diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/bart/model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/bart/model.py deleted file mode 100644 index b671c656c5283b4821f50bde533f33570df78a0e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/bart/model.py +++ /dev/null @@ -1,393 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -BART: Denoising Sequence-to-Sequence Pre-training for -Natural Language Generation, Translation, and Comprehension -""" -import logging -from typing import Optional - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer import TransformerModel -from fairseq.modules.transformer_sentence_encoder import init_bert_params - -from .hub_interface import BARTHubInterface - -logger = logging.getLogger(__name__) - - -@register_model("bart") -class BARTModel(TransformerModel): - __jit_unused_properties__ = ["supported_targets"] - - @classmethod - def hub_models(cls): - return { - "bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz", - "bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz", - "bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz", - "bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz", - "bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz", - } - - def __init__(self, args, encoder, decoder): - super().__init__(args, encoder, decoder) - - # We follow BERT's random weight initialization - self.apply(init_bert_params) - - self.classification_heads = nn.ModuleDict() - if hasattr(self.encoder, "dictionary"): - self.eos: int = self.encoder.dictionary.eos() - - @staticmethod - def add_args(parser): - super(BARTModel, BARTModel).add_args(parser) - parser.add_argument( - "--pooler-dropout", - type=float, - metavar="D", - help="dropout probability in the masked_lm pooler layers", - ) - parser.add_argument( - "--pooler-activation-fn", - choices=utils.get_available_activation_fns(), - help="activation function to use for pooler layer", - ) - parser.add_argument( - "--spectral-norm-classification-head", - action="store_true", - help="Apply spectral normalization on the classification head", - ) - - @property - def supported_targets(self): - return {"self"} - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens, - features_only: bool = False, - classification_head_name: Optional[str] = None, - token_embeddings: Optional[torch.Tensor] = None, - return_all_hiddens: bool = True, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - if classification_head_name is not None: - features_only = True - - encoder_out = self.encoder( - src_tokens, - src_lengths=src_lengths, - token_embeddings=token_embeddings, - return_all_hiddens=return_all_hiddens, - ) - x, extra = self.decoder( - prev_output_tokens, - encoder_out=encoder_out, - features_only=features_only, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - src_lengths=src_lengths, - return_all_hiddens=return_all_hiddens, - ) - eos: int = self.eos - if classification_head_name is not None: - sentence_representation = x[src_tokens.eq(eos), :].view( - x.size(0), -1, x.size(-1) - )[:, -1, :] - for k, head in self.classification_heads.items(): - # for torch script only supports iteration - if k == classification_head_name: - x = head(sentence_representation) - break - return x, extra - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - bpe="gpt2", - sample_break_mode="eos", - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - bpe=bpe, - load_checkpoint_heads=True, - sample_break_mode=sample_break_mode, - **kwargs, - ) - return BARTHubInterface(x["args"], x["task"], x["models"][0]) - - def register_classification_head( - self, name, num_classes=None, inner_dim=None, **kwargs - ): - """Register a classification head.""" - logger.info("Registering classification head: {0}".format(name)) - if name in self.classification_heads: - prev_num_classes = self.classification_heads[name].out_proj.out_features - prev_inner_dim = self.classification_heads[name].dense.out_features - if num_classes != prev_num_classes or inner_dim != prev_inner_dim: - logger.warning( - 're-registering head "{}" with num_classes {} (prev: {}) ' - "and inner_dim {} (prev: {})".format( - name, num_classes, prev_num_classes, inner_dim, prev_inner_dim - ) - ) - self.classification_heads[name] = BARTClassificationHead( - input_dim=self.args.encoder_embed_dim, - inner_dim=inner_dim or self.args.encoder_embed_dim, - num_classes=num_classes, - activation_fn=self.args.pooler_activation_fn, - pooler_dropout=self.args.pooler_dropout, - do_spectral_norm=getattr( - self.args, "spectral_norm_classification_head", False - ), - ) - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - - prefix = name + "." if name != "" else "" - current_head_names = ( - [] - if not hasattr(self, "classification_heads") - else self.classification_heads.keys() - ) - - # Handle new classification heads present in the state dict. - keys_to_delete = [] - for k in state_dict.keys(): - if not k.startswith(prefix + "classification_heads."): - continue - - head_name = k[len(prefix + "classification_heads.") :].split(".")[0] - num_classes = state_dict[ - prefix + "classification_heads." + head_name + ".out_proj.weight" - ].size(0) - inner_dim = state_dict[ - prefix + "classification_heads." + head_name + ".dense.weight" - ].size(0) - - if getattr(self.args, "load_checkpoint_heads", False): - if head_name not in current_head_names: - self.register_classification_head(head_name, num_classes, inner_dim) - else: - if head_name not in current_head_names: - logger.warning( - "deleting classification head ({}) from checkpoint " - "not present in current model: {}".format(head_name, k) - ) - keys_to_delete.append(k) - elif ( - num_classes - != self.classification_heads[head_name].out_proj.out_features - or inner_dim - != self.classification_heads[head_name].dense.out_features - ): - logger.warning( - "deleting classification head ({}) from checkpoint " - "with different dimensions than current model: {}".format( - head_name, k - ) - ) - keys_to_delete.append(k) - for k in keys_to_delete: - del state_dict[k] - - def truncate_emb(key): - if key in state_dict: - state_dict[key] = state_dict[key][:-1, :] - - # When finetuning on translation task, remove last row of - # embedding matrix that corresponds to mask_idx token. - loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0) - if ( - loaded_dict_size == len(self.encoder.dictionary) + 1 - and "" not in self.encoder.dictionary - ): - truncate_emb("encoder.embed_tokens.weight") - truncate_emb("decoder.embed_tokens.weight") - truncate_emb("encoder.output_projection.weight") - truncate_emb("decoder.output_projection.weight") - - # When continued pretraining on new set of languages for mbart, - # add extra lang embeddings at the end of embed_tokens. - # Note: newly added languages are assumed to have been added at the end. - if self.args.task == "multilingual_denoising" and loaded_dict_size < len( - self.encoder.dictionary - ): - logger.info( - "Adding extra language embeddings not found in pretrained model for " - "continued pretraining of MBART on new set of languages." - ) - loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][ - -1, : - ] - - num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size - embed_dim = state_dict["encoder.embed_tokens.weight"].size(1) - - new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim) - nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim**-0.5) - new_lang_embed_to_add = new_lang_embed_to_add.to( - dtype=state_dict["encoder.embed_tokens.weight"].dtype, - ) - - state_dict["encoder.embed_tokens.weight"] = torch.cat( - [ - state_dict["encoder.embed_tokens.weight"][ - : loaded_dict_size - 1, : - ], - new_lang_embed_to_add, - loaded_mask_token_embedding.unsqueeze(0), - ] - ) - state_dict["decoder.embed_tokens.weight"] = torch.cat( - [ - state_dict["decoder.embed_tokens.weight"][ - : loaded_dict_size - 1, : - ], - new_lang_embed_to_add, - loaded_mask_token_embedding.unsqueeze(0), - ] - ) - - # Copy any newly-added classification heads into the state dict - # with their current weights. - if hasattr(self, "classification_heads"): - cur_state = self.classification_heads.state_dict() - for k, v in cur_state.items(): - if prefix + "classification_heads." + k not in state_dict: - logger.info("Overwriting " + prefix + "classification_heads." + k) - state_dict[prefix + "classification_heads." + k] = v - - def set_beam_size(self, beam): - """Set beam size for efficient beamable enc-dec attention.""" - beamable = False - for layer in self.decoder.layers: - if layer.encoder_attn is not None: - if hasattr(layer.encoder_attn, "set_beam_size"): - layer.encoder_attn.set_beam_size(beam) - beamable = True - if beamable: - self.encoder.reorder_encoder_out = self.encoder._reorder_encoder_out - - -class BARTClassificationHead(nn.Module): - """Head for sentence-level classification tasks.""" - - def __init__( - self, - input_dim, - inner_dim, - num_classes, - activation_fn, - pooler_dropout, - do_spectral_norm=False, - ): - super().__init__() - self.dense = nn.Linear(input_dim, inner_dim) - self.activation_fn = utils.get_activation_fn(activation_fn) - self.dropout = nn.Dropout(p=pooler_dropout) - self.out_proj = nn.Linear(inner_dim, num_classes) - - if do_spectral_norm: - self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) - - def forward(self, features, **kwargs): - x = features - x = self.dropout(x) - x = self.dense(x) - x = self.activation_fn(x) - x = self.dropout(x) - x = self.out_proj(x) - return x - - -@register_model_architecture("bart", "bart_large") -def bart_large_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024) - args.encoder_layers = getattr(args, "encoder_layers", 12) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 12) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.relu_dropout = getattr(args, "relu_dropout", 0.0) - args.dropout = getattr(args, "dropout", 0.1) - args.max_target_positions = getattr(args, "max_target_positions", 1024) - args.max_source_positions = getattr(args, "max_source_positions", 1024) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", True - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", True) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - args.no_scale_embedding = getattr(args, "no_scale_embedding", True) - args.layernorm_embedding = getattr(args, "layernorm_embedding", True) - - args.activation_fn = getattr(args, "activation_fn", "gelu") - args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") - args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) - - -@register_model_architecture("bart", "bart_base") -def bart_base_architecture(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12) - bart_large_architecture(args) - - -@register_model_architecture("bart", "mbart_large") -def mbart_large_architecture(args): - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - bart_large_architecture(args) - - -@register_model_architecture("bart", "mbart_base") -def mbart_base_architecture(args): - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - bart_base_architecture(args) - - -@register_model_architecture("bart", "mbart_base_wmt20") -def mbart_base_wmt20_architecture(args): - args.layernorm_embedding = getattr(args, "layernorm_embedding", False) - mbart_base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/composite_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/composite_encoder.py deleted file mode 100644 index 4e20fe3a833a2d87876cbec294ad2bebfba7f591..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/composite_encoder.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .fairseq_encoder import FairseqEncoder - - -class CompositeEncoder(FairseqEncoder): - """ - A wrapper around a dictionary of :class:`FairseqEncoder` objects. - - We run forward on each encoder and return a dictionary of outputs. The first - encoder's dictionary is used for initialization. - - Args: - encoders (dict): a dictionary of :class:`FairseqEncoder` objects. - """ - - def __init__(self, encoders): - super().__init__(next(iter(encoders.values())).dictionary) - self.encoders = encoders - for key in self.encoders: - self.add_module(key, self.encoders[key]) - - def forward(self, src_tokens, src_lengths): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (LongTensor): lengths of each source sentence of shape - `(batch)` - - Returns: - dict: - the outputs from each Encoder - """ - encoder_out = {} - for key in self.encoders: - encoder_out[key] = self.encoders[key](src_tokens, src_lengths) - return encoder_out - - def reorder_encoder_out(self, encoder_out, new_order): - """Reorder encoder output according to new_order.""" - for key in self.encoders: - encoder_out[key] = self.encoders[key].reorder_encoder_out( - encoder_out[key], new_order - ) - return encoder_out - - def max_positions(self): - return min(self.encoders[key].max_positions() for key in self.encoders) - - def upgrade_state_dict(self, state_dict): - for key in self.encoders: - self.encoders[key].upgrade_state_dict(state_dict) - return state_dict diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/distributed_fairseq_model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/distributed_fairseq_model.py deleted file mode 100644 index 2c621f369d3accbe74b027e95a717afd2978d5e1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/distributed_fairseq_model.py +++ /dev/null @@ -1,146 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import signal -import threading - -import torch -import torch.nn as nn -from fairseq.distributed import ( - DistributedTimeoutWrapper, - LegacyDistributedDataParallel, - ModuleProxyWrapper, - TPUDistributedDataParallel, -) -from torch.nn.parallel import DistributedDataParallel - -logger = logging.getLogger(__name__) - - -_SLOWMO_DDP_DISABLED = False -try: - from fairscale.experimental.nn.data_parallel import ( - SlowMoBaseAlgorithm, - SlowMoDistributedDataParallel, - ) -except ImportError: - _SLOWMO_DDP_DISABLED = True - - -def DistributedFairseqModel(args, model, process_group, device): - """ - Wrap a *model* to support distributed data parallel training. - - This is similar to the built-in DistributedDataParallel, but allows - additional configuration of the DistributedDataParallel class to - use, and also provides easier access to the wrapped model by - forwarding requests for missing attributes to the wrapped model. - - Args: - args (argparse.Namespace): fairseq args - model (BaseFairseqModel): model to wrap - process_group: the c10d process group to be used for distributed data - parallel all-reduction. - device: device to move model to - """ - assert isinstance(model, nn.Module) - if args.tpu: - wrapped_model = TPUDistributedDataParallel( - module=model.to(device), - process_group=process_group, - ) - # forward missing getattr and state_dict/load_state_dict to orig model - wrapped_model = ModuleProxyWrapper(wrapped_model) - elif args.ddp_backend in {"c10d", "pytorch_ddp"}: - wrapped_model = DistributedDataParallel( - module=model.to(device), - device_ids=[args.device_id], - output_device=args.device_id, - broadcast_buffers=args.broadcast_buffers, - bucket_cap_mb=args.bucket_cap_mb, - process_group=process_group, - find_unused_parameters=args.find_unused_parameters, - gradient_as_bucket_view=args.gradient_as_bucket_view, - ) - if args.ddp_comm_hook == "fp16": - logger.info("enable fp16 communication hook in DDP") - try: - from torch.distributed.algorithms.ddp_comm_hooks import ( - DDPCommHookType, - register_ddp_comm_hook, - ) - except: - logger.error( - "Could not import from torch.distributed.algorithms.ddp_comm_hooks; you may need to update your pytorch version" - ) - raise - - register_ddp_comm_hook(DDPCommHookType.FP16_COMPRESS, wrapped_model) - # forward missing getattr and state_dict/load_state_dict to orig model - wrapped_model = ModuleProxyWrapper(wrapped_model) - elif args.ddp_backend in {"no_c10d", "legacy_ddp"}: - wrapped_model = LegacyDistributedDataParallel( - module=model.to(device), - buffer_size=2**28, - process_group=process_group, - ) - # forward missing getattr and state_dict/load_state_dict to orig model - wrapped_model = ModuleProxyWrapper(wrapped_model) - elif args.ddp_backend == "slowmo": - if _SLOWMO_DDP_DISABLED: - raise ImportError( - "Cannot find SlowMoDistributedDataParallel. " - "Please install fairscale with: pip install fairscale" - ) - - # The values of slowmo_momentum below were obtained by tuning on the - # En-De 16 dataset by training the transformer_wmt_en_de_large model - if args.slowmo_momentum is None: - if args.distributed_world_size <= 16: - args.slowmo_momentum = 0.0 - elif args.distributed_world_size <= 32: - args.slowmo_momentum = 0.2 - elif args.distributed_world_size <= 64: - args.slowmo_momentum = 0.5 - else: - args.slowmo_momentum = 0.6 - slowmo_base_algorithm = SlowMoBaseAlgorithm[args.slowmo_base_algorithm.upper()] - - wrapped_model = SlowMoDistributedDataParallel( - module=model.to(device), - broadcast_buffers=args.broadcast_buffers, - nprocs_per_node=args.nprocs_per_node, - slowmo_momentum=args.slowmo_momentum, - slowmo_base_algorithm=slowmo_base_algorithm, - localsgd_frequency=args.localsgd_frequency, - ) - # forward missing getattr and state_dict/load_state_dict to orig model - wrapped_model = ModuleProxyWrapper(wrapped_model) - elif args.ddp_backend == "fully_sharded": - try: - from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP - except ImportError: - raise ImportError( - "Cannot find FullyShardedDataParallel. " - "Please install fairscale with: pip install fairscale" - ) - assert isinstance(model, FSDP), "expected model to already be wrapped in FSDP" - wrapped_model = model - if args.memory_efficient_fp16: - wrapped_model = wrapped_model.half() - if not args.cpu_offload: - wrapped_model = wrapped_model.to(device=device) - else: - raise ValueError("Unknown --ddp-backend: " + args.ddp_backend) - - # kill hung distributed jobs after a timeout - if getattr(args, "heartbeat_timeout", -1) > 0: - wrapped_model = DistributedTimeoutWrapper( - wrapped_model, timeout=getattr(args, "heartbeat_timeout", -1) - ) - - return wrapped_model diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/ema/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/ema/__init__.py deleted file mode 100644 index 503ceaa609b092e48bd32a0031f4e2ffb875483f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/ema/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import importlib -import os - -from .ema import EMA - - -def build_ema(model, cfg, device): - return EMA(model, cfg, device) - - -# automatically import any Python files in the models/ema/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.models.ema." + file_name) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/ema/ema.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/ema/ema.py deleted file mode 100644 index 038f4a028145216976c0a4a8930704335de53cd3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/ema/ema.py +++ /dev/null @@ -1,208 +0,0 @@ -#!/usr/bin/env python3 - -""" -This module has the EMA class used to store a copy of the exponentially decayed -model params. - -Typical usage of EMA class involves initializing an object using an existing -model (random or from a seed model) and setting the config like ema_decay, -ema_start_update which determine how the EMA model is updated. After every -update of the model i.e. at the end of the train_step, the EMA should be updated -by passing the new model to the EMA.step function. The EMA model state dict -can be stored in the extra state under the key of "ema" and dumped -into a checkpoint and loaded. The EMA object can be passed to tasks -by setting task.uses_ema property. -EMA is a smoothed/ensemble model which might have better performance -when used for inference or further fine-tuning. EMA class has a -reverse function to load the EMA params into a model and use it -like a regular model. - -This implementation is used for trainer-level ema tracking. For EMA tracking -inside the model, please use fairseq/modules/ema_module.py instead. -""" - -import copy -import logging - -import torch -from fairseq import checkpoint_utils - - -class EMA(object): - """Exponential Moving Average of Fairseq Models - EMA keeps a copy of the exponentially decayed model params. - The set of params should include both gradient-descent and - non-gradient descent params, such as batch mean/var and buffers. - This is a modified implementation of - the open source code in https://github.com/zhawe01/fairseq-gec.git, - and internal source code in - fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py. - - Similar to TF EMA. - https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. - EMA provides a averaged and smoothed set of model weights, and has been shown to - improve vision models. EMA class does all necessary functions to update, reload, - or init EMA methods. - - EMA object is initialized from an arbitrary model. By default, it is stored in - the same device (unless device specified at initialization) and with the - same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended. - This stores the EMA parameters in fp32 only for the EMA update step, and - is used at the default precision otherwise. - EMA is usually enabled using EMAConfig with store_ema=True. Some important - parameters to configure EMA are - 1) ema_decay - The decay of EMA - 2) ema_update_freq - EMA is updated every this many model updates. - 3) ema_start_update - Start EMA update after this many model updates [default 0] - - Key methods: - 1) step - One update of EMA using new model - 2) restore - Update EMA from a state dict - 3) reverse - Load EMA into a model - 4) get_decay, _set_decay - Used to get or set the decay. Note _set_decay is - called from step. - 5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params. - Note this is enabled only when ema_fp32=True - """ - - def __init__(self, model, config, device=None, skip_keys=None): - """ - @param model model to initialize the EMA with - @param config EMAConfig object with configuration like - ema_decay, ema_update_freq, ema_fp32 - @param device If provided, copy EMA to this device (e.g. gpu). - Otherwise EMA is in the same device as the model. - """ - - self.decay = config.ema_decay - self.model = copy.deepcopy(model) - self.model.requires_grad_(False) - self.config = config - self.skip_keys = skip_keys or set() - self.fp32_params = {} - - if self.config.ema_seed_model is not None: - state = checkpoint_utils.load_ema_from_checkpoint( - self.config.ema_seed_model - ) - self.model.load_state_dict(state["model"], strict=True) - - if device is not None: - logging.info(f"Copying EMA model to device {device}") - self.model = self.model.to(device=device) - - if self.config.ema_fp32: - self.build_fp32_params() - - self.update_freq_counter = 0 - - def get_model(self): - return self.model - - def build_fp32_params(self, state_dict=None): - """ - Store a copy of the EMA params in fp32. - If state dict is passed, the EMA params is copied from - the provided state dict. Otherwise, it is copied from the - current EMA model parameters. - """ - if not self.config.ema_fp32: - raise RuntimeError( - "build_fp32_params should not be called if ema_fp32=False. " - "Use ema_fp32=True if this is really intended." - ) - - if state_dict is None: - state_dict = self.model.state_dict() - - def _to_float(t): - return t.float() if torch.is_floating_point(t) else t - - for param_key in state_dict: - if param_key in self.fp32_params: - self.fp32_params[param_key].copy_(state_dict[param_key]) - else: - self.fp32_params[param_key] = _to_float(state_dict[param_key]) - - def restore(self, state_dict, build_fp32_params=False): - """Load data from a model spec into EMA model""" - self.model.load_state_dict(state_dict, strict=False) - if build_fp32_params: - self.build_fp32_params(state_dict) - - def _set_decay(self, decay): - self.decay = decay - - def get_decay(self): - return self.decay - - def _step_internal(self, new_model, updates=None): - """One update of the EMA model based on new model weights""" - decay = self.decay - - ema_state_dict = {} - ema_params = ( - self.fp32_params if self.config.ema_fp32 else self.model.state_dict() - ) - for key, param in new_model.state_dict().items(): - if isinstance(param, dict): - continue - try: - ema_param = ema_params[key] - except KeyError: - ema_param = ( - param.float().clone() if param.ndim == 1 else copy.deepcopy(param) - ) - - if param.shape != ema_param.shape: - raise ValueError( - "incompatible tensor shapes between model param and ema param" - + "{} vs. {}".format(param.shape, ema_param.shape) - ) - - if "version" in key: - # Do not decay a model.version pytorch param - continue - - if key in self.skip_keys: - ema_param = param.to(dtype=ema_param.dtype).clone() - else: - ema_param.mul_(decay) - ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - decay) - ema_state_dict[key] = ema_param - self.restore(ema_state_dict, build_fp32_params=False) - - def step(self, new_model, updates=None): - """ - One update of EMA which is done every self.config.ema_update_freq - updates of the model. - - @param updates The current number of model updates done. - Decay is set of 0 if model updates < ema_start_update, which means - the model will be simply copied over to the EMA. - When model updates >= ema_start_updates, then EMA is updated with - a decay of self.config.ema_decay. - """ - if updates is not None: - self._set_decay( - 0 if updates < self.config.ema_start_update else self.config.ema_decay - ) - if self.config.ema_update_freq > 1: - self.update_freq_counter += 1 - if self.update_freq_counter >= self.config.ema_update_freq: - self._step_internal(new_model, updates) - self.update_freq_counter = 0 - else: - self._step_internal(new_model, updates) - - def reverse(self, model): - """ - Load the model parameters from EMA model. - Useful for inference or fine-tuning from the EMA model. - """ - d = self.model.state_dict() - if "_ema" in d: - del d["_ema"] - - model.load_state_dict(d, strict=False) - return model diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_decoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_decoder.py deleted file mode 100644 index 13b73d639e9179d3de34081d5cc5ec73cb9a1f75..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_decoder.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, List, Optional, Tuple - -import torch.nn as nn -from fairseq import utils -from torch import Tensor - - -class FairseqDecoder(nn.Module): - """Base class for decoders.""" - - def __init__(self, dictionary): - super().__init__() - self.dictionary = dictionary - self.onnx_trace = False - self.adaptive_softmax = None - - def forward(self, prev_output_tokens, encoder_out=None, **kwargs): - """ - Args: - prev_output_tokens (LongTensor): shifted output tokens of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (dict, optional): output from the encoder, used for - encoder-side attention - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - x, extra = self.extract_features( - prev_output_tokens, encoder_out=encoder_out, **kwargs - ) - x = self.output_layer(x) - return x, extra - - def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs): - """ - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - raise NotImplementedError - - def output_layer(self, features, **kwargs): - """ - Project features to the default output size, e.g., vocabulary size. - - Args: - features (Tensor): features returned by *extract_features*. - """ - raise NotImplementedError - - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - """Get normalized probabilities (or log probs) from a net's output.""" - return self.get_normalized_probs_scriptable(net_output, log_probs, sample) - - # TorchScript doesn't support super() method so that the scriptable Subclass - # can't access the base class model in Torchscript. - # Current workaround is to add a helper function with different name and - # call the helper function from scriptable Subclass. - def get_normalized_probs_scriptable( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - """Get normalized probabilities (or log probs) from a net's output.""" - - if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None: - if sample is not None: - assert "target" in sample - target = sample["target"] - else: - target = None - out = self.adaptive_softmax.get_log_prob(net_output[0], target=target) - return out.exp_() if not log_probs else out - - logits = net_output[0] - if log_probs: - return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace) - else: - return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace) - - def max_positions(self): - """Maximum input length supported by the decoder.""" - return 1e6 # an arbitrary large number - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade old state dicts to work with newer code.""" - return state_dict - - def prepare_for_onnx_export_(self): - self.onnx_trace = True diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_encoder.py deleted file mode 100644 index fd8da6258f43c94294c65e9bc1e446d7cde4fd08..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_encoder.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, List, NamedTuple, Optional - -import torch -import torch.nn as nn -from torch import Tensor - -EncoderOut = NamedTuple( - "EncoderOut", - [ - ("encoder_out", Tensor), # T x B x C - ("encoder_padding_mask", Optional[Tensor]), # B x T - ("encoder_embedding", Optional[Tensor]), # B x T x C - ("encoder_states", Optional[List[Tensor]]), # List[T x B x C] - ("src_tokens", Optional[Tensor]), # B x T - ("src_lengths", Optional[Tensor]), # B x 1 - ], -) - - -class FairseqEncoder(nn.Module): - """Base class for encoders.""" - - def __init__(self, dictionary): - super().__init__() - self.dictionary = dictionary - - def forward(self, src_tokens, src_lengths=None, **kwargs): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (LongTensor): lengths of each source sentence of shape - `(batch)` - """ - raise NotImplementedError - - def forward_torchscript(self, net_input: Dict[str, Tensor]): - """A TorchScript-compatible version of forward. - - Encoders which use additional arguments may want to override - this method for TorchScript compatibility. - """ - if torch.jit.is_scripting(): - return self.forward( - src_tokens=net_input["src_tokens"], - src_lengths=net_input["src_lengths"], - ) - else: - return self.forward_non_torchscript(net_input) - - @torch.jit.unused - def forward_non_torchscript(self, net_input: Dict[str, Tensor]): - encoder_input = { - k: v for k, v in net_input.items() if k != "prev_output_tokens" - } - return self.forward(**encoder_input) - - def reorder_encoder_out(self, encoder_out, new_order): - """ - Reorder encoder output according to `new_order`. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - `encoder_out` rearranged according to `new_order` - """ - raise NotImplementedError - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return 1e6 # an arbitrary large number - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade old state dicts to work with newer code.""" - return state_dict - - def set_num_updates(self, num_updates): - """State from trainer to pass along to model at every update.""" - - def _apply(m): - if hasattr(m, "set_num_updates") and m != self: - m.set_num_updates(num_updates) - - self.apply(_apply) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_incremental_decoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_incremental_decoder.py deleted file mode 100644 index 159057afcc4d49e594d37e55e7c8f61d839edfe4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_incremental_decoder.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from typing import Dict, Optional - -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.models import FairseqDecoder -from torch import Tensor - -logger = logging.getLogger(__name__) - - -@with_incremental_state -class FairseqIncrementalDecoder(FairseqDecoder): - """Base class for incremental decoders. - - Incremental decoding is a special mode at inference time where the Model - only receives a single timestep of input corresponding to the previous - output token (for teacher forcing) and must produce the next output - *incrementally*. Thus the model must cache any long-term state that is - needed about the sequence, e.g., hidden states, convolutional states, etc. - - Compared to the standard :class:`FairseqDecoder` interface, the incremental - decoder interface allows :func:`forward` functions to take an extra keyword - argument (*incremental_state*) that can be used to cache state across - time-steps. - - The :class:`FairseqIncrementalDecoder` interface also defines the - :func:`reorder_incremental_state` method, which is used during beam search - to select and reorder the incremental state based on the selection of beams. - - To learn more about how incremental decoding works, refer to `this blog - `_. - """ - - def __init__(self, dictionary): - super().__init__(dictionary) - - def forward( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs - ): - """ - Args: - prev_output_tokens (LongTensor): shifted output tokens of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (dict, optional): output from the encoder, used for - encoder-side attention - incremental_state (dict, optional): dictionary used for storing - state during :ref:`Incremental decoding` - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - raise NotImplementedError - - def extract_features( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs - ): - """ - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - raise NotImplementedError - - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Reorder incremental state. - - This will be called when the order of the input has changed from the - previous time step. A typical use case is beam search, where the input - order changes between time steps based on the selection of beams. - """ - pass - - def reorder_incremental_state_scripting( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Main entry point for reordering the incremental state. - - Due to limitations in TorchScript, we call this function in - :class:`fairseq.sequence_generator.SequenceGenerator` instead of - calling :func:`reorder_incremental_state` directly. - """ - for module in self.modules(): - if hasattr(module, "reorder_incremental_state"): - result = module.reorder_incremental_state(incremental_state, new_order) - if result is not None: - incremental_state = result - - def set_beam_size(self, beam_size): - """Sets the beam size in the decoder and all children.""" - if getattr(self, "_beam_size", -1) != beam_size: - seen = set() - - def apply_set_beam_size(module): - if ( - module != self - and hasattr(module, "set_beam_size") - and module not in seen - ): - seen.add(module) - module.set_beam_size(beam_size) - - self.apply(apply_set_beam_size) - self._beam_size = beam_size diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_model.py deleted file mode 100644 index 6082937a4c18b3cee9b7fa40cf6ec8d86ed94e7e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fairseq_model.py +++ /dev/null @@ -1,573 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Base classes for various fairseq models. -""" - -import logging -from argparse import Namespace -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.data import Dictionary -from fairseq.dataclass.utils import ( - convert_namespace_to_omegaconf, - gen_parser_from_dataclass, -) -from fairseq.models import FairseqDecoder, FairseqEncoder -from omegaconf import DictConfig -from torch import Tensor - -logger = logging.getLogger(__name__) - - -def check_type(module, expected_type): - if hasattr(module, "unwrapped_module"): - assert isinstance( - module.unwrapped_module, expected_type - ), f"{type(module.unwrapped_module)} != {expected_type}" - else: - assert isinstance(module, expected_type), f"{type(module)} != {expected_type}" - - -class BaseFairseqModel(nn.Module): - """Base class for fairseq models.""" - - def __init__(self): - super().__init__() - self._is_generation_fast = False - - @classmethod - def add_args(cls, parser): - """Add model-specific arguments to the parser.""" - dc = getattr(cls, "__dataclass", None) - if dc is not None: - # do not set defaults so that settings defaults from various architectures still works - gen_parser_from_dataclass(parser, dc(), delete_default=True) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - raise NotImplementedError("Model must implement the build_model method") - - def get_targets(self, sample, net_output): - """Get targets from either the sample or the net's output.""" - return sample["target"] - - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - """Get normalized probabilities (or log probs) from a net's output.""" - return self.get_normalized_probs_scriptable(net_output, log_probs, sample) - - # TorchScript doesn't support super() method so that the scriptable Subclass - # can't access the base class model in Torchscript. - # Current workaround is to add a helper function with different name and - # call the helper function from scriptable Subclass. - def get_normalized_probs_scriptable( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - """Scriptable helper function for get_normalized_probs in ~BaseFairseqModel""" - if hasattr(self, "decoder"): - return self.decoder.get_normalized_probs(net_output, log_probs, sample) - elif torch.is_tensor(net_output): - # syntactic sugar for simple models which don't have a decoder - # (e.g., the classification tutorial) - logits = net_output.float() - if log_probs: - return F.log_softmax(logits, dim=-1) - else: - return F.softmax(logits, dim=-1) - raise NotImplementedError - - def extract_features(self, *args, **kwargs): - """Similar to *forward* but only return features.""" - return self(*args, **kwargs) - - def max_positions(self): - """Maximum length supported by the model.""" - return None - - def load_state_dict( - self, - state_dict, - strict=True, - model_cfg: Optional[DictConfig] = None, - args: Optional[Namespace] = None, - ): - """Copies parameters and buffers from *state_dict* into this module and - its descendants. - - Overrides the method in :class:`nn.Module`. Compared with that method - this additionally "upgrades" *state_dicts* from old checkpoints. - """ - - if model_cfg is None and args is not None: - logger.warn( - "using 'args' is deprecated, please update your code to use dataclass config" - ) - model_cfg = convert_namespace_to_omegaconf(args).model - - self.upgrade_state_dict(state_dict) - - from fairseq.checkpoint_utils import prune_state_dict - - new_state_dict = prune_state_dict(state_dict, model_cfg) - return super().load_state_dict(new_state_dict, strict) - - def upgrade_state_dict(self, state_dict): - """Upgrade old state dicts to work with newer code.""" - self.upgrade_state_dict_named(state_dict, "") - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade old state dicts to work with newer code. - - Args: - state_dict (dict): state dictionary to upgrade, in place - name (str): the state dict key corresponding to the current module - """ - assert state_dict is not None - - def do_upgrade(m, prefix): - if len(prefix) > 0: - prefix += "." - - for n, c in m.named_children(): - name = prefix + n - if hasattr(c, "upgrade_state_dict_named"): - c.upgrade_state_dict_named(state_dict, name) - elif hasattr(c, "upgrade_state_dict"): - c.upgrade_state_dict(state_dict) - do_upgrade(c, name) - - do_upgrade(self, name) - - def set_num_updates(self, num_updates): - """State from trainer to pass along to model at every update.""" - for m in self.modules(): - if hasattr(m, "set_num_updates") and m != self: - m.set_num_updates(num_updates) - - def prepare_for_inference_(self, cfg: DictConfig): - """Prepare model for inference.""" - kwargs = {} - kwargs["beamable_mm_beam_size"] = ( - None - if getattr(cfg.generation, "no_beamable_mm", False) - else getattr(cfg.generation, "beam", 5) - ) - kwargs["need_attn"] = getattr(cfg.generation, "print_alignment", False) - if getattr(cfg.generation, "retain_dropout", False): - kwargs["retain_dropout"] = cfg.generation.retain_dropout - kwargs["retain_dropout_modules"] = cfg.generation.retain_dropout_modules - self.make_generation_fast_(**kwargs) - - def make_generation_fast_(self, **kwargs): - """ - Legacy entry point to optimize model for faster generation. - Prefer prepare_for_inference_. - """ - if self._is_generation_fast: - return # only apply once - self._is_generation_fast = True - - # remove weight norm from all modules in the network - def apply_remove_weight_norm(module): - try: - nn.utils.remove_weight_norm(module) - except (AttributeError, ValueError): # this module didn't have weight norm - return - - self.apply(apply_remove_weight_norm) - - def apply_make_generation_fast_(module, prefix): - if len(prefix) > 0: - prefix += "." - - base_func = BaseFairseqModel.make_generation_fast_ - for n, m in module.named_modules(): - if ( - m != self - and hasattr(m, "make_generation_fast_") - # don't call this implementation again, e.g., if - # children modules also inherit from BaseFairseqModel - and m.make_generation_fast_.__func__ is not base_func - ): - name = prefix + n - m.make_generation_fast_(name=name, **kwargs) - - apply_make_generation_fast_(self, "") - - def train(mode=True): - if mode: - raise RuntimeError("cannot train after make_generation_fast") - - # this model should no longer be used for training - self.eval() - self.train = train - - def prepare_for_onnx_export_(self, **kwargs): - """Make model exportable via ONNX trace.""" - seen = set() - - def apply_prepare_for_onnx_export_(module): - if ( - module != self - and hasattr(module, "prepare_for_onnx_export_") - and module not in seen - ): - seen.add(module) - module.prepare_for_onnx_export_(**kwargs) - - self.apply(apply_prepare_for_onnx_export_) - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - **kwargs, - ): - """ - Load a :class:`~fairseq.models.FairseqModel` from a pre-trained model - file. Downloads and caches the pre-trained model file if needed. - - The base implementation returns a - :class:`~fairseq.hub_utils.GeneratorHubInterface`, which can be used to - generate translations or sample from language models. The underlying - :class:`~fairseq.models.FairseqModel` can be accessed via the - *generator.models* attribute. - - Other models may override this to implement custom hub interfaces. - - Args: - model_name_or_path (str): either the name of a pre-trained model to - load or a path/URL to a pre-trained model state dict - checkpoint_file (str, optional): colon-separated list of checkpoint - files in the model archive to ensemble (default: 'model.pt') - data_name_or_path (str, optional): point args.data to the archive - at the given path/URL. Can start with '.' or './' to reuse the - model archive path. - """ - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - **kwargs, - ) - logger.info(x["args"]) - return hub_utils.GeneratorHubInterface(x["args"], x["task"], x["models"]) - - @classmethod - def hub_models(cls): - return {} - - -class FairseqEncoderDecoderModel(BaseFairseqModel): - """Base class for encoder-decoder models. - - Args: - encoder (FairseqEncoder): the encoder - decoder (FairseqDecoder): the decoder - """ - - def __init__(self, encoder, decoder): - super().__init__() - - self.encoder = encoder - self.decoder = decoder - - check_type(self.encoder, FairseqEncoder) - check_type(self.decoder, FairseqDecoder) - - def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - """ - Run the forward pass for an encoder-decoder model. - - First feed a batch of source tokens through the encoder. Then, feed the - encoder output and previous decoder outputs (i.e., teacher forcing) to - the decoder to produce the next outputs:: - - encoder_out = self.encoder(src_tokens, src_lengths) - return self.decoder(prev_output_tokens, encoder_out) - - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (LongTensor): source sentence lengths of shape `(batch)` - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - decoder_out = self.decoder( - prev_output_tokens, encoder_out=encoder_out, **kwargs - ) - return decoder_out - - def forward_decoder(self, prev_output_tokens, **kwargs): - return self.decoder(prev_output_tokens, **kwargs) - - def extract_features(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - """ - Similar to *forward* but only return features. - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - features = self.decoder.extract_features( - prev_output_tokens, encoder_out=encoder_out, **kwargs - ) - return features - - def output_layer(self, features, **kwargs): - """Project features to the default output size (typically vocabulary size).""" - return self.decoder.output_layer(features, **kwargs) - - def max_positions(self): - """Maximum length supported by the model.""" - return (self.encoder.max_positions(), self.decoder.max_positions()) - - def max_decoder_positions(self): - """Maximum length supported by the decoder.""" - return self.decoder.max_positions() - - -class FairseqModel(FairseqEncoderDecoderModel): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - utils.deprecation_warning( - "FairseqModel is deprecated, please use FairseqEncoderDecoderModel " - "or BaseFairseqModel instead", - stacklevel=4, - ) - - -class FairseqMultiModel(BaseFairseqModel): - """Base class for combining multiple encoder-decoder models.""" - - def __init__(self, encoders, decoders): - super().__init__() - assert encoders.keys() == decoders.keys() - self.keys = list(encoders.keys()) - for key in self.keys: - check_type(encoders[key], FairseqEncoder) - check_type(decoders[key], FairseqDecoder) - - self.models = nn.ModuleDict( - { - key: FairseqEncoderDecoderModel(encoders[key], decoders[key]) - for key in self.keys - } - ) - - @staticmethod - def build_shared_embeddings( - dicts: Dict[str, Dictionary], - langs: List[str], - embed_dim: int, - build_embedding: callable, - pretrained_embed_path: Optional[str] = None, - ): - """ - Helper function to build shared embeddings for a set of languages after - checking that all dicts corresponding to those languages are equivalent. - - Args: - dicts: Dict of lang_id to its corresponding Dictionary - langs: languages that we want to share embeddings for - embed_dim: embedding dimension - build_embedding: callable function to actually build the embedding - pretrained_embed_path: Optional path to load pretrained embeddings - """ - shared_dict = dicts[langs[0]] - if any(dicts[lang] != shared_dict for lang in langs): - raise ValueError( - "--share-*-embeddings requires a joined dictionary: " - "--share-encoder-embeddings requires a joined source " - "dictionary, --share-decoder-embeddings requires a joined " - "target dictionary, and --share-all-embeddings requires a " - "joint source + target dictionary." - ) - return build_embedding(shared_dict, embed_dim, pretrained_embed_path) - - def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - raise NotImplementedError - - def max_positions(self): - """Maximum length supported by the model.""" - return { - key: ( - self.models[key].encoder.max_positions(), - self.models[key].decoder.max_positions(), - ) - for key in self.keys - } - - def max_decoder_positions(self): - """Maximum length supported by the decoder.""" - return min(model.decoder.max_positions() for model in self.models.values()) - - @property - def encoder(self): - return self.models[self.keys[0]].encoder - - @property - def decoder(self): - return self.models[self.keys[0]].decoder - - def forward_decoder(self, prev_output_tokens, **kwargs): - return self.decoder(prev_output_tokens, **kwargs) - - def load_state_dict( - self, - state_dict, - strict=True, - model_cfg=None, - args: Optional[Namespace] = None, - ): - """Copies parameters and buffers from *state_dict* into this module and - its descendants. - - Overrides the method in :class:`nn.Module`. Compared with that method - this additionally "upgrades" *state_dicts* from old checkpoints. - """ - - if model_cfg is None and args is not None: - logger.warn( - "using 'args' is deprecated, please update your code to use dataclass config" - ) - model_cfg = convert_namespace_to_omegaconf(args).model - - self.upgrade_state_dict(state_dict) - - from fairseq.checkpoint_utils import prune_state_dict - - new_state_dict = prune_state_dict(state_dict, model_cfg) - return super().load_state_dict(new_state_dict, strict) - - -class FairseqLanguageModel(BaseFairseqModel): - """Base class for decoder-only models. - - Args: - decoder (FairseqDecoder): the decoder - """ - - def __init__(self, decoder): - super().__init__() - self.decoder = decoder - check_type(self.decoder, FairseqDecoder) - - def forward(self, src_tokens, **kwargs): - """ - Run the forward pass for a decoder-only model. - - Feeds a batch of tokens through the decoder to predict the next tokens. - - Args: - src_tokens (LongTensor): tokens on which to condition the decoder, - of shape `(batch, tgt_len)` - src_lengths (LongTensor): source sentence lengths of shape `(batch)` - - Returns: - tuple: - - the decoder's output of shape `(batch, seq_len, vocab)` - - a dictionary with any model-specific outputs - """ - return self.decoder(src_tokens, **kwargs) - - def forward_decoder(self, prev_output_tokens, **kwargs): - return self.decoder(prev_output_tokens, **kwargs) - - def extract_features(self, src_tokens, **kwargs): - """ - Similar to *forward* but only return features. - - Returns: - tuple: - - the decoder's features of shape `(batch, seq_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - return self.decoder.extract_features(src_tokens, **kwargs) - - def output_layer(self, features, **kwargs): - """Project features to the default output size (typically vocabulary size).""" - return self.decoder.output_layer(features, **kwargs) - - def max_positions(self): - """Maximum length supported by the model.""" - return self.decoder.max_positions() - - def max_decoder_positions(self): - """Maximum length supported by the decoder.""" - return self.decoder.max_positions() - - @property - def supported_targets(self): - return {"future"} - - -class FairseqEncoderModel(BaseFairseqModel): - """Base class for encoder-only models. - - Args: - encoder (FairseqEncoder): the encoder - """ - - def __init__(self, encoder): - super().__init__() - self.encoder = encoder - check_type(self.encoder, FairseqEncoder) - - def forward(self, src_tokens, src_lengths, **kwargs): - """ - Run the forward pass for a encoder-only model. - - Feeds a batch of tokens through the encoder to generate features. - - Args: - src_tokens (LongTensor): input tokens of shape `(batch, src_len)` - src_lengths (LongTensor): source sentence lengths of shape `(batch)` - - Returns: - the encoder's output, typically of shape `(batch, src_len, features)` - """ - return self.encoder(src_tokens, src_lengths, **kwargs) - - def get_normalized_probs(self, net_output, log_probs, sample=None): - """Get normalized probabilities (or log probs) from a net's output.""" - encoder_out = net_output["encoder_out"] - if torch.is_tensor(encoder_out): - logits = encoder_out.float() - if log_probs: - return F.log_softmax(logits, dim=-1) - else: - return F.softmax(logits, dim=-1) - raise NotImplementedError - - def max_positions(self): - """Maximum length supported by the model.""" - return self.encoder.max_positions() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv.py deleted file mode 100644 index c99a2151014d816ec9aff6f4b27d71224dd7b4cf..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv.py +++ /dev/null @@ -1,756 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) -from fairseq.modules import ( - AdaptiveSoftmax, - BeamableMM, - FairseqDropout, - GradMultiply, - LearnedPositionalEmbedding, - LinearizedConvolution, -) - - -@register_model("fconv") -class FConvModel(FairseqEncoderDecoderModel): - """ - A fully convolutional model, i.e. a convolutional encoder and a - convolutional decoder, as described in `"Convolutional Sequence to Sequence - Learning" (Gehring et al., 2017) `_. - - Args: - encoder (FConvEncoder): the encoder - decoder (FConvDecoder): the decoder - - The Convolutional model provides the following named architectures and - command-line arguments: - - .. argparse:: - :ref: fairseq.models.fconv_parser - :prog: - """ - - @classmethod - def hub_models(cls): - def moses_subword(path): - return { - "path": path, - "tokenizer": "moses", - "bpe": "subword_nmt", - } - - return { - "conv.wmt14.en-fr": moses_subword( - "https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2" - ), - "conv.wmt14.en-de": moses_subword( - "https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2" - ), - "conv.wmt17.en-de": moses_subword( - "https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2" - ), - } - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - self.encoder.num_attention_layers = sum( - layer is not None for layer in decoder.attention - ) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--encoder-embed-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--encoder-embed-path', type=str, metavar='STR', - help='path to pre-trained encoder embedding') - parser.add_argument('--encoder-layers', type=str, metavar='EXPR', - help='encoder layers [(dim, kernel_size), ...]') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-embed-path', type=str, metavar='STR', - help='path to pre-trained decoder embedding') - parser.add_argument('--decoder-layers', type=str, metavar='EXPR', - help='decoder layers [(dim, kernel_size), ...]') - parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', - help='decoder output embedding dimension') - parser.add_argument('--decoder-attention', type=str, metavar='EXPR', - help='decoder attention [True, ...]') - parser.add_argument('--share-input-output-embed', action='store_true', - help='share input and output embeddings (requires' - ' --decoder-out-embed-dim and --decoder-embed-dim' - ' to be equal)') - # fmt: on - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - # make sure that all args are properly defaulted (in case there are any new ones) - base_architecture(args) - - encoder_embed_dict = None - if args.encoder_embed_path: - encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path) - utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary) - - decoder_embed_dict = None - if args.decoder_embed_path: - decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path) - utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary) - - encoder = FConvEncoder( - dictionary=task.source_dictionary, - embed_dim=args.encoder_embed_dim, - embed_dict=encoder_embed_dict, - convolutions=eval(args.encoder_layers), - dropout=args.dropout, - max_positions=args.max_source_positions, - ) - decoder = FConvDecoder( - dictionary=task.target_dictionary, - embed_dim=args.decoder_embed_dim, - embed_dict=decoder_embed_dict, - convolutions=eval(args.decoder_layers), - out_embed_dim=args.decoder_out_embed_dim, - attention=eval(args.decoder_attention), - dropout=args.dropout, - max_positions=args.max_target_positions, - share_embed=args.share_input_output_embed, - ) - return FConvModel(encoder, decoder) - - -class FConvEncoder(FairseqEncoder): - """ - Convolutional encoder consisting of `len(convolutions)` layers. - - Args: - dictionary (~fairseq.data.Dictionary): encoding dictionary - embed_dim (int, optional): embedding dimension - embed_dict (str, optional): filename from which to load pre-trained - embeddings - max_positions (int, optional): maximum supported input sequence length - convolutions (list, optional): the convolutional layer structure. Each - list item `i` corresponds to convolutional layer `i`. Layers are - given as ``(out_channels, kernel_width, [residual])``. Residual - connections are added between layers when ``residual=1`` (which is - the default behavior). - dropout (float, optional): dropout to be applied before each conv layer - """ - - def __init__( - self, - dictionary, - embed_dim=512, - embed_dict=None, - max_positions=1024, - convolutions=((512, 3),) * 20, - dropout=0.1, - ): - super().__init__(dictionary) - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.num_attention_layers = None - - num_embeddings = len(dictionary) - self.padding_idx = dictionary.pad() - self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) - if embed_dict: - self.embed_tokens = utils.load_embedding( - embed_dict, self.dictionary, self.embed_tokens - ) - - self.embed_positions = PositionalEmbedding( - max_positions, - embed_dim, - self.padding_idx, - ) - - convolutions = extend_conv_spec(convolutions) - in_channels = convolutions[0][0] - self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) - self.projections = nn.ModuleList() - self.convolutions = nn.ModuleList() - self.residuals = [] - - layer_in_channels = [in_channels] - for _, (out_channels, kernel_size, residual) in enumerate(convolutions): - if residual == 0: - residual_dim = out_channels - else: - residual_dim = layer_in_channels[-residual] - self.projections.append( - Linear(residual_dim, out_channels) - if residual_dim != out_channels - else None - ) - if kernel_size % 2 == 1: - padding = kernel_size // 2 - else: - padding = 0 - self.convolutions.append( - ConvTBC( - in_channels, - out_channels * 2, - kernel_size, - dropout=dropout, - padding=padding, - ) - ) - self.residuals.append(residual) - in_channels = out_channels - layer_in_channels.append(out_channels) - self.fc2 = Linear(in_channels, embed_dim) - - def forward(self, src_tokens, src_lengths): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (LongTensor): lengths of each source sentence of shape - `(batch)` - - Returns: - dict: - - **encoder_out** (tuple): a tuple with two elements, where the - first element is the last encoder layer's output and the - second element is the same quantity summed with the input - embedding (used for attention). The shape of both tensors is - `(batch, src_len, embed_dim)`. - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - """ - # embed tokens and positions - x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens) - x = self.dropout_module(x) - input_embedding = x - - # project to size of convolution - x = self.fc1(x) - - # used to mask padding in input - encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B - if not encoder_padding_mask.any(): - encoder_padding_mask = None - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - residuals = [x] - # temporal convolutions - for proj, conv, res_layer in zip( - self.projections, self.convolutions, self.residuals - ): - if res_layer > 0: - residual = residuals[-res_layer] - residual = residual if proj is None else proj(residual) - else: - residual = None - - if encoder_padding_mask is not None: - x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) - - x = self.dropout_module(x) - if conv.kernel_size[0] % 2 == 1: - # padding is implicit in the conv - x = conv(x) - else: - padding_l = (conv.kernel_size[0] - 1) // 2 - padding_r = conv.kernel_size[0] // 2 - x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r)) - x = conv(x) - x = F.glu(x, dim=2) - - if residual is not None: - x = (x + residual) * math.sqrt(0.5) - residuals.append(x) - - # T x B x C -> B x T x C - x = x.transpose(1, 0) - - # project back to size of embedding - x = self.fc2(x) - - if encoder_padding_mask is not None: - encoder_padding_mask = encoder_padding_mask.t() # -> B x T - x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) - - # scale gradients (this only affects backward, not forward) - x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers)) - - # add output to input embedding for attention - y = (x + input_embedding) * math.sqrt(0.5) - - return { - "encoder_out": (x, y), - "encoder_padding_mask": encoder_padding_mask, # B x T - } - - def reorder_encoder_out(self, encoder_out, new_order): - if encoder_out["encoder_out"] is not None: - encoder_out["encoder_out"] = ( - encoder_out["encoder_out"][0].index_select(0, new_order), - encoder_out["encoder_out"][1].index_select(0, new_order), - ) - if encoder_out["encoder_padding_mask"] is not None: - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(0, new_order) - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return self.embed_positions.max_positions - - -class AttentionLayer(nn.Module): - def __init__(self, conv_channels, embed_dim, bmm=None): - super().__init__() - # projects from output of convolution to embedding dimension - self.in_projection = Linear(conv_channels, embed_dim) - # projects from embedding dimension to convolution size - self.out_projection = Linear(embed_dim, conv_channels) - - self.bmm = bmm if bmm is not None else torch.bmm - - def forward(self, x, target_embedding, encoder_out, encoder_padding_mask): - residual = x - - # attention - x = (self.in_projection(x) + target_embedding) * math.sqrt(0.5) - x = self.bmm(x, encoder_out[0]) - - # don't attend over padding - if encoder_padding_mask is not None: - x = ( - x.float() - .masked_fill(encoder_padding_mask.unsqueeze(1), float("-inf")) - .type_as(x) - ) # FP16 support: cast to float and back - - # softmax over last dim - sz = x.size() - x = F.softmax(x.view(sz[0] * sz[1], sz[2]), dim=1) - x = x.view(sz) - attn_scores = x - - x = self.bmm(x, encoder_out[1]) - - # scale attention output (respecting potentially different lengths) - s = encoder_out[1].size(1) - if encoder_padding_mask is None: - x = x * (s * math.sqrt(1.0 / s)) - else: - s = s - encoder_padding_mask.type_as(x).sum( - dim=1, keepdim=True - ) # exclude padding - s = s.unsqueeze(-1) - x = x * (s * s.rsqrt()) - - # project back - x = (self.out_projection(x) + residual) * math.sqrt(0.5) - return x, attn_scores - - def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs): - """Replace torch.bmm with BeamableMM.""" - if beamable_mm_beam_size is not None: - del self.bmm - self.add_module("bmm", BeamableMM(beamable_mm_beam_size)) - - -class FConvDecoder(FairseqIncrementalDecoder): - """Convolutional decoder""" - - def __init__( - self, - dictionary, - embed_dim=512, - embed_dict=None, - out_embed_dim=256, - max_positions=1024, - convolutions=((512, 3),) * 20, - attention=True, - dropout=0.1, - share_embed=False, - positional_embeddings=True, - adaptive_softmax_cutoff=None, - adaptive_softmax_dropout=0.0, - ): - super().__init__(dictionary) - self.register_buffer("version", torch.Tensor([2])) - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.need_attn = True - - convolutions = extend_conv_spec(convolutions) - in_channels = convolutions[0][0] - if isinstance(attention, bool): - # expand True into [True, True, ...] and do the same with False - attention = [attention] * len(convolutions) - if not isinstance(attention, list) or len(attention) != len(convolutions): - raise ValueError( - "Attention is expected to be a list of booleans of " - "length equal to the number of layers." - ) - - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) - if embed_dict: - self.embed_tokens = utils.load_embedding( - embed_dict, self.dictionary, self.embed_tokens - ) - - self.embed_positions = ( - PositionalEmbedding( - max_positions, - embed_dim, - padding_idx, - ) - if positional_embeddings - else None - ) - - self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) - self.projections = nn.ModuleList() - self.convolutions = nn.ModuleList() - self.attention = nn.ModuleList() - self.residuals = [] - - layer_in_channels = [in_channels] - for i, (out_channels, kernel_size, residual) in enumerate(convolutions): - if residual == 0: - residual_dim = out_channels - else: - residual_dim = layer_in_channels[-residual] - self.projections.append( - Linear(residual_dim, out_channels) - if residual_dim != out_channels - else None - ) - self.convolutions.append( - LinearizedConv1d( - in_channels, - out_channels * 2, - kernel_size, - padding=(kernel_size - 1), - dropout=dropout, - ) - ) - self.attention.append( - AttentionLayer(out_channels, embed_dim) if attention[i] else None - ) - self.residuals.append(residual) - in_channels = out_channels - layer_in_channels.append(out_channels) - - self.adaptive_softmax = None - self.fc2 = self.fc3 = None - - if adaptive_softmax_cutoff is not None: - assert not share_embed - self.adaptive_softmax = AdaptiveSoftmax( - num_embeddings, - in_channels, - adaptive_softmax_cutoff, - dropout=adaptive_softmax_dropout, - ) - else: - self.fc2 = Linear(in_channels, out_embed_dim) - if share_embed: - assert out_embed_dim == embed_dim, ( - "Shared embed weights implies same dimensions " - " out_embed_dim={} vs embed_dim={}".format(out_embed_dim, embed_dim) - ) - self.fc3 = nn.Linear(out_embed_dim, num_embeddings) - self.fc3.weight = self.embed_tokens.weight - else: - self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout) - - def forward( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused - ): - if encoder_out is not None: - encoder_padding_mask = encoder_out["encoder_padding_mask"] - encoder_out = encoder_out["encoder_out"] - - # split and transpose encoder outputs - encoder_a, encoder_b = self._split_encoder_out( - encoder_out, incremental_state - ) - - if self.embed_positions is not None: - pos_embed = self.embed_positions(prev_output_tokens, incremental_state) - else: - pos_embed = 0 - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - x = self._embed_tokens(prev_output_tokens, incremental_state) - - # embed tokens and combine with positional embeddings - x += pos_embed - x = self.dropout_module(x) - target_embedding = x - - # project to size of convolution - x = self.fc1(x) - - # B x T x C -> T x B x C - x = self._transpose_if_training(x, incremental_state) - - # temporal convolutions - avg_attn_scores = None - num_attn_layers = len(self.attention) - residuals = [x] - for proj, conv, attention, res_layer in zip( - self.projections, self.convolutions, self.attention, self.residuals - ): - if res_layer > 0: - residual = residuals[-res_layer] - residual = residual if proj is None else proj(residual) - else: - residual = None - - x = self.dropout_module(x) - x = conv(x, incremental_state) - x = F.glu(x, dim=2) - - # attention - if attention is not None: - x = self._transpose_if_training(x, incremental_state) - - x, attn_scores = attention( - x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask - ) - - if not self.training and self.need_attn: - attn_scores = attn_scores / num_attn_layers - if avg_attn_scores is None: - avg_attn_scores = attn_scores - else: - avg_attn_scores.add_(attn_scores) - - x = self._transpose_if_training(x, incremental_state) - - # residual - if residual is not None: - x = (x + residual) * math.sqrt(0.5) - residuals.append(x) - - # T x B x C -> B x T x C - x = self._transpose_if_training(x, incremental_state) - - # project back to size of vocabulary if not using adaptive softmax - if self.fc2 is not None and self.fc3 is not None: - x = self.fc2(x) - x = self.dropout_module(x) - x = self.fc3(x) - - return x, avg_attn_scores - - def reorder_incremental_state(self, incremental_state, new_order): - super().reorder_incremental_state(incremental_state, new_order) - encoder_out = utils.get_incremental_state( - self, incremental_state, "encoder_out" - ) - if encoder_out is not None: - encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out) - utils.set_incremental_state( - self, incremental_state, "encoder_out", encoder_out - ) - - def max_positions(self): - """Maximum output length supported by the decoder.""" - return ( - self.embed_positions.max_positions - if self.embed_positions is not None - else float("inf") - ) - - def upgrade_state_dict(self, state_dict): - if utils.item(state_dict.get("decoder.version", torch.Tensor([1]))[0]) < 2: - # old models use incorrect weight norm dimension - for i, conv in enumerate(self.convolutions): - # reconfigure weight norm - nn.utils.remove_weight_norm(conv) - self.convolutions[i] = nn.utils.weight_norm(conv, dim=0) - state_dict["decoder.version"] = torch.Tensor([1]) - return state_dict - - def make_generation_fast_(self, need_attn=False, **kwargs): - self.need_attn = need_attn - - def _embed_tokens(self, tokens, incremental_state): - if incremental_state is not None: - # keep only the last token for incremental forward pass - tokens = tokens[:, -1:] - return self.embed_tokens(tokens) - - def _split_encoder_out(self, encoder_out, incremental_state): - """Split and transpose encoder outputs. - - This is cached when doing incremental inference. - """ - cached_result = utils.get_incremental_state( - self, incremental_state, "encoder_out" - ) - if cached_result is not None: - return cached_result - - # transpose only once to speed up attention layers - encoder_a, encoder_b = encoder_out - encoder_a = encoder_a.transpose(1, 2).contiguous() - result = (encoder_a, encoder_b) - - if incremental_state is not None: - utils.set_incremental_state(self, incremental_state, "encoder_out", result) - return result - - def _transpose_if_training(self, x, incremental_state): - if incremental_state is None: - x = x.transpose(0, 1) - return x - - -def extend_conv_spec(convolutions): - """ - Extends convolutional spec that is a list of tuples of 2 or 3 parameters - (kernel size, dim size and optionally how many layers behind to look for residual) - to default the residual propagation param if it is not specified - """ - extended = [] - for spec in convolutions: - if len(spec) == 3: - extended.append(spec) - elif len(spec) == 2: - extended.append(spec + (1,)) - else: - raise Exception( - "invalid number of parameters in convolution spec " - + str(spec) - + ". expected 2 or 3" - ) - return tuple(extended) - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, 0, 0.1) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx): - m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) - nn.init.normal_(m.weight, 0, 0.1) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def Linear(in_features, out_features, dropout=0.0): - """Weight-normalized Linear layer (input: N x T x C)""" - m = nn.Linear(in_features, out_features) - nn.init.normal_(m.weight, mean=0, std=math.sqrt((1 - dropout) / in_features)) - nn.init.constant_(m.bias, 0) - return nn.utils.weight_norm(m) - - -def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): - """Weight-normalized Conv1d layer optimized for decoding""" - m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) - std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) - nn.init.normal_(m.weight, mean=0, std=std) - nn.init.constant_(m.bias, 0) - return nn.utils.weight_norm(m, dim=2) - - -def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): - """Weight-normalized Conv1d layer""" - from fairseq.modules import ConvTBC - - m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs) - std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) - nn.init.normal_(m.weight, mean=0, std=std) - nn.init.constant_(m.bias, 0) - return nn.utils.weight_norm(m, dim=2) - - -@register_model_architecture("fconv", "fconv") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 20") - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 20") - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) - args.decoder_attention = getattr(args, "decoder_attention", "True") - args.share_input_output_embed = getattr(args, "share_input_output_embed", False) - - -@register_model_architecture("fconv", "fconv_iwslt_de_en") -def fconv_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_layers = getattr(args, "encoder_layers", "[(256, 3)] * 4") - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_layers = getattr(args, "decoder_layers", "[(256, 3)] * 3") - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) - base_architecture(args) - - -@register_model_architecture("fconv", "fconv_wmt_en_ro") -def fconv_wmt_en_ro(args): - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) - base_architecture(args) - - -@register_model_architecture("fconv", "fconv_wmt_en_de") -def fconv_wmt_en_de(args): - convs = "[(512, 3)] * 9" # first 9 layers have 512 units - convs += " + [(1024, 3)] * 4" # next 4 layers have 1024 units - convs += " + [(2048, 1)] * 2" # final 2 layers use 1x1 convolutions - - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) - args.encoder_layers = getattr(args, "encoder_layers", convs) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) - args.decoder_layers = getattr(args, "decoder_layers", convs) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) - base_architecture(args) - - -@register_model_architecture("fconv", "fconv_wmt_en_fr") -def fconv_wmt_en_fr(args): - convs = "[(512, 3)] * 6" # first 6 layers have 512 units - convs += " + [(768, 3)] * 4" # next 4 layers have 768 units - convs += " + [(1024, 3)] * 3" # next 3 layers have 1024 units - convs += " + [(2048, 1)] * 1" # next 1 layer uses 1x1 convolutions - convs += " + [(4096, 1)] * 1" # final 1 layer uses 1x1 convolutions - - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) - args.encoder_layers = getattr(args, "encoder_layers", convs) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 768) - args.decoder_layers = getattr(args, "decoder_layers", convs) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv_lm.py deleted file mode 100644 index 4b243d6669cb57880353b45a01843ec22010fb5f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv_lm.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq import utils -from fairseq.models import ( - FairseqLanguageModel, - register_model, - register_model_architecture, -) -from fairseq.models.fconv import FConvDecoder -from fairseq.utils import safe_hasattr - - -@register_model("fconv_lm") -class FConvLanguageModel(FairseqLanguageModel): - def __init__(self, decoder): - super().__init__(decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-layers", - type=str, - metavar="EXPR", - help="decoder layers [(dim, kernel_size), ...]", - ) - parser.add_argument( - "--decoder-out-embed-dim", - type=int, - metavar="N", - help="decoder output embedding dimension", - ) - parser.add_argument( - "--adaptive-softmax-cutoff", - metavar="EXPR", - help="comma separated list of adaptive softmax cutoff points. " - "Must be used with adaptive_loss criterion", - ) - parser.add_argument( - "--adaptive-softmax-dropout", - type=float, - metavar="D", - help="sets adaptive softmax dropout for the tail projections", - ) - parser.add_argument( - "--decoder-attention", - type=str, - metavar="EXPR", - help="decoder attention [True, ...]", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - # make sure all arguments are present in older models - base_lm_architecture(args) - - if safe_hasattr(args, "max_target_positions") and not safe_hasattr( - args, "tokens_per_sample" - ): - args.tokens_per_sample = args.max_target_positions - - decoder = FConvDecoder( - dictionary=task.target_dictionary, - embed_dim=args.decoder_embed_dim, - convolutions=eval(args.decoder_layers), - out_embed_dim=args.decoder_embed_dim, - attention=eval(args.decoder_attention), - dropout=args.dropout, - max_positions=args.tokens_per_sample, - share_embed=False, - positional_embeddings=False, - adaptive_softmax_cutoff=( - utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) - if args.criterion == "adaptive_loss" - else None - ), - adaptive_softmax_dropout=args.adaptive_softmax_dropout, - ) - return FConvLanguageModel(decoder) - - -@register_model_architecture("fconv_lm", "fconv_lm") -def base_lm_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) - args.decoder_layers = getattr(args, "decoder_layers", "[(1268, 4)] * 13") - args.decoder_attention = getattr(args, "decoder_attention", "False") - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - - -@register_model_architecture("fconv_lm", "fconv_lm_dauphin_wikitext103") -def fconv_lm_dauphin_wikitext103(args): - layers = "[(850, 6)] * 3" - layers += " + [(850, 1)] * 1" - layers += " + [(850, 5)] * 4" - layers += " + [(850, 1)] * 1" - layers += " + [(850, 4)] * 3" - layers += " + [(1024, 4)] * 1" - layers += " + [(2048, 4)] * 1" - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 280) - args.decoder_layers = getattr(args, "decoder_layers", layers) - args.decoder_attention = getattr(args, "decoder_attention", "False") - args.adaptive_softmax_cutoff = getattr( - args, "adaptive_softmax_cutoff", "10000,20000,200000" - ) - base_lm_architecture(args) - - -@register_model_architecture("fconv_lm", "fconv_lm_dauphin_gbw") -def fconv_lm_dauphin_gbw(args): - layers = "[(512, 5)]" - layers += " + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3" - layers += " + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3" - layers += " + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6" - layers += " + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]" - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) - args.decoder_layers = getattr(args, "decoder_layers", layers) - args.decoder_attention = getattr(args, "decoder_attention", "False") - args.adaptive_softmax_cutoff = getattr( - args, "adaptive_softmax_cutoff", "10000,50000,200000" - ) - base_lm_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv_self_att.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv_self_att.py deleted file mode 100644 index 511e8d72724480088b88715464f759d65a165138..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/fconv_self_att.py +++ /dev/null @@ -1,673 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import math -import os - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import checkpoint_utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.models import ( - CompositeEncoder, - FairseqDecoder, - FairseqEncoder, - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.modules import ( - DownsampledMultiHeadAttention, - FairseqDropout, - GradMultiply, - LayerNorm, - LearnedPositionalEmbedding, - LinearizedConvolution, -) - -logger = logging.getLogger(__name__) - - -@register_model("fconv_self_att") -class FConvModelSelfAtt(FairseqEncoderDecoderModel): - @classmethod - def hub_models(cls): - return { - "conv.stories.pretrained": { - "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz", - "checkpoint_file": "pretrained_checkpoint.pt", - "tokenizer": "nltk", - }, - "conv.stories": { - "path": "https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz", - "checkpoint_file": "fusion_checkpoint.pt", - "tokenizer": "nltk", - "pretrained": "True", - "pretrained_checkpoint": "./pretrained_checkpoint.pt", - }, - # Test set containing dictionaries - "data.stories": "https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2", - } - - def __init__(self, encoder, decoder, pretrained_encoder=None): - super().__init__(encoder, decoder) - self.encoder.num_attention_layers = sum( - layer is not None for layer in decoder.attention - ) - self.pretrained_encoder = pretrained_encoder - if self.pretrained_encoder is None: - encoders = {"encoder": encoder} - else: - encoders = {"encoder": encoder, "pretrained": self.pretrained_encoder} - # for fusion model, CompositeEncoder contains both pretrained and training encoders - # these are forwarded and then combined in the decoder - self.encoder = CompositeEncoder(encoders) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--encoder-embed-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--encoder-layers', type=str, metavar='EXPR', - help='encoder layers [(dim, kernel_size), ...]') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-layers', type=str, metavar='EXPR', - help='decoder layers [(dim, kernel_size), ...]') - parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', - help='decoder output embedding dimension') - parser.add_argument('--decoder-attention', type=str, metavar='EXPR', - help='decoder attention [True, ...]') - parser.add_argument('--self-attention', type=str, metavar='EXPR', - help='decoder self-attention layers, ex: [True] + [False]*5') - parser.add_argument('--multihead-attention-nheads', type=int, - help='Number of heads to use in attention') - parser.add_argument('--multihead-self-attention-nheads', type=int, - help='Number of heads to use in self-attention') - parser.add_argument('--encoder-attention', type=str, metavar='EXPR', - help='encoder attention [True, ...]') - parser.add_argument('--encoder-attention-nheads', type=int, - help='Number of heads to use in encoder attention') - parser.add_argument('--project-input', type=str, metavar='EXPR', - help='Use projections in self-attention [True, ...]') - parser.add_argument('--gated-attention', type=str, metavar='EXPR', - help='Use GLU layers in self-attention projections [True, ...]') - parser.add_argument('--downsample', type=str, metavar='EXPR', - help='Use downsampling in self-attention [True, ...]') - parser.add_argument('--pretrained-checkpoint', metavar='DIR', - help='path to load checkpoint from pretrained model') - parser.add_argument('--pretrained', type=str, metavar='EXPR', - help='use pretrained model when training [True, ...]') - # fmt: on - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - trained_encoder, trained_decoder = None, None - pretrained = eval(args.pretrained) - if pretrained: - logger.info("loading pretrained model") - if not os.path.exists(args.pretrained_checkpoint): - new_pretrained_checkpoint = os.path.join( - args.data, args.pretrained_checkpoint - ) - if os.path.exists(new_pretrained_checkpoint): - args.pretrained_checkpoint = new_pretrained_checkpoint - trained_model = checkpoint_utils.load_model_ensemble( - filenames=[args.pretrained_checkpoint], - task=task, - )[0][0] - trained_decoder = list(trained_model.children())[1] - trained_encoder = list(trained_model.children())[0] - - # freeze pretrained model - for param in trained_decoder.parameters(): - param.requires_grad = False - for param in trained_encoder.parameters(): - param.requires_grad = False - - encoder = FConvEncoder( - task.source_dictionary, - embed_dim=args.encoder_embed_dim, - convolutions=eval(args.encoder_layers), - dropout=args.dropout, - max_positions=args.max_source_positions, - attention=eval(args.encoder_attention), - attention_nheads=args.encoder_attention_nheads, - ) - - decoder = FConvDecoder( - task.target_dictionary, - embed_dim=args.decoder_embed_dim, - convolutions=eval(args.decoder_layers), - out_embed_dim=args.decoder_out_embed_dim, - attention=eval(args.decoder_attention), - dropout=args.dropout, - max_positions=args.max_target_positions, - selfattention=eval(args.self_attention), - attention_nheads=args.multihead_attention_nheads, - selfattention_nheads=args.multihead_self_attention_nheads, - project_input=eval(args.project_input), - gated_attention=eval(args.gated_attention), - downsample=eval(args.downsample), - pretrained=pretrained, - trained_decoder=trained_decoder, - ) - model = FConvModelSelfAtt(encoder, decoder, trained_encoder) - - return model - - @property - def pretrained(self): - return self.pretrained_encoder is not None - - -class FConvEncoder(FairseqEncoder): - """Convolutional encoder""" - - def __init__( - self, - dictionary, - embed_dim=512, - max_positions=1024, - convolutions=((512, 3),) * 20, - dropout=0.1, - attention=False, - attention_nheads=1, - ): - super().__init__(dictionary) - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.num_attention_layers = None - - num_embeddings = len(dictionary) - self.padding_idx = dictionary.pad() - self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) - self.embed_positions = PositionalEmbedding( - max_positions, - embed_dim, - self.padding_idx, - ) - - def expand_bool_array(val): - if isinstance(val, bool): - # expand True into [True, True, ...] and do the same with False - return [val] * len(convolutions) - return val - - attention = expand_bool_array(attention) - - in_channels = convolutions[0][0] - self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) - self.projections = nn.ModuleList() - self.convolutions = nn.ModuleList() - self.attention = nn.ModuleList() - self.attproj = nn.ModuleList() - for i, (out_channels, kernel_size) in enumerate(convolutions): - self.projections.append( - Linear(in_channels, out_channels) - if in_channels != out_channels - else None - ) - self.convolutions.append( - ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout) - ) - - self.attention.append( - SelfAttention(out_channels, embed_dim, attention_nheads) - if attention[i] - else None - ) - in_channels = out_channels - - self.fc2 = Linear(in_channels, embed_dim) - - def forward(self, src_tokens, src_lengths): - # embed tokens and positions - x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens) - x = self.dropout_module(x) - input_embedding = x.transpose(0, 1) - - # project to size of convolution - x = self.fc1(x) - - encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B - if not encoder_padding_mask.any(): - encoder_padding_mask = None - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # temporal convolutions - for proj, conv, attention in zip( - self.projections, self.convolutions, self.attention - ): - residual = x if proj is None else proj(x) - - if encoder_padding_mask is not None: - x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) - - x = self.dropout_module(x) - padding_l = (conv.kernel_size[0] - 1) // 2 - padding_r = conv.kernel_size[0] // 2 - x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r)) - x = conv(x) - x = F.glu(x, dim=2) - if attention is not None: - x = attention(x) - x = (x + residual) * math.sqrt(0.5) - - # T x B x C -> B x T x C - x = x.transpose(1, 0) - - # project back to size of embedding - x = self.fc2(x) - - if encoder_padding_mask is not None: - encoder_padding_mask = encoder_padding_mask.t() # -> B x T - x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0) - - # scale gradients (this only affects backward, not forward) - x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers)) - - # add output to input embedding for attention - y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5) - - return { - "encoder_out": (x, y), - "encoder_padding_mask": encoder_padding_mask, # B x T - } - - def reorder_encoder_out(self, encoder_out, new_order): - encoder_out["encoder_out"] = tuple( - eo.index_select(0, new_order) for eo in encoder_out["encoder_out"] - ) - - if encoder_out["encoder_padding_mask"] is not None: - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(0, new_order) - - if "pretrained" in encoder_out: - encoder_out["pretrained"]["encoder_out"] = tuple( - eo.index_select(0, new_order) - for eo in encoder_out["pretrained"]["encoder_out"] - ) - - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return self.embed_positions.max_positions - - -@with_incremental_state -class FConvDecoder(FairseqDecoder): - """Convolutional decoder""" - - def __init__( - self, - dictionary, - embed_dim=512, - out_embed_dim=256, - max_positions=1024, - convolutions=((512, 3),) * 8, - attention=True, - dropout=0.1, - selfattention=False, - attention_nheads=1, - selfattention_nheads=1, - project_input=False, - gated_attention=False, - downsample=False, - pretrained=False, - trained_decoder=None, - ): - super().__init__(dictionary) - self.register_buffer("version", torch.Tensor([2])) - self.pretrained = pretrained - self.pretrained_decoder = trained_decoder - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.need_attn = True - in_channels = convolutions[0][0] - - def expand_bool_array(val): - if isinstance(val, bool): - # expand True into [True, True, ...] and do the same with False - return [val] * len(convolutions) - return val - - attention = expand_bool_array(attention) - selfattention = expand_bool_array(selfattention) - - if not isinstance(attention, list) or len(attention) != len(convolutions): - raise ValueError( - "Attention is expected to be a list of booleans of " - "length equal to the number of layers." - ) - - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) - - self.embed_positions = PositionalEmbedding( - max_positions, - embed_dim, - padding_idx, - ) - - self.fc1 = Linear(embed_dim, in_channels, dropout=dropout) - self.projections = nn.ModuleList() - self.convolutions = nn.ModuleList() - self.attention = nn.ModuleList() - self.selfattention = nn.ModuleList() - self.attproj = nn.ModuleList() - for i, (out_channels, kernel_size) in enumerate(convolutions): - self.projections.append( - Linear(in_channels, out_channels) - if in_channels != out_channels - else None - ) - self.convolutions.append( - LinearizedConv1d( - in_channels, - out_channels * 2, - kernel_size, - padding=(kernel_size - 1), - dropout=dropout, - ) - ) - - self.attention.append( - DownsampledMultiHeadAttention( - out_channels, - embed_dim, - attention_nheads, - project_input=project_input, - gated=False, - downsample=False, - ) - if attention[i] - else None - ) - - self.attproj.append( - Linear(out_channels, embed_dim, dropout=dropout) - if attention[i] - else None - ) - self.selfattention.append( - SelfAttention( - out_channels, - embed_dim, - selfattention_nheads, - project_input=project_input, - gated=gated_attention, - downsample=downsample, - ) - if selfattention[i] - else None - ) - in_channels = out_channels - - self.fc2 = Linear(in_channels, out_embed_dim) - self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout) - - # model fusion - if self.pretrained: - # independent gates are learned from the concatenated input - self.gate1 = nn.Sequential( - Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid() - ) - self.gate2 = nn.Sequential( - Linear(out_embed_dim * 2, out_embed_dim), nn.Sigmoid() - ) - # pretrained and trained models are joined - self.joining = nn.Sequential( - Linear(out_embed_dim * 2, out_embed_dim * 2), - LayerNorm(out_embed_dim * 2), - nn.GLU(), - Linear(out_embed_dim, out_embed_dim * 2), - LayerNorm(out_embed_dim * 2), - nn.GLU(), - Linear(out_embed_dim, out_embed_dim), - LayerNorm(out_embed_dim), - ) - # pretrained model contains an output layer that is nhid -> vocab size - # but the models are combined in their hidden state - # the hook stores the output of the pretrained model forward - self.pretrained_outputs = {} - - def save_output(): - def hook(a, b, output): - self.pretrained_outputs["out"] = output - - return hook - - self.pretrained_decoder.fc2.register_forward_hook(save_output()) - - def forward(self, prev_output_tokens, encoder_out): - trained_encoder_out = encoder_out["pretrained"] if self.pretrained else None - encoder_out = encoder_out["encoder"]["encoder_out"] - - encoder_a, encoder_b = self._split_encoder_out(encoder_out) - - # embed positions - positions = self.embed_positions(prev_output_tokens) - - # embed tokens and positions - x = self.embed_tokens(prev_output_tokens) + positions - x = self.dropout_module(x) - target_embedding = x.transpose(0, 1) - - # project to size of convolution - x = self.fc1(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # temporal convolutions - avg_attn_scores = None - for proj, conv, attention, selfattention, attproj in zip( - self.projections, - self.convolutions, - self.attention, - self.selfattention, - self.attproj, - ): - residual = x if proj is None else proj(x) - - x = self.dropout_module(x) - x = conv(x) - x = F.glu(x, dim=2) - - # attention - if attention is not None: - r = x - x, attn_scores = attention( - attproj(x) + target_embedding, encoder_a, encoder_b - ) - x = x + r - if not self.training and self.need_attn: - if avg_attn_scores is None: - avg_attn_scores = attn_scores - else: - avg_attn_scores.add_(attn_scores) - - if selfattention is not None: - x = selfattention(x) - - x = (x + residual) * math.sqrt(0.5) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - # project back to size of vocabulary - x = self.fc2(x) - x = self.dropout_module(x) - if not self.pretrained: - x = self.fc3(x) - - # fusion gating - if self.pretrained: - trained_x, _ = self.pretrained_decoder.forward( - prev_output_tokens, trained_encoder_out - ) - y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1) - gate1 = self.gate1(y) - gate2 = self.gate2(y) - gated_x1 = gate1 * x - gated_x2 = gate2 * self.pretrained_outputs["out"] - fusion = torch.cat([gated_x1, gated_x2], dim=-1) - fusion = self.joining(fusion) - fusion_output = self.fc3(fusion) - return fusion_output, avg_attn_scores - else: - return x, avg_attn_scores - - def max_positions(self): - """Maximum output length supported by the decoder.""" - return self.embed_positions.max_positions - - def make_generation_fast_(self, need_attn=False, **kwargs): - self.need_attn = need_attn - - def _split_encoder_out(self, encoder_out): - """Split and transpose encoder outputs.""" - # transpose only once to speed up attention layers - encoder_a, encoder_b = encoder_out - encoder_a = encoder_a.transpose(0, 1).contiguous() - encoder_b = encoder_b.transpose(0, 1).contiguous() - result = (encoder_a, encoder_b) - return result - - -class SelfAttention(nn.Module): - def __init__( - self, - out_channels, - embed_dim, - num_heads, - project_input=False, - gated=False, - downsample=False, - ): - super().__init__() - self.attention = DownsampledMultiHeadAttention( - out_channels, - embed_dim, - num_heads, - dropout=0, - bias=True, - project_input=project_input, - gated=gated, - downsample=downsample, - ) - self.in_proj_q = Linear(out_channels, embed_dim) - self.in_proj_k = Linear(out_channels, embed_dim) - self.in_proj_v = Linear(out_channels, embed_dim) - self.ln = LayerNorm(out_channels) - - def forward(self, x): - residual = x - query = self.in_proj_q(x) - key = self.in_proj_k(x) - value = self.in_proj_v(x) - x, _ = self.attention( - query, key, value, mask_future_timesteps=True, use_scalar_bias=True - ) - return self.ln(x + residual) - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - m.weight.data.normal_(0, 0.1) - return m - - -def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx): - m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) - m.weight.data.normal_(0, 0.1) - return m - - -def Linear(in_features, out_features, dropout=0.0): - """Weight-normalized Linear layer (input: N x T x C)""" - m = nn.Linear(in_features, out_features) - m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) - m.bias.data.zero_() - return m - - -def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): - """Weight-normalized Conv1d layer optimized for decoding""" - m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs) - std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) - m.weight.data.normal_(mean=0, std=std) - m.bias.data.zero_() - return m - - -def ConvTBC(in_channels, out_channels, kernel_size, dropout=0.0, **kwargs): - """Weight-normalized Conv1d layer""" - from fairseq.modules import ConvTBC - - m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs) - std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels)) - m.weight.data.normal_(mean=0, std=std) - m.bias.data.zero_() - return m - - -@register_model_architecture("fconv_self_att", "fconv_self_att") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_layers = getattr(args, "encoder_layers", "[(512, 3)] * 3") - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_layers = getattr(args, "decoder_layers", "[(512, 3)] * 8") - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) - args.decoder_attention = getattr(args, "decoder_attention", "True") - args.self_attention = getattr(args, "self_attention", "False") - args.encoder_attention = getattr(args, "encoder_attention", "False") - args.multihead_attention_nheads = getattr(args, "multihead_attention_nheads", 1) - args.multihead_self_attention_nheads = getattr( - args, "multihead_self_attention_nheads", 1 - ) - args.encoder_attention_nheads = getattr(args, "encoder_attention_nheads", 1) - args.project_input = getattr(args, "project_input", "False") - args.gated_attention = getattr(args, "gated_attention", "False") - args.downsample = getattr(args, "downsample", "False") - args.pretrained_checkpoint = getattr(args, "pretrained_checkpoint", "") - args.pretrained = getattr(args, "pretrained", "False") - - -@register_model_architecture("fconv_self_att", "fconv_self_att_wp") -def fconv_self_att_wp(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_layers = getattr( - args, "encoder_layers", "[(128, 3)] * 2 + [(512,3)] * 1" - ) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_layers = getattr( - args, "decoder_layers", "[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1" - ) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) - args.self_attention = getattr(args, "self_attention", "True") - args.multihead_self_attention_nheads = getattr( - args, "multihead_self_attention_nheads", 4 - ) - args.project_input = getattr(args, "project_input", "True") - args.gated_attention = getattr(args, "gated_attention", "True") - args.downsample = getattr(args, "downsample", "True") - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/__init__.py deleted file mode 100644 index a1b0eabbdbcaf12b15bb96b329ab1e276256f79a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .hubert import * # noqa -from .hubert_asr import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/hubert.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/hubert.py deleted file mode 100644 index 53d774a7e947324e3407b1e5b3d5e8924610364b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/hubert.py +++ /dev/null @@ -1,571 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple - -import numpy as np -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.data.data_utils import compute_mask_indices -from fairseq.data.dictionary import Dictionary -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.models import BaseFairseqModel, register_model -from fairseq.models.wav2vec.wav2vec2 import ( - EXTRACTOR_MODE_CHOICES, - LAYER_TYPE_CHOICES, - MASKING_DISTRIBUTION_CHOICES, - ConvFeatureExtractionModel, - TransformerEncoder, -) -from fairseq.modules import GradMultiply, LayerNorm -from fairseq.tasks.hubert_pretraining import ( - HubertPretrainingConfig, - HubertPretrainingTask, -) -from omegaconf import II - -logger = logging.getLogger(__name__) - - -@dataclass -class HubertConfig(FairseqDataclass): - label_rate: float = II("task.label_rate") - - extractor_mode: EXTRACTOR_MODE_CHOICES = field( - default="default", - metadata={ - "help": "mode for feature extractor. default has a single group " - "norm with d groups in the first conv block, whereas layer_norm " - "has layer norms in every block (meant to use with normalize=True)" - }, - ) - encoder_layers: int = field( - default=12, metadata={"help": "num encoder layers in the transformer"} - ) - encoder_embed_dim: int = field( - default=768, metadata={"help": "encoder embedding dimension"} - ) - encoder_ffn_embed_dim: int = field( - default=3072, metadata={"help": "encoder embedding dimension for FFN"} - ) - encoder_attention_heads: int = field( - default=12, metadata={"help": "num encoder attention heads"} - ) - activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( - default="gelu", metadata={"help": "activation function to use"} - ) - layer_type: LAYER_TYPE_CHOICES = field( - default="transformer", metadata={"help": "layer type in encoder"} - ) - - # dropouts - dropout: float = field( - default=0.1, - metadata={"help": "dropout probability for the transformer"}, - ) - attention_dropout: float = field( - default=0.1, - metadata={"help": "dropout probability for attention weights"}, - ) - activation_dropout: float = field( - default=0.0, - metadata={"help": "dropout probability after activation in FFN"}, - ) - encoder_layerdrop: float = field( - default=0.0, - metadata={"help": "probability of dropping a tarnsformer layer"}, - ) - dropout_input: float = field( - default=0.0, - metadata={"help": "dropout to apply to the input (after feat extr)"}, - ) - dropout_features: float = field( - default=0.0, - metadata={"help": "dropout to apply to the features (after feat extr)"}, - ) - - final_dim: int = field( - default=0, - metadata={ - "help": "project final representations and targets to this many " - "dimensions. set to encoder_embed_dim is <= 0" - }, - ) - untie_final_proj: bool = field( - default=False, - metadata={"help": "use separate projection for each target"}, - ) - layer_norm_first: bool = field( - default=False, - metadata={"help": "apply layernorm first in the transformer"}, - ) - conv_feature_layers: str = field( - default="[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", - metadata={ - "help": "string describing convolutional feature extraction " - "layers in form of a python list that contains " - "[(dim, kernel_size, stride), ...]" - }, - ) - conv_bias: bool = field( - default=False, metadata={"help": "include bias in conv encoder"} - ) - logit_temp: float = field( - default=0.1, metadata={"help": "temperature to divide logits by"} - ) - target_glu: bool = field( - default=False, metadata={"help": "adds projection + glu to targets"} - ) - feature_grad_mult: float = field( - default=1.0, - metadata={"help": "multiply feature extractor var grads by this"}, - ) - - # masking - mask_length: int = field(default=10, metadata={"help": "mask length"}) - mask_prob: float = field( - default=0.65, - metadata={"help": "probability of replacing a token with mask"}, - ) - mask_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose mask length"} - ) - mask_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument " - "(used for more complex distributions), " - "see help in compute_mask_indicesh" - }, - ) - no_mask_overlap: bool = field( - default=False, metadata={"help": "whether to allow masks to overlap"} - ) - mask_min_space: int = field( - default=1, - metadata={"help": "min space between spans (if no overlap is enabled)"}, - ) - - # channel masking - mask_channel_length: int = field( - default=10, - metadata={"help": "length of the mask for features (channels)"}, - ) - mask_channel_prob: float = field( - default=0.0, - metadata={"help": "probability of replacing a feature with 0"}, - ) - mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", - metadata={"help": "how to choose mask length for channel masking"}, - ) - mask_channel_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument " - "(used for more complex distributions), " - "see help in compute_mask_indicesh" - }, - ) - no_mask_channel_overlap: bool = field( - default=False, - metadata={"help": "whether to allow channel masks to overlap"}, - ) - mask_channel_min_space: int = field( - default=1, - metadata={"help": "min space between spans (if no overlap is enabled)"}, - ) - - # positional embeddings - conv_pos: int = field( - default=128, - metadata={"help": "number of filters for convolutional positional embeddings"}, - ) - conv_pos_groups: int = field( - default=16, - metadata={"help": "number of groups for convolutional positional embedding"}, - ) - - latent_temp: Tuple[float, float, float] = field( - default=(2, 0.5, 0.999995), - metadata={"help": "legacy (to be removed)"}, - ) - - # loss computation - skip_masked: bool = field( - default=False, - metadata={"help": "skip computing losses over masked frames"}, - ) - skip_nomask: bool = field( - default=False, - metadata={"help": "skip computing losses over unmasked frames"}, - ) - - checkpoint_activations: bool = field( - default=False, - metadata={"help": "recompute activations and save memory for extra compute"}, - ) - - # FP16 optimization - required_seq_len_multiple: int = field( - default=2, - metadata={ - "help": "pad the input to encoder such that the sequence length is divisible by multiple" - }, - ) - - # Conformer - depthwise_conv_kernel_size: int = field( - default=31, - metadata={ - "help": "depthwise-conv-kernel-size for convolution in conformer layer" - }, - ) - attn_type: str = field( - default="", - metadata={"help": "if espnet use ESPNET MHA"}, - ) - pos_enc_type: str = field( - default="abs", - metadata={"help": "Positional encoding type to use in conformer"}, - ) - fp16: bool = field(default=False, metadata={"help": "If fp16 is being used"}) - - -@register_model("hubert", dataclass=HubertConfig) -class HubertModel(BaseFairseqModel): - def __init__( - self, - cfg: HubertConfig, - task_cfg: HubertPretrainingConfig, - dictionaries: List[Dictionary], - ) -> None: - super().__init__() - logger.info(f"HubertModel Config: {cfg}") - - feature_enc_layers = eval(cfg.conv_feature_layers) # noqa - self.embed = feature_enc_layers[-1][0] - - self.feature_extractor = ConvFeatureExtractionModel( - conv_layers=feature_enc_layers, - dropout=0.0, - mode=cfg.extractor_mode, - conv_bias=cfg.conv_bias, - ) - feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) - self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / task_cfg.sample_rate - - self.post_extract_proj = ( - nn.Linear(self.embed, cfg.encoder_embed_dim) - if self.embed != cfg.encoder_embed_dim - else None - ) - - self.mask_prob = cfg.mask_prob - self.mask_selection = cfg.mask_selection - self.mask_other = cfg.mask_other - self.mask_length = cfg.mask_length - self.no_mask_overlap = cfg.no_mask_overlap - self.mask_min_space = cfg.mask_min_space - - self.mask_channel_prob = cfg.mask_channel_prob - self.mask_channel_selection = cfg.mask_channel_selection - self.mask_channel_other = cfg.mask_channel_other - self.mask_channel_length = cfg.mask_channel_length - self.no_mask_channel_overlap = cfg.no_mask_channel_overlap - self.mask_channel_min_space = cfg.mask_channel_min_space - - self.dropout_input = nn.Dropout(cfg.dropout_input) - self.dropout_features = nn.Dropout(cfg.dropout_features) - - self.feature_grad_mult = cfg.feature_grad_mult - self.logit_temp = cfg.logit_temp - self.skip_masked = cfg.skip_masked - self.skip_nomask = cfg.skip_nomask - - final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim - - self.mask_emb = nn.Parameter( - torch.FloatTensor(cfg.encoder_embed_dim).uniform_() - ) - - self.encoder = TransformerEncoder(cfg) - self.layer_norm = LayerNorm(self.embed) - - self.target_glu = None - if cfg.target_glu: - self.target_glu = nn.Sequential( - nn.Linear(final_dim, final_dim * 2), nn.GLU() - ) - - self.untie_final_proj = cfg.untie_final_proj - if self.untie_final_proj: - self.final_proj = nn.Linear( - cfg.encoder_embed_dim, final_dim * len(dictionaries) - ) - else: - self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) - - # modules below are not needed during fine-tuning - if any([d is None for d in dictionaries]): - logger.info("cannot find dictionary. assume will be used for fine-tuning") - else: - self.num_classes = [len(d) for d in dictionaries] - self.label_embs_concat = nn.Parameter( - torch.FloatTensor(sum(self.num_classes), final_dim) - ) - nn.init.uniform_(self.label_embs_concat) - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - - super().upgrade_state_dict_named(state_dict, name) - return state_dict - - @classmethod - def build_model(cls, cfg: HubertConfig, task: HubertPretrainingTask): - """Build a new model instance.""" - - model = HubertModel(cfg, task.cfg, task.dictionaries) - return model - - def apply_mask(self, x, padding_mask, target_list): - B, T, C = x.shape - if self.mask_prob > 0: - mask_indices = compute_mask_indices( - (B, T), - padding_mask, - self.mask_prob, - self.mask_length, - self.mask_selection, - self.mask_other, - min_masks=2, - no_overlap=self.no_mask_overlap, - min_space=self.mask_min_space, - ) - mask_indices = torch.from_numpy(mask_indices).to(x.device) - x[mask_indices] = self.mask_emb - else: - mask_indices = None - - if self.mask_channel_prob > 0: - mask_channel_indices = compute_mask_indices( - (B, C), - None, - self.mask_channel_prob, - self.mask_channel_length, - self.mask_channel_selection, - self.mask_channel_other, - no_overlap=self.no_mask_channel_overlap, - min_space=self.mask_channel_min_space, - ) - mask_channel_indices = ( - torch.from_numpy(mask_channel_indices) - .to(x.device) - .unsqueeze(1) - .expand(-1, T, -1) - ) - x[mask_channel_indices] = 0 - - return x, mask_indices - - def compute_nce(self, x, pos, negs): - neg_is_pos = (pos == negs).all(-1) - pos = pos.unsqueeze(0) - targets = torch.cat([pos, negs], dim=0) - - logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x) - logits /= self.logit_temp - if neg_is_pos.any(): - logits[1:][neg_is_pos] = float("-inf") - logits = logits.transpose(0, 1) # (num_x, num_cls+1) - return logits - - def forward_features(self, source: torch.Tensor) -> torch.Tensor: - if self.feature_grad_mult > 0: - features = self.feature_extractor(source) - if self.feature_grad_mult != 1.0: - features = GradMultiply.apply(features, self.feature_grad_mult) - else: - with torch.no_grad(): - features = self.feature_extractor(source) - return features - - def forward_targets( - self, - features: torch.Tensor, - target_list: List[torch.Tensor], - ) -> Tuple[torch.Tensor, torch.Tensor]: - # Trim features to ensure labels exist and then get aligned labels - feat_tsz = features.size(2) - targ_tsz = min([t.size(1) for t in target_list]) - if self.feat2tar_ratio * feat_tsz > targ_tsz: - feat_tsz = int(targ_tsz / self.feat2tar_ratio) - features = features[..., :feat_tsz] - target_inds = torch.arange(feat_tsz).float() * self.feat2tar_ratio - target_list = [t[:, target_inds.long()] for t in target_list] - return features, target_list - - def forward_padding_mask( - self, - features: torch.Tensor, - padding_mask: torch.Tensor, - ) -> torch.Tensor: - extra = padding_mask.size(1) % features.size(1) - if extra > 0: - padding_mask = padding_mask[:, :-extra] - padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1) - padding_mask = padding_mask.all(-1) - return padding_mask - - def forward( - self, - source: torch.Tensor, - target_list: Optional[List[torch.Tensor]] = None, - padding_mask: Optional[torch.Tensor] = None, - mask: bool = True, - features_only: bool = False, - output_layer: Optional[int] = None, - ) -> Dict[str, torch.Tensor]: - """output layer is 1-based""" - features = self.forward_features(source) - if target_list is not None: - features, target_list = self.forward_targets(features, target_list) - - features_pen = features.float().pow(2).mean() - - features = features.transpose(1, 2) - features = self.layer_norm(features) - unmasked_features = features.clone() - - if padding_mask is not None: - padding_mask = self.forward_padding_mask(features, padding_mask) - - if self.post_extract_proj is not None: - features = self.post_extract_proj(features) - - features = self.dropout_input(features) - unmasked_features = self.dropout_features(unmasked_features) - - if mask: - x, mask_indices = self.apply_mask(features, padding_mask, target_list) - else: - x = features - mask_indices = None - - # feature: (B, T, D), float - # target: (B, T), long - # x: (B, T, D), float - # padding_mask: (B, T), bool - # mask_indices: (B, T), bool - x, layer_results = self.encoder( - x, - padding_mask=padding_mask, - layer=None if output_layer is None else output_layer - 1, - ) - - if features_only: - return {"x": x, "padding_mask": padding_mask, "features": features,"layer_results":layer_results} - - def compute_pred(proj_x, target, label_embs): - # compute logits for the i-th label set - y = torch.index_select(label_embs, 0, target.long()) - negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1) - if self.target_glu: - y = self.target_glu(y) - negs = self.target_glu(negs) - # proj_x: (S, D) - # y: (S, D) - # negs: (Neg, S, D) - return self.compute_nce(proj_x, y, negs) - - label_embs_list = self.label_embs_concat.split(self.num_classes, 0) - - if not self.skip_masked: - masked_indices = torch.logical_and(~padding_mask, mask_indices) - proj_x_m = self.final_proj(x[masked_indices]) - if self.untie_final_proj: - proj_x_m_list = proj_x_m.chunk(len(target_list), dim=-1) - else: - proj_x_m_list = [proj_x_m for _ in range(len(target_list))] - logit_m_list = [ - compute_pred(proj_x_m, t[masked_indices], label_embs_list[i]) - for i, (proj_x_m, t) in enumerate(zip(proj_x_m_list, target_list)) - ] - else: - logit_m_list = [None for _ in target_list] - - if not self.skip_nomask: - nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) - proj_x_u = self.final_proj(x[nomask_indices]) - if self.untie_final_proj: - proj_x_u_list = proj_x_u.chunk(len(target_list), dim=-1) - else: - proj_x_u_list = [proj_x_u for _ in range(len(target_list))] - - logit_u_list = [ - compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i]) - for i, (proj_x_u, t) in enumerate(zip(proj_x_u_list, target_list)) - ] - else: - logit_u_list = [None for _ in target_list] - - result = { - "logit_m_list": logit_m_list, - "logit_u_list": logit_u_list, - "padding_mask": padding_mask, - "features_pen": features_pen, - } - return result - - def extract_features( - self, - source: torch.Tensor, - padding_mask: Optional[torch.Tensor] = None, - mask: bool = False, - ret_conv: bool = False, - output_layer: Optional[int] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - res = self.forward( - source, - padding_mask=padding_mask, - mask=mask, - features_only=True, - output_layer=output_layer, - ) - feature = res["features"] if ret_conv else res["x"] - layer_results = res["layer_results"] - avg_layer_results = sum([result[0] for result in layer_results]) / len(layer_results) - return feature, res["padding_mask"], avg_layer_results - - def get_logits(self, net_output, is_masked=True): - if is_masked: - logits_list = net_output["logit_m_list"] - else: - logits_list = net_output["logit_u_list"] - logits_list = [x.float() for x in logits_list if x is not None] - return logits_list - - def get_targets(self, net_output, is_masked=True): - logits_list = self.get_logits(net_output, is_masked) - targets_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list] - return targets_list - - def get_extra_losses(self, net_output): - extra_losses = [] - names = [] - - if "features_pen" in net_output: - extra_losses.append(net_output["features_pen"]) - names.append("features_pen") - - return extra_losses, names - - def remove_pretraining_modules(self): - self.target_glu = None - self.final_proj = None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/hubert_asr.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/hubert_asr.py deleted file mode 100644 index 0d60abe14f0975c9bf99b7c8c41556311a35a757..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/hubert/hubert_asr.py +++ /dev/null @@ -1,363 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -from argparse import Namespace -from dataclasses import dataclass, field -from typing import Any - -import torch -import torch.nn as nn -from fairseq import checkpoint_utils, tasks, utils -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.models import BaseFairseqModel, FairseqEncoder, register_model -from fairseq.models.hubert.hubert import MASKING_DISTRIBUTION_CHOICES -from fairseq.tasks import FairseqTask -from omegaconf import II, MISSING - - -@dataclass -class HubertAsrConfig(FairseqDataclass): - w2v_path: str = field(default=MISSING, metadata={"help": "path to hubert model"}) - no_pretrained_weights: bool = field( - default=False, - metadata={"help": "if true, does not load pretrained weights"}, - ) - dropout_input: float = field( - default=0.0, - metadata={"help": "dropout to apply to the input (after feat extr)"}, - ) - final_dropout: float = field( - default=0.0, - metadata={"help": "dropout after transformer and before final projection"}, - ) - dropout: float = field( - default=0.0, - metadata={"help": "dropout probability inside hubert model"}, - ) - attention_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability for attention weights " "inside hubert model" - }, - ) - activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN " "inside hubert model" - }, - ) - - # masking - apply_mask: bool = field( - default=False, metadata={"help": "apply masking during fine-tuning"} - ) - mask_length: int = field( - default=10, metadata={"help": "repeat the mask indices multiple times"} - ) - mask_prob: float = field( - default=0.5, - metadata={ - "help": "probability of replacing a token with mask " - "(normalized by length)" - }, - ) - mask_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose masks"} - ) - mask_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument " - "(used for more complex distributions), " - "see help in compute_mask_indices" - }, - ) - no_mask_overlap: bool = field( - default=False, metadata={"help": "whether to allow masks to overlap"} - ) - - # channel masking - mask_channel_length: int = field( - default=10, - metadata={"help": "length of the mask for features (channels)"}, - ) - mask_channel_prob: float = field( - default=0.0, - metadata={"help": "probability of replacing a feature with 0"}, - ) - mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", - metadata={"help": "how to choose mask length for channel masking"}, - ) - mask_channel_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument " - "(used for more complex distributions), " - "see help in compute_mask_indices" - }, - ) - no_mask_channel_overlap: bool = field( - default=False, - metadata={"help": "whether to allow channel masks to overlap"}, - ) - freeze_finetune_updates: int = field( - default=0, - metadata={"help": "dont finetune hubert for this many updates"}, - ) - feature_grad_mult: float = field( - default=0.0, - metadata={"help": "reset feature grad mult in hubert to this"}, - ) - layerdrop: float = field( - default=0.0, - metadata={"help": "probability of dropping a layer in hubert"}, - ) - normalize: bool = II("task.normalize") - data: str = II("task.data") - - # this holds the loaded hubert args - w2v_args: Any = None - - -@dataclass -class HubertCtcConfig(HubertAsrConfig): - pass - - -@register_model("hubert_ctc", dataclass=HubertCtcConfig) -class HubertCtc(BaseFairseqModel): - def __init__(self, cfg: HubertCtcConfig, w2v_encoder: BaseFairseqModel): - super().__init__() - self.cfg = cfg - self.w2v_encoder = w2v_encoder - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - return state_dict - - @classmethod - def build_model(cls, cfg: HubertCtcConfig, task: FairseqTask): - """Build a new model instance.""" - w2v_encoder = HubertEncoder(cfg, task) - return cls(cfg, w2v_encoder) - - def get_normalized_probs(self, net_output, log_probs): - """Get normalized probabilities (or log probs) from a net's output.""" - - logits = net_output["encoder_out"] - if log_probs: - return utils.log_softmax(logits.float(), dim=-1) - else: - return utils.softmax(logits.float(), dim=-1) - - def get_logits(self, net_output): - logits = net_output["encoder_out"] - padding = net_output["encoder_padding_mask"] - if padding is not None and padding.any(): - padding = padding.T - logits[padding][..., 0] = 0 - logits[padding][..., 1:] = float("-inf") - - return logits - - def forward(self, **kwargs): - x = self.w2v_encoder(**kwargs) - return x - - -@dataclass -class HubertSeq2SeqConfig(HubertAsrConfig): - decoder_embed_dim: int = field( - default=768, metadata={"help": "decoder embedding dimension"} - ) - decoder_ffn_embed_dim: int = field( - default=3072, metadata={"help": "decoder embedding dimension for FFN"} - ) - decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) - decoder_layerdrop: float = field( - default=0.0, metadata={"help": "decoder layerdrop chance"} - ) - decoder_attention_heads: int = field( - default=4, metadata={"help": "num decoder attention heads"} - ) - decoder_learned_pos: bool = field( - default=False, - metadata={"help": "use learned positional embeddings in the decoder"}, - ) - decoder_normalize_before: bool = field( - default=False, - metadata={"help": "apply layernorm before each decoder block"}, - ) - no_token_positional_embeddings: bool = field( - default=False, - metadata={ - "help": "if set, disables positional embeddings " "(outside self attention)" - }, - ) - decoder_dropout: float = field( - default=0.0, metadata={"help": "dropout probability in the decoder"} - ) - decoder_attention_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability for attention weights " "inside the decoder" - }, - ) - decoder_activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN " "inside the decoder" - }, - ) - max_target_positions: int = field( - default=2048, metadata={"help": "max target positions"} - ) - share_decoder_input_output_embed: bool = field( - default=False, - metadata={"help": "share decoder input and output embeddings"}, - ) - - -class HubertEncoder(FairseqEncoder): - def __init__(self, cfg: HubertAsrConfig, task): - self.apply_mask = cfg.apply_mask - - arg_overrides = { - "dropout": cfg.dropout, - "activation_dropout": cfg.activation_dropout, - "dropout_input": cfg.dropout_input, - "attention_dropout": cfg.attention_dropout, - "mask_length": cfg.mask_length, - "mask_prob": cfg.mask_prob, - "mask_selection": cfg.mask_selection, - "mask_other": cfg.mask_other, - "no_mask_overlap": cfg.no_mask_overlap, - "mask_channel_length": cfg.mask_channel_length, - "mask_channel_prob": cfg.mask_channel_prob, - "mask_channel_selection": cfg.mask_channel_selection, - "mask_channel_other": cfg.mask_channel_other, - "no_mask_channel_overlap": cfg.no_mask_channel_overlap, - "encoder_layerdrop": cfg.layerdrop, - "feature_grad_mult": cfg.feature_grad_mult, - } - - if cfg.w2v_args is None: - state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) - w2v_args = state.get("cfg", None) - if w2v_args is None: - w2v_args = convert_namespace_to_omegaconf(state["args"]) - cfg.w2v_args = w2v_args - else: - state = None - w2v_args = cfg.w2v_args - if isinstance(w2v_args, Namespace): - cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) - - assert cfg.normalize == w2v_args.task.normalize, ( - "Fine-tuning works best when data normalization is the same. " - "Please check that --normalize is set or unset for " - "both pre-training and here" - ) - - w2v_args.task.data = cfg.data - pretrain_task = tasks.setup_task(w2v_args.task) - if state is not None and "task_state" in state: - # This will load the stored "dictionaries" object - pretrain_task.load_state_dict(state["task_state"]) - else: - pretrain_task.load_state_dict(task.state_dict()) - - model = pretrain_task.build_model(w2v_args.model, from_checkpoint=True) - if state is not None and not cfg.no_pretrained_weights: - # set strict=False because we omit some modules - model.load_state_dict(state["model"], strict=False) - - model.remove_pretraining_modules() - - super().__init__(pretrain_task.source_dictionary) - - d = w2v_args.model.encoder_embed_dim - - self.w2v_model = model - - self.final_dropout = nn.Dropout(cfg.final_dropout) - self.freeze_finetune_updates = cfg.freeze_finetune_updates - self.num_updates = 0 - - if task.target_dictionary is not None: - self.proj = Linear(d, len(task.target_dictionary)) - elif getattr(cfg, "decoder_embed_dim", d) != d: - self.proj = Linear(d, cfg.decoder_embed_dim) - else: - self.proj = None - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - super().set_num_updates(num_updates) - self.num_updates = num_updates - - def forward(self, source, padding_mask, tbc=True, **kwargs): - - w2v_args = { - "source": source, - "padding_mask": padding_mask, - "mask": self.apply_mask and self.training, - } - - ft = self.freeze_finetune_updates <= self.num_updates - - with torch.no_grad() if not ft else contextlib.ExitStack(): - x, padding_mask = self.w2v_model.extract_features(**w2v_args) - - if tbc: - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - x = self.final_dropout(x) - - if self.proj: - x = self.proj(x) - - return { - "encoder_out": x, # T x B x C - "encoder_padding_mask": padding_mask, # B x T - "padding_mask": padding_mask, - } - - def reorder_encoder_out(self, encoder_out, new_order): - if encoder_out["encoder_out"] is not None: - encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( - 1, new_order - ) - if encoder_out["encoder_padding_mask"] is not None: - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(0, new_order) - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return None - - def upgrade_state_dict_named(self, state_dict, name): - return state_dict - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/huggingface/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/huggingface/__init__.py deleted file mode 100644 index c6906f708d6ce5df5fd7deee859ae71f1d07e07b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/huggingface/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import importlib -import os - -# automatically import any Python files in the models/huggingface/ directory -models_dir = os.path.dirname(__file__) -for file in os.listdir(models_dir): - path = os.path.join(models_dir, file) - if ( - not file.startswith("_") - and not file.startswith(".") - and (file.endswith(".py") or os.path.isdir(path)) - ): - model_name = file[: file.find(".py")] if file.endswith(".py") else file - module = importlib.import_module("fairseq.models.huggingface." + model_name) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/huggingface/hf_gpt2.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/huggingface/hf_gpt2.py deleted file mode 100644 index 75a5a367108021f49849bc1ebfcce8a3f100d568..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/huggingface/hf_gpt2.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import sys -from typing import Dict, List, Optional - -import torch -from fairseq.models import ( - FairseqIncrementalDecoder, - FairseqLanguageModel, - register_model, - register_model_architecture, -) - -logger = logging.getLogger(__name__) - - -DEFAULT_MAX_TARGET_POSITIONS = 1024 - - -@register_model("hf_gpt2") -class HuggingFaceGPT2LanguageModel(FairseqLanguageModel): - def __init__(self, decoder): - super().__init__(decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--embed-dim', type=int, metavar='N', - help='embedding dimension') - parser.add_argument('--num-attention-heads', type=int, metavar='N', - help='num attention heads') - parser.add_argument('--num-layers', type=int, metavar='N', - help='num layers') - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability for all fully connected layers ' - 'in the embeddings, encoder, and pooler') - parser.add_argument('--attention-dropout', type=float, metavar='D', - help='dropout probability for attention weights') - # fmt: on - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - default_architecture(args) - return cls(HuggingFaceGPT2Decoder(args, task)) - - -class HuggingFaceGPT2Decoder(FairseqIncrementalDecoder): - def __init__(self, args, task): - try: - from transformers import GPT2Config, GPT2LMHeadModel - except ImportError: - raise ImportError( - "\n\nPlease install huggingface/transformers with:" - "\n\n pip install transformers" - ) - - super().__init__(task.target_dictionary) - - config = GPT2Config( - vocab_size=len(task.target_dictionary), - n_positions=args.max_target_positions + 1, - n_ctx=args.max_target_positions, - n_embd=args.embed_dim, - n_layer=args.num_layers, - n_head=args.num_attention_heads, - resid_pdrop=args.dropout, - embd_pdrop=args.dropout, - attn_pdrop=args.attention_dropout, - layer_norm_epsilon=1e-6, - ) - self.model = GPT2LMHeadModel(config) - - # set zero embedding for padding symbol - self.pad_idx = task.target_dictionary.pad() - self.model.transformer.wte.weight.data[self.pad_idx].zero_() - self.model.transformer.wpe.weight.data[0].zero_() - - def forward( - self, - prev_output_tokens, - src_lengths=None, - incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, - encoder_out=None, - ): - features = self.extract_features(prev_output_tokens, incremental_state) - lm_logits = self.model.lm_head(features) - return (lm_logits,) - - def extract_features( - self, - prev_output_tokens, - incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, - ): - if incremental_state: - past = self.get_incremental_state("past") - else: - past = None - - # don't attend to padding symbols - attention_mask = prev_output_tokens.ne(self.pad_idx).int() - - # set position ids to exclude padding symbols - position_ids = attention_mask * ( - torch.arange(1, 1 + prev_output_tokens.size(1)) - .to(prev_output_tokens) - .repeat(prev_output_tokens.size(0), 1) - ) - - outputs = self.model.transformer( - input_ids=prev_output_tokens, - past=past, - attention_mask=attention_mask, - position_ids=position_ids, - ) - last_hidden_states = outputs[0] - - if incremental_state: - self.set_incremental_state(incremental_state, "past", outputs[1]) - - return last_hidden_states - - def max_positions(self): - return self.model.config.n_positions - 1 - - -@register_model_architecture("hf_gpt2", "hf_gpt2") -def default_architecture(args): - if getattr(args, "max_target_positions", None) is None: - args.max_target_positions = getattr( - args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS - ) - args.embed_dim = getattr(args, "embed_dim", 768) - args.num_attention_heads = getattr(args, "num_attention_heads", 12) - args.num_layers = getattr(args, "num_layers", 12) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - - -@register_model_architecture("hf_gpt2", "hf_gpt2_medium") -def hf_gpt2_medium(args): - args.embed_dim = getattr(args, "embed_dim", 1024) - args.num_attention_heads = getattr(args, "num_attention_heads", 16) - args.num_layers = getattr(args, "num_layers", 24) - default_architecture(args) - - -@register_model_architecture("hf_gpt2", "hf_gpt2_large") -def hf_gpt2_large(args): - args.embed_dim = getattr(args, "embed_dim", 1280) - args.num_attention_heads = getattr(args, "num_attention_heads", 20) - args.num_layers = getattr(args, "num_layers", 36) - default_architecture(args) - - -@register_model_architecture("hf_gpt2", "hf_gpt2_xl") -def hf_gpt2_xl(args): - args.embed_dim = getattr(args, "embed_dim", 1600) - args.num_attention_heads = getattr(args, "num_attention_heads", 25) - args.num_layers = getattr(args, "num_layers", 48) - default_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lightconv.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lightconv.py deleted file mode 100644 index ae200db38c5cbc3ab17fe193e76b5c42b81eb61d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lightconv.py +++ /dev/null @@ -1,1019 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) -from fairseq.modules import ( - AdaptiveSoftmax, - DynamicConv, - FairseqDropout, - LayerNorm, - LightweightConv, - MultiheadAttention, - PositionalEmbedding, -) -from fairseq.utils import safe_hasattr - - -@register_model("lightconv") -class LightConvModel(FairseqEncoderDecoderModel): - """ - LightConv and DynamicConv model from `"Pay Less Attention with Lightweight and Dynamic Convolutions" (Wu, et al, 2019) - `_. - To use LightConv please set ``--encoder-conv-type lightweight --decoder-conv-type lightweight`` - To use DynamicConv please set ``--encoder-conv-type dynamic --decoder-conv-type dynamic`` - - Args: - encoder (LightConvEncoder): the encoder - decoder (LightConvDecoder): the decoder - - The LightConv model provides the following named architectures and - command-line arguments: - - .. argparse:: - :ref: fairseq.models.lightconv_parser - :prog: - """ - - @classmethod - def hub_models(cls): - # fmt: off - - def moses_subword(path): - return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'subword_nmt', - } - - return { - 'lightconv.no_glu.iwslt14.de-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.lightconv.tar.gz'), - 'dynamicconv.no_glu.iwslt14.de-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.dynamicconv.tar.gz'), - 'lightconv.no_glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv.tar.gz'), - 'dynamicconv.no_glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv.tar.gz'), - 'lightconv.glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz'), - 'dynamicconv.glu.wmt16.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz'), - 'lightconv.glu.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz'), - 'dynamicconv.glu.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz'), - 'lightconv.glu.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.lightconv-glu.tar.gz'), - 'dynamicconv.glu.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.dynamicconv-glu.tar.gz'), - 'lightconv.glu.wmt17.zh-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.lightconv-glu.tar.gz'), - 'dynamicconv.glu.wmt17.zh-en': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.dynamicconv-glu.tar.gz'), - } - # fmt: on - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--relu-dropout", - type=float, - metavar="D", - help="dropout probability after ReLU in FFN", - ) - parser.add_argument( - "--input-dropout", - type=float, - metavar="D", - help="dropout probability of the inputs", - ) - parser.add_argument( - "--encoder-embed-path", - type=str, - metavar="STR", - help="path to pre-trained encoder embedding", - ) - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-conv-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-layers", type=int, metavar="N", help="num encoder layers" - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="N", - help="num encoder attention heads or LightConv/DynamicConv heads", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - parser.add_argument( - "--encoder-learned-pos", - action="store_true", - help="use learned positional embeddings in the encoder", - ) - parser.add_argument( - "--decoder-embed-path", - type=str, - metavar="STR", - help="path to pre-trained decoder embedding", - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-conv-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-ffn-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension for FFN", - ) - parser.add_argument( - "--decoder-layers", type=int, metavar="N", help="num decoder layers" - ) - parser.add_argument( - "--decoder-attention-heads", - type=int, - metavar="N", - help="num decoder attention heads or LightConv/DynamicConv heads", - ) - parser.add_argument( - "--decoder-learned-pos", - action="store_true", - help="use learned positional embeddings in the decoder", - ) - parser.add_argument( - "--decoder-normalize-before", - action="store_true", - help="apply layernorm before each decoder block", - ) - parser.add_argument( - "--share-decoder-input-output-embed", - action="store_true", - help="share decoder input and output embeddings", - ) - parser.add_argument( - "--share-all-embeddings", - action="store_true", - help="share encoder, decoder and output embeddings" - " (requires shared dictionary and embed dim)", - ) - parser.add_argument( - "--adaptive-softmax-cutoff", - metavar="EXPR", - help="comma separated list of adaptive softmax cutoff points. " - "Must be used with adaptive_loss criterion", - ), - parser.add_argument( - "--adaptive-softmax-dropout", - type=float, - metavar="D", - help="sets adaptive softmax dropout for the tail projections", - ) - - """LightConv and DynamicConv arguments""" - parser.add_argument( - "--encoder-kernel-size-list", - type=lambda x: utils.eval_str_list(x, int), - help='list of kernel size (default: "[3,7,15,31,31,31,31]")', - ) - parser.add_argument( - "--decoder-kernel-size-list", - type=lambda x: utils.eval_str_list(x, int), - help='list of kernel size (default: "[3,7,15,31,31,31]")', - ) - parser.add_argument( - "--encoder-glu", type=utils.eval_bool, help="glu after in proj" - ) - parser.add_argument( - "--decoder-glu", type=utils.eval_bool, help="glu after in proj" - ) - parser.add_argument( - "--encoder-conv-type", - default="dynamic", - type=str, - choices=["dynamic", "lightweight"], - help="type of convolution", - ) - parser.add_argument( - "--decoder-conv-type", - default="dynamic", - type=str, - choices=["dynamic", "lightweight"], - help="type of convolution", - ) - parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool) - parser.add_argument( - "--weight-dropout", - type=float, - metavar="D", - help="dropout probability for conv weights", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - - if not safe_hasattr(args, "max_source_positions"): - args.max_source_positions = 1024 - if not safe_hasattr(args, "max_target_positions"): - args.max_target_positions = 1024 - - src_dict, tgt_dict = task.source_dictionary, task.target_dictionary - - def build_embedding(dictionary, embed_dim, path=None): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - emb = Embedding(num_embeddings, embed_dim, padding_idx) - # if provided, load from preloaded dictionaries - if path: - embed_dict = utils.parse_embedding(path) - utils.load_embedding(embed_dict, dictionary, emb) - return emb - - if args.share_all_embeddings: - if src_dict != tgt_dict: - raise RuntimeError( - "--share-all-embeddings requires a joined dictionary" - ) - if args.encoder_embed_dim != args.decoder_embed_dim: - raise RuntimeError( - "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" - ) - if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path - ): - raise RuntimeError( - "--share-all-embeddings not compatible with --decoder-embed-path" - ) - encoder_embed_tokens = build_embedding( - src_dict, args.encoder_embed_dim, args.encoder_embed_path - ) - decoder_embed_tokens = encoder_embed_tokens - args.share_decoder_input_output_embed = True - else: - encoder_embed_tokens = build_embedding( - src_dict, args.encoder_embed_dim, args.encoder_embed_path - ) - decoder_embed_tokens = build_embedding( - tgt_dict, args.decoder_embed_dim, args.decoder_embed_path - ) - - encoder = LightConvEncoder(args, src_dict, encoder_embed_tokens) - decoder = LightConvDecoder(args, tgt_dict, decoder_embed_tokens) - return LightConvModel(encoder, decoder) - - -class LightConvEncoder(FairseqEncoder): - """ - LightConv encoder consisting of *args.encoder_layers* layers. Each layer - is a :class:`LightConvEncoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): encoding dictionary - embed_tokens (torch.nn.Embedding): input embedding - """ - - def __init__(self, args, dictionary, embed_tokens): - super().__init__(dictionary) - self.dropout_module = FairseqDropout( - args.dropout, module_name=self.__class__.__name__ - ) - - embed_dim = embed_tokens.embedding_dim - self.padding_idx = embed_tokens.padding_idx - self.max_source_positions = args.max_source_positions - - self.embed_tokens = embed_tokens - self.embed_scale = math.sqrt(embed_dim) - self.embed_positions = ( - PositionalEmbedding( - args.max_source_positions, - embed_dim, - self.padding_idx, - learned=args.encoder_learned_pos, - ) - if not args.no_token_positional_embeddings - else None - ) - - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - LightConvEncoderLayer( - args, kernel_size=args.encoder_kernel_size_list[i] - ) - for i in range(args.encoder_layers) - ] - ) - self.register_buffer("version", torch.Tensor([2])) - self.normalize = args.encoder_normalize_before - if self.normalize: - self.layer_norm = LayerNorm(embed_dim) - - def forward(self, src_tokens, **unused): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - - Returns: - dict: - - **encoder_out** (Tensor): the last encoder layer's output of - shape `(src_len, batch, embed_dim)` - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - """ - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(src_tokens) - if self.embed_positions is not None: - x += self.embed_positions(src_tokens) - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # compute padding mask - encoder_padding_mask = src_tokens.eq(self.padding_idx) - if not encoder_padding_mask.any(): - encoder_padding_mask = None - - # encoder layers - for layer in self.layers: - x = layer(x, encoder_padding_mask) - - if self.normalize: - x = self.layer_norm(x) - - return { - "encoder_out": x, # T x B x C - "encoder_padding_mask": encoder_padding_mask, # B x T - } - - def reorder_encoder_out(self, encoder_out, new_order): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - if encoder_out["encoder_out"] is not None: - encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( - 1, new_order - ) - if encoder_out["encoder_padding_mask"] is not None: - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(0, new_order) - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - if self.embed_positions is None: - return self.max_source_positions - return min(self.max_source_positions, self.embed_positions.max_positions) - - -class LightConvDecoder(FairseqIncrementalDecoder): - """ - LightConv decoder consisting of *args.decoder_layers* layers. Each layer - is a :class:`LightConvDecoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): decoding dictionary - embed_tokens (torch.nn.Embedding): output embedding - no_encoder_attn (bool, optional): whether to attend to encoder outputs. - Default: ``False`` - """ - - def __init__( - self, args, dictionary, embed_tokens, no_encoder_attn=False, final_norm=True - ): - super().__init__(dictionary) - self.dropout_module = FairseqDropout( - args.dropout, module_name=self.__class__.__name__ - ) - self.share_input_output_embed = args.share_decoder_input_output_embed - - input_embed_dim = embed_tokens.embedding_dim - embed_dim = args.decoder_embed_dim - output_embed_dim = args.decoder_output_dim - - padding_idx = embed_tokens.padding_idx - self.max_target_positions = args.max_target_positions - - self.embed_tokens = embed_tokens - self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim - - self.project_in_dim = ( - Linear(input_embed_dim, embed_dim, bias=False) - if embed_dim != input_embed_dim - else None - ) - - self.embed_positions = ( - PositionalEmbedding( - args.max_target_positions, - embed_dim, - padding_idx, - learned=args.decoder_learned_pos, - ) - if not args.no_token_positional_embeddings - else None - ) - - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - LightConvDecoderLayer( - args, no_encoder_attn, kernel_size=args.decoder_kernel_size_list[i] - ) - for i in range(args.decoder_layers) - ] - ) - - self.adaptive_softmax = None - - self.project_out_dim = ( - Linear(embed_dim, output_embed_dim, bias=False) - if embed_dim != output_embed_dim and not args.tie_adaptive_weights - else None - ) - - if args.adaptive_softmax_cutoff is not None: - self.adaptive_softmax = AdaptiveSoftmax( - len(dictionary), - output_embed_dim, - utils.eval_str_list(args.adaptive_softmax_cutoff, type=int), - dropout=args.adaptive_softmax_dropout, - adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None, - factor=args.adaptive_softmax_factor, - tie_proj=args.tie_adaptive_proj, - ) - elif not self.share_input_output_embed: - self.embed_out = nn.Parameter( - torch.Tensor(len(dictionary), output_embed_dim) - ) - nn.init.normal_(self.embed_out, mean=0, std=output_embed_dim**-0.5) - self.register_buffer("version", torch.Tensor([2])) - self.normalize = args.decoder_normalize_before and final_norm - if self.normalize: - self.layer_norm = LayerNorm(embed_dim) - - def forward( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs - ): - """ - Args: - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (Tensor, optional): output from the encoder, used for - encoder-side attention - incremental_state (dict): dictionary used for storing state during - :ref:`Incremental decoding` - - Returns: - tuple: - - the last decoder layer's output of shape `(batch, tgt_len, - vocab)` - - the last decoder layer's attention weights of shape `(batch, - tgt_len, src_len)` - """ - # embed positions - positions = ( - self.embed_positions( - prev_output_tokens, - incremental_state=incremental_state, - ) - if self.embed_positions is not None - else None - ) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - if positions is not None: - positions = positions[:, -1:] - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - attn = None - - inner_states = [x] - - # decoder layers - for layer in self.layers: - x, attn = layer( - x, - encoder_out["encoder_out"] if encoder_out is not None else None, - encoder_out["encoder_padding_mask"] - if encoder_out is not None - else None, - incremental_state, - ) - inner_states.append(x) - - if self.normalize: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - - if self.adaptive_softmax is None: - # project back to size of vocabulary - if self.share_input_output_embed: - x = F.linear(x, self.embed_tokens.weight) - else: - x = F.linear(x, self.embed_out) - - return x, {"attn": attn, "inner_states": inner_states} - - def max_positions(self): - """Maximum output length supported by the decoder.""" - if self.embed_positions is None: - return self.max_target_positions - return min(self.max_target_positions, self.embed_positions.max_positions) - - def buffered_future_mask(self, tensor): - dim = tensor.size(0) - if ( - not hasattr(self, "_future_mask") - or self._future_mask is None - or self._future_mask.device != tensor.device - ): - self._future_mask = torch.triu( - utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 - ) - if self._future_mask.size(0) < dim: - self._future_mask = torch.triu( - utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1 - ) - return self._future_mask[:dim, :dim] - - -class LightConvEncoderLayer(nn.Module): - """Encoder layer block. - - Args: - args (argparse.Namespace): parsed command-line arguments - kernel_size: kernel size of the convolution - """ - - def __init__(self, args, kernel_size=0): - super().__init__() - self.embed_dim = args.encoder_embed_dim - self.conv_dim = args.encoder_conv_dim - padding_l = ( - kernel_size // 2 - if kernel_size % 2 == 1 - else ((kernel_size - 1) // 2, kernel_size // 2) - ) - - if args.encoder_glu: - self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim) - self.act = nn.GLU() - else: - self.linear1 = Linear(self.embed_dim, self.conv_dim) - self.act = None - if args.encoder_conv_type == "lightweight": - self.conv = LightweightConv( - self.conv_dim, - kernel_size, - padding_l=padding_l, - weight_softmax=args.weight_softmax, - num_heads=args.encoder_attention_heads, - weight_dropout=args.weight_dropout, - ) - elif args.encoder_conv_type == "dynamic": - self.conv = DynamicConv( - self.conv_dim, - kernel_size, - padding_l=padding_l, - weight_softmax=args.weight_softmax, - num_heads=args.encoder_attention_heads, - weight_dropout=args.weight_dropout, - ) - else: - raise NotImplementedError - self.linear2 = Linear(self.conv_dim, self.embed_dim) - - self.dropout_module = FairseqDropout( - args.dropout, module_name=self.__class__.__name__ - ) - self.relu_dropout_module = FairseqDropout( - args.relu_dropout, module_name=self.__class__.__name__ - ) - self.input_dropout_module = FairseqDropout( - args.input_dropout, module_name=self.__class__.__name__ - ) - self.normalize_before = args.encoder_normalize_before - self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim) - self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim) - self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for _ in range(2)]) - - def forward(self, x, encoder_padding_mask): - """ - Args: - x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` - encoder_padding_mask (ByteTensor): binary ByteTensor of shape - `(batch, src_len)` where padding elements are indicated by ``1``. - - Returns: - encoded output of shape `(batch, src_len, embed_dim)` - """ - residual = x - x = self.maybe_layer_norm(0, x, before=True) - x = self.input_dropout_module(x) - x = self.linear1(x) - if self.act is not None: - x = self.act(x) - if encoder_padding_mask is not None: - x = x.masked_fill(encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0) - x = self.conv(x) - x = self.linear2(x) - x = self.dropout_module(x) - x = residual + x - x = self.maybe_layer_norm(0, x, after=True) - - residual = x - x = self.maybe_layer_norm(1, x, before=True) - x = F.relu(self.fc1(x)) - x = self.relu_dropout_module(x) - x = self.fc2(x) - x = self.dropout_module(x) - x = residual + x - x = self.maybe_layer_norm(1, x, after=True) - return x - - def maybe_layer_norm(self, i, x, before=False, after=False): - assert before ^ after - if after ^ self.normalize_before: - return self.layer_norms[i](x) - else: - return x - - def extra_repr(self): - return ( - "dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format( - self.dropout_module.p, - self.relu_dropout_module.p, - self.input_dropout_module.p, - self.normalize_before, - ) - ) - - -class LightConvDecoderLayer(nn.Module): - """Decoder layer block. - - Args: - args (argparse.Namespace): parsed command-line arguments - no_encoder_attn (bool, optional): whether to attend to encoder outputs. - Default: ``False`` - kernel_size: kernel size of the convolution - """ - - def __init__(self, args, no_encoder_attn=False, kernel_size=0): - super().__init__() - self.embed_dim = args.decoder_embed_dim - self.conv_dim = args.decoder_conv_dim - if args.decoder_glu: - self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim) - self.act = nn.GLU() - else: - self.linear1 = Linear(self.embed_dim, self.conv_dim) - self.act = None - if args.decoder_conv_type == "lightweight": - self.conv = LightweightConv( - self.conv_dim, - kernel_size, - padding_l=kernel_size - 1, - weight_softmax=args.weight_softmax, - num_heads=args.decoder_attention_heads, - weight_dropout=args.weight_dropout, - ) - elif args.decoder_conv_type == "dynamic": - self.conv = DynamicConv( - self.conv_dim, - kernel_size, - padding_l=kernel_size - 1, - weight_softmax=args.weight_softmax, - num_heads=args.decoder_attention_heads, - weight_dropout=args.weight_dropout, - ) - else: - raise NotImplementedError - self.linear2 = Linear(self.conv_dim, self.embed_dim) - - self.dropout_module = FairseqDropout( - args.dropout, module_name=self.__class__.__name__ - ) - self.relu_dropout_module = FairseqDropout( - args.relu_dropout, module_name=self.__class__.__name__ - ) - self.input_dropout_module = FairseqDropout( - args.input_dropout, module_name=self.__class__.__name__ - ) - self.normalize_before = args.decoder_normalize_before - - self.conv_layer_norm = LayerNorm(self.embed_dim) - - if no_encoder_attn: - self.encoder_attn = None - self.encoder_attn_layer_norm = None - else: - self.encoder_attn = MultiheadAttention( - self.embed_dim, - args.decoder_attention_heads, - dropout=args.attention_dropout, - encoder_decoder_attention=True, - ) - self.encoder_attn_layer_norm = LayerNorm(self.embed_dim) - - self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim) - self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim) - - self.final_layer_norm = LayerNorm(self.embed_dim) - self.need_attn = True - - def forward( - self, - x, - encoder_out, - encoder_padding_mask, - incremental_state, - prev_conv_state=None, - prev_attn_state=None, - conv_mask=None, - conv_padding_mask=None, - ): - """ - Args: - x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` - encoder_padding_mask (ByteTensor): binary ByteTensor of shape - `(batch, src_len)` where padding elements are indicated by ``1``. - - Returns: - encoded output of shape `(batch, src_len, embed_dim)` - """ - residual = x - x = self.maybe_layer_norm(self.conv_layer_norm, x, before=True) - if prev_conv_state is not None: - if incremental_state is None: - incremental_state = {} - self.conv._set_input_buffer(incremental_state, prev_conv_state) - x = self.input_dropout_module(x) - x = self.linear1(x) - if self.act is not None: - x = self.act(x) - x = self.conv(x, incremental_state=incremental_state) - x = self.linear2(x) - x = self.dropout_module(x) - x = residual + x - x = self.maybe_layer_norm(self.conv_layer_norm, x, after=True) - - attn = None - if self.encoder_attn is not None: - residual = x - x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True) - if prev_attn_state is not None: - if incremental_state is None: - incremental_state = {} - prev_key, prev_value = prev_attn_state - saved_state = {"prev_key": prev_key, "prev_value": prev_value} - self.encoder_attn._set_input_buffer(incremental_state, saved_state) - x, attn = self.encoder_attn( - query=x, - key=encoder_out, - value=encoder_out, - key_padding_mask=encoder_padding_mask, - incremental_state=incremental_state, - static_kv=True, - need_weights=(not self.training and self.need_attn), - ) - x = self.dropout_module(x) - x = residual + x - x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True) - - residual = x - x = self.maybe_layer_norm(self.final_layer_norm, x, before=True) - x = F.relu(self.fc1(x)) - x = self.relu_dropout_module(x) - x = self.fc2(x) - x = self.dropout_module(x) - x = residual + x - x = self.maybe_layer_norm(self.final_layer_norm, x, after=True) - return x, attn - - def maybe_layer_norm(self, layer_norm, x, before=False, after=False): - assert before ^ after - if after ^ self.normalize_before: - return layer_norm(x) - else: - return x - - def make_generation_fast_(self, need_attn=False, **kwargs): - self.need_attn = need_attn - - def extra_repr(self): - return ( - "dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}".format( - self.dropout_module.p, - self.relu_dropout_module.p, - self.input_dropout_module.p, - self.normalize_before, - ) - ) - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m - - -@register_model_architecture("lightconv", "lightconv") -def base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 7) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.relu_dropout = getattr(args, "relu_dropout", 0.0) - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - args.encoder_conv_dim = getattr(args, "encoder_conv_dim", args.encoder_embed_dim) - args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim) - - args.encoder_kernel_size_list = getattr( - args, "encoder_kernel_size_list", [3, 7, 15, 31, 31, 31, 31] - ) - args.decoder_kernel_size_list = getattr( - args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31] - ) - if len(args.encoder_kernel_size_list) == 1: - args.encoder_kernel_size_list = ( - args.encoder_kernel_size_list * args.encoder_layers - ) - if len(args.decoder_kernel_size_list) == 1: - args.decoder_kernel_size_list = ( - args.decoder_kernel_size_list * args.decoder_layers - ) - assert ( - len(args.encoder_kernel_size_list) == args.encoder_layers - ), "encoder_kernel_size_list doesn't match encoder_layers" - assert ( - len(args.decoder_kernel_size_list) == args.decoder_layers - ), "decoder_kernel_size_list doesn't match decoder_layers" - args.encoder_glu = getattr(args, "encoder_glu", True) - args.decoder_glu = getattr(args, "decoder_glu", True) - args.input_dropout = getattr(args, "input_dropout", 0.1) - args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout) - - -@register_model_architecture("lightconv", "lightconv_iwslt_de_en") -def lightconv_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.encoder_layers = getattr(args, "encoder_layers", 7) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.weight_dropout = getattr(args, "weight_dropout", 0.1) - args.encoder_glu = getattr(args, "encoder_glu", False) - args.decoder_glu = getattr(args, "decoder_glu", False) - args.input_dropout = getattr(args, "input_dropout", 0.0) - base_architecture(args) - - -@register_model_architecture("lightconv", "lightconv_wmt_en_de") -def lightconv_wmt_en_de(args): - base_architecture(args) - - -@register_model_architecture("lightconv", "lightconv_wmt_en_de_big") -def lightconv_wmt_en_de_big(args): - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.dropout = getattr(args, "dropout", 0.3) - base_architecture(args) - - -@register_model_architecture("lightconv", "lightconv_wmt_en_fr_big") -def lightconv_wmt_en_fr_big(args): - args.dropout = getattr(args, "dropout", 0.1) - lightconv_wmt_en_de_big(args) - - -@register_model_architecture("lightconv", "lightconv_wmt_zh_en_big") -def lightconv_wmt_zh_en_big(args): - args.dropout = getattr(args, "dropout", 0.2) - args.attention_dropout = getattr(args, "attention_dropout", 0.2) - args.weight_dropout = getattr(args, "weight_dropout", 0.2) - lightconv_wmt_en_de_big(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lightconv_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lightconv_lm.py deleted file mode 100644 index 1d9efc4e42a5ecc1b83338055f18ade5a83ea666..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lightconv_lm.py +++ /dev/null @@ -1,306 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq import utils -from fairseq.models import ( - FairseqLanguageModel, - register_model, - register_model_architecture, -) -from fairseq.models.lightconv import Embedding, LightConvDecoder -from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder - - -@register_model("lightconv_lm") -class LightConvLanguageModel(FairseqLanguageModel): - def __init__(self, decoder): - super().__init__(decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--dropout", - default=0.1, - type=float, - metavar="D", - help="dropout probability", - ) - parser.add_argument( - "--attention-dropout", - default=0.0, - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--relu-dropout", - default=0.0, - type=float, - metavar="D", - help="dropout probability after ReLU in FFN", - ) - parser.add_argument( - "--input-dropout", - type=float, - metavar="D", - help="dropout probability of the inputs", - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-output-dim", - type=int, - metavar="N", - help="decoder output dimension", - ) - parser.add_argument( - "--decoder-input-dim", type=int, metavar="N", help="decoder input dimension" - ) - parser.add_argument( - "--decoder-ffn-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension for FFN", - ) - parser.add_argument( - "--decoder-layers", type=int, metavar="N", help="num decoder layers" - ) - parser.add_argument( - "--decoder-attention-heads", - type=int, - metavar="N", - help="num decoder attention heads or LightConv/DynamicConv heads", - ) - parser.add_argument( - "--decoder-normalize-before", - default=False, - action="store_true", - help="apply layernorm before each decoder block", - ) - parser.add_argument( - "--adaptive-softmax-cutoff", - metavar="EXPR", - help="comma separated list of adaptive softmax cutoff points. " - "Must be used with adaptive_loss criterion", - ) - parser.add_argument( - "--adaptive-softmax-dropout", - type=float, - metavar="D", - help="sets adaptive softmax dropout for the tail projections", - ) - parser.add_argument( - "--adaptive-softmax-factor", - type=float, - metavar="N", - help="adaptive input factor", - ) - parser.add_argument( - "--no-token-positional-embeddings", - default=False, - action="store_true", - help="if set, disables positional embeddings (outside self attention)", - ) - parser.add_argument( - "--share-decoder-input-output-embed", - default=False, - action="store_true", - help="share decoder input and output embeddings", - ) - parser.add_argument( - "--character-embeddings", - default=False, - action="store_true", - help="if set, uses character embedding convolutions to produce token embeddings", - ) - parser.add_argument( - "--character-filters", - type=str, - metavar="LIST", - default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", - help="size of character embeddings", - ) - parser.add_argument( - "--character-embedding-dim", - type=int, - metavar="N", - default=4, - help="size of character embeddings", - ) - parser.add_argument( - "--char-embedder-highway-layers", - type=int, - metavar="N", - default=2, - help="number of highway layers for character token embeddder", - ) - parser.add_argument( - "--adaptive-input", - default=False, - action="store_true", - help="if set, uses adaptive input", - ) - parser.add_argument( - "--adaptive-input-factor", - type=float, - metavar="N", - help="adaptive input factor", - ) - parser.add_argument( - "--adaptive-input-cutoff", - metavar="EXPR", - help="comma separated list of adaptive input cutoff points.", - ) - parser.add_argument( - "--tie-adaptive-weights", - action="store_true", - help="if set, ties the weights of adaptive softmax and adaptive input", - ) - parser.add_argument( - "--tie-adaptive-proj", - action="store_true", - help="if set, ties the projection weights of adaptive softmax and adaptive input", - ) - parser.add_argument( - "--decoder-learned-pos", - action="store_true", - help="use learned positional embeddings in the decoder", - ) - - """LightConv and DynamicConv arguments""" - parser.add_argument( - "--decoder-kernel-size-list", - type=lambda x: utils.eval_str_list(x, int), - help='list of kernel size (default: "[3,7,15,31,31,31]")', - ) - parser.add_argument( - "--decoder-glu", type=utils.eval_bool, help="glu after in proj" - ) - parser.add_argument( - "--decoder-conv-type", - default="dynamic", - type=str, - choices=["dynamic", "lightweight"], - help="type of convolution", - ) - parser.add_argument("--weight-softmax", default=True, type=utils.eval_bool) - parser.add_argument( - "--weight-dropout", - type=float, - metavar="D", - help="dropout probability for conv weights", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_lm_architecture(args) - - if getattr(args, "max_source_positions", None) is None: - args.max_source_positions = args.tokens_per_sample - if getattr(args, "max_target_positions", None) is None: - args.max_target_positions = args.tokens_per_sample - - if args.character_embeddings: - embed_tokens = CharacterTokenEmbedder( - task.dictionary, - eval(args.character_filters), - args.character_embedding_dim, - args.decoder_embed_dim, - args.char_embedder_highway_layers, - ) - elif args.adaptive_input: - embed_tokens = AdaptiveInput( - len(task.dictionary), - task.dictionary.pad(), - args.decoder_input_dim, - args.adaptive_input_factor, - args.decoder_embed_dim, - utils.eval_str_list(args.adaptive_input_cutoff, type=int), - ) - else: - embed_tokens = Embedding( - len(task.dictionary), args.decoder_input_dim, task.dictionary.pad() - ) - - if args.tie_adaptive_weights: - assert args.adaptive_input - assert args.adaptive_input_factor == args.adaptive_softmax_factor - assert ( - args.adaptive_softmax_cutoff == args.adaptive_input_cutoff - ), "{} != {}".format( - args.adaptive_softmax_cutoff, args.adaptive_input_cutoff - ) - assert args.decoder_input_dim == args.decoder_output_dim - - decoder = LightConvDecoder( - args, - task.output_dictionary, - embed_tokens, - no_encoder_attn=True, - final_norm=False, - ) - return LightConvLanguageModel(decoder) - - -@register_model_architecture("lightconv_lm", "lightconv_lm") -def base_lm_architecture(args): - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.adaptive_softmax_factor = getattr(args, "adaptive_softmax_factor", 4) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - - args.character_embeddings = getattr(args, "character_embeddings", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - args.decoder_conv_dim = getattr(args, "decoder_conv_dim", args.decoder_embed_dim) - - # The model training is not stable without this - args.decoder_normalize_before = True - - args.adaptive_input = getattr(args, "adaptive_input", False) - args.adaptive_input_factor = getattr(args, "adaptive_input_factor", 4) - args.adaptive_input_cutoff = getattr(args, "adaptive_input_cutoff", None) - - args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) - args.tie_adaptive_proj = getattr(args, "tie_adaptive_proj", False) - - args.decoder_kernel_size_list = getattr( - args, "decoder_kernel_size_list", [3, 7, 15, 31, 31, 31] - ) - if len(args.decoder_kernel_size_list) == 1: - args.decoder_kernel_size_list = ( - args.decoder_kernel_size_list * args.decoder_layers - ) - assert ( - len(args.decoder_kernel_size_list) == args.decoder_layers - ), "decoder_kernel_size_list doesn't match decoder_layers" - args.decoder_glu = getattr(args, "decoder_glu", True) - args.input_dropout = getattr(args, "input_dropout", 0.1) - args.weight_dropout = getattr(args, "weight_dropout", args.attention_dropout) - - -@register_model_architecture("lightconv_lm", "lightconv_lm_gbw") -def lightconv_lm_gbw(args): - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - base_lm_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lstm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lstm.py deleted file mode 100644 index e4a02cb38fcc0b1bad2fa513cba2c17237d2cd6a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lstm.py +++ /dev/null @@ -1,754 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) -from fairseq.modules import AdaptiveSoftmax, FairseqDropout -from torch import Tensor - -DEFAULT_MAX_SOURCE_POSITIONS = 1e5 -DEFAULT_MAX_TARGET_POSITIONS = 1e5 - - -@register_model("lstm") -class LSTMModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--encoder-embed-dim', type=int, metavar='N', - help='encoder embedding dimension') - parser.add_argument('--encoder-embed-path', type=str, metavar='STR', - help='path to pre-trained encoder embedding') - parser.add_argument('--encoder-freeze-embed', action='store_true', - help='freeze encoder embeddings') - parser.add_argument('--encoder-hidden-size', type=int, metavar='N', - help='encoder hidden size') - parser.add_argument('--encoder-layers', type=int, metavar='N', - help='number of encoder layers') - parser.add_argument('--encoder-bidirectional', action='store_true', - help='make all layers of encoder bidirectional') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-embed-path', type=str, metavar='STR', - help='path to pre-trained decoder embedding') - parser.add_argument('--decoder-freeze-embed', action='store_true', - help='freeze decoder embeddings') - parser.add_argument('--decoder-hidden-size', type=int, metavar='N', - help='decoder hidden size') - parser.add_argument('--decoder-layers', type=int, metavar='N', - help='number of decoder layers') - parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', - help='decoder output embedding dimension') - parser.add_argument('--decoder-attention', type=str, metavar='BOOL', - help='decoder attention') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion') - parser.add_argument('--share-decoder-input-output-embed', default=False, - action='store_true', - help='share decoder input and output embeddings') - parser.add_argument('--share-all-embeddings', default=False, action='store_true', - help='share encoder, decoder and output embeddings' - ' (requires shared dictionary and embed dim)') - - # Granular dropout settings (if not specified these default to --dropout) - parser.add_argument('--encoder-dropout-in', type=float, metavar='D', - help='dropout probability for encoder input embedding') - parser.add_argument('--encoder-dropout-out', type=float, metavar='D', - help='dropout probability for encoder output') - parser.add_argument('--decoder-dropout-in', type=float, metavar='D', - help='dropout probability for decoder input embedding') - parser.add_argument('--decoder-dropout-out', type=float, metavar='D', - help='dropout probability for decoder output') - # fmt: on - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - # make sure that all args are properly defaulted (in case there are any new ones) - base_architecture(args) - - if args.encoder_layers != args.decoder_layers: - raise ValueError("--encoder-layers must match --decoder-layers") - - max_source_positions = getattr( - args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS - ) - max_target_positions = getattr( - args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS - ) - - def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) - embed_dict = utils.parse_embedding(embed_path) - utils.print_embed_overlap(embed_dict, dictionary) - return utils.load_embedding(embed_dict, dictionary, embed_tokens) - - if args.encoder_embed_path: - pretrained_encoder_embed = load_pretrained_embedding_from_file( - args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim - ) - else: - num_embeddings = len(task.source_dictionary) - pretrained_encoder_embed = Embedding( - num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad() - ) - - if args.share_all_embeddings: - # double check all parameters combinations are valid - if task.source_dictionary != task.target_dictionary: - raise ValueError("--share-all-embeddings requires a joint dictionary") - if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path - ): - raise ValueError( - "--share-all-embed not compatible with --decoder-embed-path" - ) - if args.encoder_embed_dim != args.decoder_embed_dim: - raise ValueError( - "--share-all-embeddings requires --encoder-embed-dim to " - "match --decoder-embed-dim" - ) - pretrained_decoder_embed = pretrained_encoder_embed - args.share_decoder_input_output_embed = True - else: - # separate decoder input embeddings - pretrained_decoder_embed = None - if args.decoder_embed_path: - pretrained_decoder_embed = load_pretrained_embedding_from_file( - args.decoder_embed_path, - task.target_dictionary, - args.decoder_embed_dim, - ) - # one last double check of parameter combinations - if args.share_decoder_input_output_embed and ( - args.decoder_embed_dim != args.decoder_out_embed_dim - ): - raise ValueError( - "--share-decoder-input-output-embeddings requires " - "--decoder-embed-dim to match --decoder-out-embed-dim" - ) - - if args.encoder_freeze_embed: - pretrained_encoder_embed.weight.requires_grad = False - if args.decoder_freeze_embed: - pretrained_decoder_embed.weight.requires_grad = False - - encoder = LSTMEncoder( - dictionary=task.source_dictionary, - embed_dim=args.encoder_embed_dim, - hidden_size=args.encoder_hidden_size, - num_layers=args.encoder_layers, - dropout_in=args.encoder_dropout_in, - dropout_out=args.encoder_dropout_out, - bidirectional=args.encoder_bidirectional, - pretrained_embed=pretrained_encoder_embed, - max_source_positions=max_source_positions, - ) - decoder = LSTMDecoder( - dictionary=task.target_dictionary, - embed_dim=args.decoder_embed_dim, - hidden_size=args.decoder_hidden_size, - out_embed_dim=args.decoder_out_embed_dim, - num_layers=args.decoder_layers, - dropout_in=args.decoder_dropout_in, - dropout_out=args.decoder_dropout_out, - attention=utils.eval_bool(args.decoder_attention), - encoder_output_units=encoder.output_units, - pretrained_embed=pretrained_decoder_embed, - share_input_output_embed=args.share_decoder_input_output_embed, - adaptive_softmax_cutoff=( - utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) - if args.criterion == "adaptive_loss" - else None - ), - max_target_positions=max_target_positions, - residuals=False, - ) - return cls(encoder, decoder) - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths) - decoder_out = self.decoder( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - ) - return decoder_out - - -class LSTMEncoder(FairseqEncoder): - """LSTM encoder.""" - - def __init__( - self, - dictionary, - embed_dim=512, - hidden_size=512, - num_layers=1, - dropout_in=0.1, - dropout_out=0.1, - bidirectional=False, - left_pad=True, - pretrained_embed=None, - padding_idx=None, - max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS, - ): - super().__init__(dictionary) - self.num_layers = num_layers - self.dropout_in_module = FairseqDropout( - dropout_in * 1.0, module_name=self.__class__.__name__ - ) - self.dropout_out_module = FairseqDropout( - dropout_out * 1.0, module_name=self.__class__.__name__ - ) - self.bidirectional = bidirectional - self.hidden_size = hidden_size - self.max_source_positions = max_source_positions - - num_embeddings = len(dictionary) - self.padding_idx = padding_idx if padding_idx is not None else dictionary.pad() - if pretrained_embed is None: - self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) - else: - self.embed_tokens = pretrained_embed - - self.lstm = LSTM( - input_size=embed_dim, - hidden_size=hidden_size, - num_layers=num_layers, - dropout=self.dropout_out_module.p if num_layers > 1 else 0.0, - bidirectional=bidirectional, - ) - self.left_pad = left_pad - - self.output_units = hidden_size - if bidirectional: - self.output_units *= 2 - - def forward( - self, - src_tokens: Tensor, - src_lengths: Tensor, - enforce_sorted: bool = True, - ): - """ - Args: - src_tokens (LongTensor): tokens in the source language of - shape `(batch, src_len)` - src_lengths (LongTensor): lengths of each source sentence of - shape `(batch)` - enforce_sorted (bool, optional): if True, `src_tokens` is - expected to contain sequences sorted by length in a - decreasing order. If False, this condition is not - required. Default: True. - """ - if self.left_pad: - # nn.utils.rnn.pack_padded_sequence requires right-padding; - # convert left-padding to right-padding - src_tokens = utils.convert_padding_direction( - src_tokens, - torch.zeros_like(src_tokens).fill_(self.padding_idx), - left_to_right=True, - ) - - bsz, seqlen = src_tokens.size() - - # embed tokens - x = self.embed_tokens(src_tokens) - x = self.dropout_in_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # pack embedded source tokens into a PackedSequence - packed_x = nn.utils.rnn.pack_padded_sequence( - x, src_lengths.cpu(), enforce_sorted=enforce_sorted - ) - - # apply LSTM - if self.bidirectional: - state_size = 2 * self.num_layers, bsz, self.hidden_size - else: - state_size = self.num_layers, bsz, self.hidden_size - h0 = x.new_zeros(*state_size) - c0 = x.new_zeros(*state_size) - packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0)) - - # unpack outputs and apply dropout - x, _ = nn.utils.rnn.pad_packed_sequence( - packed_outs, padding_value=self.padding_idx * 1.0 - ) - x = self.dropout_out_module(x) - assert list(x.size()) == [seqlen, bsz, self.output_units] - - if self.bidirectional: - final_hiddens = self.combine_bidir(final_hiddens, bsz) - final_cells = self.combine_bidir(final_cells, bsz) - - encoder_padding_mask = src_tokens.eq(self.padding_idx).t() - - return tuple( - ( - x, # seq_len x batch x hidden - final_hiddens, # num_layers x batch x num_directions*hidden - final_cells, # num_layers x batch x num_directions*hidden - encoder_padding_mask, # seq_len x batch - ) - ) - - def combine_bidir(self, outs, bsz: int): - out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous() - return out.view(self.num_layers, bsz, -1) - - def reorder_encoder_out( - self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order - ): - return tuple( - ( - encoder_out[0].index_select(1, new_order), - encoder_out[1].index_select(1, new_order), - encoder_out[2].index_select(1, new_order), - encoder_out[3].index_select(1, new_order), - ) - ) - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return self.max_source_positions - - -class AttentionLayer(nn.Module): - def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False): - super().__init__() - - self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias) - self.output_proj = Linear( - input_embed_dim + source_embed_dim, output_embed_dim, bias=bias - ) - - def forward(self, input, source_hids, encoder_padding_mask): - # input: bsz x input_embed_dim - # source_hids: srclen x bsz x source_embed_dim - - # x: bsz x source_embed_dim - x = self.input_proj(input) - - # compute attention - attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2) - - # don't attend over padding - if encoder_padding_mask is not None: - attn_scores = ( - attn_scores.float() - .masked_fill_(encoder_padding_mask, float("-inf")) - .type_as(attn_scores) - ) # FP16 support: cast to float and back - - attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz - - # sum weighted sources - x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0) - - x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1))) - return x, attn_scores - - -class LSTMDecoder(FairseqIncrementalDecoder): - """LSTM decoder.""" - - def __init__( - self, - dictionary, - embed_dim=512, - hidden_size=512, - out_embed_dim=512, - num_layers=1, - dropout_in=0.1, - dropout_out=0.1, - attention=True, - encoder_output_units=512, - pretrained_embed=None, - share_input_output_embed=False, - adaptive_softmax_cutoff=None, - max_target_positions=DEFAULT_MAX_TARGET_POSITIONS, - residuals=False, - ): - super().__init__(dictionary) - self.dropout_in_module = FairseqDropout( - dropout_in * 1.0, module_name=self.__class__.__name__ - ) - self.dropout_out_module = FairseqDropout( - dropout_out * 1.0, module_name=self.__class__.__name__ - ) - self.hidden_size = hidden_size - self.share_input_output_embed = share_input_output_embed - self.need_attn = True - self.max_target_positions = max_target_positions - self.residuals = residuals - self.num_layers = num_layers - - self.adaptive_softmax = None - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - if pretrained_embed is None: - self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) - else: - self.embed_tokens = pretrained_embed - - self.encoder_output_units = encoder_output_units - if encoder_output_units != hidden_size and encoder_output_units != 0: - self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size) - self.encoder_cell_proj = Linear(encoder_output_units, hidden_size) - else: - self.encoder_hidden_proj = self.encoder_cell_proj = None - - # disable input feeding if there is no encoder - # input feeding is described in arxiv.org/abs/1508.04025 - input_feed_size = 0 if encoder_output_units == 0 else hidden_size - self.layers = nn.ModuleList( - [ - LSTMCell( - input_size=input_feed_size + embed_dim - if layer == 0 - else hidden_size, - hidden_size=hidden_size, - ) - for layer in range(num_layers) - ] - ) - - if attention: - # TODO make bias configurable - self.attention = AttentionLayer( - hidden_size, encoder_output_units, hidden_size, bias=False - ) - else: - self.attention = None - - if hidden_size != out_embed_dim: - self.additional_fc = Linear(hidden_size, out_embed_dim) - - if adaptive_softmax_cutoff is not None: - # setting adaptive_softmax dropout to dropout_out for now but can be redefined - self.adaptive_softmax = AdaptiveSoftmax( - num_embeddings, - hidden_size, - adaptive_softmax_cutoff, - dropout=dropout_out, - ) - elif not self.share_input_output_embed: - self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out) - - def forward( - self, - prev_output_tokens, - encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - src_lengths: Optional[Tensor] = None, - ): - x, attn_scores = self.extract_features( - prev_output_tokens, encoder_out, incremental_state - ) - return self.output_layer(x), attn_scores - - def extract_features( - self, - prev_output_tokens, - encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - """ - Similar to *forward* but only return features. - """ - # get outputs from encoder - if encoder_out is not None: - encoder_outs = encoder_out[0] - encoder_hiddens = encoder_out[1] - encoder_cells = encoder_out[2] - encoder_padding_mask = encoder_out[3] - else: - encoder_outs = torch.empty(0) - encoder_hiddens = torch.empty(0) - encoder_cells = torch.empty(0) - encoder_padding_mask = torch.empty(0) - srclen = encoder_outs.size(0) - - if incremental_state is not None and len(incremental_state) > 0: - prev_output_tokens = prev_output_tokens[:, -1:] - - bsz, seqlen = prev_output_tokens.size() - - # embed tokens - x = self.embed_tokens(prev_output_tokens) - x = self.dropout_in_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # initialize previous states (or get from cache during incremental generation) - if incremental_state is not None and len(incremental_state) > 0: - prev_hiddens, prev_cells, input_feed = self.get_cached_state( - incremental_state - ) - elif encoder_out is not None: - # setup recurrent cells - prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)] - prev_cells = [encoder_cells[i] for i in range(self.num_layers)] - if self.encoder_hidden_proj is not None: - prev_hiddens = [self.encoder_hidden_proj(y) for y in prev_hiddens] - prev_cells = [self.encoder_cell_proj(y) for y in prev_cells] - input_feed = x.new_zeros(bsz, self.hidden_size) - else: - # setup zero cells, since there is no encoder - zero_state = x.new_zeros(bsz, self.hidden_size) - prev_hiddens = [zero_state for i in range(self.num_layers)] - prev_cells = [zero_state for i in range(self.num_layers)] - input_feed = None - - assert ( - srclen > 0 or self.attention is None - ), "attention is not supported if there are no encoder outputs" - attn_scores: Optional[Tensor] = ( - x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None - ) - outs = [] - for j in range(seqlen): - # input feeding: concatenate context vector from previous time step - if input_feed is not None: - input = torch.cat((x[j, :, :], input_feed), dim=1) - else: - input = x[j] - - for i, rnn in enumerate(self.layers): - # recurrent cell - hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i])) - - # hidden state becomes the input to the next layer - input = self.dropout_out_module(hidden) - if self.residuals: - input = input + prev_hiddens[i] - - # save state for next time step - prev_hiddens[i] = hidden - prev_cells[i] = cell - - # apply attention using the last layer's hidden state - if self.attention is not None: - assert attn_scores is not None - out, attn_scores[:, j, :] = self.attention( - hidden, encoder_outs, encoder_padding_mask - ) - else: - out = hidden - out = self.dropout_out_module(out) - - # input feeding - if input_feed is not None: - input_feed = out - - # save final output - outs.append(out) - - # Stack all the necessary tensors together and store - prev_hiddens_tensor = torch.stack(prev_hiddens) - prev_cells_tensor = torch.stack(prev_cells) - cache_state = torch.jit.annotate( - Dict[str, Optional[Tensor]], - { - "prev_hiddens": prev_hiddens_tensor, - "prev_cells": prev_cells_tensor, - "input_feed": input_feed, - }, - ) - self.set_incremental_state(incremental_state, "cached_state", cache_state) - - # collect outputs across time steps - x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) - - # T x B x C -> B x T x C - x = x.transpose(1, 0) - - if hasattr(self, "additional_fc") and self.adaptive_softmax is None: - x = self.additional_fc(x) - x = self.dropout_out_module(x) - # srclen x tgtlen x bsz -> bsz x tgtlen x srclen - if not self.training and self.need_attn and self.attention is not None: - assert attn_scores is not None - attn_scores = attn_scores.transpose(0, 2) - else: - attn_scores = None - return x, attn_scores - - def output_layer(self, x): - """Project features to the vocabulary size.""" - if self.adaptive_softmax is None: - if self.share_input_output_embed: - x = F.linear(x, self.embed_tokens.weight) - else: - x = self.fc_out(x) - return x - - def get_cached_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - ) -> Tuple[List[Tensor], List[Tensor], Optional[Tensor]]: - cached_state = self.get_incremental_state(incremental_state, "cached_state") - assert cached_state is not None - prev_hiddens_ = cached_state["prev_hiddens"] - assert prev_hiddens_ is not None - prev_cells_ = cached_state["prev_cells"] - assert prev_cells_ is not None - prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)] - prev_cells = [prev_cells_[j] for j in range(self.num_layers)] - input_feed = cached_state[ - "input_feed" - ] # can be None for decoder-only language models - return prev_hiddens, prev_cells, input_feed - - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - if incremental_state is None or len(incremental_state) == 0: - return - prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state) - prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens] - prev_cells = [p.index_select(0, new_order) for p in prev_cells] - if input_feed is not None: - input_feed = input_feed.index_select(0, new_order) - cached_state_new = torch.jit.annotate( - Dict[str, Optional[Tensor]], - { - "prev_hiddens": torch.stack(prev_hiddens), - "prev_cells": torch.stack(prev_cells), - "input_feed": input_feed, - }, - ) - self.set_incremental_state(incremental_state, "cached_state", cached_state_new), - return - - def max_positions(self): - """Maximum output length supported by the decoder.""" - return self.max_target_positions - - def make_generation_fast_(self, need_attn=False, **kwargs): - self.need_attn = need_attn - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.uniform_(m.weight, -0.1, 0.1) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def LSTM(input_size, hidden_size, **kwargs): - m = nn.LSTM(input_size, hidden_size, **kwargs) - for name, param in m.named_parameters(): - if "weight" in name or "bias" in name: - param.data.uniform_(-0.1, 0.1) - return m - - -def LSTMCell(input_size, hidden_size, **kwargs): - m = nn.LSTMCell(input_size, hidden_size, **kwargs) - for name, param in m.named_parameters(): - if "weight" in name or "bias" in name: - param.data.uniform_(-0.1, 0.1) - return m - - -def Linear(in_features, out_features, bias=True, dropout=0.0): - """Linear layer (input: N x T x C)""" - m = nn.Linear(in_features, out_features, bias=bias) - m.weight.data.uniform_(-0.1, 0.1) - if bias: - m.bias.data.uniform_(-0.1, 0.1) - return m - - -@register_model_architecture("lstm", "lstm") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_freeze_embed = getattr(args, "encoder_freeze_embed", False) - args.encoder_hidden_size = getattr( - args, "encoder_hidden_size", args.encoder_embed_dim - ) - args.encoder_layers = getattr(args, "encoder_layers", 1) - args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False) - args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout) - args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_freeze_embed = getattr(args, "decoder_freeze_embed", False) - args.decoder_hidden_size = getattr( - args, "decoder_hidden_size", args.decoder_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 1) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) - args.decoder_attention = getattr(args, "decoder_attention", "1") - args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) - args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.adaptive_softmax_cutoff = getattr( - args, "adaptive_softmax_cutoff", "10000,50000,200000" - ) - - -@register_model_architecture("lstm", "lstm_wiseman_iwslt_de_en") -def lstm_wiseman_iwslt_de_en(args): - args.dropout = getattr(args, "dropout", 0.1) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_dropout_in = getattr(args, "encoder_dropout_in", 0) - args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256) - args.decoder_dropout_in = getattr(args, "decoder_dropout_in", 0) - args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) - base_architecture(args) - - -@register_model_architecture("lstm", "lstm_luong_wmt_en_de") -def lstm_luong_wmt_en_de(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1000) - args.encoder_layers = getattr(args, "encoder_layers", 4) - args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1000) - args.decoder_layers = getattr(args, "decoder_layers", 4) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 1000) - args.decoder_dropout_out = getattr(args, "decoder_dropout_out", 0) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lstm_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lstm_lm.py deleted file mode 100644 index 3789d38fb11732538e7979a45c35bdf5d444215c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/lstm_lm.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq import utils -from fairseq.models import ( - FairseqLanguageModel, - register_model, - register_model_architecture, -) -from fairseq.models.lstm import Embedding, LSTMDecoder - -DEFAULT_MAX_TARGET_POSITIONS = 1e5 - - -@register_model("lstm_lm") -class LSTMLanguageModel(FairseqLanguageModel): - def __init__(self, decoder): - super().__init__(decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--dropout', type=float, metavar='D', - help='dropout probability') - parser.add_argument('--decoder-embed-dim', type=int, metavar='N', - help='decoder embedding dimension') - parser.add_argument('--decoder-embed-path', type=str, metavar='STR', - help='path to pre-trained decoder embedding') - parser.add_argument('--decoder-hidden-size', type=int, metavar='N', - help='decoder hidden size') - parser.add_argument('--decoder-layers', type=int, metavar='N', - help='number of decoder layers') - parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N', - help='decoder output embedding dimension') - parser.add_argument('--decoder-attention', type=str, metavar='BOOL', - help='decoder attention') - parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR', - help='comma separated list of adaptive softmax cutoff points. ' - 'Must be used with adaptive_loss criterion') - parser.add_argument('--residuals', default=False, - action='store_true', - help='applying residuals between LSTM layers') - - # Granular dropout settings (if not specified these default to --dropout) - parser.add_argument('--decoder-dropout-in', type=float, metavar='D', - help='dropout probability for decoder input embedding') - parser.add_argument('--decoder-dropout-out', type=float, metavar='D', - help='dropout probability for decoder output') - parser.add_argument('--share-decoder-input-output-embed', default=False, - action='store_true', - help='share decoder input and output embeddings') - # fmt: on - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - - if getattr(args, "max_target_positions", None) is not None: - max_target_positions = args.max_target_positions - else: - max_target_positions = getattr( - args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS - ) - - def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx) - embed_dict = utils.parse_embedding(embed_path) - utils.print_embed_overlap(embed_dict, dictionary) - return utils.load_embedding(embed_dict, dictionary, embed_tokens) - - pretrained_decoder_embed = None - if args.decoder_embed_path: - pretrained_decoder_embed = load_pretrained_embedding_from_file( - args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim - ) - - if args.share_decoder_input_output_embed: - # double check all parameters combinations are valid - if task.source_dictionary != task.target_dictionary: - raise ValueError( - "--share-decoder-input-output-embeddings requires a joint dictionary" - ) - - if args.decoder_embed_dim != args.decoder_out_embed_dim: - raise ValueError( - "--share-decoder-input-output-embeddings requires " - "--decoder-embed-dim to match --decoder-out-embed-dim" - ) - - decoder = LSTMDecoder( - dictionary=task.dictionary, - embed_dim=args.decoder_embed_dim, - hidden_size=args.decoder_hidden_size, - out_embed_dim=args.decoder_out_embed_dim, - num_layers=args.decoder_layers, - dropout_in=args.decoder_dropout_in, - dropout_out=args.decoder_dropout_out, - attention=False, # decoder-only language model doesn't support attention - encoder_output_units=0, - pretrained_embed=pretrained_decoder_embed, - share_input_output_embed=args.share_decoder_input_output_embed, - adaptive_softmax_cutoff=( - utils.eval_str_list(args.adaptive_softmax_cutoff, type=int) - if args.criterion == "adaptive_loss" - else None - ), - max_target_positions=max_target_positions, - residuals=args.residuals, - ) - - return cls(decoder) - - -@register_model_architecture("lstm_lm", "lstm_lm") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_hidden_size = getattr( - args, "decoder_hidden_size", args.decoder_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 1) - args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512) - args.decoder_attention = getattr(args, "decoder_attention", "0") - args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) - args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.adaptive_softmax_cutoff = getattr( - args, "adaptive_softmax_cutoff", "10000,50000,200000" - ) - args.residuals = getattr(args, "residuals", False) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/masked_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/masked_lm.py deleted file mode 100644 index 15ca726ec54cf25e0eb1c1bb548f82dad8723a2e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/masked_lm.py +++ /dev/null @@ -1,403 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderModel, - register_model, - register_model_architecture, -) -from fairseq.modules import ( - LayerNorm, - SinusoidalPositionalEmbedding, - TransformerSentenceEncoder, -) -from fairseq.modules.transformer_sentence_encoder import init_bert_params -from fairseq.utils import safe_hasattr - -logger = logging.getLogger(__name__) - - -@register_model("masked_lm") -class MaskedLMModel(FairseqEncoderModel): - """ - Class for training a Masked Language Model. It also supports an - additional sentence level prediction if the sent-loss argument is set. - """ - - def __init__(self, args, encoder): - super().__init__(encoder) - self.args = args - - # if specified then apply bert initialization on the model. We need - # to explictly call this to make sure that the output embeddings - # and projection layers are also correctly initialized - if getattr(args, "apply_bert_init", False): - self.apply(init_bert_params) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # Arguments related to dropout - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for" " attention weights", - ) - parser.add_argument( - "--act-dropout", - type=float, - metavar="D", - help="dropout probability after" " activation in FFN", - ) - - # Arguments related to hidden states and self-attention - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-layers", type=int, metavar="N", help="num encoder layers" - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="N", - help="num encoder attention heads", - ) - - # Arguments related to input and output embeddings - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--share-encoder-input-output-embed", - action="store_true", - help="share encoder input" " and output embeddings", - ) - parser.add_argument( - "--encoder-learned-pos", - action="store_true", - help="use learned positional embeddings in the encoder", - ) - parser.add_argument( - "--no-token-positional-embeddings", - action="store_true", - help="if set, disables positional embeddings" " (outside self attention)", - ) - parser.add_argument( - "--num-segment", type=int, metavar="N", help="num segment in the input" - ) - parser.add_argument( - "--max-positions", type=int, help="number of positional embeddings to learn" - ) - - # Arguments related to sentence level prediction - parser.add_argument( - "--sentence-class-num", - type=int, - metavar="N", - help="number of classes for sentence task", - ) - parser.add_argument( - "--sent-loss", - action="store_true", - help="if set," " calculate sentence level predictions", - ) - - # Arguments related to parameter initialization - parser.add_argument( - "--apply-bert-init", - action="store_true", - help="use custom param initialization for BERT", - ) - - # misc params - parser.add_argument( - "--activation-fn", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--pooler-activation-fn", - choices=utils.get_available_activation_fns(), - help="Which activation function to use for pooler layer.", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - - def forward(self, src_tokens, segment_labels=None, **kwargs): - return self.encoder(src_tokens, segment_labels=segment_labels, **kwargs) - - def max_positions(self): - return self.encoder.max_positions - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - # make sure all arguments are present in older models - base_architecture(args) - - if not safe_hasattr(args, "max_positions"): - args.max_positions = args.tokens_per_sample - - logger.info(args) - - encoder = MaskedLMEncoder(args, task.dictionary) - return cls(args, encoder) - - -class MaskedLMEncoder(FairseqEncoder): - """ - Encoder for Masked Language Modelling. - """ - - def __init__(self, args, dictionary): - super().__init__(dictionary) - - self.padding_idx = dictionary.pad() - self.vocab_size = dictionary.__len__() - self.max_positions = args.max_positions - - self.sentence_encoder = TransformerSentenceEncoder( - padding_idx=self.padding_idx, - vocab_size=self.vocab_size, - num_encoder_layers=args.encoder_layers, - embedding_dim=args.encoder_embed_dim, - ffn_embedding_dim=args.encoder_ffn_embed_dim, - num_attention_heads=args.encoder_attention_heads, - dropout=args.dropout, - attention_dropout=args.attention_dropout, - activation_dropout=args.act_dropout, - max_seq_len=self.max_positions, - num_segments=args.num_segment, - use_position_embeddings=not args.no_token_positional_embeddings, - encoder_normalize_before=args.encoder_normalize_before, - apply_bert_init=args.apply_bert_init, - activation_fn=args.activation_fn, - learned_pos_embedding=args.encoder_learned_pos, - ) - - self.share_input_output_embed = args.share_encoder_input_output_embed - self.embed_out = None - self.sentence_projection_layer = None - self.sentence_out_dim = args.sentence_class_num - self.lm_output_learned_bias = None - - # Remove head is set to true during fine-tuning - self.load_softmax = not getattr(args, "remove_head", False) - - self.masked_lm_pooler = nn.Linear( - args.encoder_embed_dim, args.encoder_embed_dim - ) - self.pooler_activation = utils.get_activation_fn(args.pooler_activation_fn) - - self.lm_head_transform_weight = nn.Linear( - args.encoder_embed_dim, args.encoder_embed_dim - ) - self.activation_fn = utils.get_activation_fn(args.activation_fn) - self.layer_norm = LayerNorm(args.encoder_embed_dim) - - self.lm_output_learned_bias = None - if self.load_softmax: - self.lm_output_learned_bias = nn.Parameter(torch.zeros(self.vocab_size)) - - if not self.share_input_output_embed: - self.embed_out = nn.Linear( - args.encoder_embed_dim, self.vocab_size, bias=False - ) - - if args.sent_loss: - self.sentence_projection_layer = nn.Linear( - args.encoder_embed_dim, self.sentence_out_dim, bias=False - ) - - def forward(self, src_tokens, segment_labels=None, masked_tokens=None, **unused): - """ - Forward pass for Masked LM encoder. This first computes the token - embedding using the token embedding matrix, position embeddings (if - specified) and segment embeddings (if specified). - - Here we assume that the sentence representation corresponds to the - output of the classification_token (see bert_task or cross_lingual_lm - task for more details). - Args: - - src_tokens: B x T matrix representing sentences - - segment_labels: B x T matrix representing segment label for tokens - Returns: - - a tuple of the following: - - logits for predictions in format B x T x C to be used in - softmax afterwards - - a dictionary of additional data, where 'pooled_output' contains - the representation for classification_token and 'inner_states' - is a list of internal model states used to compute the - predictions (similar in ELMO). 'sentence_logits' - is the prediction logit for NSP task and is only computed if - this is specified in the input arguments. - """ - - inner_states, sentence_rep = self.sentence_encoder( - src_tokens, - segment_labels=segment_labels, - ) - - x = inner_states[-1].transpose(0, 1) - # project masked tokens only - if masked_tokens is not None: - x = x[masked_tokens, :] - x = self.layer_norm(self.activation_fn(self.lm_head_transform_weight(x))) - - pooled_output = self.pooler_activation(self.masked_lm_pooler(sentence_rep)) - - # project back to size of vocabulary - if self.share_input_output_embed and hasattr( - self.sentence_encoder.embed_tokens, "weight" - ): - x = F.linear(x, self.sentence_encoder.embed_tokens.weight) - elif self.embed_out is not None: - x = self.embed_out(x) - if self.lm_output_learned_bias is not None: - x = x + self.lm_output_learned_bias - sentence_logits = None - if self.sentence_projection_layer: - sentence_logits = self.sentence_projection_layer(pooled_output) - - return x, { - "inner_states": inner_states, - "pooled_output": pooled_output, - "sentence_logits": sentence_logits, - } - - def max_positions(self): - """Maximum output length supported by the encoder.""" - return self.max_positions - - def upgrade_state_dict_named(self, state_dict, name): - if isinstance( - self.sentence_encoder.embed_positions, SinusoidalPositionalEmbedding - ): - state_dict[ - name + ".sentence_encoder.embed_positions._float_tensor" - ] = torch.FloatTensor(1) - if not self.load_softmax: - for k in list(state_dict.keys()): - if ( - "embed_out.weight" in k - or "sentence_projection_layer.weight" in k - or "lm_output_learned_bias" in k - ): - del state_dict[k] - return state_dict - - -@register_model_architecture("masked_lm", "masked_lm") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.act_dropout = getattr(args, "act_dropout", 0.0) - - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.share_encoder_input_output_embed = getattr( - args, "share_encoder_input_output_embed", False - ) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.num_segment = getattr(args, "num_segment", 2) - - args.sentence_class_num = getattr(args, "sentence_class_num", 2) - args.sent_loss = getattr(args, "sent_loss", False) - - args.apply_bert_init = getattr(args, "apply_bert_init", False) - - args.activation_fn = getattr(args, "activation_fn", "relu") - args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - - -@register_model_architecture("masked_lm", "bert_base") -def bert_base_architecture(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768) - args.share_encoder_input_output_embed = getattr( - args, "share_encoder_input_output_embed", True - ) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) - args.num_segment = getattr(args, "num_segment", 2) - - args.encoder_layers = getattr(args, "encoder_layers", 12) - - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 3072) - - args.sentence_class_num = getattr(args, "sentence_class_num", 2) - args.sent_loss = getattr(args, "sent_loss", True) - - args.apply_bert_init = getattr(args, "apply_bert_init", True) - - args.activation_fn = getattr(args, "activation_fn", "gelu") - args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - base_architecture(args) - - -@register_model_architecture("masked_lm", "bert_large") -def bert_large_architecture(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_layers = getattr(args, "encoder_layers", 24) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - bert_base_architecture(args) - - -@register_model_architecture("masked_lm", "xlm_base") -def xlm_architecture(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.share_encoder_input_output_embed = getattr( - args, "share_encoder_input_output_embed", True - ) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True) - args.num_segment = getattr(args, "num_segment", 1) - - args.encoder_layers = getattr(args, "encoder_layers", 6) - - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - - args.sent_loss = getattr(args, "sent_loss", False) - - args.activation_fn = getattr(args, "activation_fn", "gelu") - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") - args.apply_bert_init = getattr(args, "apply_bert_init", True) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/model_utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/model_utils.py deleted file mode 100644 index 732d66b1d5f695151c26d29eb7f6b53179c269f1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/model_utils.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import List, Optional - -import torch -from torch import Tensor - - -@torch.jit.script -def script_skip_tensor_list(x: List[Tensor], mask): - res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x] - outputs = [] - for i, t in enumerate(res): - if t.numel() != 0: - outputs.append(t) - else: - outputs.append(x[i]) - return outputs - - -@torch.jit.script -def script_skip_tensor(x: Tensor, mask): - # None case - if x.size(0) == 0: - return x - res = x[mask] if x.size(0) == mask.size(0) else x[:, mask] - if res.numel() == 0: - return x - else: - return res - - -@torch.jit.script -def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int): - """ - Expand 2D/3D tensor on dim=1 - """ - if x is None: - return None - - assert x.dim() == 2 or x.dim() == 3 - assert trg_dim >= x.size(1), (trg_dim, x.size()) - if trg_dim == x.size(1): - return x - - dims = [x.size(0), trg_dim - x.size(1)] - if x.dim() == 3: - dims.append(x.size(2)) - x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1) - - return x - - -@torch.jit.script -def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor: - return x if x is not None else y - - -@torch.jit.script -def fill_tensors( - x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int -) -> Optional[Tensor]: - """ - Filling tensor x with y at masked positions (dim=0). - """ - if x is None or x.size()[0] == 0 or y is None: - return x - assert x.dim() == y.dim() and mask.size(0) == x.size(0) - assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2)) - - n_selected = mask.sum() - if n_selected == 0: - return x - assert n_selected == y.size(0) - if n_selected == x.size(0): - return y - - if x.size(1) < y.size(1): - x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx) - x[mask] = y - elif x.size(1) > y.size(1): - x[mask] = torch.tensor(padding_idx).type_as(x) - if x.dim() == 2: - x[mask, : y.size(1)] = y - else: - x[mask, : y.size(1), :] = y - else: - x[mask] = y - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/multilingual_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/multilingual_transformer.py deleted file mode 100644 index e722b647edd92c95a3e93489031ae331f90e0463..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/multilingual_transformer.py +++ /dev/null @@ -1,229 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections import OrderedDict - -from fairseq import utils -from fairseq.models import ( - FairseqMultiModel, - register_model, - register_model_architecture, -) -from fairseq.models.transformer import ( - Embedding, - TransformerDecoder, - TransformerEncoder, - TransformerModel, - base_architecture, -) -from fairseq.utils import safe_hasattr - - -@register_model("multilingual_transformer") -class MultilingualTransformerModel(FairseqMultiModel): - """Train Transformer models for multiple language pairs simultaneously. - - Requires `--task multilingual_translation`. - - We inherit all arguments from TransformerModel and assume that all language - pairs use a single Transformer architecture. In addition, we provide several - options that are specific to the multilingual setting. - - Args: - --share-encoder-embeddings: share encoder embeddings across all source languages - --share-decoder-embeddings: share decoder embeddings across all target languages - --share-encoders: share all encoder params (incl. embeddings) across all source languages - --share-decoders: share all decoder params (incl. embeddings) across all target languages - """ - - def __init__(self, encoders, decoders): - super().__init__(encoders, decoders) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - TransformerModel.add_args(parser) - parser.add_argument( - "--share-encoder-embeddings", - action="store_true", - help="share encoder embeddings across languages", - ) - parser.add_argument( - "--share-decoder-embeddings", - action="store_true", - help="share decoder embeddings across languages", - ) - parser.add_argument( - "--share-encoders", - action="store_true", - help="share encoders across languages", - ) - parser.add_argument( - "--share-decoders", - action="store_true", - help="share decoders across languages", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - from fairseq.tasks.multilingual_translation import MultilingualTranslationTask - - assert isinstance(task, MultilingualTranslationTask) - - # make sure all arguments are present in older models - base_multilingual_architecture(args) - - if not safe_hasattr(args, "max_source_positions"): - args.max_source_positions = 1024 - if not safe_hasattr(args, "max_target_positions"): - args.max_target_positions = 1024 - - src_langs = [lang_pair.split("-")[0] for lang_pair in task.model_lang_pairs] - tgt_langs = [lang_pair.split("-")[1] for lang_pair in task.model_lang_pairs] - - if args.share_encoders: - args.share_encoder_embeddings = True - if args.share_decoders: - args.share_decoder_embeddings = True - - def build_embedding(dictionary, embed_dim, path=None): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - emb = Embedding(num_embeddings, embed_dim, padding_idx) - # if provided, load from preloaded dictionaries - if path: - embed_dict = utils.parse_embedding(path) - utils.load_embedding(embed_dict, dictionary, emb) - return emb - - # build shared embeddings (if applicable) - shared_encoder_embed_tokens, shared_decoder_embed_tokens = None, None - if args.share_all_embeddings: - if args.encoder_embed_dim != args.decoder_embed_dim: - raise ValueError( - "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" - ) - if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path - ): - raise ValueError( - "--share-all-embeddings not compatible with --decoder-embed-path" - ) - shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( - dicts=task.dicts, - langs=task.langs, - embed_dim=args.encoder_embed_dim, - build_embedding=build_embedding, - pretrained_embed_path=args.encoder_embed_path, - ) - shared_decoder_embed_tokens = shared_encoder_embed_tokens - args.share_decoder_input_output_embed = True - else: - if args.share_encoder_embeddings: - shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( - dicts=task.dicts, - langs=src_langs, - embed_dim=args.encoder_embed_dim, - build_embedding=build_embedding, - pretrained_embed_path=args.encoder_embed_path, - ) - if args.share_decoder_embeddings: - shared_decoder_embed_tokens = FairseqMultiModel.build_shared_embeddings( - dicts=task.dicts, - langs=tgt_langs, - embed_dim=args.decoder_embed_dim, - build_embedding=build_embedding, - pretrained_embed_path=args.decoder_embed_path, - ) - - # encoders/decoders for each language - lang_encoders, lang_decoders = {}, {} - - def get_encoder(lang): - if lang not in lang_encoders: - if shared_encoder_embed_tokens is not None: - encoder_embed_tokens = shared_encoder_embed_tokens - else: - encoder_embed_tokens = build_embedding( - task.dicts[lang], - args.encoder_embed_dim, - args.encoder_embed_path, - ) - lang_encoders[lang] = cls._get_module_class( - True, args, task.dicts[lang], encoder_embed_tokens, src_langs - ) - return lang_encoders[lang] - - def get_decoder(lang): - if lang not in lang_decoders: - if shared_decoder_embed_tokens is not None: - decoder_embed_tokens = shared_decoder_embed_tokens - else: - decoder_embed_tokens = build_embedding( - task.dicts[lang], - args.decoder_embed_dim, - args.decoder_embed_path, - ) - lang_decoders[lang] = cls._get_module_class( - False, args, task.dicts[lang], decoder_embed_tokens, tgt_langs - ) - return lang_decoders[lang] - - # shared encoders/decoders (if applicable) - shared_encoder, shared_decoder = None, None - if args.share_encoders: - shared_encoder = get_encoder(src_langs[0]) - if args.share_decoders: - shared_decoder = get_decoder(tgt_langs[0]) - - encoders, decoders = OrderedDict(), OrderedDict() - for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs): - encoders[lang_pair] = ( - shared_encoder if shared_encoder is not None else get_encoder(src) - ) - decoders[lang_pair] = ( - shared_decoder if shared_decoder is not None else get_decoder(tgt) - ) - - return MultilingualTransformerModel(encoders, decoders) - - @classmethod - def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs): - module_class = TransformerEncoder if is_encoder else TransformerDecoder - return module_class(args, lang_dict, embed_tokens) - - def load_state_dict(self, state_dict, strict=True, model_cfg=None): - state_dict_subset = state_dict.copy() - for k, _ in state_dict.items(): - assert k.startswith("models.") - lang_pair = k.split(".")[1] - if lang_pair not in self.models: - del state_dict_subset[k] - super().load_state_dict(state_dict_subset, strict=strict, model_cfg=model_cfg) - - -@register_model_architecture("multilingual_transformer", "multilingual_transformer") -def base_multilingual_architecture(args): - base_architecture(args) - args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", False) - args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", False) - args.share_encoders = getattr(args, "share_encoders", False) - args.share_decoders = getattr(args, "share_decoders", False) - - -@register_model_architecture( - "multilingual_transformer", "multilingual_transformer_iwslt_de_en" -) -def multilingual_transformer_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - args.decoder_layers = getattr(args, "decoder_layers", 6) - base_multilingual_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/__init__.py deleted file mode 100644 index 05fe822487c3bcde8346648d5826f1669c6bc1ca..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -from .fairseq_nat_model import * -from .nonautoregressive_transformer import * -from .nat_crf_transformer import * -from .iterative_nonautoregressive_transformer import * -from .cmlm_transformer import * -from .levenshtein_transformer import * -from .insertion_transformer import * diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/cmlm_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/cmlm_transformer.py deleted file mode 100644 index c876e9453c101c00bd8e93e6e6f1fb48dc26f993..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/cmlm_transformer.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -This file implements: -Ghazvininejad, Marjan, et al. -"Constant-time machine translation with conditional masked language models." -arXiv preprint arXiv:1904.09324 (2019). -""" - -from fairseq.models import register_model, register_model_architecture -from fairseq.models.nat import NATransformerModel -from fairseq.utils import new_arange - - -def _skeptical_unmasking(output_scores, output_masks, p): - sorted_index = output_scores.sort(-1)[1] - boundary_len = ( - (output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p - ).long() - skeptical_mask = new_arange(output_masks) < boundary_len - return skeptical_mask.scatter(1, sorted_index, skeptical_mask) - - -@register_model("cmlm_transformer") -class CMLMNATransformerModel(NATransformerModel): - @staticmethod - def add_args(parser): - NATransformerModel.add_args(parser) - - def forward( - self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs - ): - assert not self.decoder.src_embedding_copy, "do not support embedding copy." - - # encoding - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - # length prediction - length_out = self.decoder.forward_length( - normalize=False, encoder_out=encoder_out - ) - length_tgt = self.decoder.forward_length_prediction( - length_out, encoder_out, tgt_tokens - ) - - # decoding - word_ins_out = self.decoder( - normalize=False, - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - ) - word_ins_mask = prev_output_tokens.eq(self.unk) - - return { - "word_ins": { - "out": word_ins_out, - "tgt": tgt_tokens, - "mask": word_ins_mask, - "ls": self.args.label_smoothing, - "nll_loss": True, - }, - "length": { - "out": length_out, - "tgt": length_tgt, - "factor": self.decoder.length_loss_factor, - }, - } - - def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): - - step = decoder_out.step - max_step = decoder_out.max_step - - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - history = decoder_out.history - - # execute the decoder - output_masks = output_tokens.eq(self.unk) - _scores, _tokens = self.decoder( - normalize=True, - prev_output_tokens=output_tokens, - encoder_out=encoder_out, - ).max(-1) - output_tokens.masked_scatter_(output_masks, _tokens[output_masks]) - output_scores.masked_scatter_(output_masks, _scores[output_masks]) - - if history is not None: - history.append(output_tokens.clone()) - - # skeptical decoding (depend on the maximum decoding steps.) - if (step + 1) < max_step: - skeptical_mask = _skeptical_unmasking( - output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step - ) - - output_tokens.masked_fill_(skeptical_mask, self.unk) - output_scores.masked_fill_(skeptical_mask, 0.0) - - if history is not None: - history.append(output_tokens.clone()) - - return decoder_out._replace( - output_tokens=output_tokens, - output_scores=output_scores, - attn=None, - history=history, - ) - - -@register_model_architecture("cmlm_transformer", "cmlm_transformer") -def cmlm_base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", True) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.apply_bert_init = getattr(args, "apply_bert_init", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - # --- special arguments --- - args.sg_length_pred = getattr(args, "sg_length_pred", False) - args.pred_length_offset = getattr(args, "pred_length_offset", False) - args.length_loss_factor = getattr(args, "length_loss_factor", 0.1) - args.ngram_predictor = getattr(args, "ngram_predictor", 1) - args.src_embedding_copy = getattr(args, "src_embedding_copy", False) - - -@register_model_architecture("cmlm_transformer", "cmlm_transformer_wmt_en_de") -def cmlm_wmt_en_de(args): - cmlm_base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/fairseq_nat_model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/fairseq_nat_model.py deleted file mode 100644 index a5594a4ed9b395a1665c70fa7410d5caa16a879d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/fairseq_nat_model.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -from fairseq.models.transformer import ( - TransformerDecoder, - TransformerEncoder, - TransformerModel, -) -from fairseq.modules.transformer_sentence_encoder import init_bert_params - - -def ensemble_encoder(func): - def wrapper(self, *args, **kwargs): - if self.ensemble_models is None or len(self.ensemble_models) == 1: - return func(self, *args, **kwargs) - encoder_outs = [ - func(model, *args, **kwargs, return_all_hiddens=True) - for model in self.ensemble_models - ] - _encoder_out = encoder_outs[0].copy() - - def stack(key): - outs = [e[key][0] for e in encoder_outs] - return [torch.stack(outs, -1) if outs[0] is not None else None] - - _encoder_out["encoder_out"] = stack("encoder_out") - _encoder_out["encoder_embedding"] = stack("encoder_embedding") - - num_layers = len(_encoder_out["encoder_states"]) - if num_layers > 0: - _encoder_out["encoder_states"] = [ - torch.stack([e["encoder_states"][i] for e in encoder_outs], -1) - for i in range(num_layers) - ] - return _encoder_out - - return wrapper - - -def ensemble_decoder(func): - def wrapper(self, normalize=False, encoder_out=None, *args, **kwargs): - if self.ensemble_models is None or len(self.ensemble_models) == 1: - return func( - self, normalize=normalize, encoder_out=encoder_out, *args, **kwargs - ) - - def _replace(encoder_out, new_val): - new_encoder_out = encoder_out.copy() - new_encoder_out["encoder_out"] = [new_val] - return new_encoder_out - - action_outs = [ - func( - model, - normalize=normalize, - encoder_out=_replace( - encoder_out, encoder_out["encoder_out"][0][:, :, :, i] - ), - *args, - **kwargs - ) - for i, model in enumerate(self.ensemble_models) - ] - - if not isinstance(action_outs[0], tuple): # return multiple values - action_outs = [[a] for a in action_outs] - else: - action_outs = [list(a) for a in action_outs] - - ensembled_outs = [] - for i in range(len(action_outs[0])): - if i == 0 and normalize: - ensembled_outs += [ - torch.logsumexp( - torch.stack([a[i] for a in action_outs], -1), dim=-1 - ) - - math.log(len(self.ensemble_models)) - ] - elif action_outs[0][i] is not None: - ensembled_outs += [torch.stack([a[i] for a in action_outs], -1)] - else: - ensembled_outs += [None] - - if len(ensembled_outs) == 1: - return ensembled_outs[0] - return tuple(ensembled_outs) - - return wrapper - - -class FairseqNATModel(TransformerModel): - """ - Abstract class for all nonautoregressive-based models - """ - - def __init__(self, args, encoder, decoder): - super().__init__(args, encoder, decoder) - self.tgt_dict = decoder.dictionary - self.bos = decoder.dictionary.bos() - self.eos = decoder.dictionary.eos() - self.pad = decoder.dictionary.pad() - self.unk = decoder.dictionary.unk() - - self.ensemble_models = None - - @property - def allow_length_beam(self): - return False - - @property - def allow_ensemble(self): - return True - - def enable_ensemble(self, models): - self.encoder.ensemble_models = [m.encoder for m in models] - self.decoder.ensemble_models = [m.decoder for m in models] - - @staticmethod - def add_args(parser): - TransformerModel.add_args(parser) - parser.add_argument( - "--apply-bert-init", - action="store_true", - help="use custom param initialization for BERT", - ) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - decoder = FairseqNATDecoder(args, tgt_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - decoder.apply(init_bert_params) - return decoder - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - encoder = FairseqNATEncoder(args, src_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - encoder.apply(init_bert_params) - return encoder - - def forward_encoder(self, encoder_inputs): - return self.encoder(*encoder_inputs) - - def forward_decoder(self, *args, **kwargs): - return NotImplementedError - - def initialize_output_tokens(self, *args, **kwargs): - return NotImplementedError - - def forward(self, *args, **kwargs): - return NotImplementedError - - -class FairseqNATEncoder(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): - super().__init__(args, dictionary, embed_tokens) - self.ensemble_models = None - - @ensemble_encoder - def forward(self, *args, **kwargs): - return super().forward(*args, **kwargs) - - -class FairseqNATDecoder(TransformerDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - super().__init__(args, dictionary, embed_tokens, no_encoder_attn) - self.ensemble_models = None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/insertion_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/insertion_transformer.py deleted file mode 100644 index bc28000f59a3b9e8098f9fe710cc8335d39eea3e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/insertion_transformer.py +++ /dev/null @@ -1,280 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -import torch.nn.functional as F -from fairseq.models import register_model, register_model_architecture -from fairseq.models.nat import ( - FairseqNATModel, - LevenshteinTransformerDecoder, - LevenshteinTransformerModel, - ensemble_decoder, -) -from fairseq.models.transformer import Linear -from fairseq.modules.transformer_sentence_encoder import init_bert_params -from fairseq.utils import new_arange - - -class NegativeDistanceScore(object): - def __init__(self): - - # pre-compute some values - self.scores = {} - - self.scores[0.5] = self.compute_score_full(50, 0.5) - self.scores[1.0] = self.compute_score_full(50, 1.0) - self.scores[2.0] = self.compute_score_full(50, 2.0) - - def __call__(self, i, L, tau): - if (tau is None) or (tau > 1000): - return 1 / L - - if tau in self.scores: - if L < self.scores[tau].shape[0]: - return self.scores[tau][L - 1, i] - return self.compute_score(L, tau)[i] - - def compute_score(self, L, tau): - s = np.array([-abs(L / 2 - i) / tau for i in range(L)]) - s = np.exp(s - s.max()) - return s / s.sum() - - def compute_score_full(self, L, tau): - s = -abs(np.arange(0, L - 1)[:, None] / 2 - np.arange(L)[None, :]) / tau - s = np.tril(s, 0) + np.triu(s - float("inf"), 1) - s = np.exp(s - s.max(1, keepdims=True)) - return s / s.sum(1, keepdims=True) - - -neg_scorer = NegativeDistanceScore() - - -def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, tau=None): - try: - from fairseq import libnat - except ImportError as e: - import sys - - sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n") - raise e - - B = in_tokens.size(0) - T = in_tokens.size(1) - V = vocab_size - - with torch.cuda.device_of(in_tokens): - in_tokens_list = [ - [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist()) - ] - out_tokens_list = [ - [t for t in s if t != padding_idx] - for i, s in enumerate(out_tokens.tolist()) - ] - - full_labels = libnat.suggested_ed2_path( - in_tokens_list, out_tokens_list, padding_idx - ) - insert_labels = [a[:-1] for a in full_labels] - - # numericalize1 - insert_label_tensors = in_tokens.new_zeros(B * (T - 1) * V).float() - insert_index, insert_labels = zip( - *[ - (w + (j + i * (T - 1)) * V, neg_scorer(k, len(label), tau)) - for i, labels in enumerate(insert_labels) - for j, label in enumerate(labels[1:-1]) - for k, w in enumerate(label) - ] - ) # HACK 1:-1 - insert_index, insert_labels = [ - torch.tensor(list(a), device=in_tokens.device) - for a in [insert_index, insert_labels] - ] - insert_label_tensors.scatter_(0, insert_index.long(), insert_labels) - insert_label_tensors = insert_label_tensors.view(B, T - 1, V) - - return insert_label_tensors - - -def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx): - - padding_masks = in_tokens[:, 1:].eq(padding_idx) - word_ins_scores.masked_fill_(padding_masks, 0.0) - word_ins_pred.masked_fill_(padding_masks, padding_idx) - - in_coords = new_arange(in_tokens).type_as(in_scores) - - # shift all padding predictions to infinite - out_coords = (in_coords[:, 1:] - 0.5).masked_fill( - word_ins_pred.eq(padding_idx), float("inf") - ) - out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1] - out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords) - out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords) - return out_tokens, out_scores - - -@register_model("insertion_transformer") -class InsertionTransformerModel(LevenshteinTransformerModel): - def __init__(self, args, encoder, decoder): - super().__init__(args, encoder, decoder) - - @staticmethod - def add_args(parser): - FairseqNATModel.add_args(parser) - parser.add_argument("--label-tau", default=None, type=float) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - decoder = InsertionTransformerDecoder(args, tgt_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - decoder.apply(init_bert_params) - return decoder - - def forward( - self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs - ): - - assert tgt_tokens is not None, "forward function only supports training." - - # encoding - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - - # generate training labels for insertion - word_ins_out = self.decoder.forward_word_ins( - normalize=False, - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - ) - - word_ins_tgt = _get_ins_targets( - prev_output_tokens, - tgt_tokens, - self.pad, - self.unk, - len(self.tgt_dict), - tau=self.decoder.label_tau, - ).type_as(word_ins_out) - word_ins_masks = prev_output_tokens[:, 1:].ne(self.pad) - - return { - "word_ins": { - "out": word_ins_out, - "tgt": word_ins_tgt, - "mask": word_ins_masks, - "ls": self.args.label_smoothing, - "nll_loss": True, - } - } - - def forward_decoder( - self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs - ): - - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - history = decoder_out.history - - # TODO: decoding for InsertionTransformer - word_ins_score = self.decoder.forward_word_ins( - normalize=True, prev_output_tokens=output_tokens, encoder_out=encoder_out - ) - - if eos_penalty > 0.0: - word_ins_score[:, :, self.pad] -= eos_penalty - word_ins_score, word_ins_pred = word_ins_score.max(-1) - output_tokens, output_scores = _apply_ins_words( - output_tokens, output_scores, word_ins_pred, word_ins_score, self.pad - ) - - # delete some unnecessary paddings - cut_off = output_tokens.ne(self.pad).sum(1).max() - output_tokens = output_tokens[:, :cut_off] - output_scores = output_scores[:, :cut_off] - - if history is not None: - history.append(output_tokens.clone()) - - return decoder_out._replace( - output_tokens=output_tokens, - output_scores=output_scores, - attn=None, - history=history, - ) - - -class InsertionTransformerDecoder(LevenshteinTransformerDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - # use the TransformerDecoder's __init__ - super(LevenshteinTransformerDecoder, self).__init__( - args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn - ) - - self.dictionary = dictionary - self.bos = dictionary.bos() - self.unk = dictionary.unk() - self.eos = dictionary.eos() - self.pool_out = Linear(self.output_embed_dim * 2, self.output_embed_dim) - - self.label_tau = getattr(args, "label_tau", None) - - @ensemble_decoder - def forward_word_ins(self, normalize, encoder_out, prev_output_tokens): - features = self.extract_features(prev_output_tokens, encoder_out=encoder_out)[0] - features = self.pool_out( - torch.cat([features[:, :-1, :], features[:, 1:, :]], 2) - ) - decoder_out = self.output_layer(features) - return F.log_softmax(decoder_out, -1) if normalize else decoder_out - - def forward_mask_ins(self, *args, **kwargs): - raise NotImplementedError - - def forward_word_del(self, *args, **kwargs): - raise NotImplementedError - - -@register_model_architecture("insertion_transformer", "insertion_transformer") -def insertion_base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.apply_bert_init = getattr(args, "apply_bert_init", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - # special for insertion transformer - args.label_tau = getattr(args, "label_tau", None) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/iterative_nonautoregressive_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/iterative_nonautoregressive_transformer.py deleted file mode 100644 index bc39509980a80eb8c21e0bfdb304649ad3acc4d0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/iterative_nonautoregressive_transformer.py +++ /dev/null @@ -1,228 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from fairseq.models import register_model, register_model_architecture -from fairseq.models.nat import NATransformerModel - - -def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1): - # s: input batch - # V: vocabulary size - rand_words = torch.randint(low=4, high=V, size=s.size(), device=s.device) - choices = torch.rand(size=s.size(), device=s.device) - choices.masked_fill_((s == pad) | (s == bos) | (s == eos), 1) - - replace = choices < beta / 3 - repeat = (choices >= beta / 3) & (choices < beta * 2 / 3) - swap = (choices >= beta * 2 / 3) & (choices < beta) - safe = choices >= beta - - for i in range(s.size(1) - 1): - rand_word = rand_words[:, i] - next_word = s[:, i + 1] - self_word = s[:, i] - - replace_i = replace[:, i] - swap_i = swap[:, i] & (next_word != 3) - repeat_i = repeat[:, i] & (next_word != 3) - safe_i = safe[:, i] | ((next_word == 3) & (~replace_i)) - - s[:, i] = ( - self_word * (safe_i | repeat_i).long() - + next_word * swap_i.long() - + rand_word * replace_i.long() - ) - s[:, i + 1] = ( - next_word * (safe_i | replace_i).long() - + self_word * (swap_i | repeat_i).long() - ) - return s - - -def gumbel_noise(input, TINY=1e-8): - return ( - input.new_zeros(*input.size()) - .uniform_() - .add_(TINY) - .log_() - .neg_() - .add_(TINY) - .log_() - .neg_() - ) - - -@register_model("iterative_nonautoregressive_transformer") -class IterNATransformerModel(NATransformerModel): - @staticmethod - def add_args(parser): - NATransformerModel.add_args(parser) - parser.add_argument( - "--train-step", - type=int, - help="number of refinement iterations during training", - ) - parser.add_argument( - "--dae-ratio", - type=float, - help="the probability of switching to the denoising auto-encoder loss", - ) - parser.add_argument( - "--stochastic-approx", - action="store_true", - help="sampling from the decoder as the inputs for next iteration", - ) - - @classmethod - def build_model(cls, args, task): - model = super().build_model(args, task) - model.train_step = getattr(args, "train_step", 4) - model.dae_ratio = getattr(args, "dae_ratio", 0.5) - model.stochastic_approx = getattr(args, "stochastic_approx", False) - return model - - def forward( - self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs - ): - - B, T = prev_output_tokens.size() - - # encoding - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - - # length prediction - length_out = self.decoder.forward_length( - normalize=False, encoder_out=encoder_out - ) - length_tgt = self.decoder.forward_length_prediction( - length_out, encoder_out, tgt_tokens - ) - - # decoding - word_ins_outs, word_ins_tgts, word_ins_masks = [], [], [] - for t in range(self.train_step): - word_ins_out = self.decoder( - normalize=False, - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - step=t, - ) - word_ins_tgt = tgt_tokens - word_ins_mask = word_ins_tgt.ne(self.pad) - - word_ins_outs.append(word_ins_out) - word_ins_tgts.append(word_ins_tgt) - word_ins_masks.append(word_ins_mask) - - if t < (self.train_step - 1): - # prediction for next iteration - if self.stochastic_approx: - word_ins_prediction = ( - word_ins_out + gumbel_noise(word_ins_out) - ).max(-1)[1] - else: - word_ins_prediction = word_ins_out.max(-1)[1] - - prev_output_tokens = prev_output_tokens.masked_scatter( - word_ins_mask, word_ins_prediction[word_ins_mask] - ) - - if self.dae_ratio > 0: - # we do not perform denoising for the first iteration - corrputed = ( - torch.rand(size=(B,), device=prev_output_tokens.device) - < self.dae_ratio - ) - corrputed_tokens = _sequential_poisoning( - tgt_tokens[corrputed], - len(self.tgt_dict), - 0.33, - self.bos, - self.eos, - self.pad, - ) - prev_output_tokens[corrputed] = corrputed_tokens - - # concat everything - word_ins_out = torch.cat(word_ins_outs, 0) - word_ins_tgt = torch.cat(word_ins_tgts, 0) - word_ins_mask = torch.cat(word_ins_masks, 0) - - return { - "word_ins": { - "out": word_ins_out, - "tgt": word_ins_tgt, - "mask": word_ins_mask, - "ls": self.args.label_smoothing, - "nll_loss": True, - }, - "length": { - "out": length_out, - "tgt": length_tgt, - "factor": self.decoder.length_loss_factor, - }, - } - - -@register_model_architecture( - "iterative_nonautoregressive_transformer", "iterative_nonautoregressive_transformer" -) -def inat_base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.apply_bert_init = getattr(args, "apply_bert_init", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - # --- special arguments --- - args.sg_length_pred = getattr(args, "sg_length_pred", False) - args.pred_length_offset = getattr(args, "pred_length_offset", False) - args.length_loss_factor = getattr(args, "length_loss_factor", 0.1) - args.ngram_predictor = getattr(args, "ngram_predictor", 1) - args.src_embedding_copy = getattr(args, "src_embedding_copy", False) - - args.train_step = getattr(args, "train_step", 4) - args.dae_ratio = getattr(args, "dae_ratio", 0.5) - args.stochastic_approx = getattr(args, "stochastic_approx", False) - - -@register_model_architecture( - "iterative_nonautoregressive_transformer", - "iterative_nonautoregressive_transformer_wmt_en_de", -) -def iter_nat_wmt_en_de(args): - inat_base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/levenshtein_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/levenshtein_transformer.py deleted file mode 100644 index d60d3c52d50b1f20957039a75622ffb95d5eea24..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/levenshtein_transformer.py +++ /dev/null @@ -1,510 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.iterative_refinement_generator import DecoderOut -from fairseq.models import register_model, register_model_architecture -from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder -from fairseq.models.transformer import Embedding -from fairseq.modules import TransformerDecoderLayer -from fairseq.modules.transformer_sentence_encoder import init_bert_params - -from .levenshtein_utils import ( - _apply_del_words, - _apply_ins_masks, - _apply_ins_words, - _fill, - _get_del_targets, - _get_ins_targets, - _skip, - _skip_encoder_out, -) - - -@register_model("levenshtein_transformer") -class LevenshteinTransformerModel(FairseqNATModel): - @property - def allow_length_beam(self): - return False - - @staticmethod - def add_args(parser): - FairseqNATModel.add_args(parser) - parser.add_argument( - "--early-exit", - default="6,6,6", - type=str, - help="number of decoder layers before word_del, mask_ins, word_ins", - ) - parser.add_argument( - "--no-share-discriminator", - action="store_true", - help="separate parameters for discriminator", - ) - parser.add_argument( - "--no-share-maskpredictor", - action="store_true", - help="separate parameters for mask-predictor", - ) - parser.add_argument( - "--share-discriminator-maskpredictor", - action="store_true", - help="share the parameters for both mask-predictor and discriminator", - ) - parser.add_argument( - "--sampling-for-deletion", - action="store_true", - help="instead of argmax, use sampling to predict the tokens", - ) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - decoder = LevenshteinTransformerDecoder(args, tgt_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - decoder.apply(init_bert_params) - return decoder - - def forward( - self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs - ): - - assert tgt_tokens is not None, "forward function only supports training." - - # encoding - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - - # generate training labels for insertion - masked_tgt_masks, masked_tgt_tokens, mask_ins_targets = _get_ins_targets( - prev_output_tokens, tgt_tokens, self.pad, self.unk - ) - mask_ins_targets = mask_ins_targets.clamp(min=0, max=255) # for safe prediction - mask_ins_masks = prev_output_tokens[:, 1:].ne(self.pad) - - mask_ins_out, _ = self.decoder.forward_mask_ins( - normalize=False, - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - ) - word_ins_out, _ = self.decoder.forward_word_ins( - normalize=False, - prev_output_tokens=masked_tgt_tokens, - encoder_out=encoder_out, - ) - - # make online prediction - if self.decoder.sampling_for_deletion: - word_predictions = torch.multinomial( - F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1 - ).view(word_ins_out.size(0), -1) - else: - word_predictions = F.log_softmax(word_ins_out, dim=-1).max(2)[1] - - word_predictions.masked_scatter_( - ~masked_tgt_masks, tgt_tokens[~masked_tgt_masks] - ) - - # generate training labels for deletion - word_del_targets = _get_del_targets(word_predictions, tgt_tokens, self.pad) - word_del_out, _ = self.decoder.forward_word_del( - normalize=False, - prev_output_tokens=word_predictions, - encoder_out=encoder_out, - ) - word_del_masks = word_predictions.ne(self.pad) - - return { - "mask_ins": { - "out": mask_ins_out, - "tgt": mask_ins_targets, - "mask": mask_ins_masks, - "ls": 0.01, - }, - "word_ins": { - "out": word_ins_out, - "tgt": tgt_tokens, - "mask": masked_tgt_masks, - "ls": self.args.label_smoothing, - "nll_loss": True, - }, - "word_del": { - "out": word_del_out, - "tgt": word_del_targets, - "mask": word_del_masks, - }, - } - - def forward_decoder( - self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs - ): - - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - attn = decoder_out.attn - history = decoder_out.history - - bsz = output_tokens.size(0) - if max_ratio is None: - max_lens = torch.zeros_like(output_tokens).fill_(255) - else: - if not encoder_out["encoder_padding_mask"]: - max_src_len = encoder_out["encoder_out"].size(0) - src_lens = encoder_out["encoder_out"].new(bsz).fill_(max_src_len) - else: - src_lens = (~encoder_out["encoder_padding_mask"][0]).sum(1) - max_lens = (src_lens * max_ratio).clamp(min=10).long() - - # delete words - # do not delete tokens if it is - can_del_word = output_tokens.ne(self.pad).sum(1) > 2 - if can_del_word.sum() != 0: # we cannot delete, skip - word_del_score, word_del_attn = self.decoder.forward_word_del( - normalize=True, - prev_output_tokens=_skip(output_tokens, can_del_word), - encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_del_word), - ) - word_del_pred = word_del_score.max(-1)[1].bool() - - _tokens, _scores, _attn = _apply_del_words( - output_tokens[can_del_word], - output_scores[can_del_word], - word_del_attn, - word_del_pred, - self.pad, - self.bos, - self.eos, - ) - output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad) - output_scores = _fill(output_scores, can_del_word, _scores, 0) - attn = _fill(attn, can_del_word, _attn, 0.0) - - if history is not None: - history.append(output_tokens.clone()) - - # insert placeholders - can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens - if can_ins_mask.sum() != 0: - mask_ins_score, _ = self.decoder.forward_mask_ins( - normalize=True, - prev_output_tokens=_skip(output_tokens, can_ins_mask), - encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_mask), - ) - if eos_penalty > 0.0: - mask_ins_score[:, :, 0] = mask_ins_score[:, :, 0] - eos_penalty - mask_ins_pred = mask_ins_score.max(-1)[1] - mask_ins_pred = torch.min( - mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred) - ) - - _tokens, _scores = _apply_ins_masks( - output_tokens[can_ins_mask], - output_scores[can_ins_mask], - mask_ins_pred, - self.pad, - self.unk, - self.eos, - ) - output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad) - output_scores = _fill(output_scores, can_ins_mask, _scores, 0) - - if history is not None: - history.append(output_tokens.clone()) - - # insert words - can_ins_word = output_tokens.eq(self.unk).sum(1) > 0 - if can_ins_word.sum() != 0: - word_ins_score, word_ins_attn = self.decoder.forward_word_ins( - normalize=True, - prev_output_tokens=_skip(output_tokens, can_ins_word), - encoder_out=_skip_encoder_out(self.encoder, encoder_out, can_ins_word), - ) - word_ins_score, word_ins_pred = word_ins_score.max(-1) - _tokens, _scores = _apply_ins_words( - output_tokens[can_ins_word], - output_scores[can_ins_word], - word_ins_pred, - word_ins_score, - self.unk, - ) - - output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad) - output_scores = _fill(output_scores, can_ins_word, _scores, 0) - attn = _fill(attn, can_ins_word, word_ins_attn, 0.0) - - if history is not None: - history.append(output_tokens.clone()) - - # delete some unnecessary paddings - cut_off = output_tokens.ne(self.pad).sum(1).max() - output_tokens = output_tokens[:, :cut_off] - output_scores = output_scores[:, :cut_off] - attn = None if attn is None else attn[:, :cut_off, :] - - return decoder_out._replace( - output_tokens=output_tokens, - output_scores=output_scores, - attn=attn, - history=history, - ) - - def initialize_output_tokens(self, encoder_out, src_tokens): - initial_output_tokens = src_tokens.new_zeros(src_tokens.size(0), 2) - initial_output_tokens[:, 0] = self.bos - initial_output_tokens[:, 1] = self.eos - - initial_output_scores = initial_output_tokens.new_zeros( - *initial_output_tokens.size() - ).type_as(encoder_out["encoder_out"][0]) - - return DecoderOut( - output_tokens=initial_output_tokens, - output_scores=initial_output_scores, - attn=None, - step=0, - max_step=0, - history=None, - ) - - -class LevenshteinTransformerDecoder(FairseqNATDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - super().__init__( - args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn - ) - self.dictionary = dictionary - self.bos = dictionary.bos() - self.unk = dictionary.unk() - self.eos = dictionary.eos() - self.sampling_for_deletion = getattr(args, "sampling_for_deletion", False) - self.embed_mask_ins = Embedding(256, self.output_embed_dim * 2, None) - self.embed_word_del = Embedding(2, self.output_embed_dim, None) - - # del_word, ins_mask, ins_word - self.early_exit = [int(i) for i in args.early_exit.split(",")] - assert len(self.early_exit) == 3 - - # copy layers for mask-predict/deletion - self.layers_msk = None - if getattr(args, "no_share_maskpredictor", False): - self.layers_msk = nn.ModuleList( - [ - TransformerDecoderLayer(args, no_encoder_attn) - for _ in range(self.early_exit[1]) - ] - ) - self.layers_del = None - if getattr(args, "no_share_discriminator", False): - self.layers_del = nn.ModuleList( - [ - TransformerDecoderLayer(args, no_encoder_attn) - for _ in range(self.early_exit[0]) - ] - ) - - if getattr(args, "share_discriminator_maskpredictor", False): - assert getattr( - args, "no_share_discriminator", False - ), "must set saperate discriminator" - self.layers_msk = self.layers_del - - def extract_features( - self, - prev_output_tokens, - encoder_out=None, - early_exit=None, - layers=None, - **unused - ): - """ - Similar to *forward* but only return features. - Inputs: - prev_output_tokens: Tensor(B, T) - encoder_out: a dictionary of hidden states and masks - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - the LevenshteinTransformer decoder has full-attention to all generated tokens - """ - # embed positions - positions = ( - self.embed_positions(prev_output_tokens) - if self.embed_positions is not None - else None - ) - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - attn = None - inner_states = [x] - - # decoder layers - decoder_padding_mask = prev_output_tokens.eq(self.padding_idx) - layers = self.layers if layers is None else layers - early_exit = len(layers) if early_exit is None else early_exit - for _, layer in enumerate(layers[:early_exit]): - x, attn, _ = layer( - x, - encoder_out["encoder_out"][0] - if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) - else None, - encoder_out["encoder_padding_mask"][0] - if ( - encoder_out is not None - and len(encoder_out["encoder_padding_mask"]) > 0 - ) - else None, - self_attn_mask=None, - self_attn_padding_mask=decoder_padding_mask, - ) - inner_states.append(x) - - if self.layer_norm: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - - return x, {"attn": attn, "inner_states": inner_states} - - @ensemble_decoder - def forward_mask_ins(self, normalize, encoder_out, prev_output_tokens, **unused): - features, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - early_exit=self.early_exit[1], - layers=self.layers_msk, - **unused - ) - features_cat = torch.cat([features[:, :-1, :], features[:, 1:, :]], 2) - decoder_out = F.linear(features_cat, self.embed_mask_ins.weight) - if normalize: - return F.log_softmax(decoder_out, -1), extra["attn"] - return decoder_out, extra["attn"] - - @ensemble_decoder - def forward_word_ins(self, normalize, encoder_out, prev_output_tokens, **unused): - features, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - early_exit=self.early_exit[2], - layers=self.layers, - **unused - ) - decoder_out = self.output_layer(features) - if normalize: - return F.log_softmax(decoder_out, -1), extra["attn"] - return decoder_out, extra["attn"] - - @ensemble_decoder - def forward_word_del(self, normalize, encoder_out, prev_output_tokens, **unused): - features, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - early_exit=self.early_exit[0], - layers=self.layers_del, - **unused - ) - decoder_out = F.linear(features, self.embed_word_del.weight) - if normalize: - return F.log_softmax(decoder_out, -1), extra["attn"] - return decoder_out, extra["attn"] - - -@register_model_architecture("levenshtein_transformer", "levenshtein_transformer") -def levenshtein_base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.apply_bert_init = getattr(args, "apply_bert_init", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.sampling_for_deletion = getattr(args, "sampling_for_deletion", False) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - args.early_exit = getattr(args, "early_exit", "6,6,6") - args.no_share_discriminator = getattr(args, "no_share_discriminator", False) - args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False) - args.share_discriminator_maskpredictor = getattr( - args, "share_discriminator_maskpredictor", False - ) - args.no_share_last_layer = getattr(args, "no_share_last_layer", False) - - -@register_model_architecture( - "levenshtein_transformer", "levenshtein_transformer_wmt_en_de" -) -def levenshtein_transformer_wmt_en_de(args): - levenshtein_base_architecture(args) - - -# similar parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) -@register_model_architecture( - "levenshtein_transformer", "levenshtein_transformer_vaswani_wmt_en_de_big" -) -def levenshtein_transformer_vaswani_wmt_en_de_big(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.dropout = getattr(args, "dropout", 0.3) - levenshtein_base_architecture(args) - - -# default parameters used in tensor2tensor implementation -@register_model_architecture( - "levenshtein_transformer", "levenshtein_transformer_wmt_en_de_big" -) -def levenshtein_transformer_wmt_en_de_big_t2t(args): - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_dropout = getattr(args, "activation_dropout", 0.1) - levenshtein_transformer_vaswani_wmt_en_de_big(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/levenshtein_utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/levenshtein_utils.py deleted file mode 100644 index c4b36c6997172ca9f1549ec2a81752e7924fa14f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/levenshtein_utils.py +++ /dev/null @@ -1,292 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from fairseq.utils import new_arange - -# -------------- Helper Functions --------------------------------------------------- # - - -def load_libnat(): - try: - from fairseq import libnat_cuda - - return libnat_cuda, True - - except ImportError as e: - print(str(e) + "... fall back to CPU version") - - try: - from fairseq import libnat - - return libnat, False - - except ImportError as e: - import sys - - sys.stderr.write( - "ERROR: missing libnat_cuda. run `python setup.py build_ext --inplace`\n" - ) - raise e - - -def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx): - libnat, use_cuda = load_libnat() - - def _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx): - in_masks = in_tokens.ne(padding_idx) - out_masks = out_tokens.ne(padding_idx) - mask_ins_targets, masked_tgt_masks = libnat.generate_insertion_labels( - out_tokens.int(), - libnat.levenshtein_distance( - in_tokens.int(), - out_tokens.int(), - in_masks.sum(1).int(), - out_masks.sum(1).int(), - ), - ) - masked_tgt_masks = masked_tgt_masks.bool() & out_masks - mask_ins_targets = mask_ins_targets.type_as(in_tokens)[ - :, 1 : in_masks.size(1) - ].masked_fill_(~in_masks[:, 1:], 0) - masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx) - return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets - - def _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx): - in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1) - - in_tokens_list = [ - [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist()) - ] - out_tokens_list = [ - [t for t in s if t != padding_idx] - for i, s in enumerate(out_tokens.tolist()) - ] - - full_labels = libnat.suggested_ed2_path( - in_tokens_list, out_tokens_list, padding_idx - ) - mask_inputs = [ - [len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels - ] - - # generate labels - masked_tgt_masks = [] - for mask_input in mask_inputs: - mask_label = [] - for beam_size in mask_input[1:-1]: # HACK 1:-1 - mask_label += [0] + [1 for _ in range(beam_size)] - masked_tgt_masks.append( - mask_label + [0 for _ in range(out_seq_len - len(mask_label))] - ) - mask_ins_targets = [ - mask_input[1:-1] - + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))] - for mask_input in mask_inputs - ] - - # transform to tensor - masked_tgt_masks = torch.tensor( - masked_tgt_masks, device=out_tokens.device - ).bool() - mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device) - masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx) - return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets - - if use_cuda: - return _get_ins_targets_cuda(in_tokens, out_tokens, padding_idx, unk_idx) - return _get_ins_targets_cpu(in_tokens, out_tokens, padding_idx, unk_idx) - - -def _get_del_targets(in_tokens, out_tokens, padding_idx): - libnat, use_cuda = load_libnat() - - def _get_del_targets_cuda(in_tokens, out_tokens, padding_idx): - in_masks = in_tokens.ne(padding_idx) - out_masks = out_tokens.ne(padding_idx) - - word_del_targets = libnat.generate_deletion_labels( - in_tokens.int(), - libnat.levenshtein_distance( - in_tokens.int(), - out_tokens.int(), - in_masks.sum(1).int(), - out_masks.sum(1).int(), - ), - ) - word_del_targets = word_del_targets.type_as(in_tokens).masked_fill_( - ~in_masks, 0 - ) - return word_del_targets - - def _get_del_targets_cpu(in_tokens, out_tokens, padding_idx): - out_seq_len = out_tokens.size(1) - with torch.cuda.device_of(in_tokens): - in_tokens_list = [ - [t for t in s if t != padding_idx] - for i, s in enumerate(in_tokens.tolist()) - ] - out_tokens_list = [ - [t for t in s if t != padding_idx] - for i, s in enumerate(out_tokens.tolist()) - ] - - full_labels = libnat.suggested_ed2_path( - in_tokens_list, out_tokens_list, padding_idx - ) - word_del_targets = [b[-1] for b in full_labels] - word_del_targets = [ - labels + [0 for _ in range(out_seq_len - len(labels))] - for labels in word_del_targets - ] - - # transform to tensor - word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device) - return word_del_targets - - if use_cuda: - return _get_del_targets_cuda(in_tokens, out_tokens, padding_idx) - return _get_del_targets_cpu(in_tokens, out_tokens, padding_idx) - - -def _apply_ins_masks( - in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx -): - - in_masks = in_tokens.ne(padding_idx) - in_lengths = in_masks.sum(1) - - # HACK: hacky way to shift all the paddings to eos first. - in_tokens.masked_fill_(~in_masks, eos_idx) - mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0) - - out_lengths = in_lengths + mask_ins_pred.sum(1) - out_max_len = out_lengths.max() - out_masks = new_arange(out_lengths, out_max_len)[None, :] < out_lengths[:, None] - - reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1) - out_tokens = ( - in_tokens.new_zeros(in_tokens.size(0), out_max_len) - .fill_(padding_idx) - .masked_fill_(out_masks, unk_idx) - ) - out_tokens[:, 0] = in_tokens[:, 0] - out_tokens.scatter_(1, reordering, in_tokens[:, 1:]) - - out_scores = None - if in_scores is not None: - in_scores.masked_fill_(~in_masks, 0) - out_scores = in_scores.new_zeros(*out_tokens.size()) - out_scores[:, 0] = in_scores[:, 0] - out_scores.scatter_(1, reordering, in_scores[:, 1:]) - - return out_tokens, out_scores - - -def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx): - word_ins_masks = in_tokens.eq(unk_idx) - out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks]) - - if in_scores is not None: - out_scores = in_scores.masked_scatter( - word_ins_masks, word_ins_scores[word_ins_masks] - ) - else: - out_scores = None - - return out_tokens, out_scores - - -def _apply_del_words( - in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx -): - # apply deletion to a tensor - in_masks = in_tokens.ne(padding_idx) - bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx) - - max_len = in_tokens.size(1) - word_del_pred.masked_fill_(~in_masks, 1) - word_del_pred.masked_fill_(bos_eos_masks, 0) - - reordering = new_arange(in_tokens).masked_fill_(word_del_pred, max_len).sort(1)[1] - - out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering) - - out_scores = None - if in_scores is not None: - out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering) - - out_attn = None - if in_attn is not None: - _mask = word_del_pred[:, :, None].expand_as(in_attn) - _reordering = reordering[:, :, None].expand_as(in_attn) - out_attn = in_attn.masked_fill(_mask, 0.0).gather(1, _reordering) - - return out_tokens, out_scores, out_attn - - -def _skip(x, mask): - """ - Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors. - """ - if isinstance(x, int): - return x - - if x is None: - return None - - if isinstance(x, torch.Tensor): - if x.size(0) == mask.size(0): - return x[mask] - elif x.size(1) == mask.size(0): - return x[:, mask] - - if isinstance(x, list): - return [_skip(x_i, mask) for x_i in x] - - if isinstance(x, dict): - return {k: _skip(v, mask) for k, v in x.items()} - - raise NotImplementedError - - -def _skip_encoder_out(encoder, encoder_out, mask): - if not mask.any(): - return encoder_out - else: - return encoder.reorder_encoder_out( - encoder_out, mask.nonzero(as_tuple=False).squeeze() - ) - - -def _fill(x, mask, y, padding_idx): - """ - Filling tensor x with y at masked positions (dim=0). - """ - if x is None: - return y - assert x.dim() == y.dim() and mask.size(0) == x.size(0) - assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2)) - n_selected = mask.sum() - assert n_selected == y.size(0) - - if n_selected == x.size(0): - return y - - if x.size(1) < y.size(1): - dims = [x.size(0), y.size(1) - x.size(1)] - if x.dim() == 3: - dims.append(x.size(2)) - x = torch.cat([x, x.new_zeros(*dims).fill_(padding_idx)], 1) - x[mask] = y - elif x.size(1) > y.size(1): - x[mask] = padding_idx - if x.dim() == 2: - x[mask, : y.size(1)] = y - else: - x[mask, : y.size(1), :] = y - else: - x[mask] = y - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nat_crf_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nat_crf_transformer.py deleted file mode 100644 index d4b3cd931ceb077eb30db73df1d5d6cd714a86c2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nat_crf_transformer.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from fairseq.models import register_model, register_model_architecture -from fairseq.models.nat import NATransformerModel, base_architecture -from fairseq.modules import DynamicCRF - - -@register_model("nacrf_transformer") -class NACRFTransformerModel(NATransformerModel): - def __init__(self, args, encoder, decoder): - super().__init__(args, encoder, decoder) - self.crf_layer = DynamicCRF( - num_embedding=len(self.tgt_dict), - low_rank=args.crf_lowrank_approx, - beam_size=args.crf_beam_approx, - ) - - @property - def allow_ensemble(self): - return False - - @staticmethod - def add_args(parser): - NATransformerModel.add_args(parser) - parser.add_argument( - "--crf-lowrank-approx", - type=int, - help="the dimension of low-rank approximation of transition", - ) - parser.add_argument( - "--crf-beam-approx", - type=int, - help="the beam size for apporixmating the normalizing factor", - ) - parser.add_argument( - "--word-ins-loss-factor", - type=float, - help="weights on NAT loss used to co-training with CRF loss.", - ) - - def forward( - self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs - ): - # encoding - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - - # length prediction - length_out = self.decoder.forward_length( - normalize=False, encoder_out=encoder_out - ) - length_tgt = self.decoder.forward_length_prediction( - length_out, encoder_out, tgt_tokens - ) - - # decoding - word_ins_out = self.decoder( - normalize=False, - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - ) - word_ins_tgt, word_ins_mask = tgt_tokens, tgt_tokens.ne(self.pad) - - # compute the log-likelihood of CRF - crf_nll = -self.crf_layer(word_ins_out, word_ins_tgt, word_ins_mask) - crf_nll = (crf_nll / word_ins_mask.type_as(crf_nll).sum(-1)).mean() - - return { - "word_ins": { - "out": word_ins_out, - "tgt": word_ins_tgt, - "mask": word_ins_mask, - "ls": self.args.label_smoothing, - "nll_loss": True, - "factor": self.args.word_ins_loss_factor, - }, - "word_crf": {"loss": crf_nll}, - "length": { - "out": length_out, - "tgt": length_tgt, - "factor": self.decoder.length_loss_factor, - }, - } - - def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - history = decoder_out.history - - # execute the decoder and get emission scores - output_masks = output_tokens.ne(self.pad) - word_ins_out = self.decoder( - normalize=False, prev_output_tokens=output_tokens, encoder_out=encoder_out - ) - - # run viterbi decoding through CRF - _scores, _tokens = self.crf_layer.forward_decoder(word_ins_out, output_masks) - output_tokens.masked_scatter_(output_masks, _tokens[output_masks]) - output_scores.masked_scatter_(output_masks, _scores[output_masks]) - if history is not None: - history.append(output_tokens.clone()) - - return decoder_out._replace( - output_tokens=output_tokens, - output_scores=output_scores, - attn=None, - history=history, - ) - - -@register_model_architecture("nacrf_transformer", "nacrf_transformer") -def nacrf_base_architecture(args): - args.crf_lowrank_approx = getattr(args, "crf_lowrank_approx", 32) - args.crf_beam_approx = getattr(args, "crf_beam_approx", 64) - args.word_ins_loss_factor = getattr(args, "word_ins_loss_factor", 0.5) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nonautoregressive_ensembles.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nonautoregressive_ensembles.py deleted file mode 100644 index 0a0221f9c437afc547afa1ad019e5e4a6aaaef17..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nonautoregressive_ensembles.py +++ /dev/null @@ -1,254 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn.functional as F -from fairseq.models.nat import ( - _apply_del_words, - _apply_ins_masks, - _apply_ins_words, - _fill, - _skip, - _skip_encoder_out, -) - - -class _EnsembleModelEncoder(object): - def __init__(self, models): - self.models = models - - def reorder_encoder_out(self, encoder_outs, new_order): - encoder_outs = [ - model.encoder.reorder_encoder_out(encoder_out, new_order) - for model, encoder_out in zip(self.models, encoder_outs) - ] - return encoder_outs - - -class BasicEnsembleModel(torch.nn.Module): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__() - self.models = torch.nn.ModuleList(models) - self.bos = self.models[0].decoder.dictionary.bos() - self.eos = self.models[0].decoder.dictionary.eos() - self.pad = self.models[0].decoder.dictionary.pad() - self.unk = self.models[0].decoder.dictionary.unk() - self.encoder = _EnsembleModelEncoder(self.models) - - def has_encoder(self): - return hasattr(self.models[0], "encoder") - - def max_decoder_positions(self): - return min(m.max_decoder_positions() for m in self.models) - - @torch.no_grad() - def forward_encoder(self, encoder_input): - if not self.has_encoder(): - return None - return [model.forward_encoder(encoder_input) for model in self.models] - - @torch.no_grad() - def forward_decoder(self, *inputs): - raise NotImplementedError - - def initialize_output_tokens(self, *inputs): - raise NotImplementedError - - -class EnsembleLevT(BasicEnsembleModel): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__(models) - - @torch.no_grad() - def forward_decoder( - self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs - ): - # LevT ensembling - # A pipeline of three steps: deletion, placeholder, and word insertion. - # We need to average scores in each step in a pipeline way because of dependence. - # deletion - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - attn = decoder_out.attn - - bsz = output_tokens.size(0) - if max_ratio is None: - max_lens = output_tokens.new().fill_(255) - else: - if not encoder_outs[0]["encoder_padding_mask"]: - src_lens = ( - encoder_outs[0]["encoder_out"][0] - .new(bsz) - .fill_(encoder_outs[0]["encoder_out"][0].size(1)) - ) - else: - src_lens = (~encoder_outs[0]["encoder_padding_mask"][0]).sum(1) - max_lens = (src_lens * max_ratio).clamp(min=10).long() - - # delete words - # do not delete tokens if it is - can_del_word = output_tokens.ne(self.pad).sum(1) > 2 - if can_del_word.sum() != 0: # we cannot delete, skip - output_tokens, output_scores, attn = self.forward_word_del( - encoder_outs, - output_tokens, - output_scores, - attn, - can_del_word, - ) - - # insert placeholders - can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens - if can_ins_mask.sum() != 0: - output_tokens, output_scores = self.forward_mask_ins( - encoder_outs, - output_tokens, - output_scores, - can_ins_mask, - eos_penalty, - max_lens, - ) - - # insert words - can_ins_word = output_tokens.eq(self.unk).sum(1) > 0 - if can_ins_word.sum() != 0: - output_tokens, output_scores, attn = self.forward_word_ins( - encoder_outs, - output_tokens, - output_scores, - attn, - can_ins_word, - ) - - # delete some unnecessary paddings - cut_off = output_tokens.ne(self.pad).sum(1).max() - output_tokens = output_tokens[:, :cut_off] - output_scores = output_scores[:, :cut_off] - attn = None if attn is None else attn[:, :cut_off, :] - return decoder_out._replace( - output_tokens=output_tokens, - output_scores=output_scores, - attn=attn, - history=None, - ) - - def forward_word_del( - self, encoder_outs, output_tokens, output_scores, attn, can_del_word - ): - word_del_score_avg = [] - word_del_attn_avg = [] - for model, encoder_out in zip(self.models, encoder_outs): - word_del_out, word_del_attn = model.decoder.forward_word_del( - _skip(output_tokens, can_del_word), - _skip_encoder_out(model.encoder, encoder_out, can_del_word), - ) - word_del_score = F.log_softmax(word_del_out, 2) - word_del_score_avg.append(word_del_score) - word_del_attn_avg.append(word_del_attn) - word_del_score_avg = torch.logsumexp( - torch.stack(word_del_score_avg, dim=0), dim=0 - ) - math.log(len(self.models)) - word_del_pred = word_del_score_avg.max(-1)[1].bool() - if word_del_attn_avg[0] is not None: - word_del_attn_avg = torch.stack(word_del_attn_avg, dim=0) / len(self.models) - else: - word_del_attn_avg = None - - _tokens, _scores, _attn = _apply_del_words( - output_tokens[can_del_word], - output_scores[can_del_word], - word_del_attn_avg, - word_del_pred, - self.pad, - self.bos, - self.eos, - ) - output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad) - output_scores = _fill(output_scores, can_del_word, _scores, 0) - attn = _fill(attn, can_del_word, _attn, 0.0) - return output_tokens, output_scores, attn - - def forward_mask_ins( - self, - encoder_outs, - output_tokens, - output_scores, - can_ins_mask, - eos_penalty, - max_lens, - ): - mask_ins_score_avg = [] - for model, encoder_out in zip(self.models, encoder_outs): - mask_ins_out, _ = model.decoder.forward_mask_ins( - _skip(output_tokens, can_ins_mask), - _skip_encoder_out(model.encoder, encoder_out, can_ins_mask), - ) - mask_ins_score = F.log_softmax(mask_ins_out, 2) - if eos_penalty > 0.0: - mask_ins_score[:, :, 0] -= eos_penalty - mask_ins_score_avg.append(mask_ins_score) - mask_ins_score_avg = torch.logsumexp( - torch.stack(mask_ins_score_avg, dim=0), dim=0 - ) - math.log(len(self.models)) - mask_ins_pred = mask_ins_score_avg.max(-1)[1] - mask_ins_pred = torch.min( - mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred) - ) - _tokens, _scores = _apply_ins_masks( - output_tokens[can_ins_mask], - output_scores[can_ins_mask], - mask_ins_pred, - self.pad, - self.unk, - self.eos, - ) - output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad) - output_scores = _fill(output_scores, can_ins_mask, _scores, 0) - return output_tokens, output_scores - - def forward_word_ins( - self, encoder_outs, output_tokens, output_scores, attn, can_ins_word - ): - word_ins_score_avg = [] - word_ins_attn_avg = [] - for model, encoder_out in zip(self.models, encoder_outs): - word_ins_out, word_ins_attn = model.decoder.forward_word_ins( - _skip(output_tokens, can_ins_word), - _skip_encoder_out(model.encoder, encoder_out, can_ins_word), - ) - word_ins_score = F.log_softmax(word_ins_out, 2) - word_ins_score_avg.append(word_ins_score) - word_ins_attn_avg.append(word_ins_attn) - word_ins_score_avg = torch.logsumexp( - torch.stack(word_ins_score_avg, dim=0), dim=0 - ) - math.log(len(self.models)) - if word_ins_attn_avg[0] is not None: - word_ins_attn_avg = torch.stack(word_ins_attn_avg, dim=0) / len(self.models) - else: - word_ins_attn_avg = None - word_ins_score_max, word_ins_pred = word_ins_score_avg.max(-1) - - _tokens, _scores = _apply_ins_words( - output_tokens[can_ins_word], - output_scores[can_ins_word], - word_ins_pred, - word_ins_score_max, - self.unk, - ) - - output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad) - output_scores = _fill(output_scores, can_ins_word, _scores, 0) - attn = _fill(attn, can_ins_word, word_ins_attn, 0.0) - return output_tokens, output_scores, attn - - def initialize_output_tokens(self, encoder_outs, src_tokens): - # LevT doesn't do length prediction. - return self.models[0].initialize_output_tokens(encoder_outs[0], src_tokens) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nonautoregressive_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nonautoregressive_transformer.py deleted file mode 100644 index d114202d25fbd1dca66c7abebb0b0a8bffbe094d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/nat/nonautoregressive_transformer.py +++ /dev/null @@ -1,456 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.iterative_refinement_generator import DecoderOut -from fairseq.models import register_model, register_model_architecture -from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder -from fairseq.models.transformer import Embedding -from fairseq.modules.transformer_sentence_encoder import init_bert_params - - -def _mean_pooling(enc_feats, src_masks): - # enc_feats: T x B x C - # src_masks: B x T or None - if src_masks is None: - enc_feats = enc_feats.mean(0) - else: - src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats) - enc_feats = ( - (enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None] - ).sum(0) - return enc_feats - - -def _argmax(x, dim): - return (x == x.max(dim, keepdim=True)[0]).type_as(x) - - -def _uniform_assignment(src_lens, trg_lens): - max_trg_len = trg_lens.max() - steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size - # max_trg_len - index_t = utils.new_arange(trg_lens, max_trg_len).float() - index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len - index_t = torch.round(index_t).long().detach() - return index_t - - -@register_model("nonautoregressive_transformer") -class NATransformerModel(FairseqNATModel): - @property - def allow_length_beam(self): - return True - - @staticmethod - def add_args(parser): - FairseqNATModel.add_args(parser) - - # length prediction - parser.add_argument( - "--src-embedding-copy", - action="store_true", - help="copy encoder word embeddings as the initial input of the decoder", - ) - parser.add_argument( - "--pred-length-offset", - action="store_true", - help="predicting the length difference between the target and source sentences", - ) - parser.add_argument( - "--sg-length-pred", - action="store_true", - help="stop the gradients back-propagated from the length predictor", - ) - parser.add_argument( - "--length-loss-factor", - type=float, - help="weights on the length prediction loss", - ) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - decoder = NATransformerDecoder(args, tgt_dict, embed_tokens) - if getattr(args, "apply_bert_init", False): - decoder.apply(init_bert_params) - return decoder - - def forward( - self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs - ): - # encoding - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - - # length prediction - length_out = self.decoder.forward_length( - normalize=False, encoder_out=encoder_out - ) - length_tgt = self.decoder.forward_length_prediction( - length_out, encoder_out, tgt_tokens - ) - - # decoding - word_ins_out = self.decoder( - normalize=False, - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - ) - - return { - "word_ins": { - "out": word_ins_out, - "tgt": tgt_tokens, - "mask": tgt_tokens.ne(self.pad), - "ls": self.args.label_smoothing, - "nll_loss": True, - }, - "length": { - "out": length_out, - "tgt": length_tgt, - "factor": self.decoder.length_loss_factor, - }, - } - - def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs): - step = decoder_out.step - output_tokens = decoder_out.output_tokens - output_scores = decoder_out.output_scores - history = decoder_out.history - - # execute the decoder - output_masks = output_tokens.ne(self.pad) - _scores, _tokens = self.decoder( - normalize=True, - prev_output_tokens=output_tokens, - encoder_out=encoder_out, - step=step, - ).max(-1) - - output_tokens.masked_scatter_(output_masks, _tokens[output_masks]) - output_scores.masked_scatter_(output_masks, _scores[output_masks]) - if history is not None: - history.append(output_tokens.clone()) - - return decoder_out._replace( - output_tokens=output_tokens, - output_scores=output_scores, - attn=None, - history=history, - ) - - def initialize_output_tokens(self, encoder_out, src_tokens): - # length prediction - length_tgt = self.decoder.forward_length_prediction( - self.decoder.forward_length(normalize=True, encoder_out=encoder_out), - encoder_out=encoder_out, - ) - - max_length = length_tgt.clamp_(min=2).max() - idx_length = utils.new_arange(src_tokens, max_length) - - initial_output_tokens = src_tokens.new_zeros( - src_tokens.size(0), max_length - ).fill_(self.pad) - initial_output_tokens.masked_fill_( - idx_length[None, :] < length_tgt[:, None], self.unk - ) - initial_output_tokens[:, 0] = self.bos - initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos) - - initial_output_scores = initial_output_tokens.new_zeros( - *initial_output_tokens.size() - ).type_as(encoder_out["encoder_out"][0]) - - return DecoderOut( - output_tokens=initial_output_tokens, - output_scores=initial_output_scores, - attn=None, - step=0, - max_step=0, - history=None, - ) - - def regenerate_length_beam(self, decoder_out, beam_size): - output_tokens = decoder_out.output_tokens - length_tgt = output_tokens.ne(self.pad).sum(1) - length_tgt = ( - length_tgt[:, None] - + utils.new_arange(length_tgt, 1, beam_size) - - beam_size // 2 - ) - length_tgt = length_tgt.view(-1).clamp_(min=2) - max_length = length_tgt.max() - idx_length = utils.new_arange(length_tgt, max_length) - - initial_output_tokens = output_tokens.new_zeros( - length_tgt.size(0), max_length - ).fill_(self.pad) - initial_output_tokens.masked_fill_( - idx_length[None, :] < length_tgt[:, None], self.unk - ) - initial_output_tokens[:, 0] = self.bos - initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos) - - initial_output_scores = initial_output_tokens.new_zeros( - *initial_output_tokens.size() - ).type_as(decoder_out.output_scores) - - return decoder_out._replace( - output_tokens=initial_output_tokens, output_scores=initial_output_scores - ) - - -class NATransformerDecoder(FairseqNATDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - super().__init__( - args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn - ) - self.dictionary = dictionary - self.bos = dictionary.bos() - self.unk = dictionary.unk() - self.eos = dictionary.eos() - - self.encoder_embed_dim = args.encoder_embed_dim - self.sg_length_pred = getattr(args, "sg_length_pred", False) - self.pred_length_offset = getattr(args, "pred_length_offset", False) - self.length_loss_factor = getattr(args, "length_loss_factor", 0.1) - self.src_embedding_copy = getattr(args, "src_embedding_copy", False) - self.embed_length = Embedding(256, self.encoder_embed_dim, None) - - @ensemble_decoder - def forward(self, normalize, encoder_out, prev_output_tokens, step=0, **unused): - features, _ = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - embedding_copy=(step == 0) & self.src_embedding_copy, - ) - decoder_out = self.output_layer(features) - return F.log_softmax(decoder_out, -1) if normalize else decoder_out - - @ensemble_decoder - def forward_length(self, normalize, encoder_out): - enc_feats = encoder_out["encoder_out"][0] # T x B x C - if len(encoder_out["encoder_padding_mask"]) > 0: - src_masks = encoder_out["encoder_padding_mask"][0] # B x T - else: - src_masks = None - enc_feats = _mean_pooling(enc_feats, src_masks) - if self.sg_length_pred: - enc_feats = enc_feats.detach() - length_out = F.linear(enc_feats, self.embed_length.weight) - return F.log_softmax(length_out, -1) if normalize else length_out - - def extract_features( - self, - prev_output_tokens, - encoder_out=None, - early_exit=None, - embedding_copy=False, - **unused - ): - """ - Similar to *forward* but only return features. - - Inputs: - prev_output_tokens: Tensor(B, T) - encoder_out: a dictionary of hidden states and masks - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - the LevenshteinTransformer decoder has full-attention to all generated tokens - """ - # embedding - if embedding_copy: - src_embd = encoder_out["encoder_embedding"][0] - if len(encoder_out["encoder_padding_mask"]) > 0: - src_mask = encoder_out["encoder_padding_mask"][0] - else: - src_mask = None - src_mask = ( - ~src_mask - if src_mask is not None - else prev_output_tokens.new_ones(*src_embd.size()[:2]).bool() - ) - - x, decoder_padding_mask = self.forward_embedding( - prev_output_tokens, - self.forward_copying_source( - src_embd, src_mask, prev_output_tokens.ne(self.padding_idx) - ), - ) - - else: - - x, decoder_padding_mask = self.forward_embedding(prev_output_tokens) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - attn = None - inner_states = [x] - - # decoder layers - for i, layer in enumerate(self.layers): - - # early exit from the decoder. - if (early_exit is not None) and (i >= early_exit): - break - - x, attn, _ = layer( - x, - encoder_out["encoder_out"][0] - if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) - else None, - encoder_out["encoder_padding_mask"][0] - if ( - encoder_out is not None - and len(encoder_out["encoder_padding_mask"]) > 0 - ) - else None, - self_attn_mask=None, - self_attn_padding_mask=decoder_padding_mask, - ) - inner_states.append(x) - - if self.layer_norm: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - - return x, {"attn": attn, "inner_states": inner_states} - - def forward_embedding(self, prev_output_tokens, states=None): - # embed positions - positions = ( - self.embed_positions(prev_output_tokens) - if self.embed_positions is not None - else None - ) - - # embed tokens and positions - if states is None: - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - if self.project_in_dim is not None: - x = self.project_in_dim(x) - else: - x = states - - if positions is not None: - x += positions - x = self.dropout_module(x) - decoder_padding_mask = prev_output_tokens.eq(self.padding_idx) - return x, decoder_padding_mask - - def forward_copying_source(self, src_embeds, src_masks, tgt_masks): - length_sources = src_masks.sum(1) - length_targets = tgt_masks.sum(1) - mapped_inputs = _uniform_assignment(length_sources, length_targets).masked_fill( - ~tgt_masks, 0 - ) - copied_embedding = torch.gather( - src_embeds, - 1, - mapped_inputs.unsqueeze(-1).expand( - *mapped_inputs.size(), src_embeds.size(-1) - ), - ) - return copied_embedding - - def forward_length_prediction(self, length_out, encoder_out, tgt_tokens=None): - enc_feats = encoder_out["encoder_out"][0] # T x B x C - if len(encoder_out["encoder_padding_mask"]) > 0: - src_masks = encoder_out["encoder_padding_mask"][0] # B x T - else: - src_masks = None - if self.pred_length_offset: - if src_masks is None: - src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_( - enc_feats.size(0) - ) - else: - src_lengs = (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0) - src_lengs = src_lengs.long() - - if tgt_tokens is not None: - # obtain the length target - tgt_lengs = tgt_tokens.ne(self.padding_idx).sum(1).long() - if self.pred_length_offset: - length_tgt = tgt_lengs - src_lengs + 128 - else: - length_tgt = tgt_lengs - length_tgt = length_tgt.clamp(min=0, max=255) - - else: - # predict the length target (greedy for now) - # TODO: implementing length-beam - pred_lengs = length_out.max(-1)[1] - if self.pred_length_offset: - length_tgt = pred_lengs - 128 + src_lengs - else: - length_tgt = pred_lengs - - return length_tgt - - -@register_model_architecture( - "nonautoregressive_transformer", "nonautoregressive_transformer" -) -def base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.apply_bert_init = getattr(args, "apply_bert_init", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - # --- special arguments --- - args.sg_length_pred = getattr(args, "sg_length_pred", False) - args.pred_length_offset = getattr(args, "pred_length_offset", False) - args.length_loss_factor = getattr(args, "length_loss_factor", 0.1) - args.src_embedding_copy = getattr(args, "src_embedding_copy", False) - - -@register_model_architecture( - "nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de" -) -def nonautoregressive_transformer_wmt_en_de(args): - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/__init__.py deleted file mode 100644 index 90923139ec94d68df26be75ea3ba15ccf98075d5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .enc_dec import * # noqa -from .hub_interface import * # noqa -from .model import * # noqa -from .model_camembert import * # noqa -from .model_gottbert import * # noqa -from .model_xlmr import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/alignment_utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/alignment_utils.py deleted file mode 100644 index ccc7f74cb94d5b8baa2d4e9dfd44f653d47ee43e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/alignment_utils.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections import Counter -from typing import List - -import torch - - -def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor, other_tokens: List[str]): - """ - Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy). - - Args: - roberta (RobertaHubInterface): RoBERTa instance - bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)` - other_tokens (List[str]): other tokens of shape `(T_words)` - - Returns: - List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*. - """ - assert bpe_tokens.dim() == 1 - assert bpe_tokens[0] == 0 - - def clean(text): - return text.strip() - - # remove whitespaces to simplify alignment - bpe_tokens = [roberta.task.source_dictionary.string([x]) for x in bpe_tokens] - bpe_tokens = [ - clean(roberta.bpe.decode(x) if x not in {"", ""} else x) for x in bpe_tokens - ] - other_tokens = [clean(str(o)) for o in other_tokens] - - # strip leading - bpe_tokens = bpe_tokens[1:] - assert "".join(bpe_tokens) == "".join(other_tokens) - - # create alignment from every word to a list of BPE tokens - alignment = [] - bpe_toks = filter(lambda item: item[1] != "", enumerate(bpe_tokens, start=1)) - j, bpe_tok = next(bpe_toks) - for other_tok in other_tokens: - bpe_indices = [] - while True: - if other_tok.startswith(bpe_tok): - bpe_indices.append(j) - other_tok = other_tok[len(bpe_tok) :] - try: - j, bpe_tok = next(bpe_toks) - except StopIteration: - j, bpe_tok = None, None - elif bpe_tok.startswith(other_tok): - # other_tok spans multiple BPE tokens - bpe_indices.append(j) - bpe_tok = bpe_tok[len(other_tok) :] - other_tok = "" - else: - raise Exception('Cannot align "{}" and "{}"'.format(other_tok, bpe_tok)) - if other_tok == "": - break - assert len(bpe_indices) > 0 - alignment.append(bpe_indices) - assert len(alignment) == len(other_tokens) - - return alignment - - -def align_features_to_words(roberta, features, alignment): - """ - Align given features to words. - - Args: - roberta (RobertaHubInterface): RoBERTa instance - features (torch.Tensor): features to align of shape `(T_bpe x C)` - alignment: alignment between BPE tokens and words returned by - func:`align_bpe_to_words`. - """ - assert features.dim() == 2 - - bpe_counts = Counter(j for bpe_indices in alignment for j in bpe_indices) - assert bpe_counts[0] == 0 # shouldn't be aligned - denom = features.new([bpe_counts.get(j, 1) for j in range(len(features))]) - weighted_features = features / denom.unsqueeze(-1) - - output = [weighted_features[0]] - largest_j = -1 - for bpe_indices in alignment: - output.append(weighted_features[bpe_indices].sum(dim=0)) - largest_j = max(largest_j, *bpe_indices) - for j in range(largest_j + 1, len(features)): - output.append(weighted_features[j]) - output = torch.stack(output) - assert torch.all(torch.abs(output.sum(dim=0) - features.sum(dim=0)) < 1e-4) - return output - - -def spacy_nlp(): - if getattr(spacy_nlp, "_nlp", None) is None: - try: - from spacy.lang.en import English - - spacy_nlp._nlp = English() - except ImportError: - raise ImportError("Please install spacy with: pip install spacy") - return spacy_nlp._nlp - - -def spacy_tokenizer(): - if getattr(spacy_tokenizer, "_tokenizer", None) is None: - try: - nlp = spacy_nlp() - spacy_tokenizer._tokenizer = nlp.Defaults.create_tokenizer(nlp) - except ImportError: - raise ImportError("Please install spacy with: pip install spacy") - return spacy_tokenizer._tokenizer diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/enc_dec.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/enc_dec.py deleted file mode 100644 index 31613c520a5be81a510d6bb11d0f1932c2ffca15..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/enc_dec.py +++ /dev/null @@ -1,192 +0,0 @@ -import argparse -import logging - -import fairseq.checkpoint_utils -import torch.nn as nn -from fairseq.models import ( - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.roberta import model as roberta -from fairseq.models.transformer import TransformerDecoder - -logger = logging.getLogger(__name__) - - -@register_model("roberta_enc_dec") -class RobertaEncDecModel(FairseqEncoderDecoderModel): - @staticmethod - def add_args(parser): - parser.add_argument( - "--pretrained-mlm-checkpoint", - default=None, - type=str, - metavar="PRETRAINED", - help="path to pretrained mlm checkpoint", - ) - parser.add_argument( - "--pretrained-decoder", action="store_true", help="reload decoder" - ) - parser.add_argument( - "--hack-layernorm-embedding", - action="store_true", - help="hack to reload old models trained with encoder-normalize-before=False (no equivalent to encoder-normalize-before=False and layernorm_embedding=False", - ) - parser.add_argument( - "--share-decoder-input-output-embed", - action="store_true", - help="share decoder input and output embeddings", - ) - parser.add_argument( - "--share-all-embeddings", - action="store_true", - help="share encoder, decoder and output embeddings" - " (requires shared dictionary and embed dim)", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present - base_enc_dec_architecture(args) - if args.pretrained_mlm_checkpoint: - arg_overrides = None - if args.hack_layernorm_embedding: - arg_overrides = {"layernorm_embedding": False} - loaded = fairseq.checkpoint_utils.load_model_ensemble_and_task( - [args.pretrained_mlm_checkpoint], arg_overrides=arg_overrides - ) - ([roberta_enc], _cfg, _task) = loaded - else: - # Do we need to edit untie_weights here ? - share_in_out = ( - args.share_decoder_input_output_embed or args.share_all_embeddings - ) - args.untie_weights_roberta = not share_in_out - if args.hack_layernorm_embedding: - args.layernorm_embedding = False - args.encoder_normalize_before = False - roberta_enc = roberta.RobertaModel.build_model(args, task) - - return cls.from_roberta(roberta_enc, args, task.source_dictionary) - - @staticmethod - def from_roberta(roberta_enc: roberta.RobertaModel, args, dictionary): - encoder = roberta_enc.encoder.sentence_encoder - vocab_size, embed_dim = encoder.embed_tokens.weight.shape - - if args.share_all_embeddings: - lm_head = roberta_enc.encoder.lm_head - assert encoder.embed_tokens.weight is lm_head.weight, ( - "Can't use --share-all-embeddings with a model " - "that was pretraiend with --untie-weights-roberta_enc" - ) - else: - lm_head = roberta.RobertaLMHead( - embed_dim, vocab_size, roberta_enc.args.activation_fn - ) - - dec_embs = nn.Embedding(vocab_size, embed_dim, dictionary.pad()) - if args.share_all_embeddings or args.share_decoder_input_output_embed: - # Note: I wasn't able to use Embedding _weight parameter to achive this sharing. - dec_embs.weight = lm_head.weight - - decoder = TransformerDecoder( - RobertaEncDecModel.read_args_from_roberta(roberta_enc.args), - dictionary, - dec_embs, - no_encoder_attn=False, - output_projection=lm_head, - ) - if getattr(args, "pretrained_decoder", False): - decoder_dict = encoder.state_dict() - - # TODO: hide setting "encoder_attn" layers behind a flag. - for k, w in list(decoder_dict.items()): - if ".self_attn" in k: - k_enc_attn = k.replace(".self_attn", ".encoder_attn") - decoder_dict[k_enc_attn] = w.detach().clone() - - for k, w in lm_head.state_dict().items(): - decoder_dict["output_projection." + k] = w - - missing_keys, unexpected_keys = decoder.load_state_dict( - decoder_dict, strict=False - ) - # missing_keys = [m for m in missing_keys if ".encoder_attn" not in m] - assert not missing_keys and not unexpected_keys, ( - "Failed to load state dict. " - f"Missing keys: {missing_keys}. " - f"Unexpected keys: {unexpected_keys}." - ) - - if args.share_all_embeddings: - assert decoder.output_projection.weight is decoder.embed_tokens.weight - assert encoder.embed_tokens.weight is decoder.embed_tokens.weight - elif args.share_decoder_input_output_embed: - assert decoder.output_projection.weight is decoder.embed_tokens.weight - assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight - else: - assert decoder.output_projection.weight is not decoder.embed_tokens.weight - assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight - - return RobertaEncDecModel(encoder, decoder) - - @staticmethod - def read_args_from_roberta(roberta_args: argparse.Namespace): - # TODO: this would become easier if encoder/decoder where using a similar - # TransformerConfig object - args = argparse.Namespace(**vars(roberta_args)) - attr_map = [ - ("encoder_attention_heads", "decoder_attention_heads"), - ("encoder_embed_dim", "decoder_embed_dim"), - ("encoder_embed_dim", "decoder_output_dim"), - ("encoder_normalize_before", "decoder_normalize_before"), - ("encoder_layers_to_keep", "decoder_layers_to_keep"), - ("encoder_ffn_embed_dim", "decoder_ffn_embed_dim"), - ("encoder_layerdrop", "decoder_layerdrop"), - ("encoder_layers", "decoder_layers"), - ("encoder_learned_pos", "decoder_learned_pos"), - # should this be set from here ? - ("max_positions", "max_target_positions"), - ] - for k1, k2 in attr_map: - setattr(args, k2, getattr(roberta_args, k1)) - - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = not roberta_args.untie_weights_roberta - return args - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - super().upgrade_state_dict_named(state_dict, name) - old_keys = list(state_dict.keys()) - - # rename decoder -> encoder before upgrading children modules - for k in old_keys: - if k.startswith(prefix + "encoder.lm_head"): - state_dict.pop(k) - continue - new_k = k - new_k = new_k.replace(".sentence_encoder.", ".") - new_k = new_k.replace("decoder.lm_head.", "decoder.output_projection.") - if k == new_k: - continue - # print(k, "->", new_k) - state_dict[new_k] = state_dict.pop(k) - - -@register_model_architecture("roberta_enc_dec", "roberta_enc_dec") -def base_enc_dec_architecture(args): - args.hack_layernorm_embedding = getattr(args, "hack_layernorm_embedding", False) - args.pretrained_mlm_checkpoint = getattr(args, "pretrained_mlm_checkpoint", None) - args.pretrained_decoder = getattr(args, "pretrained_decoder", None) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - - roberta.base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/hub_interface.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/hub_interface.py deleted file mode 100644 index ba298d63ba5da2a5b2f1a44d0384a6b249277ef4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/hub_interface.py +++ /dev/null @@ -1,235 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.data import encoders - - -class RobertaHubInterface(nn.Module): - """A simple PyTorch Hub interface to RoBERTa. - - Usage: https://github.com/pytorch/fairseq/tree/main/examples/roberta - """ - - def __init__(self, cfg, task, model): - super().__init__() - self.cfg = cfg - self.task = task - self.model = model - - self.bpe = encoders.build_bpe(cfg.bpe) - - # this is useful for determining the device - self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float)) - - @property - def device(self): - return self._float_tensor.device - - def encode( - self, sentence: str, *addl_sentences, no_separator=False - ) -> torch.LongTensor: - """ - BPE-encode a sentence (or multiple sentences). - - Every sequence begins with a beginning-of-sentence (``) symbol. - Every sentence ends with an end-of-sentence (``) and we use an - extra end-of-sentence (``) as a separator. - - Example (single sentence): ` a b c ` - Example (sentence pair): ` d e f 1 2 3 ` - - The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE - requires leading spaces. For example:: - - >>> roberta.encode('Hello world').tolist() - [0, 31414, 232, 2] - >>> roberta.encode(' world').tolist() - [0, 232, 2] - >>> roberta.encode('world').tolist() - [0, 8331, 2] - """ - bpe_sentence = " " + self.bpe.encode(sentence) + " " - for s in addl_sentences: - bpe_sentence += " " if not no_separator else "" - bpe_sentence += " " + self.bpe.encode(s) + " " - tokens = self.task.source_dictionary.encode_line( - bpe_sentence, append_eos=False, add_if_not_exist=False - ) - return tokens.long() - - def decode(self, tokens: torch.LongTensor): - assert tokens.dim() == 1 - tokens = tokens.numpy() - if tokens[0] == self.task.source_dictionary.bos(): - tokens = tokens[1:] # remove - eos_mask = tokens == self.task.source_dictionary.eos() - doc_mask = eos_mask[1:] & eos_mask[:-1] - sentences = np.split(tokens, doc_mask.nonzero()[0] + 1) - sentences = [ - self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences - ] - if len(sentences) == 1: - return sentences[0] - return sentences - - def extract_features( - self, tokens: torch.LongTensor, return_all_hiddens: bool = False - ) -> torch.Tensor: - if tokens.dim() == 1: - tokens = tokens.unsqueeze(0) - if tokens.size(-1) > self.model.max_positions(): - raise ValueError( - "tokens exceeds maximum length: {} > {}".format( - tokens.size(-1), self.model.max_positions() - ) - ) - features, extra = self.model( - tokens.to(device=self.device), - features_only=True, - return_all_hiddens=return_all_hiddens, - ) - if return_all_hiddens: - # convert from T x B x C -> B x T x C - inner_states = extra["inner_states"] - return [inner_state.transpose(0, 1) for inner_state in inner_states] - else: - return features # just the last layer's features - - def register_classification_head( - self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs - ): - self.model.register_classification_head( - name, num_classes=num_classes, embedding_size=embedding_size, **kwargs - ) - - def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False): - features = self.extract_features(tokens.to(device=self.device)) - logits = self.model.classification_heads[head](features) - if return_logits: - return logits - return F.log_softmax(logits, dim=-1) - - def extract_features_aligned_to_words( - self, sentence: str, return_all_hiddens: bool = False - ) -> torch.Tensor: - """Extract RoBERTa features, aligned to spaCy's word-level tokenizer.""" - from fairseq.models.roberta import alignment_utils - from spacy.tokens import Doc - - nlp = alignment_utils.spacy_nlp() - tokenizer = alignment_utils.spacy_tokenizer() - - # tokenize both with GPT-2 BPE and spaCy - bpe_toks = self.encode(sentence) - spacy_toks = tokenizer(sentence) - spacy_toks_ws = [t.text_with_ws for t in tokenizer(sentence)] - alignment = alignment_utils.align_bpe_to_words(self, bpe_toks, spacy_toks_ws) - - # extract features and align them - features = self.extract_features( - bpe_toks, return_all_hiddens=return_all_hiddens - ) - features = features.squeeze(0) - aligned_feats = alignment_utils.align_features_to_words( - self, features, alignment - ) - - # wrap in spaCy Doc - doc = Doc( - nlp.vocab, - words=[""] + [x.text for x in spacy_toks] + [""], - spaces=[True] - + [x.endswith(" ") for x in spacy_toks_ws[:-1]] - + [True, False], - ) - assert len(doc) == aligned_feats.size(0) - doc.user_token_hooks["vector"] = lambda token: aligned_feats[token.i] - return doc - - def fill_mask(self, masked_input: str, topk: int = 5): - masked_token = "" - assert ( - masked_token in masked_input and masked_input.count(masked_token) == 1 - ), "Please add one {0} token for the input, eg: 'He is a {0} guy'".format( - masked_token - ) - - text_spans = masked_input.split(masked_token) - text_spans_bpe = ( - (" {0} ".format(masked_token)) - .join([self.bpe.encode(text_span.rstrip()) for text_span in text_spans]) - .strip() - ) - tokens = self.task.source_dictionary.encode_line( - " " + text_spans_bpe + " ", - append_eos=False, - add_if_not_exist=False, - ) - - masked_index = (tokens == self.task.mask_idx).nonzero(as_tuple=False) - if tokens.dim() == 1: - tokens = tokens.unsqueeze(0) - - with utils.model_eval(self.model): - features, extra = self.model( - tokens.long().to(device=self.device), - features_only=False, - return_all_hiddens=False, - ) - logits = features[0, masked_index, :].squeeze() - prob = logits.softmax(dim=0) - values, index = prob.topk(k=topk, dim=0) - topk_predicted_token_bpe = self.task.source_dictionary.string(index) - - topk_filled_outputs = [] - for index, predicted_token_bpe in enumerate( - topk_predicted_token_bpe.split(" ") - ): - predicted_token = self.bpe.decode(predicted_token_bpe) - # Quick hack to fix https://github.com/pytorch/fairseq/issues/1306 - if predicted_token_bpe.startswith("\u2581"): - predicted_token = " " + predicted_token - if " {0}".format(masked_token) in masked_input: - topk_filled_outputs.append( - ( - masked_input.replace( - " {0}".format(masked_token), predicted_token - ), - values[index].item(), - predicted_token, - ) - ) - else: - topk_filled_outputs.append( - ( - masked_input.replace(masked_token, predicted_token), - values[index].item(), - predicted_token, - ) - ) - return topk_filled_outputs - - def disambiguate_pronoun(self, sentence: str) -> bool: - """ - Usage:: - - >>> disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.') - True - - >>> disambiguate_pronoun('The trophy would not fit in the brown suitcase because [it] was too big.') - 'The trophy' - """ - assert hasattr( - self.task, "disambiguate_pronoun" - ), "roberta.disambiguate_pronoun() requires a model trained with the WSC task." - with utils.model_eval(self.model): - return self.task.disambiguate_pronoun( - self.model, sentence, use_cuda=self.device.type == "cuda" - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model.py deleted file mode 100644 index 18d4191cb00b41490aca39527f982e5554b525cb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model.py +++ /dev/null @@ -1,699 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -RoBERTa: A Robustly Optimized BERT Pretraining Approach. -""" - -import logging - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder -from fairseq.modules import LayerNorm -from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ -from fairseq.modules.transformer_sentence_encoder import init_bert_params -from fairseq.utils import safe_getattr, safe_hasattr - -from .hub_interface import RobertaHubInterface - -logger = logging.getLogger(__name__) - - -@register_model("roberta") -class RobertaModel(FairseqEncoderModel): - @classmethod - def hub_models(cls): - return { - "roberta.base": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz", - "roberta.large": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz", - "roberta.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz", - "roberta.large.wsc": "http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz", - } - - def __init__(self, args, encoder): - super().__init__(encoder) - self.args = args - - # We follow BERT's random weight initialization - self.apply(init_bert_params) - - self.classification_heads = nn.ModuleDict() - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--encoder-layers", type=int, metavar="L", help="num encoder layers" - ) - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="H", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="F", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="A", - help="num encoder attention heads", - ) - parser.add_argument( - "--activation-fn", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--pooler-activation-fn", - choices=utils.get_available_activation_fns(), - help="activation function to use for pooler layer", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - parser.add_argument( - "--layernorm-embedding", - action="store_true", - help="add layernorm to embedding", - ) - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--activation-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN", - ) - parser.add_argument( - "--pooler-dropout", - type=float, - metavar="D", - help="dropout probability in the masked_lm pooler layers", - ) - parser.add_argument( - "--max-positions", type=int, help="number of positional embeddings to learn" - ) - parser.add_argument( - "--load-checkpoint-heads", - action="store_true", - help="(re-)register and load heads when loading checkpoints", - ) - parser.add_argument( - "--untie-weights-roberta", - action="store_true", - help="Untie weights between embeddings and classifiers in RoBERTa", - ) - # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) - parser.add_argument( - "--encoder-layerdrop", - type=float, - metavar="D", - default=0, - help="LayerDrop probability for encoder", - ) - parser.add_argument( - "--encoder-layers-to-keep", - default=None, - help="which layers to *keep* when pruning as a comma-separated list", - ) - # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) - parser.add_argument( - "--quant-noise-pq", - type=float, - metavar="D", - default=0, - help="iterative PQ quantization noise at training time", - ) - parser.add_argument( - "--quant-noise-pq-block-size", - type=int, - metavar="D", - default=8, - help="block size of quantization noise at training time", - ) - parser.add_argument( - "--quant-noise-scalar", - type=float, - metavar="D", - default=0, - help="scalar quantization noise and scalar quantization at training time", - ) - # args for "Better Fine-Tuning by Reducing Representational Collapse" (Aghajanyan et al. 2020) - parser.add_argument( - "--spectral-norm-classification-head", - action="store_true", - default=False, - help="Apply spectral normalization on the classification head", - ) - # args for Fully Sharded Data Parallel (FSDP) training - parser.add_argument( - "--min-params-to-wrap", - type=int, - metavar="D", - default=DEFAULT_MIN_PARAMS_TO_WRAP, - help=( - "minimum number of params for a layer to be wrapped with FSDP() when " - "training with --ddp-backend=fully_sharded. Smaller values will " - "improve memory efficiency, but may make torch.distributed " - "communication less efficient due to smaller input sizes. This option " - "is set to 0 (i.e., always wrap) when --checkpoint-activations or " - "--offload-activations are passed." - ), - ) - # args for AdaPruning - # In short, it adds regularizarion for the multihead attention module and feed forward neural nets - # For more details, please refer to the paper https://openreview.net/forum?id=_CMSV7FTzGI - parser.add_argument( - "--mha-reg-scale-factor", - type=float, - metavar="D", - default=0.0, - help="scaling factor for regularization term in adptive pruning, recommendation is 0.000375", - ) - parser.add_argument( - "--ffn-reg-scale-factor", - type=float, - metavar="D", - default=0.0, - help="scaling factor for regularization term in adptive pruning, recommendation is 0.000375", - ) - parser.add_argument( - "--mha-heads-to-keep", - type=int, - metavar="D", - default=-1, - help="number of heads to keep in each multi-head attention module, -1 means keeping all heads", - ) - parser.add_argument( - "--ffn-blocks-to-remove", - type=int, - metavar="D", - default=-1, - help="number of feedforward blocks to remove in each transformer layer, -1 means keeping all ffn blocks", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - from omegaconf import OmegaConf - - if OmegaConf.is_config(args): - OmegaConf.set_struct(args, False) - - # make sure all arguments are present - base_architecture(args) - - if not safe_hasattr(args, "max_positions"): - if not safe_hasattr(args, "tokens_per_sample"): - args.tokens_per_sample = task.max_positions() - args.max_positions = args.tokens_per_sample - - encoder = RobertaEncoder(args, task.source_dictionary) - - if OmegaConf.is_config(args): - OmegaConf.set_struct(args, True) - - return cls(args, encoder) - - def forward( - self, - src_tokens, - features_only=False, - return_all_hiddens=False, - classification_head_name=None, - **kwargs, - ): - if classification_head_name is not None: - features_only = True - - x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs) - - if classification_head_name is not None: - x = self.classification_heads[classification_head_name](x) - return x, extra - - def _get_adaptive_head_loss(self): - norm_loss = 0 - scaling = float(self.args.mha_reg_scale_factor) - for layer in self.encoder.sentence_encoder.layers: - norm_loss_layer = 0 - for i in range(layer.self_attn.num_heads): - start_idx = i * layer.self_attn.head_dim - end_idx = (i + 1) * layer.self_attn.head_dim - norm_loss_layer += scaling * ( - torch.sum( - torch.abs( - layer.self_attn.q_proj.weight[ - start_idx:end_idx, - ] - ) - ) - + torch.sum( - torch.abs(layer.self_attn.q_proj.bias[start_idx:end_idx]) - ) - ) - norm_loss_layer += scaling * ( - torch.sum( - torch.abs( - layer.self_attn.k_proj.weight[ - start_idx:end_idx, - ] - ) - ) - + torch.sum( - torch.abs(layer.self_attn.k_proj.bias[start_idx:end_idx]) - ) - ) - norm_loss_layer += scaling * ( - torch.sum( - torch.abs( - layer.self_attn.v_proj.weight[ - start_idx:end_idx, - ] - ) - ) - + torch.sum( - torch.abs(layer.self_attn.v_proj.bias[start_idx:end_idx]) - ) - ) - - norm_loss += norm_loss_layer - return norm_loss - - def _get_adaptive_ffn_loss(self): - ffn_scale_factor = float(self.args.ffn_reg_scale_factor) - filter_loss = 0 - for layer in self.encoder.sentence_encoder.layers: - filter_loss += torch.sum( - torch.abs(layer.fc1.weight * ffn_scale_factor) - ) + torch.sum(torch.abs(layer.fc2.weight * ffn_scale_factor)) - filter_loss += torch.sum( - torch.abs(layer.fc1.bias * ffn_scale_factor) - ) + torch.sum(torch.abs(layer.fc2.bias * ffn_scale_factor)) - return filter_loss - - def get_normalized_probs(self, net_output, log_probs, sample=None): - """Get normalized probabilities (or log probs) from a net's output.""" - logits = net_output[0].float() - if log_probs: - return F.log_softmax(logits, dim=-1) - else: - return F.softmax(logits, dim=-1) - - def register_classification_head( - self, name, num_classes=None, inner_dim=None, **kwargs - ): - """Register a classification head.""" - if name in self.classification_heads: - prev_num_classes = self.classification_heads[name].out_proj.out_features - prev_inner_dim = self.classification_heads[name].dense.out_features - if num_classes != prev_num_classes or inner_dim != prev_inner_dim: - logger.warning( - 're-registering head "{}" with num_classes {} (prev: {}) ' - "and inner_dim {} (prev: {})".format( - name, num_classes, prev_num_classes, inner_dim, prev_inner_dim - ) - ) - self.classification_heads[name] = RobertaClassificationHead( - input_dim=self.args.encoder_embed_dim, - inner_dim=inner_dim or self.args.encoder_embed_dim, - num_classes=num_classes, - activation_fn=self.args.pooler_activation_fn, - pooler_dropout=self.args.pooler_dropout, - q_noise=self.args.quant_noise_pq, - qn_block_size=self.args.quant_noise_pq_block_size, - do_spectral_norm=self.args.spectral_norm_classification_head, - ) - - @property - def supported_targets(self): - return {"self"} - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - bpe="gpt2", - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - bpe=bpe, - load_checkpoint_heads=True, - **kwargs, - ) - - logger.info(x["args"]) - return RobertaHubInterface(x["args"], x["task"], x["models"][0]) - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - - # rename decoder -> encoder before upgrading children modules - for k in list(state_dict.keys()): - if k.startswith(prefix + "decoder"): - new_k = prefix + "encoder" + k[len(prefix + "decoder") :] - state_dict[new_k] = state_dict[k] - del state_dict[k] - - # rename emb_layer_norm -> layernorm_embedding - for k in list(state_dict.keys()): - if ".emb_layer_norm." in k: - new_k = k.replace(".emb_layer_norm.", ".layernorm_embedding.") - state_dict[new_k] = state_dict[k] - del state_dict[k] - - # upgrade children modules - super().upgrade_state_dict_named(state_dict, name) - - # Handle new classification heads present in the state dict. - current_head_names = ( - [] - if not hasattr(self, "classification_heads") - else self.classification_heads.keys() - ) - keys_to_delete = [] - for k in state_dict.keys(): - if not k.startswith(prefix + "classification_heads."): - continue - - head_name = k[len(prefix + "classification_heads.") :].split(".")[0] - num_classes = state_dict[ - prefix + "classification_heads." + head_name + ".out_proj.weight" - ].size(0) - inner_dim = state_dict[ - prefix + "classification_heads." + head_name + ".dense.weight" - ].size(0) - - if getattr(self.args, "load_checkpoint_heads", False): - if head_name not in current_head_names: - self.register_classification_head(head_name, num_classes, inner_dim) - else: - if head_name not in current_head_names: - logger.warning( - "deleting classification head ({}) from checkpoint " - "not present in current model: {}".format(head_name, k) - ) - keys_to_delete.append(k) - elif ( - num_classes - != self.classification_heads[head_name].out_proj.out_features - or inner_dim - != self.classification_heads[head_name].dense.out_features - ): - logger.warning( - "deleting classification head ({}) from checkpoint " - "with different dimensions than current model: {}".format( - head_name, k - ) - ) - keys_to_delete.append(k) - for k in keys_to_delete: - del state_dict[k] - - # Copy any newly-added classification heads into the state dict - # with their current weights. - if hasattr(self, "classification_heads"): - cur_state = self.classification_heads.state_dict() - for k, v in cur_state.items(): - if prefix + "classification_heads." + k not in state_dict: - logger.info("Overwriting " + prefix + "classification_heads." + k) - state_dict[prefix + "classification_heads." + k] = v - - # adapt data2vec models - if ( - "encoder._ema" in state_dict - and "encoder.lm_head.weight" not in state_dict - ): - lm_state = self.encoder.lm_head.state_dict() - for k, v in lm_state.items(): - state_dict["encoder.lm_head." + k] = v - - for k in list(state_dict.keys()): - if k.startswith("encoder.regression_head") or k == "encoder._ema": - del state_dict[k] - - -class RobertaLMHead(nn.Module): - """Head for masked language modeling.""" - - def __init__(self, embed_dim, output_dim, activation_fn, weight=None): - super().__init__() - self.dense = nn.Linear(embed_dim, embed_dim) - self.activation_fn = utils.get_activation_fn(activation_fn) - self.layer_norm = LayerNorm(embed_dim) - - if weight is None: - weight = nn.Linear(embed_dim, output_dim, bias=False).weight - self.weight = weight - self.bias = nn.Parameter(torch.zeros(output_dim)) - - def forward(self, features, masked_tokens=None, **kwargs): - # Only project the masked tokens while training, - # saves both memory and computation - if masked_tokens is not None: - features = features[masked_tokens, :] - - x = self.dense(features) - x = self.activation_fn(x) - x = self.layer_norm(x) - # project back to size of vocabulary with bias - x = F.linear(x, self.weight) + self.bias - return x - - -class RobertaClassificationHead(nn.Module): - """Head for sentence-level classification tasks.""" - - def __init__( - self, - input_dim, - inner_dim, - num_classes, - activation_fn, - pooler_dropout, - q_noise=0, - qn_block_size=8, - do_spectral_norm=False, - ): - super().__init__() - self.dense = nn.Linear(input_dim, inner_dim) - self.activation_fn = utils.get_activation_fn(activation_fn) - self.dropout = nn.Dropout(p=pooler_dropout) - self.out_proj = apply_quant_noise_( - nn.Linear(inner_dim, num_classes), q_noise, qn_block_size - ) - if do_spectral_norm: - if q_noise != 0: - raise NotImplementedError( - "Attempting to use Spectral Normalization with Quant Noise. This is not officially supported" - ) - self.out_proj = torch.nn.utils.spectral_norm(self.out_proj) - - def forward(self, features, **kwargs): - x = features[:, 0, :] # take token (equiv. to [CLS]) - x = self.dropout(x) - x = self.dense(x) - x = self.activation_fn(x) - x = self.dropout(x) - x = self.out_proj(x) - return x - - -class RobertaEncoder(FairseqEncoder): - """RoBERTa encoder.""" - - def __init__(self, args, dictionary): - super().__init__(dictionary) - - # set any missing default values - base_architecture(args) - self.args = args - - if args.encoder_layers_to_keep: - args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) - - embed_tokens = self.build_embedding( - len(dictionary), args.encoder_embed_dim, dictionary.pad() - ) - - self.sentence_encoder = self.build_encoder(args, dictionary, embed_tokens) - - self.lm_head = self.build_lm_head( - embed_dim=args.encoder_embed_dim, - output_dim=len(dictionary), - activation_fn=args.activation_fn, - weight=( - self.sentence_encoder.embed_tokens.weight - if not args.untie_weights_roberta - else None - ), - ) - - def build_embedding(self, vocab_size, embedding_dim, padding_idx): - return nn.Embedding(vocab_size, embedding_dim, padding_idx) - - def build_encoder(self, args, dictionary, embed_tokens): - encoder = TransformerEncoder(args, dictionary, embed_tokens) - encoder.apply(init_bert_params) - return encoder - - def build_lm_head(self, embed_dim, output_dim, activation_fn, weight): - return RobertaLMHead(embed_dim, output_dim, activation_fn, weight) - - def forward( - self, - src_tokens, - features_only=False, - return_all_hiddens=False, - masked_tokens=None, - **unused, - ): - """ - Args: - src_tokens (LongTensor): input tokens of shape `(batch, src_len)` - features_only (bool, optional): skip LM head and just return - features. If True, the output will be of shape - `(batch, src_len, embed_dim)`. - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - - Returns: - tuple: - - the LM output of shape `(batch, src_len, vocab)` - - a dictionary of additional data, where 'inner_states' - is a list of hidden states. Note that the hidden - states have shape `(src_len, batch, vocab)`. - """ - x, extra = self.extract_features( - src_tokens, return_all_hiddens=return_all_hiddens - ) - if not features_only: - x = self.output_layer(x, masked_tokens=masked_tokens) - return x, extra - - def extract_features(self, src_tokens, return_all_hiddens=False, **kwargs): - encoder_out = self.sentence_encoder( - src_tokens, - return_all_hiddens=return_all_hiddens, - token_embeddings=kwargs.get("token_embeddings", None), - ) - # T x B x C -> B x T x C - features = encoder_out["encoder_out"][0].transpose(0, 1) - inner_states = encoder_out["encoder_states"] if return_all_hiddens else None - return features, {"inner_states": inner_states} - - def output_layer(self, features, masked_tokens=None, **unused): - return self.lm_head(features, masked_tokens) - - def max_positions(self): - """Maximum output length supported by the encoder.""" - return self.args.max_positions - - -@register_model_architecture("roberta", "roberta") -def base_architecture(args): - args.encoder_layers = safe_getattr(args, "encoder_layers", 12) - args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 768) - args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 3072) - args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 12) - - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_dropout = safe_getattr(args, "activation_dropout", 0.0) - args.pooler_dropout = safe_getattr(args, "pooler_dropout", 0.0) - - args.max_source_positions = safe_getattr(args, "max_positions", 512) - args.no_token_positional_embeddings = safe_getattr( - args, "no_token_positional_embeddings", False - ) - - # BERT has a few structural differences compared to the original Transformer - args.encoder_learned_pos = safe_getattr(args, "encoder_learned_pos", True) - args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", True) - args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", True) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - args.encoder_normalize_before = safe_getattr( - args, "encoder_normalize_before", False - ) - args.pooler_activation_fn = safe_getattr(args, "pooler_activation_fn", "tanh") - args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False) - - # Adaptive input config - args.adaptive_input = safe_getattr(args, "adaptive_input", False) - - # LayerDrop config - args.encoder_layerdrop = safe_getattr(args, "encoder_layerdrop", 0.0) - args.encoder_layers_to_keep = safe_getattr(args, "encoder_layers_to_keep", None) - - # Quantization noise config - args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0) - args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8) - args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0) - - # R4F config - args.spectral_norm_classification_head = safe_getattr( - args, "spectral_norm_classification_head", False - ) - - -@register_model_architecture("roberta", "roberta_prenorm") -def roberta_prenorm_architecture(args): - args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False) - args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", True) - base_architecture(args) - - -@register_model_architecture("roberta", "roberta_base") -def roberta_base_architecture(args): - base_architecture(args) - - -@register_model_architecture("roberta", "roberta_large") -def roberta_large_architecture(args): - args.encoder_layers = safe_getattr(args, "encoder_layers", 24) - args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16) - base_architecture(args) - - -@register_model_architecture("roberta", "xlm") -def xlm_architecture(args): - args.encoder_layers = safe_getattr(args, "encoder_layers", 16) - args.encoder_embed_dim = safe_getattr(args, "encoder_embed_dim", 1280) - args.encoder_ffn_embed_dim = safe_getattr(args, "encoder_ffn_embed_dim", 1280 * 4) - args.encoder_attention_heads = safe_getattr(args, "encoder_attention_heads", 16) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_camembert.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_camembert.py deleted file mode 100644 index 46447546fafb4a0a887b481022cac07631047c80..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_camembert.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -CamemBERT: a Tasty French Language Model -""" - -from fairseq.models import register_model - -from .hub_interface import RobertaHubInterface -from .model import RobertaModel - - -@register_model("camembert") -class CamembertModel(RobertaModel): - @classmethod - def hub_models(cls): - return { - "camembert": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", - "camembert.v0": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", - "camembert-base": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz", - "camembert-large": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz", - "camembert-base-ccnet": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz", - "camembert-base-ccnet-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz", - "camembert-base-wikipedia-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz", - "camembert-base-oscar-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz", - } - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - bpe="sentencepiece", - **kwargs - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - bpe=bpe, - load_checkpoint_heads=True, - **kwargs, - ) - return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_gottbert.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_gottbert.py deleted file mode 100644 index dc7a019b33387cce2e222138a339be1c904335c7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_gottbert.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -GottBERT: a pure German Language Model -""" - -from fairseq.models import register_model - -from .hub_interface import RobertaHubInterface -from .model import RobertaModel - - -@register_model("gottbert") -class GottbertModel(RobertaModel): - @classmethod - def hub_models(cls): - return { - "gottbert-base": "https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz", - } - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - bpe="hf_byte_bpe", - bpe_vocab="vocab.json", - bpe_merges="merges.txt", - bpe_add_prefix_space=False, - **kwargs - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - bpe=bpe, - load_checkpoint_heads=True, - bpe_vocab=bpe_vocab, - bpe_merges=bpe_merges, - bpe_add_prefix_space=bpe_add_prefix_space, - **kwargs, - ) - return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_xlmr.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_xlmr.py deleted file mode 100644 index cf6e354d53b918dd4c7c78bfcd38ac0d63cab3bd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/roberta/model_xlmr.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Unsupervised Cross-lingual Representation Learning at Scale -""" - -from fairseq.models import register_model - -from .hub_interface import RobertaHubInterface -from .model import RobertaModel - - -@register_model("xlmr") -class XLMRModel(RobertaModel): - @classmethod - def hub_models(cls): - return { - "xlmr.base": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz", - "xlmr.large": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz", - "xlmr.xl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz", - "xlmr.xxl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz", - } - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - bpe="sentencepiece", - **kwargs - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - bpe=bpe, - load_checkpoint_heads=True, - **kwargs, - ) - return RobertaHubInterface(x["args"], x["task"], x["models"][0]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/__init__.py deleted file mode 100644 index 64ea7cda624959f50f5987732a821eaf0f933b3e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .modules import * # noqa -from .s2s_conformer import * # noqa -from .s2s_transformer import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/modules.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/modules.py deleted file mode 100644 index d64ce0c46edf607080abe97bb071315cb5a1cf8f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/modules.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from fairseq.models import FairseqEncoder -from fairseq.models.transformer import Linear -from torch import nn - - -class CTCDecoder(FairseqEncoder): - def __init__(self, dictionary, in_dim): - super().__init__(dictionary) - self.proj = nn.Linear(in_dim, len(dictionary)) - - def forward(self, src_tokens, src_lengths=None, **kwargs): - encoder_out = self.proj(src_tokens) - return {"encoder_out": encoder_out} - - -class StackedEmbedding(nn.Embedding): - """Embedding module that supports stacked units -> single embedding""" - - def __init__(self, num_embeddings, embed_dim, padding_idx, num_stacked=1): - super().__init__(num_embeddings, embed_dim, padding_idx) - # follow transformer.Embedding - nn.init.normal_(self.weight, mean=0, std=embed_dim**-0.5) - nn.init.constant_(self.weight[padding_idx], 0) - - self.offset = ( - 4 # skip , , , , specific to fairseq dictionary - ) - self.vocab_size = num_embeddings - self.offset - self.num_stacked = num_stacked - - if self.num_stacked > 1: - self.project_in_dim = Linear(embed_dim * num_stacked, embed_dim, bias=False) - - def forward(self, input): - if self.num_stacked == 1: - return super().forward(input) - - # expand input indices - mask = input >= self.offset - stacked_input = [] - cum_input = input.new_zeros(input.shape) - for i in range(1, self.num_stacked + 1): - div = pow(self.vocab_size, i) - next_input = torch.remainder(input - self.offset - cum_input, div) - cum_input += next_input - next_input = torch.floor_divide(next_input, div // self.vocab_size) - stacked_input.append((next_input + self.offset) * mask + input * ~mask) - - stacked_input = torch.stack(stacked_input[::-1], dim=2) - embed = super().forward(stacked_input).view(input.size(0), input.size(1), -1) - embed = self.project_in_dim(embed) - return embed diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/s2s_conformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/s2s_conformer.py deleted file mode 100644 index 1e571ad2aa8464f53679e0f1d87b12b990bedc8e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/s2s_conformer.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from pathlib import Path - -import torch -from fairseq import checkpoint_utils -from fairseq.data.audio.data_cfg import S2SDataConfig -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.speech_to_speech import ( - S2UTTransformerModel, -) -from fairseq.models.speech_to_speech import ( - s2ut_architecture_base as s2ut_transformer_architecture_base, -) -from fairseq.models.speech_to_text import S2TConformerEncoder -from fairseq.models.transformer import ( - Linear, -) - -logger = logging.getLogger(__name__) - - -class S2SConformerEncoder(S2TConformerEncoder): - """Based on S2T transformer encoder, with support - to incorporate target speaker embedding.""" - - def __init__(self, args): - super().__init__(args) - - self.spk_emb_proj = None - if args.target_speaker_embed: - self.spk_emb_proj = Linear( - args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim - ) - - def forward( - self, src_tokens, src_lengths, tgt_speaker=None, return_all_hiddens=False - ): - out = super().forward(src_tokens, src_lengths, return_all_hiddens) - - if self.spk_emb_proj: - x = out["encoder_out"][0] - seq_len, bsz, _ = x.size() - tgt_speaker_emb = tgt_speaker.view(1, bsz, -1).expand(seq_len, bsz, -1) - x = self.spk_emb_proj(torch.cat([x, tgt_speaker_emb], dim=2)) - out["encoder_out"][0] = x - - return out - - -@register_model("s2ut_conformer") -class S2UTConformerModel(S2UTTransformerModel): - """ - Direct speech-to-speech translation model with S2T Conformer encoder + Transformer discrete unit decoder - """ - - @staticmethod - def add_args(parser): - S2UTTransformerModel.add_args(parser) - parser.add_argument("--depthwise-conv-kernel-size", default=31) - parser.add_argument( - "--attn-type", - default=None, - help="If not specified uses fairseq MHA. Other valid option is espnet for using conformer", - ) - parser.add_argument( - "--pos-enc-type", - default="abs", - help="Must be specified in addition to attn-type=espnet for rel_pos and rope", - ) - - @classmethod - def build_encoder(cls, args): - print(args) - data_cfg = S2SDataConfig(Path(args.data) / args.config_yaml) - args.input_feat_per_channel = data_cfg.input_feat_per_channel - args.input_channels = data_cfg.input_transformed_channels - - encoder = S2SConformerEncoder(args) - pretraining_path = getattr(args, "load_pretrained_encoder_from", None) - if pretraining_path is not None: - if not Path(pretraining_path).exists(): - logger.warning( - f"skipped pretraining because {pretraining_path} does not exist" - ) - else: - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=pretraining_path - ) - logger.info(f"loaded pretrained encoder from: {pretraining_path}") - return encoder - - -@register_model_architecture("s2ut_conformer", "s2ut_conformer") -def s2ut_base_architecture(args): - args.attn_type = getattr(args, "attn_type", None) - args.pos_enc_type = getattr(args, "pos_enc_type", "abs") - args.max_source_positions = getattr(args, "max_source_positions", 6000) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.dropout = getattr(args, "dropout", 0.1) - args.encoder_layers = getattr(args, "encoder_layers", 16) - args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) - s2ut_transformer_architecture_base(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/s2s_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/s2s_transformer.py deleted file mode 100644 index 94fbc0d47873dc09db5ab1de077ba6cec7763405..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_speech/s2s_transformer.py +++ /dev/null @@ -1,701 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from pathlib import Path -from typing import Any, Dict, List, Optional - -import torch -from fairseq import checkpoint_utils, utils -from fairseq.models import ( - FairseqEncoderDecoderModel, - FairseqEncoderModel, - FairseqLanguageModel, - register_model, - register_model_architecture, -) -from fairseq.models.speech_to_speech.modules import CTCDecoder, StackedEmbedding -from fairseq.models.speech_to_text import S2TTransformerEncoder -from fairseq.models.text_to_speech import TTSTransformerDecoder -from fairseq.models.transformer import ( - Linear, - TransformerDecoder, - TransformerModelBase, -) -from torch import Tensor - -logger = logging.getLogger(__name__) - - -class S2STransformerEncoder(S2TTransformerEncoder): - """Based on S2T transformer encoder, with support - to incorporate target speaker embedding.""" - - def __init__(self, args): - super().__init__(args) - - self.spk_emb_proj = None - if args.target_speaker_embed: - self.spk_emb_proj = Linear( - args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim - ) - - def forward( - self, src_tokens, src_lengths, tgt_speaker=None, return_all_hiddens=False - ): - out = super().forward(src_tokens, src_lengths, return_all_hiddens) - - if self.spk_emb_proj: - x = out["encoder_out"][0] - seq_len, bsz, _ = x.size() - tgt_speaker_emb = tgt_speaker.view(1, bsz, -1).expand(seq_len, bsz, -1) - x = self.spk_emb_proj(torch.cat([x, tgt_speaker_emb], dim=2)) - out["encoder_out"][0] = x - - return out - - -class TransformerUnitDecoder(TransformerDecoder): - """Based on Transformer decoder, with support to decoding stacked units""" - - def __init__( - self, - args, - dictionary, - embed_tokens, - no_encoder_attn=False, - output_projection=None, - ): - super().__init__( - args, dictionary, embed_tokens, no_encoder_attn, output_projection - ) - self.n_frames_per_step = args.n_frames_per_step - - self.out_proj_n_frames = ( - Linear( - self.output_embed_dim, - self.output_embed_dim * self.n_frames_per_step, - bias=False, - ) - if self.n_frames_per_step > 1 - else None - ) - - def forward( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - features_only: bool = False, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - src_lengths: Optional[Any] = None, - return_all_hiddens: bool = False, - ): - """ - Args: - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (optional): output from the encoder, used for - encoder-side attention, should be of size T x B x C - incremental_state (dict): dictionary used for storing state during - :ref:`Incremental decoding` - features_only (bool, optional): only return features without - applying output layer (default: False). - full_context_alignment (bool, optional): don't apply - auto-regressive mask to self-attention (default: False). - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - full_context_alignment=full_context_alignment, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - ) - - if not features_only: - bsz, seq_len, d = x.size() - if self.out_proj_n_frames: - x = self.out_proj_n_frames(x) - x = self.output_layer(x.view(bsz, seq_len, self.n_frames_per_step, d)) - x = x.view(bsz, seq_len * self.n_frames_per_step, -1) - if ( - incremental_state is None and self.n_frames_per_step > 1 - ): # teacher-forcing mode in training - x = x[ - :, : -(self.n_frames_per_step - 1), : - ] # remove extra frames after - - return x, extra - - def upgrade_state_dict_named(self, state_dict, name): - if self.n_frames_per_step > 1: - move_keys = [ - ( - f"{name}.project_in_dim.weight", - f"{name}.embed_tokens.project_in_dim.weight", - ) - ] - for from_k, to_k in move_keys: - if from_k in state_dict and to_k not in state_dict: - state_dict[to_k] = state_dict[from_k] - del state_dict[from_k] - - -class S2STransformerMultitaskModelBase(FairseqEncoderDecoderModel): - @classmethod - def build_encoder(cls, args): - encoder = S2STransformerEncoder(args) - pretraining_path = getattr(args, "load_pretrained_encoder_from", None) - if pretraining_path is not None: - if not Path(pretraining_path).exists(): - logger.warning( - f"skipped pretraining because {pretraining_path} does not exist" - ) - else: - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=pretraining_path - ) - logger.info(f"loaded pretrained encoder from: {pretraining_path}") - return encoder - - @classmethod - def build_multitask_decoder(cls, args, tgt_dict, in_dim): - decoder_args = args.decoder_args - decoder_args.encoder_embed_dim = in_dim - if args.decoder_type == "transformer": - base_multitask_text_transformer_decoder_arch(decoder_args) - task_decoder = TransformerDecoder( - decoder_args, - tgt_dict, - embed_tokens=TransformerModelBase.build_embedding( - decoder_args, - tgt_dict, - decoder_args.decoder_embed_dim, - ), - ) - elif args.decoder_type == "ctc": - task_decoder = CTCDecoder( - dictionary=tgt_dict, - in_dim=in_dim, - ) - else: - raise NotImplementedError( - "currently only support multitask decoder_type 'transformer', 'ctc'" - ) - - return task_decoder - - @classmethod - def build_model(cls, args, task): - encoder = cls.build_encoder(args) - decoder = ( - cls.build_decoder(args, task.target_dictionary) - if task.args.target_is_code - else cls.build_decoder(args) - ) - base_model = cls(encoder, decoder) - - # set up multitask decoders - base_model.multitask_decoders = {} - for task_name, task_obj in task.multitask_tasks.items(): - in_dim = ( - args.encoder_embed_dim - if task_obj.args.input_from == "encoder" - else args.decoder_embed_dim - ) - task_decoder = cls.build_multitask_decoder( - task_obj.args, task_obj.target_dictionary, in_dim - ) - - setattr(base_model, f"{task_name}_decoder", task_decoder) - decoder_model_cls = ( - FairseqEncoderModel - if task_obj.args.decoder_type == "ctc" - else FairseqLanguageModel - ) - base_model.multitask_decoders[task_name] = decoder_model_cls( - getattr(base_model, f"{task_name}_decoder") - ) - - return base_model - - def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs): - return self.encoder( - src_tokens, src_lengths=src_lengths, tgt_speaker=speaker, **kwargs - ) - - -@register_model("s2ut_transformer") -class S2UTTransformerModel(S2STransformerMultitaskModelBase): - """ - Direct speech-to-speech translation model with S2T Transformer encoder + Transformer discrete unit decoder - https://arxiv.org/abs/2107.05604 - """ - - @staticmethod - def add_args(parser): - # input - parser.add_argument( - "--conv-kernel-sizes", - type=str, - metavar="N", - help="kernel sizes of Conv1d subsampling layers", - ) - parser.add_argument( - "--conv-channels", - type=int, - metavar="N", - help="# of channels in Conv1d subsampling layers", - ) - # Transformer - parser.add_argument( - "--activation-fn", - type=str, - default="relu", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--activation-dropout", - "--relu-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN.", - ) - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-layers", type=int, metavar="N", help="num encoder layers" - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="N", - help="num encoder attention heads", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-ffn-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension for FFN", - ) - parser.add_argument( - "--decoder-layers", type=int, metavar="N", help="num decoder layers" - ) - parser.add_argument( - "--decoder-attention-heads", - type=int, - metavar="N", - help="num decoder attention heads", - ) - parser.add_argument( - "--decoder-normalize-before", - action="store_true", - help="apply layernorm before each decoder block", - ) - parser.add_argument( - "--share-decoder-input-output-embed", - action="store_true", - help="share decoder input and output embeddings", - ) - parser.add_argument( - "--layernorm-embedding", - action="store_true", - help="add layernorm to embedding", - ) - parser.add_argument( - "--no-scale-embedding", - action="store_true", - help="if True, dont scale embeddings", - ) - parser.add_argument( - "--load-pretrained-encoder-from", - type=str, - metavar="STR", - help="model to take encoder weights from (for initialization)", - ) - parser.add_argument( - "--encoder-freezing-updates", - type=int, - metavar="N", - help="freeze encoder for first N updates", - ) - # speaker - parser.add_argument( - "--speaker-embed-dim", - type=int, - metavar="N", - help="speaker embedding dimension", - ) - - @classmethod - def build_decoder(cls, args, tgt_dict): - num_embeddings = len(tgt_dict) - padding_idx = tgt_dict.pad() - embed_tokens = StackedEmbedding( - num_embeddings, - args.decoder_embed_dim, - padding_idx, - num_stacked=args.n_frames_per_step, - ) - - return TransformerUnitDecoder( - args, - tgt_dict, - embed_tokens, - ) - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens, - tgt_speaker=None, - return_all_hiddens=False, - ): - encoder_out = self.encoder( - src_tokens, - src_lengths=src_lengths, - tgt_speaker=tgt_speaker, - return_all_hiddens=return_all_hiddens, - ) - decoder_out = self.decoder( - prev_output_tokens, - encoder_out=encoder_out, - ) - if return_all_hiddens: - decoder_out[-1]["encoder_states"] = encoder_out["encoder_states"] - decoder_out[-1]["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ] - return decoder_out - - -@register_model("s2spect_transformer") -class S2SpecTTransformerModel(S2STransformerMultitaskModelBase): - """ - Speech-to-spectrogram model with S2T Transformer encoder + TTS Transformer decoder - """ - - @staticmethod - def add_args(parser): - # input - parser.add_argument( - "--conv-kernel-sizes", - type=str, - metavar="N", - help="kernel sizes of Conv1d subsampling layers", - ) - parser.add_argument( - "--conv-channels", - type=int, - metavar="N", - help="# of channels in Conv1d subsampling layers", - ) - # Transformer - parser.add_argument( - "--activation-fn", - type=str, - default="relu", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--activation-dropout", - "--relu-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN.", - ) - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-layers", type=int, metavar="N", help="num encoder layers" - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="N", - help="num encoder attention heads", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - parser.add_argument( - "--no-scale-embedding", - action="store_true", - help="if True, dont scale embeddings", - ) - parser.add_argument( - "--load-pretrained-encoder-from", - type=str, - metavar="STR", - help="model to take encoder weights from (for initialization)", - ) - parser.add_argument( - "--encoder-freezing-updates", - type=int, - metavar="N", - help="freeze encoder for first N updates", - ) - # speaker - parser.add_argument( - "--speaker-embed-dim", - type=int, - metavar="N", - help="speaker embedding dimension", - ) - # decoder - parser.add_argument("--output-frame-dim", type=int) - # decoder prenet - parser.add_argument("--prenet-dropout", type=float) - parser.add_argument("--prenet-layers", type=int) - parser.add_argument("--prenet-dim", type=int) - # decoder postnet - parser.add_argument("--postnet-dropout", type=float) - parser.add_argument("--postnet-layers", type=int) - parser.add_argument("--postnet-conv-dim", type=int) - parser.add_argument("--postnet-conv-kernel-size", type=int) - # decoder transformer layers - parser.add_argument("--decoder-transformer-layers", type=int) - parser.add_argument("--decoder-embed-dim", type=int) - parser.add_argument("--decoder-ffn-embed-dim", type=int) - parser.add_argument("--decoder-normalize-before", action="store_true") - parser.add_argument("--decoder-attention-heads", type=int) - - @classmethod - def build_decoder(cls, args): - return TTSTransformerDecoder(args, None, padding_idx=1) - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens, - tgt_speaker=None, - incremental_state=None, - target_lengths=None, - speaker=None, - return_all_hiddens=False, - ): - encoder_out = self.encoder( - src_tokens, - src_lengths=src_lengths, - tgt_speaker=tgt_speaker, - return_all_hiddens=return_all_hiddens, - ) - decoder_out = self.decoder( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - target_lengths=target_lengths, - speaker=speaker, - ) - if return_all_hiddens: - decoder_out[-1]["encoder_states"] = encoder_out["encoder_states"] - decoder_out[-1]["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ] - return decoder_out - - -def base_multitask_text_transformer_decoder_arch(args): - args.dropout = getattr(args, "dropout", 0.3) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", True - ) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - args.max_target_positions = getattr(args, "max_target_positions", 1024) - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - - args.adaptive_input = getattr(args, "adaptive_input", False) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) - - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - - args.decoder_layers = getattr(args, "decoder_layers", 2) - - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - - # decoder layer - args.activation_dropout = getattr(args, "activation_dropout", args.dropout) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 2048) - - args.attention_dropout = getattr(args, "attention_dropout", args.dropout) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - - -def base_s2st_transformer_encoder_architecture(args): - args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) - - # Convolutional subsampler - args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") - args.conv_channels = getattr(args, "conv_channels", 1024) - # Transformer - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 12) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", args.dropout) - args.activation_dropout = getattr(args, "activation_dropout", args.dropout) - args.activation_fn = getattr(args, "activation_fn", "relu") - - args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 256) - - -@register_model_architecture( - model_name="s2ut_transformer", arch_name="s2ut_transformer" -) -def s2ut_architecture_base(args): - base_s2st_transformer_encoder_architecture(args) - - # decoder - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) - - -@register_model_architecture("s2ut_transformer", "s2ut_transformer_fisher") -def s2ut_architecture_fisher(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.dropout = getattr(args, "dropout", 0.1) - - s2ut_architecture_base(args) - - -@register_model_architecture( - model_name="s2spect_transformer", arch_name="s2spect_transformer" -) -def s2spect_architecture_base(args): - base_s2st_transformer_encoder_architecture(args) - - # decoder - args.output_frame_dim = getattr(args, "output_frame_dim", 80) - # decoder prenet - args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) - args.prenet_layers = getattr(args, "prenet_layers", 2) - args.prenet_dim = getattr(args, "prenet_dim", 256) - # decoder postnet - args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) - args.postnet_layers = getattr(args, "postnet_layers", 5) - args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) - args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) - # decoder transformer layers - args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim - ) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - - -@register_model_architecture("s2spect_transformer", "s2spect_transformer_fisher") -def s2spect_architecture_fisher(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.dropout = getattr(args, "dropout", 0.1) - - # decoder - args.prenet_dim = getattr(args, "prenet_dim", 32) - - s2spect_architecture_base(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/__init__.py deleted file mode 100644 index e3fadc8cddce809530f6c4d0c0baf21598756f56..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .berard import * # noqa -from .convtransformer import * # noqa -from .multi_modality_model import * # noqa -from .s2t_conformer import * # noqa -from .s2t_transformer import * # noqa -from .s2t_wav_transformer import * # noqa -from .xm_transformer import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/berard.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/berard.py deleted file mode 100644 index c505e3acaa84e5f3263ccbfaf9556f77123f09fc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/berard.py +++ /dev/null @@ -1,606 +0,0 @@ -#!/usr/bin/env python3 - -from ast import literal_eval -from typing import List, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import checkpoint_utils, utils -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) - - -@register_model("s2t_berard") -class BerardModel(FairseqEncoderDecoderModel): - """Implementation of a model similar to https://arxiv.org/abs/1802.04200 - - Paper title: End-to-End Automatic Speech Translation of Audiobooks - An implementation is available in tensorflow at - https://github.com/eske/seq2seq - Relevant files in this implementation are the config - (https://github.com/eske/seq2seq/blob/master/config/LibriSpeech/AST.yaml) - and the model code - (https://github.com/eske/seq2seq/blob/master/translate/models.py). - The encoder and decoder try to be close to the original implementation. - The attention is an MLP as in Bahdanau et al. - (https://arxiv.org/abs/1409.0473). - There is no state initialization by averaging the encoder outputs. - """ - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - parser.add_argument( - "--input-layers", - type=str, - metavar="EXPR", - help="List of linear layer dimensions. These " - "layers are applied to the input features and " - "are followed by tanh and possibly dropout.", - ) - parser.add_argument( - "--dropout", - type=float, - metavar="D", - help="Dropout probability to use in the encoder/decoder. " - "Note that this parameters control dropout in various places, " - "there is no fine-grained control for dropout for embeddings " - "vs LSTM layers for example.", - ) - parser.add_argument( - "--in-channels", - type=int, - metavar="N", - help="Number of encoder input channels. " "Typically value is 1.", - ) - parser.add_argument( - "--conv-layers", - type=str, - metavar="EXPR", - help="List of conv layers " "(format: (channels, kernel, stride)).", - ) - parser.add_argument( - "--num-blstm-layers", - type=int, - metavar="N", - help="Number of encoder bi-LSTM layers.", - ) - parser.add_argument( - "--lstm-size", type=int, metavar="N", help="LSTM hidden size." - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="Embedding dimension of the decoder target tokens.", - ) - parser.add_argument( - "--decoder-hidden-dim", - type=int, - metavar="N", - help="Decoder LSTM hidden dimension.", - ) - parser.add_argument( - "--decoder-num-layers", - type=int, - metavar="N", - help="Number of decoder LSTM layers.", - ) - parser.add_argument( - "--attention-dim", - type=int, - metavar="N", - help="Hidden layer dimension in MLP attention.", - ) - parser.add_argument( - "--output-layer-dim", - type=int, - metavar="N", - help="Hidden layer dim for linear layer prior to output projection.", - ) - parser.add_argument( - "--load-pretrained-encoder-from", - type=str, - metavar="STR", - help="model to take encoder weights from (for initialization)", - ) - parser.add_argument( - "--load-pretrained-decoder-from", - type=str, - metavar="STR", - help="model to take decoder weights from (for initialization)", - ) - - @classmethod - def build_encoder(cls, args, task): - encoder = BerardEncoder( - input_layers=literal_eval(args.input_layers), - conv_layers=literal_eval(args.conv_layers), - in_channels=args.input_channels, - input_feat_per_channel=args.input_feat_per_channel, - num_blstm_layers=args.num_blstm_layers, - lstm_size=args.lstm_size, - dropout=args.dropout, - ) - if getattr(args, "load_pretrained_encoder_from", None): - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=args.load_pretrained_encoder_from - ) - return encoder - - @classmethod - def build_decoder(cls, args, task): - decoder = LSTMDecoder( - dictionary=task.target_dictionary, - embed_dim=args.decoder_embed_dim, - num_layers=args.decoder_num_layers, - hidden_size=args.decoder_hidden_dim, - dropout=args.dropout, - encoder_output_dim=2 * args.lstm_size, # bidirectional - attention_dim=args.attention_dim, - output_layer_dim=args.output_layer_dim, - ) - if getattr(args, "load_pretrained_decoder_from", None): - decoder = checkpoint_utils.load_pretrained_component_from_model( - component=decoder, checkpoint=args.load_pretrained_decoder_from - ) - return decoder - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - encoder = cls.build_encoder(args, task) - decoder = cls.build_decoder(args, task) - - return cls(encoder, decoder) - - def get_normalized_probs(self, net_output, log_probs, sample=None): - # net_output['encoder_out'] is a (B, T, D) tensor - lprobs = super().get_normalized_probs(net_output, log_probs, sample) - # lprobs is a (B, T, D) tensor - lprobs.batch_first = True - return lprobs - - -class BerardEncoder(FairseqEncoder): - def __init__( - self, - input_layers: List[int], - conv_layers: List[Tuple[int]], - in_channels: int, - input_feat_per_channel: int, - num_blstm_layers: int, - lstm_size: int, - dropout: float, - ): - """ - Args: - input_layers: list of linear layer dimensions. These layers are - applied to the input features and are followed by tanh and - possibly dropout. - conv_layers: list of conv2d layer configurations. A configuration is - a tuple (out_channels, conv_kernel_size, stride). - in_channels: number of input channels. - input_feat_per_channel: number of input features per channel. These - are speech features, typically 40 or 80. - num_blstm_layers: number of bidirectional LSTM layers. - lstm_size: size of the LSTM hidden (and cell) size. - dropout: dropout probability. Dropout can be applied after the - linear layers and LSTM layers but not to the convolutional - layers. - """ - super().__init__(None) - - self.input_layers = nn.ModuleList() - in_features = input_feat_per_channel - for out_features in input_layers: - if dropout > 0: - self.input_layers.append( - nn.Sequential( - nn.Linear(in_features, out_features), nn.Dropout(p=dropout) - ) - ) - else: - self.input_layers.append(nn.Linear(in_features, out_features)) - in_features = out_features - - self.in_channels = in_channels - self.input_dim = input_feat_per_channel - self.conv_kernel_sizes_and_strides = [] - self.conv_layers = nn.ModuleList() - lstm_input_dim = input_layers[-1] - for conv_layer in conv_layers: - out_channels, conv_kernel_size, conv_stride = conv_layer - self.conv_layers.append( - nn.Conv2d( - in_channels, - out_channels, - conv_kernel_size, - stride=conv_stride, - padding=conv_kernel_size // 2, - ) - ) - self.conv_kernel_sizes_and_strides.append((conv_kernel_size, conv_stride)) - in_channels = out_channels - lstm_input_dim //= conv_stride - - lstm_input_dim *= conv_layers[-1][0] - self.lstm_size = lstm_size - self.num_blstm_layers = num_blstm_layers - self.lstm = nn.LSTM( - input_size=lstm_input_dim, - hidden_size=lstm_size, - num_layers=num_blstm_layers, - dropout=dropout, - bidirectional=True, - ) - self.output_dim = 2 * lstm_size # bidirectional - if dropout > 0: - self.dropout = nn.Dropout(p=dropout) - else: - self.dropout = None - - def forward(self, src_tokens, src_lengths=None, **kwargs): - """ - Args - src_tokens: padded tensor (B, T, C * feat) - src_lengths: tensor of original lengths of input utterances (B,) - """ - bsz, max_seq_len, _ = src_tokens.size() - # (B, C, T, feat) - x = ( - src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) - .transpose(1, 2) - .contiguous() - ) - - for input_layer in self.input_layers: - x = input_layer(x) - x = torch.tanh(x) - - for conv_layer in self.conv_layers: - x = conv_layer(x) - - bsz, _, output_seq_len, _ = x.size() - - # (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> - # (T, B, C * feat) - x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) - - input_lengths = src_lengths.clone() - for k, s in self.conv_kernel_sizes_and_strides: - p = k // 2 - input_lengths = (input_lengths.float() + 2 * p - k) / s + 1 - input_lengths = input_lengths.floor().long() - - packed_x = nn.utils.rnn.pack_padded_sequence(x, input_lengths) - - h0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_() - c0 = x.new(2 * self.num_blstm_layers, bsz, self.lstm_size).zero_() - packed_outs, _ = self.lstm(packed_x, (h0, c0)) - - # unpack outputs and apply dropout - x, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_outs) - if self.dropout is not None: - x = self.dropout(x) - - encoder_padding_mask = ( - lengths_to_padding_mask(output_lengths).to(src_tokens.device).t() - ) - - return { - "encoder_out": x, # (T, B, C) - "encoder_padding_mask": encoder_padding_mask, # (T, B) - } - - def reorder_encoder_out(self, encoder_out, new_order): - encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( - 1, new_order - ) - encoder_out["encoder_padding_mask"] = encoder_out[ - "encoder_padding_mask" - ].index_select(1, new_order) - return encoder_out - - -class MLPAttention(nn.Module): - """The original attention from Badhanau et al. (2014) - - https://arxiv.org/abs/1409.0473, based on a Multi-Layer Perceptron. - The attention score between position i in the encoder and position j in the - decoder is: alpha_ij = V_a * tanh(W_ae * enc_i + W_ad * dec_j + b_a) - """ - - def __init__(self, decoder_hidden_state_dim, context_dim, attention_dim): - super().__init__() - - self.context_dim = context_dim - self.attention_dim = attention_dim - # W_ae and b_a - self.encoder_proj = nn.Linear(context_dim, self.attention_dim, bias=True) - # W_ad - self.decoder_proj = nn.Linear( - decoder_hidden_state_dim, self.attention_dim, bias=False - ) - # V_a - self.to_scores = nn.Linear(self.attention_dim, 1, bias=False) - - def forward(self, decoder_state, source_hids, encoder_padding_mask): - """The expected input dimensions are: - decoder_state: bsz x decoder_hidden_state_dim - source_hids: src_len x bsz x context_dim - encoder_padding_mask: src_len x bsz - """ - src_len, bsz, _ = source_hids.size() - # (src_len*bsz) x context_dim (to feed through linear) - flat_source_hids = source_hids.view(-1, self.context_dim) - # (src_len*bsz) x attention_dim - encoder_component = self.encoder_proj(flat_source_hids) - # src_len x bsz x attention_dim - encoder_component = encoder_component.view(src_len, bsz, self.attention_dim) - # 1 x bsz x attention_dim - decoder_component = self.decoder_proj(decoder_state).unsqueeze(0) - # Sum with broadcasting and apply the non linearity - # src_len x bsz x attention_dim - hidden_att = torch.tanh( - (decoder_component + encoder_component).view(-1, self.attention_dim) - ) - # Project onto the reals to get attentions scores (src_len x bsz) - attn_scores = self.to_scores(hidden_att).view(src_len, bsz) - - # Mask + softmax (src_len x bsz) - if encoder_padding_mask is not None: - attn_scores = ( - attn_scores.float() - .masked_fill_(encoder_padding_mask, float("-inf")) - .type_as(attn_scores) - ) # FP16 support: cast to float and back - # srclen x bsz - normalized_masked_attn_scores = F.softmax(attn_scores, dim=0) - - # Sum weighted sources (bsz x context_dim) - attn_weighted_context = ( - source_hids * normalized_masked_attn_scores.unsqueeze(2) - ).sum(dim=0) - - return attn_weighted_context, normalized_masked_attn_scores - - -class LSTMDecoder(FairseqIncrementalDecoder): - def __init__( - self, - dictionary, - embed_dim, - num_layers, - hidden_size, - dropout, - encoder_output_dim, - attention_dim, - output_layer_dim, - ): - """ - Args: - dictionary: target text dictionary. - embed_dim: embedding dimension for target tokens. - num_layers: number of LSTM layers. - hidden_size: hidden size for LSTM layers. - dropout: dropout probability. Dropout can be applied to the - embeddings, the LSTM layers, and the context vector. - encoder_output_dim: encoder output dimension (hidden size of - encoder LSTM). - attention_dim: attention dimension for MLP attention. - output_layer_dim: size of the linear layer prior to output - projection. - """ - super().__init__(dictionary) - self.num_layers = num_layers - self.hidden_size = hidden_size - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - self.embed_tokens = nn.Embedding(num_embeddings, embed_dim, padding_idx) - if dropout > 0: - self.dropout = nn.Dropout(p=dropout) - else: - self.dropout = None - - self.layers = nn.ModuleList() - for layer_id in range(num_layers): - input_size = embed_dim if layer_id == 0 else encoder_output_dim - self.layers.append( - nn.LSTMCell(input_size=input_size, hidden_size=hidden_size) - ) - - self.context_dim = encoder_output_dim - self.attention = MLPAttention( - decoder_hidden_state_dim=hidden_size, - context_dim=encoder_output_dim, - attention_dim=attention_dim, - ) - - self.deep_output_layer = nn.Linear( - hidden_size + encoder_output_dim + embed_dim, output_layer_dim - ) - self.output_projection = nn.Linear(output_layer_dim, num_embeddings) - - def forward( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs - ): - encoder_padding_mask = encoder_out["encoder_padding_mask"] - encoder_outs = encoder_out["encoder_out"] - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - bsz, seqlen = prev_output_tokens.size() - - srclen = encoder_outs.size(0) - - # embed tokens - embeddings = self.embed_tokens(prev_output_tokens) - x = embeddings - if self.dropout is not None: - x = self.dropout(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # initialize previous states (or get from cache during incremental - # generation) - cached_state = utils.get_incremental_state( - self, incremental_state, "cached_state" - ) - if cached_state is not None: - prev_hiddens, prev_cells = cached_state - else: - prev_hiddens = [encoder_out["encoder_out"].mean(dim=0)] * self.num_layers - prev_cells = [x.new_zeros(bsz, self.hidden_size)] * self.num_layers - - attn_scores = x.new_zeros(bsz, srclen) - attention_outs = [] - outs = [] - for j in range(seqlen): - input = x[j, :, :] - attention_out = None - for i, layer in enumerate(self.layers): - # the previous state is one layer below except for the bottom - # layer where the previous state is the state emitted by the - # top layer - hidden, cell = layer( - input, - ( - prev_hiddens[(i - 1) % self.num_layers], - prev_cells[(i - 1) % self.num_layers], - ), - ) - if self.dropout is not None: - hidden = self.dropout(hidden) - prev_hiddens[i] = hidden - prev_cells[i] = cell - if attention_out is None: - attention_out, attn_scores = self.attention( - hidden, encoder_outs, encoder_padding_mask - ) - if self.dropout is not None: - attention_out = self.dropout(attention_out) - attention_outs.append(attention_out) - input = attention_out - - # collect the output of the top layer - outs.append(hidden) - - # cache previous states (no-op except during incremental generation) - utils.set_incremental_state( - self, incremental_state, "cached_state", (prev_hiddens, prev_cells) - ) - - # collect outputs across time steps - x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size) - attention_outs_concat = torch.cat(attention_outs, dim=0).view( - seqlen, bsz, self.context_dim - ) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - attention_outs_concat = attention_outs_concat.transpose(0, 1) - - # concat LSTM output, attention output and embedding - # before output projection - x = torch.cat((x, attention_outs_concat, embeddings), dim=2) - x = self.deep_output_layer(x) - x = torch.tanh(x) - if self.dropout is not None: - x = self.dropout(x) - # project back to size of vocabulary - x = self.output_projection(x) - - # to return the full attn_scores tensor, we need to fix the decoder - # to account for subsampling input frames - # return x, attn_scores - return x, None - - def reorder_incremental_state(self, incremental_state, new_order): - super().reorder_incremental_state(incremental_state, new_order) - cached_state = utils.get_incremental_state( - self, incremental_state, "cached_state" - ) - if cached_state is None: - return - - def reorder_state(state): - if isinstance(state, list): - return [reorder_state(state_i) for state_i in state] - return state.index_select(0, new_order) - - new_state = tuple(map(reorder_state, cached_state)) - utils.set_incremental_state(self, incremental_state, "cached_state", new_state) - - -@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard") -def berard(args): - """The original version: "End-to-End Automatic Speech Translation of - Audiobooks" (https://arxiv.org/abs/1802.04200) - """ - args.input_layers = getattr(args, "input_layers", "[256, 128]") - args.conv_layers = getattr(args, "conv_layers", "[(16, 3, 2), (16, 3, 2)]") - args.num_blstm_layers = getattr(args, "num_blstm_layers", 3) - args.lstm_size = getattr(args, "lstm_size", 256) - args.dropout = getattr(args, "dropout", 0.2) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 128) - args.decoder_num_layers = getattr(args, "decoder_num_layers", 2) - args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 512) - args.attention_dim = getattr(args, "attention_dim", 512) - args.output_layer_dim = getattr(args, "output_layer_dim", 128) - args.load_pretrained_encoder_from = getattr( - args, "load_pretrained_encoder_from", None - ) - args.load_pretrained_decoder_from = getattr( - args, "load_pretrained_decoder_from", None - ) - - -@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_256_3_3") -def berard_256_3_3(args): - """Used in - * "Harnessing Indirect Training Data for End-to-End Automatic Speech - Translation: Tricks of the Trade" (https://arxiv.org/abs/1909.06515) - * "CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus" - (https://arxiv.org/pdf/2002.01320.pdf) - * "Self-Supervised Representations Improve End-to-End Speech Translation" - (https://arxiv.org/abs/2006.12124) - """ - args.decoder_num_layers = getattr(args, "decoder_num_layers", 3) - berard(args) - - -@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_3_2") -def berard_512_3_2(args): - args.num_blstm_layers = getattr(args, "num_blstm_layers", 3) - args.lstm_size = getattr(args, "lstm_size", 512) - args.dropout = getattr(args, "dropout", 0.3) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_num_layers = getattr(args, "decoder_num_layers", 2) - args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024) - args.attention_dim = getattr(args, "attention_dim", 512) - args.output_layer_dim = getattr(args, "output_layer_dim", 256) - berard(args) - - -@register_model_architecture(model_name="s2t_berard", arch_name="s2t_berard_512_5_3") -def berard_512_5_3(args): - args.num_blstm_layers = getattr(args, "num_blstm_layers", 5) - args.lstm_size = getattr(args, "lstm_size", 512) - args.dropout = getattr(args, "dropout", 0.3) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_num_layers = getattr(args, "decoder_num_layers", 3) - args.decoder_hidden_dim = getattr(args, "decoder_hidden_dim", 1024) - args.attention_dim = getattr(args, "attention_dim", 512) - args.output_layer_dim = getattr(args, "output_layer_dim", 256) - berard(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/convtransformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/convtransformer.py deleted file mode 100644 index eba000d7b0826d2ecf5dc471156f8f8cc9f5e402..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/convtransformer.py +++ /dev/null @@ -1,448 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import math -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import checkpoint_utils, utils -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.transformer import Embedding, TransformerDecoder -from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer -from torch import Tensor - -logger = logging.getLogger(__name__) - - -@register_model("convtransformer") -class ConvTransformerModel(FairseqEncoderDecoderModel): - """ - Transformer-based Speech translation model from ESPNet-ST - https://arxiv.org/abs/2004.10234 - """ - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - parser.add_argument( - "--input-feat-per-channel", - type=int, - metavar="N", - help="encoder input dimension per input channel", - ) - parser.add_argument( - "--activation-fn", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--activation-dropout", - "--relu-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN.", - ) - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-layers", type=int, metavar="N", help="num encoder layers" - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="N", - help="num encoder attention heads", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-ffn-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension for FFN", - ) - parser.add_argument( - "--decoder-layers", type=int, metavar="N", help="num decoder layers" - ) - parser.add_argument( - "--decoder-attention-heads", - type=int, - metavar="N", - help="num decoder attention heads", - ) - parser.add_argument( - "--decoder-normalize-before", - action="store_true", - help="apply layernorm before each decoder block", - ) - parser.add_argument( - "--decoder-output-dim", - type=int, - metavar="N", - help="decoder output dimension (extra linear layer if different from decoder embed dim)", - ) - parser.add_argument( - "--share-decoder-input-output-embed", - action="store_true", - help="share decoder input and output embeddings", - ) - parser.add_argument( - "--layernorm-embedding", - action="store_true", - help="add layernorm to embedding", - ) - parser.add_argument( - "--no-scale-embedding", - action="store_true", - help="if True, dont scale embeddings", - ) - parser.add_argument( - "--load-pretrained-encoder-from", - type=str, - metavar="STR", - help="model to take encoder weights from (for initialization)", - ) - parser.add_argument( - "--load-pretrained-decoder-from", - type=str, - metavar="STR", - help="model to take decoder weights from (for initialization)", - ) - parser.add_argument( - "--conv-out-channels", - type=int, - metavar="INT", - help="the number of output channels of conv layer", - ) - - @classmethod - def build_encoder(cls, args): - encoder = ConvTransformerEncoder(args) - if getattr(args, "load_pretrained_encoder_from", None): - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=args.load_pretrained_encoder_from - ) - return encoder - - @classmethod - def build_decoder(cls, args, task, embed_tokens): - decoder = TransformerDecoderNoExtra(args, task.target_dictionary, embed_tokens) - if getattr(args, "load_pretrained_decoder_from", None): - decoder = checkpoint_utils.load_pretrained_component_from_model( - component=decoder, checkpoint=args.load_pretrained_decoder_from - ) - return decoder - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - - def build_embedding(dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - return Embedding(num_embeddings, embed_dim, padding_idx) - - decoder_embed_tokens = build_embedding( - task.target_dictionary, args.decoder_embed_dim - ) - encoder = cls.build_encoder(args) - decoder = cls.build_decoder(args, task, decoder_embed_tokens) - return cls(encoder, decoder) - - @staticmethod - @torch.jit.unused - def set_batch_first(lprobs): - lprobs.batch_first = True - - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - # net_output['encoder_out'] is a (B, T, D) tensor - lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) - if self.training: - self.set_batch_first(lprobs) - return lprobs - - def output_layout(self): - return "BTD" - - """ - The forward method inherited from the base class has a **kwargs argument in - its input, which is not supported in torchscript. This method overrites the forward - method definition without **kwargs. - """ - - def forward(self, src_tokens, src_lengths, prev_output_tokens): - encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths) - decoder_out = self.decoder( - prev_output_tokens=prev_output_tokens, encoder_out=encoder_out - ) - return decoder_out - - -class ConvTransformerEncoder(FairseqEncoder): - """Conv + Transformer encoder""" - - def __init__(self, args): - """Construct an Encoder object.""" - super().__init__(None) - - self.dropout = args.dropout - self.embed_scale = ( - 1.0 if args.no_scale_embedding else math.sqrt(args.encoder_embed_dim) - ) - self.padding_idx = 1 - self.in_channels = 1 - self.input_dim = args.input_feat_per_channel - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, args.conv_out_channels, 3, stride=2, padding=3 // 2), - torch.nn.ReLU(), - torch.nn.Conv2d( - args.conv_out_channels, - args.conv_out_channels, - 3, - stride=2, - padding=3 // 2, - ), - torch.nn.ReLU(), - ) - transformer_input_dim = self.infer_conv_output_dim( - self.in_channels, self.input_dim, args.conv_out_channels - ) - self.out = torch.nn.Linear(transformer_input_dim, args.encoder_embed_dim) - self.embed_positions = PositionalEmbedding( - args.max_source_positions, - args.encoder_embed_dim, - self.padding_idx, - learned=False, - ) - - self.transformer_layers = nn.ModuleList([]) - self.transformer_layers.extend( - [TransformerEncoderLayer(args) for i in range(args.encoder_layers)] - ) - if args.encoder_normalize_before: - self.layer_norm = LayerNorm(args.encoder_embed_dim) - else: - self.layer_norm = None - - def pooling_ratio(self): - return 4 - - def infer_conv_output_dim(self, in_channels, input_dim, out_channels): - sample_seq_len = 200 - sample_bsz = 10 - x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim) - x = torch.nn.Conv2d(1, out_channels, 3, stride=2, padding=3 // 2)(x) - x = torch.nn.Conv2d(out_channels, out_channels, 3, stride=2, padding=3 // 2)(x) - x = x.transpose(1, 2) - mb, seq = x.size()[:2] - return x.contiguous().view(mb, seq, -1).size(-1) - - def forward(self, src_tokens, src_lengths): - """Encode input sequence. - :param torch.Tensor xs: input tensor - :param torch.Tensor masks: input mask - :return: position embedded tensor and mask - :rtype Tuple[torch.Tensor, torch.Tensor]: - """ - bsz, max_seq_len, _ = src_tokens.size() - x = ( - src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) - .transpose(1, 2) - .contiguous() - ) - x = self.conv(x) - bsz, _, output_seq_len, _ = x.size() - x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) - x = self.out(x) - x = self.embed_scale * x - - subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5) - input_len_0 = (src_lengths.float() / subsampling_factor).ceil().long() - input_len_1 = x.size(0) * torch.ones([src_lengths.size(0)]).long().to( - input_len_0.device - ) - input_lengths = torch.min(input_len_0, input_len_1) - - encoder_padding_mask = lengths_to_padding_mask(input_lengths) - - positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) - x += positions - x = F.dropout(x, p=self.dropout, training=self.training) - - for layer in self.transformer_layers: - x = layer(x, encoder_padding_mask) - - if not encoder_padding_mask.any(): - maybe_encoder_padding_mask = None - else: - maybe_encoder_padding_mask = encoder_padding_mask - - return { - "encoder_out": [x], - "encoder_padding_mask": [maybe_encoder_padding_mask] - if maybe_encoder_padding_mask is not None - else [], - "encoder_embedding": [], - "encoder_states": [], - "src_tokens": [], - "src_lengths": [], - } - - @torch.jit.export - def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)] - if len(encoder_out["encoder_padding_mask"]) == 0: - new_encoder_padding_mask = [] - else: - new_encoder_padding_mask = [ - (encoder_out["encoder_padding_mask"][0]).index_select(0, new_order) - ] - if len(encoder_out["encoder_embedding"]) == 0: - new_encoder_embedding = [] - else: - new_encoder_embedding = [ - (encoder_out["encoder_embedding"][0]).index_select(0, new_order) - ] - encoder_states = encoder_out["encoder_states"] - if len(encoder_states) > 0: - for idx, state in enumerate(encoder_states): - encoder_states[idx] = state.index_select(1, new_order) - - return { - "encoder_out": new_encoder_out, - "encoder_padding_mask": new_encoder_padding_mask, - "encoder_embedding": new_encoder_embedding, - "encoder_states": encoder_states, - "src_tokens": [], - "src_lengths": [], - } - - -class TransformerDecoderNoExtra(TransformerDecoder): - def extract_features( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - # call scriptable method from parent class - x, _ = self.extract_features_scriptable( - prev_output_tokens, - encoder_out, - incremental_state, - full_context_alignment, - alignment_layer, - alignment_heads, - ) - return x, None - - -@register_model_architecture(model_name="convtransformer", arch_name="convtransformer") -def base_architecture(args): - args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) - args.max_source_positions = getattr(args, "max_source_positions", 3000) - args.max_target_positions = getattr(args, "max_target_positions", 1024) - args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) - args.conv_out_channels = getattr(args, "conv_out_channels", args.encoder_embed_dim) - - -@register_model_architecture("convtransformer", "convtransformer_espnet") -def convtransformer_espnet(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_layers = getattr(args, "encoder_layers", 12) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/hub_interface.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/hub_interface.py deleted file mode 100644 index 57f29e4b09e401acdf2a1c33c497945e0a456897..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/hub_interface.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from argparse import Namespace -from typing import Optional, Tuple, Union - -import fairseq.data.audio.feature_transforms.utterance_cmvn as utt_cmvn -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.data import encoders -from fairseq.data.audio.audio_utils import convert_waveform as convert_wav -from fairseq.data.audio.audio_utils import ( - get_fbank, -) -from fairseq.data.audio.audio_utils import get_waveform as get_wav -from fairseq.data.audio.speech_to_text_dataset import SpeechToTextDataset - -logger = logging.getLogger(__name__) - - -class S2THubInterface(nn.Module): - def __init__(self, cfg, task, model): - super().__init__() - self.cfg = cfg - self.task = task - self.model = model - self.model.eval() - self.generator = self.task.build_generator([self.model], self.cfg) - - @classmethod - def get_model_input(cls, task, audio: Union[str, torch.Tensor]): - input_type = task.data_cfg.hub.get("input_type", "fbank80") - if input_type == "fbank80_w_utt_cmvn": - if isinstance(audio, str): - feat = utt_cmvn.UtteranceCMVN()(get_fbank(audio)) - feat = feat.unsqueeze(0) # T x D -> 1 x T x D - else: - import torchaudio.compliance.kaldi as kaldi - - feat = kaldi.fbank(audio, num_mel_bins=80).numpy() # 1 x T x D - elif input_type in {"waveform", "standardized_waveform"}: - if isinstance(audio, str): - feat, sr = get_wav(audio) # C x T - feat, _ = convert_wav( - feat, sr, to_sample_rate=16_000, to_mono=True - ) # C x T -> 1 x T - else: - feat = audio.numpy() - else: - raise ValueError(f"Unknown value: input_type = {input_type}") - - src_lengths = torch.Tensor([feat.shape[1]]).long() - src_tokens = torch.from_numpy(feat) # 1 x T (x D) - if input_type == "standardized_waveform": - with torch.no_grad(): - src_tokens = F.layer_norm(src_tokens, src_tokens.shape) - - return { - "net_input": { - "src_tokens": src_tokens, - "src_lengths": src_lengths, - "prev_output_tokens": None, - }, - "target_lengths": None, - "speaker": None, - } - - @classmethod - def detokenize(cls, task, tokens): - text = task.tgt_dict.string(tokens) - tkn_cfg = task.data_cfg.bpe_tokenizer - tokenizer = encoders.build_bpe(Namespace(**tkn_cfg)) - return text if tokenizer is None else tokenizer.decode(text) - - @classmethod - def get_prefix_token(cls, task, lang): - prefix_size = int(task.data_cfg.prepend_tgt_lang_tag) - prefix_tokens = None - if prefix_size > 0: - assert lang is not None - lang_tag = SpeechToTextDataset.get_lang_tag_idx(lang, task.tgt_dict) - prefix_tokens = torch.Tensor([lang_tag]).long().unsqueeze(0) - return prefix_tokens - - @classmethod - def get_prediction( - cls, task, model, generator, sample, tgt_lang=None, synthesize_speech=False - ) -> Union[str, Tuple[str, Tuple[torch.Tensor, int]]]: - _tgt_lang = tgt_lang or task.data_cfg.hub.get("tgt_lang", None) - prefix = cls.get_prefix_token(task, _tgt_lang) - pred_tokens = generator.generate([model], sample, prefix_tokens=prefix) - pred = cls.detokenize(task, pred_tokens[0][0]["tokens"]) - - if synthesize_speech: - pfx = f"{_tgt_lang}_" if task.data_cfg.prepend_tgt_lang_tag else "" - tts_model_id = task.data_cfg.hub.get(f"{pfx}tts_model_id", None) - if tts_model_id is None: - logger.warning("TTS model configuration not found") - else: - _repo, _id = tts_model_id.split(":") - tts_model = torch.hub.load(_repo, _id, verbose=False) - pred = (pred, tts_model.predict(pred)) - return pred - - def predict( - self, - audio: Union[str, torch.Tensor], - tgt_lang: Optional[str] = None, - synthesize_speech: bool = False, - ) -> Union[str, Tuple[str, Tuple[torch.Tensor, int]]]: - # `audio` is either a file path or a 1xT Tensor - # return either text or (text, synthetic speech) - sample = self.get_model_input(self.task, audio) - return self.get_prediction( - self.task, - self.model, - self.generator, - sample, - tgt_lang=tgt_lang, - synthesize_speech=synthesize_speech, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/modules/augmented_memory_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/modules/augmented_memory_attention.py deleted file mode 100644 index 7f9adf858295c08b789312d2453e111f2e1f8412..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/modules/augmented_memory_attention.py +++ /dev/null @@ -1,488 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import List, Tuple - -import torch -import torch.nn.functional as F -from fairseq.models import FairseqEncoder -from fairseq.models.speech_to_text import ( - ConvTransformerEncoder, -) -from fairseq.models.speech_to_text.utils import ( - attention_suppression, - lengths_to_encoder_padding_mask, - segments_to_sequence, - sequence_to_segments, -) -from fairseq.modules import MultiheadAttention, TransformerEncoderLayer -from torch import Tensor, nn - -# ------------------------------------------------------------------------------ -# AugmentedMemoryConvTransformerEncoder -# ------------------------------------------------------------------------------ - - -class AugmentedMemoryConvTransformerEncoder(ConvTransformerEncoder): - def __init__(self, args): - super().__init__(args) - - args.encoder_stride = self.stride() - - self.left_context = args.left_context // args.encoder_stride - - self.right_context = args.right_context // args.encoder_stride - - self.left_context_after_stride = args.left_context // args.encoder_stride - self.right_context_after_stride = args.right_context // args.encoder_stride - - self.transformer_layers = nn.ModuleList([]) - self.transformer_layers.extend( - [ - AugmentedMemoryTransformerEncoderLayer(args) - for i in range(args.encoder_layers) - ] - ) - - def stride(self): - # Hard coded here. Should infer from convs in future - stride = 4 - return stride - - def forward(self, src_tokens, src_lengths, states=None): - """Encode input sequence. - :param torch.Tensor xs: input tensor - :param torch.Tensor masks: input mask - :return: position embedded tensor and mask - :rtype Tuple[torch.Tensor, torch.Tensor]: - """ - bsz, max_seq_len, _ = src_tokens.size() - x = ( - src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim) - .transpose(1, 2) - .contiguous() - ) - x = self.conv(x) - bsz, _, output_seq_len, _ = x.size() - x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1) - x = self.out(x) - x = self.embed_scale * x - - subsampling_factor = 1.0 * max_seq_len / output_seq_len - input_lengths = torch.max( - (src_lengths.float() / subsampling_factor).ceil().long(), - x.size(0) * src_lengths.new_ones([src_lengths.size(0)]).long(), - ) - - encoder_padding_mask, _ = lengths_to_encoder_padding_mask( - input_lengths, batch_first=True - ) - - # TODO: fix positional embedding - positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) - - x += positions - x = F.dropout(x, p=self.dropout, training=self.training) - - # State to store memory banks etc. - if states is None: - states = [ - {"memory_banks": None, "encoder_states": None} - for i in range(len(self.transformer_layers)) - ] - - for i, layer in enumerate(self.transformer_layers): - # x size: - # (self.left_size + self.segment_size + self.right_size) - # / self.stride, num_heads, dim - # TODO: Consider mask here - x = layer(x, states[i]) - states[i]["encoder_states"] = x[ - self.left_context_after_stride : -self.right_context_after_stride - ] - - lengths = ( - ( - ~encoder_padding_mask[ - :, self.left_context_after_stride : -self.right_context_after_stride - ] - ) - .sum(dim=1, keepdim=True) - .long() - ) - - return states[-1]["encoder_states"], lengths, states - - -# ------------------------------------------------------------------------------ -# AugmentedMemoryTransformerEncoderLayer -# ------------------------------------------------------------------------------ -class AugmentedMemoryTransformerEncoderLayer(TransformerEncoderLayer): - def __init__(self, args): - super().__init__(args) - - self.left_context = args.left_context // args.encoder_stride - self.right_context = args.right_context // args.encoder_stride - - def forward(self, x, state): - - length, batch_size, x_dim = x.size() - - residual = x - - if self.normalize_before: - x = self.self_attn_layer_norm(x) - - # init_state - if state.get("memory_banks", None) is None: - state["memory_banks"] = [] - - # TODO reseach new sum_query method - seg_start = self.left_context - seg_end = length - self.right_context - if seg_start < seg_end: - summarization_query = torch.mean(x[seg_start:seg_end], keepdim=True, dim=0) - else: - summarization_query = x.new_zeros(1, batch_size, x_dim) - - x = torch.cat([x, summarization_query], dim=0) - - x = self.self_attn(input_and_summary=x, state=state) - - x = self.dropout_module(x) - x = residual + x - - if not self.normalize_before: - x = self.self_attn_layer_norm(x) - - residual = x - if self.normalize_before: - x = self.final_layer_norm(x) - - x = self.activation_fn(self.fc1(x)) - x = self.activation_dropout_module(x) - x = self.fc2(x) - x = self.dropout_module(x) - x = residual + x - if not self.normalize_before: - x = self.final_layer_norm(x) - - return x - - def build_self_attention(self, embed_dim, args): - return AugmentedMemoryMultiheadAttention( - embed_dim=embed_dim, - num_heads=args.encoder_attention_heads, - dropout=args.attention_dropout, - self_attention=True, - q_noise=self.quant_noise, - qn_block_size=self.quant_noise_block_size, - tanh_on_mem=True, - max_memory_size=args.max_memory_size, - ) - - -# ------------------------------------------------------------------------------ -# AugmentedMemoryMultiheadAttention -# ------------------------------------------------------------------------------ -class AugmentedMemoryMultiheadAttention(MultiheadAttention): - """ - Augmented Memory Attention from - Streaming Transformer-based Acoustic Models - Using Self-attention with Augmented Memory - https://arxiv.org/abs/2005.08042 - """ - - def __init__( - self, - embed_dim, - num_heads, - kdim=None, - vdim=None, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - self_attention=False, - encoder_decoder_attention=False, - q_noise=0.0, - qn_block_size=8, - tanh_on_mem=False, - memory_dim=None, - std_scale=0.5, # 0.5 based on https://arxiv.org/abs/2005.09137 - max_memory_size=-1, - disable_mem_on_mem_attn=True, - ): - super().__init__( - embed_dim, - num_heads, - kdim, - vdim, - dropout, - bias, - add_bias_kv, - add_zero_attn, - self_attention, - encoder_decoder_attention, - q_noise, - qn_block_size, - ) - - self.memory_dim = memory_dim if memory_dim is not None else embed_dim - self.std_scale = std_scale - self.disable_mem_on_mem_attn = disable_mem_on_mem_attn - - # This Operator was used for factorization in PySpeech - self.v2e = lambda x: x - - if tanh_on_mem: - self.squash_mem = torch.tanh - self.nonlinear_squash_mem = True - else: - self.squash_mem = lambda x: x - self.nonlinear_squash_mem = False - - self.max_memory_size = max_memory_size - - def forward(self, input_and_summary, state): - """ - input: Encoder states of current segment with left or right context, - plus one summarization query - - """ - - length, batch_size, _ = input_and_summary.shape - length = length - 1 # not include sum_query, last index - - memory = state["memory_banks"] - # TODO: positional embedding on memory - - if self.max_memory_size > -1 and len(memory) > self.max_memory_size: - # TODO: need to fix here - if self.max_memory_size == 0: - memory = memory.new_zeros(1, memory.size(1), self.memory_dim) - else: - memory = memory[-self.max_memory_size :] - - memory_and_input = torch.cat(memory + [input_and_summary[:-1]], dim=0) - input_and_sum_query = input_and_summary - - q = self.q_proj(self.v2e(input_and_sum_query)) - k = self.k_proj(self.v2e(memory_and_input)) - v = self.v_proj(self.v2e(memory_and_input)) - - q = ( - q.contiguous() - .view(-1, batch_size * self.num_heads, self.head_dim) - .transpose(0, 1) - * self.scaling - ) - k = ( - k.contiguous() - .view(-1, batch_size * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - v = ( - v.contiguous() - .view(-1, batch_size * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - attention_weights = torch.bmm(q, k.transpose(1, 2)) - - if self.disable_mem_on_mem_attn: - attention_weights = self.suppress_mem_on_mem_attention( - batch_size, self.num_heads, len(memory), attention_weights - ) - - if self.std_scale is not None: - attention_weights = attention_suppression(attention_weights, self.std_scale) - - assert list(attention_weights.shape) == [ - batch_size * self.num_heads, - length + 1, - length + len(memory), - ] - - attention_weights = torch.nn.functional.softmax( - attention_weights.float(), dim=-1 - ).type_as(attention_weights) - - attention_probs = self.dropout_module(attention_weights) - - # [T, T, B, n_head] + [T, B, n_head, d_head] -> [T, B, n_head, d_head] - attention = torch.bmm(attention_probs, v) - - assert list(attention.shape) == [ - batch_size * self.num_heads, - length + 1, - self.head_dim, - ] - - attention = ( - attention.transpose(0, 1) - .contiguous() - .view(length + 1, batch_size, self.embed_dim) - ) - - output_and_memory = self.out_proj(attention) - - next_m = output_and_memory[-1:] - next_m = self.squash_mem(next_m) - output = output_and_memory[:-1] - - state["memory_banks"].append(next_m) - - return output - - def suppress_mem_on_mem_attention( - self, B: int, num_heads: int, mem_size: int, attention_weight: Tensor - ): - """ - Arguments: - - B: batch size - - num_heads: number of attention heads - - mem_size: size of memory bank - - attention_weight: a [B*num_heads, T + 1, T + mem_size] vector - - Return: - modified attention_weight with [B*num_heads, -1, :mem_size] = -inf - """ - attention_weight[:, -1, :mem_size] = float("-inf") - return attention_weight - - -# ------------------------------------------------------------------------------ -# SequenceEncoder -# ------------------------------------------------------------------------------ -class SequenceEncoder(FairseqEncoder): - """ - SequenceEncoder encodes sequences. - - More specifically, `src_tokens` and `src_lengths` in `forward()` should - describe a batch of "complete" sequences rather than segments. - - Segment-by-segment inference can be triggered by `segment_size`: - 1) `segment_size` is None: - SequenceEncoder treats the input sequence as one single segment. - 2) `segment_size` is not None (some int instead): - SequenceEncoder does the following: - 1. breaks the input sequence into several segments - 2. inference on each segment and collect the outputs - 3. concatanete segment outputs into the output sequence. - Note that `segment_size` here shouldn't include additional left/right - contexts needed, for example if we wish to infer with LC-BLSTM where the - middle chunk size is 100 and right context is 20, `segment_size` should be - 100. - """ - - def __init__(self, args, module): - super().__init__(None) - - self.module = module - self.input_time_axis = 1 - self.output_time_axis = 0 - self.segment_size = args.segment_size - self.left_context = args.left_context - self.right_context = args.right_context - - def forward( - self, - src_tokens: Tensor, - src_lengths: Tensor, - states=None, - ): - - seg_src_tokens_lengths = sequence_to_segments( - sequence=src_tokens, - time_axis=self.input_time_axis, - lengths=src_lengths, - segment_size=self.segment_size, - extra_left_context=self.left_context, - extra_right_context=self.right_context, - ) - - seg_encoder_states_lengths: List[Tuple[Tensor, Tensor]] = [] - - for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths: - (seg_encoder_states, seg_enc_lengths, states) = self.module( - seg_src_tokens, - seg_src_lengths, - states=states, - ) - - seg_encoder_states_lengths.append((seg_encoder_states, seg_enc_lengths)) - - encoder_out, enc_lengths = segments_to_sequence( - segments=seg_encoder_states_lengths, time_axis=self.output_time_axis - ) - - encoder_padding_mask, _ = lengths_to_encoder_padding_mask( - enc_lengths, batch_first=True - ) - - if not encoder_padding_mask.any(): - encoder_padding_mask = None - - return { - "encoder_out": [encoder_out], - "encoder_padding_mask": [encoder_padding_mask], - "encoder_embedding": [], - "encoder_states": [states], - "src_tokens": [], - "src_lengths": [], - } - - def incremental_encode( - self, - seg_src_tokens: Tensor, - seg_src_lengths: Tensor, - states=None, - ): - """ - Different from forward function, this function takes segmented speech - as input, and append encoder states to previous states - """ - (seg_encoder_states, seg_enc_lengths, states) = self.module( - seg_src_tokens, - seg_src_lengths, - states=states, - ) - return seg_encoder_states, seg_enc_lengths, states - - -# ------------------------------------------------------------------------------ -# Augmented memory model decorator -# ------------------------------------------------------------------------------ -def augmented_memory(klass): - class StreamSeq2SeqModel(klass): - @staticmethod - def add_args(parser): - super(StreamSeq2SeqModel, StreamSeq2SeqModel).add_args(parser) - parser.add_argument( - "--segment-size", type=int, required=True, help="Length of the segment." - ) - parser.add_argument( - "--left-context", - type=int, - default=0, - help="Left context for the segment.", - ) - parser.add_argument( - "--right-context", - type=int, - default=0, - help="Right context for the segment.", - ) - parser.add_argument( - "--max-memory-size", - type=int, - default=-1, - help="Right context for the segment.", - ) - - StreamSeq2SeqModel.__name__ = klass.__name__ - return StreamSeq2SeqModel diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/modules/emformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/modules/emformer.py deleted file mode 100644 index 4114b0c5563df08aedf09cbcbbae71e79ab8ab0a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/modules/emformer.py +++ /dev/null @@ -1,1843 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - - -import math -import re -from functools import partial -from typing import List, Optional, Tuple - -import torch -import torch.nn as nn -from fairseq.models import FairseqEncoder -from fairseq.models.speech_to_text.utils import ( - NoOp, - attention_suppression, - layer_norm_backward_hook, - lengths_to_padding_mask, - segments_to_sequence, -) -from torch import Tensor -from torch import device as Device - -try: - import torch.ao.quantization as quantization - from torch.ao.quantization.qconfig import ( - default_dynamic_qconfig, - per_channel_dynamic_qconfig, - ) -except ImportError: - import torch.quantization as quantization - from torch.quantization.qconfig import ( - default_dynamic_qconfig, - per_channel_dynamic_qconfig, - ) - - -class RelativePositionEmbedding(nn.Module): - """ - Implementation according to https://arxiv.org/abs/1803.02155 - """ - - def __init__(self, head_dim, max_position, norm_init=True): - super().__init__() - self.head_dim = head_dim - self.max_position = max_position - self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim)) - if norm_init: - nn.init.xavier_normal_(self.embeddings) - else: - nn.init.xavier_uniform_(self.embeddings) - - def forward(self, input: Tensor): - output = nn.functional.embedding(input.long(), self.embeddings) - return output - - -class Fp32LayerNorm(nn.Module): - def __init__( - self, - input_dim, - clamp_grad=True, - max_grad_value=256, - eps=1e-5, - elementwise_affine=True, - ): - super().__init__() - self.torch_module = torch.nn.LayerNorm( - input_dim, eps=eps, elementwise_affine=elementwise_affine - ) - if clamp_grad: - hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value) - self.torch_module.register_backward_hook(hook) - - def forward(self, input): - output = torch.nn.functional.layer_norm( - input.float(), - self.torch_module.normalized_shape, - self.torch_module.weight.float() - if self.torch_module.weight is not None - else None, - self.torch_module.bias.float() - if self.torch_module.bias is not None - else None, - self.torch_module.eps, - ).type_as(input) - return output - - -# ------------------------------------------------------------------------------ -# PositionwiseFF -# ------------------------------------------------------------------------------ - - -class PositionwiseFF(nn.Module): - """ - FFN layer in transformer. - - Args: - input_dim: input embedding dimension - ffn_dim: FFN layer inner dimension - dropout_on_fc1: dropout for first linear layer - dropout_on_fc2: dropout fr second linear layer - activation_fn: activation function used after first linear layer. \ - Only relu or gelu is supported. - - """ - - def __init__( - self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn - ): - super(PositionwiseFF, self).__init__() - - self.input_dim = input_dim - self.ffn_dim = ffn_dim - if activation_fn == "relu": - ac = nn.ReLU() - elif activation_fn == "gelu": - ac = nn.GELU() - else: - raise ValueError("Unsupported activation_fn = ({})".format(activation_fn)) - - # fc1 -> ac -> dropout -> fc2 -> dropout - self.module = nn.Sequential( - nn.Linear(input_dim, ffn_dim), - ac, - nn.Dropout(dropout_on_fc1), - nn.Linear(ffn_dim, input_dim), - nn.Dropout(dropout_on_fc2), - ) - - self.layer_norm = Fp32LayerNorm(input_dim) - - def forward(self, input): - module_out = self.module(self.layer_norm(input)) - output = module_out + input - - return output - - def quantize_(self, params=None): - if params and "per_channel" in params and params["per_channel"]: - qconfig = per_channel_dynamic_qconfig - else: - qconfig = default_dynamic_qconfig - quantization.quantize_dynamic( - self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True - ) - return self - - -# ------------------------------------------------------------------------------ -# SummarizationLayer -# ------------------------------------------------------------------------------ - - -class SummarizationLayer(nn.Module): - def __init__(self, method, segment_size, embedding_dim): - super(SummarizationLayer, self).__init__() - self.segment_size = segment_size - self.embedding_dim = embedding_dim - nonlin_match = re.match(r"nonlinear\((?P[a-z]+),(?P[0-9]+)\)", method) - self.method = method - if method == "mean": - self.module = nn.AvgPool1d( - kernel_size=segment_size, - stride=segment_size, - ceil_mode=True, - ) - elif method == "max": - self.module = nn.MaxPool1d( - kernel_size=segment_size, - stride=segment_size, - ceil_mode=True, - ) - elif method == "linear": - self.module = nn.Linear(segment_size, 1) - elif nonlin_match: - nonlin_args = nonlin_match.groupdict() - act_type = nonlin_args["act"] - hid_dim = int(nonlin_args["dim"]) - if act_type == "relu": - act = nn.ReLU() - elif act_type == "gelu": - act = nn.GELU() - else: - raise ValueError("Unsupported activation_fn = ({})".format(act_type)) - self.module = nn.Sequential( - nn.Linear(segment_size, hid_dim), - act, - nn.Linear(hid_dim, 1), - ) - else: - raise ValueError("Unsupported summarization method = ({})".format(method)) - - def forward(self, input): - # T, B, D -> B, D, T - input = input.permute(1, 2, 0) - - if self.method == "mean" or self.method == "max": - output = self.module(input) - output = output.permute(2, 0, 1) - return output - - full_seg_length = input.size(2) // self.segment_size * self.segment_size - if full_seg_length > 0: - # at least one seg is full - B = input.size(0) - D = input.size(1) - input_todo = ( - input[:, :, :full_seg_length] - .contiguous() - .view(B, -1, self.segment_size) - ) - output = self.module(input_todo) - output = output.view(B, D, -1) - else: - output = input.new_zeros(input.size(0), input.size(1), 0) - left = input.size(2) - full_seg_length - if left > 0: - # when last seg is not full, use zeros as last memory placeholder - zeros = input.new_zeros(input.size(0), input.size(1), 1) - output = torch.cat([output, zeros], dim=2) - output = output.permute(2, 0, 1) - return output - - -# ------------------------------------------------------------------------------ -# NoSegAugmentedMemoryMultiheadAttentionBmm -# ------------------------------------------------------------------------------ - - -class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module): - """ - Whole utterance augmented memory multihead attention using BMM. - - Different with previous augmented memory multihead attention where - the utterance is chunked into segments. Here we use attention mask - achieve so. The input embedding [right_context, utterance, summary] - is a concatenation of right context, utterance and summary. - - Right context block is the concatenation of all the right context for - each segments. [right_context_0, right_context_1, ..., right_context_n] - For example, if we have utterance = [v0, v1, v2, ...., v20]. segment - size 8, right_context size 4. Then the right context blocks = - [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10, - and v11 are the right context for first segment. v16, v17, v18 and v19 - are the right context for second segment. 0, 0, 0 and 0 are right context - for the last segment. - - utterance is corresponding to input embedding sequence - - summary is concatenation of average of each segments. [summary_0, - summary_1, ..., ]. - - In augmented memory multihead attention, the query is [right_context, - utterance, summary], key is [memory, right_context, utterance]. Different - with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from - previous attention layer. For the first attention layer, memory is average - of each segment. - - Memory is a concatenation of memory from each segments in previous attention - layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n]. - Each m_k is the output from seg_k in layer i-1. - - args: - input_dim: input embedding dimension - num_heads: number of heads in multihead self-attention - dropout: attention dropout - std_scale: if std_scale is not None. The weak attention suppression is - turned on. For std_scale = 0.5, all the attention smaller than - mean + 0.5 * std will be suppressed. - scaled_init: whether to use scaled init for linear weight - tanh_on_mem: whether to use tanh on memory output - use_mem: whether to use memory or not. When max_memory_size is 0, then - we don't have memory anymore. - layer_index: current self-attention layer index that is used in depth - initialization - max_relative_position: max relative position used in relative position - embedding - rpe_old_option: To be compatible with previous model. The previous model - was trained with attention += attention + rpe. The correct equation - should be attention = attention + rpe - - """ - - def __init__( - self, - input_dim, - num_heads, - dropout=0.0, - std_scale=None, - scaled_init=False, - tanh_on_mem=False, - use_mem=True, - mini_batches=False, - negative_inf="-inf", - layer_index=-1, - max_relative_position=0, - rpe_old_option=True, - ): - if input_dim % num_heads: - raise ValueError( - "input_dim ({}) must be divisible by num_heads ({})".format( - input_dim, num_heads - ) - ) - - super().__init__() - - embed_dim = input_dim - self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True) - self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True) - self.rpe_old_option = rpe_old_option - if max_relative_position > 0: - self.use_rpe = True - self.rpe_k = RelativePositionEmbedding( - head_dim=input_dim // num_heads, - max_position=max_relative_position, - ) - self.rpe_v = RelativePositionEmbedding( - head_dim=input_dim // num_heads, - max_position=max_relative_position, - ) - else: - self.use_rpe = False - self.rpe_k = None - self.rpe_v = None - if scaled_init: - if layer_index == -1: - gain = 1.0 / math.sqrt(2) - else: - # https://arxiv.org/abs/2005.09684 depthwise initialization - # stablize the training greatly. Use depthwise initialization to - # replace incremental loss. - gain = 1.0 / math.sqrt(layer_index + 1) - torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain) - torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain) - - self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True) - - self.embed_dim = embed_dim - self.num_heads = num_heads - self.dropout = dropout - - self.head_dim = embed_dim // num_heads - self.scaling = self.head_dim**-0.5 - - self.std_scale = std_scale - self.use_mem = use_mem - self.mini_batches = mini_batches - self.negative_inf = negative_inf - - if tanh_on_mem: - self.squash_mem = torch.tanh - self.nonlinear_squash_mem = True - else: - self.squash_mem = NoOp() - self.nonlinear_squash_mem = False - - def prepare_qkv( - self, - input: Tensor, - mems: Tensor, - lengths: Tensor, - summary_length: int, - lc_length: int, - ): - # T: right_context length + utterance_length + summary_length - T, B, D = input.shape - mem_length = mems.size(0) - utterance_length = torch.max(lengths) - - right_context_blocks_length = T - utterance_length - summary_length - rc_block = input[:right_context_blocks_length, :, :] - utterance_block = input[right_context_blocks_length : T - summary_length, :, :] - - if B == 1: - padding_mask = None - else: - klengths = lengths + mem_length + right_context_blocks_length + lc_length - padding_mask = lengths_to_padding_mask(lengths=klengths) - - mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0) - - # In training lc_length = 0 - key_length = mem_rc_input.size(0) + lc_length - rc_input_sum = input - q = self.e2h_q(rc_input_sum) - kv = self.e2h_kv(mem_rc_input) - k, v = kv.chunk(chunks=2, dim=2) - result_qkv = (q, k, v) - input_shape = (T, B, D) - result_lengths_info = ( - mem_length, - utterance_length, - right_context_blocks_length, - key_length, - ) - if padding_mask is not None: - assert padding_mask.size(0) == B - assert padding_mask.size(1) == key_length - - return result_qkv, input_shape, result_lengths_info, padding_mask - - def prepare_attention_weights( - self, - q: Tensor, - new_k: Tensor, - new_v: Tensor, - input_shape: Tuple[int, int, int], - rpe: Optional[Tensor], - ) -> Tuple[Tensor, Tensor, Tensor]: - T, B, D = input_shape - q = ( - q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1) - * self.scaling - ) - - k = ( - new_k.contiguous() - .view(-1, B * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - v = ( - new_v.contiguous() - .view(-1, B * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - attention_weights = torch.bmm(q, k.transpose(1, 2)) - if self.use_rpe and rpe is not None and self.rpe_v is not None: - r_k = self.rpe_k(rpe) - # [q, B*h, d] * [q, k, d] -> [B*h, q, k] - attention_weights_rpe = torch.matmul( - q.transpose(0, 1), r_k.transpose(1, 2) - ).transpose(0, 1) - attention_weights = attention_weights + attention_weights_rpe - attention_weights_float = attention_weights.float() - - return attention_weights, attention_weights_float, v - - def prepare_attention_output( - self, - attention_weights: Tensor, - attention_weights_float: Tensor, - v: Tensor, - input_shape: Tuple[int, int, int], - key_length: int, - padding_mask: Optional[Tensor], - rpe: Optional[Tensor], - ) -> Tensor: - T, B, D = input_shape - if padding_mask is not None: - attention_weights_float = attention_weights_float.view( - B, self.num_heads, T, key_length - ) - attention_weights_float = attention_weights_float.masked_fill( - padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf") - ) - attention_weights_float = attention_weights_float.view( - B * self.num_heads, T, key_length - ) - - if self.std_scale is not None: - attention_weights_float = attention_suppression( - attention_weights_float, self.std_scale - ) - - attention_weights_float = torch.nn.functional.softmax( - attention_weights_float, dim=-1 - ) - attention_weights = attention_weights_float.type_as(attention_weights) - - attention_probs = torch.nn.functional.dropout( - attention_weights, p=self.dropout, training=self.training - ) - - # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head] - # -> [T, B, n_head, d_head] - attention = torch.bmm(attention_probs, v) - if self.use_rpe and rpe is not None and self.rpe_v is not None: - r_v = self.rpe_v(rpe) - attention_rpe = torch.matmul( - attention_probs.transpose(0, 1), r_v - ).transpose(0, 1) - - if self.rpe_old_option: - attention += attention + attention_rpe - else: - attention = attention + attention_rpe - - assert list(attention.shape) == [B * self.num_heads, T, self.head_dim] - - attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim) - - rc_output_memory = self.out_proj(attention) - return rc_output_memory - - @torch.jit.unused - def forward( - self, - input: Tensor, - lengths: Tensor, - mems: Tensor, - attention_mask: Tensor, - pre_mems: Optional[Tensor] = None, - left_context_key: Optional[Tensor] = None, - left_context_val: Optional[Tensor] = None, - rpe: Optional[Tensor] = None, - ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - """ - forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training. - - args: - input: formed in the following way - [right_context_0, right_contex_1, ..., seg_0, seg_1, - ..., summary_0, summary_1,..] - lengths: the length of query which is [seg_0, seg_1, ....] - mems: [mem_0, mem_1, ...]. - attention_mask: attention mask for query = [right_context, query, summary] - key = [mem, right_context, query]. This is only used for traing. - - """ - if self.use_mem: - mem_length = mems.size(0) - summary_length = mem_length + 1 - if pre_mems is not None: - mems = torch.cat([pre_mems, mems], dim=0) - else: - mem_length = 0 - summary_length = 0 - - # In training, lc_length = 0 - if left_context_key is not None: - lc_length = left_context_key.size(0) - else: - lc_length = 0 - results = self.prepare_qkv( - input=input, - mems=mems, - lengths=lengths, - summary_length=summary_length, - lc_length=lc_length, - ) - result_qkv, input_shape, result_lengths_info, padding_mask = results - q, k, v = result_qkv - ( - mem_length, - utterance_length, - right_context_blocks_length, - key_length, - ) = result_lengths_info - - if left_context_key is not None: - # add the cache key and value - new_k = torch.cat( - [ - k[: mem_length + right_context_blocks_length, :, :], - left_context_key, - k[-utterance_length:, :, :], - ], - dim=0, - ) - new_v = torch.cat( - [ - v[: mem_length + right_context_blocks_length, :, :], - left_context_val, - v[-utterance_length:, :, :], - ], - dim=0, - ) - next_k = new_k[mem_length + right_context_blocks_length :, :, :] - next_v = new_v[mem_length + right_context_blocks_length :, :, :] - else: - new_k = k - new_v = v - next_k = None - next_v = None - - attention_weights, attention_weights_float, v = self.prepare_attention_weights( - q=q, - new_k=new_k, - new_v=new_v, - input_shape=input_shape, - rpe=rpe, - ) - - # mask attention - attention_mask = attention_mask.unsqueeze(0) - attention_weights_float = attention_weights_float.masked_fill( - attention_mask, float(self.negative_inf) - ) - - rc_output_memory = self.prepare_attention_output( - attention_weights=attention_weights, - attention_weights_float=attention_weights_float, - v=v, - input_shape=input_shape, - key_length=key_length, - padding_mask=padding_mask, - rpe=rpe, - ) - - if self.use_mem: - # next_m length equals to summary length - 1 - # last memory is ignored - if self.mini_batches: - next_m = rc_output_memory[-summary_length:] - else: - next_m = rc_output_memory[-summary_length:-1] - - next_m = self.squash_mem(next_m) - # rc and output - rc_output = rc_output_memory[:-summary_length] - if not self.nonlinear_squash_mem: - next_m = torch.clamp(next_m, min=-10, max=10) - else: - next_m = mems - rc_output = rc_output_memory - - return rc_output, next_m, next_k, next_v - - @torch.jit.export - def forward_jit( - self, - input: Tensor, - lengths: Tensor, - mems: Tensor, - left_context_key: Tensor, - left_context_val: Tensor, - rpe: Optional[Tensor], - ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - """ - forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding. - - args: - input: formed in the following way - [right_context_0, right_contex_1, ..., seg_0, seg_1, - ..., summary_0, summary_1,..] - lengths: the length of query which is [seg_0, seg_1, ....] - mems: [mem_0, mem_1, ...]. - left_context_key: left_context for key part. This is only used for online - decoding. In training, this is empty tensor - left_context_val: left_context for value part. This is only used for online - decoding. In training, this is empty tensor - - """ - lc_length = left_context_key.size(0) - - # In decoding, summary_length = 1 or 0 - if self.use_mem: - summary_length = 1 - else: - summary_length = 0 - - results = self.prepare_qkv( - input=input, - mems=mems, - lengths=lengths, - summary_length=summary_length, - lc_length=lc_length, - ) - result_qkv, input_shape, result_lengths_info, padding_mask = results - q, k, v = result_qkv - ( - mem_length, - utterance_length, - right_context_blocks_length, - key_length, - ) = result_lengths_info - - # add the cache key and value - new_k = torch.cat( - [ - k[: mem_length + right_context_blocks_length, :, :], - left_context_key, - k[-utterance_length:, :, :], - ], - dim=0, - ) - new_v = torch.cat( - [ - v[: mem_length + right_context_blocks_length, :, :], - left_context_val, - v[-utterance_length:, :, :], - ], - dim=0, - ) - next_k = new_k[mem_length + right_context_blocks_length :, :, :] - next_v = new_v[mem_length + right_context_blocks_length :, :, :] - - attention_weights, attention_weights_float, v = self.prepare_attention_weights( - q=q, - new_k=new_k, - new_v=new_v, - input_shape=input_shape, - rpe=rpe, - ) - # In online decoding, we don't have attention mask. But we still need - # to disable the attention from summary query to memory - attention_weights_float[:, -1, :mem_length] = float(self.negative_inf) - rc_output_memory = self.prepare_attention_output( - attention_weights=attention_weights, - attention_weights_float=attention_weights_float, - v=v, - input_shape=input_shape, - key_length=key_length, - padding_mask=padding_mask, - rpe=rpe, - ) - - # In decoding, summary length is 1 - if self.use_mem: - next_m = rc_output_memory[-1:] - next_m = self.squash_mem(next_m) - # rc and output - rc_output = rc_output_memory[:-1] - if not self.nonlinear_squash_mem: - next_m = torch.clamp(next_m, min=-10, max=10) - else: - rc_output = rc_output_memory - # empty tensor as input mems - next_m = mems - - return rc_output, next_m, next_k, next_v - - def quantize_(self, params=None): - if params and "per_channel" in params and params["per_channel"]: - qconfig = per_channel_dynamic_qconfig - else: - qconfig = default_dynamic_qconfig - quantization.quantize_dynamic( - self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True - ) - return self - - -class NoSegAugmentedMemoryTransformer(nn.Module): - """ - Whole utterance augmented memory transformer. - - This is not pyspeech nn layer. It is used as a module in a master layer where - multiple transformers is used. - """ - - def __init__( - self, - input_dim, - num_heads, - ffn_dim, - dropout_in_attn=0.0, - dropout_on_attn=None, - dropout_on_fc1=None, - dropout_on_fc2=None, - activation_fn="relu", - tanh_on_mem=False, - std_scale=None, - scaled_init=False, - segment_size=128, - use_mem=True, - mini_batches=False, - negative_inf="-inf", - layer_index=-1, - summarization_method="mean", - max_relative_position=0, - rpe_old_option=True, - ): - super(NoSegAugmentedMemoryTransformer, self).__init__() - - self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm( - input_dim=input_dim, - num_heads=num_heads, - dropout=dropout_in_attn, - scaled_init=scaled_init, - tanh_on_mem=tanh_on_mem, - std_scale=std_scale, - use_mem=use_mem, - mini_batches=mini_batches, - negative_inf=negative_inf, - layer_index=layer_index, - max_relative_position=max_relative_position, - ) - self.dropout = nn.Dropout(dropout_on_attn) - self.pos_ff = PositionwiseFF( - input_dim=input_dim, - ffn_dim=ffn_dim, - dropout_on_fc1=dropout_on_fc1, - dropout_on_fc2=dropout_on_fc2, - activation_fn=activation_fn, - ) - self.layer_norm_pre = Fp32LayerNorm(input_dim) - self.layer_norm = Fp32LayerNorm(input_dim) - self.segment_size = segment_size - self.use_mem = use_mem - - self.memory_op = SummarizationLayer( - summarization_method, segment_size, input_dim - ) - - def set_mini_batches(self, mini_batches): - self.attention.mini_batches = mini_batches - - def gen_summary_queries(self, input): - sum_input = self.memory_op(input) - return sum_input - - def pre_attention_ops(self, input, right_context_blocks): - rc_length = right_context_blocks.size(0) - input_length = input.size(0) - - rc_and_input = torch.cat([right_context_blocks, input], dim=0) - residual_input = rc_and_input - rc_and_input = self.layer_norm_pre(rc_and_input) - - query_input = rc_and_input[-input_length:, :, :] - return rc_length, input_length, residual_input, query_input, rc_and_input - - def after_attention_ops(self, attention_output, residual_input): - output = self.dropout(attention_output) - output = output + residual_input - output = self.pos_ff(output) - output = self.layer_norm(output) - return output - - @torch.jit.export - def forward_jit( - self, - input: Tensor, - lengths: Tensor, - mems: Tensor, - left_context_key: Tensor, - left_context_val: Tensor, - right_context_blocks: Tensor, - rpe: Optional[Tensor], - ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: - - results = self.pre_attention_ops(input, right_context_blocks) - rc_length, input_length, residual_input, query_input, rc_and_input = results - - # In online decoding, the summary query size is always 1 or 0 - if self.use_mem: - summary_query = self.gen_summary_queries(query_input) - summary_query = summary_query[0:1, :, :] - rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0) - else: - rc_qu_su = rc_and_input - - rc_output, next_m, next_k, next_v = self.attention.forward_jit( - input=rc_qu_su, - lengths=lengths, - mems=mems, - left_context_key=left_context_key, - left_context_val=left_context_val, - rpe=rpe, - ) - rc_output = self.after_attention_ops(rc_output, residual_input) - results = ( - rc_output[-input_length:, :, :], - next_m, - rc_output[0:rc_length, :, :], - next_k, - next_v, - ) - return results - - @torch.jit.unused - def forward( - self, - input, - lengths, - mems, - right_context_blocks, - attention_mask, - pre_mems, - left_context_key, - left_context_val, - rpe, - ): - - results = self.pre_attention_ops(input, right_context_blocks) - rc_length, input_length, residual_input, query_input, rc_and_input = results - if self.use_mem: - summary_query = self.gen_summary_queries(query_input) - rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0) - else: - rc_qu_su = rc_and_input - - rc_output, next_m, next_k, next_v = self.attention( - input=rc_qu_su, - lengths=lengths, - mems=mems, - attention_mask=attention_mask, - pre_mems=pre_mems, - left_context_key=left_context_key, - left_context_val=left_context_val, - rpe=rpe, - ) - - # [TODO] Note memory did not go through pos_ff. What happen if we pass - # memory through the pos_ff as well? - rc_output = self.after_attention_ops(rc_output, residual_input) - results = ( - rc_output[-input_length:, :, :], - next_m, - rc_output[0:rc_length, :, :], - next_k, - next_v, - ) - - return results - - -class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder): - """ - Whole utterance augmented memory transformer encoder layer. This is a master layer - where we can define multiple augmented memory transformers. There are two reasons - to setup the master layer. - 1. We only need to define once about the attention mask. All the layers in the master - layer share the same mask. - 2. pyspeech nn layer has special input and output format. Defining one master layer is - easier to passing memory between different layes inside the master layer - - args: - input_dim: input embedding dimension - num_heads: number of heads in multihead self-attention - ffn_dim: ffn dimension in FFN layer - num_layers: number of augmented memory transformer layers - dropout_in_attn: dropout used in multi-head self-attention - dropout_on_attn: dropout used for output from te multihead self-attention - dropout_on_fc1: dropout used in FFN layer for the first linear layer - dropout_on_fc2: dropout used in FFN layer for the second linear layer - segment_size: segment size for each segment - context_config: (left_context_size, right_context_size) defines the surround context size - for each segment - max_memory_size: maximum memory size used for each segment - scaled_init: whether use scaled init for weight initialization in attention layer - std_scale: if std_scale is not None. The weak attention suppression is - turned on. For std_scale = 0.5, all the attention smaller than - mean + 0.5 * std will be suppressed. - activation_fn: activation function used in FFN layer. [ReLU, GELU] supported - tanh_on_mem: whether use tanh on memory - mini_batches: use mini-btach training - negative_inf: the negative infinity value used in attention masking. default is "-inf". - For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue. - summarization_method: method to generate segment summrization embedding - max_relative_position: max relatie position for relative position embedding - rpe_old_option: To be compatible with previous model. The previous model - was trained with attention += attention + rpe. The correct equation - should be attention = attention + rpe - [TODO]: remove the rpe_old_option by the end of 2021 Q1. - - """ - - def __init__( - self, - input_dim, - num_heads, - ffn_dim, - num_layers=1, - dropout_in_attn=0.0, - dropout_on_attn=0.0, - dropout_on_fc1=0.0, - dropout_on_fc2=0.0, - segment_size=128, - context_config=(0, 0), - max_memory_size=0, - scaled_init=True, - std_scale=None, - activation_fn="relu", - tanh_on_mem=False, - mini_batches=False, - negative_inf="-inf", - deep_init=True, - summarization_method="mean", - max_relative_position=0, - rpe_old_option=True, - ): - super().__init__(None) - if input_dim % num_heads: - raise ValueError( - "input_dim ({}) must be divisible by num_heads ({})".format( - input_dim, num_heads - ) - ) - - # we used to support growing memory size. However, it will cause - # cross stream batching failure. Now we need to have exact max memory size - if max_memory_size < 0: - raise ValueError("max_memory_size must be >= 0") - - # Only assign right_context. In decoding, left context will be cached. - # No need to let the online decoder to re-assign the left context - self.left_context, self.right_context = context_config - self.segment_size = segment_size - self.memory_dim = input_dim - self.max_memory_size = max_memory_size - self.mini_batches = mini_batches - if self.max_memory_size != 0: - self.use_mem = True - else: - self.use_mem = False - - self.memory_op = SummarizationLayer( - summarization_method, segment_size, input_dim - ) - - self.layers = torch.nn.ModuleList() - self.num_layers = num_layers - self.max_relative_position = max_relative_position - if self.max_relative_position > 0: - self.use_rpe = True - else: - self.use_rpe = False - for i in range(self.num_layers): - if deep_init: - layer_index = i - else: - layer_index = -1 - - self.layers.append( - NoSegAugmentedMemoryTransformer( - num_heads=num_heads, - input_dim=input_dim, - ffn_dim=ffn_dim, - dropout_in_attn=dropout_in_attn, - dropout_on_attn=dropout_on_attn, - dropout_on_fc1=dropout_on_fc1, - dropout_on_fc2=dropout_on_fc2, - segment_size=segment_size, - std_scale=std_scale, - activation_fn=activation_fn, - tanh_on_mem=tanh_on_mem, - scaled_init=scaled_init, - use_mem=self.use_mem, - mini_batches=mini_batches, - negative_inf=negative_inf, - layer_index=layer_index, - summarization_method=summarization_method, - max_relative_position=max_relative_position, - rpe_old_option=rpe_old_option, - ) - ) - - def set_mini_batches(self, mini_batches): - # handy function only used for unit test - self.mini_batches = mini_batches - for layer in self.layers: - layer.set_mini_batches(mini_batches) - - def _get_relative_position( - self, - input: Tensor, - max_relative_position: int, - left_context_length: int, - past_length: int, - is_decoding: bool, - ): - # For training, we copy the right context to the start of the utterance - # First dimension in distance is corresponding to query. - # [right context, utterance, summary vector] - # Second dimension in distance is corresponding to key. - # [Memory bank, right context, utterance] - # For summary vector in query part, the distance with - # all other position is 2*max_position. For memory bank in key, - # the distance with all other positions is 0. - - T, B, D = input.shape - num_segs = math.ceil((T - self.right_context) / self.segment_size) - - # utterance - u_st = past_length * self.segment_size - u_ed = u_st + T - utterance_ranges = torch.arange(u_st, u_ed - self.right_context) - - # left context. Only in minibatch or decoding - left_context_ranges = torch.arange(u_st - left_context_length, u_st) - - # Right context block - # right context + utterance - right_context_blocks = [] - for i in range(0, num_segs - 1): - st = (i + 1) * self.segment_size + u_st - ed = st + self.right_context - assert ed < u_ed - temp = torch.arange(st, ed) - right_context_blocks.append(temp) - right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed)) - right_context_ranges = torch.cat(right_context_blocks) - - if self.use_mem: - # Memory bank - # The position for memory -n, .., -1 - if is_decoding: - memory_size = min(past_length, self.max_memory_size) - else: - memory_size = num_segs + past_length - 1 - memory_bank_ranges = torch.arange( - -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1 - ) - - # summary vector - # The position for summary vector as the T+max_relative_position+1. - # After the clamping, the relative position is max_relative_position - summary_pos_st = u_ed + max_relative_position + 1 - summary_vector_ranges = torch.arange( - summary_pos_st, summary_pos_st + num_segs - ) - - key_ranges = torch.cat( - [ - memory_bank_ranges, - right_context_ranges, - left_context_ranges, - utterance_ranges, - ] - ) - - query_ranges = torch.cat( - [right_context_ranges, utterance_ranges, summary_vector_ranges] - ) - else: - key_ranges = torch.cat( - [right_context_ranges, left_context_ranges, utterance_ranges] - ) - - query_ranges = torch.cat([right_context_ranges, utterance_ranges]) - - distance = key_ranges[None, :] - query_ranges[:, None] - distance_clamp = ( - torch.clamp(distance, -max_relative_position, max_relative_position) - + max_relative_position - ) - distance_clamp = distance_clamp.to(input.device).long().detach() - return distance_clamp - - def _get_attention_mask(self, input, past_length=0, left_context_cache=0): - # attention mask for each query contains three parts: - # 1. memory part - # 2. left_context + segment - # 3. right_context_block - # so for each segment and its correspoinding right context block, - # the attention matrix is formed by 9 parts: - # [0, m, 0, 0, right_context, 0, 0, seg, 0] - # [before memory, memory, after memory, before right context, right_context, - # after right context, before seg, seg, after seg] - # - # Query is formed in the way as [right_context_blocks, utterance, summary] - # - # Note: put m and right_context before segment is convenient - # for padding_mask operation. - # Key lengths = m_length + right_context_block_length + lengths - utterance_length, batch_size, _ = input.shape - summary_length = math.ceil(utterance_length / self.segment_size) - num_segs = summary_length - rc_length = self.right_context * num_segs - rc = self.right_context - lc = self.left_context - - # using mini-batches, there is left context cache available for current - # sequence. - lcc = left_context_cache - - # max_memory_size is 0 then we don't have memory and summary - # past_length is the memory carry from previous sequence - if self.use_mem: - mem_length = num_segs - 1 + past_length - else: - mem_length = 0 - rc_mask = [] - query_mask = [] - summary_mask = [] - for j in range(0, num_segs): - ssize = min(self.segment_size, utterance_length - j * self.segment_size) - - rc_size = rc - rc_mat = [] - q_mat = [] - s_mat = [] - m_start = max(j + past_length - self.max_memory_size, 0) - - # max_memory_size is 0, then we don't use memory - if self.use_mem: - # part 0: before memory - rc_mat.append(input.new_zeros(rc_size, m_start)) - q_mat.append(input.new_zeros(ssize, m_start)) - s_mat.append(input.new_zeros(1, m_start)) - - # part 1: memory - col_1 = j + past_length - m_start - rc_mat.append(torch.ones(rc_size, col_1, device=input.device)) - q_mat.append(torch.ones(ssize, col_1, device=input.device)) - # based on D22875746, disable summary query attention - # on memeory is better for long form utterance - s_mat.append(input.new_zeros(1, col_1)) - - # part 2: after memory - col_2 = mem_length - (j + past_length) - rc_mat.append(input.new_zeros(rc_size, col_2)) - q_mat.append(input.new_zeros(ssize, col_2)) - s_mat.append(input.new_zeros(1, col_2)) - - # part 3: before right context - rc_start = j * rc - rc_mat.append(input.new_zeros(rc_size, rc_start)) - q_mat.append(input.new_zeros(ssize, rc_start)) - s_mat.append(input.new_zeros(1, rc_start)) - - # part 4: right context - rc_end = rc_start + rc - col_4 = rc - rc_mat.append(torch.ones(rc_size, col_4, device=input.device)) - q_mat.append(torch.ones(ssize, col_4, device=input.device)) - s_mat.append(torch.ones(1, col_4, device=input.device)) - - # part 5: after right context - col_5 = rc_length - rc_end - rc_mat.append(input.new_zeros(rc_size, col_5)) - q_mat.append(input.new_zeros(ssize, col_5)) - s_mat.append(input.new_zeros(1, col_5)) - - # part 6: before query segment - seg_start = max(j * self.segment_size + lcc - lc, 0) - rc_mat.append(input.new_zeros(rc_size, seg_start)) - q_mat.append(input.new_zeros(ssize, seg_start)) - s_mat.append(input.new_zeros(1, seg_start)) - - # part 7: query segment - # note: right context is put in right context block - # here we only need to consider about left context - seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc) - col_7 = seg_end - seg_start - rc_mat.append(torch.ones(rc_size, col_7, device=input.device)) - q_mat.append(torch.ones(ssize, col_7, device=input.device)) - s_mat.append(torch.ones(1, col_7, device=input.device)) - - # part 8: after query segment - col_8 = utterance_length + lcc - seg_end - rc_mat.append(input.new_zeros(rc_size, col_8)) - q_mat.append(input.new_zeros(ssize, col_8)) - s_mat.append(input.new_zeros(1, col_8)) - - rc_mask.append(torch.cat(rc_mat, dim=1)) - query_mask.append(torch.cat(q_mat, dim=1)) - summary_mask.append(torch.cat(s_mat, dim=1)) - - # no memory, then we don't need summary either - if self.use_mem: - attention_mask = ( - 1 - - torch.cat( - [ - torch.cat(rc_mask, dim=0), - torch.cat(query_mask, dim=0), - torch.cat(summary_mask, dim=0), - ], - dim=0, - ) - ).to(torch.bool) - else: - attention_mask = ( - 1 - - torch.cat( - [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0 - ) - ).to(torch.bool) - - return attention_mask - - @torch.jit.export - def init_state( - self, batch_size: int, device: Optional[Device] = None - ) -> List[Tensor]: - empty_memory = torch.zeros( - self.num_layers, - self.max_memory_size, - batch_size, - self.memory_dim, - device=device, - ) - left_context_key = torch.zeros( - self.num_layers, - self.left_context, - batch_size, - self.memory_dim, - device=device, - ) - left_context_val = torch.zeros( - self.num_layers, - self.left_context, - batch_size, - self.memory_dim, - device=device, - ) - past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device) - - return [empty_memory, left_context_key, left_context_val, past_length] - - @torch.jit.export - def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]: - if len(states) == 0: - return [] - batched_m = [] - batched_lc_key = [] - batched_lc_val = [] - batched_past_length = [] - for state in states: - if len(state) == 0: - continue - m, lc_key, lc_val, past_length = state - batched_m.append(m) - batched_lc_key.append(lc_key) - batched_lc_val.append(lc_val) - batched_past_length.append(past_length) - - if ( - (len(batched_m) == 0) - or (len(batched_lc_key) == 0) - or (len(batched_lc_val) == 0) - or (len(batched_past_length) == 0) - ): - return [ - torch.tensor([]), - torch.tensor([]), - torch.tensor([]), - torch.tensor([]), - ] - - batched_m = torch.cat(batched_m, dim=2) - batched_lc_key = torch.cat(batched_lc_key, dim=2) - batched_lc_val = torch.cat(batched_lc_val, dim=2) - batched_past_length = torch.cat(batched_past_length, dim=1) - return [batched_m, batched_lc_key, batched_lc_val, batched_past_length] - - @torch.jit.export - def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]: - if len(state) == 0: - return [] - m, lc_key, lc_val, past_length = state - indices = indices.to(device=m.device) - reord_m = torch.index_select(m, 2, indices) - reord_lc_key = torch.index_select(lc_key, 2, indices) - reord_lc_val = torch.index_select(lc_val, 2, indices) - reord_past_length = torch.index_select(past_length, 1, indices) - return [reord_m, reord_lc_key, reord_lc_val, reord_past_length] - - @torch.jit.export - def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]: - m, lc_key, lc_val, past_length = state - m = m.index_fill(dim=2, index=indices, value=0.0) - lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0) - lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0) - past_length = past_length.index_fill(dim=1, index=indices, value=0) - - return [m, lc_key, lc_val, past_length] - - @torch.jit.export - def state_size(self) -> int: - return 4 - - @torch.jit.export - def batch_size_in_state( - self, state: Optional[List[Tensor]], sloppy: bool = True - ) -> Optional[int]: - if state is None: - return None - return state[0].size(2) - - def gen_summary_queries(self, input): - sum_input = self.memory_op(input) - return sum_input - - def _gen_right_context_padded_input(self, input): - # This function deals with input that is already - # padded with right context (e.g. minibatch training) - right_context_blocks = [] - T, B, D = input.shape - num_segs = math.ceil((T - self.right_context) / self.segment_size) - for i in range(0, num_segs - 1): - st = (i + 1) * self.segment_size - ed = st + self.right_context - assert ed < T - temp = input[st:ed, :, :] - right_context_blocks.append(temp) - - # last segment right context is already available - right_context_blocks.append(input[T - self.right_context :, :, :]) - return torch.cat(right_context_blocks, dim=0) - - def _gen_segs_right_context(self, input, lengths): - segments = [] - T, B, D = input.size() - nT = T - self.right_context - - # assume input is right context padded - num_segs = math.ceil(nT / self.segment_size) - # pad zeros to the utterance to make sure each - # segment has the same right context. For the - for i in range(0, num_segs - 1): - st = i * self.segment_size - ed = min(T, st + self.segment_size + self.right_context) - temp = input[st:ed, :, :] - rest_lengths = torch.clamp( - lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size - ) - segments.append((temp, lengths - rest_lengths + self.right_context)) - lengths = rest_lengths - - last_seg = input[st + self.segment_size :, :, :] - segments.append((last_seg, rest_lengths + self.right_context)) - - return segments - - @torch.jit.unused - def forward( - self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None - ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]: - # Xutai: originally the second argument is lengths. - lengths = (~padding_masks).sum(dim=1).long() - # mini batch training. - if self.mini_batches: - return self.forward_mini_batches(input, lengths, state) - - # regular full sequence training. Note, assume the right context in provided - # in the input. - T, B, D = input.size() - right_context_blocks = self._gen_right_context_padded_input(input) - - # generate the relative positional embedding - if self.use_rpe: - rpe = self._get_relative_position( - input=input, - max_relative_position=self.max_relative_position, - left_context_length=0, - past_length=0, - is_decoding=False, - ) - else: - rpe = None - input = input[: T - self.right_context, :, :] - - attention_mask = self._get_attention_mask(input) - - # firt layer use each segment mean as memory - # ignore the last one seg average - if self.use_mem: - mems = self.gen_summary_queries(input)[:-1, :, :] - else: - mems = torch.zeros(0, input.size(1), input.size(2), device=input.device) - mems = mems.type_as(input) - - output = input - all_outputs = [] - - for layer in self.layers: - output, mems, right_context_blocks, _, _ = layer( - input=output, - lengths=lengths, - attention_mask=attention_mask, - mems=mems, - right_context_blocks=right_context_blocks, - pre_mems=None, - left_context_key=None, - left_context_val=None, - rpe=rpe, - ) - all_outputs.append(output) - return output, padding_masks, [], all_outputs - - def forward_jit_mini_batch_init( - self, - seg: Tensor, - state: Optional[List[Tensor]] = None, - is_decoding: bool = False, - ): - # Prepare state. In whole sequence training, state is ignored. - # For minibatch training, we need to prepare state - if state is None: - state = self.init_state(batch_size=seg.size(1), device=seg.device) - if seg.dtype == torch.half: - state = [state[0].half(), state[1].half(), state[2].half(), state[3]] - - if self.use_mem: - # note input average only on seg, not on right context - # first layer use each segmetn mean as memory. the last - # one segment average is used in state - full_mems = self.gen_summary_queries(seg) - if is_decoding: - mems = full_mems[0:1, :, :] - state_mems = torch.cat([state[0][0], mems], dim=0) - else: - mems = full_mems[:-1, :, :] - state_mems = torch.cat([state[0][0], full_mems], dim=0) - else: - mems = state[0][0] - state_mems = mems - - # track processed segment number or memory number - # the same batch as the same bumber of past length - past_length = state[3][0][0].item() - past_left_context = min(past_length * self.segment_size, self.left_context) - past_length = min(self.max_memory_size, past_length) - - return state, mems, state_mems, past_length, past_left_context - - def state_update_before( - self, layer: int, state: List[Tensor], past_length: int, past_left_context: int - ): - pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :] - lc_key = state[1][layer][self.left_context - past_left_context :, :, :] - lc_val = state[2][layer][self.left_context - past_left_context :, :, :] - return pre_mems, lc_key, lc_val - - def state_update_after( - self, - layer: int, - state: List[Tensor], - mems: Tensor, - next_key: Tensor, - next_val: Tensor, - mems_list: List[Tensor], - lc_key_list: List[Tensor], - lc_val_list: List[Tensor], - ): - # mems is used for next layer - if layer < self.num_layers - 1: - state_mems = torch.cat([state[0][layer + 1], mems], dim=0) - mems_list.append(state_mems[-self.max_memory_size :, :, :]) - - # when mems pass to next sequence, we need the last memory. when mems - # use for the next layer, we can ignore the last memory - mems = mems[:-1, :, :] - - # note state[1][i] and state[2][i] original length equals to self.left_context - new_k = torch.cat([state[1][layer], next_key], dim=0) - new_v = torch.cat([state[2][layer], next_val], dim=0) - lc_key_list.append(new_k[-self.left_context :, :, :]) - lc_val_list.append(new_v[-self.left_context :, :, :]) - return mems_list, lc_key_list, lc_val_list, mems - - def state_update_after_loop( - self, - state: List[Tensor], - mems_list: List[Tensor], - lc_key_list: List[Tensor], - lc_val_list: List[Tensor], - update_length: int, - ): - state[0] = torch.stack(mems_list, dim=0) - state[1] = torch.stack(lc_key_list, dim=0) - state[2] = torch.stack(lc_val_list, dim=0) - state[3] = state[3] + update_length - return state - - @torch.jit.unused - def forward_mini_batches( - self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None - ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]: - T, B, D = input.size() - - # input without right context - seg = input[: T - self.right_context, :, :] - - # get right context blocks - right_context_blocks = self._gen_right_context_padded_input(input) - - mems_list = [] - lc_key_list = [] - lc_val_list = [] - results = self.forward_jit_mini_batch_init(seg, state, False) - state, mems, state_mems, past_length, past_left_context = results - - # relative position embedding - if self.use_rpe: - rpe = self._get_relative_position( - input=input, - max_relative_position=self.max_relative_position, - left_context_length=past_left_context, - past_length=past_length, - is_decoding=False, - ) - else: - rpe = None - - # get attention mask based on seg (not include right context) and available - # left context - attention_mask = self._get_attention_mask(seg, past_length, past_left_context) - mems_list.append(state_mems[-self.max_memory_size :, :, :]) - output = seg - i = 0 - all_outputs = [] - for layer in self.layers: - # In order to make cross stream batching work, mem, left context key - # and left context value in the state should always be the same shape. - # We use the past length to track the processed segment number. In this - # way, we take out the essential memory, left context key and left - # context val from the state. After finish the forward for current segment - # we add the new memory, left context key and left context value into the - # staate and trim out the oldest part to keep the shape consistent. - pre_mems, lc_key, lc_val = self.state_update_before( - i, state, past_length, past_left_context - ) - - output, mems, right_context_blocks, next_key, next_val = layer.forward( - input=output, - lengths=lengths, - attention_mask=attention_mask, - mems=mems, - right_context_blocks=right_context_blocks, - pre_mems=pre_mems, - left_context_key=lc_key, - left_context_val=lc_val, - rpe=rpe, - ) - all_outputs.append(output) - mems_list, lc_key_list, lc_val_list, mems = self.state_update_after( - layer=i, - state=state, - mems=mems, - next_key=next_key, - next_val=next_val, - mems_list=mems_list, - lc_key_list=lc_key_list, - lc_val_list=lc_val_list, - ) - - i += 1 - - # update state - update_length = math.ceil((T - self.right_context) / self.segment_size) - state = self.state_update_after_loop( - state=state, - mems_list=mems_list, - lc_key_list=lc_key_list, - lc_val_list=lc_val_list, - update_length=update_length, - ) - - return output, lengths, state, all_outputs - - def forward_jit_test( - self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None - ) -> Tuple[Tensor, Tensor, List[Tensor]]: - """ - This one simulate sequence encoder forward jit. This is for unit test purpose. - It is not used in training or decoding. Note, extra_right_context is set in - the model. In unit test, input = [utterance, right_context], lengths = - [utterance_length]. - args: - input: input utterance - lengths: utterance input length - state: None here. input is whole utterance - """ - # [TODO] sequence_to_segment has bug in lengths. - seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths) - - seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = [] - state: Optional[List[Tensor]] = None - for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths: - seg_enc_tokens, seg_enc_lengths, state = self.forward_jit( - input=seg_src_tokens, lengths=seg_src_lengths, state=state - ) - seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths)) - - enc_tokens, enc_lengths = segments_to_sequence( - segments=seg_enc_tokens_lengths, time_axis=0 - ) - - state = [] # returns trivial state - - return enc_tokens, enc_lengths, state - - @torch.jit.export - def forward_jit( - self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None - ) -> Tuple[Tensor, Tensor, List[Tensor]]: - """ - Forward helper for online decoding. - - args: - input: [seg, right_context]. We assume in online we - always padding the right context to the preset right context size. - For the last segment, we may have short segment size, but right - context size is the same as other segments - lengths: utterance input length is the utterance segment length and - right context size - state: [memory, left_context_key, left_context_val]. To improve throughput, - in addition to memory, we also cache key and value for left_context in - multihead self-attention - """ - # In online decoding, input = [segment, right_context] - # Lengths = [segment_length, right_context_length] - # so we need strip right context in output - T, B, D = input.size() - rc_str = T - self.right_context - rc_end = T - right_context_blocks = input[rc_str:rc_end, :, :] - seg = input[:rc_str, :, :] - lengths = torch.clamp(lengths - self.right_context, min=0) - mems_list = [] - lc_key_list = [] - lc_val_list = [] - - results = self.forward_jit_mini_batch_init(seg, state, True) - state, mems, state_mems, past_length, past_left_context = results - - # relative position embedding - if self.use_rpe: - rpe = self._get_relative_position( - input=input, - max_relative_position=self.max_relative_position, - left_context_length=past_left_context, - past_length=past_length, - is_decoding=True, - ) - else: - rpe = None - - # memory for first layer. - mems_list.append(state_mems[-self.max_memory_size :, :, :]) - output = seg - i = 0 - for layer in self.layers: - # In order to make cross stream batching work, mem, left context key - # and left context value in the state should always be the same shape. - # We use the past length to track the processed segment number. In this - # way, we take out the essential memory, left context key and left - # context val from the state. After finish the forward for current segment - # we add the new memory, left context key and left context value into the - # staate and trim out the oldest part to keep the shape consistent. - true_mems, lc_key, lc_val = self.state_update_before( - layer=i, - state=state, - past_length=past_length, - past_left_context=past_left_context, - ) - - output, mems, right_context_blocks, next_key, next_val = layer.forward_jit( - input=output, - lengths=lengths, - mems=true_mems, - right_context_blocks=right_context_blocks, - left_context_key=lc_key, - left_context_val=lc_val, - rpe=rpe, - ) - # mems is used for next layer - mems_list, lc_key_list, lc_val_list, _ = self.state_update_after( - layer=i, - state=state, - mems_list=mems_list, - mems=mems, - next_key=next_key, - next_val=next_val, - lc_key_list=lc_key_list, - lc_val_list=lc_val_list, - ) - i += 1 - - # update state - state = self.state_update_after_loop( - state=state, - mems_list=mems_list, - lc_key_list=lc_key_list, - lc_val_list=lc_val_list, - update_length=1, - ) - - return output, lengths, state - - def quantize_(self, params=None): - if params and "per_channel" in params and params["per_channel"]: - qconfig = per_channel_dynamic_qconfig - else: - qconfig = default_dynamic_qconfig - quantization.quantize_dynamic( - self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True - ) - return self - - -# ------------------------------------------------------------------------------ -# Emformer encoder for seq2seq model -# This is a wrapper over the original emformer -# ------------------------------------------------------------------------------ -def emformer_encoder(klass): - class SpeechEncoder(klass): - def __init__(self, args): - super().__init__(args) - stride = SpeechEncoder.conv_layer_stride(args) - trf_left_context = args.segment_left_context // stride - trf_right_context = args.segment_right_context // stride - context_config = [trf_left_context, trf_right_context] - self.transformer_layers = nn.ModuleList( - [ - NoSegAugmentedMemoryTransformerEncoderLayer( - input_dim=args.encoder_embed_dim, - num_heads=args.encoder_attention_heads, - ffn_dim=args.encoder_ffn_embed_dim, - num_layers=args.encoder_layers, - dropout_in_attn=args.dropout, - dropout_on_attn=args.dropout, - dropout_on_fc1=args.dropout, - dropout_on_fc2=args.dropout, - activation_fn=args.activation_fn, - context_config=context_config, - segment_size=args.segment_length, - max_memory_size=args.max_memory_size, - scaled_init=True, # TODO: use constant for now. - tanh_on_mem=args.amtrf_tanh_on_mem, - ) - ] - ) - - def forward(self, src_tokens, src_lengths): - encoder_out = super().forward(src_tokens, src_lengths) - output = encoder_out["encoder_out"][0] - encoder_padding_masks = encoder_out["encoder_padding_mask"][0] - - # This is because that in the original implementation - # the output didn't consider the last segment as right context. - encoder_padding_masks = encoder_padding_masks[:, : output.size(0)] - - return { - "encoder_out": [output], - "encoder_padding_mask": [encoder_padding_masks], - "encoder_embedding": [], - "encoder_states": [], - "src_tokens": [], - "src_lengths": [], - } - - @staticmethod - def conv_layer_stride(args): - # TODO: make it configurable from the args - return 4 - - SpeechEncoder.__name__ = klass.__name__ - return SpeechEncoder diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/multi_modality_model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/multi_modality_model.py deleted file mode 100644 index 046421620ae9e78a6138632c26e8a25ce3b64478..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/multi_modality_model.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.models import FairseqDecoder, FairseqEncoder - - -# a container for different encoders with training samples from different modality -# each time, only one encoder is selected -class MultiModalityEncoder(FairseqEncoder): - def __init__(self, dictionary): - super().__init__(dictionary) - - def select_encoder(self, mode, **kwargs): - raise NotImplementedError("Model must implement the select_encoder method") - return None, kwargs - - # def post_encoder(self, encoder_out, src_tokens, src_lengths, mode, **kwargs): - # # Default do nothing - # return encoder_out - - # get sample data from JointSpeechTextDataset - def forward(self, src_tokens, src_lengths=None, mode="", **kwargs): - encoder, kwargs = self.select_encoder(mode, **kwargs) - # return self.post_encoder(encoder(src_tokens, src_lengths, **kwargs), src_tokens, src_lengths, mode, **kwargs) - return encoder(src_tokens, src_lengths, **kwargs) - - -# a container for different decoders with training samples from different modality -# each time, only one decoder is selected -class MultiInputDecoder(FairseqDecoder): - def __init__(self, dictionary): - super().__init__(dictionary) - - def select_decoder(self, mode, **kwargs): - raise NotImplementedError("Model must implement the select_decoder method") - return None, kwargs - - def forward( - self, prev_output_tokens, encoder_out, incremental_state=None, mode="", **kwargs - ): - decoder, kwargs = self.select_decoder(mode, **kwargs) - return decoder( - prev_output_tokens, - encoder_out, - incremental_state=incremental_state, - **kwargs - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_conformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_conformer.py deleted file mode 100644 index 24878c2c4f28d43ecfb0059dc7f2f31b08a1c0ab..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_conformer.py +++ /dev/null @@ -1,162 +0,0 @@ -import logging -import math - -import torch -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import FairseqEncoder, register_model, register_model_architecture -from fairseq.models.speech_to_text.s2t_transformer import ( - Conv1dSubsampler, - S2TTransformerEncoder, - S2TTransformerModel, -) -from fairseq.models.speech_to_text.s2t_transformer import ( - base_architecture as transformer_base_architecture, -) -from fairseq.modules import PositionalEmbedding, RelPositionalEncoding -from fairseq.modules.conformer_layer import ConformerEncoderLayer - -logger = logging.getLogger(__name__) - - -class S2TConformerEncoder(FairseqEncoder): - """Conformer Encoder for speech translation based on https://arxiv.org/abs/2005.08100""" - - def __init__(self, args): - super().__init__(None) - self.embed_scale = math.sqrt(args.encoder_embed_dim) - if args.no_scale_embedding: - self.embed_scale = 1.0 - self.padding_idx = 1 - self.subsample = Conv1dSubsampler( - args.input_feat_per_channel * args.input_channels, - args.conv_channels, - args.encoder_embed_dim, - [int(k) for k in args.conv_kernel_sizes.split(",")], - ) - self.pos_enc_type = args.pos_enc_type - if self.pos_enc_type == "rel_pos": - self.embed_positions = RelPositionalEncoding( - args.max_source_positions, args.encoder_embed_dim - ) - elif self.pos_enc_type == "rope": - self.embed_positions = None - else: # Use absolute positional embedding - self.pos_enc_type = "abs" - self.embed_positions = PositionalEmbedding( - args.max_source_positions, args.encoder_embed_dim, self.padding_idx - ) - - self.linear = torch.nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim) - self.dropout = torch.nn.Dropout(args.dropout) - self.conformer_layers = torch.nn.ModuleList( - [ - ConformerEncoderLayer( - embed_dim=args.encoder_embed_dim, - ffn_embed_dim=args.encoder_ffn_embed_dim, - attention_heads=args.encoder_attention_heads, - dropout=args.dropout, - depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, - attn_type=args.attn_type, - pos_enc_type=self.pos_enc_type, - use_fp16=args.fp16, - ) - for _ in range(args.encoder_layers) - ] - ) - - def forward(self, src_tokens, src_lengths, return_all_hiddens=False): - """ - Args: - src_tokens: Input source tokens Tensor of shape B X T X C - src_lengths: Lengths Tensor corresponding to input source tokens - return_all_hiddens: If true will append the self attention states to the encoder states - Returns: - encoder_out: Tensor of shape B X T X C - encoder_padding_mask: Optional Tensor with mask - encoder_embedding: Optional Tensor. Always empty here - encoder_states: List of Optional Tensors wih self attention states - src_tokens: Optional Tensor. Always empty here - src_lengths: Optional Tensor. Always empty here - """ - x, input_lengths = self.subsample(src_tokens, src_lengths) # returns T X B X C - encoder_padding_mask = lengths_to_padding_mask(input_lengths) - x = self.embed_scale * x - if self.pos_enc_type == "rel_pos": - positions = self.embed_positions(x) - - elif self.pos_enc_type == "rope": - positions = None - - else: - positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) - x += positions - positions = None - - x = self.linear(x) - x = self.dropout(x) - encoder_states = [] - - # x is T X B X C - for layer in self.conformer_layers: - x, _ = layer(x, encoder_padding_mask, positions) - if return_all_hiddens: - encoder_states.append(x) - - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [encoder_padding_mask] - if encoder_padding_mask.any() - else [], # B x T - "encoder_embedding": [], # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - } - - def reorder_encoder_out(self, encoder_out, new_order): - """Required method for a FairseqEncoder. Calls the method from the parent class""" - return S2TTransformerEncoder.reorder_encoder_out(self, encoder_out, new_order) - - -@register_model("s2t_conformer") -class S2TConformerModel(S2TTransformerModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - S2TTransformerModel.add_args(parser) - parser.add_argument("--input-feat-per-channel", default=80) - parser.add_argument("--depthwise-conv-kernel-size", default=31) - parser.add_argument("--input-channels", default=1) - parser.add_argument( - "--attn-type", - default=None, - help="If not specified uses fairseq MHA. Other valid option is espnet", - ) - parser.add_argument( - "--pos-enc-type", - default="abs", - help="Must be specified in addition to attn-type=espnet for rel_pos and rope", - ) - - @classmethod - def build_encoder(cls, args): - encoder = S2TConformerEncoder(args) - return encoder - - -@register_model_architecture("s2t_conformer", "s2t_conformer") -def base_architecture(args): - args.attn_type = getattr(args, "attn_type", None) - args.pos_enc_type = getattr(args, "pos_enc_type", "abs") - args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80) - args.input_channels = getattr(args, "input_channels", 1) - args.max_source_positions = getattr(args, "max_source_positions", 6000) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.dropout = getattr(args, "dropout", 0.1) - args.encoder_layers = getattr(args, "encoder_layers", 16) - args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31) - transformer_base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_transformer.py deleted file mode 100644 index a40c3dfa6455bc9200281e70cfec62b4b27500e5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_transformer.py +++ /dev/null @@ -1,568 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import math -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -from fairseq import checkpoint_utils, utils -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.speech_to_text.hub_interface import S2THubInterface -from fairseq.models.transformer import Embedding, TransformerDecoder -from fairseq.modules import ( - FairseqDropout, - LayerNorm, - PositionalEmbedding, - TransformerEncoderLayer, -) -from torch import Tensor - -logger = logging.getLogger(__name__) - - -class Conv1dSubsampler(nn.Module): - """Convolutional subsampler: a stack of 1D convolution (along temporal - dimension) followed by non-linear activation via gated linear units - (https://arxiv.org/abs/1911.08460) - - Args: - in_channels (int): the number of input channels - mid_channels (int): the number of intermediate channels - out_channels (int): the number of output channels - kernel_sizes (List[int]): the kernel size for each convolutional layer - """ - - def __init__( - self, - in_channels: int, - mid_channels: int, - out_channels: int, - kernel_sizes: List[int] = (3, 3), - ): - super(Conv1dSubsampler, self).__init__() - self.n_layers = len(kernel_sizes) - self.conv_layers = nn.ModuleList( - nn.Conv1d( - in_channels if i == 0 else mid_channels // 2, - mid_channels if i < self.n_layers - 1 else out_channels * 2, - k, - stride=2, - padding=k // 2, - ) - for i, k in enumerate(kernel_sizes) - ) - - def get_out_seq_lens_tensor(self, in_seq_lens_tensor): - out = in_seq_lens_tensor.clone() - for _ in range(self.n_layers): - out = ((out.float() - 1) / 2 + 1).floor().long() - return out - - def forward(self, src_tokens, src_lengths): - bsz, in_seq_len, _ = src_tokens.size() # B x T x (C x D) - x = src_tokens.transpose(1, 2).contiguous() # -> B x (C x D) x T - for conv in self.conv_layers: - x = conv(x) - x = nn.functional.glu(x, dim=1) - _, _, out_seq_len = x.size() - x = x.transpose(1, 2).transpose(0, 1).contiguous() # -> T x B x (C x D) - return x, self.get_out_seq_lens_tensor(src_lengths) - - -@register_model("s2t_transformer") -class S2TTransformerModel(FairseqEncoderDecoderModel): - """Adapted Transformer model (https://arxiv.org/abs/1706.03762) for - speech-to-text tasks. The Transformer encoder/decoder remains the same. - A trainable input subsampler is prepended to the Transformer encoder to - project inputs into the encoder dimension as well as downsample input - sequence for computational efficiency.""" - - @classmethod - def hub_models(cls): - base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t" - model_ids = [ - "s2t_transformer_s-en-asr-librispeech", - "s2t_transformer_m-en-asr-librispeech", - "s2t_transformer_l-en-asr-librispeech", - ] - return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - config_yaml="config.yaml", - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - config_yaml=config_yaml, - **kwargs, - ) - return S2THubInterface(x["args"], x["task"], x["models"][0]) - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # input - parser.add_argument( - "--conv-kernel-sizes", - type=str, - metavar="N", - help="kernel sizes of Conv1d subsampling layers", - ) - parser.add_argument( - "--conv-channels", - type=int, - metavar="N", - help="# of channels in Conv1d subsampling layers", - ) - # Transformer - parser.add_argument( - "--activation-fn", - type=str, - default="relu", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--activation-dropout", - "--relu-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN.", - ) - parser.add_argument( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-ffn-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension for FFN", - ) - parser.add_argument( - "--encoder-layers", type=int, metavar="N", help="num encoder layers" - ) - parser.add_argument( - "--encoder-attention-heads", - type=int, - metavar="N", - help="num encoder attention heads", - ) - parser.add_argument( - "--encoder-normalize-before", - action="store_true", - help="apply layernorm before each encoder block", - ) - parser.add_argument( - "--decoder-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension", - ) - parser.add_argument( - "--decoder-ffn-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension for FFN", - ) - parser.add_argument( - "--decoder-layers", type=int, metavar="N", help="num decoder layers" - ) - parser.add_argument( - "--decoder-attention-heads", - type=int, - metavar="N", - help="num decoder attention heads", - ) - parser.add_argument( - "--decoder-normalize-before", - action="store_true", - help="apply layernorm before each decoder block", - ) - parser.add_argument( - "--share-decoder-input-output-embed", - action="store_true", - help="share decoder input and output embeddings", - ) - parser.add_argument( - "--layernorm-embedding", - action="store_true", - help="add layernorm to embedding", - ) - parser.add_argument( - "--no-scale-embedding", - action="store_true", - help="if True, dont scale embeddings", - ) - parser.add_argument( - "--load-pretrained-encoder-from", - type=str, - metavar="STR", - help="model to take encoder weights from (for initialization)", - ) - parser.add_argument( - "--encoder-freezing-updates", - type=int, - metavar="N", - help="freeze encoder for first N updates", - ) - - @classmethod - def build_encoder(cls, args): - encoder = S2TTransformerEncoder(args) - pretraining_path = getattr(args, "load_pretrained_encoder_from", None) - if pretraining_path is not None: - if not Path(pretraining_path).exists(): - logger.warning( - f"skipped pretraining because {pretraining_path} does not exist" - ) - else: - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=pretraining_path - ) - logger.info(f"loaded pretrained encoder from: {pretraining_path}") - return encoder - - @classmethod - def build_decoder(cls, args, task, embed_tokens): - return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - - def build_embedding(dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - return Embedding(num_embeddings, embed_dim, padding_idx) - - decoder_embed_tokens = build_embedding( - task.target_dictionary, args.decoder_embed_dim - ) - args.tgt_dict_size = len(task.target_dictionary) - encoder = cls.build_encoder(args) - decoder = cls.build_decoder(args, task, decoder_embed_tokens) - return cls(encoder, decoder) - - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - # net_output['encoder_out'] is a (B, T, D) tensor - lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample) - lprobs.batch_first = True - return lprobs - - def get_ctc_target(self, sample: Optional[Dict[str, Tensor]]): - return sample["target"], sample["target_lengths"] - - def get_ctc_output( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - sample: Optional[Dict[str, Tensor]], - ): - encoder_out = net_output[1]["encoder_out"]["encoder_out"][0] - logits = self.encoder.ctc_proj(encoder_out) # T x B x C - out = utils.log_softmax(logits.float(), dim=-1) - padding_mask = net_output[1]["encoder_out"]["encoder_padding_mask"] - lens = out.new_full((out.shape[1],), out.shape[0]).long() - if len(padding_mask) > 0: - lens -= padding_mask[0].sum(dim=-1) - return out, lens - - def forward(self, src_tokens, src_lengths, prev_output_tokens): - """ - The forward method inherited from the base class has a **kwargs - argument in its input, which is not supported in torchscript. This - method overwrites the forward method definition without **kwargs. - """ - encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths) - decoder_out = self.decoder( - prev_output_tokens=prev_output_tokens, encoder_out=encoder_out - ) - return decoder_out - - -class S2TTransformerEncoder(FairseqEncoder): - """Speech-to-text Transformer encoder that consists of input subsampler and - Transformer encoder.""" - - def __init__(self, args): - super().__init__(None) - - self.encoder_freezing_updates = args.encoder_freezing_updates - self.num_updates = 0 - - self.dropout_module = FairseqDropout( - p=args.dropout, module_name=self.__class__.__name__ - ) - self.embed_scale = math.sqrt(args.encoder_embed_dim) - if args.no_scale_embedding: - self.embed_scale = 1.0 - self.padding_idx = 1 - - self.subsample = Conv1dSubsampler( - args.input_feat_per_channel * args.input_channels, - args.conv_channels, - args.encoder_embed_dim, - [int(k) for k in args.conv_kernel_sizes.split(",")], - ) - - self.embed_positions = PositionalEmbedding( - args.max_source_positions, args.encoder_embed_dim, self.padding_idx - ) - - self.transformer_layers = nn.ModuleList( - [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)] - ) - if args.encoder_normalize_before: - self.layer_norm = LayerNorm(args.encoder_embed_dim) - else: - self.layer_norm = None - - self.ctc_proj = None - if getattr(args, "ctc_weight", 0.0) > 0.0: - self.ctc_proj = nn.Linear(args.encoder_embed_dim, args.tgt_dict_size) - - def _forward(self, src_tokens, src_lengths, return_all_hiddens=False): - x, input_lengths = self.subsample(src_tokens, src_lengths) - x = self.embed_scale * x - - encoder_padding_mask = lengths_to_padding_mask(input_lengths) - positions = self.embed_positions(encoder_padding_mask).transpose(0, 1) - x += positions - x = self.dropout_module(x) - - encoder_states = [] - - for layer in self.transformer_layers: - x = layer(x, encoder_padding_mask) - if return_all_hiddens: - encoder_states.append(x) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [encoder_padding_mask] - if encoder_padding_mask.any() - else [], # B x T - "encoder_embedding": [], # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - } - - def forward(self, src_tokens, src_lengths, return_all_hiddens=False): - if self.num_updates < self.encoder_freezing_updates: - with torch.no_grad(): - x = self._forward( - src_tokens, src_lengths, return_all_hiddens=return_all_hiddens - ) - else: - x = self._forward( - src_tokens, src_lengths, return_all_hiddens=return_all_hiddens - ) - return x - - def reorder_encoder_out(self, encoder_out, new_order): - new_encoder_out = ( - [] - if len(encoder_out["encoder_out"]) == 0 - else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] - ) - - new_encoder_padding_mask = ( - [] - if len(encoder_out["encoder_padding_mask"]) == 0 - else [ - x.index_select(0, new_order) - for x in encoder_out["encoder_padding_mask"] - ] - ) - - new_encoder_embedding = ( - [] - if len(encoder_out["encoder_embedding"]) == 0 - else [ - x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] - ] - ) - - encoder_states = encoder_out["encoder_states"] - if len(encoder_states) > 0: - for idx, state in enumerate(encoder_states): - encoder_states[idx] = state.index_select(1, new_order) - - return { - "encoder_out": new_encoder_out, # T x B x C - "encoder_padding_mask": new_encoder_padding_mask, # B x T - "encoder_embedding": new_encoder_embedding, # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], # B x T - "src_lengths": [], # B x 1 - } - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - self.num_updates = num_updates - - -class TransformerDecoderScriptable(TransformerDecoder): - def extract_features( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - # call scriptable method from parent class - x, _ = self.extract_features_scriptable( - prev_output_tokens, - encoder_out, - incremental_state, - full_context_alignment, - alignment_layer, - alignment_heads, - ) - extra = {"encoder_out": encoder_out} if incremental_state is None else None - return x, extra - - -@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer") -def base_architecture(args): - args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0) - # Convolutional subsampler - args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5") - args.conv_channels = getattr(args, "conv_channels", 1024) - # Transformer - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 12) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", args.dropout) - args.activation_dropout = getattr(args, "activation_dropout", args.dropout) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_s") -def s2t_transformer_s(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - args.dropout = getattr(args, "dropout", 0.1) - base_architecture(args) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_xs") -def s2t_transformer_xs(args): - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.decoder_layers = getattr(args, "decoder_layers", 3) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4) - args.dropout = getattr(args, "dropout", 0.3) - s2t_transformer_s(args) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_sp") -def s2t_transformer_sp(args): - args.encoder_layers = getattr(args, "encoder_layers", 16) - s2t_transformer_s(args) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_m") -def s2t_transformer_m(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.dropout = getattr(args, "dropout", 0.15) - base_architecture(args) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_mp") -def s2t_transformer_mp(args): - args.encoder_layers = getattr(args, "encoder_layers", 16) - s2t_transformer_m(args) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_l") -def s2t_transformer_l(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.dropout = getattr(args, "dropout", 0.2) - base_architecture(args) - - -@register_model_architecture("s2t_transformer", "s2t_transformer_lp") -def s2t_transformer_lp(args): - args.encoder_layers = getattr(args, "encoder_layers", 16) - s2t_transformer_l(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_wav_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_wav_transformer.py deleted file mode 100644 index 6105b23f1bbc4412214517c6dc31a596c8188084..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/s2t_wav_transformer.py +++ /dev/null @@ -1,484 +0,0 @@ -#!/usr/bin/env python3 - -import math - -import torch -import torch.nn as nn -from fairseq.data.data_utils import compute_mask_indices -from fairseq.models import FairseqEncoder -from fairseq.models.wav2vec import ConvFeatureExtractionModel -from fairseq.modules import GradMultiply, LayerNorm, SamePad, TransformerEncoderLayer - - -# Transformer encoder with wave input, it is adopted from wav2vec 2.0 Encoder. -# use wav input -# use trained position embedding so it is easier to match with text input -class SpeechWavTransformerEncoder(FairseqEncoder): - - # extra parameters for speech encoder besides those defined in transformermodel - @staticmethod - def add_args(parser): - parser.add_argument( - "--dropout-input", - type=float, - metavar="D", - help="dropout to apply to the input (after feat extr)", - ) - parser.add_argument( - "--dropout-features", - type=float, - metavar="D", - help="dropout to apply to the unmasked features (after feat extr)", - ) - parser.add_argument( - "--speech-extractor-mode", - type=str, - default="layer_norm", - choices=["default", "layer_norm"], - help="feature extractor norm", - ) - - parser.add_argument( - "--speech-conv-bias", - action="store_true", - help="include bias in speech conv encoder", - ) - - parser.add_argument( - "--conv-feature-layers", - default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", - help="string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]", - ) - - parser.add_argument( - "--speech-mask-length", - type=int, - help="repeat the mask indices multiple times", - ) - - parser.add_argument( - "--speech-mask-prob", - type=float, - help="probability of replacing a token with mask", - ) - - parser.add_argument( - "--speech-mask-selection", - type=str, - choices=["static", "uniform", "normal", "poisson"], - help="how to choose masks", - ) - - parser.add_argument( - "--speech-mask-other", - type=float, - help="stdev of the mask length in case of 'normal' selection strategy", - ) - - parser.add_argument( - "--speech-no-mask-overlap", - action="store_true", - help="whether to allow masks to overlap", - ) - - parser.add_argument( - "--speech-mask-min-space", - type=int, - help="min space between spans (if no overlap is enabled)", - ) - - parser.add_argument( - "--speech-mask-channel-length", - type=int, - help="repeat the mask indices multiple times", - ) - - parser.add_argument( - "--speech-mask-channel-prob", - type=float, - help="probability of replacing a token with mask", - ) - - parser.add_argument( - "--speech-mask-channel-selection", - type=str, - choices=["static", "uniform", "normal", "poisson"], - help="how to choose masks", - ) - - parser.add_argument( - "--speech-mask-channel-other", - type=float, - help="stdev of the mask length in case of 'normal' selection strategy", - ) - - parser.add_argument( - "--speech-no-mask-channel-overlap", - action="store_true", - help="whether to allow masks to overlap", - ) - - parser.add_argument( - "--no-scale-feature", - action="store_true", - help="no scale for the calculated features", - ) - - parser.add_argument( - "--speech-mask-channel-min-space", - type=int, - help="min space between spans (if no overlap is enabled)", - ) - - parser.add_argument( - "--feature-grad-mult", - type=float, - help="reset feature grad mult in wav2vec 2.0 to this", - ) - - # positional embeddings - parser.add_argument( - "--conv-pos", - type=int, - default=128, - help="number of filters for convolutional positional embeddings", - ) - - parser.add_argument( - "--conv-pos-groups", - type=int, - default=16, - help="number of groups for convolutional positional embedding", - ) - # model configures - parser.add_argument( - "--speech-encoder-layers", - type=int, - help="number of speech encoder layers", - ) - parser.add_argument( - "--text-encoder-layers", - type=int, - help="number of text encoder layers", - ) - - def __init__(self, args, alway_mask=False): - super().__init__(args) - self.args = args - self.dropout = args.dropout - self.embedding_dim = args.encoder_embed_dim - self.feat_scale = math.sqrt(args.encoder_embed_dim) - if args.no_scale_feature: - self.feat_scale = 1.0 - - subsample = ConvFeatureExtractionModel( - conv_layers=eval(args.conv_feature_layers), - dropout=0.0, - mode=args.speech_extractor_mode, # default, layer_norm - conv_bias=args.speech_conv_bias, - ) - feature_enc_layers = eval(args.conv_feature_layers) - self.subsample = subsample - self.feat_proj = ( - nn.Linear(feature_enc_layers[-1][0], self.embedding_dim) - if feature_enc_layers[-1][0] != self.embedding_dim - else None - ) - - self.feat_layer_norm = LayerNorm(feature_enc_layers[-1][0]) - - self.embed_positions = nn.Conv1d( - self.embedding_dim, - self.embedding_dim, - kernel_size=args.conv_pos, - padding=args.conv_pos // 2, - groups=args.conv_pos_groups, - ) - std = math.sqrt(4 / (args.conv_pos * self.embedding_dim)) - nn.init.normal_(self.embed_positions.weight, mean=0, std=std) - nn.init.constant_(self.embed_positions.bias, 0) - - self.embed_positions = nn.utils.weight_norm( - self.embed_positions, name="weight", dim=2 - ) - self.embed_positions = nn.Sequential( - self.embed_positions, SamePad(args.conv_pos), nn.GELU() - ) - - self.mask_prob = args.speech_mask_prob - self.mask_selection = args.speech_mask_selection - self.mask_other = args.speech_mask_other - self.mask_length = args.speech_mask_length - self.no_mask_overlap = args.speech_no_mask_overlap - self.mask_min_space = args.speech_mask_min_space - - self.mask_channel_prob = args.speech_mask_channel_prob - self.mask_channel_selection = args.speech_mask_channel_selection - self.mask_channel_other = args.speech_mask_channel_other - self.mask_channel_length = args.speech_mask_channel_length - self.no_mask_channel_overlap = args.speech_no_mask_channel_overlap - self.mask_channel_min_space = args.speech_mask_channel_min_space - - self.dropout_input = nn.Dropout(args.dropout_input) - self.dropout_features = nn.Dropout(args.dropout_features) - - self.feature_grad_mult = args.feature_grad_mult - - self.mask_emb = nn.Parameter( - torch.FloatTensor(args.encoder_embed_dim).uniform_() - ) - - self.layers = nn.ModuleList( - [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)] - ) - self.layer_norm = LayerNorm(args.encoder_embed_dim) - self.normalize_before = args.encoder_normalize_before - self.alway_mask = alway_mask - - def apply_mask(self, x, padding_mask): - B, T, C = x.shape - if self.mask_prob > 0: - mask_indices = compute_mask_indices( - (B, T), - padding_mask, - self.mask_prob, - self.mask_length, - self.mask_selection, - self.mask_other, - min_masks=2, - no_overlap=self.no_mask_overlap, - min_space=self.mask_min_space, - ) - mask_indices = torch.from_numpy(mask_indices).to(x.device) - x[mask_indices] = self.mask_emb - else: - mask_indices = None - - if self.mask_channel_prob > 0: - mask_channel_indices = compute_mask_indices( - (B, C), - None, - self.mask_channel_prob, - self.mask_channel_length, - self.mask_channel_selection, - self.mask_channel_other, - no_overlap=self.no_mask_channel_overlap, - min_space=self.mask_channel_min_space, - ) - mask_channel_indices = ( - torch.from_numpy(mask_channel_indices) - .to(x.device) - .unsqueeze(1) - .expand(-1, T, -1) - ) - x[mask_channel_indices] = 0 - - return x, mask_indices - - def forward( - self, - src_tokens, - src_lengths, - return_all_hiddens=False, - padding_mask=None, - features_only=True, - ): - mask = self.training or self.alway_mask - if self.feature_grad_mult > 0 and self.training: - features = self.subsample(src_tokens) - if self.feature_grad_mult != 1.0: - features = GradMultiply.apply(features, self.feature_grad_mult) - else: - with torch.no_grad(): - features = self.subsample(src_tokens) - features = features.transpose(1, 2) - features = self.feat_layer_norm(features) - if self.feat_proj is not None: - features = self.feat_proj(features) - - if padding_mask is not None: - input_lengths = (1 - padding_mask.long()).sum(-1) - # apply conv formula to get real output_lengths - output_lengths = self._get_feat_extract_output_lengths(input_lengths) - - padding_mask = torch.zeros( - features.shape[:2], dtype=features.dtype, device=features.device - ) - - # these two operations makes sure that all values - # before the output lengths indices are attended to - padding_mask[ - ( - torch.arange(padding_mask.shape[0], device=padding_mask.device), - output_lengths - 1, - ) - ] = 1 - padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() - - features = self.feat_scale * features if self.feat_scale != 1.0 else features - unmasked_features = features.clone() - - features = self.dropout_input(features) - unmasked_features = self.dropout_features(unmasked_features) - if mask: - x, mask_indices = self.apply_mask(features, padding_mask) - else: - x = features - mask_indices = None - - def cal_transformer_layers(x, encoder_padding_mask, return_all_hiddens=False): - # x: B x T x C - positions = self.embed_positions(x.transpose(1, 2)).transpose(1, 2) - x = x + positions - if not self.normalize_before: - x = self.layer_norm(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - encoder_states = [] - for layer in self.layers: - x = layer(x, encoder_padding_mask) - if return_all_hiddens: - encoder_states.append(x) - if self.normalize_before: - x = self.layer_norm(x) - return x, encoder_states - - x, encoder_states = cal_transformer_layers(x, padding_mask, return_all_hiddens) - if features_only: - return { - "encoder_out": [x], # [T x B x C] - "encoder_padding_mask": [padding_mask] - if padding_mask is not None - else [], # B x T - "encoder_embedding": [], # - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - "mask_indices": [mask_indices], - } - - x_unmasked = x - if self.mask_prob > 0 or self.mask_channel_prob > 0: - x_unmasked, _ = cal_transformer_layers(unmasked_features, padding_mask) - return { - "encoder_out": [x], # [T x B x C] - "encoder_unmasked_out": [x_unmasked], # [T x B x C] - "encoder_padding_mask": [padding_mask] - if padding_mask is not None - else [], # B x T - "encoder_embedding": [], # - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - "mask_indices": [mask_indices] if mask_indices is not None else [], # B X T - } - - def reorder_encoder_out(self, encoder_out, new_order): - new_encoder_out = ( - [] - if len(encoder_out["encoder_out"]) == 0 - else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] - ) - - new_encoder_padding_mask = ( - [] - if len(encoder_out["encoder_padding_mask"]) == 0 - else [ - x.index_select(0, new_order) - for x in encoder_out["encoder_padding_mask"] - ] - ) - - new_encoder_embedding = ( - [] - if len(encoder_out["encoder_embedding"]) == 0 - else [ - x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] - ] - ) - - encoder_states = encoder_out["encoder_states"] - if len(encoder_states) > 0: - for idx, state in enumerate(encoder_states): - encoder_states[idx] = state.index_select(1, new_order) - - return { - "encoder_out": new_encoder_out, # T x B x C - "encoder_padding_mask": new_encoder_padding_mask, # B x T - "encoder_embedding": new_encoder_embedding, # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], # B x T - "src_lengths": [], # B x 1 - } - - -class StackedSpeechWavTransformerEncoder(FairseqEncoder): - def __init__(self, speech_enc, text_enc_layers, text_layer_norm): - super().__init__(None) - self.speech_encoder = speech_enc - self.text_encoder_layers = text_enc_layers - self.final_layer_norm = text_layer_norm - - def forward( - self, - src_tokens, - src_lengths=None, - return_all_hiddens=False, - padding_mask=None, - features_only=True, - ): - - out = self.speech_encoder.forward( - src_tokens, - src_lengths, - return_all_hiddens, - padding_mask=padding_mask, - features_only=features_only, - ) - x = out["encoder_out"][0] - encoder_padding_mask = None - if len(out["encoder_padding_mask"]) > 0: - encoder_padding_mask = out["encoder_padding_mask"][0] - - def cal_text_layers(x, padding_mask, return_all_hiddens=False): - encoder_states = [] - for layer in self.text_encoder_layers: - x = layer(x, padding_mask) - if return_all_hiddens: - encoder_states.append(x) - if self.final_layer_norm is not None: - x = self.final_layer_norm(x) - return x, encoder_states - - x, encoder_states = cal_text_layers(x, encoder_padding_mask, return_all_hiddens) - if features_only: - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [encoder_padding_mask] - if encoder_padding_mask is not None - else [], # B x T - "encoder_embedding": [], # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - } - - x_u = out["encoder_unmasked_out"][0] - x_u, _ = cal_text_layers(x_u, encoder_padding_mask) - - return { - "encoder_out": [x], # [T x B x C] - "encoder_unmasked_out": [x_u], # [T x B x C] - "encoder_padding_mask": [encoder_padding_mask] - if encoder_padding_mask is not None - else [], # B x T - "encoder_embedding": [], # - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - "mask_indices": out["mask_indices"], # B X T - } - - def reorder_encoder_out(self, encoder_out, new_order): - return self.speech_encoder.reorder_encoder_out(encoder_out, new_order) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/utils.py deleted file mode 100644 index 33117446a5e2f3b71c64f0a7b6b8122a1ac7c182..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/utils.py +++ /dev/null @@ -1,562 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - - -import logging -from collections.abc import Iterable -from itertools import repeat -from typing import List, Optional, Tuple - -import torch -from torch import Tensor - -# ------------------------------------------------------------------------------ -# assert_equal() -# ------------------------------------------------------------------------------ - - -def assert_equal(value1, value2, name1=None, name2=None): - """Asserts two values are equal otherwise raise an error.""" - - str_name1 = "" if name1 is None else "{} ".format(name1) - str_name2 = "" if name2 is None else "{} ".format(name2) - if value1 != value2: - str_value1 = "{}" if name1 is None else "({})" - str_value1 = str_value1.format(value1) - str_value2 = "{}" if name2 is None else "({})" - str_value2 = str_value2.format(value2) - raise ValueError( - "Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2) - ) - - -def fill_config(config, key, value): - if value is not None: - if key not in config or config[key] is None: - config[key] = value - assert_equal(value, config[key], "value", f'config["{key}"]') - - -# ------------------------------------------------------------------------------ -# check_and_return_expected() -# ------------------------------------------------------------------------------ - - -def check_and_return_expected(value, undefined_value, expected_value, name=None): - """ - Return the expected value while checking if the given value is undefined or - equal to the expected value. - """ - if (undefined_value is None and value is None) or (undefined_value == value): - return expected_value - if value != expected_value: - str_name = "" if name is None else "{} ".format(name) - str_value = "{}" if name is None else "({})" - str_value = str_value.format(value) - raise ValueError( - "Expected {}{} == {}".format(str_name, str_value, expected_value) - ) - return expected_value - - -# ------------------------------------------------------------------------------ -# get_time_axis() -# ------------------------------------------------------------------------------ - - -def get_time_axis(layout): - """ - Extract the time axis from the layout, for example for breaking sequence into - segments. - """ - if layout in ["TB", "TBD"]: - return 0 - if layout in ["BT", "BTD"]: - return 1 - if layout in ["BCTD"]: - return 2 - raise ValueError("Unsupported layout = {}".format(layout)) - - -# ------------------------------------------------------------------------------ -# get_batch_axis() -# ------------------------------------------------------------------------------ - - -def get_batch_axis(layout): - """ - Extract the batch axis from the layout - """ - if layout in ["TB", "TBD"]: - return 1 - if layout in ["BT", "BTD", "BCTD"]: - return 0 - raise ValueError("Unsupported layout = {}".format(layout)) - - -# ------------------------------------------------------------------------------ -# monotonically_increasing_and_bounded() -# ------------------------------------------------------------------------------ - - -def monotonically_increasing_and_bounded(iterable, min=None, max=None): - """ - Check if the elements in the given iterable are monotonically increasing and - bounded by upper/lower bounds. - """ - if not isinstance(iterable, Iterable): - raise TypeError( - "Expected iterable to be of type Iterable, got ({})".format( - iterable.__class__.__name__ - ) - ) - for i in range(len(iterable)): - if min is not None and iterable[i] < min: - return False - if max is not None and iterable[i] > max: - return False - if i > 0 and iterable[i] <= iterable[i - 1]: - return False - return True - - -# ------------------------------------------------------------------------------ -# to_pair() -# ------------------------------------------------------------------------------ - - -def to_pair(value, name): - """Make a pair (of type tuple) of given value.""" - if isinstance(value, Iterable): - if len(value) != 2: - raise ValueError( - "Expected `{}` to have exactly 2 elements, got: ({})".format( - name, value - ) - ) - return value - return tuple(repeat(value, 2)) - - -# ------------------------------------------------------------------------------ -# infer_conv_output_attrs() -# ------------------------------------------------------------------------------ - - -# TODO(cfyeh): figure out if we can get `output_dim` without calling the module. -def infer_conv_output_attrs( - module, input_channels, input_dim, batch_size=1, max_length=8 -): - """Get output attributes of a module with input.""" - input = torch.randn(batch_size, input_channels, max_length, input_dim) - output = module(input) - output_channels = output.shape[1] - output_dim = output.shape[-1] - return output_channels, output_dim - - -# ------------------------------------------------------------------------------ -# NoOp -# ------------------------------------------------------------------------------ - - -class NoOp(torch.nn.Module): - """ - NoOp simply passes the input as the output. - """ - - def __init__(self): - super().__init__() - - def forward(self, input: Tensor) -> Tensor: - return input - - -# ------------------------------------------------------------------------------ -# Permute: a torch.nn.Module applies permutation on the input tensor. -# ------------------------------------------------------------------------------ - - -class Permute(torch.nn.Module): - def __init__(self, dims): - super().__init__() - self.dims = dims - - def forward(self, input: Tensor) -> Tensor: - return input.permute(self.dims).contiguous() - - -# ------------------------------------------------------------------------------ -# lengths_to_padding_mask() -# ------------------------------------------------------------------------------ - - -def lengths_to_padding_mask(lengths: Tensor) -> Tensor: - """Convert lengths of shape (B, ) to padding mask.""" - batch_size = lengths.shape[0] - max_length = int(torch.max(lengths).item()) - padding_mask = torch.arange( # [0, ..., T-1] - max_length, device=lengths.device, dtype=lengths.dtype - ).expand(batch_size, max_length) >= lengths.unsqueeze(1) - - return padding_mask - - -# ------------------------------------------------------------------------------ -# lengths_to_attention_mask() -# ------------------------------------------------------------------------------ - - -def lengths_to_attention_mask( - lengths: Tensor, - left_context: Optional[int] = None, - right_context: Optional[int] = None, -) -> Optional[Tensor]: - """ - Generate attention mask based on (lengths, left_context, right_context). - left_context is None means unlimited left context. - right_context is None means unlimited right context. - """ - - if left_context is None and right_context is None: - return None - - max_length = int(torch.max(lengths).item()) - - # For example, with `max_length` == 5, - # indices = tensor([ - # [ 0, 1, 2, 3, 4, 5], - # [-1, 0, 1, 2, 3, 4], - # [-2, -1, 0, 1, 2, 3], - # [-3, -2, -1, 0, 1, 2], - # [-4, -3, -2, -1, 0, 1], - # [-5, -4, -3, -2, -1, 0], - # ]) - - # In some cases the second torch.arange is created on cpu which causes a - # failure. Adding the device option to guard against it. - indices = torch.arange( - max_length, device=lengths.device, dtype=lengths.dtype - ).expand(max_length, max_length) - torch.arange( - max_length, device=lengths.device - ).view( - max_length, -1 - ) - - # For example, with `max_length` == 5, - # bool_mask = tensor([ - # [True, True, True, True, True], - # [True, True, True, True, True], - # [True, True, True, True, True], - # [True, True, True, True, True], - # [True, True, True, True, True], - # ]) - bool_mask = ( - torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length) - ) - - # For example, with `max_length` == 5, left_context == 2 - # left_mask = tensor([ - # [ True, True, True, True, True], - # [ True, True, True, True, True], - # [ True, True, True, True, True], - # [False, True, True, True, True], - # [False, False, True, True, True], - # ]) - if left_context is not None: - left_mask = indices >= -left_context - bool_mask = bool_mask & left_mask - - # For example, with `max_length` == 5, right_context == 1 - # right_mask = tensor([ - # [True, True, False, False, False], - # [True, True, True, False, False], - # [True, True, True, True, False], - # [True, True, True, True, True], - # [True, True, True, True, True], - # ]) - if right_context is not None: - right_mask = indices <= right_context - bool_mask = bool_mask & right_mask - - bool_mask = (~bool_mask).to(device=lengths.device) - return bool_mask - - -# ------------------------------------------------------------------------------ -# infer_output_norm() -# ------------------------------------------------------------------------------ - - -def infer_output_norm(module, output_norm=None): - """ - Infer the output norm (string and module) needed on the module gvien desired - output normalization. - """ - if output_norm == module.output_norm(): - # output_norm already matches module.output_norm(). - return (None, NoOp()) - - if output_norm is None and module.output_norm() is not None: - logger = logging.getLogger("infer_output_norm()") - logger.warning( - "trying to set output_norm ({}) ".format(output_norm) - + "but got module.output_norm() ({}), ".format(module.output_norm()) - + "the combined output_norm() will be ({})".format(module.output_norm()) - ) - return (None, NoOp()) - - if output_norm == "log_softmax": - if module.output_norm() is not None: - raise ValueError( - "incompatible output_norm ({}) ".format(output_norm) - + "and module.output_norm() ({})".format(module.output_norm()) - ) - else: - return ("log_softmax", torch.nn.LogSoftmax(dim=-1)) - - if output_norm == "softmax": - if module.output_norm() is not None: - raise ValueError( - "incompatible output_norm ({}) ".format(output_norm) - + "and module.output_norm() ({})".format(module.output_norm()) - ) - else: - return ("softmax", torch.nn.Softmax(dim=-1)) - - raise ValueError( - "output_norm ({}) not in ".format(output_norm) - + "supported list = [None, softmax, log_softmax]" - ) - - -# ------------------------------------------------------------------------------ -# infer_channels_from_layout() -# ------------------------------------------------------------------------------ - - -def infer_channels_from_layout(layout, channels): - """Extract the number of channels from the layout.""" - if layout in ("TBD", "BTD"): - if channels is not None and channels != 1: - raise ValueError( - "Expected channels ({}) to be 1 for layout = {}".format( - channels, layout - ) - ) - if channels is None: - return 1 - return channels - - -# ------------------------------------------------------------------------------ -# pad_sequence() -# ------------------------------------------------------------------------------ - - -@torch.jit.export -def pad_sequence( - sequence: Tensor, - time_axis: int, - extra_left_context: int = 0, - extra_right_context: int = 0, -) -> Tensor: - """Pad extra left/right contexts to the sequence.""" - - if extra_left_context == 0 and extra_right_context == 0: - return sequence - - tensors_to_concat = [] - - if extra_left_context: - size = (extra_left_context,) - fill_value = 0 - indices = torch.full( - size=size, - fill_value=fill_value, - dtype=torch.long, - device=sequence.device, - ) - left_padding = torch.index_select(sequence, time_axis, indices) - tensors_to_concat.append(left_padding) - - tensors_to_concat.append(sequence) - - # NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for - # extra right contexts. - if extra_right_context: - size = list(sequence.shape) - size[time_axis] = extra_right_context - right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device) - tensors_to_concat.append(right_padding) - - padded_sequence = torch.cat(tensors_to_concat, dim=time_axis) - return padded_sequence - - -# ------------------------------------------------------------------------------ -# sequence_to_segments() -# ------------------------------------------------------------------------------ - - -@torch.jit.export -def sequence_to_segments( - sequence: Tensor, - time_axis: int, - lengths: Tensor, - segment_size: Optional[int] = None, - extra_left_context: int = 0, - extra_right_context: int = 0, -) -> List[Tuple[Tensor, Tensor]]: - """Breaks sequence into segments.""" - - sequence = pad_sequence( - sequence=sequence, - time_axis=time_axis, - extra_left_context=extra_left_context, - extra_right_context=extra_right_context, - ) - - lengths = lengths + extra_left_context + extra_right_context - - segments: List[Tuple[Tensor, Tensor]] = [] - - if segment_size is None: - segments.append((sequence, lengths)) - return segments - - offset = 0 - end = sequence.shape[time_axis] - step = segment_size - size = extra_left_context + segment_size + extra_right_context - - while offset + extra_left_context + extra_right_context < end: - clamped_size = min(size, end - offset) - segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size) - indices = torch.arange( - start=offset, - end=(offset + clamped_size), - step=1, - dtype=torch.long, - device=sequence.device, - ) - segment_tensor = torch.index_select(sequence, time_axis, indices) - segments.append((segment_tensor, segment_lengths)) - offset = offset + step - - return segments - - -# ------------------------------------------------------------------------------ -# segments_to_sequence() -# ------------------------------------------------------------------------------ - - -@torch.jit.export -def segments_to_sequence( - segments: List[Tuple[Tensor, Tensor]], time_axis: int -) -> Tuple[Tensor, Tensor]: - """Concatenate segments into a full sequence.""" - if len(segments) == 1: - return segments[0] - - tensors_to_concat: List[Tensor] = [] - lengths_to_stack: List[Tensor] = [] - - for tensor, lengths in segments: - tensors_to_concat.append(tensor) - lengths_to_stack.append(lengths) - - sequence = torch.cat(tensors_to_concat, dim=time_axis) - lengths = torch.stack(lengths_to_stack, dim=0) - lengths = torch.sum(lengths, dim=0) - - return sequence, lengths - - -def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False): - """ - convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor - - Args: - lengths: a (B, )-shaped tensor - batch_first: whether to return a (B, T) tensor - - Return: - max_length: maximum length of B sequences - encoder_padding_mask: a (max_length, B) binary mask, where - [t, b] = False for t < lengths[b] and True otherwise - - TODO: - kernelize this function if benchmarking shows this function is slow - """ - max_lengths = torch.max(lengths).item() - bsz = lengths.size(0) - encoder_padding_mask = torch.arange( - max_lengths - ).to( # a (T, ) tensor with [0, ..., T-1] - lengths.device - ).view( # move to the right device - 1, max_lengths - ).expand( # reshape to (1, T)-shaped tensor - bsz, -1 - ) > lengths.view( # expand to (B, T)-shaped tensor - bsz, 1 - ).expand( - -1, max_lengths - ) - if not batch_first: - return encoder_padding_mask.t(), max_lengths - else: - return encoder_padding_mask, max_lengths - - -# ------------------------------------------------------------------------------ -# attention suppression -# ------------------------------------------------------------------------------ - - -def attention_suppression(attention_weights: Tensor, scale: float): - # B, H, qlen, klen -> B, H, qlen, 1 - attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1) - attention_nozeros = attention_prob.to(torch.bool) - nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True) - - # For very sparse situation, we need get round about 0s - key_sum = torch.sum(attention_prob, dim=-1, keepdim=True) - - # nozeros_sum should > 1 - key_mean = key_sum / (nozeros_sum + 1e-8) - - # std calculation - dis = (attention_prob - key_mean) * (attention_prob - key_mean) - - # if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i - dis_masked = torch.where( - attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size()) - ) - - key_var = torch.sum(dis_masked, dim=-1, keepdim=True) - key_var = key_var / (nozeros_sum - 1.0 + 1e-8) - key_std = torch.sqrt(key_var) - key_thread = key_mean - scale * key_std - - # if attention_prob[i] >= key_thread, then attention_prob[i] - # , otherwise "-inf" - inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach() - inf_tensor[:] = float("-inf") - attention_weights_float = torch.where( - attention_prob < key_thread, - inf_tensor, - attention_weights.float(), - ) - - return attention_weights_float.type_as(attention_weights) - - -def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value): - return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/xm_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/xm_transformer.py deleted file mode 100644 index 0c7ffbd233b6516f34219c1830100c3dc2e42d03..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/speech_to_text/xm_transformer.py +++ /dev/null @@ -1,746 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import copy -import logging -from typing import Dict, List, Optional, Tuple - -import numpy as np -import torch -import torch.nn as nn -from fairseq import checkpoint_utils, utils -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.speech_to_text.hub_interface import S2THubInterface -from fairseq.models.transformer import Embedding, TransformerDecoder -from fairseq.models.wav2vec import Wav2VecEncoder -from fairseq.modules.layer_norm import LayerNorm -from torch import Tensor - -logger = logging.getLogger(__name__) - - -class Conv1dAdaptor(nn.Module): - def __init__( - self, - in_dim, - out_dim, - n_layers=3, - kernel_size=3, - stride=2, - layerdrop=0.0, - layernorm=False, - proj=False, - ): - super().__init__() - self.proj, self.proj_ln = None, None - self.post_proj, self.post_proj_ln = None, None - if proj: - self.proj = nn.Sequential( - nn.Linear(in_dim, in_dim * 4), nn.ReLU(), nn.Linear(in_dim * 4, in_dim) - ) - self.proj_ln = LayerNorm(in_dim) - self.post_proj = nn.Sequential( - nn.Linear(out_dim, out_dim * 4), - nn.ReLU(), - nn.Linear(out_dim * 4, out_dim), - ) - self.post_proj_ln = LayerNorm(out_dim) - - self.layers = nn.ModuleList( - nn.Conv1d( - in_dim if i == 0 else out_dim, - out_dim * 2, - kernel_size, - stride=stride, - padding=kernel_size // 2, - ) - for i in range(n_layers) - ) - self.stride = stride - self.layerdrop = layerdrop - self.layernorm = LayerNorm(in_dim) if layernorm else None - - @classmethod - def add_args(cls, parser): - parser.add_argument("--adaptor-n-layers", type=int) - parser.add_argument("--adaptor-kernel-size", type=int) - parser.add_argument("--adaptor-stride", type=int) - parser.add_argument("--adaptor-layerdrop", type=float) - parser.add_argument("--adaptor-layernorm", action="store_true") - parser.add_argument("--adaptor-proj", action="store_true") - - def forward(self, x, padding_mask: Optional[torch.Tensor]): - if self.layernorm is not None: - x = self.layernorm(x) - - if self.proj is not None: - x = x + 0.5 * self.proj(x) - x = self.proj_ln(x) - - # T x B x C -> B x C x T - x = x.transpose(0, 1).transpose(1, 2) - out_lens = None - if padding_mask is not None: - out_lens = (~padding_mask).sum(1).float() - - for layer in self.layers: - layerdrop_prob = np.random.random() - if not self.training or (layerdrop_prob > self.layerdrop): - x = nn.functional.glu(layer(x), dim=1) - if padding_mask is not None: - out_lens = ((out_lens - 1) / self.stride + 1).floor() - # B x C x T -> T x B x C - x = x.transpose(1, 2).transpose(0, 1) - - if self.post_proj is not None: - x = x + 0.5 * self.post_proj(x) - x = self.post_proj_ln(x) - - out_padding_mask = None - if padding_mask is not None: - out_padding_mask = lengths_to_padding_mask(out_lens.long()) - return x, out_padding_mask - - -def add_wav2vec_asr_args(parser): - parser.add_argument("--w2v-path", help="path to wav2vec 2.0 model") - parser.add_argument( - "--no-pretrained-weights", - action="store_true", - help="if true, does not load pretrained weights", - ) - parser.add_argument( - "--dropout-input", - type=float, - metavar="D", - help="dropout to apply to the input (after feat extr)", - ) - parser.add_argument( - "--final-dropout", - type=float, - metavar="D", - help="dropout after transformer and before final projection", - ) - parser.add_argument( - "--apply-mask", action="store_true", help="apply masking during fine-tuning" - ) - parser.add_argument( - "--dropout", - type=float, - metavar="D", - help="dropout probability inside wav2vec 2.0 model", - ) - parser.add_argument( - "--attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights inside wav2vec 2.0 model", - ) - parser.add_argument( - "--activation-dropout", - "--relu-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN inside wav2vec 2.0 model", - ) - - parser.add_argument( - "--mask-length", type=int, help="repeat the mask indices multiple times" - ) - - parser.add_argument( - "--mask-prob", type=float, help="probability of replacing a token with mask" - ) - - parser.add_argument( - "--mask-selection", - type=str, - choices=["static", "uniform", "normal", "poisson"], - help="how to choose masks", - ) - - parser.add_argument( - "--mask-other", - type=float, - help="stdev of the mask length in case of 'normal' selection strategy", - ) - - parser.add_argument( - "--no-mask-overlap", - action="store_true", - help="whether to allow masks to overlap", - ) - - parser.add_argument( - "--mask-channel-length", type=int, help="repeat the mask indices multiple times" - ) - - parser.add_argument( - "--mask-channel-prob", - type=float, - help="probability of replacing a token with mask", - ) - - parser.add_argument( - "--mask-channel-selection", - type=str, - choices=["static", "uniform", "normal", "poisson"], - help="how to choose masks", - ) - - parser.add_argument( - "--mask-channel-other", - type=float, - help="stdev of the mask length in case of 'normal' selection strategy", - ) - - parser.add_argument( - "--no-mask-channel-overlap", - action="store_true", - help="whether to allow masks to overlap", - ) - - parser.add_argument( - "--freeze-finetune-updates", - default=0, - type=int, - help="dont finetune wav2vec for this many updates", - ) - - parser.add_argument( - "--feature-grad-mult", - default=None, - type=float, - help="reset feature grad mult in wav2vec 2.0 to this", - ) - - parser.add_argument( - "--layerdrop", - default=0.0, - type=float, - help="probability of dropping a layer in wav2vec 2.0", - ) - parser.add_argument( - "--max-positions", - type=int, - help="Max input positions to be used in the conformer encoder in wav2vec 2.0", - ) - - parser.add_argument("--encoder-proj", action="store_true") - - parser.add_argument("--w2v-args", default=None) - - parser.add_argument( - "--remove-weight-norm", - action="store_true", - help="if set, then the weight-norm (in one pos_conv layer) is removed from the model", - ) - - -def need_finetuning(ft_params, param_name): - if ft_params == "all": - return True - ft_params_list = ft_params.split(",") - for ft_param in ft_params_list: - if ft_param in param_name: - return True - return False - - -class Wav2VecEncoderWithAdaptor(FairseqEncoder): - def build_adaptor(self, args): - adaptor = None - if args.adaptor_n_layers > 0: - adaptor = Conv1dAdaptor( - args.decoder_embed_dim, - args.decoder_embed_dim, - n_layers=args.adaptor_n_layers, - kernel_size=args.adaptor_kernel_size, - stride=args.adaptor_stride, - layerdrop=args.adaptor_layerdrop, - layernorm=args.adaptor_layernorm, - proj=args.adaptor_proj, - ) - return adaptor - - def __init__(self, args): - super().__init__(None) - self.w2v_encoder = Wav2VecEncoder(args) - self.is_v0_arch = not args.adaptor_proj - self.w2v_proj_ln = None - if not self.is_v0_arch and self.w2v_encoder.proj is not None: - self.w2v_proj_ln = LayerNorm(args.decoder_embed_dim) - self.adaptor = self.build_adaptor(args) - - self.num_updates = 0 - self.freezing_updates = args.w2v_freezing_updates - self.finetuning_params = args.finetune_w2v_params - for k, p in self.w2v_encoder.w2v_model.named_parameters(): - p.requires_grad = need_finetuning(self.finetuning_params, k) - - @classmethod - def add_args(cls, parser): - add_wav2vec_asr_args(parser) - parser.add_argument( - "--normalize", - action="store_true", - help="if set, normalizes input to have 0 mean and unit variance", - ) - parser.add_argument( - "--finetune-w2v-params", - type=str, - metavar="STR", - help="comma-separated param strings to finetune.", - ) - parser.add_argument("--w2v-freezing-updates", type=int) - parser.add_argument("--load-pretrained-encoder-from", type=str, metavar="STR") - Conv1dAdaptor.add_args(parser) - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - self.num_updates = num_updates - - def forward(self, src_tokens, src_lengths=None, **kwargs): - if ( - self.freezing_updates is not None - and self.num_updates > self.freezing_updates - ): - for p in self.w2v_encoder.w2v_model.parameters(): - p.requires_grad = True - - padding_mask = lengths_to_padding_mask(src_lengths) - out = self.w2v_encoder.forward(src_tokens, padding_mask, tbc=True) - x, padding_mask = out["encoder_out"], out["padding_mask"] - if self.w2v_proj_ln is not None: - x = self.w2v_proj_ln(x) - - if self.adaptor is not None: - x, padding_mask = self.adaptor(x, padding_mask) - - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [] - if padding_mask is None - else [padding_mask], # B x T - "encoder_embedding": [], # B x T x C - "encoder_states": [], # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - } - - def reorder_encoder_out(self, encoder_out, new_order): - new_encoder_out = ( - [] - if len(encoder_out["encoder_out"]) == 0 - else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]] - ) - - new_encoder_padding_mask = ( - [] - if len(encoder_out["encoder_padding_mask"]) == 0 - else [ - x.index_select(0, new_order) - for x in encoder_out["encoder_padding_mask"] - ] - ) - - new_encoder_embedding = ( - [] - if len(encoder_out["encoder_embedding"]) == 0 - else [ - x.index_select(0, new_order) for x in encoder_out["encoder_embedding"] - ] - ) - - encoder_states = encoder_out["encoder_states"] - if len(encoder_states) > 0: - for idx, state in enumerate(encoder_states): - encoder_states[idx] = state.index_select(1, new_order) - - return { - "encoder_out": new_encoder_out, # T x B x C - "encoder_padding_mask": new_encoder_padding_mask, # B x T - "encoder_embedding": new_encoder_embedding, # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], # B x T - "src_lengths": [], # B x 1 - } - - -def add_decoder_args(parser): - parser.add_argument( - "--activation-fn", - type=str, - default="relu", - choices=utils.get_available_activation_fns(), - help="activation function to use", - ) - parser.add_argument( - "--decoder-dropout", type=float, metavar="D", help="dropout probability" - ) - parser.add_argument( - "--decoder-attention-dropout", - type=float, - metavar="D", - help="dropout probability for attention weights", - ) - parser.add_argument( - "--decoder-activation-dropout", - type=float, - metavar="D", - help="dropout probability after activation in FFN.", - ) - parser.add_argument( - "--decoder-embed-dim", type=int, metavar="N", help="decoder embedding dimension" - ) - parser.add_argument( - "--decoder-ffn-embed-dim", - type=int, - metavar="N", - help="decoder embedding dimension for FFN", - ) - parser.add_argument( - "--decoder-layers", type=int, metavar="N", help="num decoder layers" - ) - parser.add_argument( - "--decoder-attention-heads", - type=int, - metavar="N", - help="num decoder attention heads", - ) - parser.add_argument( - "--decoder-normalize-before", - action="store_true", - help="apply layernorm before each decoder block", - ) - parser.add_argument( - "--layernorm-embedding", action="store_true", help="add layernorm to embedding" - ) - parser.add_argument("--decoder-layerdrop", type=float, metavar="D") - parser.add_argument("--decoder-learned-pos", action="store_true") - parser.add_argument("--share-decoder-input-output-embed", action="store_true") - parser.add_argument( - "--no-scale-embedding", - action="store_true", - help="if True, dont scale embeddings", - ) - parser.add_argument( - "--load-pretrained-decoder-from", - type=str, - metavar="STR", - help="model to take decoder weights from (for initialization)", - ) - parser.add_argument( - "--finetune-decoder-params", - type=str, - metavar="STR", - help="comma-separated param strings to finetune.", - ) - - -def remove_weight_norm_from_model(model): - from functools import reduce - - layers_with_wn = [] - for param_name, _ in model.named_parameters(): - if param_name.endswith("_g"): - # retrieve the module with this param_name - module_names = param_name.split(".")[ - :-1 - ] # exclude the actual parameter name - wn_module = reduce(getattr, module_names, model) - layers_with_wn.append(wn_module) - for wn_module in layers_with_wn: - torch.nn.utils.remove_weight_norm(wn_module) - logger.warning(f"Weight norm removed from module with {wn_module}\n") - - -@register_model("xm_transformer") -class XMTransformerModel(FairseqEncoderDecoderModel): - @classmethod - def hub_models(cls): - base_url = "http://dl.fbaipublicfiles.com/fairseq/s2t" - model_ids = [ - "xm_transformer_600m-es_en-multi_domain", - "xm_transformer_600m-ru_en-multi_domain", - "xm_transformer_600m-fr_en-multi_domain", - "xm_transformer_600m-en_es-multi_domain", - "xm_transformer_600m-en_ru-multi_domain", - "xm_transformer_600m-en_fr-multi_domain", - "xm_transformer_600m-en_zh-multi_domain", - "xm_transformer_600m-en_ar-multi_domain", - "xm_transformer_600m-en_tr-multi_domain", - "xm_transformer_600m-en_vi-multi_domain", - "xm_transformer-21_en-xls_r_300m", - "xm_transformer-en_15-xls_r_300m", - "xm_transformer-21_en-xls_r_1b", - "xm_transformer-en_15-xls_r_1b", - "xm_transformer-21_en-xls_r_2b", - "xm_transformer-en_15-xls_r_2b", - "xm_transformer-22_16-xls_r_2b", - ] - return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - config_yaml="config.yaml", - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - config_yaml=config_yaml, - **kwargs, - ) - return S2THubInterface(x["args"], x["task"], x["models"][0]) - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def add_args(cls, parser): - """Add model-specific arguments to the parser.""" - Wav2VecEncoderWithAdaptor.add_args(parser) - add_decoder_args(parser) - parser.add_argument("--checkpoint-activations", action="store_true") - parser.add_argument("--offload-activations", action="store_true") - parser.add_argument("--min-params-to-wrap", type=int) - - @classmethod - def maybe_load_pretrained(cls, component, checkpoint: Optional[str] = None): - if checkpoint is None: - return component - - _load = checkpoint_utils.load_pretrained_component_from_model - try: - return _load(component, checkpoint) - except RuntimeError as e: - logger.warning(e) - return _load(component, checkpoint, strict=False) - - @classmethod - def build_encoder(cls, args): - _args = copy.deepcopy(args) - if not args.adaptor_proj and not args.encoder_proj: # V0 arch - state = checkpoint_utils.load_checkpoint_to_cpu(args.w2v_path) - if state.get("cfg") is not None: - encoder_embed_dim = state["cfg"]._content["model"]["encoder_embed_dim"] - elif state.get("args") is not None: - encoder_embed_dim = state["args"].encoder_embed_dim - else: - raise ValueError(f"Invalid config in {args.w2v_path}") - _args.decoder_embed_dim = encoder_embed_dim - del state - - encoder = Wav2VecEncoderWithAdaptor(_args) - encoder = cls.maybe_load_pretrained( - encoder, getattr(args, "load_pretrained_encoder_from", None) - ) - if args.remove_weight_norm: - # remove the wn for EMA usage - logger.warning("Removing weight norm from wav2vec encoder") - remove_weight_norm_from_model(encoder) - - return encoder - - @classmethod - def get_decoder_args_from_checkpoint(cls, ckpt_args): - assert "model" in ckpt_args, "Model args not found in checkpoint cfg!" - decoder_args = {} - for k, v in ckpt_args["model"].__dict__.items(): - if "decoder" in k: - decoder_args[k] = v - - return decoder_args - - @classmethod - def override_decoder_args(cls, cli_args, decoder_args_dict): - for k, v in decoder_args_dict.items(): - if v != getattr(cli_args, k, None): - logger.warning( - f"Overriding decoder arg {k}: from {getattr(cli_args, k, None)} to {v}" - ) - setattr(cli_args, k, v) - - return cli_args - - @classmethod - def build_decoder(cls, args, task, embed_tokens): - _args = copy.deepcopy(args) - if args.adaptor_proj or args.encoder_proj: # not V0 arch - _args.encoder_embed_dim = _args.decoder_embed_dim - _args.dropout = args.decoder_dropout - _args.attention_dropout = args.decoder_attention_dropout - _args.activation_dropout = args.decoder_activation_dropout - - decoder = TransformerDecoder(_args, task.target_dictionary, embed_tokens) - decoder = cls.maybe_load_pretrained( - decoder, getattr(args, "load_pretrained_decoder_from", None) - ) - - for k, p in decoder.named_parameters(): - p.requires_grad = need_finetuning(args.finetune_decoder_params, k) - return decoder - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - if getattr(args, "load_pretrained_decoder_from", None): - ckpt = torch.load(getattr(args, "load_pretrained_decoder_from", None)) - decoder_args_dict = cls.get_decoder_args_from_checkpoint(ckpt["cfg"]) - args = cls.override_decoder_args(args, decoder_args_dict) - - def build_embedding(dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - return Embedding(num_embeddings, embed_dim, padding_idx) - - decoder_embed_tokens = build_embedding( - task.target_dictionary, args.decoder_embed_dim - ) - - encoder = cls.build_encoder(args) - decoder = cls.build_decoder(args, task, decoder_embed_tokens) - return cls(encoder, decoder) - - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - return self.get_normalized_probs_scriptable(net_output, log_probs, sample) - - def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - """ - The forward method inherited from the base class has a **kwargs - argument in its input, which is not supported in torchscript. This - method overwrites the forward method definition without **kwargs. - """ - encoder_out = self.encoder( - src_tokens=src_tokens, src_lengths=src_lengths, **kwargs - ) - decoder_out = self.decoder( - prev_output_tokens=prev_output_tokens, encoder_out=encoder_out - ) - return decoder_out - - def upgrade_state_dict(self, state_dict): - for k, _ in state_dict.items(): - if "adaptor.layers" in state_dict: - new = k.replace("adaptor.layers", "adaptor_layers") - state_dict[new] = state_dict[k] - del state_dict[k] - - -def set_default_w2v_encoder_args(args): - args.no_pretrained_weights = getattr(args, "no_pretrained_weights", False) - args.dropout_input = getattr(args, "dropout_input", 0) - args.final_dropout = getattr(args, "final_dropout", 0) - args.apply_mask = getattr(args, "apply_mask", False) - args.dropout = getattr(args, "dropout", 0) - args.attention_dropout = getattr(args, "attention_dropout", 0) - args.activation_dropout = getattr(args, "activation_dropout", 0) - args.encoder_proj = getattr(args, "encoder_proj", False) - args.remove_weight_norm = getattr(args, "remove_weight_norm", False) - - args.mask_length = getattr(args, "mask_length", 10) - args.mask_prob = getattr(args, "mask_prob", 0.5) - args.mask_selection = getattr(args, "mask_selection", "static") - args.mask_other = getattr(args, "mask_other", 0) - args.no_mask_overlap = getattr(args, "no_mask_overlap", False) - args.mask_channel_length = getattr(args, "mask_channel_length", 10) - args.mask_channel_prob = getattr(args, "mask_channel_prob", 0.5) - args.mask_channel_before = getattr(args, "mask_channel_before", False) - args.mask_channel_selection = getattr(args, "mask_channel_selection", "static") - args.mask_channel_other = getattr(args, "mask_channel_other", 0) - args.no_mask_channel_overlap = getattr(args, "no_mask_channel_overlap", False) - - args.freeze_finetune_updates = getattr(args, "freeze_finetune_updates", 0) - args.feature_grad_mult = 0.1 - args.layerdrop = getattr(args, "layerdrop", 0.0) - - args.normalize = getattr(args, "normalize", False) - args.finetune_w2v_params = getattr(args, "finetune_w2v_params", "all") - args.w2v_freezing_updates = getattr(args, "w2v_freezing_updates", None) - - -def set_default_adaptor_args(args): - args.adaptor_n_layers = getattr(args, "adaptor_n_layers", 3) - args.adaptor_kernel_size = getattr(args, "adaptor_kernel_size", 3) - args.adaptor_stride = getattr(args, "adaptor_stride", 2) - args.adaptor_layerdrop = getattr(args, "adaptor_layerdrop", 0.0) - args.adaptor_layernorm = getattr(args, "adaptor_layernorm", False) - args.adaptor_proj = getattr(args, "adaptor_proj", False) - - -def set_default_transformer_decoder_args(args): - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * 1024) - args.decoder_layers = getattr(args, "decoder_layers", 12) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.decoder_attention_dropout = getattr(args, "decoder_attention_dropout", 0.0) - args.decoder_activation_dropout = getattr(args, "decoder_activation_dropout", 0.0) - args.decoder_dropout = getattr(args, "decoder_dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) - args.layernorm_embedding = getattr(args, "layernorm_embedding", False) - - args.activation_fn = getattr(args, "activation_fn", "gelu") - args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh") - args.pooler_dropout = getattr(args, "pooler_dropout", 0.0) - - args.finetune_decoder_params = getattr(args, "finetune_decoder_params", "all") - - -def set_default_general_args(args): - args.checkpoint_activations = getattr(args, "checkpoint_activations", False) - args.offload_activations = getattr(args, "offload_activations", False) - args.min_params_to_wrap = getattr(args, "min_params_to_wrap", int(1e8)) - args.max_positions = getattr(args, "max_positions", 3000) - - -@register_model_architecture(model_name="xm_transformer", arch_name="xm_transformer") -def base_architecture(args): - set_default_general_args(args) - set_default_w2v_encoder_args(args) - set_default_adaptor_args(args) - set_default_transformer_decoder_args(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/__init__.py deleted file mode 100644 index 87143ff44183c99546f557d5b2d2a3789a4064f8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .fastspeech2 import * # noqa -from .tacotron2 import * # noqa -from .tts_transformer import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/codehifigan.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/codehifigan.py deleted file mode 100644 index 5571c7a48651e14a986a5827b489907ee259cfe2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/codehifigan.py +++ /dev/null @@ -1,95 +0,0 @@ -from argparse import Namespace - -import torch -import torch.nn as nn -from fairseq.models.text_to_speech.fastspeech2 import VariancePredictor -from fairseq.models.text_to_speech.hifigan import Generator - - -class CodeGenerator(Generator): - def __init__(self, cfg): - super().__init__(cfg) - self.dict = nn.Embedding(cfg["num_embeddings"], cfg["embedding_dim"]) - self.multispkr = cfg.get("multispkr", None) - self.embedder = cfg.get("embedder_params", None) - - if self.multispkr and not self.embedder: - self.spkr = nn.Embedding(cfg.get("num_speakers", 200), cfg["embedding_dim"]) - elif self.embedder: - self.spkr = nn.Linear(cfg.get("embedder_dim", 256), cfg["embedding_dim"]) - - self.dur_predictor = None - if cfg.get("dur_predictor_params", None): - self.dur_predictor = VariancePredictor( - Namespace(**cfg["dur_predictor_params"]) - ) - - self.f0 = cfg.get("f0", None) - n_f0_bin = cfg.get("f0_quant_num_bin", 0) - self.f0_quant_embed = ( - None if n_f0_bin <= 0 else nn.Embedding(n_f0_bin, cfg["embedding_dim"]) - ) - - @staticmethod - def _upsample(signal, max_frames): - if signal.dim() == 3: - bsz, channels, cond_length = signal.size() - elif signal.dim() == 2: - signal = signal.unsqueeze(2) - bsz, channels, cond_length = signal.size() - else: - signal = signal.view(-1, 1, 1) - bsz, channels, cond_length = signal.size() - - signal = signal.unsqueeze(3).repeat(1, 1, 1, max_frames // cond_length) - - # pad zeros as needed (if signal's shape does not divide completely with max_frames) - reminder = (max_frames - signal.shape[2] * signal.shape[3]) // signal.shape[3] - if reminder > 0: - raise NotImplementedError( - "Padding condition signal - misalignment between condition features." - ) - - signal = signal.view(bsz, channels, max_frames) - return signal - - def forward(self, **kwargs): - x = self.dict(kwargs["code"]).transpose(1, 2) - - if self.dur_predictor and kwargs.get("dur_prediction", False): - assert x.size(0) == 1, "only support single sample" - log_dur_pred = self.dur_predictor(x.transpose(1, 2)) - dur_out = torch.clamp( - torch.round((torch.exp(log_dur_pred) - 1)).long(), min=1 - ) - # B x C x T - x = torch.repeat_interleave(x, dur_out.view(-1), dim=2) - - if self.f0: - if self.f0_quant_embed: - kwargs["f0"] = self.f0_quant_embed(kwargs["f0"].long()).transpose(1, 2) - else: - kwargs["f0"] = kwargs["f0"].unsqueeze(1) - - if x.shape[-1] < kwargs["f0"].shape[-1]: - x = self._upsample(x, kwargs["f0"].shape[-1]) - elif x.shape[-1] > kwargs["f0"].shape[-1]: - kwargs["f0"] = self._upsample(kwargs["f0"], x.shape[-1]) - x = torch.cat([x, kwargs["f0"]], dim=1) - - if self.multispkr: - assert ( - "spkr" in kwargs - ), 'require "spkr" input for multispeaker CodeHiFiGAN vocoder' - spkr = self.spkr(kwargs["spkr"]).transpose(1, 2) - spkr = self._upsample(spkr, x.shape[-1]) - x = torch.cat([x, spkr], dim=1) - - for k, feat in kwargs.items(): - if k in ["spkr", "code", "f0", "dur_prediction"]: - continue - - feat = self._upsample(feat, x.shape[-1]) - x = torch.cat([x, feat], dim=1) - - return super().forward(x) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/fastspeech2.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/fastspeech2.py deleted file mode 100644 index 9847b299e5babf1ad3f9f9edb4c0ed8c6975c625..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/fastspeech2.py +++ /dev/null @@ -1,447 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import torch -from fairseq import utils -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.text_to_speech.hub_interface import TTSHubInterface -from fairseq.models.text_to_speech.tacotron2 import Postnet -from fairseq.modules import ( - FairseqDropout, - LayerNorm, - MultiheadAttention, - PositionalEmbedding, -) -from torch import nn - -logger = logging.getLogger(__name__) - - -def model_init(m): - if isinstance(m, nn.Conv1d): - nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) - - -def Embedding(num_embeddings, embedding_dim, padding_idx=None): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - return m - - -class PositionwiseFeedForward(nn.Module): - def __init__(self, in_dim, hidden_dim, kernel_size, dropout): - super().__init__() - self.ffn = nn.Sequential( - nn.Conv1d( - in_dim, - hidden_dim, - kernel_size=kernel_size, - padding=(kernel_size - 1) // 2, - ), - nn.ReLU(), - nn.Conv1d( - hidden_dim, - in_dim, - kernel_size=kernel_size, - padding=(kernel_size - 1) // 2, - ), - ) - self.layer_norm = LayerNorm(in_dim) - self.dropout = self.dropout_module = FairseqDropout( - p=dropout, module_name=self.__class__.__name__ - ) - - def forward(self, x): - # B x T x C - residual = x - x = self.ffn(x.transpose(1, 2)).transpose(1, 2) - x = self.dropout(x) - return self.layer_norm(x + residual) - - -class FFTLayer(torch.nn.Module): - def __init__( - self, embed_dim, n_heads, hidden_dim, kernel_size, dropout, attention_dropout - ): - super().__init__() - self.self_attn = MultiheadAttention( - embed_dim, n_heads, dropout=attention_dropout, self_attention=True - ) - self.layer_norm = LayerNorm(embed_dim) - self.ffn = PositionwiseFeedForward( - embed_dim, hidden_dim, kernel_size, dropout=dropout - ) - - def forward(self, x, padding_mask=None): - # B x T x C - residual = x - x = x.transpose(0, 1) - x, _ = self.self_attn( - query=x, key=x, value=x, key_padding_mask=padding_mask, need_weights=False - ) - x = x.transpose(0, 1) - x = self.layer_norm(x + residual) - return self.ffn(x) - - -class LengthRegulator(nn.Module): - def forward(self, x, durations): - # x: B x T x C - out_lens = durations.sum(dim=1) - max_len = out_lens.max() - bsz, seq_len, dim = x.size() - out = x.new_zeros((bsz, max_len, dim)) - - for b in range(bsz): - indices = [] - for t in range(seq_len): - indices.extend([t] * utils.item(durations[b, t])) - indices = torch.tensor(indices, dtype=torch.long).to(x.device) - out_len = utils.item(out_lens[b]) - out[b, :out_len] = x[b].index_select(0, indices) - - return out, out_lens - - -class VariancePredictor(nn.Module): - def __init__(self, args): - super().__init__() - self.conv1 = nn.Sequential( - nn.Conv1d( - args.encoder_embed_dim, - args.var_pred_hidden_dim, - kernel_size=args.var_pred_kernel_size, - padding=(args.var_pred_kernel_size - 1) // 2, - ), - nn.ReLU(), - ) - self.ln1 = nn.LayerNorm(args.var_pred_hidden_dim) - self.dropout_module = FairseqDropout( - p=args.var_pred_dropout, module_name=self.__class__.__name__ - ) - self.conv2 = nn.Sequential( - nn.Conv1d( - args.var_pred_hidden_dim, - args.var_pred_hidden_dim, - kernel_size=args.var_pred_kernel_size, - padding=1, - ), - nn.ReLU(), - ) - self.ln2 = nn.LayerNorm(args.var_pred_hidden_dim) - self.proj = nn.Linear(args.var_pred_hidden_dim, 1) - - def forward(self, x): - # Input: B x T x C; Output: B x T - x = self.conv1(x.transpose(1, 2)).transpose(1, 2) - x = self.dropout_module(self.ln1(x)) - x = self.conv2(x.transpose(1, 2)).transpose(1, 2) - x = self.dropout_module(self.ln2(x)) - return self.proj(x).squeeze(dim=2) - - -class VarianceAdaptor(nn.Module): - def __init__(self, args): - super().__init__() - self.args = args - self.length_regulator = LengthRegulator() - self.duration_predictor = VariancePredictor(args) - self.pitch_predictor = VariancePredictor(args) - self.energy_predictor = VariancePredictor(args) - - n_bins, steps = self.args.var_pred_n_bins, self.args.var_pred_n_bins - 1 - self.pitch_bins = torch.linspace(args.pitch_min, args.pitch_max, steps) - self.embed_pitch = Embedding(n_bins, args.encoder_embed_dim) - self.energy_bins = torch.linspace(args.energy_min, args.energy_max, steps) - self.embed_energy = Embedding(n_bins, args.encoder_embed_dim) - - def get_pitch_emb(self, x, tgt=None, factor=1.0): - out = self.pitch_predictor(x) - bins = self.pitch_bins.to(x.device) - if tgt is None: - out = out * factor - emb = self.embed_pitch(torch.bucketize(out, bins)) - else: - emb = self.embed_pitch(torch.bucketize(tgt, bins)) - return out, emb - - def get_energy_emb(self, x, tgt=None, factor=1.0): - out = self.energy_predictor(x) - bins = self.energy_bins.to(x.device) - if tgt is None: - out = out * factor - emb = self.embed_energy(torch.bucketize(out, bins)) - else: - emb = self.embed_energy(torch.bucketize(tgt, bins)) - return out, emb - - def forward( - self, - x, - padding_mask, - durations=None, - pitches=None, - energies=None, - d_factor=1.0, - p_factor=1.0, - e_factor=1.0, - ): - # x: B x T x C - log_dur_out = self.duration_predictor(x) - dur_out = torch.clamp( - torch.round((torch.exp(log_dur_out) - 1) * d_factor).long(), min=0 - ) - dur_out.masked_fill_(padding_mask, 0) - - pitch_out, pitch_emb = self.get_pitch_emb(x, pitches, p_factor) - x = x + pitch_emb - energy_out, energy_emb = self.get_energy_emb(x, energies, e_factor) - x = x + energy_emb - - x, out_lens = self.length_regulator( - x, dur_out if durations is None else durations - ) - - return x, out_lens, log_dur_out, pitch_out, energy_out - - -class FastSpeech2Encoder(FairseqEncoder): - def __init__(self, args, src_dict, embed_speaker): - super().__init__(src_dict) - self.args = args - self.padding_idx = src_dict.pad() - self.n_frames_per_step = args.n_frames_per_step - self.out_dim = args.output_frame_dim * args.n_frames_per_step - - self.embed_speaker = embed_speaker - self.spk_emb_proj = None - if embed_speaker is not None: - self.spk_emb_proj = nn.Linear( - args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim - ) - - self.dropout_module = FairseqDropout( - p=args.dropout, module_name=self.__class__.__name__ - ) - self.embed_tokens = Embedding( - len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx - ) - - self.embed_positions = PositionalEmbedding( - args.max_source_positions, args.encoder_embed_dim, self.padding_idx - ) - self.pos_emb_alpha = nn.Parameter(torch.ones(1)) - self.dec_pos_emb_alpha = nn.Parameter(torch.ones(1)) - - self.encoder_fft_layers = nn.ModuleList( - FFTLayer( - args.encoder_embed_dim, - args.encoder_attention_heads, - args.fft_hidden_dim, - args.fft_kernel_size, - dropout=args.dropout, - attention_dropout=args.attention_dropout, - ) - for _ in range(args.encoder_layers) - ) - - self.var_adaptor = VarianceAdaptor(args) - - self.decoder_fft_layers = nn.ModuleList( - FFTLayer( - args.decoder_embed_dim, - args.decoder_attention_heads, - args.fft_hidden_dim, - args.fft_kernel_size, - dropout=args.dropout, - attention_dropout=args.attention_dropout, - ) - for _ in range(args.decoder_layers) - ) - - self.out_proj = nn.Linear(args.decoder_embed_dim, self.out_dim) - - self.postnet = None - if args.add_postnet: - self.postnet = Postnet( - self.out_dim, - args.postnet_conv_dim, - args.postnet_conv_kernel_size, - args.postnet_layers, - args.postnet_dropout, - ) - - self.apply(model_init) - - def forward( - self, - src_tokens, - src_lengths=None, - speaker=None, - durations=None, - pitches=None, - energies=None, - **kwargs, - ): - x = self.embed_tokens(src_tokens) - - enc_padding_mask = src_tokens.eq(self.padding_idx) - x += self.pos_emb_alpha * self.embed_positions(enc_padding_mask) - x = self.dropout_module(x) - - for layer in self.encoder_fft_layers: - x = layer(x, enc_padding_mask) - - if self.embed_speaker is not None: - bsz, seq_len, _ = x.size() - emb = self.embed_speaker(speaker).expand(bsz, seq_len, -1) - x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) - - x, out_lens, log_dur_out, pitch_out, energy_out = self.var_adaptor( - x, enc_padding_mask, durations, pitches, energies - ) - - dec_padding_mask = lengths_to_padding_mask(out_lens) - x += self.dec_pos_emb_alpha * self.embed_positions(dec_padding_mask) - for layer in self.decoder_fft_layers: - x = layer(x, dec_padding_mask) - - x = self.out_proj(x) - x_post = None - if self.postnet is not None: - x_post = x + self.postnet(x) - return x, x_post, out_lens, log_dur_out, pitch_out, energy_out - - -@register_model("fastspeech2") -class FastSpeech2Model(FairseqEncoderModel): - """ - Implementation for https://arxiv.org/abs/2006.04558 - """ - - NON_AUTOREGRESSIVE = True - - @classmethod - def hub_models(cls): - base_url = "http://dl.fbaipublicfiles.com/fairseq/s2" - model_ids = [ - "fastspeech2-en-ljspeech", - "fastspeech2-en-200_speaker-cv4", - ] - return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - config_yaml="config.yaml", - vocoder: str = "griffin_lim", - fp16: bool = False, - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - config_yaml=config_yaml, - vocoder=vocoder, - fp16=fp16, - **kwargs, - ) - return TTSHubInterface(x["args"], x["task"], x["models"][0]) - - @staticmethod - def add_args(parser): - parser.add_argument("--dropout", type=float) - parser.add_argument("--output-frame-dim", type=int) - parser.add_argument("--speaker-embed-dim", type=int) - # FFT blocks - parser.add_argument("--fft-hidden-dim", type=int) - parser.add_argument("--fft-kernel-size", type=int) - parser.add_argument("--attention-dropout", type=float) - parser.add_argument("--encoder-layers", type=int) - parser.add_argument("--encoder-embed-dim", type=int) - parser.add_argument("--encoder-attention-heads", type=int) - parser.add_argument("--decoder-layers", type=int) - parser.add_argument("--decoder-embed-dim", type=int) - parser.add_argument("--decoder-attention-heads", type=int) - # variance predictor - parser.add_argument("--var-pred-n-bins", type=int) - parser.add_argument("--var-pred-hidden-dim", type=int) - parser.add_argument("--var-pred-kernel-size", type=int) - parser.add_argument("--var-pred-dropout", type=float) - # postnet - parser.add_argument("--add-postnet", action="store_true") - parser.add_argument("--postnet-dropout", type=float) - parser.add_argument("--postnet-layers", type=int) - parser.add_argument("--postnet-conv-dim", type=int) - parser.add_argument("--postnet-conv-kernel-size", type=int) - - def __init__(self, encoder, args, src_dict): - super().__init__(encoder) - self._num_updates = 0 - - out_dim = args.output_frame_dim * args.n_frames_per_step - self.ctc_proj = None - if getattr(args, "ctc_weight", 0.0) > 0.0: - self.ctc_proj = nn.Linear(out_dim, len(src_dict)) - - @classmethod - def build_model(cls, args, task): - embed_speaker = task.get_speaker_embeddings(args) - encoder = FastSpeech2Encoder(args, task.src_dict, embed_speaker) - return cls(encoder, args, task.src_dict) - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - self._num_updates = num_updates - - def get_normalized_probs(self, net_output, log_probs, sample=None): - logits = self.ctc_proj(net_output[0]) - if log_probs: - return utils.log_softmax(logits.float(), dim=-1) - else: - return utils.softmax(logits.float(), dim=-1) - - -@register_model_architecture("fastspeech2", "fastspeech2") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.2) - args.output_frame_dim = getattr(args, "output_frame_dim", 80) - args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64) - # FFT blocks - args.fft_hidden_dim = getattr(args, "fft_hidden_dim", 1024) - args.fft_kernel_size = getattr(args, "fft_kernel_size", 9) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.encoder_layers = getattr(args, "encoder_layers", 4) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2) - args.decoder_layers = getattr(args, "decoder_layers", 4) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2) - # variance predictor - args.var_pred_n_bins = getattr(args, "var_pred_n_bins", 256) - args.var_pred_hidden_dim = getattr(args, "var_pred_hidden_dim", 256) - args.var_pred_kernel_size = getattr(args, "var_pred_kernel_size", 3) - args.var_pred_dropout = getattr(args, "var_pred_dropout", 0.5) - # postnet - args.add_postnet = getattr(args, "add_postnet", False) - args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) - args.postnet_layers = getattr(args, "postnet_layers", 5) - args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) - args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/hifigan.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/hifigan.py deleted file mode 100644 index a852beefecd57c4f62f75be0341943678beb17c2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/hifigan.py +++ /dev/null @@ -1,179 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn import Conv1d, ConvTranspose1d -from torch.nn.utils import remove_weight_norm, weight_norm - -LRELU_SLOPE = 0.1 - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return (kernel_size * dilation - dilation) // 2 - - -class ResBlock(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for layer in self.convs1: - remove_weight_norm(layer) - for layer in self.convs2: - remove_weight_norm(layer) - - -class Generator(torch.nn.Module): - def __init__(self, cfg): - super(Generator, self).__init__() - self.num_kernels = len(cfg["resblock_kernel_sizes"]) - self.num_upsamples = len(cfg["upsample_rates"]) - self.conv_pre = weight_norm( - Conv1d( - cfg.get("model_in_dim", 80), - cfg["upsample_initial_channel"], - 7, - 1, - padding=3, - ) - ) - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate( - zip(cfg["upsample_rates"], cfg["upsample_kernel_sizes"]) - ): - self.ups.append( - weight_norm( - ConvTranspose1d( - cfg["upsample_initial_channel"] // (2**i), - cfg["upsample_initial_channel"] // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = cfg["upsample_initial_channel"] // (2 ** (i + 1)) - for k, d in zip( - cfg["resblock_kernel_sizes"], cfg["resblock_dilation_sizes"] - ): - self.resblocks.append(ResBlock(ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - - def forward(self, x): - x = self.conv_pre(x) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print("Removing weight norm...") - for layer in self.ups: - remove_weight_norm(layer) - for layer in self.resblocks: - layer.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/hub_interface.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/hub_interface.py deleted file mode 100644 index 26c7ccaca29f9f414396067003bf624752be08d1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/hub_interface.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import random -from pathlib import Path -from typing import Dict, Optional, Tuple - -import torch -import torch.nn as nn - -logger = logging.getLogger(__name__) - - -class TTSHubInterface(nn.Module): - def __init__(self, cfg, task, model): - super().__init__() - self.cfg = cfg - self.task = task - self.model = model - self.model.eval() - - self.update_cfg_with_data_cfg(self.cfg, self.task.data_cfg) - self.generator = self.task.build_generator([self.model], self.cfg) - - @classmethod - def phonemize( - cls, - text: str, - lang: Optional[str], - phonemizer: Optional[str] = None, - preserve_punct: bool = False, - to_simplified_zh: bool = False, - ): - if to_simplified_zh: - import hanziconv - - text = hanziconv.HanziConv.toSimplified(text) - - if phonemizer == "g2p": - import g2p_en - - g2p = g2p_en.G2p() - if preserve_punct: - return " ".join("|" if p == " " else p for p in g2p(text)) - else: - res = [{",": "sp", ";": "sp"}.get(p, p) for p in g2p(text)] - return " ".join(p for p in res if p.isalnum()) - if phonemizer == "g2pc": - import g2pc - - g2p = g2pc.G2pC() - return " ".join([w[3] for w in g2p(text)]) - elif phonemizer == "ipa": - assert lang is not None - import phonemizer - from phonemizer.separator import Separator - - lang_map = {"en": "en-us", "fr": "fr-fr"} - return phonemizer.phonemize( - text, - backend="espeak", - language=lang_map.get(lang, lang), - separator=Separator(word="| ", phone=" "), - ) - else: - return text - - @classmethod - def tokenize(cls, text: str, tkn_cfg: Dict[str, str]): - sentencepiece_model = tkn_cfg.get("sentencepiece_model", None) - if sentencepiece_model is not None: - assert Path(sentencepiece_model).exists() - import sentencepiece as sp - - spm = sp.SentencePieceProcessor() - spm.Load(sentencepiece_model) - return " ".join(spm.Encode(text, out_type=str)) - else: - return text - - @classmethod - def update_cfg_with_data_cfg(cls, cfg, data_cfg): - cfg["task"].vocoder = data_cfg.vocoder.get("type", "griffin_lim") - - @classmethod - def get_model_input( - cls, task, text: str, speaker: Optional[int] = None, verbose: bool = False - ): - phonemized = cls.phonemize( - text, - task.data_cfg.hub.get("lang", None), - task.data_cfg.hub.get("phonemizer", None), - task.data_cfg.hub.get("preserve_punct", False), - task.data_cfg.hub.get("to_simplified_zh", False), - ) - tkn_cfg = task.data_cfg.bpe_tokenizer - tokenized = cls.tokenize(phonemized, tkn_cfg) - if verbose: - logger.info(f"text: {text}") - logger.info(f"phonemized: {phonemized}") - logger.info(f"tokenized: {tokenized}") - - spk = task.data_cfg.hub.get("speaker", speaker) - n_speakers = len(task.speaker_to_id or {}) - if spk is None and n_speakers > 0: - spk = random.randint(0, n_speakers - 1) - if spk is not None: - spk = max(0, min(spk, n_speakers - 1)) - if verbose: - logger.info(f"speaker: {spk}") - spk = None if spk is None else torch.Tensor([[spk]]).long() - - src_tokens = task.src_dict.encode_line(tokenized, add_if_not_exist=False).view( - 1, -1 - ) - src_lengths = torch.Tensor([len(tokenized.split())]).long() - return { - "net_input": { - "src_tokens": src_tokens, - "src_lengths": src_lengths, - "prev_output_tokens": None, - }, - "target_lengths": None, - "speaker": spk, - } - - @classmethod - def get_prediction(cls, task, model, generator, sample) -> Tuple[torch.Tensor, int]: - prediction = generator.generate(model, sample) - return prediction[0]["waveform"], task.sr - - def predict( - self, text: str, speaker: Optional[int] = None, verbose: bool = False - ) -> Tuple[torch.Tensor, int]: - sample = self.get_model_input(self.task, text, speaker, verbose=verbose) - return self.get_prediction(self.task, self.model, self.generator, sample) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/tacotron2.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/tacotron2.py deleted file mode 100644 index f67b654c22ce3cb8c1e2c82cebadc481fde2a573..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/tacotron2.py +++ /dev/null @@ -1,378 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import torch -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) -from fairseq.modules import LocationAttention, LSTMCellWithZoneOut -from torch import nn -from torch.nn import functional as F - -logger = logging.getLogger(__name__) - - -def encoder_init(m): - if isinstance(m, nn.Conv1d): - nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) - - -class Tacotron2Encoder(FairseqEncoder): - def __init__(self, args, src_dict, embed_speaker): - super().__init__(src_dict) - self.padding_idx = src_dict.pad() - self.embed_speaker = embed_speaker - self.spk_emb_proj = None - if embed_speaker is not None: - self.spk_emb_proj = nn.Linear( - args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim - ) - - self.embed_tokens = nn.Embedding( - len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx - ) - - assert args.encoder_conv_kernel_size % 2 == 1 - self.convolutions = nn.ModuleList( - nn.Sequential( - nn.Conv1d( - args.encoder_embed_dim, - args.encoder_embed_dim, - kernel_size=args.encoder_conv_kernel_size, - padding=((args.encoder_conv_kernel_size - 1) // 2), - ), - nn.BatchNorm1d(args.encoder_embed_dim), - nn.ReLU(), - nn.Dropout(args.encoder_dropout), - ) - for _ in range(args.encoder_conv_layers) - ) - - self.lstm = nn.LSTM( - args.encoder_embed_dim, - args.encoder_embed_dim // 2, - num_layers=args.encoder_lstm_layers, - batch_first=True, - bidirectional=True, - ) - - self.apply(encoder_init) - - def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs): - x = self.embed_tokens(src_tokens) - x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T - for conv in self.convolutions: - x = conv(x) - x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C - - src_lengths = src_lengths.cpu().long() - x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True) - x = self.lstm(x)[0] - x = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)[0] - - encoder_padding_mask = src_tokens.eq(self.padding_idx) - - if self.embed_speaker is not None: - seq_len, bsz, _ = x.size() - emb = self.embed_speaker(speaker).expand(seq_len, bsz, -1) - x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) - - return { - "encoder_out": [x], # B x T x C - "encoder_padding_mask": encoder_padding_mask, # B x T - } - - -class Prenet(nn.Module): - def __init__(self, in_dim, n_layers, n_units, dropout): - super().__init__() - self.layers = nn.ModuleList( - nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units), nn.ReLU()) - for i in range(n_layers) - ) - self.dropout = dropout - - def forward(self, x): - for layer in self.layers: - x = F.dropout(layer(x), p=self.dropout) # always applies dropout - return x - - -class Postnet(nn.Module): - def __init__(self, in_dim, n_channels, kernel_size, n_layers, dropout): - super(Postnet, self).__init__() - self.convolutions = nn.ModuleList() - assert kernel_size % 2 == 1 - for i in range(n_layers): - cur_layers = ( - [ - nn.Conv1d( - in_dim if i == 0 else n_channels, - n_channels if i < n_layers - 1 else in_dim, - kernel_size=kernel_size, - padding=((kernel_size - 1) // 2), - ), - nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim), - ] - + ([nn.Tanh()] if i < n_layers - 1 else []) - + [nn.Dropout(dropout)] - ) - nn.init.xavier_uniform_( - cur_layers[0].weight, - torch.nn.init.calculate_gain("tanh" if i < n_layers - 1 else "linear"), - ) - self.convolutions.append(nn.Sequential(*cur_layers)) - - def forward(self, x): - x = x.transpose(1, 2) # B x T x C -> B x C x T - for conv in self.convolutions: - x = conv(x) - return x.transpose(1, 2) - - -def decoder_init(m): - if isinstance(m, torch.nn.Conv1d): - nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh")) - - -class Tacotron2Decoder(FairseqIncrementalDecoder): - def __init__(self, args, src_dict): - super().__init__(None) - self.args = args - self.n_frames_per_step = args.n_frames_per_step - self.out_dim = args.output_frame_dim * args.n_frames_per_step - - self.prenet = Prenet( - self.out_dim, args.prenet_layers, args.prenet_dim, args.prenet_dropout - ) - - # take prev_context, prev_frame, (speaker embedding) as input - self.attention_lstm = LSTMCellWithZoneOut( - args.zoneout, - args.prenet_dim + args.encoder_embed_dim, - args.decoder_lstm_dim, - ) - - # take attention_lstm output, attention_state, encoder_out as input - self.attention = LocationAttention( - args.attention_dim, - args.encoder_embed_dim, - args.decoder_lstm_dim, - (1 + int(args.attention_use_cumprob)), - args.attention_conv_dim, - args.attention_conv_kernel_size, - ) - - # take attention_lstm output, context, (gated_latent) as input - self.lstm = nn.ModuleList( - LSTMCellWithZoneOut( - args.zoneout, - args.encoder_embed_dim + args.decoder_lstm_dim, - args.decoder_lstm_dim, - ) - for i in range(args.decoder_lstm_layers) - ) - - proj_in_dim = args.encoder_embed_dim + args.decoder_lstm_dim - self.feat_proj = nn.Linear(proj_in_dim, self.out_dim) - self.eos_proj = nn.Linear(proj_in_dim, 1) - - self.postnet = Postnet( - self.out_dim, - args.postnet_conv_dim, - args.postnet_conv_kernel_size, - args.postnet_layers, - args.postnet_dropout, - ) - - self.ctc_proj = None - if getattr(args, "ctc_weight", 0.0) > 0.0: - self.ctc_proj = nn.Linear(self.out_dim, len(src_dict)) - - self.apply(decoder_init) - - def _get_states(self, incremental_state, enc_out): - bsz, in_len, _ = enc_out.size() - alstm_h = self.get_incremental_state(incremental_state, "alstm_h") - if alstm_h is None: - alstm_h = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) - alstm_c = self.get_incremental_state(incremental_state, "alstm_c") - if alstm_c is None: - alstm_c = enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) - - lstm_h = self.get_incremental_state(incremental_state, "lstm_h") - if lstm_h is None: - lstm_h = [ - enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) - for _ in range(self.args.decoder_lstm_layers) - ] - lstm_c = self.get_incremental_state(incremental_state, "lstm_c") - if lstm_c is None: - lstm_c = [ - enc_out.new_zeros(bsz, self.args.decoder_lstm_dim) - for _ in range(self.args.decoder_lstm_layers) - ] - - attn_w = self.get_incremental_state(incremental_state, "attn_w") - if attn_w is None: - attn_w = enc_out.new_zeros(bsz, in_len) - attn_w_cum = self.get_incremental_state(incremental_state, "attn_w_cum") - if attn_w_cum is None: - attn_w_cum = enc_out.new_zeros(bsz, in_len) - return alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum - - def _get_init_attn_c(self, enc_out, enc_mask): - bsz = enc_out.size(0) - if self.args.init_attn_c == "zero": - return enc_out.new_zeros(bsz, self.args.encoder_embed_dim) - elif self.args.init_attn_c == "avg": - enc_w = (~enc_mask).type(enc_out.type()) - enc_w = enc_w / enc_w.sum(dim=1, keepdim=True) - return torch.sum(enc_out * enc_w.unsqueeze(2), dim=1) - else: - raise ValueError(f"{self.args.init_attn_c} not supported") - - def forward( - self, - prev_output_tokens, - encoder_out=None, - incremental_state=None, - target_lengths=None, - **kwargs, - ): - enc_mask = encoder_out["encoder_padding_mask"] - enc_out = encoder_out["encoder_out"][0] - in_len = enc_out.size(1) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:, :] - bsz, out_len, _ = prev_output_tokens.size() - - prenet_out = self.prenet(prev_output_tokens) - (alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum) = self._get_states( - incremental_state, enc_out - ) - attn_ctx = self._get_init_attn_c(enc_out, enc_mask) - - attn_out = enc_out.new_zeros(bsz, in_len, out_len) - feat_out = enc_out.new_zeros(bsz, out_len, self.out_dim) - eos_out = enc_out.new_zeros(bsz, out_len) - for t in range(out_len): - alstm_in = torch.cat((attn_ctx, prenet_out[:, t, :]), dim=1) - alstm_h, alstm_c = self.attention_lstm(alstm_in, (alstm_h, alstm_c)) - - attn_state = attn_w.unsqueeze(1) - if self.args.attention_use_cumprob: - attn_state = torch.stack((attn_w, attn_w_cum), dim=1) - attn_ctx, attn_w = self.attention(enc_out, enc_mask, alstm_h, attn_state) - attn_w_cum = attn_w_cum + attn_w - attn_out[:, :, t] = attn_w - - for i, cur_lstm in enumerate(self.lstm): - if i == 0: - lstm_in = torch.cat((attn_ctx, alstm_h), dim=1) - else: - lstm_in = torch.cat((attn_ctx, lstm_h[i - 1]), dim=1) - lstm_h[i], lstm_c[i] = cur_lstm(lstm_in, (lstm_h[i], lstm_c[i])) - - proj_in = torch.cat((attn_ctx, lstm_h[-1]), dim=1) - feat_out[:, t, :] = self.feat_proj(proj_in) - eos_out[:, t] = self.eos_proj(proj_in).squeeze(1) - self.attention.clear_cache() - - self.set_incremental_state(incremental_state, "alstm_h", alstm_h) - self.set_incremental_state(incremental_state, "alstm_c", alstm_c) - self.set_incremental_state(incremental_state, "lstm_h", lstm_h) - self.set_incremental_state(incremental_state, "lstm_c", lstm_c) - self.set_incremental_state(incremental_state, "attn_w", attn_w) - self.set_incremental_state(incremental_state, "attn_w_cum", attn_w_cum) - - post_feat_out = feat_out + self.postnet(feat_out) - eos_out = eos_out.view(bsz, out_len, 1) - return post_feat_out, eos_out, {"attn": attn_out, "feature_out": feat_out} - - -@register_model("tacotron_2") -class Tacotron2Model(FairseqEncoderDecoderModel): - """ - Implementation for https://arxiv.org/pdf/1712.05884.pdf - """ - - @staticmethod - def add_args(parser): - # encoder - parser.add_argument("--encoder-dropout", type=float) - parser.add_argument("--encoder-embed-dim", type=int) - parser.add_argument("--encoder-conv-layers", type=int) - parser.add_argument("--encoder-conv-kernel-size", type=int) - parser.add_argument("--encoder-lstm-layers", type=int) - # decoder - parser.add_argument("--attention-dim", type=int) - parser.add_argument("--attention-conv-dim", type=int) - parser.add_argument("--attention-conv-kernel-size", type=int) - parser.add_argument("--prenet-dropout", type=float) - parser.add_argument("--prenet-layers", type=int) - parser.add_argument("--prenet-dim", type=int) - parser.add_argument("--postnet-dropout", type=float) - parser.add_argument("--postnet-layers", type=int) - parser.add_argument("--postnet-conv-dim", type=int) - parser.add_argument("--postnet-conv-kernel-size", type=int) - parser.add_argument("--init-attn-c", type=str) - parser.add_argument("--attention-use-cumprob", action="store_true") - parser.add_argument("--zoneout", type=float) - parser.add_argument("--decoder-lstm-layers", type=int) - parser.add_argument("--decoder-lstm-dim", type=int) - parser.add_argument("--output-frame-dim", type=int) - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._num_updates = 0 - - @classmethod - def build_model(cls, args, task): - embed_speaker = task.get_speaker_embeddings(args) - encoder = Tacotron2Encoder(args, task.src_dict, embed_speaker) - decoder = Tacotron2Decoder(args, task.src_dict) - return cls(encoder, decoder) - - def forward_encoder(self, src_tokens, src_lengths, **kwargs): - return self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - self._num_updates = num_updates - - -@register_model_architecture("tacotron_2", "tacotron_2") -def base_architecture(args): - # encoder - args.encoder_dropout = getattr(args, "encoder_dropout", 0.5) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3) - args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5) - args.encoder_lstm_layers = getattr(args, "encoder_lstm_layers", 1) - # decoder - args.attention_dim = getattr(args, "attention_dim", 128) - args.attention_conv_dim = getattr(args, "attention_conv_dim", 32) - args.attention_conv_kernel_size = getattr(args, "attention_conv_kernel_size", 15) - args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) - args.prenet_layers = getattr(args, "prenet_layers", 2) - args.prenet_dim = getattr(args, "prenet_dim", 256) - args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) - args.postnet_layers = getattr(args, "postnet_layers", 5) - args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) - args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) - args.init_attn_c = getattr(args, "init_attn_c", "zero") - args.attention_use_cumprob = getattr(args, "attention_use_cumprob", True) - args.zoneout = getattr(args, "zoneout", 0.1) - args.decoder_lstm_layers = getattr(args, "decoder_lstm_layers", 2) - args.decoder_lstm_dim = getattr(args, "decoder_lstm_dim", 1024) - args.output_frame_dim = getattr(args, "output_frame_dim", 80) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/tts_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/tts_transformer.py deleted file mode 100644 index 6b4a12b9a6f877b8b159a72ca7a9d2eeac171be0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/tts_transformer.py +++ /dev/null @@ -1,453 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from typing import List, Optional - -import torch -from fairseq import utils -from fairseq.data.data_utils import lengths_to_padding_mask -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) -from fairseq.models.text_to_speech.hub_interface import TTSHubInterface -from fairseq.models.text_to_speech.tacotron2 import Postnet, Prenet -from fairseq.modules import ( - FairseqDropout, - LayerNorm, - PositionalEmbedding, - TransformerDecoderLayer, - TransformerEncoderLayer, -) -from torch import nn - -logger = logging.getLogger(__name__) - - -def encoder_init(m): - if isinstance(m, nn.Conv1d): - nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("relu")) - - -def Embedding(num_embeddings, embedding_dim): - m = nn.Embedding(num_embeddings, embedding_dim) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - return m - - -class TTSTransformerEncoder(FairseqEncoder): - def __init__(self, args, src_dict, embed_speaker): - super().__init__(src_dict) - self.padding_idx = src_dict.pad() - self.embed_speaker = embed_speaker - self.spk_emb_proj = None - if embed_speaker is not None: - self.spk_emb_proj = nn.Linear( - args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim - ) - - self.dropout_module = FairseqDropout( - p=args.dropout, module_name=self.__class__.__name__ - ) - self.embed_tokens = nn.Embedding( - len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx - ) - assert args.encoder_conv_kernel_size % 2 == 1 - self.prenet = nn.ModuleList( - nn.Sequential( - nn.Conv1d( - args.encoder_embed_dim, - args.encoder_embed_dim, - kernel_size=args.encoder_conv_kernel_size, - padding=((args.encoder_conv_kernel_size - 1) // 2), - ), - nn.BatchNorm1d(args.encoder_embed_dim), - nn.ReLU(), - nn.Dropout(args.encoder_dropout), - ) - for _ in range(args.encoder_conv_layers) - ) - self.prenet_proj = nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim) - self.embed_positions = PositionalEmbedding( - args.max_source_positions, args.encoder_embed_dim, self.padding_idx - ) - self.pos_emb_alpha = nn.Parameter(torch.ones(1)) - - self.transformer_layers = nn.ModuleList( - TransformerEncoderLayer(args) - for _ in range(args.encoder_transformer_layers) - ) - if args.encoder_normalize_before: - self.layer_norm = LayerNorm(args.encoder_embed_dim) - else: - self.layer_norm = None - - self.apply(encoder_init) - - def forward(self, src_tokens, src_lengths=None, speaker=None, **kwargs): - x = self.embed_tokens(src_tokens) - x = x.transpose(1, 2).contiguous() # B x T x C -> B x C x T - for conv in self.prenet: - x = conv(x) - x = x.transpose(1, 2).contiguous() # B x C x T -> B x T x C - x = self.prenet_proj(x) - - padding_mask = src_tokens.eq(self.padding_idx) - positions = self.embed_positions(padding_mask) - x += self.pos_emb_alpha * positions - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - for layer in self.transformer_layers: - x = layer(x, padding_mask) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - if self.embed_speaker is not None: - seq_len, bsz, _ = x.size() - emb = self.embed_speaker(speaker).transpose(0, 1) - emb = emb.expand(seq_len, bsz, -1) - x = self.spk_emb_proj(torch.cat([x, emb], dim=2)) - - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [padding_mask] - if padding_mask.any() - else [], # B x T - "encoder_embedding": [], # B x T x C - "encoder_states": [], # List[T x B x C] - "src_tokens": [], - "src_lengths": [], - } - - -def decoder_init(m): - if isinstance(m, torch.nn.Conv1d): - nn.init.xavier_uniform_(m.weight, torch.nn.init.calculate_gain("tanh")) - - -class TTSTransformerDecoder(FairseqIncrementalDecoder): - def __init__(self, args, src_dict, padding_idx=1): - super().__init__(None) - self._future_mask = torch.empty(0) - - self.args = args - self.padding_idx = src_dict.pad() if src_dict else padding_idx - self.n_frames_per_step = args.n_frames_per_step - self.out_dim = args.output_frame_dim * args.n_frames_per_step - - self.dropout_module = FairseqDropout( - args.dropout, module_name=self.__class__.__name__ - ) - self.embed_positions = PositionalEmbedding( - args.max_target_positions, args.decoder_embed_dim, self.padding_idx - ) - self.pos_emb_alpha = nn.Parameter(torch.ones(1)) - self.prenet = nn.Sequential( - Prenet( - self.out_dim, args.prenet_layers, args.prenet_dim, args.prenet_dropout - ), - nn.Linear(args.prenet_dim, args.decoder_embed_dim), - ) - - self.n_transformer_layers = args.decoder_transformer_layers - self.transformer_layers = nn.ModuleList( - TransformerDecoderLayer(args) for _ in range(self.n_transformer_layers) - ) - if args.decoder_normalize_before: - self.layer_norm = LayerNorm(args.decoder_embed_dim) - else: - self.layer_norm = None - - self.feat_proj = nn.Linear(args.decoder_embed_dim, self.out_dim) - self.eos_proj = nn.Linear(args.decoder_embed_dim, 1) - - self.postnet = Postnet( - self.out_dim, - args.postnet_conv_dim, - args.postnet_conv_kernel_size, - args.postnet_layers, - args.postnet_dropout, - ) - - self.ctc_proj = None - if getattr(args, "ctc_weight", 0.0) > 0.0: - self.ctc_proj = nn.Linear(self.out_dim, len(src_dict)) - - self.apply(decoder_init) - - def extract_features( - self, - prev_outputs, - encoder_out=None, - incremental_state=None, - target_lengths=None, - speaker=None, - **kwargs, - ): - alignment_layer = self.n_transformer_layers - 1 - self_attn_padding_mask = lengths_to_padding_mask(target_lengths) - positions = self.embed_positions( - self_attn_padding_mask, incremental_state=incremental_state - ) - - if incremental_state is not None: - prev_outputs = prev_outputs[:, -1:, :] - self_attn_padding_mask = self_attn_padding_mask[:, -1:] - if positions is not None: - positions = positions[:, -1:] - - x = self.prenet(prev_outputs) - x += self.pos_emb_alpha * positions - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - if not self_attn_padding_mask.any(): - self_attn_padding_mask = None - - attn: Optional[torch.Tensor] = None - inner_states: List[Optional[torch.Tensor]] = [x] - for idx, transformer_layer in enumerate(self.transformer_layers): - if incremental_state is None: - self_attn_mask = self.buffered_future_mask(x) - else: - self_attn_mask = None - - x, layer_attn, _ = transformer_layer( - x, - encoder_out["encoder_out"][0] - if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) - else None, - encoder_out["encoder_padding_mask"][0] - if ( - encoder_out is not None - and len(encoder_out["encoder_padding_mask"]) > 0 - ) - else None, - incremental_state, - self_attn_mask=self_attn_mask, - self_attn_padding_mask=self_attn_padding_mask, - need_attn=bool((idx == alignment_layer)), - need_head_weights=bool((idx == alignment_layer)), - ) - inner_states.append(x) - if layer_attn is not None and idx == alignment_layer: - attn = layer_attn.float().to(x) - - if attn is not None: - # average probabilities over heads, transpose to - # (B, src_len, tgt_len) - attn = attn.mean(dim=0).transpose(2, 1) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - return x, {"attn": attn, "inner_states": inner_states} - - def forward( - self, - prev_output_tokens, - encoder_out=None, - incremental_state=None, - target_lengths=None, - speaker=None, - **kwargs, - ): - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - target_lengths=target_lengths, - speaker=speaker, - **kwargs, - ) - attn = extra["attn"] - feat_out = self.feat_proj(x) - bsz, seq_len, _ = x.size() - eos_out = self.eos_proj(x) - post_feat_out = feat_out + self.postnet(feat_out) - return ( - post_feat_out, - eos_out, - { - "attn": attn, - "feature_out": feat_out, - "inner_states": extra["inner_states"], - }, - ) - - def get_normalized_probs(self, net_output, log_probs, sample): - logits = self.ctc_proj(net_output[2]["feature_out"]) - if log_probs: - return utils.log_softmax(logits.float(), dim=-1) - else: - return utils.softmax(logits.float(), dim=-1) - - def buffered_future_mask(self, tensor): - dim = tensor.size(0) - # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. - if ( - self._future_mask.size(0) == 0 - or (not self._future_mask.device == tensor.device) - or self._future_mask.size(0) < dim - ): - self._future_mask = torch.triu( - utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 - ) - self._future_mask = self._future_mask.to(tensor) - return self._future_mask[:dim, :dim] - - -@register_model("tts_transformer") -class TTSTransformerModel(FairseqEncoderDecoderModel): - """ - Implementation for https://arxiv.org/pdf/1809.08895.pdf - """ - - @classmethod - def hub_models(cls): - base_url = "http://dl.fbaipublicfiles.com/fairseq/s2" - model_ids = [ - "tts_transformer-en-ljspeech", - "tts_transformer-en-200_speaker-cv4", - "tts_transformer-es-css10", - "tts_transformer-fr-cv7_css10", - "tts_transformer-ru-cv7_css10", - "tts_transformer-zh-cv7_css10", - "tts_transformer-ar-cv7_css10", - "tts_transformer-tr-cv7_css10", - "tts_transformer-vi-cv7", - ] - return {i: f"{base_url}/{i}.tar.gz" for i in model_ids} - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - config_yaml="config.yaml", - vocoder: str = "griffin_lim", - fp16: bool = False, - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - config_yaml=config_yaml, - vocoder=vocoder, - fp16=fp16, - **kwargs, - ) - return TTSHubInterface(x["args"], x["task"], x["models"][0]) - - @staticmethod - def add_args(parser): - parser.add_argument("--dropout", type=float) - parser.add_argument("--output-frame-dim", type=int) - parser.add_argument("--speaker-embed-dim", type=int) - # encoder prenet - parser.add_argument("--encoder-dropout", type=float) - parser.add_argument("--encoder-conv-layers", type=int) - parser.add_argument("--encoder-conv-kernel-size", type=int) - # encoder transformer layers - parser.add_argument("--encoder-transformer-layers", type=int) - parser.add_argument("--encoder-embed-dim", type=int) - parser.add_argument("--encoder-ffn-embed-dim", type=int) - parser.add_argument("--encoder-normalize-before", action="store_true") - parser.add_argument("--encoder-attention-heads", type=int) - parser.add_argument("--attention-dropout", type=float) - parser.add_argument("--activation-dropout", "--relu-dropout", type=float) - parser.add_argument("--activation-fn", type=str, default="relu") - # decoder prenet - parser.add_argument("--prenet-dropout", type=float) - parser.add_argument("--prenet-layers", type=int) - parser.add_argument("--prenet-dim", type=int) - # decoder postnet - parser.add_argument("--postnet-dropout", type=float) - parser.add_argument("--postnet-layers", type=int) - parser.add_argument("--postnet-conv-dim", type=int) - parser.add_argument("--postnet-conv-kernel-size", type=int) - # decoder transformer layers - parser.add_argument("--decoder-transformer-layers", type=int) - parser.add_argument("--decoder-embed-dim", type=int) - parser.add_argument("--decoder-ffn-embed-dim", type=int) - parser.add_argument("--decoder-normalize-before", action="store_true") - parser.add_argument("--decoder-attention-heads", type=int) - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._num_updates = 0 - - @classmethod - def build_model(cls, args, task): - embed_speaker = task.get_speaker_embeddings(args) - encoder = TTSTransformerEncoder(args, task.src_dict, embed_speaker) - decoder = TTSTransformerDecoder(args, task.src_dict) - return cls(encoder, decoder) - - def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs): - return self.encoder( - src_tokens, src_lengths=src_lengths, speaker=speaker, **kwargs - ) - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - self._num_updates = num_updates - - -@register_model_architecture("tts_transformer", "tts_transformer") -def base_architecture(args): - args.dropout = getattr(args, "dropout", 0.1) - args.output_frame_dim = getattr(args, "output_frame_dim", 80) - args.speaker_embed_dim = getattr(args, "speaker_embed_dim", 64) - # encoder prenet - args.encoder_dropout = getattr(args, "encoder_dropout", 0.5) - args.encoder_conv_layers = getattr(args, "encoder_conv_layers", 3) - args.encoder_conv_kernel_size = getattr(args, "encoder_conv_kernel_size", 5) - # encoder transformer layers - args.encoder_transformer_layers = getattr(args, "encoder_transformer_layers", 6) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr( - args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim - ) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - # decoder prenet - args.prenet_dropout = getattr(args, "prenet_dropout", 0.5) - args.prenet_layers = getattr(args, "prenet_layers", 2) - args.prenet_dim = getattr(args, "prenet_dim", 256) - # decoder postnet - args.postnet_dropout = getattr(args, "postnet_dropout", 0.5) - args.postnet_layers = getattr(args, "postnet_layers", 5) - args.postnet_conv_dim = getattr(args, "postnet_conv_dim", 512) - args.postnet_conv_kernel_size = getattr(args, "postnet_conv_kernel_size", 5) - # decoder transformer layers - args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim - ) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/vocoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/vocoder.py deleted file mode 100644 index da1984678f88b1441228c60a89a8828cd9dbcc9b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/text_to_speech/vocoder.py +++ /dev/null @@ -1,258 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import logging -from typing import Dict - -import numpy as np -import torch -import torch.nn.functional as F -from fairseq.data.audio.audio_utils import ( - TTSSpectrogram, - get_fourier_basis, - get_mel_filters, - get_window, -) -from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig -from fairseq.models.text_to_speech.codehifigan import CodeGenerator as CodeHiFiGANModel -from fairseq.models.text_to_speech.hifigan import Generator as HiFiGANModel -from torch import nn - -logger = logging.getLogger(__name__) - - -class PseudoInverseMelScale(torch.nn.Module): - def __init__(self, n_stft, n_mels, sample_rate, f_min, f_max) -> None: - super(PseudoInverseMelScale, self).__init__() - self.n_mels = n_mels - basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max) - basis = torch.pinverse(basis) # F x F_mel - self.register_buffer("basis", basis) - - def forward(self, melspec: torch.Tensor) -> torch.Tensor: - # pack batch - shape = melspec.shape # B_1 x ... x B_K x F_mel x T - n_mels, time = shape[-2], shape[-1] - melspec = melspec.view(-1, n_mels, time) - - freq, _ = self.basis.size() # F x F_mel - assert self.n_mels == n_mels, (self.n_mels, n_mels) - specgram = self.basis.matmul(melspec).clamp(min=0) - - # unpack batch - specgram = specgram.view(shape[:-2] + (freq, time)) - return specgram - - -class GriffinLim(torch.nn.Module): - def __init__( - self, - n_fft: int, - win_length: int, - hop_length: int, - n_iter: int, - window_fn=torch.hann_window, - ): - super(GriffinLim, self).__init__() - self.transform = TTSSpectrogram( - n_fft, win_length, hop_length, return_phase=True - ) - - basis = get_fourier_basis(n_fft) - basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :] - basis *= get_window(window_fn, n_fft, win_length) - self.register_buffer("basis", basis) - - self.n_fft = n_fft - self.win_length = win_length - self.hop_length = hop_length - self.n_iter = n_iter - - self.tiny = 1.1754944e-38 - - @classmethod - def get_window_sum_square( - cls, n_frames, hop_length, win_length, n_fft, window_fn=torch.hann_window - ) -> torch.Tensor: - w_sq = get_window(window_fn, n_fft, win_length) ** 2 - n = n_fft + hop_length * (n_frames - 1) - x = torch.zeros(n, dtype=torch.float32) - for i in range(n_frames): - ofst = i * hop_length - x[ofst : min(n, ofst + n_fft)] += w_sq[: max(0, min(n_fft, n - ofst))] - return x - - def inverse(self, magnitude: torch.Tensor, phase) -> torch.Tensor: - x = torch.cat( - [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 - ) - x = F.conv_transpose1d(x, self.basis, stride=self.hop_length) - win_sum_sq = self.get_window_sum_square( - magnitude.shape[-1], - hop_length=self.hop_length, - win_length=self.win_length, - n_fft=self.n_fft, - ).to(magnitude.device) - # remove modulation effects - approx_nonzero_indices = win_sum_sq > self.tiny - x[:, :, approx_nonzero_indices] /= win_sum_sq[approx_nonzero_indices] - x *= self.n_fft / self.hop_length - x = x[:, :, self.n_fft // 2 :] - x = x[:, :, : -self.n_fft // 2 :] - return x - - def forward(self, specgram: torch.Tensor) -> torch.Tensor: - angles = np.angle(np.exp(2j * np.pi * np.random.rand(*specgram.shape))) - angles = torch.from_numpy(angles).to(specgram) - _specgram = specgram.view(-1, specgram.shape[-2], specgram.shape[-1]) - waveform = self.inverse(_specgram, angles).squeeze(1) - for _ in range(self.n_iter): - _, angles = self.transform(waveform) - waveform = self.inverse(_specgram, angles).squeeze(1) - return waveform.squeeze(0) - - -class GriffinLimVocoder(nn.Module): - def __init__( - self, - sample_rate, - win_size, - hop_size, - n_fft, - n_mels, - f_min, - f_max, - window_fn, - spec_bwd_max_iter=32, - fp16=False, - ): - super().__init__() - self.inv_mel_transform = PseudoInverseMelScale( - n_stft=n_fft // 2 + 1, - n_mels=n_mels, - sample_rate=sample_rate, - f_min=f_min, - f_max=f_max, - ) - self.gl_transform = GriffinLim( - n_fft=n_fft, - win_length=win_size, - hop_length=hop_size, - window_fn=window_fn, - n_iter=spec_bwd_max_iter, - ) - if fp16: - self.half() - self.inv_mel_transform.half() - self.gl_transform.half() - else: - self.float() - self.inv_mel_transform.float() - self.gl_transform.float() - - def forward(self, x): - # x: (B x) T x D -> (B x) 1 x T - # NOTE: batched forward produces noisier waveform. recommend running - # one utterance at a time - self.eval() - x = x.exp().transpose(-1, -2) - x = self.inv_mel_transform(x) - x = self.gl_transform(x) - return x - - @classmethod - def from_data_cfg(cls, args, data_cfg: S2TDataConfig): - feat_cfg = data_cfg.config["features"] - window_fn = getattr(torch, feat_cfg["window_fn"] + "_window") - return cls( - sample_rate=feat_cfg["sample_rate"], - win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]), - hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]), - n_fft=feat_cfg["n_fft"], - n_mels=feat_cfg["n_mels"], - f_min=feat_cfg["f_min"], - f_max=feat_cfg["f_max"], - window_fn=window_fn, - spec_bwd_max_iter=args.spec_bwd_max_iter, - fp16=args.fp16, - ) - - -class HiFiGANVocoder(nn.Module): - def __init__( - self, checkpoint_path: str, model_cfg: Dict[str, str], fp16: bool = False - ) -> None: - super().__init__() - self.model = HiFiGANModel(model_cfg) - state_dict = torch.load(checkpoint_path) - self.model.load_state_dict(state_dict["generator"]) - if fp16: - self.model.half() - logger.info(f"loaded HiFiGAN checkpoint from {checkpoint_path}") - - def forward(self, x: torch.Tensor) -> torch.Tensor: - # (B x) T x D -> (B x) 1 x T - model = self.model.eval() - if len(x.shape) == 2: - return model(x.unsqueeze(0).transpose(1, 2)).detach().squeeze(0) - else: - return model(x.transpose(-1, -2)).detach() - - @classmethod - def from_data_cfg(cls, args, data_cfg: S2TDataConfig): - vocoder_cfg = data_cfg.vocoder - assert vocoder_cfg.get("type", "griffin_lim") == "hifigan" - with open(vocoder_cfg["config"]) as f: - model_cfg = json.load(f) - return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16) - - -class CodeHiFiGANVocoder(nn.Module): - def __init__( - self, checkpoint_path: str, model_cfg: Dict[str, str], fp16: bool = False - ) -> None: - super().__init__() - self.model = CodeHiFiGANModel(model_cfg) - state_dict = torch.load(checkpoint_path) - self.model.load_state_dict(state_dict["generator"]) - self.model.eval() - if fp16: - self.model.half() - self.model.remove_weight_norm() - logger.info(f"loaded CodeHiFiGAN checkpoint from {checkpoint_path}") - - def forward(self, x: Dict[str, torch.Tensor], dur_prediction=False) -> torch.Tensor: - assert "code" in x - x["dur_prediction"] = dur_prediction - - # remove invalid code - mask = x["code"] >= 0 - x["code"] = x["code"][mask].unsqueeze(dim=0) - if "f0" in x: - f0_up_ratio = x["f0"].size(1) // x["code"].size(1) - mask = mask.unsqueeze(2).repeat(1, 1, f0_up_ratio).view(-1, x["f0"].size(1)) - x["f0"] = x["f0"][mask].unsqueeze(dim=0) - - return self.model(**x).detach().squeeze() - - @classmethod - def from_data_cfg(cls, args, data_cfg): - vocoder_cfg = data_cfg.vocoder - assert vocoder_cfg is not None, "vocoder not specified in the data config" - with open(vocoder_cfg["config"]) as f: - model_cfg = json.load(f) - return cls(vocoder_cfg["checkpoint"], model_cfg, fp16=args.fp16) - - -def get_vocoder(args, data_cfg: S2TDataConfig): - if args.vocoder == "griffin_lim": - return GriffinLimVocoder.from_data_cfg(args, data_cfg) - elif args.vocoder == "hifigan": - return HiFiGANVocoder.from_data_cfg(args, data_cfg) - elif args.vocoder == "code_hifigan": - return CodeHiFiGANVocoder.from_data_cfg(args, data_cfg) - else: - raise ValueError("Unknown vocoder") diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/__init__.py deleted file mode 100644 index 681fca3d4553f6832a65f61fc186793bc4ee0679..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/__init__.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) Facebook Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -from .transformer_config import ( - TransformerConfig, - DEFAULT_MAX_SOURCE_POSITIONS, - DEFAULT_MAX_TARGET_POSITIONS, - DEFAULT_MIN_PARAMS_TO_WRAP, -) -from .transformer_decoder import TransformerDecoder, TransformerDecoderBase, Linear -from .transformer_encoder import TransformerEncoder, TransformerEncoderBase -from .transformer_legacy import ( - TransformerModel, - base_architecture, - tiny_architecture, - transformer_iwslt_de_en, - transformer_wmt_en_de, - transformer_vaswani_wmt_en_de_big, - transformer_vaswani_wmt_en_fr_big, - transformer_wmt_en_de_big, - transformer_wmt_en_de_big_t2t, -) -from .transformer_base import TransformerModelBase, Embedding - - -__all__ = [ - "TransformerModelBase", - "TransformerConfig", - "TransformerDecoder", - "TransformerDecoderBase", - "TransformerEncoder", - "TransformerEncoderBase", - "TransformerModel", - "Embedding", - "Linear", - "base_architecture", - "tiny_architecture", - "transformer_iwslt_de_en", - "transformer_wmt_en_de", - "transformer_vaswani_wmt_en_de_big", - "transformer_vaswani_wmt_en_fr_big", - "transformer_wmt_en_de_big", - "transformer_wmt_en_de_big_t2t", - "DEFAULT_MAX_SOURCE_POSITIONS", - "DEFAULT_MAX_TARGET_POSITIONS", - "DEFAULT_MIN_PARAMS_TO_WRAP", -] diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_base.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_base.py deleted file mode 100644 index 502204f7d7e968dd83ef0f6dcd290cc84d859ac3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_base.py +++ /dev/null @@ -1,175 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.distributed import fsdp_wrap -from fairseq.models import FairseqEncoderDecoderModel -from fairseq.models.transformer import ( - TransformerConfig, - TransformerDecoderBase, - TransformerEncoderBase, -) -from torch import Tensor - - -class TransformerModelBase(FairseqEncoderDecoderModel): - """ - Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017) - `_. - - Args: - encoder (TransformerEncoder): the encoder - decoder (TransformerDecoder): the decoder - - The Transformer model provides the following named architectures and - command-line arguments: - - .. argparse:: - :ref: fairseq.models.transformer_parser - :prog: - """ - - def __init__(self, cfg, encoder, decoder): - super().__init__(encoder, decoder) - self.cfg = cfg - self.supports_align_args = True - - @classmethod - def add_args(cls, parser): - """Add model-specific arguments to the parser.""" - # we want to build the args recursively in this case. - gen_parser_from_dataclass( - parser, TransformerConfig(), delete_default=False, with_prefix="" - ) - - @classmethod - def build_model(cls, cfg, task): - """Build a new model instance.""" - - # -- TODO T96535332 - # bug caused by interaction between OmegaConf II and argparsing - cfg.decoder.input_dim = int(cfg.decoder.input_dim) - cfg.decoder.output_dim = int(cfg.decoder.output_dim) - # -- - - if cfg.encoder.layers_to_keep: - cfg.encoder.layers = len(cfg.encoder.layers_to_keep.split(",")) - if cfg.decoder.layers_to_keep: - cfg.decoder.layers = len(cfg.decoder.layers_to_keep.split(",")) - - src_dict, tgt_dict = task.source_dictionary, task.target_dictionary - - if cfg.share_all_embeddings: - if src_dict != tgt_dict: - raise ValueError("--share-all-embeddings requires a joined dictionary") - if cfg.encoder.embed_dim != cfg.decoder.embed_dim: - raise ValueError( - "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" - ) - if cfg.decoder.embed_path and ( - cfg.decoder.embed_path != cfg.encoder.embed_path - ): - raise ValueError( - "--share-all-embeddings not compatible with --decoder-embed-path" - ) - encoder_embed_tokens = cls.build_embedding( - cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path - ) - decoder_embed_tokens = encoder_embed_tokens - cfg.share_decoder_input_output_embed = True - else: - encoder_embed_tokens = cls.build_embedding( - cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path - ) - decoder_embed_tokens = cls.build_embedding( - cfg, tgt_dict, cfg.decoder.embed_dim, cfg.decoder.embed_path - ) - if cfg.offload_activations: - cfg.checkpoint_activations = True # offloading implies checkpointing - encoder = cls.build_encoder(cfg, src_dict, encoder_embed_tokens) - decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) - return cls(cfg, encoder, decoder) - - @classmethod - def build_embedding(cls, cfg, dictionary, embed_dim, path=None): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - - emb = Embedding(num_embeddings, embed_dim, padding_idx) - # if provided, load from preloaded dictionaries - if path: - embed_dict = utils.parse_embedding(path) - utils.load_embedding(embed_dict, dictionary, emb) - return emb - - @classmethod - def build_encoder(cls, cfg, src_dict, embed_tokens): - return TransformerEncoderBase(cfg, src_dict, embed_tokens) - - @classmethod - def build_decoder(cls, cfg, tgt_dict, embed_tokens): - return TransformerDecoderBase( - cfg, - tgt_dict, - embed_tokens, - no_encoder_attn=cfg.no_cross_attention, - ) - - # TorchScript doesn't support optional arguments with variable length (**kwargs). - # Current workaround is to add union of all arguments in child classes. - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens, - return_all_hiddens: bool = True, - features_only: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - """ - Run the forward pass for an encoder-decoder model. - - Copied from the base class, but without ``**kwargs``, - which are not supported by TorchScript. - """ - encoder_out = self.encoder( - src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens - ) - decoder_out = self.decoder( - prev_output_tokens, - encoder_out=encoder_out, - features_only=features_only, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - src_lengths=src_lengths, - return_all_hiddens=return_all_hiddens, - ) - return decoder_out - - # Since get_normalized_probs is in the Fairseq Model which is not scriptable, - # I rewrite the get_normalized_probs from Base Class to call the - # helper function in the Base Class. - @torch.jit.export - def get_normalized_probs( - self, - net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]], - log_probs: bool, - sample: Optional[Dict[str, Tensor]] = None, - ): - """Get normalized probabilities (or log probs) from a net's output.""" - return self.get_normalized_probs_scriptable(net_output, log_probs, sample) - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - nn.init.constant_(m.weight[padding_idx], 0) - return m diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_config.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_config.py deleted file mode 100644 index 0d67de6915f99bfdb124a11333410beb06d1dfb5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_config.py +++ /dev/null @@ -1,332 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import re -from dataclasses import dataclass, field, fields -from typing import List, Optional - -from fairseq import utils -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.utils import safe_getattr, safe_hasattr -from omegaconf import II - -DEFAULT_MAX_SOURCE_POSITIONS = 1024 -DEFAULT_MAX_TARGET_POSITIONS = 1024 - -DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8) - -_NAME_PARSER = r"(decoder|encoder|quant_noise)_(.*)" - - -@dataclass -class EncDecBaseConfig(FairseqDataclass): - embed_path: Optional[str] = field( - default=None, metadata={"help": "path to pre-trained embedding"} - ) - embed_dim: Optional[int] = field( - default=512, metadata={"help": "embedding dimension"} - ) - ffn_embed_dim: int = field( - default=2048, metadata={"help": "embedding dimension for FFN"} - ) - layers: int = field(default=6, metadata={"help": "number of layers"}) - attention_heads: int = field( - default=8, metadata={"help": "number of attention heads"} - ) - normalize_before: bool = field( - default=False, metadata={"help": "apply layernorm before each block"} - ) - learned_pos: bool = field( - default=False, metadata={"help": "use learned positional embeddings"} - ) - # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) - layerdrop: float = field(default=0, metadata={"help": "LayerDrop probability"}) - layers_to_keep: Optional[List[int]] = field( - default=None, metadata={"help": "which layers to *keep* when pruning"} - ) - - xformers_att_config: Optional[str] = field( - default=None, - metadata={ - "help": "config for xFormers attention, defined in xformers.components.attention.AttentionConfig" - }, - ) - - -@dataclass -class DecoderConfig(EncDecBaseConfig): - input_dim: int = II("model.decoder.embed_dim") - output_dim: int = field( - default=II("model.decoder.embed_dim"), - metadata={ - "help": "decoder output dimension (extra linear layer if different from decoder embed dim)" - }, - ) - - def __post_init__(self): - # II doesn't work if we are just creating the object outside of hydra so fix that - if self.input_dim == II("model.decoder.embed_dim"): - self.input_dim = self.embed_dim - if self.output_dim == II("model.decoder.embed_dim"): - self.output_dim = self.embed_dim - - -@dataclass -class QuantNoiseConfig(FairseqDataclass): - pq: float = field( - default=0.0, - metadata={"help": "iterative PQ quantization noise at training time"}, - ) - pq_block_size: int = field( - default=8, - metadata={"help": "block size of quantization noise at training time"}, - ) - scalar: float = field( - default=0.0, - metadata={ - "help": "scalar quantization noise and scalar quantization at training time" - }, - ) - - -@dataclass -class TransformerConfig(FairseqDataclass): - activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( - default="relu", - metadata={"help": "activation function to use"}, - ) - dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) - attention_dropout: float = field( - default=0.0, metadata={"help": "dropout probability for attention weights"} - ) - activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN.", - "alias": "--relu-dropout", - }, - ) - adaptive_input: bool = False - encoder: EncDecBaseConfig = EncDecBaseConfig() - # TODO should really be in the encoder config - max_source_positions: int = field( - default=DEFAULT_MAX_SOURCE_POSITIONS, - metadata={"help": "Maximum input length supported by the encoder"}, - ) - decoder: DecoderConfig = DecoderConfig() - # TODO should really be in the decoder config - max_target_positions: int = field( - default=DEFAULT_MAX_TARGET_POSITIONS, - metadata={"help": "Maximum output length supported by the decoder"}, - ) - share_decoder_input_output_embed: bool = field( - default=False, metadata={"help": "share decoder input and output embeddings"} - ) - share_all_embeddings: bool = field( - default=False, - metadata={ - "help": "share encoder, decoder and output embeddings (requires shared dictionary and embed dim)" - }, - ) - no_token_positional_embeddings: bool = field( - default=False, - metadata={ - "help": "if True, disables positional embeddings (outside self attention)" - }, - ) - adaptive_softmax_cutoff: Optional[List[int]] = field( - default=None, - metadata={ - "help": "list of adaptive softmax cutoff points. Must be used with adaptive_loss criterion" - }, - ) - adaptive_softmax_dropout: float = field( - default=0.0, - metadata={"help": "sets adaptive softmax dropout for the tail projections"}, - ) - adaptive_softmax_factor: float = field( - default=4, metadata={"help": "adaptive input factor"} - ) - layernorm_embedding: bool = field( - default=False, metadata={"help": "add layernorm to embedding"} - ) - tie_adaptive_weights: bool = field( - default=False, - metadata={ - "help": "if set, ties the weights of adaptive softmax and adaptive input" - }, - ) - tie_adaptive_proj: bool = field( - default=False, - metadata={ - "help": "if set, ties the projection weights of adaptive softmax and adaptive input" - }, - ) - no_scale_embedding: bool = field( - default=False, metadata={"help": "if True, dont scale embeddings"} - ) - checkpoint_activations: bool = field( - default=False, - metadata={ - "help": "checkpoint activations at each layer, which saves GPU memory usage at the cost of some additional compute" - }, - ) - offload_activations: bool = field( - default=False, - metadata={ - "help": "checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations." - }, - ) - # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019) - no_cross_attention: bool = field( - default=False, metadata={"help": "do not perform cross-attention"} - ) - cross_self_attention: bool = field( - default=False, metadata={"help": "perform cross+self-attention"} - ) - # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) - quant_noise: QuantNoiseConfig = field(default=QuantNoiseConfig()) - min_params_to_wrap: int = field( - default=DEFAULT_MIN_PARAMS_TO_WRAP, - metadata={ - "help": "minimum number of params for a layer to be wrapped with FSDP() when " - "training with --ddp-backend=fully_sharded. Smaller values will " - "improve memory efficiency, but may make torch.distributed " - "communication less efficient due to smaller input sizes. This option " - "is set to 0 (i.e., always wrap) when --checkpoint-activations or " - "--offload-activations are passed." - }, - ) - # DEPRECATED field, but some old checkpoints might have it - char_inputs: bool = field( - default=False, metadata={"help": "if set, model takes character ids as input"} - ) - relu_dropout: float = 0.0 - # config for "BASE Layers: Simplifying Training of Large, Sparse Models" - base_layers: Optional[int] = field( - default=0, metadata={"help": "number of BASE layers in total"} - ) - base_sublayers: Optional[int] = field( - default=1, metadata={"help": "number of sublayers in each BASE layer"} - ) - base_shuffle: Optional[int] = field( - default=1, - metadata={"help": "shuffle tokens between workers before computing assignment"}, - ) - - export: bool = field( - default=False, - metadata={"help": "make the layernorm exportable with torchscript."}, - ) - - # copied from transformer_lm but expected in transformer_decoder: - no_decoder_final_norm: bool = field( - default=False, - metadata={"help": "don't add an extra layernorm after the last decoder block"}, - ) - - # We need to make this hierarchical dataclass like the flat namespace - # __getattr__ and __setattr__ here allow backward compatibility - # for subclasses of Transformer(Legacy) that depend on read/write on - # the flat namespace. - - def __getattr__(self, name): - match = re.match(_NAME_PARSER, name) - if match: - sub = safe_getattr(self, match[1]) - return safe_getattr(sub, match[2]) - raise AttributeError(f"invalid argument {name}.") - - def __setattr__(self, name, value): - match = re.match(_NAME_PARSER, name) - if match: - sub = safe_getattr(self, match[1]) - setattr(sub, match[2], value) - else: - super().__setattr__(name, value) - - @staticmethod - def _copy_keys(args, cls, prefix, seen): - """ - copy the prefixed keys (decoder_embed_dim) to the DC fields: decoder.embed_dim - """ - cfg = cls() - for fld in fields(cls): - # for all the fields in the DC, find the fields (e.g. embed_dim) - # in the namespace with the prefix (e.g. decoder) - # and set it on the dc. - args_key = f"{prefix}_{fld.name}" - if safe_hasattr(args, args_key): - seen.add(args_key) - setattr(cfg, fld.name, safe_getattr(args, args_key)) - if safe_hasattr(args, fld.name): - seen.add(fld.name) - setattr(cfg, fld.name, safe_getattr(args, fld.name)) - return cfg - - @classmethod - def from_namespace(cls, args): - if args is None: - return None - if not isinstance(args, cls): - seen = set() - config = cls() - # currently, we can go generically from DC fields to args hierarchically - # but we can't easily deconstruct a flat namespace to a hierarchical - # DC. Mostly because we could have a sub-dc called `decoder-foo` that should not - # go to the sub struct called `decoder`. There are ways to go around this, but let's keep it simple - # for now. - for fld in fields(cls): - # concretelly, the transformer_config know what sub-dc it has, so we go through all the dc fields - # and if it's one that has a sub-dc, we build that sub-dc with `copy_keys()` - if fld.name == "decoder": - if safe_hasattr(args, "decoder"): - # in some cases, the args we receive is already structured (as DictConfigs), so let's just build the correct DC - seen.add("decoder") - config.decoder = DecoderConfig(**args.decoder) - else: - config.decoder = cls._copy_keys( - args, DecoderConfig, "decoder", seen - ) - elif fld.name == "encoder": - # same but for encoder - if safe_hasattr(args, "encoder"): - seen.add("encoder") - config.encoder = EncDecBaseConfig(**args.encoder) - else: - config.encoder = cls._copy_keys( - args, EncDecBaseConfig, "encoder", seen - ) - elif fld.name == "quant_noise": - # same but for quant_noise - if safe_hasattr(args, "quant_noise"): - seen.add("quant_noise") - config.quant_noise = QuantNoiseConfig(**args.quant_noise) - else: - config.quant_noise = cls._copy_keys( - args, QuantNoiseConfig, "quant_noise", seen - ) - elif safe_hasattr(args, fld.name): - # if it's not a structure field, it's just a normal field, copy it over - seen.add(fld.name) - setattr(config, fld.name, safe_getattr(args, fld.name)) - # we got all the fields defined in the dataclass, but - # the argparse namespace might have extra args for two reasons: - # - we are in a legacy class so all the args are not declared in the dataclass. Ideally once everyone has defined a dataclass for their model, we won't need this - # - some places expect args to be there but never define them - args_dict = ( - args._asdict() - if safe_hasattr(args, "_asdict") - else vars(args) - if safe_hasattr(args, "__dict__") - else {} - ) # namedtupled doesn't have __dict__ :-/ - for key, value in args_dict.items(): - if key not in seen: - setattr(config, key, value) - return config - else: - return args diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_decoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_decoder.py deleted file mode 100644 index a3bdf14fcee059b607fd82e6deecef816cb8bcc1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_decoder.py +++ /dev/null @@ -1,479 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Any, Dict, List, Optional - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.distributed import fsdp_wrap -from fairseq.models import FairseqIncrementalDecoder -from fairseq.models.transformer import TransformerConfig -from fairseq.modules import ( - AdaptiveSoftmax, - BaseLayer, - FairseqDropout, - LayerDropModuleList, - LayerNorm, - PositionalEmbedding, - SinusoidalPositionalEmbedding, - transformer_layer, -) -from fairseq.modules.checkpoint_activations import checkpoint_wrapper -from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ -from torch import Tensor - - -# rewrite name for backward compatibility in `make_generation_fast_` -def module_name_fordropout(module_name: str) -> str: - if module_name == "TransformerDecoderBase": - return "TransformerDecoder" - else: - return module_name - - -class TransformerDecoderBase(FairseqIncrementalDecoder): - """ - Transformer decoder consisting of *cfg.decoder.layers* layers. Each layer - is a :class:`TransformerDecoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): decoding dictionary - embed_tokens (torch.nn.Embedding): output embedding - no_encoder_attn (bool, optional): whether to attend to encoder outputs - (default: False). - """ - - def __init__( - self, - cfg, - dictionary, - embed_tokens, - no_encoder_attn=False, - output_projection=None, - ): - self.cfg = cfg - super().__init__(dictionary) - self.register_buffer("version", torch.Tensor([3])) - self._future_mask = torch.empty(0) - - self.dropout_module = FairseqDropout( - cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__) - ) - self.decoder_layerdrop = cfg.decoder.layerdrop - self.share_input_output_embed = cfg.share_decoder_input_output_embed - - input_embed_dim = embed_tokens.embedding_dim - embed_dim = cfg.decoder.embed_dim - self.embed_dim = embed_dim - self.output_embed_dim = cfg.decoder.output_dim - - self.padding_idx = embed_tokens.padding_idx - self.max_target_positions = cfg.max_target_positions - - self.embed_tokens = embed_tokens - - self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim) - - if not cfg.adaptive_input and cfg.quant_noise.pq > 0: - self.quant_noise = apply_quant_noise_( - nn.Linear(embed_dim, embed_dim, bias=False), - cfg.quant_noise.pq, - cfg.quant_noise.pq_block_size, - ) - else: - self.quant_noise = None - - self.project_in_dim = ( - Linear(input_embed_dim, embed_dim, bias=False) - if embed_dim != input_embed_dim - else None - ) - self.embed_positions = ( - PositionalEmbedding( - self.max_target_positions, - embed_dim, - self.padding_idx, - learned=cfg.decoder.learned_pos, - ) - if not cfg.no_token_positional_embeddings - else None - ) - if cfg.layernorm_embedding: - self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export) - else: - self.layernorm_embedding = None - - self.cross_self_attention = cfg.cross_self_attention - - if self.decoder_layerdrop > 0.0: - self.layers = LayerDropModuleList(p=self.decoder_layerdrop) - else: - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - self.build_decoder_layer(cfg, no_encoder_attn) - for _ in range(cfg.decoder.layers) - ] - ) - self.num_layers = len(self.layers) - - if cfg.decoder.normalize_before and not cfg.no_decoder_final_norm: - self.layer_norm = LayerNorm(embed_dim, export=cfg.export) - else: - self.layer_norm = None - - self.project_out_dim = ( - Linear(embed_dim, self.output_embed_dim, bias=False) - if embed_dim != self.output_embed_dim and not cfg.tie_adaptive_weights - else None - ) - - self.adaptive_softmax = None - self.output_projection = output_projection - if self.output_projection is None: - self.build_output_projection(cfg, dictionary, embed_tokens) - - def build_output_projection(self, cfg, dictionary, embed_tokens): - if cfg.adaptive_softmax_cutoff is not None: - self.adaptive_softmax = AdaptiveSoftmax( - len(dictionary), - self.output_embed_dim, - utils.eval_str_list(cfg.adaptive_softmax_cutoff, type=int), - dropout=cfg.adaptive_softmax_dropout, - adaptive_inputs=embed_tokens if cfg.tie_adaptive_weights else None, - factor=cfg.adaptive_softmax_factor, - tie_proj=cfg.tie_adaptive_proj, - ) - elif self.share_input_output_embed: - self.output_projection = nn.Linear( - self.embed_tokens.weight.shape[1], - self.embed_tokens.weight.shape[0], - bias=False, - ) - self.output_projection.weight = self.embed_tokens.weight - else: - self.output_projection = nn.Linear( - self.output_embed_dim, len(dictionary), bias=False - ) - nn.init.normal_( - self.output_projection.weight, mean=0, std=self.output_embed_dim**-0.5 - ) - num_base_layers = cfg.base_layers - for i in range(num_base_layers): - self.layers.insert( - ((i + 1) * cfg.decoder.layers) // (num_base_layers + 1), - BaseLayer(cfg), - ) - - def build_decoder_layer(self, cfg, no_encoder_attn=False): - layer = transformer_layer.TransformerDecoderLayerBase(cfg, no_encoder_attn) - checkpoint = cfg.checkpoint_activations - if checkpoint: - offload_to_cpu = cfg.offload_activations - layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) - # if we are checkpointing, enforce that FSDP always wraps the - # checkpointed layer, regardless of layer size - min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 - layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) - return layer - - def forward( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - features_only: bool = False, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - src_lengths: Optional[Any] = None, - return_all_hiddens: bool = False, - ): - """ - Args: - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (optional): output from the encoder, used for - encoder-side attention, should be of size T x B x C - incremental_state (dict): dictionary used for storing state during - :ref:`Incremental decoding` - features_only (bool, optional): only return features without - applying output layer (default: False). - full_context_alignment (bool, optional): don't apply - auto-regressive mask to self-attention (default: False). - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - full_context_alignment=full_context_alignment, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - ) - - if not features_only: - x = self.output_layer(x) - return x, extra - - def extract_features( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - return self.extract_features_scriptable( - prev_output_tokens, - encoder_out, - incremental_state, - full_context_alignment, - alignment_layer, - alignment_heads, - ) - - """ - A scriptable subclass of this class has an extract_features method and calls - super().extract_features, but super() is not supported in torchscript. A copy of - this function is made to be used in the subclass instead. - """ - - def extract_features_scriptable( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - """ - Similar to *forward* but only return features. - - Includes several features from "Jointly Learning to Align and - Translate with Transformer Models" (Garg et al., EMNLP 2019). - - Args: - full_context_alignment (bool, optional): don't apply - auto-regressive mask to self-attention (default: False). - alignment_layer (int, optional): return mean alignment over - heads at this layer (default: last layer). - alignment_heads (int, optional): only average alignment over - this many heads (default: all heads). - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - bs, slen = prev_output_tokens.size() - if alignment_layer is None: - alignment_layer = self.num_layers - 1 - - enc: Optional[Tensor] = None - padding_mask: Optional[Tensor] = None - if encoder_out is not None and len(encoder_out["encoder_out"]) > 0: - enc = encoder_out["encoder_out"][0] - if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0: - padding_mask = encoder_out["encoder_padding_mask"][0] - - # embed positions - positions = None - if self.embed_positions is not None: - positions = self.embed_positions( - prev_output_tokens, incremental_state=incremental_state - ) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - if positions is not None: - positions = positions[:, -1:] - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - - if self.quant_noise is not None: - x = self.quant_noise(x) - - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - self_attn_padding_mask: Optional[Tensor] = None - if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): - self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) - - # decoder layers - attn: Optional[Tensor] = None - inner_states: List[Optional[Tensor]] = [x] - for idx, layer in enumerate(self.layers): - if incremental_state is None and not full_context_alignment: - self_attn_mask = self.buffered_future_mask(x) - else: - self_attn_mask = None - - x, layer_attn, _ = layer( - x, - enc, - padding_mask, - incremental_state, - self_attn_mask=self_attn_mask, - self_attn_padding_mask=self_attn_padding_mask, - need_attn=bool((idx == alignment_layer)), - need_head_weights=bool((idx == alignment_layer)), - ) - inner_states.append(x) - if layer_attn is not None and idx == alignment_layer: - attn = layer_attn.float().to(x) - - if attn is not None: - if alignment_heads is not None: - attn = attn[:alignment_heads] - - # average probabilities over heads - attn = attn.mean(dim=0) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - - return x, {"attn": [attn], "inner_states": inner_states} - - def output_layer(self, features): - """Project features to the vocabulary size.""" - if self.adaptive_softmax is None: - # project back to size of vocabulary - return self.output_projection(features) - else: - return features - - def max_positions(self): - """Maximum output length supported by the decoder.""" - if self.embed_positions is None: - return self.max_target_positions - return min(self.max_target_positions, self.embed_positions.max_positions) - - def buffered_future_mask(self, tensor): - dim = tensor.size(0) - # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround. - if ( - self._future_mask.size(0) == 0 - or (not self._future_mask.device == tensor.device) - or self._future_mask.size(0) < dim - ): - self._future_mask = torch.triu( - utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1 - ) - self._future_mask = self._future_mask.to(tensor) - return self._future_mask[:dim, :dim] - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): - weights_key = "{}.embed_positions.weights".format(name) - if weights_key in state_dict: - del state_dict[weights_key] - state_dict[ - "{}.embed_positions._float_tensor".format(name) - ] = torch.FloatTensor(1) - - if f"{name}.output_projection.weight" not in state_dict: - if self.share_input_output_embed: - embed_out_key = f"{name}.embed_tokens.weight" - else: - embed_out_key = f"{name}.embed_out" - if embed_out_key in state_dict: - state_dict[f"{name}.output_projection.weight"] = state_dict[ - embed_out_key - ] - if not self.share_input_output_embed: - del state_dict[embed_out_key] - - for i in range(self.num_layers): - # update layer norms - layer_norm_map = { - "0": "self_attn_layer_norm", - "1": "encoder_attn_layer_norm", - "2": "final_layer_norm", - } - for old, new in layer_norm_map.items(): - for m in ("weight", "bias"): - k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m) - if k in state_dict: - state_dict[ - "{}.layers.{}.{}.{}".format(name, i, new, m) - ] = state_dict[k] - del state_dict[k] - - version_key = "{}.version".format(name) - if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2: - # earlier checkpoints did not normalize after the stack of layers - self.layer_norm = None - self.normalize = False - state_dict[version_key] = torch.Tensor([1]) - - return state_dict - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m - - -class TransformerDecoder(TransformerDecoderBase): - def __init__( - self, - args, - dictionary, - embed_tokens, - no_encoder_attn=False, - output_projection=None, - ): - self.args = args - super().__init__( - TransformerConfig.from_namespace(args), - dictionary, - embed_tokens, - no_encoder_attn=no_encoder_attn, - output_projection=output_projection, - ) - - def build_output_projection(self, args, dictionary, embed_tokens): - super().build_output_projection( - TransformerConfig.from_namespace(args), dictionary, embed_tokens - ) - - def build_decoder_layer(self, args, no_encoder_attn=False): - return super().build_decoder_layer( - TransformerConfig.from_namespace(args), no_encoder_attn=no_encoder_attn - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_encoder.py deleted file mode 100644 index b1c37132c8825ab61ab56960bc1e9a6fbd4fa0f7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_encoder.py +++ /dev/null @@ -1,365 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, List, Optional - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.distributed import fsdp_wrap -from fairseq.models import FairseqEncoder -from fairseq.models.transformer import ( - TransformerConfig, -) -from fairseq.modules import ( - FairseqDropout, - LayerDropModuleList, - LayerNorm, - PositionalEmbedding, - SinusoidalPositionalEmbedding, - transformer_layer, -) -from fairseq.modules.checkpoint_activations import checkpoint_wrapper -from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ -from torch import Tensor - - -# rewrite name for backward compatibility in `make_generation_fast_` -def module_name_fordropout(module_name: str) -> str: - if module_name == "TransformerEncoderBase": - return "TransformerEncoder" - else: - return module_name - - -class TransformerEncoderBase(FairseqEncoder): - """ - Transformer encoder consisting of *cfg.encoder.layers* layers. Each layer - is a :class:`TransformerEncoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): encoding dictionary - embed_tokens (torch.nn.Embedding): input embedding - """ - - def __init__(self, cfg, dictionary, embed_tokens, return_fc=False): - self.cfg = cfg - super().__init__(dictionary) - self.register_buffer("version", torch.Tensor([3])) - - self.dropout_module = FairseqDropout( - cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__) - ) - self.encoder_layerdrop = cfg.encoder.layerdrop - self.return_fc = return_fc - - embed_dim = embed_tokens.embedding_dim - self.padding_idx = embed_tokens.padding_idx - self.max_source_positions = cfg.max_source_positions - - self.embed_tokens = embed_tokens - - self.embed_scale = 1.0 if cfg.no_scale_embedding else math.sqrt(embed_dim) - - self.embed_positions = ( - PositionalEmbedding( - cfg.max_source_positions, - embed_dim, - self.padding_idx, - learned=cfg.encoder.learned_pos, - ) - if not cfg.no_token_positional_embeddings - else None - ) - if cfg.layernorm_embedding: - self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export) - else: - self.layernorm_embedding = None - - if not cfg.adaptive_input and cfg.quant_noise.pq > 0: - self.quant_noise = apply_quant_noise_( - nn.Linear(embed_dim, embed_dim, bias=False), - cfg.quant_noise.pq, - cfg.quant_noise.pq_block_size, - ) - else: - self.quant_noise = None - - if self.encoder_layerdrop > 0.0: - self.layers = LayerDropModuleList(p=self.encoder_layerdrop) - else: - self.layers = nn.ModuleList([]) - self.layers.extend( - [self.build_encoder_layer(cfg) for i in range(cfg.encoder.layers)] - ) - self.num_layers = len(self.layers) - - if cfg.encoder.normalize_before: - self.layer_norm = LayerNorm(embed_dim, export=cfg.export) - else: - self.layer_norm = None - - def build_encoder_layer(self, cfg): - layer = transformer_layer.TransformerEncoderLayerBase( - cfg, return_fc=self.return_fc - ) - checkpoint = cfg.checkpoint_activations - if checkpoint: - offload_to_cpu = cfg.offload_activations - layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) - # if we are checkpointing, enforce that FSDP always wraps the - # checkpointed layer, regardless of layer size - min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 - layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) - return layer - - def forward_embedding( - self, src_tokens, token_embedding: Optional[torch.Tensor] = None - ): - # embed tokens and positions - if token_embedding is None: - token_embedding = self.embed_tokens(src_tokens) - x = embed = self.embed_scale * token_embedding - if self.embed_positions is not None: - x = embed + self.embed_positions(src_tokens) - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - x = self.dropout_module(x) - if self.quant_noise is not None: - x = self.quant_noise(x) - return x, embed - - def forward( - self, - src_tokens, - src_lengths: Optional[torch.Tensor] = None, - return_all_hiddens: bool = False, - token_embeddings: Optional[torch.Tensor] = None, - ): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (torch.LongTensor): lengths of each source sentence of - shape `(batch)` - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - token_embeddings (torch.Tensor, optional): precomputed embeddings - default `None` will recompute embeddings - - Returns: - dict: - - **encoder_out** (Tensor): the last encoder layer's output of - shape `(src_len, batch, embed_dim)` - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - - **encoder_embedding** (Tensor): the (scaled) embedding lookup - of shape `(batch, src_len, embed_dim)` - - **encoder_states** (List[Tensor]): all intermediate - hidden states of shape `(src_len, batch, embed_dim)`. - Only populated if *return_all_hiddens* is True. - """ - return self.forward_scriptable( - src_tokens, src_lengths, return_all_hiddens, token_embeddings - ) - - # TorchScript doesn't support super() method so that the scriptable Subclass - # can't access the base class model in Torchscript. - # Current workaround is to add a helper function with different name and - # call the helper function from scriptable Subclass. - def forward_scriptable( - self, - src_tokens, - src_lengths: Optional[torch.Tensor] = None, - return_all_hiddens: bool = False, - token_embeddings: Optional[torch.Tensor] = None, - ): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (torch.LongTensor): lengths of each source sentence of - shape `(batch)` - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - token_embeddings (torch.Tensor, optional): precomputed embeddings - default `None` will recompute embeddings - - Returns: - dict: - - **encoder_out** (Tensor): the last encoder layer's output of - shape `(src_len, batch, embed_dim)` - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - - **encoder_embedding** (Tensor): the (scaled) embedding lookup - of shape `(batch, src_len, embed_dim)` - - **encoder_states** (List[Tensor]): all intermediate - hidden states of shape `(src_len, batch, embed_dim)`. - Only populated if *return_all_hiddens* is True. - """ - # compute padding mask - encoder_padding_mask = src_tokens.eq(self.padding_idx) - has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any() - - x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings) - - # account for padding while computing the representation - if has_pads: - x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x)) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - encoder_states = [] - fc_results = [] - - if return_all_hiddens: - encoder_states.append(x) - - # encoder layers - for layer in self.layers: - lr = layer( - x, encoder_padding_mask=encoder_padding_mask if has_pads else None - ) - - if isinstance(lr, tuple) and len(lr) == 2: - x, fc_result = lr - else: - x = lr - fc_result = None - - if return_all_hiddens and not torch.jit.is_scripting(): - assert encoder_states is not None - encoder_states.append(x) - fc_results.append(fc_result) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in - # `forward` so we use a dictionary instead. - # TorchScript does not support mixed values so the values are all lists. - # The empty list is equivalent to None. - src_lengths = ( - src_tokens.ne(self.padding_idx) - .sum(dim=1, dtype=torch.int32) - .reshape(-1, 1) - .contiguous() - ) - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [encoder_padding_mask], # B x T - "encoder_embedding": [encoder_embedding], # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "fc_results": fc_results, # List[T x B x C] - "src_tokens": [], - "src_lengths": [src_lengths], - } - - @torch.jit.export - def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - if len(encoder_out["encoder_out"]) == 0: - new_encoder_out = [] - else: - new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)] - if len(encoder_out["encoder_padding_mask"]) == 0: - new_encoder_padding_mask = [] - else: - new_encoder_padding_mask = [ - encoder_out["encoder_padding_mask"][0].index_select(0, new_order) - ] - if len(encoder_out["encoder_embedding"]) == 0: - new_encoder_embedding = [] - else: - new_encoder_embedding = [ - encoder_out["encoder_embedding"][0].index_select(0, new_order) - ] - - if len(encoder_out["src_tokens"]) == 0: - src_tokens = [] - else: - src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)] - - if len(encoder_out["src_lengths"]) == 0: - src_lengths = [] - else: - src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)] - - encoder_states = encoder_out["encoder_states"] - if len(encoder_states) > 0: - for idx, state in enumerate(encoder_states): - encoder_states[idx] = state.index_select(1, new_order) - - return { - "encoder_out": new_encoder_out, # T x B x C - "encoder_padding_mask": new_encoder_padding_mask, # B x T - "encoder_embedding": new_encoder_embedding, # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": src_tokens, # B x T - "src_lengths": src_lengths, # B x 1 - } - - @torch.jit.export - def _reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): - """Dummy re-order function for beamable enc-dec attention""" - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - if self.embed_positions is None: - return self.max_source_positions - return min(self.max_source_positions, self.embed_positions.max_positions) - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - if isinstance(self.embed_positions, SinusoidalPositionalEmbedding): - weights_key = "{}.embed_positions.weights".format(name) - if weights_key in state_dict: - print("deleting {0}".format(weights_key)) - del state_dict[weights_key] - state_dict[ - "{}.embed_positions._float_tensor".format(name) - ] = torch.FloatTensor(1) - for i in range(self.num_layers): - # update layer norms - self.layers[i].upgrade_state_dict_named( - state_dict, "{}.layers.{}".format(name, i) - ) - - version_key = "{}.version".format(name) - if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2: - # earlier checkpoints did not normalize after the stack of layers - self.layer_norm = None - self.normalize = False - state_dict[version_key] = torch.Tensor([1]) - return state_dict - - -class TransformerEncoder(TransformerEncoderBase): - def __init__(self, args, dictionary, embed_tokens, return_fc=False): - self.args = args - super().__init__( - TransformerConfig.from_namespace(args), - dictionary, - embed_tokens, - return_fc=return_fc, - ) - - def build_encoder_layer(self, args): - return super().build_encoder_layer( - TransformerConfig.from_namespace(args), - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_legacy.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_legacy.py deleted file mode 100644 index a6eb68b23d6181d140e04dc90846cabd72f0ba69..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer/transformer_legacy.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.transformer.transformer_base import ( - TransformerModelBase, -) -from fairseq.models.transformer.transformer_config import ( - DEFAULT_MAX_SOURCE_POSITIONS, - DEFAULT_MAX_TARGET_POSITIONS, - DEFAULT_MIN_PARAMS_TO_WRAP, - TransformerConfig, -) - - -@register_model("transformer") -class TransformerModel(TransformerModelBase): - """ - This is the legacy implementation of the transformer model that - uses argparse for configuration. - """ - - @classmethod - def hub_models(cls): - # fmt: off - - def moses_subword(path): - return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'subword_nmt', - } - - def moses_fastbpe(path): - return { - 'path': path, - 'tokenizer': 'moses', - 'bpe': 'fastbpe', - } - - def spm(path): - return { - 'path': path, - 'bpe': 'sentencepiece', - 'tokenizer': 'space', - } - - return { - 'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'), - 'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2', - 'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'), - 'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'), - 'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'), - 'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'), - 'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'), - 'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'), - 'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'), - 'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'), - 'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'), - 'transformer.wmt20.en-ta': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz'), - 'transformer.wmt20.en-iu.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz'), - 'transformer.wmt20.en-iu.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz'), - 'transformer.wmt20.ta-en': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz'), - 'transformer.wmt20.iu-en.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz'), - 'transformer.wmt20.iu-en.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz'), - 'transformer.flores101.mm100.615M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz'), - 'transformer.flores101.mm100.175M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz'), - } - # fmt: on - - def __init__(self, args, encoder, decoder): - cfg = TransformerConfig.from_namespace(args) - super().__init__(cfg, encoder, decoder) - self.args = args - - @classmethod - def add_args(cls, parser): - """Add model-specific arguments to the parser.""" - # we want to build the args recursively in this case. - # do not set defaults so that settings defaults from various architectures still works - gen_parser_from_dataclass( - parser, TransformerConfig(), delete_default=True, with_prefix="" - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present in older models - base_architecture(args) - - if args.encoder_layers_to_keep: - args.encoder_layers = len(args.encoder_layers_to_keep.split(",")) - if args.decoder_layers_to_keep: - args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) - - if getattr(args, "max_source_positions", None) is None: - args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS - if getattr(args, "max_target_positions", None) is None: - args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS - - src_dict, tgt_dict = task.source_dictionary, task.target_dictionary - - if args.share_all_embeddings: - if src_dict != tgt_dict: - raise ValueError("--share-all-embeddings requires a joined dictionary") - if args.encoder_embed_dim != args.decoder_embed_dim: - raise ValueError( - "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim" - ) - if args.decoder_embed_path and ( - args.decoder_embed_path != args.encoder_embed_path - ): - raise ValueError( - "--share-all-embeddings not compatible with --decoder-embed-path" - ) - args.share_decoder_input_output_embed = True - - if getattr(args, "offload_activations", False): - args.checkpoint_activations = True # offloading implies checkpointing - - if not args.share_all_embeddings: - args.min_params_to_wrap = getattr( - args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP - ) - cfg = TransformerConfig.from_namespace(args) - return super().build_model(cfg, task) - - @classmethod - def build_embedding(cls, args, dictionary, embed_dim, path=None): - return super().build_embedding( - TransformerConfig.from_namespace(args), dictionary, embed_dim, path - ) - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return super().build_encoder( - TransformerConfig.from_namespace(args), src_dict, embed_tokens - ) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - return super().build_decoder( - TransformerConfig.from_namespace(args), tgt_dict, embed_tokens - ) - - -# architectures - - -@register_model_architecture("transformer", "transformer_tiny") -def tiny_architecture(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 64) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 64) - args.encoder_layers = getattr(args, "encoder_layers", 2) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2) - args.decoder_layers = getattr(args, "decoder_layers", 2) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2) - return base_architecture(args) - - -@register_model_architecture("transformer", "transformer") -def base_architecture(args): - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False) - args.decoder_embed_path = getattr(args, "decoder_embed_path", None) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim) - args.decoder_ffn_embed_dim = getattr( - args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim - ) - args.decoder_layers = getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False) - args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False) - args.attention_dropout = getattr(args, "attention_dropout", 0.0) - args.activation_dropout = getattr(args, "activation_dropout", 0.0) - args.activation_fn = getattr(args, "activation_fn", "relu") - args.dropout = getattr(args, "dropout", 0.1) - args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0) - args.share_decoder_input_output_embed = getattr( - args, "share_decoder_input_output_embed", False - ) - args.share_all_embeddings = getattr(args, "share_all_embeddings", False) - args.no_token_positional_embeddings = getattr( - args, "no_token_positional_embeddings", False - ) - args.adaptive_input = getattr(args, "adaptive_input", False) - args.no_cross_attention = getattr(args, "no_cross_attention", False) - args.cross_self_attention = getattr(args, "cross_self_attention", False) - - args.decoder_output_dim = getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim) - - args.no_scale_embedding = getattr(args, "no_scale_embedding", False) - args.layernorm_embedding = getattr(args, "layernorm_embedding", False) - args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False) - args.checkpoint_activations = getattr(args, "checkpoint_activations", False) - args.offload_activations = getattr(args, "offload_activations", False) - if args.offload_activations: - args.checkpoint_activations = True - args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None) - args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None) - args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0) - args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0) - args.quant_noise_pq = getattr(args, "quant_noise_pq", 0) - args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8) - args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0) - - -@register_model_architecture("transformer", "transformer_iwslt_de_en") -def transformer_iwslt_de_en(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4) - args.encoder_layers = getattr(args, "encoder_layers", 6) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4) - args.decoder_layers = getattr(args, "decoder_layers", 6) - base_architecture(args) - - -@register_model_architecture("transformer", "transformer_wmt_en_de") -def transformer_wmt_en_de(args): - base_architecture(args) - - -# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) -@register_model_architecture("transformer", "transformer_vaswani_wmt_en_de_big") -def transformer_vaswani_wmt_en_de_big(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False) - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16) - args.dropout = getattr(args, "dropout", 0.3) - base_architecture(args) - - -@register_model_architecture("transformer", "transformer_vaswani_wmt_en_fr_big") -def transformer_vaswani_wmt_en_fr_big(args): - args.dropout = getattr(args, "dropout", 0.1) - transformer_vaswani_wmt_en_de_big(args) - - -@register_model_architecture("transformer", "transformer_wmt_en_de_big") -def transformer_wmt_en_de_big(args): - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - transformer_vaswani_wmt_en_de_big(args) - - -# default parameters used in tensor2tensor implementation -@register_model_architecture("transformer", "transformer_wmt_en_de_big_t2t") -def transformer_wmt_en_de_big_t2t(args): - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_dropout = getattr(args, "activation_dropout", 0.1) - transformer_vaswani_wmt_en_de_big(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_align.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_align.py deleted file mode 100644 index eaf585bd10e630ae6cd89920f197cd165f55ad58..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_align.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer import ( - TransformerModel, - base_architecture, - transformer_wmt_en_de_big, -) - - -@register_model("transformer_align") -class TransformerAlignModel(TransformerModel): - """ - See "Jointly Learning to Align and Translate with Transformer - Models" (Garg et al., EMNLP 2019). - """ - - def __init__(self, encoder, decoder, args): - super().__init__(args, encoder, decoder) - self.alignment_heads = args.alignment_heads - self.alignment_layer = args.alignment_layer - self.full_context_alignment = args.full_context_alignment - - @staticmethod - def add_args(parser): - # fmt: off - super(TransformerAlignModel, TransformerAlignModel).add_args(parser) - parser.add_argument('--alignment-heads', type=int, metavar='D', - help='Number of cross attention heads per layer to supervised with alignments') - parser.add_argument('--alignment-layer', type=int, metavar='D', - help='Layer number which has to be supervised. 0 corresponding to the bottommost layer.') - parser.add_argument('--full-context-alignment', action='store_true', - help='Whether or not alignment is supervised conditioned on the full target context.') - # fmt: on - - @classmethod - def build_model(cls, args, task): - # set any default arguments - transformer_align(args) - - transformer_model = TransformerModel.build_model(args, task) - return TransformerAlignModel( - transformer_model.encoder, transformer_model.decoder, args - ) - - def forward(self, src_tokens, src_lengths, prev_output_tokens): - encoder_out = self.encoder(src_tokens, src_lengths) - return self.forward_decoder(prev_output_tokens, encoder_out) - - def forward_decoder( - self, - prev_output_tokens, - encoder_out=None, - incremental_state=None, - features_only=False, - **extra_args, - ): - attn_args = { - "alignment_layer": self.alignment_layer, - "alignment_heads": self.alignment_heads, - } - decoder_out = self.decoder(prev_output_tokens, encoder_out, **attn_args) - - if self.full_context_alignment: - attn_args["full_context_alignment"] = self.full_context_alignment - _, alignment_out = self.decoder( - prev_output_tokens, - encoder_out, - features_only=True, - **attn_args, - **extra_args, - ) - decoder_out[1]["attn"] = alignment_out["attn"] - - return decoder_out - - -@register_model_architecture("transformer_align", "transformer_align") -def transformer_align(args): - args.alignment_heads = getattr(args, "alignment_heads", 1) - args.alignment_layer = getattr(args, "alignment_layer", 4) - args.full_context_alignment = getattr(args, "full_context_alignment", False) - base_architecture(args) - - -@register_model_architecture("transformer_align", "transformer_wmt_en_de_big_align") -def transformer_wmt_en_de_big_align(args): - args.alignment_heads = getattr(args, "alignment_heads", 1) - args.alignment_layer = getattr(args, "alignment_layer", 4) - transformer_wmt_en_de_big(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_from_pretrained_xlm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_from_pretrained_xlm.py deleted file mode 100644 index aa124514556bb4e252903b4aba11b86eed7a8d6b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_from_pretrained_xlm.py +++ /dev/null @@ -1,154 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -from typing import Any, Dict - -from fairseq import checkpoint_utils -from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary -from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer import ( - TransformerDecoder, - TransformerEncoder, - TransformerModel, -) -from fairseq.models.transformer import ( - base_architecture as transformer_base_architecture, -) - - -@register_model("transformer_from_pretrained_xlm") -class TransformerFromPretrainedXLMModel(TransformerModel): - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - TransformerModel.add_args(parser) - parser.add_argument( - "--pretrained-xlm-checkpoint", - type=str, - metavar="STR", - help="XLM model to use for initializing transformer encoder and/or decoder", - ) - parser.add_argument( - "--init-encoder-only", - action="store_true", - help="if set, don't load the XLM weights and embeddings into decoder", - ) - parser.add_argument( - "--init-decoder-only", - action="store_true", - help="if set, don't load the XLM weights and embeddings into encoder", - ) - - @classmethod - def build_model(self, args, task, cls_dictionary=MaskedLMDictionary): - assert hasattr(args, "pretrained_xlm_checkpoint"), ( - "You must specify a path for --pretrained-xlm-checkpoint to use " - "--arch transformer_from_pretrained_xlm" - ) - assert isinstance(task.source_dictionary, cls_dictionary) and isinstance( - task.target_dictionary, cls_dictionary - ), ( - "You should use a MaskedLMDictionary when using --arch " - "transformer_from_pretrained_xlm because the pretrained XLM model " - "was trained using data binarized with MaskedLMDictionary. " - "For translation, you may want to use --task " - "translation_from_pretrained_xlm" - ) - assert not ( - getattr(args, "init_encoder_only", False) - and getattr(args, "init_decoder_only", False) - ), "Only one of --init-encoder-only and --init-decoder-only can be set." - return super().build_model(args, task) - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return TransformerEncoderFromPretrainedXLM(args, src_dict, embed_tokens) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - return TransformerDecoderFromPretrainedXLM(args, tgt_dict, embed_tokens) - - -def upgrade_state_dict_with_xlm_weights( - state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str -) -> Dict[str, Any]: - """ - Load XLM weights into a Transformer encoder or decoder model. - - Args: - state_dict: state dict for either TransformerEncoder or - TransformerDecoder - pretrained_xlm_checkpoint: checkpoint to load XLM weights from - - Raises: - AssertionError: If architecture (num layers, attention heads, etc.) - does not match between the current Transformer encoder or - decoder and the pretrained_xlm_checkpoint - """ - if not os.path.exists(pretrained_xlm_checkpoint): - raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint)) - - state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint) - xlm_state_dict = state["model"] - for key in xlm_state_dict.keys(): - - for search_key in ["embed_tokens", "embed_positions", "layers"]: - if search_key in key: - subkey = key[key.find(search_key) :] - assert subkey in state_dict, ( - "{} Transformer encoder / decoder " - "state_dict does not contain {}. Cannot " - "load {} from pretrained XLM checkpoint " - "{} into Transformer.".format( - str(state_dict.keys()), subkey, key, pretrained_xlm_checkpoint - ) - ) - - state_dict[subkey] = xlm_state_dict[key] - return state_dict - - -class TransformerEncoderFromPretrainedXLM(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): - super().__init__(args, dictionary, embed_tokens) - if getattr(args, "init_decoder_only", False): - # Don't load XLM weights for encoder if --init-decoder-only - return - - assert hasattr(args, "pretrained_xlm_checkpoint"), ( - "--pretrained-xlm-checkpoint must be specified to load Transformer " - "encoder from pretrained XLM" - ) - xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights( - state_dict=self.state_dict(), - pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint, - ) - self.load_state_dict(xlm_loaded_state_dict, strict=True) - - -class TransformerDecoderFromPretrainedXLM(TransformerDecoder): - def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False): - super().__init__(args, dictionary, embed_tokens, no_encoder_attn) - if getattr(args, "init_encoder_only", False): - # Don't load XLM weights for decoder if --init-encoder-only - return - assert hasattr(args, "pretrained_xlm_checkpoint"), ( - "--pretrained-xlm-checkpoint must be specified to load Transformer " - "decoder from pretrained XLM" - ) - - xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights( - state_dict=self.state_dict(), - pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint, - ) - self.load_state_dict(xlm_loaded_state_dict, strict=True) - - -@register_model_architecture( - "transformer_from_pretrained_xlm", "transformer_from_pretrained_xlm" -) -def base_architecture(args): - transformer_base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_lm.py deleted file mode 100644 index 1a87d0c4a1e928736114626355969ab2a71cd9e5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_lm.py +++ /dev/null @@ -1,606 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from dataclasses import dataclass, field -from typing import Optional - -from fairseq import options, utils -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.models import ( - FairseqLanguageModel, - register_model, - register_model_architecture, -) -from fairseq.models.transformer import ( - DEFAULT_MIN_PARAMS_TO_WRAP, - Embedding, - TransformerDecoder, -) -from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder -from fairseq.utils import safe_getattr, safe_hasattr -from omegaconf import II - -DEFAULT_MAX_TARGET_POSITIONS = 1024 - - -@dataclass -class TransformerLanguageModelConfig(FairseqDataclass): - activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( - default="relu", metadata={"help": "activation function to use"} - ) - dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) - attention_dropout: float = field( - default=0.0, metadata={"help": "dropout probability for attention weights"} - ) - activation_dropout: float = field( - default=0.0, metadata={"help": "dropout probability after activation in FFN."} - ) - relu_dropout: float = field( - default=0.0, metadata={"help": "dropout probability after activation in FFN."} - ) - decoder_embed_dim: int = field( - default=512, metadata={"help": "decoder embedding dimension"} - ) - decoder_output_dim: int = field( - default=512, metadata={"help": "decoder output dimension"} - ) - decoder_input_dim: int = field( - default=512, metadata={"help": "decoder input dimension"} - ) - decoder_ffn_embed_dim: int = field( - default=2048, metadata={"help": "decoder embedding dimension for FFN"} - ) - decoder_layers: int = field(default=6, metadata={"help": "num decoder layers"}) - decoder_attention_heads: int = field( - default=8, metadata={"help": "num decoder attention heads"} - ) - decoder_normalize_before: bool = field( - default=False, metadata={"help": "apply layernorm before each decoder block"} - ) - no_decoder_final_norm: bool = field( - default=False, - metadata={"help": "don't add an extra layernorm after the last decoder block"}, - ) - adaptive_softmax_cutoff: Optional[str] = field( - default=None, - metadata={ - "help": "comma separated list of adaptive softmax cutoff points. " - "Must be used with adaptive_loss criterion" - }, - ) - adaptive_softmax_dropout: float = field( - default=0, - metadata={"help": "sets adaptive softmax dropout for the tail projections"}, - ) - adaptive_softmax_factor: float = field( - default=4, metadata={"help": "adaptive input factor"} - ) - no_token_positional_embeddings: bool = field( - default=False, - metadata={ - "help": "if set, disables positional embeddings (outside self attention)" - }, - ) - share_decoder_input_output_embed: bool = field( - default=False, metadata={"help": "share decoder input and output embeddings"} - ) - character_embeddings: bool = field( - default=False, - metadata={ - "help": "if set, uses character embedding convolutions to produce token embeddings" - }, - ) - character_filters: str = field( - default="[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]", - metadata={"help": "size of character embeddings"}, - ) - character_embedding_dim: int = field( - default=4, metadata={"help": "size of character embeddings"} - ) - char_embedder_highway_layers: int = field( - default=2, - metadata={"help": "number of highway layers for character token embeddder"}, - ) - adaptive_input: bool = field( - default=False, metadata={"help": "if set, uses adaptive input"} - ) - adaptive_input_factor: float = field( - default=4, metadata={"help": "adaptive input factor"} - ) - adaptive_input_cutoff: Optional[str] = field( - default=None, - metadata={"help": "comma separated list of adaptive input cutoff points."}, - ) - tie_adaptive_weights: bool = field( - default=False, - metadata={ - "help": "if set, ties the weights of adaptive softmax and adaptive input" - }, - ) - tie_adaptive_proj: bool = field( - default=False, - metadata={ - "help": "if set, ties the projection weights of adaptive softmax and adaptive input" - }, - ) - decoder_learned_pos: bool = field( - default=False, - metadata={"help": "use learned positional embeddings in the decoder"}, - ) - layernorm_embedding: bool = field( - default=False, metadata={"help": "add layernorm to embedding"} - ) - no_scale_embedding: bool = field( - default=False, metadata={"help": "if True, dont scale embeddings"} - ) - checkpoint_activations: bool = field( - default=False, metadata={"help": "checkpoint activations at each layer"} - ) - offload_activations: bool = field( - default=False, - metadata={"help": "move checkpointed activations to CPU after they are used."}, - ) - # config for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019) - decoder_layerdrop: float = field( - default=0.0, metadata={"help": "LayerDrop probability for decoder"} - ) - decoder_layers_to_keep: Optional[str] = field( - default=None, - metadata={ - "help": "which layers to *keep* when pruning as a comma-separated list" - }, - ) - # config for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020) - quant_noise_pq: float = field( - default=0.0, - metadata={"help": "iterative PQ quantization noise at training time"}, - ) - quant_noise_pq_block_size: int = field( - default=8, - metadata={"help": "block size of quantization noise at training time"}, - ) - quant_noise_scalar: float = field( - default=0.0, - metadata={ - "help": "scalar quantization noise and scalar quantization at training time" - }, - ) - # config for Fully Sharded Data Parallel (FSDP) training - min_params_to_wrap: int = field( - default=DEFAULT_MIN_PARAMS_TO_WRAP, - metadata={ - "help": ( - "minimum number of params for a layer to be wrapped with FSDP() when " - "training with --ddp-backend=fully_sharded. Smaller values will " - "improve memory efficiency, but may make torch.distributed " - "communication less efficient due to smaller input sizes. This option " - "is set to 0 (i.e., always wrap) when --checkpoint-activations or " - "--offload-activations are passed." - ) - }, - ) - # config for "BASE Layers: Simplifying Training of Large, Sparse Models" - base_layers: Optional[int] = field( - default=0, metadata={"help": "number of BASE layers in total"} - ) - base_sublayers: Optional[int] = field( - default=1, metadata={"help": "number of sublayers in each BASE layer"} - ) - base_shuffle: Optional[int] = field( - default=1, - metadata={"help": "shuffle tokens between workers before computing assignment"}, - ) - # NormFormer - scale_fc: Optional[bool] = field( - default=False, - metadata={"help": "Insert LayerNorm between fully connected layers"}, - ) - scale_attn: Optional[bool] = field( - default=False, metadata={"help": "Insert LayerNorm after attention"} - ) - scale_heads: Optional[bool] = field( - default=False, - metadata={"help": "Learn a scale coefficient for each attention head"}, - ) - scale_resids: Optional[bool] = field( - default=False, - metadata={"help": "Learn a scale coefficient for each residual connection"}, - ) - - # xFormers arguments - decoder_xformers_att_config: Optional[str] = field( - default=None, - metadata={ - "help": "config for xFormers library attention, defined in xformers.components.attention.AttentionConfig", - }, - ) - - # options from other parts of the config - add_bos_token: bool = II("task.add_bos_token") - tokens_per_sample: int = II("task.tokens_per_sample") - max_target_positions: Optional[int] = II("task.max_target_positions") - tpu: bool = II("common.tpu") - - -@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) -class TransformerLanguageModel(FairseqLanguageModel): - @classmethod - def hub_models(cls): - def moses_fastbpe(path): - return {"path": path, "tokenizer": "moses", "bpe": "fastbpe"} - - def spm(path): - return {"path": path, "tokenizer": "space", "bpe": "sentencepiece"} - - return { - "transformer_lm.gbw.adaptive_huge": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2", - "transformer_lm.wiki103.adaptive": "https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2", - "transformer_lm.wmt19.en": moses_fastbpe( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2" - ), - "transformer_lm.wmt19.de": moses_fastbpe( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2" - ), - "transformer_lm.wmt19.ru": moses_fastbpe( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2" - ), - "transformer_lm.wmt20.en": spm( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.en.tar.gz" - ), - "transformer_lm.wmt20.ta": spm( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.ta.tar.gz" - ), - "transformer_lm.wmt20.iu.news": spm( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.news.tar.gz" - ), - "transformer_lm.wmt20.iu.nh": spm( - "https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt20.iu.nh.tar.gz" - ), - } - - def __init__(self, decoder): - super().__init__(decoder) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - if args.decoder_layers_to_keep: - args.decoder_layers = len(args.decoder_layers_to_keep.split(",")) - - if safe_getattr(args, "max_target_positions", None) is None: - args.max_target_positions = safe_getattr( - args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS - ) - - if args.character_embeddings: - embed_tokens = CharacterTokenEmbedder( - task.source_dictionary, - eval(args.character_filters), - args.character_embedding_dim, - args.decoder_embed_dim, - args.char_embedder_highway_layers, - ) - elif args.adaptive_input: - embed_tokens = AdaptiveInput( - len(task.source_dictionary), - task.source_dictionary.pad(), - args.decoder_input_dim, - args.adaptive_input_factor, - args.decoder_embed_dim, - options.eval_str_list(args.adaptive_input_cutoff, type=int), - args.quant_noise_pq, - args.quant_noise_pq_block_size, - ) - else: - embed_tokens = cls.build_embedding( - args, task.source_dictionary, args.decoder_input_dim - ) - - if args.tie_adaptive_weights: - assert args.adaptive_input - assert args.adaptive_input_factor == args.adaptive_softmax_factor - assert ( - args.adaptive_softmax_cutoff == args.adaptive_input_cutoff - ), "{} != {}".format( - args.adaptive_softmax_cutoff, args.adaptive_input_cutoff - ) - assert args.decoder_input_dim == args.decoder_output_dim - - decoder = TransformerDecoder( - args, task.target_dictionary, embed_tokens, no_encoder_attn=True - ) - return cls(decoder) - - @classmethod - def build_embedding(cls, args, dictionary, embed_dim, path=None): - embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad()) - return embed_tokens - - -def base_lm_architecture(args): - # backward compatibility for older model checkpoints - if safe_hasattr(args, "no_tie_adaptive_proj"): - # previous models defined --no-tie-adaptive-proj, so use the existence of - # that option to determine if this is an "old" model checkpoint - args.no_decoder_final_norm = True # old models always set this to True - if args.no_tie_adaptive_proj is False: - args.tie_adaptive_proj = True - if safe_hasattr(args, "decoder_final_norm"): - args.no_decoder_final_norm = not args.decoder_final_norm - - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0) - - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 2048) - args.decoder_layers = safe_getattr(args, "decoder_layers", 6) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8) - args.adaptive_softmax_cutoff = safe_getattr(args, "adaptive_softmax_cutoff", None) - args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0) - args.adaptive_softmax_factor = safe_getattr(args, "adaptive_softmax_factor", 4) - args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", False) - args.activation_fn = safe_getattr(args, "activation_fn", "relu") - - args.decoder_layerdrop = safe_getattr(args, "decoder_layerdrop", 0) - args.decoder_layers_to_keep = safe_getattr(args, "decoder_layers_to_keep", None) - args.quant_noise_pq = safe_getattr(args, "quant_noise_pq", 0) - args.quant_noise_pq_block_size = safe_getattr(args, "quant_noise_pq_block_size", 8) - args.quant_noise_scalar = safe_getattr(args, "quant_noise_scalar", 0) - - args.base_layers = safe_getattr(args, "base_layers", 0) - args.base_sublayers = safe_getattr(args, "base_sublayers", 1) - args.base_shuffle = safe_getattr(args, "base_shuffle", False) - - args.add_bos_token = safe_getattr(args, "add_bos_token", False) - args.no_token_positional_embeddings = safe_getattr( - args, "no_token_positional_embeddings", False - ) - args.share_decoder_input_output_embed = safe_getattr( - args, "share_decoder_input_output_embed", False - ) - args.character_embeddings = safe_getattr(args, "character_embeddings", False) - - args.decoder_output_dim = safe_getattr( - args, "decoder_output_dim", args.decoder_embed_dim - ) - args.decoder_input_dim = safe_getattr( - args, "decoder_input_dim", args.decoder_embed_dim - ) - - # Model training is not stable without this - args.decoder_normalize_before = True - args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", False) - - args.adaptive_input = safe_getattr(args, "adaptive_input", False) - args.adaptive_input_factor = safe_getattr(args, "adaptive_input_factor", 4) - args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", None) - - args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", False) - args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", False) - - args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", False) - args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False) - args.checkpoint_activations = safe_getattr(args, "checkpoint_activations", False) - args.offload_activations = safe_getattr(args, "offload_activations", False) - args.scale_fc = safe_getattr(args, "scale_fc", False) - args.scale_attn = safe_getattr(args, "scale_attn", False) - args.scale_heads = safe_getattr(args, "scale_heads", False) - args.scale_resids = safe_getattr(args, "scale_resids", False) - if args.offload_activations: - args.checkpoint_activations = True - - -@register_model_architecture("transformer_lm", "transformer_lm_big") -def transformer_lm_big(args): - args.decoder_layers = safe_getattr(args, "decoder_layers", 12) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_wiki103") -@register_model_architecture("transformer_lm", "transformer_lm_baevski_wiki103") -def transformer_lm_baevski_wiki103(args): - args.decoder_layers = safe_getattr(args, "decoder_layers", 16) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 8) - args.dropout = safe_getattr(args, "dropout", 0.3) - args.adaptive_input = safe_getattr(args, "adaptive_input", True) - args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", True) - args.adaptive_input_cutoff = safe_getattr( - args, "adaptive_input_cutoff", "20000,60000" - ) - args.adaptive_softmax_cutoff = safe_getattr( - args, "adaptive_softmax_cutoff", "20000,60000" - ) - args.adaptive_softmax_dropout = safe_getattr(args, "adaptive_softmax_dropout", 0.2) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_dropout = safe_getattr(args, "activation_dropout", 0.1) - args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True) - args.tie_adaptive_proj = safe_getattr(args, "tie_adaptive_proj", True) - transformer_lm_big(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gbw") -@register_model_architecture("transformer_lm", "transformer_lm_baevski_gbw") -def transformer_lm_baevski_gbw(args): - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 512) - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.no_decoder_final_norm = safe_getattr(args, "no_decoder_final_norm", True) - transformer_lm_big(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt") -def transformer_lm_gpt(args): - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 3072) - args.decoder_layers = safe_getattr(args, "decoder_layers", 12) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12) - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt2_small") -def transformer_lm_gpt2_small(args): - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 4096) - args.decoder_layers = safe_getattr(args, "decoder_layers", 24) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt2_tiny") -def transformer_lm_gpt2_tiny(args): - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 64) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 64) - args.decoder_layers = safe_getattr(args, "decoder_layers", 2) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 1) - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt2_medium") -def transformer_lm_gpt2_medium(args): - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1280) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 5120) - args.decoder_layers = safe_getattr(args, "decoder_layers", 36) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 20) - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big") -def transformer_lm_gpt2_big(args): - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1600) - args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", 6400) - args.decoder_layers = safe_getattr(args, "decoder_layers", 48) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 25) - args.dropout = safe_getattr(args, "dropout", 0.1) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.1) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt2_big_wide") -def transformer_lm_gpt2_big_wide(args): - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2048) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 8192) - args.decoder_layers = getattr(args, "decoder_layers", 24) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_fn = getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt2_bigger") -def transformer_lm_gpt2_bigger(args): - args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 2048) - args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 8192) - args.decoder_layers = getattr(args, "decoder_layers", 48) - args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 32) - args.dropout = getattr(args, "dropout", 0.1) - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - args.activation_fn = getattr(args, "activation_fn", "gelu") - base_lm_architecture(args) - - -def base_gpt3_architecture(args): - args.decoder_input_dim = args.decoder_embed_dim - args.decoder_output_dim = args.decoder_embed_dim - args.decoder_ffn_embed_dim = safe_getattr( - args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4 - ) - # GPT-3 used learned positional embeddings, rather than sinusoidal - args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", True) - args.dropout = safe_getattr(args, "dropout", 0.0) - args.attention_dropout = safe_getattr(args, "attention_dropout", 0.0) - args.activation_fn = safe_getattr(args, "activation_fn", "gelu") - args.share_decoder_input_output_embed = True - base_lm_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_small") -def transformer_lm_gpt3_small(args): - # 125M params - args.decoder_layers = safe_getattr(args, "decoder_layers", 12) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 768) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 12) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_medium") -def transformer_lm_gpt3_medium(args): - # 350M params - args.decoder_layers = safe_getattr(args, "decoder_layers", 24) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1024) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_large") -def transformer_lm_gpt3_large(args): - # 760M params - args.decoder_layers = safe_getattr(args, "decoder_layers", 24) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 1536) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 16) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_xl") -def transformer_lm_gpt3_xl(args): - # 1.3B params - args.decoder_layers = safe_getattr(args, "decoder_layers", 24) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2048) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_2_7") -def transformer_lm_gpt3_2_7(args): - # 2.7B params - args.decoder_layers = safe_getattr(args, "decoder_layers", 32) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 2560) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_6_7") -def transformer_lm_gpt3_6_7(args): - # 6.7B params - args.decoder_layers = safe_getattr(args, "decoder_layers", 32) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 4096) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 32) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_13") -def transformer_lm_gpt3_13(args): - # 13B params - args.decoder_layers = safe_getattr(args, "decoder_layers", 40) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 5120) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 40) - base_gpt3_architecture(args) - - -@register_model_architecture("transformer_lm", "transformer_lm_gpt3_175") -def transformer_lm_gpt3_175(args): - # 175B params - args.decoder_layers = safe_getattr(args, "decoder_layers", 96) - args.decoder_embed_dim = safe_getattr(args, "decoder_embed_dim", 12288) - args.decoder_attention_heads = safe_getattr(args, "decoder_attention_heads", 96) - base_gpt3_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_ulm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_ulm.py deleted file mode 100644 index d7e8fc2d341adabd6b81748186879c86b49107aa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/transformer_ulm.py +++ /dev/null @@ -1,407 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional - -import numpy as np -import torch -from fairseq.data.data_utils import compute_mask_indices -from fairseq.dataclass import ChoiceEnum -from fairseq.models import ( - FairseqLanguageModel, - register_model, - register_model_architecture, -) -from fairseq.models.fairseq_decoder import FairseqDecoder -from fairseq.models.transformer import Embedding, Linear, TransformerDecoder -from fairseq.models.transformer_lm import TransformerLanguageModelConfig -from fairseq.tasks.speech_ulm_task import SpeechUnitLanguageModelingTask -from torch import Tensor, nn - -DEFAULT_MAX_TARGET_POSITIONS = 1024 -MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"]) - - -@dataclass -class SpeechUnitLanguageModelConfig(TransformerLanguageModelConfig): - mask_unit_seg_prob: float = field( - default=0.0, metadata={"help": "probability to mask a segment of unit sequence"} - ) - mask_unit_seg_leng: int = field( - default=5, metadata={"help": "length of unit segment mask"} - ) - mask_unit_seg_type: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose unit mask length"} - ) - - mask_dur_prob: float = field( - default=0.0, metadata={"help": "probability to mask entire duration sequence"} - ) - mask_dur_seg_prob: float = field( - default=0.0, - metadata={"help": "probability to mask a segment of duration sequence"}, - ) - mask_dur_seg_leng: int = field( - default=5, metadata={"help": "length of duration segment mask"} - ) - mask_dur_seg_type: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose duration mask length"} - ) - - mask_f0_prob: float = field( - default=0.0, metadata={"help": "probability to mask entire duration sequence"} - ) - mask_f0_seg_prob: float = field( - default=0.0, metadata={"help": "probability to mask a segment of f0 sequence"} - ) - mask_f0_seg_leng: int = field( - default=5, metadata={"help": "length of f0 segment mask"} - ) - mask_f0_seg_type: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose f0 mask length"} - ) - - -@register_model("transformer_ulm", dataclass=SpeechUnitLanguageModelConfig) -class TransformerUnitLanguageModel(FairseqLanguageModel): - def __init__( - self, - cfg: SpeechUnitLanguageModelConfig, - task: SpeechUnitLanguageModelingTask, - decoder: FairseqDecoder, - ): - super().__init__(decoder) - self.cfg = cfg - - self.channel_names = task.channel_names - self.channel_sizes = task.channel_sizes - - self.unit_mask_val = task.source_dictionary.unk() - self.dur_mask_val = ( - task.source_duration_dictionary.unk() if task.cfg.discrete_duration else 0 - ) - self.f0_mask_val = ( - task.source_f0_dictionary.unk() if task.cfg.discrete_f0 else 0 - ) - - self.ignore_duration_input = task.cfg.ignore_duration_input - self.ignore_f0_input = task.cfg.ignore_f0_input - - @classmethod - def build_model(cls, args, task): - base_ulm_architecture(args) - - if getattr(args, "max_target_positions", None) is None: - args.max_target_positions = getattr( - args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS - ) - - embed_tokens = Embedding( - len(task.source_dictionary), - args.decoder_input_dim, - padding_idx=task.source_dictionary.pad(), - ) - embed_duration = None - if task.cfg.discrete_duration: - embed_duration = Embedding( - len(task.source_duration_dictionary), - args.decoder_input_dim, - padding_idx=0, # duration uses 0 for padding - ) - embed_f0 = None - if task.cfg.discrete_f0: - embed_f0 = Embedding( - len(task.source_f0_dictionary), - args.decoder_input_dim, - padding_idx=task.source_f0_dictionary.pad(), - ) - - decoder = MultiStreamTransformerDecoder( - args, - task.target_dictionary, - embed_tokens, - [embed_duration, embed_f0], - no_encoder_attn=True, - channel_sizes=task.channel_sizes, - ) - - return cls(args, task, decoder) - - def apply_seg_dropout(self, inp, mask_prob, mask_leng, mask_type, mask_val): - B, T = inp.size() - if mask_prob > 0: - mask_indices = compute_mask_indices( - (B, T), None, mask_prob, mask_leng, mask_type # may mask padding - ) - mask_indices = torch.from_numpy(mask_indices).to(inp.device) - inp[mask_indices] = mask_val - else: - mask_indices = torch.zeros_like(inp).bool() - return inp, mask_indices - - def apply_seq_dropout(self, inp, mask_prob, mask_val): - B, T = inp.size() - if mask_prob > 0: - mask_indices = np.random.uniform(0, 1, (B,)) < mask_prob - mask_indices = ( - torch.from_numpy(mask_indices).to(inp.device).unsqueeze(1).expand(-1, T) - ) - inp[mask_indices] = mask_val - else: - mask_indices = torch.zeros_like(inp).bool() - return inp, mask_indices - - def apply_dropout(self, src_tokens, dur_src, f0_src): - src_tokens, unit_mask = self.apply_seg_dropout( - src_tokens, - self.cfg.mask_unit_seg_prob, - self.cfg.mask_unit_seg_leng, - self.cfg.mask_unit_seg_type, - self.unit_mask_val, - ) - - dur_src, dur_mask = self.apply_seq_dropout( - dur_src, self.cfg.mask_dur_prob, self.dur_mask_val - ) - dur_src, _dur_mask = self.apply_seg_dropout( - dur_src, - self.cfg.mask_dur_seg_prob, - self.cfg.mask_dur_seg_leng, - self.cfg.mask_dur_seg_type, - self.dur_mask_val, - ) - dur_mask = dur_mask.logical_or(_dur_mask) - - f0_src, f0_mask = self.apply_seq_dropout( - f0_src, self.cfg.mask_f0_prob, self.f0_mask_val - ) - f0_src, _f0_mask = self.apply_seg_dropout( - f0_src, - self.cfg.mask_f0_seg_prob, - self.cfg.mask_f0_seg_leng, - self.cfg.mask_f0_seg_type, - self.f0_mask_val, - ) - f0_mask = f0_mask.logical_or(_f0_mask) - - return src_tokens, unit_mask, dur_src, dur_mask, f0_src, f0_mask - - def forward( - self, - src_tokens: torch.Tensor, - dur_src: torch.Tensor, - f0_src: torch.Tensor, - src_lengths: Optional[Any] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - if self.ignore_duration_input: - dur_src = torch.zeros_like(dur_src) - - if self.ignore_f0_input: - f0_src = torch.zeros_like(f0_src) - - if self.training: - ( - src_tokens, - unit_mask, - dur_src, - dur_mask, - f0_src, - f0_mask, - ) = self.apply_dropout(src_tokens, dur_src, f0_src) - else: - unit_masks = dur_mask = f0_mask = None - - prediction, _ = self.decoder( - prev_output_tokens=(src_tokens, dur_src, f0_src), - incremental_state=incremental_state, - src_lengths=src_lengths, - features_only=True, - ) - - result = dict(zip(self.channel_names, prediction)) - - return result - - -def base_ulm_architecture(args): - from .transformer_lm import base_lm_architecture - - base_lm_architecture(args) - - -@register_model_architecture("transformer_ulm", "transformer_ulm_big") -def transformer_ulm_big(args): - from .transformer_lm import transformer_lm_big - - transformer_lm_big(args) - base_ulm_architecture(args) - - -@register_model_architecture("transformer_ulm", "transformer_ulm_tiny") -def transformer_ulm_tiny(args): - from .transformer_lm import transformer_lm_gpt2_tiny - - transformer_lm_gpt2_tiny(args) - base_ulm_architecture(args) - - -class MultiStreamTransformerDecoder(TransformerDecoder): - def __init__( - self, - args, - dictionary, - embed_tokens, - embed_other_list, - no_encoder_attn, - channel_sizes, - ): - super().__init__( - args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn - ) - - # embed each channel and project if dimensions do not match - self.embed_other_list = torch.nn.ModuleList(embed_other_list) - self.proj_other_list = torch.nn.ModuleList() - dim = embed_tokens.embedding_dim - for embed_other in embed_other_list: - other_dim = 1 if embed_other is None else embed_other.embedding_dim - self.proj_other_list.append( - nn.Linear(other_dim, dim) if other_dim != dim else None - ) - - # tranformer output to prediction - self.channel_sizes = channel_sizes - self.project_out_dim = Linear( - embed_tokens.embedding_dim, sum(channel_sizes), bias=False - ) - - def extract_features_scriptable( - self, - prev_output_tokens, - encoder_out: Optional[Dict[str, List[Tensor]]], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - full_context_alignment: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - ): - if alignment_layer is None: - alignment_layer = self.num_layers - 1 - - # XXX: first multi-channel change start - prev_output_tokens, *other_channels = prev_output_tokens - # XXX: first multi-channel change end - - # embed positions - positions = None - if self.embed_positions is not None: - positions = self.embed_positions( - prev_output_tokens, incremental_state=incremental_state - ) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - other_channels = [o[:, -1:] for o in other_channels] - if positions is not None: - positions = positions[:, -1:] - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - - # XXX: second multi-channel change start - other_channels = [ - o.unsqueeze(-1).to(dtype=x.dtype) if emb is None else emb(o) - for o, emb in zip(other_channels, self.embed_other_list) - ] - other_channels = [ - o if proj_other is None else proj_other(o) - for o, proj_other in zip(other_channels, self.proj_other_list) - ] - for o in other_channels: - x = x + o - # XXX: second multi-channel change end - - if self.quant_noise is not None: - x = self.quant_noise(x) - - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - - x = self.dropout_module(x) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - self_attn_padding_mask: Optional[Tensor] = None - if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any(): - self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) - - # decoder layers - attn: Optional[Tensor] = None - inner_states: List[Optional[Tensor]] = [x] - for idx, layer in enumerate(self.layers): - if incremental_state is None and not full_context_alignment: - self_attn_mask = self.buffered_future_mask(x) - else: - self_attn_mask = None - - x, layer_attn, _ = layer( - x, - encoder_out["encoder_out"][0] - if (encoder_out is not None and len(encoder_out["encoder_out"]) > 0) - else None, - encoder_out["encoder_padding_mask"][0] - if ( - encoder_out is not None - and len(encoder_out["encoder_padding_mask"]) > 0 - ) - else None, - incremental_state, - self_attn_mask=self_attn_mask, - self_attn_padding_mask=self_attn_padding_mask, - need_attn=bool((idx == alignment_layer)), - need_head_weights=bool((idx == alignment_layer)), - ) - inner_states.append(x) - if layer_attn is not None and idx == alignment_layer: - attn = layer_attn.float().to(x) - - if attn is not None: - if alignment_heads is not None: - attn = attn[:alignment_heads] - - # average probabilities over heads - attn = attn.mean(dim=0) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - if self.project_out_dim is not None: - x = self.project_out_dim(x) - else: - assert False - - # XXX: the last change start - result = [] - start = 0 - for channel_size in self.channel_sizes: - end = start + channel_size - result.append(x[:, :, start:end]) - start = end - assert end == x.size(-1) - # XXX: the last change end - - return result, {"attn": [attn], "inner_states": inner_states} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/__init__.py deleted file mode 100644 index 06cec18183ca14cd534d14558e8b44e25f3e69d5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .wav2vec import * # noqa -from .wav2vec2 import * # noqa -from .wav2vec2_asr import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/utils.py deleted file mode 100644 index 38f68610d697f42551479eea85b0a1a7d65bca94..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/utils.py +++ /dev/null @@ -1,22 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch.nn.functional as F - - -def pad_to_multiple(x, multiple, dim=-1, value=0): - # Inspired from https://github.com/lucidrains/local-attention/blob/master/local_attention/local_attention.py#L41 - if x is None: - return None, 0 - tsz = x.size(dim) - m = tsz / multiple - remainder = math.ceil(m) * multiple - tsz - if m.is_integer(): - return x, 0 - pad_offset = (0,) * (-1 - dim) * 2 - - return F.pad(x, (*pad_offset, 0, remainder), value=value), remainder diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec.py deleted file mode 100644 index be6fa7deb515feb972b6753cd698afef69de51ef..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec.py +++ /dev/null @@ -1,629 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import math -import sys -from dataclasses import dataclass, field -from typing import Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.models import BaseFairseqModel, register_model -from fairseq.modules import ( - Fp32GroupNorm, - Fp32LayerNorm, - GumbelVectorQuantizer, - KmeansVectorQuantizer, - TransposeLast, -) -from fairseq.tasks import FairseqTask -from fairseq.utils import buffered_arange -from omegaconf import II - -logger = logging.getLogger(__name__) - - -AGGREGATOR_CHOICES = ChoiceEnum(["cnn", "gru"]) -PROJECT_FEATURES_CHOICES = ChoiceEnum(["none", "same", "new"]) -ACTIVATION_CHOICES = ChoiceEnum(["relu", "gelu"]) -VQ_TYPE_CHOICES = ChoiceEnum(["none", "gumbel", "kmeans"]) - - -@dataclass -class Wav2VecConfig(FairseqDataclass): - prediction_steps: int = field( - default=12, metadata={"help": "number of steps ahead to predict"} - ) - sample_distance: Optional[int] = field( - default=None, - metadata={ - "help": "sample distance from target. does not work properly with cross-sampling" - }, - ) - cross_sample_negatives: int = field( - default=0, metadata={"help": "num of cross sampled negatives"} - ) - num_negatives: int = field( - default=10, metadata={"help": "num of sampled negatives"} - ) - conv_feature_layers: str = field( - default="[(512, 10, 5), (512, 8, 4), (512, 4, 2), (512, 4, 2), (512, 4, 2), (512, 1, 1), (512, 1, 1), (512, 1, 1)]", - metadata={ - "help": "convolutional feature extraction layers [(dim, kernel_size, stride), ...]" - }, - ) - conv_aggregator_layers: str = field( - default="[(512, 2, 1), (512, 3, 1), (512, 4, 1), (512, 5, 1), (512, 6, 1), (512, 7, 1), (512, 8, 1), (512, 9, 1), (512, 10, 1), (512, 11, 1), (512, 12, 1), (512, 13, 1)]", - metadata={ - "help": "convolutional aggregator layers [(dim, kernel_size, stride), ...]" - }, - ) - dropout: float = field( - default=0.0, metadata={"help": "dropout to apply within the model"} - ) - dropout_features: float = field( - default=0.0, metadata={"help": "dropout to apply to the features"} - ) - dropout_agg: float = field( - default=0.0, metadata={"help": "dropout to apply after aggregation step"} - ) - aggregator: AGGREGATOR_CHOICES = field( - default="cnn", metadata={"help": "type of aggregator to use"} - ) - gru_dim: int = field(default=512, metadata={"help": "GRU dimensionality"}) - no_conv_bias: bool = field( - default=False, metadata={"help": "if set, does not learn bias for conv layers"} - ) - agg_zero_pad: bool = field( - default=False, - metadata={"help": "if set, zero pads in aggregator instead of repl pad"}, - ) - skip_connections_feat: bool = field( - default=False, - metadata={"help": "if set, adds skip connections to the feature extractor"}, - ) - skip_connections_agg: bool = field( - default=True, - metadata={"help": "if set, adds skip connections to the aggregator"}, - ) - residual_scale: float = field( - default=0.5, metadata={"help": "scales residual by sqrt(value)"} - ) - log_compression: bool = field( - default=True, - metadata={"help": "if set, adds a log compression to feature extractor"}, - ) - balanced_classes: bool = field( - default=False, - metadata={"help": "if set, loss is scaled to balance for number of negatives"}, - ) - project_features: PROJECT_FEATURES_CHOICES = field( - default="none", - metadata={ - "help": "if not none, features are projected using the (same or new) aggregator" - }, - ) - non_affine_group_norm: bool = field( - default=False, metadata={"help": "if set, group norm is not affine"} - ) - offset: str = field( - default="auto", - metadata={ - "help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value" - }, - ) - activation: ACTIVATION_CHOICES = field( - default="relu", - metadata={ - "help": "if set to 'auto', it is computed automatically from the receptive field, else set to int value" - }, - ) - vq_type: VQ_TYPE_CHOICES = field( - default="none", metadata={"help": "which type of quantizer to use"} - ) - vq_vars: int = field( - default=320, - metadata={"help": "project to this many vector quantized variables per group"}, - ) - vq_groups: int = field( - default=2, metadata={"help": "number of groups of latent variables"} - ) - vq_dim: int = field( - default=0, - metadata={ - "help": "uses this dimensionality for quantized vectors. 0 to use model dim // groups" - }, - ) - vq_depth: int = field( - default=1, metadata={"help": "number of layers for vq weight projection"} - ) - combine_groups: bool = field( - default=False, metadata={"help": "if set, variables are shared among groups"} - ) - vq_temp: Tuple[float, float, float] = field( - default=(2.0, 0.5, 0.999995), - metadata={ - "help": "temperature for latent variable sampling with gumbel softmax. should be a tuple of 3 values (start, end, decay)" - }, - ) - vq_gamma: float = field( - default=0.25, - metadata={"help": "gamma parameter for kmeans style vector quantization"}, - ) - infonce: bool = II("criterion.infonce") - - -@register_model("wav2vec", dataclass=Wav2VecConfig) -class Wav2VecModel(BaseFairseqModel): - @classmethod - def build_model(cls, cfg: Wav2VecConfig, task: FairseqTask): - """Build a new model instance.""" - - model = Wav2VecModel(cfg) - logger.info(model) - return model - - def __init__(self, cfg: Wav2VecConfig): - super().__init__() - - self.prediction_steps = cfg.prediction_steps - offset = cfg.offset - - if cfg.activation == "relu": - activation = nn.ReLU() - elif cfg.activation == "gelu": - activation = nn.GELU() - else: - raise Exception("unknown activation " + cfg.activation) - - feature_enc_layers = eval(cfg.conv_feature_layers) - self.feature_extractor = ConvFeatureExtractionModel( - conv_layers=feature_enc_layers, - dropout=0.0, - log_compression=cfg.log_compression, - skip_connections=cfg.skip_connections_feat, - residual_scale=cfg.residual_scale, - non_affine_group_norm=cfg.non_affine_group_norm, - activation=activation, - ) - embed = feature_enc_layers[-1][0] - - self.vector_quantizer = None - if cfg.vq_type == "gumbel": - self.vector_quantizer = GumbelVectorQuantizer( - dim=embed, - num_vars=cfg.vq_vars, - temp=cfg.vq_temp, - groups=cfg.vq_groups, - combine_groups=cfg.combine_groups, - vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed, - time_first=False, - activation=activation, - weight_proj_depth=cfg.vq_depth, - weight_proj_factor=2, - ) - elif cfg.vq_type == "kmeans": - self.vector_quantizer = KmeansVectorQuantizer( - dim=embed, - num_vars=cfg.vq_vars, - groups=cfg.vq_groups, - combine_groups=cfg.combine_groups, - vq_dim=cfg.vq_dim if cfg.vq_dim > 0 else embed, - time_first=False, - gamma=cfg.vq_gamma, - ) - else: - assert ( - cfg.vq_type == "none" or cfg.vq_type is None - ), "Unknown quantizer type" - - if cfg.offset == "auto": - jin = 0 - rin = 0 - for _, k, stride in feature_enc_layers: - if rin == 0: - rin = k - rin = rin + (k - 1) * jin - if jin == 0: - jin = stride - else: - jin *= stride - offset = math.ceil(rin / jin) - - offset = int(offset) - - def make_aggregator(): - if cfg.aggregator == "cnn": - agg_layers = eval(cfg.conv_aggregator_layers) - agg_dim = agg_layers[-1][0] - feature_aggregator = ConvAggegator( - conv_layers=agg_layers, - embed=embed, - dropout=cfg.dropout, - skip_connections=cfg.skip_connections_agg, - residual_scale=cfg.residual_scale, - non_affine_group_norm=cfg.non_affine_group_norm, - conv_bias=not cfg.no_conv_bias, - zero_pad=cfg.agg_zero_pad, - activation=activation, - ) - elif cfg.aggregator == "gru": - agg_dim = cfg.gru_dim - feature_aggregator = nn.Sequential( - TransposeLast(), - nn.GRU( - input_size=embed, - hidden_size=agg_dim, - num_layers=1, - dropout=cfg.dropout, - ), - TransposeLast(deconstruct_idx=0), - ) - else: - raise Exception("unknown aggregator type " + cfg.aggregator) - - return feature_aggregator, agg_dim - - self.feature_aggregator, agg_dim = make_aggregator() - - self.wav2vec_predictions = Wav2VecPredictionsModel( - in_dim=agg_dim, - out_dim=embed, - prediction_steps=cfg.prediction_steps, - n_negatives=cfg.num_negatives, - cross_sample_negatives=cfg.cross_sample_negatives, - sample_distance=cfg.sample_distance, - dropout=cfg.dropout, - offset=offset, - balanced_classes=cfg.balanced_classes, - infonce=cfg.infonce, - ) - - self.dropout_feats = nn.Dropout(p=cfg.dropout_features) - self.dropout_agg = nn.Dropout(p=cfg.dropout_agg) - - if cfg.project_features == "none": - self.project_features = None - elif cfg.project_features == "same": - self.project_features = self.feature_aggregator - elif cfg.project_features == "new": - self.project_features, _ = make_aggregator() - - def forward(self, source): - result = {} - - features = self.feature_extractor(source) - if self.vector_quantizer: - q_res = self.vector_quantizer(features) - features = q_res["x"] - for k in q_res.keys(): - if k != "x": - result[k] = q_res[k] - - x = self.dropout_feats(features) - x = self.feature_aggregator(x) - x = self.dropout_agg(x) - - if self.project_features is not None: - features = self.project_features(features) - x, targets = self.wav2vec_predictions(x, features) - result["cpc_logits"] = x - result["cpc_targets"] = targets - - return result - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - - def max_positions(self): - """Maximum length supported by the model.""" - return sys.maxsize - - def get_logits(self, net_output): - logits = net_output["cpc_logits"] - return logits - - def get_targets(self, sample, net_output): - t = net_output["cpc_targets"] - if isinstance(t, tuple): - t = t[0] - return t.contiguous() - - def get_target_weights(self, targets, net_output): - targets = net_output["cpc_targets"] - if isinstance(targets, tuple) and targets[-1] is not None: - return targets[-1] - return None - - def get_extra_losses(self, net_output): - loss = None - if "prob_perplexity" in net_output: - loss = net_output["num_vars"] - net_output["prob_perplexity"] - elif "kmeans_loss" in net_output: - loss = net_output["kmeans_loss"] - - return loss - - -def norm_block(is_layer_norm, dim, affine=True): - if is_layer_norm: - mod = nn.Sequential( - TransposeLast(), - Fp32LayerNorm(dim, elementwise_affine=affine), - TransposeLast(), - ) - else: - mod = Fp32GroupNorm(1, dim, affine=affine) - - return mod - - -class ConvFeatureExtractionModel(nn.Module): - def __init__( - self, - conv_layers, - dropout, - log_compression, - skip_connections, - residual_scale, - non_affine_group_norm, - activation, - ): - super().__init__() - - def block(n_in, n_out, k, stride): - return nn.Sequential( - nn.Conv1d(n_in, n_out, k, stride=stride, bias=False), - nn.Dropout(p=dropout), - norm_block( - is_layer_norm=False, dim=n_out, affine=not non_affine_group_norm - ), - activation, - ) - - in_d = 1 - self.conv_layers = nn.ModuleList() - for dim, k, stride in conv_layers: - self.conv_layers.append(block(in_d, dim, k, stride)) - in_d = dim - - self.log_compression = log_compression - self.skip_connections = skip_connections - self.residual_scale = math.sqrt(residual_scale) - - def forward(self, x): - # BxT -> BxCxT - x = x.unsqueeze(1) - - for conv in self.conv_layers: - residual = x - x = conv(x) - if self.skip_connections and x.size(1) == residual.size(1): - tsz = x.size(2) - r_tsz = residual.size(2) - residual = residual[..., :: r_tsz // tsz][..., :tsz] - x = (x + residual) * self.residual_scale - - if self.log_compression: - x = x.abs() - x = x + 1 - x = x.log() - - return x - - -class ZeroPad1d(nn.Module): - def __init__(self, pad_left, pad_right): - super().__init__() - self.pad_left = pad_left - self.pad_right = pad_right - - def forward(self, x): - return F.pad(x, (self.pad_left, self.pad_right)) - - -class ConvAggegator(nn.Module): - def __init__( - self, - conv_layers, - embed, - dropout, - skip_connections, - residual_scale, - non_affine_group_norm, - conv_bias, - zero_pad, - activation, - ): - super().__init__() - - def block(n_in, n_out, k, stride): - # padding dims only really make sense for stride = 1 - ka = k // 2 - kb = ka - 1 if k % 2 == 0 else ka - - pad = ( - ZeroPad1d(ka + kb, 0) if zero_pad else nn.ReplicationPad1d((ka + kb, 0)) - ) - - return nn.Sequential( - pad, - nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias), - nn.Dropout(p=dropout), - norm_block(False, n_out, affine=not non_affine_group_norm), - activation, - ) - - in_d = embed - self.conv_layers = nn.ModuleList() - self.residual_proj = nn.ModuleList() - for dim, k, stride in conv_layers: - if in_d != dim and skip_connections: - self.residual_proj.append(nn.Conv1d(in_d, dim, 1, bias=False)) - else: - self.residual_proj.append(None) - - self.conv_layers.append(block(in_d, dim, k, stride)) - in_d = dim - self.conv_layers = nn.Sequential(*self.conv_layers) - self.skip_connections = skip_connections - self.residual_scale = math.sqrt(residual_scale) - - def forward(self, x): - for rproj, conv in zip(self.residual_proj, self.conv_layers): - residual = x - x = conv(x) - if self.skip_connections: - if rproj is not None: - residual = rproj(residual) - x = (x + residual) * self.residual_scale - return x - - -class Wav2VecPredictionsModel(nn.Module): - def __init__( - self, - in_dim, - out_dim, - prediction_steps, - n_negatives, - cross_sample_negatives, - sample_distance, - dropout, - offset, - balanced_classes, - infonce, - ): - super().__init__() - - self.n_negatives = n_negatives - self.cross_sample_negatives = cross_sample_negatives - self.sample_distance = sample_distance - self.project_to_steps = nn.ConvTranspose2d( - in_dim, out_dim, (1, prediction_steps) - ) - self.dropout = nn.Dropout(p=dropout) - self.offset = offset - self.balanced_classes = balanced_classes - self.infonce = infonce - - def sample_negatives(self, y): - bsz, fsz, tsz = y.shape - - y = y.transpose(0, 1) # BCT -> CBT - y = y.contiguous().view(fsz, -1) # CBT => C(BxT) - - cross_high = tsz * bsz - high = tsz if self.sample_distance is None else min(tsz, self.sample_distance) - assert high > 1 - - neg_idxs = torch.randint(low=0, high=high, size=(bsz, self.n_negatives * tsz)) - - with torch.no_grad(): - if self.n_negatives > 0: - tszs = ( - buffered_arange(tsz) - .unsqueeze(-1) - .expand(-1, self.n_negatives) - .flatten() - ) - - neg_idxs = torch.randint( - low=0, high=high - 1, size=(bsz, self.n_negatives * tsz) - ) - neg_idxs[neg_idxs >= tszs] += 1 - - if self.cross_sample_negatives > 0: - tszs = ( - buffered_arange(tsz) - .unsqueeze(-1) - .expand(-1, self.cross_sample_negatives) - .flatten() - ) - - cross_neg_idxs = torch.randint( - low=0, - high=cross_high - 1, - size=(bsz, self.cross_sample_negatives * tsz), - ) - cross_neg_idxs[cross_neg_idxs >= tszs] += 1 - - if self.n_negatives > 0: - for i in range(1, bsz): - neg_idxs[i] += i * high - else: - neg_idxs = cross_neg_idxs - - if self.cross_sample_negatives > 0 and self.n_negatives > 0: - neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1) - - negs = y[..., neg_idxs.view(-1)] - negs = negs.view( - fsz, bsz, self.n_negatives + self.cross_sample_negatives, tsz - ).permute( - 2, 1, 0, 3 - ) # to NxBxCxT - - return negs - - def forward(self, x, y): - - x = x.unsqueeze(-1) - x = self.project_to_steps(x) # BxCxTxS - x = self.dropout(x) - - negatives = self.sample_negatives(y) - y = y.unsqueeze(0) - targets = torch.cat([y, negatives], dim=0) # Copies x B x C x T - - copies = targets.size(0) - bsz, dim, tsz, steps = x.shape - steps = min(steps, tsz - self.offset) - - predictions = x.new( - bsz * copies * (tsz - self.offset + 1) * steps - - ((steps + 1) * steps // 2) * copies * bsz - ) - if self.infonce: - labels = predictions.new_full( - (predictions.shape[0] // copies,), 0, dtype=torch.long - ) - else: - labels = torch.zeros_like(predictions) - weights = ( - torch.full_like(labels, 1 / self.n_negatives) - if self.balanced_classes and not self.infonce - else None - ) - - start = end = 0 - for i in range(steps): - offset = i + self.offset - end = start + (tsz - offset) * bsz * copies - if self.infonce: - predictions[start:end] = torch.einsum( - "bct,nbct->tbn", x[..., :-offset, i], targets[..., offset:] - ).flatten() - else: - pos_num = (end - start) // copies - predictions[start:end] = torch.einsum( - "bct,nbct->nbt", x[..., :-offset, i], targets[..., offset:] - ).flatten() - labels[start : start + pos_num] = 1.0 - if weights is not None: - weights[start : start + pos_num] = 1.0 - start = end - assert end == predictions.numel(), "{} != {}".format(end, predictions.numel()) - - if self.infonce: - predictions = predictions.view(-1, copies) - else: - if weights is not None: - labels = (labels, weights) - - return predictions, labels diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec2.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec2.py deleted file mode 100644 index 6ce0e2297ecd5ac427e5e7514686894d3dad303b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec2.py +++ /dev/null @@ -1,1283 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from dataclasses import dataclass, field -from typing import List, Tuple - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.data.data_utils import compute_mask_indices -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.distributed import fsdp_wrap -from fairseq.models import BaseFairseqModel, register_model -from fairseq.modules import ( - Fp32GroupNorm, - Fp32LayerNorm, - GradMultiply, - GumbelVectorQuantizer, - LayerNorm, - MultiheadAttention, - RelPositionalEncoding, - SamePad, - TransposeLast, -) -from fairseq.modules.checkpoint_activations import checkpoint_wrapper -from fairseq.modules.conformer_layer import ConformerWav2Vec2EncoderLayer -from fairseq.modules.transformer_sentence_encoder import init_bert_params -from fairseq.utils import buffered_arange, index_put, is_xla_tensor - -from .utils import pad_to_multiple - -EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"]) -MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"]) -LAYER_TYPE_CHOICES = ChoiceEnum(["transformer", "conformer"]) - - -@dataclass -class Wav2Vec2Config(FairseqDataclass): - extractor_mode: EXTRACTOR_MODE_CHOICES = field( - default="default", - metadata={ - "help": "mode for feature extractor. default has a single group norm with d " - "groups in the first conv block, whereas layer_norm has layer norms in " - "every block (meant to use with normalize=True)" - }, - ) - encoder_layers: int = field( - default=12, metadata={"help": "num encoder layers in the transformer"} - ) - encoder_embed_dim: int = field( - default=768, metadata={"help": "encoder embedding dimension"} - ) - encoder_ffn_embed_dim: int = field( - default=3072, metadata={"help": "encoder embedding dimension for FFN"} - ) - encoder_attention_heads: int = field( - default=12, metadata={"help": "num encoder attention heads"} - ) - activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field( - default="gelu", metadata={"help": "activation function to use"} - ) - layer_type: LAYER_TYPE_CHOICES = field( - default="transformer", metadata={"help": "layer type in encoder"} - ) - # dropouts - dropout: float = field( - default=0.1, metadata={"help": "dropout probability for the transformer"} - ) - attention_dropout: float = field( - default=0.1, metadata={"help": "dropout probability for attention weights"} - ) - activation_dropout: float = field( - default=0.0, metadata={"help": "dropout probability after activation in FFN"} - ) - encoder_layerdrop: float = field( - default=0.0, metadata={"help": "probability of dropping a tarnsformer layer"} - ) - dropout_input: float = field( - default=0.0, - metadata={"help": "dropout to apply to the input (after feat extr)"}, - ) - dropout_features: float = field( - default=0.0, - metadata={"help": "dropout to apply to the features (after feat extr)"}, - ) - - final_dim: int = field( - default=0, - metadata={ - "help": "project final representations and targets to this many dimensions." - "set to encoder_embed_dim is <= 0" - }, - ) - layer_norm_first: bool = field( - default=False, metadata={"help": "apply layernorm first in the transformer"} - ) - conv_feature_layers: str = field( - default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", - metadata={ - "help": "string describing convolutional feature extraction layers in form of a python list that contains " - "[(dim, kernel_size, stride), ...]" - }, - ) - conv_bias: bool = field( - default=False, metadata={"help": "include bias in conv encoder"} - ) - logit_temp: float = field( - default=0.1, metadata={"help": "temperature to divide logits by"} - ) - quantize_targets: bool = field( - default=False, metadata={"help": "use quantized targets"} - ) - quantize_input: bool = field( - default=False, metadata={"help": "use quantized inputs"} - ) - same_quantizer: bool = field( - default=False, metadata={"help": "use same quantizer for inputs and targets"} - ) - target_glu: bool = field( - default=False, metadata={"help": "adds projection + glu to targets"} - ) - feature_grad_mult: float = field( - default=1.0, metadata={"help": "multiply feature extractor var grads by this"} - ) - quantizer_depth: int = field( - default=1, - metadata={"help": "number of quantizer layers"}, - ) - quantizer_factor: int = field( - default=3, - metadata={ - "help": "dimensionality increase for inner quantizer layers (if depth > 1)" - }, - ) - latent_vars: int = field( - default=320, - metadata={"help": "number of latent variables V in each group of the codebook"}, - ) - latent_groups: int = field( - default=2, - metadata={"help": "number of groups G of latent variables in the codebook"}, - ) - latent_dim: int = field( - default=0, - metadata={ - "help": "if > 0, uses this dimensionality for latent variables. " - "otherwise uses final_dim / latent_groups" - }, - ) - - # masking - mask_length: int = field(default=10, metadata={"help": "mask length"}) - mask_prob: float = field( - default=0.65, metadata={"help": "probability of replacing a token with mask"} - ) - mask_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose mask length"} - ) - mask_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument (used for more complex distributions), " - "see help in compute_mask_indices" - }, - ) - no_mask_overlap: bool = field( - default=False, metadata={"help": "whether to allow masks to overlap"} - ) - mask_min_space: int = field( - default=1, - metadata={"help": "min space between spans (if no overlap is enabled)"}, - ) - require_same_masks: bool = field( - default=True, - metadata={ - "help": "whether to number of masked timesteps must be the same across all " - "examples in a batch" - }, - ) - mask_dropout: float = field( - default=0.0, - metadata={"help": "percent of masks to unmask for each sample"}, - ) - - # channel masking - mask_channel_length: int = field( - default=10, metadata={"help": "length of the mask for features (channels)"} - ) - mask_channel_prob: float = field( - default=0.0, metadata={"help": "probability of replacing a feature with 0"} - ) - mask_channel_before: bool = False - mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", - metadata={"help": "how to choose mask length for channel masking"}, - ) - mask_channel_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument (used for more complex distributions), " - "see help in compute_mask_indicesh" - }, - ) - no_mask_channel_overlap: bool = field( - default=False, metadata={"help": "whether to allow channel masks to overlap"} - ) - mask_channel_min_space: int = field( - default=1, - metadata={"help": "min space between spans (if no overlap is enabled)"}, - ) - - # negative selection - num_negatives: int = field( - default=100, - metadata={"help": "number of negative examples from the same sample"}, - ) - negatives_from_everywhere: bool = field( - default=False, - metadata={"help": "sample negatives from everywhere, not just masked states"}, - ) - cross_sample_negatives: int = field( - default=0, metadata={"help": "number of negative examples from the any sample"} - ) - codebook_negatives: int = field( - default=0, metadata={"help": "number of negative examples codebook"} - ) - - # positional embeddings - conv_pos: int = field( - default=128, - metadata={"help": "number of filters for convolutional positional embeddings"}, - ) - conv_pos_groups: int = field( - default=16, - metadata={"help": "number of groups for convolutional positional embedding"}, - ) - pos_conv_depth: int = field( - default=1, - metadata={"help": "depth of positional encoder network"}, - ) - - latent_temp: Tuple[float, float, float] = field( - default=(2, 0.5, 0.999995), - metadata={ - "help": "temperature for latent variable sampling. " - "can be tuple of 3 values (start, end, decay)" - }, - ) - max_positions: int = field(default=100000, metadata={"help": "Max positions"}) - checkpoint_activations: bool = field( - default=False, - metadata={"help": "recompute activations and save memory for extra compute"}, - ) - - # FP16 optimization - required_seq_len_multiple: int = field( - default=2, - metadata={ - "help": "pad the input to encoder such that the sequence length is divisible by multiple" - }, - ) - crop_seq_to_multiple: int = field( - default=1, - metadata={ - "help": "crop convolutional feature extractor output such that the sequence length is divisible by multiple" - }, - ) - - # Conformer - depthwise_conv_kernel_size: int = field( - default=31, - metadata={ - "help": "depthwise-conv-kernel-size for convolution in conformer layer" - }, - ) - attn_type: str = field( - default="", - metadata={"help": "if espnet use ESPNET MHA"}, - ) - pos_enc_type: str = field( - default="abs", - metadata={"help": "Positional encoding type to use in conformer"}, - ) - fp16: bool = field(default=False, metadata={"help": "If fp16 is being used"}) - - -@register_model("wav2vec2", dataclass=Wav2Vec2Config) -class Wav2Vec2Model(BaseFairseqModel): - def __init__(self, cfg: Wav2Vec2Config): - super().__init__() - self.cfg = cfg - - feature_enc_layers = eval(cfg.conv_feature_layers) - self.embed = feature_enc_layers[-1][0] - - self.feature_extractor = ConvFeatureExtractionModel( - conv_layers=feature_enc_layers, - dropout=0.0, - mode=cfg.extractor_mode, - conv_bias=cfg.conv_bias, - ) - - self.post_extract_proj = ( - nn.Linear(self.embed, cfg.encoder_embed_dim) - if self.embed != cfg.encoder_embed_dim and not cfg.quantize_input - else None - ) - - self.crop_seq_to_multiple = cfg.crop_seq_to_multiple - - self.mask_prob = cfg.mask_prob - self.mask_selection = cfg.mask_selection - self.mask_other = cfg.mask_other - self.mask_length = cfg.mask_length - self.no_mask_overlap = cfg.no_mask_overlap - self.mask_min_space = cfg.mask_min_space - - self.mask_channel_prob = cfg.mask_channel_prob - self.mask_channel_before = cfg.mask_channel_before - self.mask_channel_selection = cfg.mask_channel_selection - self.mask_channel_other = cfg.mask_channel_other - self.mask_channel_length = cfg.mask_channel_length - self.no_mask_channel_overlap = cfg.no_mask_channel_overlap - self.mask_channel_min_space = cfg.mask_channel_min_space - - self.dropout_input = nn.Dropout(cfg.dropout_input) - self.dropout_features = nn.Dropout(cfg.dropout_features) - - self.feature_grad_mult = cfg.feature_grad_mult - - self.quantizer = None - self.input_quantizer = None - - self.n_negatives = cfg.num_negatives - self.cross_sample_negatives = cfg.cross_sample_negatives - self.codebook_negatives = cfg.codebook_negatives - self.negatives_from_everywhere = cfg.negatives_from_everywhere - - self.logit_temp = cfg.logit_temp - - final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim - - if cfg.quantize_targets: - vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else final_dim - self.quantizer = GumbelVectorQuantizer( - dim=self.embed, - num_vars=cfg.latent_vars, - temp=cfg.latent_temp, - groups=cfg.latent_groups, - combine_groups=False, - vq_dim=vq_dim, - time_first=True, - weight_proj_depth=cfg.quantizer_depth, - weight_proj_factor=cfg.quantizer_factor, - ) - self.project_q = nn.Linear(vq_dim, final_dim) - else: - self.project_q = nn.Linear(self.embed, final_dim) - - if cfg.quantize_input: - if cfg.same_quantizer and self.quantizer is not None: - vq_dim = final_dim - self.input_quantizer = self.quantizer - else: - vq_dim = cfg.latent_dim if cfg.latent_dim > 0 else cfg.encoder_embed_dim - self.input_quantizer = GumbelVectorQuantizer( - dim=self.embed, - num_vars=cfg.latent_vars, - temp=cfg.latent_temp, - groups=cfg.latent_groups, - combine_groups=False, - vq_dim=vq_dim, - time_first=True, - weight_proj_depth=cfg.quantizer_depth, - weight_proj_factor=cfg.quantizer_factor, - ) - self.project_inp = nn.Linear(vq_dim, cfg.encoder_embed_dim) - - self.mask_emb = nn.Parameter( - torch.FloatTensor(cfg.encoder_embed_dim).uniform_() - ) - encoder_cls = TransformerEncoder - if cfg.layer_type == "conformer" and cfg.pos_enc_type in ["rel_pos", "rope"]: - encoder_cls = ConformerEncoder - - self.encoder = encoder_cls(cfg) - self.layer_norm = LayerNorm(self.embed) - - self.target_glu = None - if cfg.target_glu: - self.target_glu = nn.Sequential( - nn.Linear(final_dim, final_dim * 2), nn.GLU() - ) - - self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - return state_dict - - @classmethod - def build_model(cls, cfg: Wav2Vec2Config, task=None): - """Build a new model instance.""" - - return cls(cfg) - - def apply_mask( - self, - x, - padding_mask, - mask_indices=None, - mask_channel_indices=None, - ): - B, T, C = x.shape - - if self.mask_channel_prob > 0 and self.mask_channel_before: - mask_channel_indices = compute_mask_indices( - (B, C), - None, - self.mask_channel_prob, - self.mask_channel_length, - self.mask_channel_selection, - self.mask_channel_other, - no_overlap=self.no_mask_channel_overlap, - min_space=self.mask_channel_min_space, - ) - mask_channel_indices = ( - torch.from_numpy(mask_channel_indices) - .to(x.device) - .unsqueeze(1) - .expand(-1, T, -1) - ) - x[mask_channel_indices] = 0 - - if self.mask_prob > 0: - if mask_indices is None: - mask_indices = compute_mask_indices( - (B, T), - padding_mask, - self.mask_prob, - self.mask_length, - self.mask_selection, - self.mask_other, - min_masks=2, - no_overlap=self.no_mask_overlap, - min_space=self.mask_min_space, - require_same_masks=self.cfg.require_same_masks, - mask_dropout=self.cfg.mask_dropout, - ) - mask_indices = torch.from_numpy(mask_indices).to(x.device) - x = index_put(x, mask_indices, self.mask_emb) - else: - mask_indices = None - - if self.mask_channel_prob > 0 and not self.mask_channel_before: - if mask_channel_indices is None: - mask_channel_indices = compute_mask_indices( - (B, C), - None, - self.mask_channel_prob, - self.mask_channel_length, - self.mask_channel_selection, - self.mask_channel_other, - no_overlap=self.no_mask_channel_overlap, - min_space=self.mask_channel_min_space, - ) - mask_channel_indices = ( - torch.from_numpy(mask_channel_indices) - .to(x.device) - .unsqueeze(1) - .expand(-1, T, -1) - ) - x = index_put(x, mask_channel_indices, 0) - - return x, mask_indices - - def sample_negatives(self, y, num, padding_count=None): - - if self.n_negatives == 0 and self.cross_sample_negatives == 0: - return y.new(0) - - bsz, tsz, fsz = y.shape - y = y.view(-1, fsz) # BTC => (BxT)C - - # FIXME: what happens if padding_count is specified? - cross_high = tsz * bsz - high = tsz - (padding_count or 0) - with torch.no_grad(): - assert high > 1, f"{bsz,tsz,fsz}" - - if self.n_negatives > 0: - tszs = ( - buffered_arange(num) - .unsqueeze(-1) - .expand(-1, self.n_negatives) - .flatten() - ) - - neg_idxs = torch.randint( - low=0, high=high - 1, size=(bsz, self.n_negatives * num) - ) - neg_idxs[neg_idxs >= tszs] += 1 - - if self.cross_sample_negatives > 0: - tszs = ( - buffered_arange(num) - .unsqueeze(-1) - .expand(-1, self.cross_sample_negatives) - .flatten() - ) - - cross_neg_idxs = torch.randint( - low=0, - high=cross_high - 1, - size=(bsz, self.cross_sample_negatives * num), - ) - cross_neg_idxs[cross_neg_idxs >= tszs] += 1 - - if self.n_negatives > 0: - neg_idxs = neg_idxs + (torch.arange(bsz).unsqueeze(1) * high) - else: - neg_idxs = cross_neg_idxs - - if self.cross_sample_negatives > 0 and self.n_negatives > 0: - neg_idxs = torch.cat([neg_idxs, cross_neg_idxs], dim=1) - - negs = y[neg_idxs.view(-1)] - negs = negs.view( - bsz, num, self.n_negatives + self.cross_sample_negatives, fsz - ).permute( - 2, 0, 1, 3 - ) # to NxBxTxC - return negs, neg_idxs - - def compute_preds(self, x, y, negatives): - - neg_is_pos = (y == negatives).all(-1) - y = y.unsqueeze(0) - targets = torch.cat([y, negatives], dim=0) - - logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1) - logits = logits / self.logit_temp - logits = logits.type_as(x) - - if is_xla_tensor(logits) or neg_is_pos.any(): - if not hasattr(self, "_inftensor"): - fillval = -float(2**30) - self._inftensor = ( - torch.tensor(fillval).to(x.device) - if is_xla_tensor(logits) - else float("-inf") - ) - logits[1:] = index_put(logits[1:], neg_is_pos, self._inftensor) - - return logits - - def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): - """ - Computes the output length of the convolutional layers - """ - - def _conv_out_length(input_length, kernel_size, stride): - return torch.floor((input_length - kernel_size) / stride + 1) - - conv_cfg_list = eval(self.cfg.conv_feature_layers) - - for i in range(len(conv_cfg_list)): - input_lengths = _conv_out_length( - input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2] - ) - - return input_lengths.to(torch.long) - - def forward( - self, - source, - padding_mask=None, - mask=True, - features_only=False, - layer=None, - mask_indices=None, - mask_channel_indices=None, - padding_count=None, - ): - - if self.feature_grad_mult > 0: - features = self.feature_extractor(source) - if self.feature_grad_mult != 1.0: - features = GradMultiply.apply(features, self.feature_grad_mult) - else: - with torch.no_grad(): - features = self.feature_extractor(source) - - features_pen = features.float().pow(2).mean() - - features = features.transpose(1, 2) - features = self.layer_norm(features) - unmasked_features = features.clone() - - if padding_mask is not None and padding_mask.any(): - input_lengths = (1 - padding_mask.long()).sum(-1) - # apply conv formula to get real output_lengths - output_lengths = self._get_feat_extract_output_lengths(input_lengths) - - padding_mask = torch.zeros( - features.shape[:2], dtype=features.dtype, device=features.device - ) - - # these two operations makes sure that all values - # before the output lengths indices are attended to - padding_mask[ - ( - torch.arange(padding_mask.shape[0], device=padding_mask.device), - output_lengths - 1, - ) - ] = 1 - padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool() - else: - padding_mask = None - - time_steps_to_drop = features.size(1) % self.crop_seq_to_multiple - if time_steps_to_drop != 0: - features = features[:, :-time_steps_to_drop] - unmasked_features = unmasked_features[:, :-time_steps_to_drop] - if padding_mask is not None: - padding_mask = padding_mask[:, :-time_steps_to_drop] - - if self.post_extract_proj is not None: - features = self.post_extract_proj(features) - - features = self.dropout_input(features) - unmasked_features = self.dropout_features(unmasked_features) - - num_vars = None - code_ppl = None - prob_ppl = None - curr_temp = None - - if self.input_quantizer: - q = self.input_quantizer(features, produce_targets=False) - features = q["x"] - num_vars = q["num_vars"] - code_ppl = q["code_perplexity"] - prob_ppl = q["prob_perplexity"] - curr_temp = q["temp"] - features = self.project_inp(features) - - if mask: - x, mask_indices = self.apply_mask( - features, - padding_mask, - mask_indices=mask_indices, - mask_channel_indices=mask_channel_indices, - ) - if not is_xla_tensor(x) and mask_indices is not None: - # tpu-comment: reducing the size in a dynamic way causes - # too many recompilations on xla. - y = unmasked_features[mask_indices].view( - unmasked_features.size(0), -1, unmasked_features.size(-1) - ) - else: - y = unmasked_features - else: - x = features - y = unmasked_features - mask_indices = None - - x, layer_results = self.encoder(x, padding_mask=padding_mask, layer=layer) - - if features_only: - return { - "x": x, - "padding_mask": padding_mask, - "features": unmasked_features, - "layer_results": layer_results, - } - - if self.quantizer: - if self.negatives_from_everywhere: - q = self.quantizer(unmasked_features, produce_targets=False) - y = q["x"] - num_vars = q["num_vars"] - code_ppl = q["code_perplexity"] - prob_ppl = q["prob_perplexity"] - curr_temp = q["temp"] - y = self.project_q(y) - - negs, _ = self.sample_negatives( - y, - mask_indices[0].sum(), - padding_count=padding_count, - ) - y = y[mask_indices].view(y.size(0), -1, y.size(-1)) - - else: - q = self.quantizer(y, produce_targets=False) - y = q["x"] - num_vars = q["num_vars"] - code_ppl = q["code_perplexity"] - prob_ppl = q["prob_perplexity"] - curr_temp = q["temp"] - - y = self.project_q(y) - - negs, _ = self.sample_negatives( - y, - y.size(1), - padding_count=padding_count, - ) - - if self.codebook_negatives > 0: - cb_negs = self.quantizer.sample_from_codebook( - y.size(0) * y.size(1), self.codebook_negatives - ) - cb_negs = cb_negs.view( - self.codebook_negatives, y.size(0), y.size(1), -1 - ) # order doesnt matter - cb_negs = self.project_q(cb_negs) - negs = torch.cat([negs, cb_negs], dim=0) - else: - y = self.project_q(y) - - if self.negatives_from_everywhere: - negs, _ = self.sample_negatives( - unmasked_features, - y.size(1), - padding_count=padding_count, - ) - negs = self.project_q(negs) - else: - negs, _ = self.sample_negatives( - y, - y.size(1), - padding_count=padding_count, - ) - - if not is_xla_tensor(x): - # tpu-comment: reducing the size in a dynamic way causes - # too many recompilations on xla. - x = x[mask_indices].view(x.size(0), -1, x.size(-1)) - - if self.target_glu: - y = self.target_glu(y) - negs = self.target_glu(negs) - - x = self.final_proj(x) - x = self.compute_preds(x, y, negs) - - result = { - "x": x, - "padding_mask": padding_mask, - "features_pen": features_pen, - } - - if prob_ppl is not None: - result["prob_perplexity"] = prob_ppl - result["code_perplexity"] = code_ppl - result["num_vars"] = num_vars - result["temp"] = curr_temp - - return result - - def quantize(self, x): - assert self.quantizer is not None - x = self.feature_extractor(x) - x = x.transpose(1, 2) - x = self.layer_norm(x) - return self.quantizer.forward_idx(x) - - def extract_features(self, source, padding_mask, mask=False, layer=None): - res = self.forward( - source, padding_mask, mask=mask, features_only=True, layer=layer - ) - return res - - def get_logits(self, net_output): - logits = net_output["x"] - logits = logits.transpose(0, 2) - logits = logits.reshape(-1, logits.size(-1)) - return logits - - def get_targets(self, sample, net_output, expand_steps=True): - x = net_output["x"] - return x.new_zeros(x.size(1) * x.size(2), dtype=torch.long) - - def get_extra_losses(self, net_output): - pen = [] - - if "prob_perplexity" in net_output: - pen.append( - (net_output["num_vars"] - net_output["prob_perplexity"]) - / net_output["num_vars"] - ) - - if "features_pen" in net_output: - pen.append(net_output["features_pen"]) - - return pen - - def remove_pretraining_modules(self, last_layer=None): - self.quantizer = None - self.project_q = None - self.target_glu = None - self.final_proj = None - - if last_layer is not None: - self.encoder.layers = nn.ModuleList( - l for i, l in enumerate(self.encoder.layers) if i <= last_layer - ) - - -class ConvFeatureExtractionModel(nn.Module): - def __init__( - self, - conv_layers: List[Tuple[int, int, int]], - dropout: float = 0.0, - mode: str = "default", - conv_bias: bool = False, - ): - super().__init__() - - assert mode in {"default", "layer_norm"} - - def block( - n_in, - n_out, - k, - stride, - is_layer_norm=False, - is_group_norm=False, - conv_bias=False, - ): - def make_conv(): - conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) - nn.init.kaiming_normal_(conv.weight) - return conv - - assert ( - is_layer_norm and is_group_norm - ) == False, "layer norm and group norm are exclusive" - - if is_layer_norm: - return nn.Sequential( - make_conv(), - nn.Dropout(p=dropout), - nn.Sequential( - TransposeLast(), - Fp32LayerNorm(dim, elementwise_affine=True), - TransposeLast(), - ), - nn.GELU(), - ) - elif is_group_norm: - return nn.Sequential( - make_conv(), - nn.Dropout(p=dropout), - Fp32GroupNorm(dim, dim, affine=True), - nn.GELU(), - ) - else: - return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) - - in_d = 1 - self.conv_layers = nn.ModuleList() - for i, cl in enumerate(conv_layers): - assert len(cl) == 3, "invalid conv definition: " + str(cl) - (dim, k, stride) = cl - - self.conv_layers.append( - block( - in_d, - dim, - k, - stride, - is_layer_norm=mode == "layer_norm", - is_group_norm=mode == "default" and i == 0, - conv_bias=conv_bias, - ) - ) - in_d = dim - - def forward(self, x): - - # BxT -> BxCxT - x = x.unsqueeze(1) - - for conv in self.conv_layers: - x = conv(x) - - return x - - -def make_conv_pos(e, k, g): - pos_conv = nn.Conv1d( - e, - e, - kernel_size=k, - padding=k // 2, - groups=g, - ) - dropout = 0 - std = math.sqrt((4 * (1.0 - dropout)) / (k * e)) - nn.init.normal_(pos_conv.weight, mean=0, std=std) - nn.init.constant_(pos_conv.bias, 0) - - pos_conv = nn.utils.weight_norm(pos_conv, name="weight", dim=2) - pos_conv = nn.Sequential(pos_conv, SamePad(k), nn.GELU()) - - return pos_conv - - -class TransformerEncoder(nn.Module): - def build_encoder_layer(self, args: Wav2Vec2Config): - if args.layer_type == "transformer": - layer = TransformerSentenceEncoderLayer( - embedding_dim=self.embedding_dim, - ffn_embedding_dim=args.encoder_ffn_embed_dim, - num_attention_heads=args.encoder_attention_heads, - dropout=self.dropout, - attention_dropout=args.attention_dropout, - activation_dropout=args.activation_dropout, - activation_fn=args.activation_fn, - layer_norm_first=args.layer_norm_first, - ) - elif args.layer_type == "conformer": - layer = ConformerWav2Vec2EncoderLayer( - embed_dim=self.embedding_dim, - ffn_embed_dim=args.encoder_ffn_embed_dim, - attention_heads=args.encoder_attention_heads, - dropout=args.dropout, - depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, - activation_fn="swish", - attn_type=args.attn_type, - use_fp16=args.fp16, - pos_enc_type="abs", - ) - layer = fsdp_wrap(layer) - if args.checkpoint_activations: - layer = checkpoint_wrapper(layer) - return layer - - def __init__(self, args: Wav2Vec2Config): - super().__init__() - - self.dropout = args.dropout - self.embedding_dim = args.encoder_embed_dim - self.required_seq_len_multiple = args.required_seq_len_multiple - - pos_conv_depth = getattr(args, "pos_conv_depth", 1) - if pos_conv_depth > 1: - num_layers = args.pos_conv_depth - k = max(3, args.conv_pos // num_layers) - - def make_conv_block(e, k, g, l): - return nn.Sequential( - *[ - nn.Sequential( - nn.Conv1d( - e, - e, - kernel_size=k, - padding=k // 2, - groups=g, - ), - SamePad(k), - TransposeLast(), - LayerNorm(e, elementwise_affine=False), - TransposeLast(), - nn.GELU(), - ) - for _ in range(l) - ] - ) - - self.pos_conv = make_conv_block( - self.embedding_dim, k, args.conv_pos_groups, num_layers - ) - - else: - self.pos_conv = make_conv_pos( - self.embedding_dim, - args.conv_pos, - args.conv_pos_groups, - ) - - self.layers = nn.ModuleList( - [self.build_encoder_layer(args) for _ in range(args.encoder_layers)] - ) - self.layer_norm_first = args.layer_norm_first - self.layer_norm = LayerNorm(self.embedding_dim) - self.layerdrop = args.encoder_layerdrop - - self.apply(init_bert_params) - - def forward(self, x, padding_mask=None, layer=None): - x, layer_results = self.extract_features(x, padding_mask, layer) - - if self.layer_norm_first and layer is None: - x = self.layer_norm(x) - - return x, layer_results - - def extract_features( - self, - x, - padding_mask=None, - tgt_layer=None, - min_layer=0, - ): - - if padding_mask is not None: - x = index_put(x, padding_mask, 0) - - x_conv = self.pos_conv(x.transpose(1, 2)) - x_conv = x_conv.transpose(1, 2) - x = x + x_conv - - if not self.layer_norm_first: - x = self.layer_norm(x) - - # pad to the sequence length dimension - x, pad_length = pad_to_multiple( - x, self.required_seq_len_multiple, dim=-2, value=0 - ) - if pad_length > 0 and padding_mask is None: - padding_mask = x.new_zeros((x.size(0), x.size(1)), dtype=torch.bool) - padding_mask[:, -pad_length:] = True - else: - padding_mask, _ = pad_to_multiple( - padding_mask, self.required_seq_len_multiple, dim=-1, value=True - ) - x = F.dropout(x, p=self.dropout, training=self.training) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - layer_results = [] - r = None - for i, layer in enumerate(self.layers): - dropout_probability = np.random.random() if self.layerdrop > 0 else 1 - if not self.training or (dropout_probability > self.layerdrop): - x, (z, lr) = layer( - x, self_attn_padding_mask=padding_mask, need_weights=False - ) - if i >= min_layer: - layer_results.append((x, z, lr)) - if i == tgt_layer: - r = x - break - - if r is not None: - x = r - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - # undo paddding - if pad_length > 0: - x = x[:, :-pad_length] - - def undo_pad(a, b, c): - return ( - a[:-pad_length], - b[:-pad_length] if b is not None else b, - c[:-pad_length], - ) - - layer_results = [undo_pad(*u) for u in layer_results] - - return x, layer_results - - def max_positions(self): - """Maximum output length supported by the encoder.""" - return self.args.max_positions - - def upgrade_state_dict_named(self, state_dict, name): - """Upgrade a (possibly old) state dict for new versions of fairseq.""" - return state_dict - - -class ConformerEncoder(TransformerEncoder): - def build_encoder_layer(self, args): - layer = ConformerWav2Vec2EncoderLayer( - embed_dim=self.embedding_dim, - ffn_embed_dim=args.encoder_ffn_embed_dim, - attention_heads=args.encoder_attention_heads, - dropout=args.dropout, - depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, - activation_fn="swish", - attn_type=args.attn_type, - pos_enc_type=args.pos_enc_type, - use_fp16=args.fp16, # only used for rope - ) - layer = fsdp_wrap(layer) - if args.checkpoint_activations: - layer = checkpoint_wrapper(layer) - return layer - - def __init__(self, args): - super().__init__(args) - self.args = args - self.dropout = args.dropout - self.embedding_dim = args.encoder_embed_dim - self.pos_enc_type = args.pos_enc_type - max_source_positions = self.max_positions() - - if self.pos_enc_type == "rel_pos": - self.embed_positions = RelPositionalEncoding( - max_source_positions, self.embedding_dim - ) - elif self.pos_enc_type == "rope": - self.embed_positions = None - else: - raise Exception("Unsupported positional encoding type") - - self.layers = nn.ModuleList( - [self.build_encoder_layer(args) for _ in range(args.encoder_layers)] - ) - self.layer_norm_first = args.layer_norm_first - self.layer_norm = LayerNorm(self.embedding_dim) - self.layerdrop = args.encoder_layerdrop - - self.apply(init_bert_params) - - def extract_features(self, x, padding_mask=None, tgt_layer=None): - if padding_mask is not None: - x = index_put(x, padding_mask, 0) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # B X T X C here - position_emb = None - if self.pos_enc_type == "rel_pos": - position_emb = self.embed_positions(x) - - if not self.layer_norm_first: - x = self.layer_norm(x) - - x = F.dropout(x, p=self.dropout, training=self.training) - - layer_results = [] - r = None - for i, layer in enumerate(self.layers): - dropout_probability = np.random.random() - if not self.training or (dropout_probability > self.layerdrop): - x, z = layer( - x, - self_attn_padding_mask=padding_mask, - need_weights=False, - position_emb=position_emb, - ) - if tgt_layer is not None: - layer_results.append((x, z)) - if i == tgt_layer: - r = x - break - - if r is not None: - x = r - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - return x, layer_results - - -class TransformerSentenceEncoderLayer(nn.Module): - """ - Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained - models. - """ - - def __init__( - self, - embedding_dim: float = 768, - ffn_embedding_dim: float = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - activation_fn: str = "relu", - layer_norm_first: bool = False, - ) -> None: - - super().__init__() - # Initialize parameters - self.embedding_dim = embedding_dim - self.dropout = dropout - self.activation_dropout = activation_dropout - - # Initialize blocks - self.activation_fn = utils.get_activation_fn(activation_fn) - self.self_attn = MultiheadAttention( - self.embedding_dim, - num_attention_heads, - dropout=attention_dropout, - self_attention=True, - ) - - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(self.activation_dropout) - self.dropout3 = nn.Dropout(dropout) - - self.layer_norm_first = layer_norm_first - - # layer norm associated with the self attention layer - self.self_attn_layer_norm = LayerNorm(self.embedding_dim) - self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) - self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) - - # layer norm associated with the position wise feed-forward NN - self.final_layer_norm = LayerNorm(self.embedding_dim) - - def forward( - self, - x: torch.Tensor, - self_attn_mask: torch.Tensor = None, - self_attn_padding_mask: torch.Tensor = None, - need_weights: bool = False, - att_args=None, - ): - """ - LayerNorm is applied either before or after the self-attention/ffn - modules similar to the original Transformer imlementation. - """ - residual = x - - if self.layer_norm_first: - x = self.self_attn_layer_norm(x) - x, attn = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=self_attn_padding_mask, - attn_mask=self_attn_mask, - need_weights=False, - ) - x = self.dropout1(x) - x = residual + x - - residual = x - x = self.final_layer_norm(x) - x = self.activation_fn(self.fc1(x)) - x = self.dropout2(x) - x = self.fc2(x) - - layer_result = x - - x = self.dropout3(x) - x = residual + x - else: - x, attn = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=self_attn_padding_mask, - need_weights=False, - ) - - x = self.dropout1(x) - x = residual + x - - x = self.self_attn_layer_norm(x) - - residual = x - x = self.activation_fn(self.fc1(x)) - x = self.dropout2(x) - x = self.fc2(x) - - layer_result = x - - x = self.dropout3(x) - x = residual + x - x = self.final_layer_norm(x) - - return x, (attn, layer_result) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec2_asr.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec2_asr.py deleted file mode 100644 index 21ece3bc554e29ab14211bbde85c09c595beae75..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/wav2vec/wav2vec2_asr.py +++ /dev/null @@ -1,747 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import copy -import logging -import math -import re -from argparse import Namespace -from dataclasses import dataclass, field -from typing import Any, Optional - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import checkpoint_utils, tasks, utils -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.models import ( - BaseFairseqModel, - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, -) -from fairseq.models.wav2vec.wav2vec2 import MASKING_DISTRIBUTION_CHOICES -from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerDecoderLayer -from fairseq.tasks import FairseqTask -from omegaconf import II, MISSING, open_dict - -logger = logging.getLogger(__name__) - - -@dataclass -class Wav2Vec2AsrConfig(FairseqDataclass): - w2v_path: str = field( - default=MISSING, metadata={"help": "path to wav2vec 2.0 model"} - ) - no_pretrained_weights: bool = field( - default=False, metadata={"help": "if true, does not load pretrained weights"} - ) - dropout_input: float = field( - default=0.0, - metadata={"help": "dropout to apply to the input (after feat extr)"}, - ) - final_dropout: float = field( - default=0.0, - metadata={"help": "dropout after transformer and before final projection"}, - ) - dropout: float = field( - default=0.0, metadata={"help": "dropout probability inside wav2vec 2.0 model"} - ) - attention_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability for attention weights inside wav2vec 2.0 model" - }, - ) - activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN inside wav2vec 2.0 model" - }, - ) - conv_feature_layers: Optional[str] = field( - default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]", - metadata={ - "help": ( - "string describing convolutional feature extraction " - "layers in form of a python list that contains " - "[(dim, kernel_size, stride), ...]" - ), - }, - ) - encoder_embed_dim: Optional[int] = field( - default=768, metadata={"help": "encoder embedding dimension"} - ) - - # masking - apply_mask: bool = field( - default=False, metadata={"help": "apply masking during fine-tuning"} - ) - mask_length: int = field( - default=10, metadata={"help": "repeat the mask indices multiple times"} - ) - mask_prob: float = field( - default=0.5, - metadata={ - "help": "probability of replacing a token with mask (normalized by length)" - }, - ) - mask_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", metadata={"help": "how to choose masks"} - ) - mask_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument (used for more complex distributions), " - "see help in compute_mask_indices" - }, - ) - no_mask_overlap: bool = field( - default=False, metadata={"help": "whether to allow masks to overlap"} - ) - mask_min_space: Optional[int] = field( - default=1, - metadata={"help": "min space between spans (if no overlap is enabled)"}, - ) - require_same_masks: bool = field( - default=True, - metadata={ - "help": "whether to number of masked timesteps must be the same across all " - "examples in a batch" - }, - ) - mask_dropout: float = field( - default=0.0, - metadata={"help": "percent of masks to unmask for each sample"}, - ) - - # channel masking - mask_channel_length: int = field( - default=10, metadata={"help": "length of the mask for features (channels)"} - ) - mask_channel_prob: float = field( - default=0.0, metadata={"help": "probability of replacing a feature with 0"} - ) - mask_channel_selection: MASKING_DISTRIBUTION_CHOICES = field( - default="static", - metadata={"help": "how to choose mask length for channel masking"}, - ) - mask_channel_other: float = field( - default=0, - metadata={ - "help": "secondary mask argument (used for more complex distributions), " - "see help in compute_mask_indicesh" - }, - ) - no_mask_channel_overlap: bool = field( - default=False, metadata={"help": "whether to allow channel masks to overlap"} - ) - freeze_finetune_updates: int = field( - default=0, metadata={"help": "dont finetune wav2vec for this many updates"} - ) - feature_grad_mult: float = field( - default=0.0, metadata={"help": "reset feature grad mult in wav2vec 2.0 to this"} - ) - layerdrop: float = field( - default=0.0, metadata={"help": "probability of dropping a layer in wav2vec 2.0"} - ) - mask_channel_min_space: Optional[int] = field( - default=1, - metadata={"help": "min space between spans (if no overlap is enabled)"}, - ) - mask_channel_before: bool = False - normalize: bool = II("task.normalize") - data: str = II("task.data") - # this holds the loaded wav2vec args - w2v_args: Any = None - offload_activations: bool = field( - default=False, metadata={"help": "offload_activations"} - ) - min_params_to_wrap: int = field( - default=int(1e8), - metadata={ - "help": "minimum number of params for a layer to be wrapped with FSDP() when " - "training with --ddp-backend=fully_sharded. Smaller values will " - "improve memory efficiency, but may make torch.distributed " - "communication less efficient due to smaller input sizes. This option " - "is set to 0 (i.e., always wrap) when --checkpoint-activations or " - "--offload-activations are passed." - }, - ) - - checkpoint_activations: bool = field( - default=False, - metadata={"help": "recompute activations and save memory for extra compute"}, - ) - ddp_backend: str = II("distributed_training.ddp_backend") - - -@dataclass -class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig): - blank_weight: float = 0 - blank_mode: str = "add" - - -@register_model("wav2vec_ctc", dataclass=Wav2Vec2CtcConfig) -class Wav2VecCtc(BaseFairseqModel): - def __init__(self, cfg: Wav2Vec2CtcConfig, w2v_encoder: BaseFairseqModel): - super().__init__() - self.cfg = cfg - self.w2v_encoder = w2v_encoder - self.blank_weight = cfg.blank_weight - self.blank_mode = cfg.blank_mode - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - return state_dict - - @classmethod - def build_model(cls, cfg: Wav2Vec2CtcConfig, task: FairseqTask): - """Build a new model instance.""" - w2v_encoder = Wav2VecEncoder(cfg, len(task.target_dictionary)) - return cls(cfg, w2v_encoder) - - def get_logits(self, net_output, normalize=False): - logits = net_output["encoder_out"] - if self.blank_weight != 0: - if self.blank_mode == "add": - logits[..., 0] += self.blank_weight - elif self.blank_mode == "set": - logits[..., 0] = self.blank_weight - else: - raise Exception(f"invalid blank mode {self.blank_mode}") - - if net_output["padding_mask"] is not None and net_output["padding_mask"].any(): - number_of_classes = logits.size(-1) - masking_tensor = torch.ones( - number_of_classes, device=logits.device - ) * float("-inf") - masking_tensor[0] = 0 - logits[net_output["padding_mask"].T] = masking_tensor.type_as(logits) - - if normalize: - logits = utils.log_softmax(logits.float(), dim=-1) - - return logits - - def get_normalized_probs(self, net_output, log_probs): - """Get normalized probabilities (or log probs) from a net's output.""" - - logits = self.get_logits(net_output) - - if log_probs: - return utils.log_softmax(logits.float(), dim=-1) - else: - return utils.softmax(logits.float(), dim=-1) - - def forward(self, **kwargs): - x = self.w2v_encoder(**kwargs) - return x - - -@dataclass -class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig): - decoder_embed_dim: int = field( - default=768, metadata={"help": "decoder embedding dimension"} - ) - decoder_ffn_embed_dim: int = field( - default=3072, metadata={"help": "decoder embedding dimension for FFN"} - ) - decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"}) - decoder_layerdrop: float = field( - default=0.0, metadata={"help": "decoder layerdrop chance"} - ) - decoder_attention_heads: int = field( - default=4, metadata={"help": "num decoder attention heads"} - ) - decoder_learned_pos: bool = field( - default=False, - metadata={"help": "use learned positional embeddings in the decoder"}, - ) - decoder_normalize_before: bool = field( - default=False, metadata={"help": "apply layernorm before each decoder block"} - ) - no_token_positional_embeddings: bool = field( - default=False, - metadata={ - "help": "if set, disables positional embeddings (outside self attention)" - }, - ) - decoder_dropout: float = field( - default=0.0, metadata={"help": "dropout probability in the decoder"} - ) - decoder_attention_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability for attention weights inside the decoder" - }, - ) - decoder_activation_dropout: float = field( - default=0.0, - metadata={ - "help": "dropout probability after activation in FFN inside the decoder" - }, - ) - max_target_positions: int = field( - default=2048, metadata={"help": "max target positions"} - ) - share_decoder_input_output_embed: bool = field( - default=False, metadata={"help": "share decoder input and output embeddings"} - ) - autoregressive: bool = II("task.autoregressive") - - -@register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig) -class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, cfg: Wav2Vec2Seq2SeqConfig, task: FairseqTask): - """Build a new model instance.""" - - assert ( - cfg.autoregressive - ), "Please set task.autoregressive=true for seq2seq asr models" - - src_dict, tgt_dict = task.source_dictionary, task.target_dictionary - - def build_embedding(dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - emb = Embedding(num_embeddings, embed_dim, padding_idx) - return emb - - decoder_embed_tokens = build_embedding(tgt_dict, cfg.decoder_embed_dim) - - encoder = cls.build_encoder(cfg) - decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens) - - return Wav2Vec2Seq2SeqModel(encoder, decoder) - - @classmethod - def build_encoder(cls, cfg: Wav2Vec2AsrConfig): - return Wav2VecEncoder(cfg) - - @classmethod - def build_decoder(cls, cfg: Wav2Vec2Seq2SeqConfig, tgt_dict, embed_tokens): - return TransformerDecoder(cfg, tgt_dict, embed_tokens) - - def forward(self, **kwargs): - encoder_out = self.encoder(**kwargs) - decoder_out = self.decoder(encoder_out=encoder_out, **kwargs) - return decoder_out - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - return state_dict - - -class Wav2VecEncoder(FairseqEncoder): - def __init__(self, cfg: Wav2Vec2AsrConfig, output_size=None): - self.apply_mask = cfg.apply_mask - - arg_overrides = { - "dropout": cfg.dropout, - "activation_dropout": cfg.activation_dropout, - "dropout_input": cfg.dropout_input, - "attention_dropout": cfg.attention_dropout, - "mask_length": cfg.mask_length, - "mask_prob": cfg.mask_prob, - "require_same_masks": getattr(cfg, "require_same_masks", True), - "pct_holes": getattr(cfg, "mask_dropout", 0), - "mask_selection": cfg.mask_selection, - "mask_other": cfg.mask_other, - "no_mask_overlap": cfg.no_mask_overlap, - "mask_channel_length": cfg.mask_channel_length, - "mask_channel_prob": cfg.mask_channel_prob, - "mask_channel_before": cfg.mask_channel_before, - "mask_channel_selection": cfg.mask_channel_selection, - "mask_channel_other": cfg.mask_channel_other, - "no_mask_channel_overlap": cfg.no_mask_channel_overlap, - "encoder_layerdrop": cfg.layerdrop, - "feature_grad_mult": cfg.feature_grad_mult, - "checkpoint_activations": cfg.checkpoint_activations, - "offload_activations": cfg.offload_activations, - "min_params_to_wrap": cfg.min_params_to_wrap, - } - - if cfg.w2v_args is None: - state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides) - w2v_args = state.get("cfg", None) - if w2v_args is None: - w2v_args = convert_namespace_to_omegaconf(state["args"]) - w2v_args.criterion = None - w2v_args.lr_scheduler = None - cfg.w2v_args = w2v_args - - logger.info(w2v_args) - - else: - state = None - w2v_args = cfg.w2v_args - if isinstance(w2v_args, Namespace): - cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args) - - model_normalized = w2v_args.task.get( - "normalize", w2v_args.model.get("normalize", False) - ) - assert cfg.normalize == model_normalized, ( - "Fine-tuning works best when data normalization is the same. " - "Please check that --normalize is set or unset for both pre-training and here" - ) - - if hasattr(cfg, "checkpoint_activations") and cfg.checkpoint_activations: - with open_dict(w2v_args): - w2v_args.model.checkpoint_activations = cfg.checkpoint_activations - - w2v_args.task.data = cfg.data - task = tasks.setup_task(w2v_args.task) - model = task.build_model(w2v_args.model, from_checkpoint=True) - - model.remove_pretraining_modules() - - if state is not None and not cfg.no_pretrained_weights: - self.load_model_weights(state, model, cfg) - - super().__init__(task.source_dictionary) - - d = w2v_args.model.encoder_embed_dim - - self.w2v_model = model - - self.final_dropout = nn.Dropout(cfg.final_dropout) - self.freeze_finetune_updates = cfg.freeze_finetune_updates - self.num_updates = 0 - - targ_d = None - self.proj = None - - if output_size is not None: - targ_d = output_size - elif getattr(cfg, "decoder_embed_dim", d) != d: - targ_d = cfg.decoder_embed_dim - - if targ_d is not None: - self.proj = Linear(d, targ_d) - - def load_model_weights(self, state, model, cfg): - if cfg.ddp_backend == "fully_sharded": - from fairseq.distributed import FullyShardedDataParallel - - for name, module in model.named_modules(): - if "encoder.layers" in name and len(name.split(".")) == 3: - # Only for layers, we do a special handling and load the weights one by one - # We dont load all weights together as that wont be memory efficient and may - # cause oom - new_dict = { - k.replace(name + ".", ""): v - for (k, v) in state["model"].items() - if name + "." in k - } - assert isinstance(module, FullyShardedDataParallel) - with module.summon_full_params(): - module.load_state_dict(new_dict, strict=True) - module._reset_lazy_init() - - # Once layers are loaded, filter them out and load everything else. - r = re.compile("encoder.layers.\d.") - filtered_list = list(filter(r.match, state["model"].keys())) - - new_big_dict = { - k: v for (k, v) in state["model"].items() if k not in filtered_list - } - - model.load_state_dict(new_big_dict, strict=False) - else: - if "_ema" in state["model"]: - del state["model"]["_ema"] - model.load_state_dict(state["model"], strict=True) - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - super().set_num_updates(num_updates) - self.num_updates = num_updates - - def forward(self, source, padding_mask, **kwargs): - - w2v_args = { - "source": source, - "padding_mask": padding_mask, - "mask": self.apply_mask and self.training, - } - - ft = self.freeze_finetune_updates <= self.num_updates - - with torch.no_grad() if not ft else contextlib.ExitStack(): - res = self.w2v_model.extract_features(**w2v_args) - - x = res["x"] - padding_mask = res["padding_mask"] - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - x = self.final_dropout(x) - - if self.proj: - x = self.proj(x) - - return { - "encoder_out": x, # T x B x C - "padding_mask": padding_mask, # B x T, - "layer_results": res["layer_results"], - } - - def forward_torchscript(self, net_input): - if torch.jit.is_scripting(): - return self.forward(net_input["source"], net_input["padding_mask"]) - else: - return self.forward_non_torchscript(net_input) - - def reorder_encoder_out(self, encoder_out, new_order): - if encoder_out["encoder_out"] is not None: - encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select( - 1, new_order - ) - if encoder_out["padding_mask"] is not None: - encoder_out["padding_mask"] = encoder_out["padding_mask"].index_select( - 0, new_order - ) - return encoder_out - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return None - - def upgrade_state_dict_named(self, state_dict, name): - return state_dict - - -class TransformerDecoder(FairseqIncrementalDecoder): - """ - Transformer decoder consisting of *args.decoder_layers* layers. Each layer - is a :class:`TransformerDecoderLayer`. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): decoding dictionary - embed_tokens (torch.nn.Embedding): output embedding - no_encoder_attn (bool, optional): whether to attend to encoder outputs - (default: False). - """ - - def __init__( - self, - cfg: Wav2Vec2Seq2SeqConfig, - dictionary, - embed_tokens, - no_encoder_attn=False, - ): - super().__init__(dictionary) - - self.dropout = cfg.decoder_dropout - self.share_input_output_embed = cfg.share_decoder_input_output_embed - - input_embed_dim = embed_tokens.embedding_dim - embed_dim = cfg.decoder_embed_dim - self.output_embed_dim = cfg.decoder_embed_dim - - self.layerdrop = cfg.decoder_layerdrop - - self.padding_idx = embed_tokens.padding_idx - self.max_target_positions = cfg.max_target_positions - - self.embed_tokens = embed_tokens - self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim - - self.project_in_dim = ( - Linear(input_embed_dim, embed_dim, bias=False) - if embed_dim != input_embed_dim - else None - ) - - self.embed_positions = ( - PositionalEmbedding( - cfg.max_target_positions, - embed_dim, - self.padding_idx, - learned=cfg.decoder_learned_pos, - ) - if not cfg.no_token_positional_embeddings - else None - ) - - # TODO: update this when transformer gets converted to dataclass configs - transformer_cfg = copy.deepcopy(cfg) - with open_dict(transformer_cfg): - transformer_cfg.dropout = transformer_cfg.decoder_dropout - transformer_cfg.attention_dropout = ( - transformer_cfg.decoder_attention_dropout - ) - transformer_cfg.activation_dropout = ( - transformer_cfg.decoder_activation_dropout - ) - - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - TransformerDecoderLayer(transformer_cfg, no_encoder_attn) - for _ in range(transformer_cfg.decoder_layers) - ] - ) - - if not self.share_input_output_embed: - self.embed_out = nn.Parameter( - torch.Tensor(len(dictionary), self.output_embed_dim) - ) - nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim**-0.5) - - if transformer_cfg.decoder_normalize_before: - self.layer_norm = LayerNorm(embed_dim) - else: - self.layer_norm = None - - def forward( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused - ): - """ - Args: - prev_output_tokens (LongTensor): previous decoder outputs of shape - `(batch, tgt_len)`, for teacher forcing - encoder_out (Tensor, optional): output from the encoder, used for - encoder-side attention - incremental_state (dict): dictionary used for storing state during - :ref:`Incremental decoding` - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - prev_output_tokens = prev_output_tokens.long() - x, extra = self.extract_features( - prev_output_tokens, encoder_out, incremental_state - ) - x = self.output_layer(x) - return x, extra - - def extract_features( - self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused - ): - """ - Similar to *forward* but only return features. - - Returns: - tuple: - - the decoder's features of shape `(batch, tgt_len, embed_dim)` - - a dictionary with any model-specific outputs - """ - - # embed positions - positions = ( - self.embed_positions( - prev_output_tokens, incremental_state=incremental_state - ) - if self.embed_positions is not None - else None - ) - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - if positions is not None: - positions = positions[:, -1:] - - # embed tokens and positions - x = self.embed_scale * self.embed_tokens(prev_output_tokens) - - if self.project_in_dim is not None: - x = self.project_in_dim(x) - - if positions is not None: - x += positions - x = F.dropout(x, p=self.dropout, training=self.training) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - attn = None - - inner_states = [x] - - # decoder layers - self_attn_padding_mask = None - if prev_output_tokens.eq(self.padding_idx).any(): - self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx) - for layer in self.layers: - dropout_probability = np.random.random() - if not self.training or (dropout_probability > self.layerdrop): - x, attn, _ = layer( - x, - encoder_out["encoder_out"] if encoder_out is not None else None, - encoder_out["padding_mask"] if encoder_out is not None else None, - incremental_state, - self_attn_mask=self.buffered_future_mask(x) - if incremental_state is None - else None, - self_attn_padding_mask=self_attn_padding_mask, - ) - inner_states.append(x) - - if self.layer_norm: - x = self.layer_norm(x) - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - return x, {"attn": attn, "inner_states": inner_states} - - def output_layer(self, features, **kwargs): - """Project features to the vocabulary size.""" - # project back to size of vocabulary - if self.share_input_output_embed: - return F.linear(features, self.embed_tokens.weight) - else: - return F.linear(features, self.embed_out) - - def max_positions(self): - """Maximum output length supported by the decoder.""" - if self.embed_positions is None: - return self.max_target_positions - return min(self.max_target_positions, self.embed_positions.max_positions) - - def buffered_future_mask(self, tensor): - dim = tensor.size(0) - if ( - not hasattr(self, "_future_mask") - or self._future_mask is None - or self._future_mask.device != tensor.device - or self._future_mask.size(0) < dim - ): - self._future_mask = torch.triu( - utils.fill_with_neg_inf(tensor.new(dim, dim)), 1 - ) - return self._future_mask[:dim, :dim] - - def upgrade_state_dict_named(self, state_dict, name): - return state_dict - - -def Embedding(num_embeddings, embedding_dim, padding_idx): - m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - nn.init.constant_(m.weight[padding_idx], 0) - return m - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/__init__.py deleted file mode 100644 index bbf7694920eb00bed27e17dac272611be1ab44f9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .model import * # noqa -from .transformer_layer_xmod import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/hub_interface.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/hub_interface.py deleted file mode 100644 index 4596adc7a43207944de95b30a5c072e573b09bed..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/hub_interface.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import torch -import torch.nn.functional as F -from fairseq.models.roberta.hub_interface import RobertaHubInterface - - -class XMODHubInterface(RobertaHubInterface): - def extract_features( - self, - tokens: torch.LongTensor, - return_all_hiddens: bool = False, - lang_id=None, - ) -> torch.Tensor: - if tokens.dim() == 1: - tokens = tokens.unsqueeze(0) - if tokens.size(-1) > self.model.max_positions(): - raise ValueError( - "tokens exceeds maximum length: {} > {}".format( - tokens.size(-1), self.model.max_positions() - ) - ) - features, extra = self.model( - tokens.to(device=self.device), - features_only=True, - return_all_hiddens=return_all_hiddens, - lang_id=lang_id, - ) - if return_all_hiddens: - # convert from T x B x C -> B x T x C - inner_states = extra["inner_states"] - return [inner_state.transpose(0, 1) for inner_state in inner_states] - else: - return features # just the last layer's features - - def predict( - self, - head: str, - tokens: torch.LongTensor, - return_logits: bool = False, - lang_id=None, - ): - features = self.extract_features(tokens.to(device=self.device), lang_id=lang_id) - logits = self.model.classification_heads[head](features) - if return_logits: - return logits - return F.log_softmax(logits, dim=-1) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/model.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/model.py deleted file mode 100644 index d27ac1cb674db1cd73792991ff223318187e29d4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/model.py +++ /dev/null @@ -1,743 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Optional - -import torch -from fairseq.distributed import fsdp_wrap -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.transformer import TransformerEncoder -from fairseq.models.xmod.hub_interface import XMODHubInterface -from fairseq.models.xmod.transformer_layer_xmod import XMODTransformerEncoderLayerBase -from fairseq.modules.checkpoint_activations import checkpoint_wrapper -from fairseq.modules.transformer_sentence_encoder import init_bert_params - -from ..roberta.model import RobertaEncoder, base_architecture -from ..roberta.model_xlmr import XLMRModel - -DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8) - - -@register_model("xmod") -class XMODModel(XLMRModel): - @classmethod - def hub_models(cls): - return { - "xmod.base": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.81.1M.tar.gz", - "xmod.large.prenorm": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.large.prenorm.81.500k.tar.gz", - "xmod.base.13.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.13.125k.tar.gz", - "xmod.base.30.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.30.125k.tar.gz", - "xmod.base.30.195k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.30.195k.tar.gz", - "xmod.base.60.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.60.125k.tar.gz", - "xmod.base.60.265k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.60.265k.tar.gz", - "xmod.base.75.125k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.75.125k.tar.gz", - "xmod.base.75.269k": "https://dl.fbaipublicfiles.com/fairseq/models/xmod/xmod.base.75.269k.tar.gz", - } - - @classmethod - def from_pretrained( - cls, - model_name_or_path, - checkpoint_file="model.pt", - data_name_or_path=".", - bpe="sentencepiece", - **kwargs, - ): - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - model_name_or_path, - checkpoint_file, - data_name_or_path, - archive_map=cls.hub_models(), - bpe=bpe, - load_checkpoint_heads=True, - **kwargs, - ) - return XMODHubInterface(x["args"], x["task"], x["models"][0]) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - from omegaconf import OmegaConf - - if OmegaConf.is_config(args): - OmegaConf.set_struct(args, False) - - # make sure all arguments are present - base_architecture(args) - - if not hasattr(args, "max_positions"): - if not hasattr(args, "tokens_per_sample"): - args.tokens_per_sample = task.max_positions() - args.max_positions = args.tokens_per_sample - - encoder = XMODEncoder(args, task.source_dictionary) - - if OmegaConf.is_config(args): - OmegaConf.set_struct(args, True) - - return cls(args, encoder) - - def forward( - self, - src_tokens, - features_only=False, - return_all_hiddens=False, - classification_head_name=None, - lang_id=None, - **kwargs, - ): - if classification_head_name is not None: - features_only = True - x, extra = self.encoder( - src_tokens, features_only, return_all_hiddens, lang_id=lang_id, **kwargs - ) - - if classification_head_name is not None: - x = self.classification_heads[classification_head_name](x) - return x, extra - - -class XMODEncoder(RobertaEncoder): - """XMOD encoder.""" - - def build_encoder(self, args, dictionary, embed_tokens): - encoder = XMODTransformerEncoder(args, dictionary, embed_tokens) - encoder.apply(init_bert_params) - return encoder - - def forward( - self, - src_tokens, - features_only=False, - return_all_hiddens=False, - masked_tokens=None, - lang_id=None, - **unused, - ): - """ - Args: - src_tokens (LongTensor): input tokens of shape `(batch, src_len)` - features_only (bool, optional): skip LM head and just return - features. If True, the output will be of shape - `(batch, src_len, embed_dim)`. - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - - Returns: - tuple: - - the LM output of shape `(batch, src_len, vocab)` - - a dictionary of additional data, where 'inner_states' - is a list of hidden states. Note that the hidden - states have shape `(src_len, batch, vocab)`. - """ - x, extra = self.extract_features( - src_tokens, return_all_hiddens=return_all_hiddens, lang_id=lang_id - ) - if not features_only: - x = self.output_layer(x, masked_tokens=masked_tokens) - return x, extra - - def extract_features( - self, src_tokens, return_all_hiddens=False, lang_id=None, **kwargs - ): - encoder_out = self.sentence_encoder( - src_tokens, - return_all_hiddens=return_all_hiddens, - lang_id=lang_id, - token_embeddings=kwargs.get("token_embeddings", None), - ) - # T x B x C -> B x T x C - features = encoder_out["encoder_out"][0].transpose(0, 1) - inner_states = encoder_out["encoder_states"] if return_all_hiddens else None - return features, {"inner_states": inner_states} - - -class XMODTransformerEncoder(TransformerEncoder): - def build_encoder_layer(self, cfg): - layer = XMODTransformerEncoderLayerBase(cfg) - checkpoint = cfg.checkpoint_activations - if checkpoint: - offload_to_cpu = cfg.offload_activations - layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu) - # if we are checkpointing, enforce that FSDP always wraps the - # checkpointed layer, regardless of layer size - min_params_to_wrap = cfg.min_params_to_wrap if not checkpoint else 0 - layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap) - return layer - - def forward( - self, - src_tokens, - src_lengths: Optional[torch.Tensor] = None, - return_all_hiddens: bool = False, - token_embeddings: Optional[torch.Tensor] = None, - lang_id=None, - ): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (torch.LongTensor): lengths of each source sentence of - shape `(batch)` - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - token_embeddings (torch.Tensor, optional): precomputed embeddings - default `None` will recompute embeddings - - Returns: - dict: - - **encoder_out** (Tensor): the last encoder layer's output of - shape `(src_len, batch, embed_dim)` - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - - **encoder_embedding** (Tensor): the (scaled) embedding lookup - of shape `(batch, src_len, embed_dim)` - - **encoder_states** (List[Tensor]): all intermediate - hidden states of shape `(src_len, batch, embed_dim)`. - Only populated if *return_all_hiddens* is True. - """ - return self.forward_scriptable( - src_tokens, - src_lengths, - return_all_hiddens, - token_embeddings, - lang_id=lang_id, - ) - # TorchScript doesn't support super() method so that the scriptable Subclass - # can't access the base class model in Torchscript. - # Current workaround is to add a helper function with different name and - # call the helper function from scriptable Subclass. - - def forward_scriptable( - self, - src_tokens, - src_lengths: Optional[torch.Tensor] = None, - return_all_hiddens: bool = False, - token_embeddings: Optional[torch.Tensor] = None, - lang_id=None, - ): - """ - Args: - src_tokens (LongTensor): tokens in the source language of shape - `(batch, src_len)` - src_lengths (torch.LongTensor): lengths of each source sentence of - shape `(batch)` - return_all_hiddens (bool, optional): also return all of the - intermediate hidden states (default: False). - token_embeddings (torch.Tensor, optional): precomputed embeddings - default `None` will recompute embeddings - - Returns: - dict: - - **encoder_out** (Tensor): the last encoder layer's output of - shape `(src_len, batch, embed_dim)` - - **encoder_padding_mask** (ByteTensor): the positions of - padding elements of shape `(batch, src_len)` - - **encoder_embedding** (Tensor): the (scaled) embedding lookup - of shape `(batch, src_len, embed_dim)` - - **encoder_states** (List[Tensor]): all intermediate - hidden states of shape `(src_len, batch, embed_dim)`. - Only populated if *return_all_hiddens* is True. - """ - # compute padding mask - encoder_padding_mask = src_tokens.eq(self.padding_idx) - has_pads = src_tokens.device.type == "xla" or encoder_padding_mask.any() - - x, encoder_embedding = self.forward_embedding(src_tokens, token_embeddings) - - # account for padding while computing the representation - if has_pads: - x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x)) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - encoder_states = [] - - if return_all_hiddens: - encoder_states.append(x) - - # encoder layers - for layer in self.layers: - x = layer( - x, - encoder_padding_mask=encoder_padding_mask if has_pads else None, - lang_id=lang_id, - ) - if return_all_hiddens: - assert encoder_states is not None - encoder_states.append(x) - - if self.layer_norm is not None: - x = self.layer_norm(x) - - # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in - # `forward` so we use a dictionary instead. - # TorchScript does not support mixed values so the values are all lists. - # The empty list is equivalent to None. - src_lengths = ( - src_tokens.ne(self.padding_idx) - .sum(dim=1, dtype=torch.int32) - .reshape(-1, 1) - .contiguous() - ) - return { - "encoder_out": [x], # T x B x C - "encoder_padding_mask": [encoder_padding_mask], # B x T - "encoder_embedding": [encoder_embedding], # B x T x C - "encoder_states": encoder_states, # List[T x B x C] - "src_tokens": [], - "src_lengths": [src_lengths], - } - - -@register_model_architecture("xmod", "xmod_base_13") -def roberta_base_architecture(args): - args.ffn_modules = getattr(args, "ffn_modules", False) - args.adapter_modules = getattr(args, "adapter_modules", True) - args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) - args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) - args.ln_before_adapter = getattr(args, "ln_before_adapter", True) - args.languages = getattr( - args, - "languages", - [ - "ar_AR", - "en_XX", - "fi_FI", - "fr_XX", - "hi_IN", - "id_ID", - "ka_GE", - "ko_KR", - "ru_RU", - "sw_KE", - "ta_IN", - "th_TH", - "vi_VN", - ], - ) - base_architecture(args) - - -@register_model_architecture("xmod", "xmod_base_30") -def roberta_base_architecture(args): - args.ffn_modules = getattr(args, "ffn_modules", False) - args.adapter_modules = getattr(args, "adapter_modules", True) - args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) - args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) - args.ln_before_adapter = getattr(args, "ln_before_adapter", True) - args.languages = getattr( - args, - "languages", - [ - "ar_AR", - "cs_CZ", - "en_XX", - "eu_ES", - "fi_FI", - "fr_XX", - "hi_IN", - "hr_HR", - "hu_HU", - "hy_AM", - "id_ID", - "it_IT", - "ka_GE", - "ko_KR", - "lt_LT", - "ml_IN", - "mn_MN", - "ms_MY", - "pl_PL", - "ro_RO", - "ru_RU", - "si_LK", - "sk_SK", - "sq_AL", - "sv_SE", - "sw_KE", - "ta_IN", - "th_TH", - "tl_XX", - "vi_VN", - ], - ) - base_architecture(args) - - -@register_model_architecture("xmod", "xmod_base_60") -def roberta_base_architecture(args): - args.ffn_modules = getattr(args, "ffn_modules", False) - args.adapter_modules = getattr(args, "adapter_modules", True) - args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) - args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) - args.ln_before_adapter = getattr(args, "ln_before_adapter", True) - args.languages = getattr( - args, - "languages", - [ - "af_ZA", - "am_ET", - "ar_AR", - "be_BY", - "bn_IN", - "ca_ES", - "cs_CZ", - "cy_GB", - "da_DK", - "en_XX", - "eo_EO", - "et_EE", - "eu_ES", - "fa_IR", - "fi_FI", - "fr_XX", - "ga_IE", - "gl_ES", - "gu_IN", - "ha_NG", - "hi_IN", - "hr_HR", - "hu_HU", - "hy_AM", - "id_ID", - "is_IS", - "it_IT", - "ka_GE", - "ko_KR", - "ku_TR", - "la_VA", - "lt_LT", - "lv_LV", - "mk_MK", - "ml_IN", - "mn_MN", - "ms_MY", - "ne_NP", - "nl_XX", - "no_XX", - "pl_PL", - "ps_AF", - "pt_XX", - "ro_RO", - "ru_RU", - "sa_IN", - "sd_PK", - "si_LK", - "sk_SK", - "sl_SI", - "so_SO", - "sq_AL", - "sr_RS", - "sv_SE", - "sw_KE", - "ta_IN", - "te_IN", - "th_TH", - "tl_XX", - "vi_VN", - ], - ) - base_architecture(args) - - -@register_model_architecture("xmod", "xmod_base_75") -def roberta_base_architecture(args): - args.ffn_modules = getattr(args, "ffn_modules", False) - args.adapter_modules = getattr(args, "adapter_modules", True) - args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) - args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) - args.ln_before_adapter = getattr(args, "ln_before_adapter", True) - args.languages = getattr( - args, - "languages", - [ - "af_ZA", - "am_ET", - "ar_AR", - "as_IN", - "be_BY", - "bn_IN", - "br_FR", - "bs_BA", - "ca_ES", - "cs_CZ", - "cy_GB", - "da_DK", - "en_XX", - "eo_EO", - "et_EE", - "eu_ES", - "fa_IR", - "fi_FI", - "fr_XX", - "fy_NL", - "ga_IE", - "gd_GB", - "gl_ES", - "gu_IN", - "ha_NG", - "hi_IN", - "hr_HR", - "hu_HU", - "hy_AM", - "id_ID", - "is_IS", - "it_IT", - "jv_ID", - "ka_GE", - "kn_IN", - "ko_KR", - "ku_TR", - "la_VA", - "lt_LT", - "lv_LV", - "mg_MG", - "mk_MK", - "ml_IN", - "mn_MN", - "mr_IN", - "ms_MY", - "ne_NP", - "nl_XX", - "no_XX", - "om_KE", - "or_IN", - "pa_IN", - "pl_PL", - "ps_AF", - "pt_XX", - "ro_RO", - "ru_RU", - "sa_IN", - "sd_PK", - "si_LK", - "sk_SK", - "sl_SI", - "so_SO", - "sq_AL", - "sr_RS", - "su_ID", - "sv_SE", - "sw_KE", - "ta_IN", - "te_IN", - "th_TH", - "tl_XX", - "vi_VN", - "xh_ZA", - "yi_DE", - ], - ) - base_architecture(args) - - -@register_model_architecture("xmod", "xmod_base") -def roberta_base_architecture(args): - args.ffn_modules = getattr(args, "ffn_modules", False) - args.adapter_modules = getattr(args, "adapter_modules", True) - args.adapter_layer_norm = getattr(args, "adapter_layer_norm", False) - args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", True) - args.ln_before_adapter = getattr(args, "ln_before_adapter", True) - args.languages = getattr( - args, - "languages", - [ - "en_XX", - "id_ID", - "vi_VN", - "ru_RU", - "fa_IR", - "sv_SE", - "ja_XX", - "fr_XX", - "de_DE", - "ro_RO", - "ko_KR", - "hu_HU", - "es_XX", - "fi_FI", - "uk_UA", - "da_DK", - "pt_XX", - "no_XX", - "th_TH", - "pl_PL", - "bg_BG", - "nl_XX", - "zh_CN", - "he_IL", - "el_GR", - "it_IT", - "sk_SK", - "hr_HR", - "tr_TR", - "ar_AR", - "cs_CZ", - "lt_LT", - "hi_IN", - "zh_TW", - "ca_ES", - "ms_MY", - "sl_SI", - "lv_LV", - "ta_IN", - "bn_IN", - "et_EE", - "az_AZ", - "sq_AL", - "sr_RS", - "kk_KZ", - "ka_GE", - "tl_XX", - "ur_PK", - "is_IS", - "hy_AM", - "ml_IN", - "mk_MK", - "be_BY", - "la_VA", - "te_IN", - "eu_ES", - "gl_ES", - "mn_MN", - "kn_IN", - "ne_NP", - "sw_KE", - "si_LK", - "mr_IN", - "af_ZA", - "gu_IN", - "cy_GB", - "eo_EO", - "km_KH", - "ky_KG", - "uz_UZ", - "ps_AF", - "pa_IN", - "ga_IE", - "ha_NG", - "am_ET", - "lo_LA", - "ku_TR", - "so_SO", - "my_MM", - "or_IN", - "sa_IN", - ], - ) - base_architecture(args) - - -@register_model_architecture("xmod", "xmod_large_prenorm") -def roberta_base_architecture(args): - args.ffn_modules = getattr(args, "ffn_modules", False) - args.adapter_modules = getattr(args, "adapter_modules", True) - args.adapter_layer_norm = getattr(args, "adapter_layer_norm", True) - args.adapter_reuse_layer_norm = getattr(args, "adapter_reuse_layer_norm", False) - args.ln_before_adapter = getattr(args, "ln_before_adapter", False) - # args.bottleneck = getattr(args, "bottleneck", 8) - args.bottleneck = getattr(args, "bottleneck", 4) - args.languages = getattr( - args, - "languages", - [ - "en_XX", - "id_ID", - "vi_VN", - "ru_RU", - "fa_IR", - "sv_SE", - "ja_XX", - "fr_XX", - "de_DE", - "ro_RO", - "ko_KR", - "hu_HU", - "es_XX", - "fi_FI", - "uk_UA", - "da_DK", - "pt_XX", - "no_XX", - "th_TH", - "pl_PL", - "bg_BG", - "nl_XX", - "zh_CN", - "he_IL", - "el_GR", - "it_IT", - "sk_SK", - "hr_HR", - "tr_TR", - "ar_AR", - "cs_CZ", - "lt_LT", - "hi_IN", - "zh_TW", - "ca_ES", - "ms_MY", - "sl_SI", - "lv_LV", - "ta_IN", - "bn_IN", - "et_EE", - "az_AZ", - "sq_AL", - "sr_RS", - "kk_KZ", - "ka_GE", - "tl_XX", - "ur_PK", - "is_IS", - "hy_AM", - "ml_IN", - "mk_MK", - "be_BY", - "la_VA", - "te_IN", - "eu_ES", - "gl_ES", - "mn_MN", - "kn_IN", - "ne_NP", - "sw_KE", - "si_LK", - "mr_IN", - "af_ZA", - "gu_IN", - "cy_GB", - "eo_EO", - "km_KH", - "ky_KG", - "uz_UZ", - "ps_AF", - "pa_IN", - "ga_IE", - "ha_NG", - "am_ET", - "lo_LA", - "ku_TR", - "so_SO", - "my_MM", - "or_IN", - "sa_IN", - ], - ) - - args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True) - args.encoder_layers = getattr(args, "encoder_layers", 24) - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024) - args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096) - args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/transformer_layer_xmod.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/transformer_layer_xmod.py deleted file mode 100644 index d6dfdf8e78957c7984536450dea6e729cce67e67..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/models/xmod/transformer_layer_xmod.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Optional - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.modules import LayerNorm -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise -from fairseq.modules.transformer_layer import TransformerEncoderLayer -from torch import Tensor - - -class Adapter(nn.Module): - def __init__(self, cfg, red_fac=2): - super(Adapter, self).__init__() - self.cfg = cfg - self.embed_dim = cfg.encoder_embed_dim - self.quant_noise = getattr(cfg, "quant_noise_pq", 0) - self.quant_noise_block_size = getattr(cfg, "quant_noise_pq_block_size", 8) or 8 - self.activation_fn = utils.get_activation_fn( - activation=getattr(cfg, "activation_fn", "relu") or "relu" - ) - self.fc1 = quant_noise( - nn.Linear(self.embed_dim, self.embed_dim // red_fac), - p=self.quant_noise, - block_size=self.quant_noise_block_size, - ) - self.fc2 = quant_noise( - nn.Linear(self.embed_dim // red_fac, self.embed_dim), - p=self.quant_noise, - block_size=self.quant_noise_block_size, - ) - activation_dropout_p = getattr(cfg, "activation_dropout", 0) or 0 - if activation_dropout_p == 0: - # for backwards compatibility with models that use cfg.relu_dropout - activation_dropout_p = getattr(cfg, "relu_dropout", 0) or 0 - self.activation_dropout_module = FairseqDropout( - float(activation_dropout_p), module_name=self.__class__.__name__ - ) - - def forward(self, x): - x = self.activation_fn(self.fc1(x)) - if not hasattr(self.cfg, "adapter_dropout") or self.cfg.adapter_dropout: - x = self.activation_dropout_module(x) - x = self.fc2(x) - return x - - -class XMODTransformerEncoderLayerBase(TransformerEncoderLayer): - """Encoder layer block. - - In the original paper each operation (multi-head attention or FFN) is - postprocessed with: `dropout -> add residual -> layernorm`. In the - tensor2tensor code they suggest that learning is more robust when - preprocessing each layer with layernorm and postprocessing with: - `dropout -> add residual`. We default to the approach in the paper, but the - tensor2tensor approach can be enabled by setting - *cfg.encoder.normalize_before* to ``True``. - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - - def __init__(self, cfg): - super().__init__(cfg) - if hasattr(cfg, "adapter_modules") and cfg.adapter_modules: - export = getattr(cfg, "export", False) - if cfg.adapter_layer_norm: - self.adapter_layer_norm = LayerNorm(self.embed_dim, export=export) - self.adapter_modules = nn.ModuleDict(dict()) - if hasattr(self.cfg, "bottleneck"): - bottleneck = self.cfg.bottleneck - else: - bottleneck = 2 - for language in cfg.languages: - self.adapter_modules[str(language)] = Adapter(cfg, red_fac=bottleneck) - - def lang_adapter(self, lang_id, x): - # If language adapters exist pass throught them - if hasattr(self.cfg, "adapter_modules") and self.cfg.adapter_modules: - if lang_id is None: - lang_id = ["en_XX"] * x.shape[1] - d_langs = [lang_id[0]] - lang_lengths = [1] - for lang in lang_id[1:]: - if lang == d_langs[-1]: - lang_lengths[-1] += 1 - else: - d_langs.append(lang) - lang_lengths.append(1) - - if ( - not hasattr(self.cfg, "ln_before_adapter") - or not self.cfg.ln_before_adapter - ): - residual = x - if self.cfg.adapter_layer_norm: - x = self.adapter_layer_norm(x) - elif self.cfg.adapter_reuse_layer_norm: - x = self.final_layer_norm(x) - if hasattr(self.cfg, "ln_before_adapter") and self.cfg.ln_before_adapter: - residual = x - - split_x = torch.split(x, lang_lengths, 1) - x_ = [] - for i, (lang, s_x) in enumerate(zip(d_langs, split_x)): - lang = lang.replace("_rom", "").replace("_zaw", "") - x_.append(self.adapter_modules[str(lang)](s_x)) - x = torch.cat(x_, 1) - - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - - return x - - def forward( - self, - x, - encoder_padding_mask: Optional[Tensor], - attn_mask: Optional[Tensor] = None, - lang_id: Optional[list] = None, - ): - """ - Args: - x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` - encoder_padding_mask (ByteTensor): binary ByteTensor of shape - `(batch, seq_len)` where padding elements are indicated by ``1``. - attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`, - where `tgt_len` is the length of output and `src_len` is the - length of input, though here both are equal to `seq_len`. - `attn_mask[tgt_i, src_j] = 1` means that when calculating the - embedding for `tgt_i`, we exclude (mask out) `src_j`. This is - useful for strided self-attention. - - Returns: - encoded output of shape `(seq_len, batch, embed_dim)` - """ - # anything in original attn_mask = 1, becomes -1e8 - # anything in original attn_mask = 0, becomes 0 - # Note that we cannot use -inf here, because at some edge cases, - # the attention weight (before softmax) for some padded element in query - # will become -inf, which results in NaN in model parameters - if attn_mask is not None: - attn_mask = attn_mask.masked_fill(attn_mask.to(torch.bool), -1e8) - - residual = x - if self.normalize_before: - x = self.self_attn_layer_norm(x) - x, _ = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=encoder_padding_mask, - need_weights=False, - attn_mask=attn_mask, - ) - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.self_attn_layer_norm(x) - - residual = x - if self.normalize_before: - x = self.final_layer_norm(x) - x = self.activation_fn(self.fc1(x)) - x = self.activation_dropout_module(x) - x = self.fc2(x) - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - - x = self.lang_adapter(lang_id, x) - - if not self.normalize_before: - x = self.final_layer_norm(x) - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/__init__.py deleted file mode 100644 index 8ad7f647d69cda437d3a7e68444b2cff5612adb0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/__init__.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -from .adaptive_input import AdaptiveInput -from .adaptive_softmax import AdaptiveSoftmax -from .base_layer import BaseLayer -from .beamable_mm import BeamableMM -from .character_token_embedder import CharacterTokenEmbedder -from .conv_tbc import ConvTBC -from .cross_entropy import cross_entropy -from .downsampled_multihead_attention import DownsampledMultiHeadAttention -from .dynamic_convolution import DynamicConv, DynamicConv1dTBC -from .dynamic_crf_layer import DynamicCRF -from .ema_module import EMAModuleConfig, EMAModule -from .fairseq_dropout import FairseqDropout -from .fp32_batch_norm import Fp32BatchNorm -from .fp32_group_norm import Fp32GroupNorm -from .fp32_instance_norm import Fp32InstanceNorm -from .gelu import gelu, gelu_accurate -from .grad_multiply import GradMultiply -from .gumbel_vector_quantizer import GumbelVectorQuantizer -from .kmeans_vector_quantizer import KmeansVectorQuantizer -from .layer_drop import LayerDropModuleList -from .layer_norm import Fp32LayerNorm, LayerNorm -from .learned_positional_embedding import LearnedPositionalEmbedding -from .lightweight_convolution import LightweightConv, LightweightConv1dTBC -from .linearized_convolution import LinearizedConvolution -from .location_attention import LocationAttention -from .lstm_cell_with_zoneout import LSTMCellWithZoneOut -from .multihead_attention import MultiheadAttention -from .positional_embedding import PositionalEmbedding -from .same_pad import SamePad -from .scalar_bias import ScalarBias -from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding -from .transformer_sentence_encoder_layer import TransformerSentenceEncoderLayer -from .transformer_sentence_encoder import TransformerSentenceEncoder -from .transpose_last import TransposeLast -from .unfold import unfold1d -from .transformer_layer import TransformerDecoderLayer, TransformerEncoderLayer -from .vggblock import VGGBlock -from .espnet_multihead_attention import ( - ESPNETMultiHeadedAttention, - RelPositionMultiHeadedAttention, - RotaryPositionMultiHeadedAttention, -) -from .rotary_positional_embedding import RotaryPositionalEmbedding -from .positional_encoding import ( - RelPositionalEncoding, -) - -__all__ = [ - "AdaptiveInput", - "AdaptiveSoftmax", - "BaseLayer", - "BeamableMM", - "CharacterTokenEmbedder", - "ConvTBC", - "cross_entropy", - "DownsampledMultiHeadAttention", - "DynamicConv1dTBC", - "DynamicConv", - "DynamicCRF", - "EMAModule", - "EMAModuleConfig", - "FairseqDropout", - "Fp32BatchNorm", - "Fp32GroupNorm", - "Fp32LayerNorm", - "Fp32InstanceNorm", - "gelu", - "gelu_accurate", - "GradMultiply", - "GumbelVectorQuantizer", - "KmeansVectorQuantizer", - "LayerDropModuleList", - "LayerNorm", - "LearnedPositionalEmbedding", - "LightweightConv1dTBC", - "LightweightConv", - "LinearizedConvolution", - "LocationAttention", - "LSTMCellWithZoneOut", - "MultiheadAttention", - "PositionalEmbedding", - "SamePad", - "ScalarBias", - "SinusoidalPositionalEmbedding", - "TransformerSentenceEncoderLayer", - "TransformerSentenceEncoder", - "TransformerDecoderLayer", - "TransformerEncoderLayer", - "TransposeLast", - "VGGBlock", - "unfold1d", - "ESPNETMultiheadedAttention", - "PositionalEmbedding", - "RelPositionMultiHeadedAttention", - "RelPositionalEncoding", - "RotaryPositionalEmbedding", - "RotaryPositionMultiHeadedAttention", -] diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/adaptive_input.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/adaptive_input.py deleted file mode 100644 index b973caf0a93194d75e3b2ef088385911df4e3427..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/adaptive_input.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from typing import List - -import torch -from fairseq.modules.quant_noise import quant_noise -from torch import nn - - -class AdaptiveInput(nn.Module): - def __init__( - self, - vocab_size: int, - padding_idx: int, - initial_dim: int, - factor: float, - output_dim: int, - cutoff: List[int], - q_noise: float = 0, - qn_block_size: int = 8, - ): - super().__init__() - - if vocab_size > cutoff[-1]: - cutoff = cutoff + [vocab_size] - else: - assert ( - vocab_size == cutoff[-1] - ), "cannot specify cutoff larger than vocab size" - - self.cutoff = cutoff - self.embedding_dim = output_dim - self.padding_idx = padding_idx - - self.embeddings = nn.ModuleList() - for i in range(len(self.cutoff)): - prev = self.cutoff[i - 1] if i > 0 else 0 - size = self.cutoff[i] - prev - dim = int(initial_dim // (factor**i)) - seq = nn.Sequential( - nn.Embedding(size, dim, self.padding_idx), - quant_noise( - nn.Linear(dim, output_dim, bias=False), q_noise, qn_block_size - ), - ) - - self.embeddings.append(seq) - self.padding_idx = None - self.padding_idx = padding_idx - - def init_weights(m): - if isinstance(m, nn.Embedding): - nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5) - nn.init.constant_(m.weight[padding_idx], 0) - elif hasattr(m, "weight"): - nn.init.xavier_uniform_(m.weight) - - self.apply(init_weights) - - self.register_buffer("_float_tensor", torch.FloatTensor(1)) - - def weights_for_band(self, band: int): - return self.embeddings[band][0].weight, self.embeddings[band][1].weight - - def forward(self, input: torch.Tensor): - result = self._float_tensor.new(input.shape + (self.embedding_dim,)) - for i in range(len(self.cutoff)): - mask = input.lt(self.cutoff[i]) - if i > 0: - mask.mul_(input.ge(self.cutoff[i - 1])) - chunk_input = input[mask] - self.cutoff[i - 1] - else: - chunk_input = input[mask] - if mask.any(): - result[mask] = self.embeddings[i](chunk_input) - return result diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/adaptive_softmax.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/adaptive_softmax.py deleted file mode 100644 index ae0c77ba0f6ee98501306d66cbc4a948b4ade0f7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/adaptive_softmax.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import functools -import operator - -import torch -import torch.nn.functional as F -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise -from torch import nn - - -class TiedLinear(nn.Module): - def __init__(self, weight, transpose): - super().__init__() - self.weight = weight - self.transpose = transpose - - def forward(self, input): - return F.linear(input, self.weight.t() if self.transpose else self.weight) - - -class TiedHeadModule(nn.Module): - def __init__(self, weights, input_dim, num_classes, q_noise, qn_block_size): - super().__init__() - tied_emb, _ = weights - self.num_words, emb_dim = tied_emb.size() - - self.word_proj = quant_noise( - TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size - ) - if input_dim != emb_dim: - self.word_proj = nn.Sequential( - quant_noise( - nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size - ), - self.word_proj, - ) - - self.class_proj = quant_noise( - nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size - ) - self.out_dim = self.num_words + num_classes - - self.register_buffer("_float_tensor", torch.FloatTensor(1)) - - def forward(self, input): - inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1) - out = self._float_tensor.new(inp_sz, self.out_dim) - out[:, : self.num_words] = self.word_proj(input.view(inp_sz, -1)) - out[:, self.num_words :] = self.class_proj(input.view(inp_sz, -1)) - return out - - -class AdaptiveSoftmax(nn.Module): - """ - This is an implementation of the efficient softmax approximation for - graphical processing units (GPU), described in the paper "Efficient softmax - approximation for GPUs" (http://arxiv.org/abs/1609.04309). - """ - - def __init__( - self, - vocab_size, - input_dim, - cutoff, - dropout, - factor=4.0, - adaptive_inputs=None, - tie_proj=False, - q_noise=0, - qn_block_size=8, - ): - super().__init__() - - if vocab_size > cutoff[-1]: - cutoff = cutoff + [vocab_size] - else: - assert ( - vocab_size == cutoff[-1] - ), "cannot specify cutoff larger than vocab size" - - output_dim = cutoff[0] + len(cutoff) - 1 - - self.vocab_size = vocab_size - self.cutoff = cutoff - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.input_dim = input_dim - self.factor = factor - self.q_noise = q_noise - self.qn_block_size = qn_block_size - - self.lsm = nn.LogSoftmax(dim=1) - - if adaptive_inputs is not None: - self.head = TiedHeadModule( - adaptive_inputs.weights_for_band(0), - input_dim, - len(cutoff) - 1, - self.q_noise, - self.qn_block_size, - ) - else: - self.head = quant_noise( - nn.Linear(input_dim, output_dim, bias=False), - self.q_noise, - self.qn_block_size, - ) - - self._make_tail(adaptive_inputs, tie_proj) - - def init_weights(m): - if ( - hasattr(m, "weight") - and not isinstance(m, TiedLinear) - and not isinstance(m, TiedHeadModule) - ): - nn.init.xavier_uniform_(m.weight) - - self.apply(init_weights) - - self.register_buffer("version", torch.LongTensor([1])) - - def _make_tail(self, adaptive_inputs=None, tie_proj=False): - self.tail = nn.ModuleList() - for i in range(len(self.cutoff) - 1): - dim = int(self.input_dim // self.factor ** (i + 1)) - - tied_emb, tied_proj = ( - adaptive_inputs.weights_for_band(i + 1) - if adaptive_inputs is not None - else (None, None) - ) - - if tied_proj is not None: - if tie_proj: - proj = quant_noise( - TiedLinear(tied_proj, transpose=True), - self.q_noise, - self.qn_block_size, - ) - else: - proj = quant_noise( - nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False), - self.q_noise, - self.qn_block_size, - ) - else: - proj = quant_noise( - nn.Linear(self.input_dim, dim, bias=False), - self.q_noise, - self.qn_block_size, - ) - - if tied_emb is None: - out_proj = nn.Linear( - dim, self.cutoff[i + 1] - self.cutoff[i], bias=False - ) - else: - out_proj = TiedLinear(tied_emb, transpose=False) - - m = nn.Sequential( - proj, - nn.Dropout(self.dropout_module.p), - quant_noise(out_proj, self.q_noise, self.qn_block_size), - ) - - self.tail.append(m) - - def upgrade_state_dict_named(self, state_dict, name): - version_name = name + ".version" - if version_name not in state_dict: - raise Exception("This version of the model is no longer supported") - - def adapt_target(self, target): - """ - In order to be efficient, the AdaptiveSoftMax does not compute the - scores for all the word of the vocabulary for all the examples. It is - thus necessary to call the method adapt_target of the AdaptiveSoftMax - layer inside each forward pass. - """ - - target = target.view(-1) - new_target = [target.clone()] - target_idxs = [] - - for i in range(len(self.cutoff) - 1): - mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1])) - new_target[0][mask] = self.cutoff[0] + i - - if mask.any(): - target_idxs.append(mask.nonzero(as_tuple=False).squeeze(1)) - new_target.append(target[mask].add(-self.cutoff[i])) - else: - target_idxs.append(None) - new_target.append(None) - - return new_target, target_idxs - - def forward(self, input, target): - """ - Args: - input: (b x t x d) - target: (b x t) - Returns: - 2 lists: output for each cutoff section and new targets by cut off - """ - - input = input.contiguous().view(-1, input.size(-1)) - input = self.dropout_module(input) - - new_target, target_idxs = self.adapt_target(target) - output = [self.head(input)] - - for i in range(len(target_idxs)): - if target_idxs[i] is not None: - output.append(self.tail[i](input.index_select(0, target_idxs[i]))) - else: - output.append(None) - - return output, new_target - - def get_log_prob(self, input, target): - """ - Computes the log probabilities for all the words of the vocabulary, - given a 2D tensor of hidden vectors. - """ - - bsz, length, dim = input.size() - input = input.contiguous().view(-1, dim) - - if target is not None: - _, target_idxs = self.adapt_target(target) - else: - target_idxs = None - - head_y = self.head(input) - log_probs = head_y.new_zeros(input.size(0), self.vocab_size) - - head_sz = self.cutoff[0] + len(self.tail) - log_probs[:, :head_sz] = self.lsm(head_y) - tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone() - - for i in range(len(self.tail)): - start = self.cutoff[i] - end = self.cutoff[i + 1] - - if target_idxs is None: - tail_out = log_probs[:, start:end] - tail_out.copy_(self.tail[i](input)) - log_probs[:, start:end] = self.lsm(tail_out).add_( - tail_priors[:, i, None] - ) - elif target_idxs[i] is not None: - idxs = target_idxs[i] - tail_out = log_probs[idxs, start:end] - tail_out.copy_(self.tail[i](input[idxs])) - log_probs[idxs, start:end] = self.lsm(tail_out).add_( - tail_priors[idxs, i, None] - ) - - log_probs = log_probs.view(bsz, length, -1) - return log_probs diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/base_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/base_layer.py deleted file mode 100644 index 49bfd44e075ebe571ca02e1eb27f50ae9efb4072..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/base_layer.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import sys - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.distributed import utils as distributed_utils -from fairseq.modules.layer_norm import LayerNorm - - -class BaseLayer(nn.Module): - def __init__(self, args): - super().__init__() - self.num_workers = distributed_utils.get_data_parallel_world_size() - expert_centroids = torch.empty(self.num_workers, args.decoder_embed_dim) - torch.nn.init.orthogonal_(expert_centroids, gain=0.1) - self.register_parameter( - "expert_centroids", torch.nn.Parameter(expert_centroids) - ) - self.expert_network = nn.Sequential( - *([BaseSublayer(args) for _ in range(args.base_sublayers)]) - ) - self.expert_id = distributed_utils.get_data_parallel_rank() - self.shuffle = args.base_shuffle - self.cpp = self.load_assignment() - - # Add a special attribute to the expert parameters, so we know not to sync their gradients - for param in self.expert_network.parameters(): - param.expert = True - - def forward(self, input_features, *args, **kwargs): - features = input_features.reshape(-1, input_features.size(-1)) - is_training = input_features.requires_grad - - if self.shuffle and is_training: - # Send each token to a random worker, to break correlations within the batch - shuffle_sort = torch.randperm(features.size(0), device=features.device) - features = All2All.apply(features[shuffle_sort]) - - with torch.no_grad(): - # Compute similarity of each token to each expert, for routing - token_expert_affinities = features.matmul( - self.expert_centroids.transpose(0, 1) - ) - - # Compute which token goes to which expert - sort_by_expert, input_splits, output_splits = ( - self.balanced_assignment(token_expert_affinities) - if is_training - else self.greedy_assignment(token_expert_affinities) - ) - # Swap these tokens for the right ones for our expert - routed_features = All2All.apply( - features[sort_by_expert], output_splits, input_splits - ) - - if routed_features.size(0) > 0: - # Mix in the expert network based on how appropriate it is for these tokens - alpha = torch.sigmoid( - routed_features.mv(self.expert_centroids[self.expert_id]) - ).unsqueeze(1) - routed_features = ( - alpha * self.expert_network(routed_features) - + (1 - alpha) * routed_features - ) - # Return to original worker and ordering - result = All2All.apply(routed_features, input_splits, output_splits)[ - self.inverse_sort(sort_by_expert) - ] - - if self.shuffle and is_training: - # Undo shuffling - result = All2All.apply(result)[self.inverse_sort(shuffle_sort)] - - # Return additional Nones for compatibility with TransformerDecoderLayer - return result.view(input_features.size()), None, None - - def inverse_sort(self, order): - # Creates an index that undoes a sort: xs==xs[order][inverse_sort(order)] - return torch.empty_like(order).scatter_( - 0, order, torch.arange(0, order.size(0), device=order.device) - ) - - def balanced_assignment(self, scores): - ok = scores.isfinite() - if not ok.all(): - # NaNs here can break the assignment algorithm - scores[~ok] = scores[ok].min() - return self.cpp.balanced_assignment(scores), None, None - - # Assigns each token to the top k experts - def greedy_assignment(self, scores, k=1): - token_to_workers = torch.topk(scores, dim=1, k=k, largest=True).indices.view(-1) - token_to_workers, sort_ordering = torch.sort(token_to_workers) - worker2token = sort_ordering // k - - # Find how many tokens we're sending to each other worker (being careful for sending 0 tokens to some workers) - output_splits = torch.zeros( - (self.num_workers,), dtype=torch.long, device=scores.device - ) - workers, counts = torch.unique_consecutive(token_to_workers, return_counts=True) - output_splits[workers] = counts - # Tell other workers how many tokens to expect from us - input_splits = All2All.apply(output_splits) - return worker2token, input_splits.tolist(), output_splits.tolist() - - def load_assignment(self): - try: - from fairseq import libbase - - return libbase - - except ImportError as e: - sys.stderr.write( - "ERROR: missing libbase. run `python setup.py build_ext --inplace`\n" - ) - raise e - - -class BaseSublayer(nn.Module): - def __init__(self, args): - super().__init__() - self.activation_fn = utils.get_activation_fn( - activation=getattr(args, "activation_fn", "relu") or "relu" - ) - self.norm = LayerNorm(args.decoder_embed_dim, export=False) - self.ff1 = torch.nn.Linear(args.decoder_embed_dim, args.decoder_ffn_embed_dim) - self.ff2 = torch.nn.Linear(args.decoder_ffn_embed_dim, args.decoder_embed_dim) - self.ff2.weight.data.zero_() - - def forward(self, xs): - return xs + self.ff2(self.activation_fn(self.ff1(self.norm(xs)))) - - -# Wraps torch.distributed.all_to_all_single as a function that supports autograd -class All2All(torch.autograd.Function): - @staticmethod - def forward(ctx, xs, input_splits=None, output_splits=None): - ctx.input_splits = input_splits - ctx.output_splits = output_splits - - ys = ( - torch.empty_like(xs) - if output_splits is None - else xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:])) - ) - torch.distributed.all_to_all_single( - ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits - ) - return ys - - @staticmethod - def backward(ctx, grad_output): - result = ( - torch.empty_like(grad_output) - if ctx.input_splits is None - else grad_output.new_empty( - size=[sum(ctx.input_splits)] + list(grad_output.size()[1:]) - ) - ) - torch.distributed.all_to_all_single( - result, - grad_output, - output_split_sizes=ctx.input_splits, - input_split_sizes=ctx.output_splits, - ) - return result, None, None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/beamable_mm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/beamable_mm.py deleted file mode 100644 index eff1a4607f600c71210e6b914985dc48731aae86..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/beamable_mm.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn - - -class BeamableMM(nn.Module): - """This module provides an optimized MM for beam decoding with attention. - - It leverage the fact that the source-side of the input is replicated beam - times and the target-side of the input is of width one. This layer speeds up - inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)} - with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}. - """ - - def __init__(self, beam_size=None): - super(BeamableMM, self).__init__() - self.beam_size = beam_size - - def forward(self, input1, input2): - if ( - not self.training - and self.beam_size is not None # test mode - and input1.dim() == 3 # beam size is set - and input1.size(1) # only support batched input - == 1 # single time step update - ): - bsz, beam = input1.size(0), self.beam_size - - # bsz x 1 x nhu --> bsz/beam x beam x nhu - input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1) - - # bsz x sz2 x nhu --> bsz/beam x sz2 x nhu - input2 = input2.unfold(0, beam, beam)[:, :, :, 0] - - # use non batched operation if bsz = beam - if input1.size(0) == 1: - output = torch.mm(input1[0, :, :], input2[0, :, :]) - else: - output = input1.bmm(input2) - return output.view(bsz, 1, -1) - else: - return input1.bmm(input2) - - def set_beam_size(self, beam_size): - self.beam_size = beam_size diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/character_token_embedder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/character_token_embedder.py deleted file mode 100644 index f329002e1dacc6c828ea14885060d4013df386a0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/character_token_embedder.py +++ /dev/null @@ -1,213 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from typing import List, Tuple - -import torch -import torch.nn.functional as F -from fairseq.data import Dictionary -from torch import nn - -CHAR_PAD_IDX = 0 -CHAR_EOS_IDX = 257 - - -logger = logging.getLogger(__name__) - - -class CharacterTokenEmbedder(torch.nn.Module): - def __init__( - self, - vocab: Dictionary, - filters: List[Tuple[int, int]], - char_embed_dim: int, - word_embed_dim: int, - highway_layers: int, - max_char_len: int = 50, - char_inputs: bool = False, - ): - super(CharacterTokenEmbedder, self).__init__() - - self.onnx_trace = False - self.embedding_dim = word_embed_dim - self.max_char_len = max_char_len - self.char_embeddings = nn.Embedding(257, char_embed_dim, padding_idx=0) - self.symbol_embeddings = nn.Parameter(torch.FloatTensor(2, word_embed_dim)) - self.eos_idx, self.unk_idx = 0, 1 - self.char_inputs = char_inputs - - self.convolutions = nn.ModuleList() - for width, out_c in filters: - self.convolutions.append( - nn.Conv1d(char_embed_dim, out_c, kernel_size=width) - ) - - last_dim = sum(f[1] for f in filters) - - self.highway = Highway(last_dim, highway_layers) if highway_layers > 0 else None - - self.projection = nn.Linear(last_dim, word_embed_dim) - - assert ( - vocab is not None or char_inputs - ), "vocab must be set if not using char inputs" - self.vocab = None - if vocab is not None: - self.set_vocab(vocab, max_char_len) - - self.reset_parameters() - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def set_vocab(self, vocab, max_char_len): - word_to_char = torch.LongTensor(len(vocab), max_char_len) - - truncated = 0 - for i in range(len(vocab)): - if i < vocab.nspecial: - char_idxs = [0] * max_char_len - else: - chars = vocab[i].encode() - # +1 for padding - char_idxs = [c + 1 for c in chars] + [0] * (max_char_len - len(chars)) - if len(char_idxs) > max_char_len: - truncated += 1 - char_idxs = char_idxs[:max_char_len] - word_to_char[i] = torch.LongTensor(char_idxs) - - if truncated > 0: - logger.info( - "truncated {} words longer than {} characters".format( - truncated, max_char_len - ) - ) - - self.vocab = vocab - self.word_to_char = word_to_char - - @property - def padding_idx(self): - return Dictionary().pad() if self.vocab is None else self.vocab.pad() - - def reset_parameters(self): - nn.init.xavier_normal_(self.char_embeddings.weight) - nn.init.xavier_normal_(self.symbol_embeddings) - nn.init.xavier_uniform_(self.projection.weight) - - nn.init.constant_( - self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.0 - ) - nn.init.constant_(self.projection.bias, 0.0) - - def forward( - self, - input: torch.Tensor, - ): - if self.char_inputs: - chars = input.view(-1, self.max_char_len) - pads = chars[:, 0].eq(CHAR_PAD_IDX) - eos = chars[:, 0].eq(CHAR_EOS_IDX) - if eos.any(): - if self.onnx_trace: - chars = torch.where(eos.unsqueeze(1), chars.new_zeros(1), chars) - else: - chars[eos] = 0 - - unk = None - else: - flat_words = input.view(-1) - chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as( - input - ) - pads = flat_words.eq(self.vocab.pad()) - eos = flat_words.eq(self.vocab.eos()) - unk = flat_words.eq(self.vocab.unk()) - - word_embs = self._convolve(chars) - if self.onnx_trace: - if pads.any(): - word_embs = torch.where( - pads.unsqueeze(1), word_embs.new_zeros(1), word_embs - ) - if eos.any(): - word_embs = torch.where( - eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs - ) - if unk is not None and unk.any(): - word_embs = torch.where( - unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs - ) - else: - if pads.any(): - word_embs[pads] = 0 - if eos.any(): - word_embs[eos] = self.symbol_embeddings[self.eos_idx] - if unk is not None and unk.any(): - word_embs[unk] = self.symbol_embeddings[self.unk_idx] - - return word_embs.view(input.size()[:2] + (-1,)) - - def _convolve( - self, - char_idxs: torch.Tensor, - ): - char_embs = self.char_embeddings(char_idxs) - char_embs = char_embs.transpose(1, 2) # BTC -> BCT - - conv_result = [] - - for conv in self.convolutions: - x = conv(char_embs) - x, _ = torch.max(x, -1) - x = F.relu(x) - conv_result.append(x) - - x = torch.cat(conv_result, dim=-1) - - if self.highway is not None: - x = self.highway(x) - x = self.projection(x) - - return x - - -class Highway(torch.nn.Module): - """ - A `Highway layer `_. - Adopted from the AllenNLP implementation. - """ - - def __init__(self, input_dim: int, num_layers: int = 1): - super(Highway, self).__init__() - self.input_dim = input_dim - self.layers = nn.ModuleList( - [nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)] - ) - self.activation = nn.ReLU() - - self.reset_parameters() - - def reset_parameters(self): - for layer in self.layers: - # As per comment in AllenNLP: - # We should bias the highway layer to just carry its input forward. We do that by - # setting the bias on `B(x)` to be positive, because that means `g` will be biased to - # be high, so we will carry the input forward. The bias on `B(x)` is the second half - # of the bias vector in each Linear layer. - nn.init.constant_(layer.bias[self.input_dim :], 1) - - nn.init.constant_(layer.bias[: self.input_dim], 0) - nn.init.xavier_normal_(layer.weight) - - def forward(self, x: torch.Tensor): - for layer in self.layers: - projection = layer(x) - proj_x, gate = projection.chunk(2, dim=-1) - proj_x = self.activation(proj_x) - gate = torch.sigmoid(gate) - x = gate * x + (gate.new_tensor([1]) - gate) * proj_x - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/checkpoint_activations.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/checkpoint_activations.py deleted file mode 100644 index aa0b5929a39d5acec91038d7335276b4e5098aed..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/checkpoint_activations.py +++ /dev/null @@ -1,242 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import functools -from typing import Any, Dict, List, Tuple, Union - -import torch -import torch.utils.checkpoint as checkpoint -from fairseq import utils - - -def checkpoint_wrapper(m, offload_to_cpu=False): - """ - A friendlier wrapper for performing activation checkpointing. - - Compared to the PyTorch version, this version: - - wraps an nn.Module, so that all subsequent calls will use checkpointing - - handles keyword arguments in the forward - - handles non-Tensor outputs from the forward - - Usage:: - - checkpointed_module = checkpoint_wrapper(my_module, offload_to_cpu=True) - a, b = checkpointed_module(x, y=3, z=torch.Tensor([1])) - """ - # should I check whether original_forward has already been set? - assert not hasattr( - m, "precheckpoint_forward" - ), "checkpoint function has already been applied?" - m.precheckpoint_forward = m.forward - m.forward = functools.partial( - _checkpointed_forward, - m.precheckpoint_forward, # original_forward - offload_to_cpu, - ) - return m - - -def unwrap_checkpoint(m: torch.nn.Module): - """ - unwrap a module and its children from checkpoint_wrapper - """ - for module in m.modules(): - if hasattr(module, "precheckpoint_forward"): - module.forward = module.precheckpoint_forward - del module.precheckpoint_forward - if hasattr(module, "old_deepcopy_method"): - module.__deepcopy__ = module.old_deepcopy_method - del module.old_deepcopy_method - return m - - -def _checkpointed_forward(original_forward, offload_to_cpu, *args, **kwargs): - # Autograd Functions in PyTorch work best with positional args, since - # the backward must return gradients (or None) for every input argument. - # We can flatten keyword arguments to make this easier. - kwarg_keys, flat_args = pack_kwargs(*args, **kwargs) - parent_ctx_dict = {"offload": offload_to_cpu} - output = CheckpointFunction.apply( - original_forward, parent_ctx_dict, kwarg_keys, *flat_args - ) - if isinstance(output, torch.Tensor): - return output - else: - packed_non_tensor_outputs = parent_ctx_dict["packed_non_tensor_outputs"] - if packed_non_tensor_outputs: - output = unpack_non_tensors(output, packed_non_tensor_outputs) - return output - - -def pack_kwargs(*args, **kwargs) -> Tuple[List[str], List[Any]]: - """ - Usage:: - - kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4) - args, kwargs = unpack_kwargs(kwarg_keys, flat_args) - assert args == [1, 2] - assert kwargs == {"a": 3, "b": 4} - """ - kwarg_keys = [] - flat_args = list(args) - for k, v in kwargs.items(): - kwarg_keys.append(k) - flat_args.append(v) - return kwarg_keys, flat_args - - -def unpack_kwargs( - kwarg_keys: List[str], flat_args: List[Any] -) -> Tuple[List[Any], Dict[str, Any]]: - if len(kwarg_keys) == 0: - return flat_args, {} - args = flat_args[: -len(kwarg_keys)] - kwargs = {k: v for k, v in zip(kwarg_keys, flat_args[-len(kwarg_keys) :])} - return args, kwargs - - -def split_non_tensors( - mixed: Union[torch.Tensor, Tuple[Any]] -) -> Tuple[Tuple[torch.Tensor], Dict[str, List[Any]]]: - """ - Usage:: - - x = torch.Tensor([1]) - y = torch.Tensor([2]) - tensors, packed_non_tensors = split_non_tensors((x, y, None, 3)) - recon = unpack_non_tensors(tensors, packed_non_tensors) - assert recon == (x, y, None, 3) - """ - if isinstance(mixed, torch.Tensor): - return (mixed,), None - tensors = [] - packed_non_tensors = {"is_tensor": [], "objects": []} - for o in mixed: - if isinstance(o, torch.Tensor): - packed_non_tensors["is_tensor"].append(True) - tensors.append(o) - else: - packed_non_tensors["is_tensor"].append(False) - packed_non_tensors["objects"].append(o) - return tuple(tensors), packed_non_tensors - - -def unpack_non_tensors( - tensors: Tuple[torch.Tensor], - packed_non_tensors: Dict[str, List[Any]], -) -> Tuple[Any]: - if packed_non_tensors is None: - return tensors - assert isinstance(packed_non_tensors, dict) - mixed = [] - is_tensor_list = packed_non_tensors["is_tensor"] - objects = packed_non_tensors["objects"] - assert len(tensors) + len(objects) == len(is_tensor_list) - obj_i = tnsr_i = 0 - for is_tensor in is_tensor_list: - if is_tensor: - mixed.append(tensors[tnsr_i]) - tnsr_i += 1 - else: - mixed.append(objects[obj_i]) - obj_i += 1 - return tuple(mixed) - - -class CheckpointFunction(torch.autograd.Function): - """Similar to the torch version, but support non-Tensor outputs. - - The caller is expected to provide a dict (*parent_ctx_dict*) that will hold - the non-Tensor outputs. These should be combined with the Tensor *outputs* - by calling ``unpack_non_tensors``. - """ - - @staticmethod - def forward(ctx, run_function, parent_ctx_dict, kwarg_keys, *args): - if torch.is_grad_enabled(): # grad may be disabled, e.g., during validation - checkpoint.check_backward_validity(args) - - ctx.run_function = run_function - ctx.kwarg_keys = kwarg_keys - ctx.fwd_rng_state = utils.get_rng_state() - - tensor_inputs, packed_non_tensor_inputs = split_non_tensors(args) - if parent_ctx_dict["offload"]: - ctx.fwd_device = tuple(x.device for x in tensor_inputs) - ctx.grad_requirements = tuple(x.requires_grad for x in tensor_inputs) - tensor_inputs = tuple( - x.to(torch.device("cpu"), non_blocking=True) for x in tensor_inputs - ) - - else: - ctx.fwd_device, ctx.grad_requirements = None, None - - ctx.save_for_backward(*tensor_inputs) - ctx.packed_non_tensor_inputs = packed_non_tensor_inputs - - with torch.no_grad(): - unpacked_args, unpacked_kwargs = unpack_kwargs(kwarg_keys, args) - outputs = run_function(*unpacked_args, **unpacked_kwargs) - - if isinstance(outputs, torch.Tensor): - return outputs - else: - # Autograd Functions don't like non-Tensor outputs. We can split the - # non-Tensor and Tensor outputs, returning the former by reference - # through *parent_ctx_dict* and returning the latter directly. - outputs, packed_non_tensor_outputs = split_non_tensors(outputs) - parent_ctx_dict["packed_non_tensor_outputs"] = packed_non_tensor_outputs - return outputs - - @staticmethod - def backward(ctx, *args): - if not torch.autograd._is_checkpoint_valid(): - raise RuntimeError( - "Checkpointing is not compatible with .grad(), please use .backward() if possible" - ) - - tensor_inputs: Tuple = ctx.saved_tensors - tensor_inputs = checkpoint.detach_variable(tensor_inputs) - if ctx.fwd_device is not None: - tensor_inputs = [ - t.to(ctx.fwd_device[i], non_blocking=True) - for i, t in enumerate(tensor_inputs) - ] - for i, need_grad in enumerate(ctx.grad_requirements): - tensor_inputs[i].requires_grad = need_grad - inputs = unpack_non_tensors(tensor_inputs, ctx.packed_non_tensor_inputs) - - # Store the current states. - bwd_rng_state = utils.get_rng_state() - - # Set the states to what it used to be before the forward pass. - utils.set_rng_state(ctx.fwd_rng_state) - - with torch.enable_grad(): - unpacked_args, unpacked_kwargs = unpack_kwargs(ctx.kwarg_keys, inputs) - outputs = ctx.run_function(*unpacked_args, **unpacked_kwargs) - tensor_outputs, _ = split_non_tensors(outputs) - # Set the states back to what it was at the start of this function. - utils.set_rng_state(bwd_rng_state) - - # Run backward() with only Tensors that require grad - outputs_with_grad = [] - args_with_grad = [] - for i in range(len(tensor_outputs)): - if tensor_outputs[i].requires_grad: - outputs_with_grad.append(tensor_outputs[i]) - args_with_grad.append(args[i]) - if len(outputs_with_grad) == 0: - raise RuntimeError( - "None of the outputs have requires_grad=True, " - "this checkpoint() is not necessary" - ) - - torch.autograd.backward(outputs_with_grad, args_with_grad) - - grads = tuple( - inp.grad if isinstance(inp, torch.Tensor) else None for inp in inputs - ) - return (None, None, None) + grads diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/conformer_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/conformer_layer.py deleted file mode 100644 index 4846ce1c30e8b5721a0a1919c3d2b4b97a0d9ca0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/conformer_layer.py +++ /dev/null @@ -1,300 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from typing import Optional - -import torch -from fairseq.modules import ( - ESPNETMultiHeadedAttention, - LayerNorm, - MultiheadAttention, - RelPositionMultiHeadedAttention, - RotaryPositionMultiHeadedAttention, -) -from fairseq.utils import get_activation_fn - - -class ConvolutionModule(torch.nn.Module): - """Convolution block used in the conformer block""" - - def __init__( - self, - embed_dim, - channels, - depthwise_kernel_size, - dropout, - activation_fn="swish", - bias=False, - export=False, - ): - """ - Args: - embed_dim: Embedding dimension - channels: Number of channels in depthwise conv layers - depthwise_kernel_size: Depthwise conv layer kernel size - dropout: dropout value - activation_fn: Activation function to use after depthwise convolution kernel - bias: If bias should be added to conv layers - export: If layernorm should be exported to jit - """ - super(ConvolutionModule, self).__init__() - assert ( - depthwise_kernel_size - 1 - ) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding" - self.layer_norm = LayerNorm(embed_dim, export=export) - self.pointwise_conv1 = torch.nn.Conv1d( - embed_dim, - 2 * channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - self.glu = torch.nn.GLU(dim=1) - self.depthwise_conv = torch.nn.Conv1d( - channels, - channels, - depthwise_kernel_size, - stride=1, - padding=(depthwise_kernel_size - 1) // 2, - groups=channels, - bias=bias, - ) - self.batch_norm = torch.nn.BatchNorm1d(channels) - self.activation = get_activation_fn(activation_fn)(channels) - self.pointwise_conv2 = torch.nn.Conv1d( - channels, - embed_dim, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - self.dropout = torch.nn.Dropout(dropout) - - def forward(self, x): - """ - Args: - x: Input of shape B X T X C - Returns: - Tensor of shape B X T X C - """ - x = self.layer_norm(x) - # exchange the temporal dimension and the feature dimension - x = x.transpose(1, 2) - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channel, dim) - x = self.glu(x) # (batch, channel, dim) - - # 1D Depthwise Conv - x = self.depthwise_conv(x) - x = self.batch_norm(x) - x = self.activation(x) - - x = self.pointwise_conv2(x) - x = self.dropout(x) - return x.transpose(1, 2) - - -class FeedForwardModule(torch.nn.Module): - """Positionwise feed forward layer used in conformer""" - - def __init__( - self, - input_feat, - hidden_units, - dropout1, - dropout2, - activation_fn="swish", - bias=True, - ): - """ - Args: - input_feat: Input feature dimension - hidden_units: Hidden unit dimension - dropout1: dropout value for layer1 - dropout2: dropout value for layer2 - activation_fn: Name of activation function - bias: If linear layers should have bias - """ - - super(FeedForwardModule, self).__init__() - self.layer_norm = LayerNorm(input_feat) - self.w_1 = torch.nn.Linear(input_feat, hidden_units, bias=bias) - self.w_2 = torch.nn.Linear(hidden_units, input_feat, bias=bias) - self.dropout1 = torch.nn.Dropout(dropout1) - self.dropout2 = torch.nn.Dropout(dropout2) - self.activation = get_activation_fn(activation_fn)(hidden_units) - - def forward(self, x): - """ - Args: - x: Input Tensor of shape T X B X C - Returns: - Tensor of shape T X B X C - """ - x = self.layer_norm(x) - x = self.w_1(x) - x = self.activation(x) - x = self.dropout1(x) - x = self.w_2(x) - return self.dropout2(x) - - -class ConformerEncoderLayer(torch.nn.Module): - """Conformer block based on https://arxiv.org/abs/2005.08100. We currently don't support relative positional encoding in MHA""" - - def __init__( - self, - embed_dim, - ffn_embed_dim, - attention_heads, - dropout, - use_fp16, - depthwise_conv_kernel_size=31, - activation_fn="swish", - attn_type=None, - pos_enc_type="abs", - ): - """ - Args: - embed_dim: Input embedding dimension - ffn_embed_dim: FFN layer dimension - attention_heads: Number of attention heads in MHA - dropout: dropout value - depthwise_conv_kernel_size: Size of kernel in depthwise conv layer in convolution module - activation_fn: Activation function name to use in convulation block and feed forward block - attn_type: MHA implementation from ESPNET vs fairseq - pos_enc_type: Positional encoding type - abs, rope, rel_pos - """ - self.pos_enc_type = pos_enc_type - super(ConformerEncoderLayer, self).__init__() - - self.ffn1 = FeedForwardModule( - embed_dim, - ffn_embed_dim, - dropout, - dropout, - ) - - self.self_attn_layer_norm = LayerNorm(embed_dim, export=False) - self.self_attn_dropout = torch.nn.Dropout(dropout) - if attn_type == "espnet": - if self.pos_enc_type == "rel_pos": - self.self_attn = RelPositionMultiHeadedAttention( - embed_dim, - attention_heads, - dropout=dropout, - ) - elif self.pos_enc_type == "rope": - self.self_attn = RotaryPositionMultiHeadedAttention( - embed_dim, attention_heads, dropout=dropout, precision=use_fp16 - ) - elif self.pos_enc_type == "abs": - self.self_attn = ESPNETMultiHeadedAttention( - embed_dim, - attention_heads, - dropout=dropout, - ) - else: - raise Exception(f"Unsupported attention type {self.pos_enc_type}") - else: - # Default to fairseq MHA - self.self_attn = MultiheadAttention( - embed_dim, - attention_heads, - dropout=dropout, - ) - - self.conv_module = ConvolutionModule( - embed_dim=embed_dim, - channels=embed_dim, - depthwise_kernel_size=depthwise_conv_kernel_size, - dropout=dropout, - activation_fn=activation_fn, - ) - - self.ffn2 = FeedForwardModule( - embed_dim, - ffn_embed_dim, - dropout, - dropout, - activation_fn=activation_fn, - ) - self.final_layer_norm = LayerNorm(embed_dim, export=False) - - def forward( - self, - x, - encoder_padding_mask: Optional[torch.Tensor], - position_emb: Optional[torch.Tensor] = None, - ): - """ - Args: - x: Tensor of shape T X B X C - encoder_padding_mask: Optional mask tensor - positions: - Returns: - Tensor of shape T X B X C - """ - residual = x - x = self.ffn1(x) - x = x * 0.5 + residual - residual = x - x = self.self_attn_layer_norm(x) - if self.pos_enc_type == "rel_pos": - x, attn = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=encoder_padding_mask, - pos_emb=position_emb, - need_weights=False, - ) - else: - x, attn = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=encoder_padding_mask, - need_weights=False, - ) - x = self.self_attn_dropout(x) - x = x + residual - - residual = x - # TBC to BTC - x = x.transpose(0, 1) - x = self.conv_module(x) - # BTC to TBC - x = x.transpose(0, 1) - x = residual + x - - residual = x - x = self.ffn2(x) - - layer_result = x - - x = x * 0.5 + residual - - x = self.final_layer_norm(x) - return x, (attn, layer_result) - - -class ConformerWav2Vec2EncoderLayer(ConformerEncoderLayer): - """Encoder layer for Wav2vec2 encoder""" - - def forward( - self, - x: torch.Tensor, - self_attn_mask: torch.Tensor = None, - self_attn_padding_mask: torch.Tensor = None, - need_weights: bool = False, - att_args=None, - position_emb=None, - ): - return super().forward(x, self_attn_padding_mask, position_emb) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/conv_tbc.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/conv_tbc.py deleted file mode 100644 index 404dd7dbfbfe97e53a998615da3bf2c694484be3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/conv_tbc.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from torch import Tensor, nn -from torch.nn.modules.utils import _single - - -class ConvTBC(torch.nn.Module): - """1D convolution over an input of shape (time x batch x channel) - - The implementation uses gemm to perform the convolution. This implementation - is faster than cuDNN for small kernel sizes. - """ - - def __init__(self, in_channels, out_channels, kernel_size, padding=0): - super(ConvTBC, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _single(kernel_size) - self.padding = _single(padding) - - self.weight = torch.nn.Parameter( - torch.Tensor(self.kernel_size[0], in_channels, out_channels) - ) - self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) - - self.reset_parameters() - - def reset_parameters(self): - nn.init.xavier_normal_(self.weight) - nn.init.zeros_(self.bias) - - def conv_tbc(self, input: Tensor): - return torch.conv_tbc( - input.contiguous(), self.weight, self.bias, self.padding[0] - ) - - def forward(self, input: Tensor): - return self.conv_tbc(input) - - def __repr__(self): - s = ( - "{name}({in_channels}, {out_channels}, kernel_size={kernel_size}" - ", padding={padding}" - ) - if self.bias is None: - s += ", bias=False" - s += ")" - return s.format(name=self.__class__.__name__, **self.__dict__) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/cross_entropy.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/cross_entropy.py deleted file mode 100644 index 286c00eeccbc9d5596a793a20d9c7fdf3dd092f1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/cross_entropy.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import torch -import torch.nn.functional as F - -logger = logging.getLogger(__name__) - - -def _cross_entropy_pytorch(logits, target, ignore_index=None, reduction="mean"): - lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) - return F.nll_loss( - lprobs, - target, - ignore_index=ignore_index, - reduction=reduction, - ) - - -try: - import xentropy_cuda - from apex.contrib import xentropy - - def cross_entropy(logits, target, ignore_index=-100, reduction="mean"): - if logits.device == torch.device("cpu"): - return _cross_entropy_pytorch(logits, target, ignore_index, reduction) - else: - if not getattr(cross_entropy, "_has_logged_once", False): - logger.info("using fused cross entropy") - cross_entropy._has_logged_once = True - - half_to_float = logits.dtype == torch.half - losses = xentropy.SoftmaxCrossEntropyLoss.apply( - logits, - target, - 0.0, - ignore_index, - half_to_float, - ) - if reduction == "sum": - return losses.sum() - elif reduction == "mean": - if ignore_index >= 0: - return losses.sum() / target.ne(ignore_index).sum() - else: - return losses.mean() - elif reduction == "none": - return losses - else: - raise NotImplementedError - -except ImportError: - - def cross_entropy(logits, target, ignore_index=-100, reduction="mean"): - return _cross_entropy_pytorch(logits, target, ignore_index, reduction) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/cuda_utils.cu b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/cuda_utils.cu deleted file mode 100644 index 924f852758ee654e462546274db4b5e7199a9c90..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/cuda_utils.cu +++ /dev/null @@ -1,202 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -template -constexpr __host__ __device__ auto divUp(U a, V b) -> decltype(a + b) { - return (a + b - 1) / b; -} - -template -__inline__ __device__ void zeroSharedMem(scalar_t* data) { - /* - Given an array of length FS + SB, zero out the first padding_l and last - (FS - padding_l) values in the array - */ - - int tid = threadIdx.x; - - if (FS < SB) { - // zero all if we have enough threads in a block to do all of them - if (tid < padding_l || tid > SB - FS + padding_l - 1) { - data[tid] = scalar_t(0.0); - } - } else { - // otherwise zero out one block at a time - const int numIterations = divUp(FS, SB); - for (int i = 0; i < numIterations; i++) { - int offset = i * SB; - if (tid + offset < padding_l) { - data[tid + offset] = scalar_t(0.0); - } else if (tid + offset < FS) { - data[SB + tid + offset] = scalar_t(0.0); - } - } - } -} - -template -__inline__ __device__ scalar_t warpReduce(scalar_t data) { - /* - Reduce an array within each warp. After processing all values in warp will - caontain the sum of all original values in that warp. - - data - pointer to data to reduce - */ - data += __shfl_xor_sync(SHFL_MASK, data, 16); - data += __shfl_xor_sync(SHFL_MASK, data, 8); - data += __shfl_xor_sync(SHFL_MASK, data, 4); - data += __shfl_xor_sync(SHFL_MASK, data, 2); - data += __shfl_xor_sync(SHFL_MASK, data, 1); - return data; -} - -template -__inline__ __device__ scalar_t blockReduce(scalar_t data) { - /* - Reduce an entire array on the block level. After processing, the - first value in the array will contain the reduced sum. - - data - pointer to data to reduce - */ - - static __shared__ scalar_t warpSum[32]; - const int tid = threadIdx.x; - int wid = tid / 32; - int lane = tid % 32; - - __syncthreads(); - - // reduce each warp then write to shared memory - scalar_t sum = warpReduce(data); - if (lane == 0) { - warpSum[wid] = sum; - } - - __syncthreads(); - - scalar_t v; - // perform final sum of partial warp sums - if (tid < blockDim.x / 32) { - v = warpSum[lane]; - } else { - v = scalar_t(0.0); - } - - if (wid == 0) { - v = warpReduce(v); - } - __syncthreads(); - - return v; -} - -void checkCudaStatus(cudaError_t status, int lineNumber = -1) { - if (status != cudaSuccess) { - std::cout << cudaGetErrorString(status) << " at line " << lineNumber - << std::endl; - std::cout << "Exiting" << std::endl; - exit(1); - } -} - -template -__device__ void load_input_to_shared( - const scalar_t* input, // global memory - int inputOffset, - int sequenceLength, - int iteration, - int numIterations, - bool no_prev, - scalar_t* output /* shared memory */) { - /* - Load a block size of input into shared memory with - right and left overhang of total size FS. If previously - loaded memory, overlap will be shifted over to reduce - global memory access - - input - pointer to start of channel sequence - inputOffset - how far in the sequence to start loading - sequenceLength - total length of sequence - iteration - which block of sequence we are loading - numIterations - total number of blocks to load - no_prev - whether to load the whole block if the previous block - wasn't loaded - output - shared memory to write input to - */ - - const int tid = threadIdx.x; - - // Load the left "overhang" of input - if (iteration > 0) { - if (padding_l < SB) { - // load all at once - if (tid < padding_l) { - output[tid] = - (no_prev) ? input[inputOffset - padding_l + tid] : output[tid + SB]; - } - } else { - // load in chunks of size SB - int numIterations = divUp(padding_l, SB); - for (int i = 0; i < numIterations; i++) { - int offset = i * SB; - if ((tid + offset) < padding_l) { - output[tid + offset] = (no_prev) - ? input[inputOffset - padding_l + tid + offset] - : output[tid + offset + SB]; - } - } - } - } - - // Load the right "overhang" of input - if (iteration < (numIterations - 1)) { - const int elementsLeft = sequenceLength - (iteration + 1) * SB; - - if ((FS - padding_l) < SB) { - // load all at once - if (tid < (FS - padding_l)) { - output[padding_l + SB + tid] = (tid < elementsLeft) - ? input[inputOffset + SB + tid] - : scalar_t(0.0); - } - } else { - // load in chunks of size SB - int numIterations = divUp(FS - padding_l, SB); - for (int i = 0; i < numIterations; i++) { - int offset = i * SB; - if ((tid + offset) < (FS - padding_l)) { - output[padding_l + SB + tid + offset] = - ((tid + offset) < elementsLeft) - ? input[inputOffset + SB + tid + offset] - : scalar_t(0.0); - } - } - } - } - - // We should also clear out the right "overhang" - if (iteration == (numIterations - 1)) { - if ((FS - padding_l) < SB) { - // clear out all at once - if (tid < (FS - padding_l)) { - output[padding_l + SB + tid] = scalar_t(0.0); - } - } else { - // clear in chunks of size SB - int numIterations = divUp(FS - padding_l, SB); - for (int i = 0; i < numIterations; i++) { - int offset = i * SB; - if ((tid + offset) < (FS - padding_l)) { - output[padding_l + SB + tid + offset] = scalar_t(0.0); - } - } - } - } - output[tid + padding_l] = ((inputOffset + tid) < sequenceLength) - ? input[inputOffset + tid] - : scalar_t(0.0); -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/downsampled_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/downsampled_multihead_attention.py deleted file mode 100644 index c746e8b3932b4b1e209fb012a1b0125c22136f7a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/downsampled_multihead_attention.py +++ /dev/null @@ -1,316 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.scalar_bias import scalar_bias - - -class SingleHeadAttention(nn.Module): - """ - Single-head attention that supports Gating and Downsampling - """ - - def __init__( - self, - out_channels, - embed_dim, - head_dim, - head_index, - dropout=0.0, - bias=True, - project_input=True, - gated=False, - downsample=False, - num_heads=1, - ): - super().__init__() - self.embed_dim = embed_dim - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.head_index = head_index - self.head_dim = head_dim - self.project_input = project_input - self.gated = gated - self.downsample = downsample - self.num_heads = num_heads - self.projection = None - - k_layers = [] - v_layers = [] - if self.downsample: - k_layers.append(Downsample(self.head_index)) - v_layers.append(Downsample(self.head_index)) - out_proj_size = self.head_dim - else: - out_proj_size = self.head_dim * self.num_heads - if self.gated: - k_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias)) - self.in_proj_q = GatedLinear(self.embed_dim, out_proj_size, bias=bias) - v_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias)) - else: - k_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias)) - self.in_proj_q = Linear(self.embed_dim, out_proj_size, bias=bias) - v_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias)) - - self.in_proj_k = nn.Sequential(*k_layers) - self.in_proj_v = nn.Sequential(*v_layers) - - if self.downsample: - self.out_proj = Linear(out_proj_size, self.head_dim, bias=bias) - else: - self.out_proj = Linear(out_proj_size, out_channels, bias=bias) - - self.scaling = self.head_dim**-0.5 - - def forward( - self, - query, - key, - value, - mask_future_timesteps=False, - key_padding_mask=None, - use_scalar_bias=False, - ): - """Input shape: Time x Batch x Channel - Self-attention can be implemented by passing in the same arguments for - query, key and value. Future timesteps can be masked with the - `mask_future_timesteps` argument. Padding elements can be excluded from - the key by passing a binary ByteTensor (`key_padding_mask`) with shape: - batch x src_len, where padding elements are indicated by 1s. - """ - src_len, bsz, out_channels = key.size() - tgt_len = query.size(0) - assert list(query.size()) == [tgt_len, bsz, out_channels] - assert key.size() == value.size() - - if key_padding_mask is not None: - assert key_padding_mask.size(0) == bsz - assert key_padding_mask.size(1) == src_len - - if self.downsample: - size = bsz - else: - size = bsz * self.num_heads - - k = key - v = value - q = query - if self.project_input: - q = self.in_proj_q(q) - k = self.in_proj_k(k) - v = self.in_proj_v(v) - src_len = k.size()[0] - q *= self.scaling - - if not self.downsample: - q = q.view(tgt_len, size, self.head_dim) - k = k.view(src_len, size, self.head_dim) - v = v.view(src_len, size, self.head_dim) - - q = q.transpose(0, 1) - k = k.transpose(0, 1) - v = v.transpose(0, 1) - - attn_weights = torch.bmm(q, k.transpose(1, 2)) - if mask_future_timesteps: - assert ( - query.size() == key.size() - ), "mask_future_timesteps only applies to self-attention" - attn_weights *= torch.tril( - attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(), - diagonal=-1, - )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0) - attn_weights += torch.triu( - attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(), - diagonal=0, - )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0) - tgt_size = tgt_len - if use_scalar_bias: - attn_weights = scalar_bias(attn_weights, 2) - v = scalar_bias(v, 1) - tgt_size += 1 - - if key_padding_mask is not None: - # don't attend to padding symbols - if key_padding_mask.max() > 0: - if self.downsample: - attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len) - else: - attn_weights = attn_weights.view( - size, self.num_heads, tgt_len, src_len - ) - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2), - -math.inf, - ) - attn_weights = attn_weights.view(size, tgt_len, src_len) - attn_weights = F.softmax(attn_weights, dim=-1) - attn_weights = self.dropout_module(attn_weights) - - attn = torch.bmm(attn_weights, v) - if self.downsample: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim) - else: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim) - - attn = self.out_proj(attn) - - return attn, attn_weights - - -class DownsampledMultiHeadAttention(nn.ModuleList): - """ - Multi-headed attention with Gating and Downsampling - """ - - def __init__( - self, - out_channels, - embed_dim, - num_heads, - dropout=0.0, - bias=True, - project_input=True, - gated=False, - downsample=False, - ): - self.embed_dim = embed_dim - self.num_heads = num_heads - self.head_dim = embed_dim // num_heads - self.downsample = downsample - self.gated = gated - self.project_input = project_input - assert self.head_dim * num_heads == embed_dim - - if self.downsample: - attention_heads = [] - for index in range(self.num_heads): - attention_heads.append( - SingleHeadAttention( - out_channels, - self.embed_dim, - self.head_dim, - index, - dropout, - bias, - self.project_input, - self.gated, - self.downsample, - self.num_heads, - ) - ) - super().__init__(modules=attention_heads) - self.out_proj = Linear(embed_dim, out_channels, bias=bias) - else: - # either we have a list of attention heads, or just one attention head - # if not being downsampled, we can do the heads with one linear layer instead of separate ones - super().__init__() - self.attention_module = SingleHeadAttention( - out_channels, - self.embed_dim, - self.head_dim, - 1, - dropout, - bias, - self.project_input, - self.gated, - self.downsample, - self.num_heads, - ) - - def forward( - self, - query, - key, - value, - mask_future_timesteps=False, - key_padding_mask=None, - use_scalar_bias=False, - ): - src_len, bsz, embed_dim = key.size() - tgt_len = query.size(0) - assert embed_dim == self.embed_dim - assert list(query.size()) == [tgt_len, bsz, embed_dim] - assert key.size() == value.size() - - tgt_size = tgt_len - if use_scalar_bias: - tgt_size += 1 - - attn = [] - attn_weights = [] - if self.downsample: - for attention_head_number in range(self.num_heads): - # call the forward of each attention head - _attn, _attn_weight = self[attention_head_number]( - query, - key, - value, - mask_future_timesteps, - key_padding_mask, - use_scalar_bias, - ) - attn.append(_attn) - attn_weights.append(_attn_weight) - full_attn = torch.cat(attn, dim=2) - full_attn = self.out_proj(full_attn) - return full_attn, attn_weights[0].clone() - else: - _attn, _attn_weight = self.attention_module( - query, - key, - value, - mask_future_timesteps, - key_padding_mask, - use_scalar_bias, - ) - attn.append(_attn) - attn_weights.append(_attn_weight) - full_attn = torch.cat(attn, dim=2) - full_attn_weights = torch.cat(attn_weights) - full_attn_weights = full_attn_weights.view( - bsz, self.num_heads, tgt_size, src_len - ) - full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads - return full_attn, full_attn_weights - - -class Downsample(nn.Module): - """ - Selects every nth element, where n is the index - """ - - def __init__(self, index): - super().__init__() - self.index = index - - def forward(self, x): - return x[:: self.index + 1] - - -def Linear(in_features, out_features, dropout=0.0, bias=True): - """Weight-normalized Linear layer (input: B x T x C)""" - m = nn.Linear(in_features, out_features, bias=bias) - m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features)) - m.bias.data.zero_() - return nn.utils.weight_norm(m) - - -def GatedLinear(in_features, out_features, dropout=0.0, bias=True): - """Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units""" - return nn.Sequential( - Linear(in_features, out_features * 4, dropout, bias), - nn.GLU(), - Linear(out_features * 2, out_features * 2, dropout, bias), - nn.GLU(), - Linear(out_features, out_features, dropout, bias), - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamic_convolution.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamic_convolution.py deleted file mode 100644 index 0121d453b9e026f5128dd41fce691aa1b4486448..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamic_convolution.py +++ /dev/null @@ -1,310 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout - -from .unfold import unfold1d - - -def DynamicConv( - input_size, - kernel_size=1, - padding_l=None, - num_heads=1, - weight_dropout=0.0, - weight_softmax=False, - renorm_padding=False, - bias=False, - conv_bias=False, - query_size=None, - in_proj=False, -): - if torch.cuda.is_available(): - try: - from fairseq.modules.dynamicconv_layer import DynamicconvLayer - - return DynamicconvLayer( - input_size, - kernel_size=kernel_size, - padding_l=padding_l, - num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, - renorm_padding=renorm_padding, - bias=bias, - conv_bias=conv_bias, - query_size=query_size, - ) - except ImportError as e: - print(e) - return DynamicConv1dTBC( - input_size, - kernel_size=kernel_size, - padding_l=padding_l, - num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, - renorm_padding=renorm_padding, - bias=bias, - conv_bias=conv_bias, - query_size=query_size, - ) - - -def Linear(in_features, out_features, bias=True): - m = nn.Linear(in_features, out_features, bias) - nn.init.xavier_uniform_(m.weight) - if bias: - nn.init.constant_(m.bias, 0.0) - return m - - -@with_incremental_state -class DynamicConv1dTBC(nn.Module): - """Dynamic lightweight convolution taking T x B x C inputs - Args: - input_size: # of channels of the input - kernel_size: convolution channels - padding_l: padding to the left when using "same" padding - num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size) - weight_dropout: the drop rate of the DropConnect to drop the weight - weight_softmax: normalize the weight with softmax before the convolution - renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1) - bias: use bias - conv_bias: bias of the convolution - query_size: specified when feeding a different input as the query - in_proj: project the input and generate the filter together - - Shape: - Input: TxBxC, i.e. (timesteps, batch_size, input_size) - Output: TxBxC, i.e. (timesteps, batch_size, input_size) - - Attributes: - weight: the learnable weights of the module of shape - `(num_heads, 1, kernel_size)` - bias: the learnable bias of the module of shape `(input_size)` - """ - - def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - num_heads=1, - weight_dropout=0.0, - weight_softmax=False, - renorm_padding=False, - bias=False, - conv_bias=False, - query_size=None, - in_proj=False, - ): - super().__init__() - self.input_size = input_size - self.query_size = input_size if query_size is None else query_size - self.kernel_size = kernel_size - self.padding_l = padding_l - self.num_heads = num_heads - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - self.weight_softmax = weight_softmax - self.renorm_padding = renorm_padding - - if in_proj: - self.weight_linear = Linear( - self.input_size, self.input_size + num_heads * kernel_size * 1 - ) - else: - self.weight_linear = Linear( - self.query_size, num_heads * kernel_size * 1, bias=bias - ) - if conv_bias: - self.conv_bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.conv_bias = None - self.reset_parameters() - - @property - def in_proj(self): - return ( - self.weight_linear.out_features - == self.input_size + self.num_heads * self.kernel_size - ) - - def reset_parameters(self): - self.weight_linear.reset_parameters() - if self.conv_bias is not None: - nn.init.constant_(self.conv_bias, 0.0) - - def forward(self, x, incremental_state=None, query=None, unfold=None): - """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C - args: - x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) - incremental_state: A dict to keep the state - unfold: unfold the input or not. If not, we use the matrix trick instead - query: use the specified query to predict the conv filters - """ - unfold = ( - x.size(0) > 512 if unfold is None else unfold - ) # use unfold mode as default for long sequence to save memory - unfold = unfold or (incremental_state is not None) - assert query is None or not self.in_proj - - if query is None: - query = x - if unfold: - output = self._forward_unfolded(x, incremental_state, query) - else: - output = self._forward_expanded(x, incremental_state, query) - - if self.conv_bias is not None: - output = output + self.conv_bias.view(1, 1, -1) - return output - - def _forward_unfolded(self, x, incremental_state, query): - """The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.""" - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - - if self.in_proj: - proj = self.weight_linear(x) - x = proj.narrow(2, 0, self.input_size).contiguous() - weight = ( - proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1) - ) - else: - weight = self.weight_linear(query).view(T * B * H, -1) - - # renorm_padding is only implemented in _forward_expanded - assert not self.renorm_padding or incremental_state is not None - - if incremental_state is not None: - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = x.new() - x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) - if self.kernel_size > 1: - self._set_input_buffer( - incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] - ) - x_unfold = x_unfold.view(T * B * H, R, -1) - else: - padding_l = self.padding_l - if K > T and padding_l == K - 1: - weight = weight.narrow(1, K - T, T) - K, padding_l = T, T - 1 - # unfold the input: T x B x C --> T' x B x C x K - x_unfold = unfold1d(x, K, padding_l, 0) - x_unfold = x_unfold.view(T * B * H, R, K) - - if self.weight_softmax and not self.renorm_padding: - weight = F.softmax(weight, dim=1) - weight = weight.narrow(1, 0, K) - - if incremental_state is not None: - weight = weight[:, -x_unfold.size(2) :] - K = weight.size(1) - - if self.weight_softmax and self.renorm_padding: - weight = F.softmax(weight, dim=1) - - weight = self.weight_dropout_module(weight, inplace=False) - - output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1 - output = output.view(T, B, C) - return output - - def _forward_expanded(self, x, incremental_stat, query): - """Turn the convolution filters into band matrices and do matrix multiplication. - This is faster when the sequence is short, but less memory efficient. - This is not used in the decoder during inference. - """ - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - if self.in_proj: - proj = self.weight_linear(x) - x = proj.narrow(2, 0, self.input_size).contiguous() - weight = ( - proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1) - ) - else: - weight = self.weight_linear(query).view(T * B * H, -1) - - if not self.renorm_padding: - if self.weight_softmax: - weight = F.softmax(weight, dim=1) - weight = self.weight_dropout_module(weight, inplace=False) - weight = weight.narrow(1, 0, K).contiguous() - weight = weight.view(T, B * H, K).transpose(0, 1) - - x = x.view(T, B * H, R).transpose(0, 1) - if self.weight_softmax and self.renorm_padding: - # turn the convolution filters into band matrices - weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf")) - weight_expanded.as_strided( - (B * H, T, K), (T * (T + K - 1), T + K, 1) - ).copy_(weight) - weight_expanded = weight_expanded.narrow(2, self.padding_l, T) - # normalize the weight over valid positions like self-attention - weight_expanded = F.softmax(weight_expanded, dim=2) - weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False) - else: - P = self.padding_l - # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length - if K > T and P == K - 1: - weight = weight.narrow(2, K - T, T) - K, P = T, T - 1 - # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) - weight_expanded.as_strided( - (B * H, T, K), (T * (T + K - 1), T + K, 1) - ).copy_(weight) - weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T - output = torch.bmm(weight_expanded, x) - output = output.transpose(0, 1).contiguous().view(T, B, C) - return output - - def reorder_incremental_state(self, incremental_state, new_order): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(1, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - def extra_repr(self): - s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}".format( - self.input_size, - self.kernel_size, - self.padding_l, - self.num_heads, - self.weight_softmax, - self.conv_bias is not None, - self.renorm_padding, - self.in_proj, - ) - - if self.query_size != self.input_size: - s += ", query_size={}".format(self.query_size) - if self.weight_dropout_module.p > 0.0: - s += ", weight_dropout={}".format(self.weight_dropout_module.p) - return s diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamic_crf_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamic_crf_layer.py deleted file mode 100644 index 8fcc6b8d2672d2eacc6d01b9688bac44d5e1ce26..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamic_crf_layer.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -This file is to re-implemented the low-rank and beam approximation of CRF layer -Proposed by: - -Sun, Zhiqing, et al. -Fast Structured Decoding for Sequence Models -https://arxiv.org/abs/1910.11555 - -The CRF implementation is mainly borrowed from -https://github.com/kmkurn/pytorch-crf/blob/master/torchcrf/__init__.py - -""" - -import numpy as np -import torch -import torch.nn as nn - - -def logsumexp(x, dim=1): - return torch.logsumexp(x.float(), dim=dim).type_as(x) - - -class DynamicCRF(nn.Module): - """Dynamic CRF layer is used to approximate the traditional - Conditional Random Fields (CRF) - $P(y | x) = 1/Z(x) exp(sum_i s(y_i, x) + sum_i t(y_{i-1}, y_i, x))$ - - where in this function, we assume the emition scores (s) are given, - and the transition score is a |V| x |V| matrix $M$ - - in the following two aspects: - (1) it used a low-rank approximation for the transition matrix: - $M = E_1 E_2^T$ - (2) it used a beam to estimate the normalizing factor Z(x) - """ - - def __init__(self, num_embedding, low_rank=32, beam_size=64): - super().__init__() - - self.E1 = nn.Embedding(num_embedding, low_rank) - self.E2 = nn.Embedding(num_embedding, low_rank) - - self.vocb = num_embedding - self.rank = low_rank - self.beam = beam_size - - def extra_repr(self): - return "vocab_size={}, low_rank={}, beam_size={}".format( - self.vocb, self.rank, self.beam - ) - - def forward(self, emissions, targets, masks, beam=None): - """ - Compute the conditional log-likelihood of a sequence of target tokens given emission scores - - Args: - emissions (`~torch.Tensor`): Emission score are usually the unnormalized decoder output - ``(batch_size, seq_len, vocab_size)``. We assume batch-first - targets (`~torch.LongTensor`): Sequence of target token indices - ``(batch_size, seq_len) - masks (`~torch.ByteTensor`): Mask tensor with the same size as targets - - Returns: - `~torch.Tensor`: approximated log-likelihood - """ - numerator = self._compute_score(emissions, targets, masks) - denominator = self._compute_normalizer(emissions, targets, masks, beam) - return numerator - denominator - - def forward_decoder(self, emissions, masks=None, beam=None): - """ - Find the most likely output sequence using Viterbi algorithm. - - Args: - emissions (`~torch.Tensor`): Emission score are usually the unnormalized decoder output - ``(batch_size, seq_len, vocab_size)``. We assume batch-first - masks (`~torch.ByteTensor`): Mask tensor with the same size as targets - - Returns: - `~torch.LongTensor`: decoded sequence from the CRF model - """ - return self._viterbi_decode(emissions, masks, beam) - - def _compute_score(self, emissions, targets, masks=None): - batch_size, seq_len = targets.size() - emission_scores = emissions.gather(2, targets[:, :, None])[:, :, 0] # B x T - transition_scores = (self.E1(targets[:, :-1]) * self.E2(targets[:, 1:])).sum(2) - - scores = emission_scores - scores[:, 1:] += transition_scores - - if masks is not None: - scores = scores * masks.type_as(scores) - return scores.sum(-1) - - def _compute_normalizer(self, emissions, targets=None, masks=None, beam=None): - # HACK: we include "target" which is a hueristic for training - # HACK: we use a beam of tokens to approximate the normalizing factor (which is bad?) - - beam = beam if beam is not None else self.beam - batch_size, seq_len = emissions.size()[:2] - if targets is not None: - _emissions = emissions.scatter(2, targets[:, :, None], np.float("inf")) - beam_targets = _emissions.topk(beam, 2)[1] - beam_emission_scores = emissions.gather(2, beam_targets) - else: - beam_emission_scores, beam_targets = emissions.topk(beam, 2) - beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D - beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D - beam_transition_matrix = torch.bmm( - beam_transition_score1.view(-1, beam, self.rank), - beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2), - ) - beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam) - - # compute the normalizer in the log-space - score = beam_emission_scores[:, 0] # B x K - for i in range(1, seq_len): - next_score = score[:, :, None] + beam_transition_matrix[:, i - 1] - next_score = logsumexp(next_score, dim=1) + beam_emission_scores[:, i] - - if masks is not None: - score = torch.where(masks[:, i : i + 1], next_score, score) - else: - score = next_score - - # Sum (log-sum-exp) over all possible tags - return logsumexp(score, dim=1) - - def _viterbi_decode(self, emissions, masks=None, beam=None): - # HACK: we use a beam of tokens to approximate the normalizing factor (which is bad?) - - beam = beam if beam is not None else self.beam - batch_size, seq_len = emissions.size()[:2] - beam_emission_scores, beam_targets = emissions.topk(beam, 2) - beam_transition_score1 = self.E1(beam_targets[:, :-1]) # B x (T-1) x K x D - beam_transition_score2 = self.E2(beam_targets[:, 1:]) # B x (T-1) x K x D - beam_transition_matrix = torch.bmm( - beam_transition_score1.view(-1, beam, self.rank), - beam_transition_score2.view(-1, beam, self.rank).transpose(1, 2), - ) - beam_transition_matrix = beam_transition_matrix.view(batch_size, -1, beam, beam) - - traj_tokens, traj_scores = [], [] - finalized_tokens, finalized_scores = [], [] - - # compute the normalizer in the log-space - score = beam_emission_scores[:, 0] # B x K - dummy = ( - torch.arange(beam, device=score.device).expand(*score.size()).contiguous() - ) - - for i in range(1, seq_len): - traj_scores.append(score) - _score = score[:, :, None] + beam_transition_matrix[:, i - 1] - _score, _index = _score.max(dim=1) - _score = _score + beam_emission_scores[:, i] - - if masks is not None: - score = torch.where(masks[:, i : i + 1], _score, score) - index = torch.where(masks[:, i : i + 1], _index, dummy) - else: - score, index = _score, _index - traj_tokens.append(index) - - # now running the back-tracing and find the best - best_score, best_index = score.max(dim=1) - finalized_tokens.append(best_index[:, None]) - finalized_scores.append(best_score[:, None]) - - for idx, scs in zip(reversed(traj_tokens), reversed(traj_scores)): - previous_index = finalized_tokens[-1] - finalized_tokens.append(idx.gather(1, previous_index)) - finalized_scores.append(scs.gather(1, previous_index)) - - finalized_tokens.reverse() - finalized_tokens = torch.cat(finalized_tokens, 1) - finalized_tokens = beam_targets.gather(2, finalized_tokens[:, :, None])[:, :, 0] - - finalized_scores.reverse() - finalized_scores = torch.cat(finalized_scores, 1) - finalized_scores[:, 1:] = finalized_scores[:, 1:] - finalized_scores[:, :-1] - - return finalized_scores, finalized_tokens diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/__init__.py deleted file mode 100644 index 22dc6f403d2a0ecdb1b9e7e69ed96bd560e93b2c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .dynamicconv_layer import DynamicconvLayer # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/cuda_function_gen.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/cuda_function_gen.py deleted file mode 100644 index 9304f99eb8169a614f39babc830c84cac80e080b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/cuda_function_gen.py +++ /dev/null @@ -1,223 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -def gen_forward(): - - kernels = [3, 5, 7, 15, 31, 63, 127, 255] - blocks = [32, 64, 128, 256] - - head = """ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include "dynamicconv_cuda.cuh" - -std::vector dynamicconv_cuda_forward(at::Tensor input, at::Tensor weight, int padding_l) { - - at::DeviceGuard g(input.device()); - const auto minibatch = input.size(0); - const auto numFeatures = input.size(1); - const auto sequenceLength = input.size(2); - - const auto numHeads = weight.size(1); - const auto filterSize = weight.size(2); - - const auto numFiltersInBlock = numFeatures / numHeads; - const dim3 blocks(minibatch, numFeatures); - - auto output = at::zeros_like(input); - auto stream = at::cuda::getCurrentCUDAStream(); -""" - - switch = """ - switch(filterSize) { -""" - - case_k = """ - case {k}: -""" - - main_block = """ - if (padding_l == {pad}) {{ - AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "dynamicconv_forward", ([&] {{ - dynamicconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t> - <<>>( - input.data(), - weight.data(), - minibatch, - sequenceLength, - numFeatures, - numFiltersInBlock, - numHeads, - output.data()); - }})); - }} else -""" - - bad_padding = """ - { - std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl; - } - break;\n -""" - - end = """ - default: - std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl; - } - - return {output}; -} -""" - - with open("dynamicconv_cuda_forward.cu", "w") as forward: - forward.write(head) - forward.write(switch) - for k in kernels: - b_size = 32 - for b in blocks: - if b > k: - b_size = b - break - forward.write(case_k.format(k=k)) - for pad in [k // 2, k - 1]: - forward.write(main_block.format(k=k, b_size=b_size, pad=pad)) - forward.write(bad_padding) - forward.write(end) - - -def gen_backward(): - - kernels = [3, 5, 7, 15, 31, 63, 127, 255] - thresh = [512, 512, 512, 512, 512, 380, 256, 256] - min_block = [64, 64, 64, 64, 64, 64, 128, 256] - seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]] - - head = """ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include "dynamicconv_cuda.cuh" - -std::vector dynamicconv_cuda_backward(at::Tensor gradOutput, int padding_l, at::Tensor input, at::Tensor weight) { - - at::DeviceGuard g(input.device()); - const auto minibatch = input.size(0); - const auto numFeatures = input.size(1); - const auto sequenceLength = input.size(2); - - const auto numHeads = weight.size(1); - const auto filterSize = weight.size(2); - - const auto numFiltersInBlock = numFeatures / numHeads; - auto numChunks = 1; - - auto gradInput = at::zeros_like(input); - auto gradWeight = at::zeros_like(weight); - auto stream = at::cuda::getCurrentCUDAStream(); - - dim3 blocks(minibatch, numHeads, numChunks); -""" - - sequence_if = """ - if (sequenceLength < {seq}) {{ - switch(filterSize) {{ -""" - - case_k = """ - case {k}: -""" - - chunks_reset = """ - numChunks = int(ceilf(sequenceLength/float({b_size}))); - blocks = dim3(minibatch, numHeads, numChunks); -""" - - main_block = """ - if (padding_l == {p}) {{ - AT_DISPATCH_FLOATING_TYPES_AND_HALF(gradOutput.scalar_type(), "dynamicconv_backward", ([&] {{ - dynamicconv_backward_kernel<{k}, {b_size}, {p}, scalar_t> - <<>>( - gradOutput.data(), - input.data(), - weight.data(), - minibatch, - sequenceLength, - numFeatures, - numFiltersInBlock, - numHeads, - gradWeight.data(), - gradInput.data()); - }})); - }} else -""" - - bad_padding = """ - { - std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl; - } - break;\n -""" - - bad_filter = """ - default: - std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl; - } -""" - - con_else = """ - } else -""" - - final_else = """ - { - switch(filterSize) { -""" - - last_return = """ - } - return {gradInput, gradWeight}; -} -""" - - with open("dynamicconv_cuda_backward.cu", "w") as backward: - backward.write(head) - for seq in seqs: - backward.write(sequence_if.format(seq=seq)) - for k, t, m in zip(kernels, thresh, min_block): - backward.write(case_k.format(k=k)) - if seq <= t: - b_size = seq - else: - b_size = m - backward.write(chunks_reset.format(b_size=b_size)) - for p in [k // 2, k - 1]: - backward.write(main_block.format(k=k, b_size=b_size, p=p)) - backward.write(bad_padding) - backward.write(bad_filter) - backward.write(con_else) - backward.write(final_else) - for k, m in zip(kernels, min_block): - backward.write(case_k.format(k=k)) - backward.write(chunks_reset.format(b_size=m)) - for p in [k // 2, k - 1]: - backward.write(main_block.format(k=k, b_size=m, p=p)) - backward.write(bad_padding) - backward.write(bad_filter) - backward.write(last_return) - - -if __name__ == "__main__": - gen_forward() - gen_backward() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp deleted file mode 100644 index 744c363e550231b8e0fbb94f998d46039daf5c00..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include -#include - -std::vector -dynamicconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l); - -std::vector dynamicconv_cuda_backward( - at::Tensor gradOutput, - int padding_l, - at::Tensor input, - at::Tensor filters); - -#define CHECK_CUDA(x) \ - AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) \ - AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x) - -std::vector -dynamicconv_forward(at::Tensor input, at::Tensor filters, int padding_l) { - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return dynamicconv_cuda_forward(input, filters, padding_l); -} - -std::vector dynamicconv_backward( - at::Tensor gradOutput, - int padding_l, - at::Tensor input, - at::Tensor filters) { - CHECK_INPUT(gradOutput); - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return dynamicconv_cuda_backward(gradOutput, padding_l, input, filters); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("forward", &dynamicconv_forward, "dynamicconv forward (CUDA)"); - m.def("backward", &dynamicconv_backward, "dynamicconv backward (CUDA)"); -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh deleted file mode 100644 index 44baf21bdd2d4a7a692ae6f7953a413ea6513268..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda.cuh +++ /dev/null @@ -1,50 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include -#include - -#include -#include -#include - -#include -#include -#include -#include -#include -#include - -#include -#include -#include - -#define SHFL_MASK 0xffffffff - -template -__global__ void dynamicconv_forward_kernel( - const scalar_t* input, - const scalar_t* weight, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - scalar_t* output); - -template -__global__ void dynamicconv_backward_kernel( - const scalar_t* gradOutput, // B * C * T - const scalar_t* input, // B * C * T - const scalar_t* weight, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - scalar_t* gradWeight, - scalar_t* gradInput); // B * H * k * T diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu deleted file mode 100644 index 4630f1e9826aea61973f2e82feb57ac0a4390735..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_cuda_kernel.cu +++ /dev/null @@ -1,176 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include "../cuda_utils.cu" -#include "dynamicconv_cuda.cuh" -#include "dynamicconv_cuda_backward.cu" -#include "dynamicconv_cuda_forward.cu" - -// FS is filter size and kernels are specialized for filter sizes -template -__global__ void dynamicconv_forward_kernel( - const scalar_t* input, - const scalar_t* weight, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - scalar_t* output) { - assert(blockDim.x == SB); - - const int tid = threadIdx.x; - const int batchIdx = blockIdx.x; - const int featureIdx = blockIdx.y; - const int head = featureIdx / numFiltersInBlock; - - const int IOOffset = - batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; - const scalar_t* inputFeature = &input[IOOffset]; - scalar_t* outputFeature = &output[IOOffset]; - - scalar_t filter[FS]; - - __shared__ scalar_t tempInput[SB + FS]; - zeroSharedMem(tempInput); - - const int numIterations = divUp(sequenceLength, SB); - - for (int i = 0; i < numIterations; ++i) { - __syncthreads(); - const int inputOffset = i * SB; - load_input_to_shared( - inputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - false, - tempInput); - __syncthreads(); - if (inputOffset + tid < sequenceLength) { -#pragma unroll - for (int k = 0; k < FS; ++k) { - const int filterOffset = batchIdx * numHeads * FS * sequenceLength + - head * FS * sequenceLength + k * sequenceLength + i * SB + tid; - filter[k] = weight[filterOffset]; - } - - scalar_t out = scalar_t(0.0); -#pragma unroll - for (int k = 0; k < FS; ++k) { - out += filter[k] * tempInput[tid + k]; - } - - outputFeature[inputOffset + tid] = out; - } - } -} - -template -__global__ void dynamicconv_backward_kernel( - const scalar_t* gradOutput, // B * C * T - const scalar_t* input, // B * C * T - const scalar_t* weight, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - scalar_t* gradWeight, - scalar_t* gradInput) { // B * H * k * T - - assert(blockDim.x == SB); - - // each block operates on a single batch and filter head - const int tid = threadIdx.x; - const int batchIdx = blockIdx.x; - const int headIdx = blockIdx.y; - const int chunkIdx = blockIdx.z; - - const int numChunks = divUp(sequenceLength, SB); - const int inputOffset = chunkIdx * SB; - - // initialize shared memory for output gradient and input - __shared__ scalar_t tempGradOutput[SB + FS]; - __shared__ scalar_t tempInput[SB + FS]; - const int padding = FS - padding_l - 1; - - zeroSharedMem(tempGradOutput); - zeroSharedMem(tempInput); - - // initialize local filter and weight gradient sum arrays - scalar_t tempGradSum[FS]; - scalar_t bfilter[FS]; - for (int k = 0; k < FS; ++k) { - tempGradSum[k] = scalar_t(0.0); - - int idxOffset = inputOffset + tid + k - padding; - if (idxOffset >= 0 && idxOffset < sequenceLength) { - int bfilterOffset = batchIdx * numHeads * FS * sequenceLength + - headIdx * FS * sequenceLength + (FS - k - 1) * sequenceLength + - idxOffset; - bfilter[k] = weight[bfilterOffset]; - } else { - bfilter[k] = scalar_t(0.0); - } - } - - // iterate over filter block - for (int featureIdx = 0; featureIdx < numFiltersInBlock; ++featureIdx) { - __syncthreads(); - - // load input and output gradient for this channel and chunk - const int IOOffset = batchIdx * numFeatures * sequenceLength + - (headIdx * numFiltersInBlock + featureIdx) * sequenceLength; - const scalar_t* inputFeature = &input[IOOffset]; - const scalar_t* gradOutputFeature = &gradOutput[IOOffset]; - scalar_t* gradInputFeature = &gradInput[IOOffset]; - - load_input_to_shared( - gradOutputFeature, - inputOffset, - sequenceLength, - chunkIdx, - numChunks, - true, - tempGradOutput); - load_input_to_shared( - inputFeature, - inputOffset, - sequenceLength, - chunkIdx, - numChunks, - true, - tempInput); - __syncthreads(); - - // sum input and weight gradients - scalar_t out = scalar_t(0.0); -#pragma unroll - for (int k = 0; k < FS; ++k) { - tempGradSum[k] += tempInput[tid + k] * tempGradOutput[tid + padding]; - out += bfilter[k] * tempGradOutput[tid + k]; - } - - if (inputOffset + tid < sequenceLength) { - gradInputFeature[inputOffset + tid] = out; - } - } - - const int gradOffset = - batchIdx * numHeads * FS * sequenceLength + headIdx * FS * sequenceLength; - scalar_t* gradWeightFeature = &gradWeight[gradOffset]; - - // write weight gradient - if (inputOffset + tid < sequenceLength) { - for (int k = 0; k < FS; ++k) { - const int outputOffset = k * sequenceLength + inputOffset + tid; - gradWeightFeature[outputOffset] = tempGradSum[k]; - } - } -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py deleted file mode 100644 index 711ed03483f4089dbe91964a89021b49eeffbedc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py +++ /dev/null @@ -1,227 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import dynamicconv_cuda -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.unfold import unfold1d -from torch import nn -from torch.autograd import Function - - -class dynamicconvFunction(Function): - @staticmethod - def forward(ctx, x, weights, padding_l): - ctx.padding_l = padding_l - outputs = dynamicconv_cuda.forward(x, weights, padding_l) - variables = [x, weights] - ctx.save_for_backward(*variables) - return outputs[0] - - @staticmethod - def backward(ctx, grad_output): - outputs = dynamicconv_cuda.backward( - grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors - ) - grad_input, grad_weights = outputs - return grad_input, grad_weights, None - - -@with_incremental_state -class DynamicconvLayer(nn.Module): - def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - weight_softmax=False, - num_heads=1, - weight_dropout=0.0, - bias=False, - renorm_padding=False, - conv_bias=False, - query_size=None, - ): - - super(DynamicconvLayer, self).__init__() - self.input_size = input_size - self.query_size = input_size if query_size is None else query_size - self.kernel_size = kernel_size - self.padding_l = padding_l - self.num_heads = num_heads - self.weight_softmax = weight_softmax - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - self.renorm_padding = renorm_padding - self.bias = bias - - self.weight_linear = nn.Linear(input_size, num_heads * kernel_size, bias) - if conv_bias: - self.conv_bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.conv_bias = None - self.reset_parameters() - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight_linear.weight) - if self.conv_bias is not None: - nn.init.constant_(self.conv_bias, 0.0) - nn.init.constant_(self.weight_linaer.bias, 0.0) - - def forward(self, x, incremental_state=None, query=None, unfold=None): - - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - # R = C // H - - # during inference time, incremental BMM is faster - if incremental_state is not None: - unfold = ( - x.size(0) > 512 if unfold is None else unfold - ) # use unfold mode as default for long sequence to save memory - unfold = unfold or (incremental_state is not None) - assert query is None - - if query is None: - query = x - if unfold: - output = self._forward_unfolded(x, incremental_state, query) - else: - output = self._forward_expanded(x, incremental_state, query) - - if self.conv_bias is not None: - output = output + self.conv_bias.view(1, 1, -1) - - return output - - # during training time, use CUDA kernel - else: - weight = self.weight_linear(x).view(T, B, H, K) - if self.weight_softmax: - weight = F.softmax(weight, dim=-1) - if self.weight_dropout_module.p: - weight = self.weight_dropout_module(weight) - - weight = weight.permute(1, 2, 3, 0).contiguous() - self.filters = weight - x = x.permute(1, 2, 0).contiguous() - output = dynamicconvFunction.apply(x, weight, self.padding_l).permute( - 2, 0, 1 - ) - if self.conv_bias is not None: - output = output + self.conv_bias.view(1, 1, -1) - return output - - def reorder_incremental_state(self, incremental_state, new_order): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(1, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - def _forward_unfolded(self, x, incremental_state, query): - """The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.""" - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - - weight = self.weight_linear(query).view(T * B * H, -1) - - # renorm_padding is only implemented in _forward_expanded - assert not self.renorm_padding or incremental_state is not None - - if incremental_state is not None: - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = x.new() - x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) - if self.kernel_size > 1: - self._set_input_buffer( - incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] - ) - x_unfold = x_unfold.view(T * B * H, R, -1) - else: - padding_l = self.padding_l - if K > T and padding_l == K - 1: - weight = weight.narrow(1, K - T, T) - K, padding_l = T, T - 1 - # unfold the input: T x B x C --> T' x B x C x K - x_unfold = unfold1d(x, K, padding_l, 0) - x_unfold = x_unfold.view(T * B * H, R, K) - - if self.weight_softmax and not self.renorm_padding: - weight = F.softmax(weight, dim=1) - weight = weight.narrow(1, 0, K) - - if incremental_state is not None: - weight = weight[:, -x_unfold.size(2) :] - K = weight.size(1) - - if self.weight_softmax and self.renorm_padding: - weight = F.softmax(weight, dim=1) - - weight = self.weight_dropout_module(weight, inplace=False) - - output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1 - output = output.view(T, B, C) - return output - - def _forward_expanded(self, x, incremental_stat, query): - """Turn the convolution filters into band matrices and do matrix multiplication. - This is faster when the sequence is short, but less memory efficient. - This is not used in the decoder during inference. - """ - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - weight = self.weight_linear(query).view(T * B * H, -1) - - if not self.renorm_padding: - if self.weight_softmax: - weight = F.softmax(weight, dim=1) - weight = self.weight_dropout_module(weight, inplace=False) - weight = weight.narrow(1, 0, K).contiguous() - weight = weight.view(T, B * H, K).transpose(0, 1) - - x = x.view(T, B * H, R).transpose(0, 1) - if self.weight_softmax and self.renorm_padding: - # turn the convolution filters into band matrices - weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf")) - weight_expanded.as_strided( - (B * H, T, K), (T * (T + K - 1), T + K, 1) - ).copy_(weight) - weight_expanded = weight_expanded.narrow(2, self.padding_l, T) - # normalize the weight over valid positions like self-attention - weight_expanded = F.softmax(weight_expanded, dim=2) - weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False) - else: - P = self.padding_l - # For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length - if K > T and P == K - 1: - weight = weight.narrow(2, K - T, T) - K, P = T, T - 1 - # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) - weight_expanded.as_strided( - (B * H, T, K), (T * (T + K - 1), T + K, 1) - ).copy_(weight) - weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T - output = torch.bmm(weight_expanded, x) - output = output.transpose(0, 1).contiguous().view(T, B, C) - return output diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp deleted file mode 100644 index d7e57c859085f98ec10960330ca763ae2764585a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/dynamiconv_cpu.cpp +++ /dev/null @@ -1,29 +0,0 @@ -#include -#include - -std::vector -dynamicconv_cpu_forward(float* input, float* filters, int padding_l); - -std::vector dynamicconv_cpu_backward( - float* gradOutput, - int padding_l, - float* input, - float* filters); - -std::vector -dynamicconv_forward(float* input, float* filters, int padding_l) { - return dynamicconv_cpu_forward(input, filters, padding_l); -} - -std::vector dynamicconv_backward( - float* gradOutput, - int padding_l, - float* input, - float* filters) { - return dynamicconv_cpu_backward(gradOutput, padding_l, input, filters); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("forward", &dynamicconv_forward, "dynamicconv forward (CPU)"); - m.def("backward", &dynamicconv_backward, "dynamicconv backward (CPU)"); -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/setup.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/setup.py deleted file mode 100644 index 5c5f22ad50ea62b3f79ef63f0ff5ede666fb4888..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/dynamicconv_layer/setup.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from setuptools import setup -from torch.utils.cpp_extension import BuildExtension, CUDAExtension - -setup( - name="dynamicconv_layer", - ext_modules=[ - CUDAExtension( - name="dynamicconv_cuda", - sources=[ - "dynamicconv_cuda.cpp", - "dynamicconv_cuda_kernel.cu", - ], - ), - ], - cmdclass={"build_ext": BuildExtension}, -) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/ema_module.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/ema_module.py deleted file mode 100644 index 9e6a00a021b8e24d6fbad73b3df3c5a268c123e9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/ema_module.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python3 - -""" -Used for EMA tracking a given pytorch module. The user is responsible for calling step() -and setting the appropriate decay -""" - -import copy -import logging -from dataclasses import dataclass, field - -import torch -from fairseq.dataclass import FairseqDataclass - - -@dataclass -class EMAModuleConfig(FairseqDataclass): - ema_decay: float = field( - default=0.9999, metadata={"help": "decay for exponential moving average model"} - ) - ema_fp32: bool = field( - default=False, - metadata={"help": "If true, store EMA model in fp32 even if model is in fp16"}, - ) - - -class EMAModule: - """Exponential Moving Average of Fairseq Models""" - - def __init__(self, model, config: EMAModuleConfig, device=None, skip_keys=None): - """ - @param model model to initialize the EMA with - @param config EMAConfig object with configuration like - ema_decay, ema_update_freq, ema_fp32 - @param device If provided, copy EMA to this device (e.g. gpu). - Otherwise EMA is in the same device as the model. - """ - - self.decay = config.ema_decay - self.model = copy.deepcopy(model) - self.model.requires_grad_(False) - self.config = config - self.skip_keys = skip_keys or set() - self.fp32_params = {} - - if device is not None: - logging.info(f"Copying EMA model to device {device}") - self.model = self.model.to(device=device) - - if self.config.ema_fp32: - self.build_fp32_params() - - self.update_freq_counter = 0 - - def build_fp32_params(self, state_dict=None): - """ - Store a copy of the EMA params in fp32. - If state dict is passed, the EMA params is copied from - the provided state dict. Otherwise, it is copied from the - current EMA model parameters. - """ - if not self.config.ema_fp32: - raise RuntimeError( - "build_fp32_params should not be called if ema_fp32=False. " - "Use ema_fp32=True if this is really intended." - ) - - if state_dict is None: - state_dict = self.model.state_dict() - - def _to_float(t): - return t.float() if torch.is_floating_point(t) else t - - for param_key in state_dict: - if param_key in self.fp32_params: - self.fp32_params[param_key].copy_(state_dict[param_key]) - else: - self.fp32_params[param_key] = _to_float(state_dict[param_key]) - - def restore(self, state_dict, build_fp32_params=False): - """Load data from a model spec into EMA model""" - self.model.load_state_dict(state_dict, strict=False) - if build_fp32_params: - self.build_fp32_params(state_dict) - - def set_decay(self, decay): - self.decay = decay - - def get_decay(self): - return self.decay - - def _step_internal(self, new_model): - """One update of the EMA model based on new model weights""" - decay = self.decay - - ema_state_dict = {} - ema_params = ( - self.fp32_params if self.config.ema_fp32 else self.model.state_dict() - ) - for key, param in new_model.named_parameters(): - if isinstance(param, dict): - continue - try: - ema_param = ema_params[key] - except KeyError: - ema_param = ( - param.float().clone() if param.ndim == 1 else copy.deepcopy(param) - ) - ema_params[key] = ema_param - - if param.shape != ema_param.shape: - raise ValueError( - "incompatible tensor shapes between model param and ema param" - + "{} vs. {}".format(param.shape, ema_param.shape) - ) - - if "version" in key: - # Do not decay a model.version pytorch param - continue - - if key in self.skip_keys or not param.requires_grad: - ema_params[key].copy_(param.to(dtype=ema_param.dtype).data) - ema_param = ema_params[key] - else: - ema_param.mul_(decay) - ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - decay) - - ema_state_dict[key] = ema_param - - for key, param in new_model.named_buffers(): - ema_state_dict[key] = param - - self.restore(ema_state_dict, build_fp32_params=False) - - @torch.no_grad() - def step(self, new_model): - self._step_internal(new_model) - - def reverse(self, model): - """ - Load the model parameters from EMA model. - Useful for inference or fine-tuning from the EMA model. - """ - d = self.model.state_dict() - if "_ema" in d: - del d["_ema"] - - model.load_state_dict(d, strict=False) - return model diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/espnet_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/espnet_multihead_attention.py deleted file mode 100644 index 96625cf1f19777c03aa8c9b3294aca4e9cb0a0b1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/espnet_multihead_attention.py +++ /dev/null @@ -1,255 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -# Copyright 2019 Shigeki Karita -# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) - -"""Multi-Head Attention layer definition.""" - -import math - -import torch -from fairseq.modules.rotary_positional_embedding import ( - RotaryPositionalEmbedding, - apply_rotary_pos_emb, -) -from torch import nn - - -class ESPNETMultiHeadedAttention(nn.Module): - """Multi-Head Attention layer. - Args: - n_head: The number of heads. - n_feat: The number of features. - dropout: Dropout rate. - """ - - def __init__(self, n_feat, n_head, dropout): - """Construct an MultiHeadedAttention object.""" - super(ESPNETMultiHeadedAttention, self).__init__() - assert n_feat % n_head == 0 - # We assume d_v always equals d_k - self.d_k = n_feat // n_head - self.h = n_head - self.linear_q = nn.Linear(n_feat, n_feat) - self.linear_k = nn.Linear(n_feat, n_feat) - self.linear_v = nn.Linear(n_feat, n_feat) - self.linear_out = nn.Linear(n_feat, n_feat) - self.attn = None - self.dropout = nn.Dropout(p=dropout) - - def forward_qkv(self, query, key, value, **kwargs): - """Transform query, key and value. - Args: - query: Query tensor B X T1 X C - key: Key tensor B X T2 X C - value: Value tensor B X T2 X C - Returns: - torch.Tensor: Transformed query tensor B X n_head X T1 X d_k - torch.Tensor: Transformed key tensor B X n_head X T2 X d_k - torch.Tensor: Transformed value tensor B X n_head X T2 X d_k - """ - n_batch = query.size(0) - q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) - k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) - v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) - q = q.transpose(1, 2) # (batch, head, time1, d_k) - k = k.transpose(1, 2) # (batch, head, time2, d_k) - v = v.transpose(1, 2) # (batch, head, time2, d_k) - return q, k, v - - def forward_attention(self, value, scores, mask): - """Compute attention context vector. - Args: - value: Transformed value B X n_head X T2 X d_k. - scores: Attention score B X n_head X T1 X T2 - mask: Mask T2 X B - Returns: - torch.Tensor: Transformed value B X T1 X d_model - weighted by the attention score B X T1 X T2 - """ - n_batch = value.size(0) - if mask is not None: - scores = scores.masked_fill( - mask.unsqueeze(1).unsqueeze(2).to(bool), - float("-inf"), # (batch, head, time1, time2) - ) - self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - - else: - self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - p_attn = self.dropout(self.attn) - x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) - x = ( - x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) - ) # (batch, time1, d_model) - - return self.linear_out(x) # (batch, time1, d_model) - - def forward(self, query, key, value, key_padding_mask=None, **kwargs): - """Compute scaled dot product attention. - Args: - query (torch.Tensor): Query tensor T X B X C - key (torch.Tensor): Key tensor T X B X C - value (torch.Tensor): Value tensor T X B X C - mask (torch.Tensor): Mask tensor T X B - Returns: - torch.Tensor: Output tensor T X B X D. - """ - query = query.transpose(0, 1) - key = key.transpose(0, 1) - value = value.transpose(0, 1) - - q, k, v = self.forward_qkv(query, key, value) - scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) - scores = self.forward_attention(v, scores, key_padding_mask) - scores = scores.transpose(0, 1) - return scores, None - - -class RelPositionMultiHeadedAttention(ESPNETMultiHeadedAttention): - """Multi-Head Attention layer with relative position encoding. - Paper: https://arxiv.org/abs/1901.02860 - Args: - n_head: The number of heads. - n_feat: The number of features. - dropout: Dropout rate. - zero_triu: Whether to zero the upper triangular part of attention matrix. - """ - - def __init__(self, n_feat, n_head, dropout, zero_triu=False): - """Construct an RelPositionMultiHeadedAttention object.""" - super().__init__(n_feat, n_head, dropout) - self.zero_triu = zero_triu - # linear transformation for positional encoding - self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) - # these two learnable bias are used in matrix c and matrix d - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) - self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) - torch.nn.init.xavier_uniform_(self.pos_bias_u) - torch.nn.init.xavier_uniform_(self.pos_bias_v) - - def rel_shift(self, x): - """Compute relative positional encoding. - Args: - x: Input tensor B X n_head X T X 2T-1 - Returns: - torch.Tensor: Output tensor. - """ - zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) - x_padded = torch.cat([zero_pad, x], dim=-1) - - x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) - x = x_padded[:, :, 1:].view_as(x)[ - :, :, :, : x.size(-1) // 2 + 1 - ] # only keep the positions from 0 to time2 - - if self.zero_triu: - ones = torch.ones((x.size(2), x.size(3)), device=x.device) - x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] - - return x - - def forward(self, query, key, value, pos_emb, key_padding_mask=None, **kwargs): - """Compute scaled dot product attention. - Args: - query: Query tensor T X B X C - key: Key tensor T X B X C - value: Value tensor T X B X C - pos_emb: Positional embedding tensor B X 2T-1 X C - key_padding_mask: Mask tensor T X B - Returns: - torch.Tensor: Output tensor T X B X C. - """ - query = query.transpose(0, 1) - key = key.transpose(0, 1) - value = value.transpose(0, 1) - pos_emb = pos_emb.transpose(0, 1) - q, k, v = self.forward_qkv(query, key, value) - q = q.transpose(1, 2) # (batch, time1, head, d_k) - n_batch_pos = pos_emb.size(0) - p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) - p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) - - # (batch, head, time1, d_k) - q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) - # (batch, head, time1, d_k) - q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) - - # compute attention score - # first compute matrix a and matrix c - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - # (batch, head, time1, time2) - matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) - - # compute matrix b and matrix d - # (batch, head, time1, 2*time1-1) - matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) - matrix_bd = self.rel_shift(matrix_bd) - - scores = (matrix_ac + matrix_bd) / math.sqrt( - self.d_k - ) # (batch, head, time1, time2) - - scores = self.forward_attention(v, scores, key_padding_mask) - scores = scores.transpose(0, 1) - return scores, None - - -class RotaryPositionMultiHeadedAttention(ESPNETMultiHeadedAttention): - def __init__( - self, - n_feat, - n_head, - dropout, - precision, - rotary_emd_base=10000, - ): - """Construct an RotaryPositionMultiHeadedAttention object.""" - super().__init__(n_feat, n_head, dropout) - precision = torch.float - self.rotary_ndims = self.d_k # also try self.d_k//2 - if precision == "fp16": - precision = torch.half - - self.rotary_emb = RotaryPositionalEmbedding( - self.rotary_ndims, base=rotary_emd_base, precision=precision - ) - - def forward(self, query, key, value, key_padding_mask=None, **kwargs): - """Compute rotary position attention. - Args: - query: Query tensor T X B X C - key: Key tensor T X B X C - value: Value tensor T X B X C - key_padding_mask: Mask tensor T X B - Returns: - torch.Tensor: Output tensor T X B X D. - Notes: - Assumes self attn - """ - - T, B, C = value.size() - query = query.view(T, B, self.h, self.d_k) - key = key.view(T, B, self.h, self.d_k) - value = value.view(T, B, self.h, self.d_k) - cos, sin = self.rotary_emb(value, seq_len=T) - query, key = apply_rotary_pos_emb( - query, key, cos, sin, offset=0 - ) # offset is based on layer_past - - query = query.view(T, B, self.h * self.d_k) - key = key.view(T, B, self.h * self.d_k) - value = value.view(T, B, self.h * self.d_k) - - # TBD to BTD - query = query.transpose(0, 1) - key = key.transpose(0, 1) - value = value.transpose(0, 1) - - q, k, v = self.forward_qkv(query, key, value) - scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) - scores = self.forward_attention(v, scores, key_padding_mask) - scores = scores.transpose(0, 1) - return scores, None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fairseq_dropout.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fairseq_dropout.py deleted file mode 100644 index 032033166464c504fa14863e646f4bbe4851d45f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fairseq_dropout.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from typing import List, Optional - -import torch.nn as nn -import torch.nn.functional as F - -logger = logging.getLogger(__name__) - - -class FairseqDropout(nn.Module): - def __init__(self, p, module_name=None): - super().__init__() - self.p = p - self.module_name = module_name - self.apply_during_inference = False - - def forward(self, x, inplace: bool = False): - if self.p > 0 and (self.training or self.apply_during_inference): - return F.dropout(x, p=self.p, training=True, inplace=inplace) - else: - return x - - def make_generation_fast_( - self, - name: str, - retain_dropout: bool = False, - retain_dropout_modules: Optional[List[str]] = None, - **kwargs - ): - if retain_dropout: - if retain_dropout_modules is not None and self.module_name is None: - logger.warning( - "Cannot enable dropout during inference for module {} " - "because module_name was not set".format(name) - ) - elif ( - retain_dropout_modules is None # if None, apply to all modules - or self.module_name in retain_dropout_modules - ): - logger.info( - "Enabling dropout during inference for module: {}".format(name) - ) - self.apply_during_inference = True - else: - logger.info("Disabling dropout for module: {}".format(name)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_batch_norm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_batch_norm.py deleted file mode 100644 index c560f338fdb79870c49aa70ec3f65fc9d6184989..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_batch_norm.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -batch norm done in fp32 (for fp16 training) -""" -import torch -import torch.nn as nn - - -class Fp32BatchNorm(nn.Module): - def __init__(self, sync=False, *args, **kwargs): - super().__init__() - - if sync: - from fairseq.distributed import utils - - if utils.get_global_world_size() == 1: - sync = False - - if sync: - self.bn = nn.SyncBatchNorm(*args, **kwargs) - else: - self.bn = nn.BatchNorm1d(*args, **kwargs) - - self.sync = sync - - def forward(self, input): - if self.bn.running_mean.dtype != torch.float: - if self.sync: - self.bn.running_mean = self.bn.running_mean.float() - self.bn.running_var = self.bn.running_var.float() - if self.bn.affine: - try: - self.bn.weight = self.bn.weight.float() - self.bn.bias = self.bn.bias.float() - except: - self.bn.float() - else: - self.bn.float() - - output = self.bn(input.float()) - return output.type_as(input) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_group_norm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_group_norm.py deleted file mode 100644 index d03aac022e30c8c14a600062d1d86429504ba003..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_group_norm.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Layer norm done in fp32 (for fp16 training) -""" - -import torch.nn as nn -import torch.nn.functional as F - - -class Fp32GroupNorm(nn.GroupNorm): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, input): - output = F.group_norm( - input.float(), - self.num_groups, - self.weight.float() if self.weight is not None else None, - self.bias.float() if self.bias is not None else None, - self.eps, - ) - return output.type_as(input) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_instance_norm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_instance_norm.py deleted file mode 100644 index 30a54496ded633ee005cf11cbdbd2cc2ebb29ca0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/fp32_instance_norm.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Layer norm done in fp32 (for fp16 training) -""" - -import torch.nn as nn -import torch.nn.functional as F - - -class Fp32InstanceNorm(nn.InstanceNorm1d): - def __init__(self, *args, **kwargs): - self.transpose_last = "transpose_last" in kwargs and kwargs["transpose_last"] - if "transpose_last" in kwargs: - del kwargs["transpose_last"] - super().__init__(*args, **kwargs) - - def forward(self, input): - if self.transpose_last: - input = input.transpose(1, 2) - output = F.instance_norm( - input.float(), - running_mean=self.running_mean, - running_var=self.running_var, - weight=self.weight.float() if self.weight is not None else None, - bias=self.bias.float() if self.bias is not None else None, - use_input_stats=self.training or not self.track_running_stats, - momentum=self.momentum, - eps=self.eps, - ) - if self.transpose_last: - output = output.transpose(1, 2) - return output.type_as(input) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/gelu.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/gelu.py deleted file mode 100644 index a2f1ecff4a3ae3de3eb7d327b9163c46b18a15ed..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/gelu.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with -the corresponding GitHub repo: https://github.com/hendrycks/GELUs -""" - -import math - -import torch -import torch.nn as nn - - -def gelu_accurate(x): - if not hasattr(gelu_accurate, "_a"): - gelu_accurate._a = math.sqrt(2 / math.pi) - return ( - 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3)))) - ) - - -def gelu(x: torch.Tensor) -> torch.Tensor: - return torch.nn.functional.gelu(x.float()).type_as(x) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/grad_multiply.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/grad_multiply.py deleted file mode 100644 index 08d15f55dfda9c61a1cf8641ea31424fe1d97f57..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/grad_multiply.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - - -class GradMultiply(torch.autograd.Function): - @staticmethod - def forward(ctx, x, scale): - ctx.scale = scale - res = x.new(x) - return res - - @staticmethod - def backward(ctx, grad): - return grad * ctx.scale, None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/gumbel_vector_quantizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/gumbel_vector_quantizer.py deleted file mode 100644 index 91655bc5e1f07f91f187dd00f364bbca1427b791..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/gumbel_vector_quantizer.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class GumbelVectorQuantizer(nn.Module): - def __init__( - self, - dim, - num_vars, - temp, - groups, - combine_groups, - vq_dim, - time_first, - activation=nn.GELU(), - weight_proj_depth=1, - weight_proj_factor=1, - ): - """Vector quantization using gumbel softmax - - Args: - dim: input dimension (channels) - num_vars: number of quantized vectors per group - temp: temperature for training. this should be a tuple of 3 elements: (start, stop, decay factor) - groups: number of groups for vector quantization - combine_groups: whether to use the vectors for all groups - vq_dim: dimensionality of the resulting quantized vector - time_first: if true, expect input in BxTxC format, otherwise in BxCxT - activation: what activation to use (should be a module). this is only used if weight_proj_depth is > 1 - weight_proj_depth: number of layers (with activation in between) to project input before computing logits - weight_proj_factor: this is used only if weight_proj_depth is > 1. scales the inner dimensionality of - projections by this factor - """ - super().__init__() - - self.groups = groups - self.combine_groups = combine_groups - self.input_dim = dim - self.num_vars = num_vars - self.time_first = time_first - - assert ( - vq_dim % groups == 0 - ), f"dim {vq_dim} must be divisible by groups {groups} for concatenation" - - var_dim = vq_dim // groups - num_groups = groups if not combine_groups else 1 - - self.vars = nn.Parameter(torch.FloatTensor(1, num_groups * num_vars, var_dim)) - nn.init.uniform_(self.vars) - - if weight_proj_depth > 1: - - def block(input_dim, output_dim): - return nn.Sequential(nn.Linear(input_dim, output_dim), activation) - - inner_dim = self.input_dim * weight_proj_factor - self.weight_proj = nn.Sequential( - *[ - block(self.input_dim if i == 0 else inner_dim, inner_dim) - for i in range(weight_proj_depth - 1) - ], - nn.Linear(inner_dim, groups * num_vars), - ) - else: - self.weight_proj = nn.Linear(self.input_dim, groups * num_vars) - nn.init.normal_(self.weight_proj.weight, mean=0, std=1) - nn.init.zeros_(self.weight_proj.bias) - - if isinstance(temp, str): - import ast - - temp = ast.literal_eval(temp) - assert len(temp) == 3, f"{temp}, {len(temp)}" - - self.max_temp, self.min_temp, self.temp_decay = temp - self.curr_temp = self.max_temp - self.codebook_indices = None - - def set_num_updates(self, num_updates): - self.curr_temp = max( - self.max_temp * self.temp_decay**num_updates, self.min_temp - ) - - def get_codebook_indices(self): - if self.codebook_indices is None: - from itertools import product - - p = [range(self.num_vars)] * self.groups - inds = list(product(*p)) - self.codebook_indices = torch.tensor( - inds, dtype=torch.long, device=self.vars.device - ).flatten() - - if not self.combine_groups: - self.codebook_indices = self.codebook_indices.view( - self.num_vars**self.groups, -1 - ) - for b in range(1, self.groups): - self.codebook_indices[:, b] += self.num_vars * b - self.codebook_indices = self.codebook_indices.flatten() - return self.codebook_indices - - def codebook(self): - indices = self.get_codebook_indices() - return ( - self.vars.squeeze(0) - .index_select(0, indices) - .view(self.num_vars**self.groups, -1) - ) - - def sample_from_codebook(self, b, n): - indices = self.get_codebook_indices() - indices = indices.view(-1, self.groups) - cb_size = indices.size(0) - assert ( - n < cb_size - ), f"sample size {n} is greater than size of codebook {cb_size}" - sample_idx = torch.randint(low=0, high=cb_size, size=(b * n,)) - indices = indices[sample_idx] - - z = self.vars.squeeze(0).index_select(0, indices.flatten()).view(b, n, -1) - return z - - def to_codebook_index(self, indices): - res = indices.new_full(indices.shape[:-1], 0) - for i in range(self.groups): - exponent = self.groups - i - 1 - res += indices[..., i] * (self.num_vars**exponent) - return res - - def forward_idx(self, x): - res = self.forward(x, produce_targets=True) - return res["x"], res["targets"] - - def forward(self, x, produce_targets=False): - - result = {"num_vars": self.num_vars * self.groups} - - if not self.time_first: - x = x.transpose(1, 2) - - bsz, tsz, fsz = x.shape - x = x.reshape(-1, fsz) - x = self.weight_proj(x) - x = x.view(bsz * tsz * self.groups, -1) - - _, k = x.max(-1) - hard_x = ( - x.new_zeros(*x.shape) - .scatter_(-1, k.view(-1, 1), 1.0) - .view(bsz * tsz, self.groups, -1) - ) - hard_probs = torch.mean(hard_x.float(), dim=0) - result["code_perplexity"] = torch.exp( - -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) - ).sum() - - avg_probs = torch.softmax( - x.view(bsz * tsz, self.groups, -1).float(), dim=-1 - ).mean(dim=0) - result["prob_perplexity"] = torch.exp( - -torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1) - ).sum() - - result["temp"] = self.curr_temp - - if self.training: - x = F.gumbel_softmax(x.float(), tau=self.curr_temp, hard=True).type_as(x) - else: - x = hard_x - - x = x.view(bsz * tsz, -1) - - vars = self.vars - if self.combine_groups: - vars = vars.repeat(1, self.groups, 1) - - if produce_targets: - result["targets"] = ( - x.view(bsz * tsz * self.groups, -1) - .argmax(dim=-1) - .view(bsz, tsz, self.groups) - .detach() - ) - - x = x.unsqueeze(-1) * vars - x = x.view(bsz * tsz, self.groups, self.num_vars, -1) - x = x.sum(-2) - x = x.view(bsz, tsz, -1) - - if not self.time_first: - x = x.transpose(1, 2) # BTC -> BCT - - result["x"] = x - - return result diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/kmeans_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/kmeans_attention.py deleted file mode 100644 index 538e18a4c73168298d34f2ece1112b2e206fcd41..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/kmeans_attention.py +++ /dev/null @@ -1,743 +0,0 @@ -import math -from functools import reduce, wraps -from inspect import isfunction -from operator import mul - -import torch -import torch.nn as nn -import torch.nn.functional as F -from aml.multimodal_video.utils.einops.lib import rearrange, repeat -from aml.multimodal_video.utils.einops.lib.layers.torch import Rearrange -from fairseq.modules.local_attention import LocalAttention - -# constants - -TOKEN_SELF_ATTN_VALUE = -5e4 -KMEAN_INIT_ITERS = 10 - -# helper functions - - -def exists(val): - return val is not None - - -def identity(x, *args, **kwargs): - return x - - -def default(x, d): - if not exists(x): - return d if not isfunction(d) else d() - return x - - -def cast_tuple(x): - return x if isinstance(x, tuple) else (x,) - - -def cache_fn(f): - cache = None - - @wraps(f) - def cached_fn(*args, **kwargs): - nonlocal cache - if exists(cache): - return cache - cache = f(*args, **kwargs) - return cache - - return cached_fn - - -def to(t): - return {"device": t.device, "dtype": t.dtype} - - -def find_modules(nn_module, type): - return [module for module in nn_module.modules() if isinstance(module, type)] - - -def is_empty(t): - return t.nelement() == 0 - - -def max_neg_value(tensor): - return -torch.finfo(tensor.dtype).max - - -def batched_index_select(values, indices): - last_dim = values.shape[-1] - return values.gather(2, expand_dim(indices, -1, last_dim)) - - -def merge_dims(ind_from, ind_to, tensor): - shape = list(tensor.shape) - arr_slice = slice(ind_from, ind_to + 1) - shape[arr_slice] = [reduce(mul, shape[arr_slice])] - return tensor.reshape(*shape) - - -def expand_dim(t, dim, k): - t = t.unsqueeze(dim) - expand_shape = [-1] * len(t.shape) - expand_shape[dim] = k - return t.expand(*expand_shape) - - -def scatter_mean(src, t, index, dim, eps=1e-5): - numer = src.scatter_add(dim, index, t) - denom = src.scatter_add(dim, index, torch.ones_like(t)) - return numer / (denom + eps) - - -def split_at_index(dim, index, t): - pre_slices = (slice(None),) * dim - l = (*pre_slices, slice(None, index)) - r = (*pre_slices, slice(index, None)) - return t[l], t[r] - - -def reshape_dim(t, dim, split_dims): - shape = list(t.shape) - num_dims = len(shape) - dim = (dim + num_dims) % num_dims - shape[dim : dim + 1] = split_dims - return t.reshape(shape) - - -def ema(old, new, decay): - if not exists(old): - return new - return old * decay + new * (1 - decay) - - -def ema_inplace(moving_avg, new, decay): - if is_empty(moving_avg): - moving_avg.data.copy_(new) - return - moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) - - -# helper classes - - -def map_first_tuple_or_el(x, fn): - if isinstance(x, tuple): - return (fn(x[0]),) + x[1:] - return fn(x) - - -class Chunk(nn.Module): - def __init__(self, chunks, fn, along_dim=-1): - super().__init__() - self.dim = along_dim - self.chunks = chunks - self.fn = fn - - def forward(self, x, **kwargs): - if self.chunks <= 1: - return self.fn(x, **kwargs) - chunks = x.chunk(self.chunks, dim=self.dim) - return torch.cat([self.fn(c, **kwargs) for c in chunks], dim=self.dim) - - -class PreNorm(nn.ModuleList): - def __init__(self, norm_class, dim, fn): - super().__init__() - self.norm = norm_class(dim) - self.fn = fn - - def forward(self, x, **kwargs): - x = self.norm(x) - return self.fn(x, **kwargs) - - -class ReZero(nn.Module): - def __init__(self, fn): - super().__init__() - self.residual_weight = nn.Parameter(torch.zeros(1)) - self.fn = fn - - def forward(self, x, **kwargs): - x = self.fn(x, **kwargs) - return map_first_tuple_or_el(x, lambda t: t * self.residual_weight) - - -class ScaleNorm(nn.Module): - def __init__(self, dim, eps=1e-5): - super().__init__() - self.g = nn.Parameter(torch.ones(1)) - self.eps = eps - - def forward(self, x): - def norm(t): - n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps) - return t / n * self.g - - return map_first_tuple_or_el(x, norm) - - -class ProjectInOut(nn.Module): - def __init__(self, fn, dim_in, dim_out, project_out=True): - super().__init__() - self.fn = fn - self.project_in = nn.Linear(dim_in, dim_out) - self.project_out = nn.Linear(dim_out, dim_in) if project_out else identity - - def forward(self, x, **kwargs): - x = self.project_in(x) - x, loss = self.fn(x, **kwargs) - x = self.project_out(x) - return x, loss - - -class MatrixMultiply(nn.Module): - def __init__(self, tensor, transpose=False): - super().__init__() - self.tensor = tensor - self.transpose = transpose - - def forward(self, x): - tensor = self.tensor - if self.transpose: - tensor = tensor.t() - return x @ tensor - - -# positional embeddings - - -class DepthWiseConv1d(nn.Module): - def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False): - super().__init__() - self.padding = ( - ((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2) - ) - - self.net = nn.Sequential( - nn.Conv1d( - dim_in, - dim_in, - kernel_size=kernel_size, - groups=dim_in, - stride=stride, - bias=bias, - ), - nn.Conv1d(dim_in, dim_out, 1, bias=bias), - ) - - def forward(self, x): - x = F.pad(x, self.padding, value=0.0) - return self.net(x) - - -class FixedPositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) - position = torch.arange(0, max_seq_len, dtype=torch.float) - sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq) - emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) - self.register_buffer("emb", emb) - - def forward(self, x): - return self.emb[None, : x.shape[1], :].to(x) - - -def rotate_every_two(x): - x = rearrange(x, "... (d j) -> ... d j", j=2) - x1, x2 = x.unbind(dim=-1) - x = torch.stack((-x2, x1), dim=-1) - return rearrange(x, "... d j -> ... (d j)") - - -def apply_rotary_pos_emb(q, k, sinu_pos): - sinu_pos = rearrange(sinu_pos, "() n (j d) -> n j d", j=2) - sin, cos = sinu_pos.unbind(dim=-2) - sin, cos = map(lambda t: repeat(t, "b n -> b (n j)", j=2), (sin, cos)) - q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) - return q, k - - -# kmeans related function and class - - -def update_kmeans_on_backwards(module): - module.kmean_modules = find_modules(module, Kmeans) - - def hook(_, grad_in, grad_out): - for m in module.kmean_modules: - m.update() - - return module.register_backward_hook(hook) - - -def similarity(x, means): - return torch.einsum("bhld,hcd->bhlc", x, means) - - -def dists_and_buckets(x, means): - dists = similarity(x, means) - _, buckets = torch.max(dists, dim=-1) - return dists, buckets - - -def batched_bincount(index, num_classes, dim=-1): - shape = list(index.shape) - shape[dim] = num_classes - out = index.new_zeros(shape) - out.scatter_add_(dim, index, torch.ones_like(index, dtype=index.dtype)) - return out - - -def kmeans_iter(x, means, buckets=None): - b, h, _, d, dtype, num_clusters = *x.shape, x.dtype, means.shape[1] - - if not exists(buckets): - _, buckets = dists_and_buckets(x, means) - - bins = batched_bincount(buckets, num_clusters).sum(0, keepdim=True) - zero_mask = bins.long() == 0 - - means_ = buckets.new_zeros(b, h, num_clusters, d, dtype=dtype) - means_.scatter_add_(-2, expand_dim(buckets, -1, d), x) - means_ = F.normalize(means_.sum(0, keepdim=True), dim=-1).type(dtype) - - means = torch.where(zero_mask.unsqueeze(-1), means, means_) - means = means.squeeze(0) - return means - - -def distribution(dists, window_size): - _, topk_indices = dists.topk(k=window_size, dim=-2) - indices = topk_indices.transpose(-2, -1) - return indices.reshape(*indices.size()[:2], -1) - - -class Kmeans(nn.Module): - def __init__( - self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4 - ): - super().__init__() - self.commitment = commitment - self.ema_decay = ema_decay - - self.register_buffer("means", torch.randn(num_heads, num_clusters, head_dim)) - self.register_buffer("initted", torch.tensor(False)) - self.num_new_means = 0 - self.new_means = None - - @torch.no_grad() - def init(self, x): - if self.initted: - return - _, h, _, d, device, _ = *x.shape, x.device, x.dtype - - num_clusters = self.means.shape[1] - - means = x.transpose(0, 1).contiguous().view(h, -1, d) - num_samples = means.shape[1] - - if num_samples >= num_clusters: - indices = torch.randperm(num_samples, device=device)[:num_clusters] - else: - indices = torch.randint(0, num_samples, (num_clusters,), device=device) - - means = means[:, indices] - - for _ in range(KMEAN_INIT_ITERS): - means = kmeans_iter(x, means) - - self.num_new_means = 0 - self.means.data.copy_(means) - self.initted.data.copy_(torch.tensor(True)) - - @torch.no_grad() - def update(self, new_means=None): - new_means = default(new_means, self.new_means) - assert exists(new_means), "new kmeans has not been supplied" - ema_inplace(self.means, new_means, self.ema_decay) - - del self.new_means - self.new_means = None - self.num_new_means = 0 - - def forward(self, x, update_means=False): - self.init(x) - - b, dtype = x.shape[0], x.dtype - means = self.means.type(dtype) - x = F.normalize(x, 2, dim=-1).type(dtype) - - with torch.no_grad(): - dists, buckets = dists_and_buckets(x, means) - - routed_means = batched_index_select(expand_dim(means, 0, b), buckets) - loss = F.mse_loss(x, routed_means) * self.commitment - - if update_means: - with torch.no_grad(): - means = kmeans_iter(x, means, buckets) - self.new_means = ema( - self.new_means, means, self.num_new_means / (self.num_new_means + 1) - ) - self.num_new_means += 1 - - return dists, loss - - -# kmeans attention class - - -class KmeansAttention(nn.Module): - def __init__( - self, - num_clusters, - window_size, - num_heads, - head_dim, - causal=False, - dropout=0.0, - ema_decay=0.999, - commitment=1e-4, - context_window_size=None, - receives_context=False, - num_mem_kv=0, - shared_qk=False, - ): - super().__init__() - self.num_heads = num_heads - self.num_clusters = num_clusters - self.head_dim = head_dim - - self.window_size = window_size - self.context_window_size = default(context_window_size, window_size) - self.causal = causal - - self.shared_qk = shared_qk - self.receives_context = receives_context - self.kmeans = Kmeans(num_heads, head_dim, num_clusters, ema_decay, commitment) - self.dropout = nn.Dropout(dropout) - - self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0) - self.mem_key = nn.Parameter( - torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim) - ) - self.mem_value = nn.Parameter( - torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim) - ) - - def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs): - b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = ( - *q.shape, - k.shape[2], - self.window_size, - self.context_window_size, - self.num_clusters, - q.device, - q.dtype, - ) - is_reverse = kwargs.pop("_reverse", False) - - out = torch.zeros_like(q, dtype=dtype) - - update_kmeans = self.training and not is_reverse - - key_mask = ( - default(key_mask, query_mask) if not self.receives_context else key_mask - ) - kv_wsz = wsz if not self.receives_context else c_wsz - - wsz = min(wsz, t) - kv_wsz = min(kv_wsz, kv_t) - - if not self.shared_qk or self.receives_context: - dists, aux_loss = self.kmeans(torch.cat((q, k), dim=2), update_kmeans) - q_dists, k_dists = split_at_index(2, t, dists) - indices = distribution(q_dists, wsz) - kv_indices = distribution(k_dists, kv_wsz) - else: - dists, aux_loss = self.kmeans(q, update_kmeans) - k = F.normalize(k, dim=-1).to(q) - indices = distribution(dists, wsz) - kv_indices = indices - - q = batched_index_select(q, indices) - k = batched_index_select(k, kv_indices) - v = batched_index_select(v, kv_indices) - - reshape_with_window = lambda x: x.reshape(b, h, nc, -1, d) - q, k, v = map(reshape_with_window, (q, k, v)) - - m_k, m_v = map( - lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value) - ) - k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v))) - - dots = torch.einsum("bhnid,bhnjd->bhnij", q, k) * (d**-0.5) - - mask_value = max_neg_value(dots) - - if exists(query_mask) or exists(key_mask): - query_mask = default( - query_mask, lambda: torch.ones((b, t), device=device).bool() - ) - key_mask = default( - key_mask, lambda: torch.ones((b, kv_t), device=device).bool() - ) - - q_mask = expand_dim(query_mask, 1, h).gather(2, indices) - kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices) - q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (q_mask, kv_mask)) - mask = q_mask[:, :, :, :, None] * kv_mask[:, :, :, None, :] - mask = F.pad(mask, (self.num_mem_kv, 0), value=1) - dots.masked_fill_(~mask, mask_value) - del mask - - if self.causal: - q_mask, kv_mask = map( - lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices) - ) - mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :] - mask = F.pad(mask, (self.num_mem_kv, 0), value=1) - dots.masked_fill_(~mask, mask_value) - del mask - - if self.shared_qk: - q_mask, kv_mask = map( - lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices) - ) - mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :] - mask = F.pad(mask, (self.num_mem_kv, 0), value=0) - dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE) - del mask - - dots = dots.softmax(dim=-1) - dots = self.dropout(dots) - - bo = torch.einsum("bhcij,bhcjd->bhcid", dots, v) - so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype) - out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2) - return out, aux_loss - - -# feedforward - - -class GELU_(nn.Module): - def forward(self, x): - return ( - 0.5 - * x - * ( - 1 - + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))) - ) - ) - - -GELU = nn.GELU if hasattr(nn, "GELU") else GELU_ - - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4, dropout=0.0, activation=None, glu=False): - super().__init__() - activation = default(activation, GELU) - - self.glu = glu - self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) - self.act = activation() - self.dropout = nn.Dropout(dropout) - self.w2 = nn.Linear(dim * mult, dim) - - def forward(self, x, **kwargs): - if not self.glu: - x = self.w1(x) - x = self.act(x) - else: - x, v = self.w1(x).chunk(2, dim=-1) - x = self.act(x) * v - - x = self.dropout(x) - x = self.w2(x) - return x - - -# self attention - - -class SelfAttention(nn.Module): - def __init__( - self, - dim, - max_seq_len, - heads, - local_attn_heads, - window_size, - dim_head=None, - local_attn_window_size=None, - local_attn_radius_blocks=1, - causal=False, - attn_dropout=0.0, - dropout=0.0, - kmeans_ema_decay=0.999, - commitment_factor=1e-4, - receives_context=False, - context_window_size=None, - rel_pos_emb=True, - num_mem_kv=0, - shared_qk=False, - conv_query_kernel=9, - ): - super().__init__() - assert ( - dim_head or (dim % heads) == 0 - ), "hidden dimension must be divisible by number of heads" - assert ( - max_seq_len % window_size - ) == 0, "maximum sequence length must be divisible by the target window size" - assert ( - local_attn_heads <= heads - ), "number of local attention heads must be less than total heads" - assert not ( - receives_context and local_attn_heads > 0 - ), "local attention cannot be used for self attention with context" - assert not ( - receives_context and causal - ), "contextual attention layer cannot be causal" - - local_attn_window_size = default(local_attn_window_size, window_size) - context_window_size = default(context_window_size, window_size) - - self.shared_qk = shared_qk - self.receives_context = receives_context - self.heads = heads - self.local_attn_heads = local_attn_heads - self.global_attn_heads = heads - local_attn_heads - - self.causal = causal - self.window_size = window_size - - dim_head = default(dim_head, dim // heads) - dim_heads = dim_head * heads - self.dim_head = dim_head - - num_clusters = max_seq_len // window_size - - # local - - local_dim_heads = dim_head * self.local_attn_heads - - if self.local_attn_heads > 0: - rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None - self.local_attn = LocalAttention( - dim=dim_head, - window_size=local_attn_window_size, - causal=causal, - dropout=attn_dropout, - rel_pos_emb_config=rel_pos_emb_config, - look_backward=local_attn_radius_blocks, - look_forward=0 if causal else local_attn_radius_blocks, - ) - self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads) - - # global - - global_dim_heads = dim_head * self.global_attn_heads - - if self.global_attn_heads > 0: - self.global_attn = KmeansAttention( - num_clusters, - window_size, - self.global_attn_heads, - dim_head, - causal=causal, - dropout=attn_dropout, - ema_decay=kmeans_ema_decay, - commitment=commitment_factor, - receives_context=receives_context, - num_mem_kv=num_mem_kv, - shared_qk=shared_qk, - ) - - self.to_q = nn.Sequential( - Rearrange("b n c -> b c n"), - DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal), - Rearrange("b c n -> b n c"), - ) - - self.to_v = nn.Linear(dim, global_dim_heads, bias=False) - - if not self.shared_qk: - self.to_k = nn.Linear(dim, global_dim_heads, bias=False) - - # out - - self.to_out = nn.Linear(dim_heads, dim, bias=False) - self.dropout = nn.Dropout(dropout) - - def forward( - self, - query, - key, - value, - context=None, - key_padding_mask=None, - context_mask=None, - pos_emb=None, - **kwargs - ): - assert not ( - self.receives_context and not exists(context) - ), "context must be passed if self attention is set to receive context" - input_mask = key_padding_mask - x = query.transpose(0, 1) - b, t, _, h, dh = *x.shape, self.heads, self.dim_head - has_local, has_global = map( - lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads) - ) - - split_heads = ( - lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous() - ) - - if has_local: - local_qkv = self.local_to_qkv(x).chunk(3, dim=-1) - lq, lk, lv = map(split_heads, local_qkv) - - if has_global: - kv_input = x if not self.receives_context else context - - q, v = self.to_q(x), self.to_v(kv_input) - - if not self.shared_qk: - k = self.to_k(kv_input) - else: - k = self.to_q(kv_input) if self.receives_context else q - - q, k, v = map(split_heads, (q, k, v)) - - out = [] - total_loss = torch.tensor(0.0, requires_grad=True, **to(x)) - - if has_local: - local_out = self.local_attn(lq, lk, lv, input_mask=input_mask) - out.append(local_out) - - if has_global: - if not self.receives_context and exists(pos_emb): - q, k = apply_rotary_pos_emb(q, k, pos_emb) - - global_out, loss = self.global_attn( - q, k, v, query_mask=input_mask, key_mask=context_mask - ) - total_loss = total_loss + loss - - out.append(global_out) - - out = torch.cat(out, dim=1) - out = out.reshape(b, h, t, -1).transpose(1, 2).reshape(b, t, -1) - out = self.dropout(out.transpose(0, 1)) - # out = self.to_out(out) - return out, total_loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/kmeans_vector_quantizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/kmeans_vector_quantizer.py deleted file mode 100644 index 040db1e83e775a3bb59d5263d22aae9276a83f22..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/kmeans_vector_quantizer.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -from fairseq.modules import Fp32GroupNorm - - -class KmeansVectorQuantizer(nn.Module): - def __init__( - self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25 - ): - """Vector quantization using straight pass-through estimator (i.e. kmeans) - - Args: - dim: input dimension (channels) - num_vars: number of quantized vectors per group - groups: number of groups for vector quantization - combine_groups: whether to use the vectors for all groups - vq_dim: dimensionality of the resulting quantized vector - time_first: if true, expect input in BxTxC format, otherwise in BxCxT - gamma: commitment loss coefficient - """ - super().__init__() - - self.groups = groups - self.combine_groups = combine_groups - self.input_dim = dim - self.num_vars = num_vars - self.vq_dim = vq_dim - self.time_first = time_first - - assert ( - vq_dim % groups == 0 - ), f"dim {vq_dim} must be divisible by groups {groups} for concatenation" - - self.var_dim = vq_dim // groups - num_groups = groups if not combine_groups else 1 - - self.embedding = nn.Parameter( - 0.01 * torch.randn(num_vars, num_groups, self.var_dim) - ) - self.projection = nn.Sequential( - nn.Conv1d(dim, dim, kernel_size=1, groups=groups, bias=False), - Fp32GroupNorm(groups, dim), - ) - self.gamma = gamma - self.mse_mean = nn.MSELoss(reduction="mean") - - def _pass_grad(self, x, y): - """Manually set gradient for backward pass. - for y = f(x), ensure that during the backward pass, - dL/dy = dL/dx regardless of f(x). - Returns: - y, with the gradient forced to be dL/dy = dL/dx. - """ - - return y.detach() + (x - x.detach()) - - @property - def expand_embedding(self): - if self.combine_groups: - return self.embedding.expand(self.num_vars, self.groups, self.var_dim) - return self.embedding - - def forward_idx(self, x): - res = self.forward(x, produce_targets=True) - return res["x"], res["targets"] - - def forward(self, x, produce_targets=False): - - result = {"num_vars": self.num_vars} - - if self.time_first: - x = x.transpose(1, 2) - - bsz, fsz, tsz = x.shape - - ze = self.projection(x) - ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2) - d = ( - (ze_.unsqueeze(0) - self.expand_embedding.unsqueeze(1).unsqueeze(1)) - .view(self.num_vars, bsz, tsz, self.groups, -1) - .norm(dim=-1, p=2) - ) - idx = d.argmin(dim=0) - zq = ( - torch.stack( - [ - self.expand_embedding[idx[..., group], group] - for group in range(self.groups) - ], - dim=-2, - ) - .view(bsz, tsz, self.groups * self.var_dim) - .permute(0, 2, 1) - ) - assert ze.shape == zq.shape, (ze.shape, zq.shape) - x = self._pass_grad(ze, zq) - - hard_x = ( - idx.new_zeros(bsz * tsz * self.groups, self.num_vars) - .scatter_(-1, idx.view(-1, 1), 1.0) - .view(bsz * tsz, self.groups, -1) - ) - hard_probs = torch.mean(hard_x.float(), dim=0) - result["code_perplexity"] = torch.exp( - -torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1) - ).sum() - - if produce_targets: - result["targets"] = idx - - if self.time_first: - x = x.transpose(1, 2) # BCT -> BTC - result["x"] = x - - ze = ze.float() - zq = zq.float() - latent_loss = self.mse_mean(zq, ze.detach()) - commitment_loss = self.mse_mean(ze, zq.detach()) - - result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss - - return result diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/layer_drop.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/layer_drop.py deleted file mode 100644 index 8961d8bcbc492c40c6b30973234416ce5a414f5a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/layer_drop.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -LayerDrop as described in https://arxiv.org/abs/1909.11556. -""" - -import torch -import torch.nn as nn - - -class LayerDropModuleList(nn.ModuleList): - """ - A LayerDrop implementation based on :class:`torch.nn.ModuleList`. - - We refresh the choice of which layers to drop every time we iterate - over the LayerDropModuleList instance. During evaluation we always - iterate over all layers. - - Usage:: - - layers = LayerDropList(p=0.5, modules=[layer1, layer2, layer3]) - for layer in layers: # this might iterate over layers 1 and 3 - x = layer(x) - for layer in layers: # this might iterate over all layers - x = layer(x) - for layer in layers: # this might not iterate over any layers - x = layer(x) - - Args: - p (float): probability of dropping out each layer - modules (iterable, optional): an iterable of modules to add - """ - - def __init__(self, p, modules=None): - super().__init__(modules) - self.p = p - - def __iter__(self): - dropout_probs = torch.empty(len(self)).uniform_() - for i, m in enumerate(super().__iter__()): - if not self.training or (dropout_probs[i] > self.p): - yield m diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/layer_norm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/layer_norm.py deleted file mode 100644 index 0b276ce02fc6bcb9619c9e8a0f7ec10cd28bc420..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/layer_norm.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - -try: - from apex.normalization import FusedLayerNorm as _FusedLayerNorm - - has_fused_layernorm = True - - class FusedLayerNorm(_FusedLayerNorm): - @torch.jit.unused - def forward(self, x): - if not x.is_cuda: - return super().forward(x) - else: - with torch.cuda.device(x.device): - return super().forward(x) - -except ImportError: - has_fused_layernorm = False - - -def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): - if torch.jit.is_scripting() or torch.jit.is_tracing(): - export = True - if not export and torch.cuda.is_available() and has_fused_layernorm: - return FusedLayerNorm(normalized_shape, eps, elementwise_affine) - return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) - - -class Fp32LayerNorm(nn.LayerNorm): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, input): - output = F.layer_norm( - input.float(), - self.normalized_shape, - self.weight.float() if self.weight is not None else None, - self.bias.float() if self.bias is not None else None, - self.eps, - ) - return output.type_as(input) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/learned_positional_embedding.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/learned_positional_embedding.py deleted file mode 100644 index 378d0f707183dd344dbb9288dda394b11053acf0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/learned_positional_embedding.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from torch import Tensor - - -class LearnedPositionalEmbedding(nn.Embedding): - """ - This module learns positional embeddings up to a fixed maximum size. - Padding ids are ignored by either offsetting based on padding_idx - or by setting padding_idx to None and ensuring that the appropriate - position ids are passed to the forward function. - """ - - def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int): - super().__init__(num_embeddings, embedding_dim, padding_idx) - self.onnx_trace = False - if self.padding_idx is not None: - self.max_positions = self.num_embeddings - self.padding_idx - 1 - else: - self.max_positions = self.num_embeddings - - def forward( - self, - input: Tensor, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - positions: Optional[Tensor] = None, - ): - """Input is expected to be of size [bsz x seqlen].""" - assert (positions is None) or ( - self.padding_idx is None - ), "If positions is pre-computed then padding_idx should not be set." - - if positions is None: - if incremental_state is not None: - # positions is the same for every token when decoding a single step - # Without the int() cast, it doesn't work in some cases when exporting to ONNX - positions = torch.zeros( - (1, 1), device=input.device, dtype=input.dtype - ).fill_(int(self.padding_idx + input.size(1))) - else: - positions = utils.make_positions( - input, self.padding_idx, onnx_trace=self.onnx_trace - ) - return F.embedding( - positions, - self.weight, - self.padding_idx, - self.max_norm, - self.norm_type, - self.scale_grad_by_freq, - self.sparse, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/__init__.py deleted file mode 100644 index 3b2a99c1227f827768911e5e22e79f6865ffbfd3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .lightconv_layer import LightconvLayer # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/cuda_function_gen.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/cuda_function_gen.py deleted file mode 100644 index a25433dd8edae2f0b52d7d0eeeb829cabc6b4b89..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/cuda_function_gen.py +++ /dev/null @@ -1,289 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -def gen_forward(): - - kernels = [3, 5, 7, 15, 31, 63, 127, 255] - seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]] - - head = """ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include "lightconv_cuda.cuh" - -std::vector lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l) { - - at::DeviceGuard g(input.device()); - const auto minibatch = input.size(0); - const auto numFeatures = input.size(1); - const auto sequenceLength = input.size(2); - - const auto numHeads = filters.size(0); - const auto filterSize = filters.size(1); - - const auto numFiltersInBlock = numFeatures / numHeads; - - const dim3 blocks(minibatch, numFeatures); - - auto output = at::zeros_like(input); - auto stream = at::cuda::getCurrentCUDAStream(); -""" - - sequence_if = """ - if (sequenceLength <= {seq}) {{ - switch(filterSize) {{ -""" - - case_k = """ - case {k}: -""" - - main_block = """ - if (padding_l == {pad}) {{ - AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_forward", ([&] {{ - lightconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t> - <<>>( - input.data(), - filters.data(), - minibatch, - sequenceLength, - numFeatures, - numFiltersInBlock, - output.data()); - }})); - }} else -""" - - bad_padding = """ - { - std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl; - } - break; -""" - - bad_filter = """ - default: - std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl; - } -""" - - con_else = """ - } else -""" - - final_else = """ - { - switch(filterSize) { -""" - - final_return = """ - } - - return {output}; -} -""" - - with open("lightconv_cuda_forward.cu", "w") as forward: - forward.write(head) - for seq in seqs: - forward.write(sequence_if.format(seq=seq)) - for k in kernels: - forward.write(case_k.format(k=k)) - for pad in [k // 2, k - 1]: - forward.write(main_block.format(k=k, b_size=seq, pad=pad)) - forward.write(bad_padding) - forward.write(bad_filter) - forward.write(con_else) - - forward.write(final_else) - for k in kernels: - forward.write(case_k.format(k=k)) - for pad in [k // 2, k - 1]: - forward.write(main_block.format(k=k, b_size=seq, pad=pad)) - forward.write(bad_padding) - forward.write(bad_filter) - forward.write(final_return) - - -def gen_backward(): - - head = """ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include "lightconv_cuda.cuh" - -std::vector lightconv_cuda_backward( - at::Tensor gradOutput, - int padding_l, - at::Tensor input, - at::Tensor filters) { - - // gradWrtInput - const int minibatch = input.size(0); - const int numFeatures = input.size(1); - const int sequenceLength = input.size(2); - - const int numHeads = filters.size(0); - const int filterSize = filters.size(1); - - const dim3 gradBlocks(minibatch, numFeatures); - const dim3 weightGradFirstpassShortBlocks(minibatch, numHeads); - const dim3 weightGradSecondpassBlocks(numHeads, filterSize); - - const int numFiltersInBlock = numFeatures / numHeads; - - auto gradInput = at::zeros_like(input); - auto gradFilters = at::zeros_like(filters); - - at::DeviceGuard g(input.device()); - auto stream = at::cuda::getCurrentCUDAStream(); - - switch(filterSize) { -""" - - sequence_if = """ - if (sequenceLength <= {seq}) {{ -""" - - case_k = """ - case {k}: -""" - - main_block = """ - if (padding_l == {p}) {{ - AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_backward", ([&] {{ - lightconv_grad_wrt_input_kernel<{k}, {b_size}, {p}, scalar_t> - <<>>( - gradOutput.data(), - filters.data(), - minibatch, - sequenceLength, - numFeatures, - numFiltersInBlock, - gradInput.data()); - -""" - - weight_grad_short = """ - at::Tensor tempSumGradFilters = at::zeros({{minibatch, numHeads, filterSize}}, input.options().dtype(at::kFloat)); - lightconv_grad_wrt_weights_firstpass_short_kernel<{k}, {b_size}, {p}, scalar_t> - <<>>( - input.data(), - gradOutput.data(), - minibatch, - sequenceLength, - numFeatures, - numFiltersInBlock, - numHeads, - tempSumGradFilters.data() - ); - - lightconv_grad_wrt_weights_secondpass_short_kernel<{k}, {b_size}, scalar_t> - <<>>( - tempSumGradFilters.data(), - minibatch, - numFiltersInBlock, - gradFilters.data() - ); - }})); - }} else -""" - - weight_grad = """ - at::Tensor tempSumGradFilters = at::zeros({{minibatch, numFeatures, filterSize}}, input.options().dtype(at::kFloat)); - lightconv_grad_wrt_weights_firstpass_kernel<{k}, {b_size}, {p}, scalar_t> - <<>>( - input.data(), - gradOutput.data(), - minibatch, - sequenceLength, - numFeatures, - numFiltersInBlock, - tempSumGradFilters.data() - ); - - lightconv_grad_wrt_weights_secondpass_kernel<{k}, {b_size}, scalar_t> - <<>>( - tempSumGradFilters.data(), - minibatch, - numFiltersInBlock, - gradFilters.data() - ); - }})); - }} else -""" - - bad_padding = """ - { - std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl; - } -""" - - breakout = """ - break; -""" - - bad_filter = """ - default: - std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl; -""" - - con_else = """ - } else -""" - - final_else = """ - { - switch(filterSize) { -""" - - last_return = """ - } - return {gradInput, gradFilters}; -} -""" - - kernels = [3, 5, 7, 15, 31, 63, 127, 255] - seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]] - thresh = [32, 32, 64, 128, 256, -1, -1, -1] - max_mem = [-1, -1, -1, -1, -1, 192, 96, 64] - - with open("lightconv_cuda_backward.cu", "w") as backward: - backward.write(head) - for (k, t, mem) in zip(kernels, thresh, max_mem): - backward.write(case_k.format(k=k)) - for seq in seqs: - if (t == -1 or seq <= t) and (mem == -1 or seq < mem): - backward.write(sequence_if.format(seq=seq)) - for p in [k // 2, k - 1]: - backward.write(main_block.format(k=k, b_size=seq, p=p)) - backward.write(weight_grad_short.format(k=k, b_size=seq, p=p)) - backward.write(bad_padding) - else: - for p in [k // 2, k - 1]: - backward.write(main_block.format(k=k, b_size=32, p=p)) - backward.write(weight_grad.format(k=k, b_size=32, p=p)) - backward.write(bad_padding) - backward.write(breakout) - break - backward.write(con_else) - backward.write(bad_filter) - backward.write(last_return) - - -if __name__ == "__main__": - gen_forward() - gen_backward() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda.cpp b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda.cpp deleted file mode 100644 index ece47a8d908b93cec102743070c9057986d39d3f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include -#include - -std::vector -lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l); - -std::vector lightconv_cuda_backward( - at::Tensor gradOutput, - int padding_l, - at::Tensor input, - at::Tensor filters); - -#define CHECK_CUDA(x) \ - AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) \ - AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x) - -std::vector -lightconv_forward(at::Tensor input, at::Tensor filters, int padding_l) { - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return lightconv_cuda_forward(input, filters, padding_l); -} - -std::vector lightconv_backward( - at::Tensor gradOutput, - int padding_l, - at::Tensor input, - at::Tensor filters) { - CHECK_INPUT(gradOutput); - CHECK_INPUT(input); - CHECK_INPUT(filters); - - return lightconv_cuda_backward(gradOutput, padding_l, input, filters); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("forward", &lightconv_forward, "lighconv forward (CUDA)"); - m.def("backward", &lightconv_backward, "lighconv backward (CUDA)"); -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda.cuh b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda.cuh deleted file mode 100644 index 610ab399e9b201cd8b0fb87a91e09b6f7aab9803..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda.cuh +++ /dev/null @@ -1,79 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include -#include - -#include -#include - -#include -#include -#include -#include -#include -#include - -#include -#include - -#define SHFL_MASK 0xffffffff - -template -__global__ void lightconv_forward_kernel( - const scalar_t* input, - const scalar_t* filters, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - scalar_t* output); - -template -__global__ void lightconv_grad_wrt_input_kernel( - const scalar_t* input, - const scalar_t* filters, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - scalar_t* output); - -template -__global__ void lightconv_grad_wrt_weights_firstpass_short_kernel( - const scalar_t* input, - const scalar_t* gradInput, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - float* output); - -template -__global__ void lightconv_grad_wrt_weights_secondpass_short_kernel( - const float* input, - const int minibatch, - const int numFiltersInBlock, - scalar_t* output); - -template -__global__ void lightconv_grad_wrt_weights_firstpass_kernel( - const scalar_t* input, - const scalar_t* gradInput, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - float* output); - -template -__global__ void lightconv_grad_wrt_weights_secondpass_kernel( - const float* input, - const int minibatch, - const int numFiltersInBlock, - scalar_t* output); diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu deleted file mode 100644 index cdf31d5d2df2d3433c66e167f098e48a99f96db2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_cuda_kernel.cu +++ /dev/null @@ -1,400 +0,0 @@ -/** - * Copyright (c) Facebook, Inc. and its affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */ - -#include "../cuda_utils.cu" -#include "lightconv_cuda.cuh" -#include "lightconv_cuda_backward.cu" -#include "lightconv_cuda_forward.cu" - -template -__global__ void lightconv_forward_kernel( - const scalar_t* input, - const scalar_t* filters, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - scalar_t* output) { - const int tid = threadIdx.x; - const int batchIdx = blockIdx.x; - const int featureIdx = blockIdx.y; - const int filterIdx = featureIdx / numFiltersInBlock; - - const int IOOffset = - numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; - const scalar_t* inputFeature = &input[IOOffset]; - scalar_t* outputFeature = &output[IOOffset]; - const scalar_t* inputFilter = &filters[filterIdx * FS]; - - assert(blockDim.x == SB); - - scalar_t filter[FS]; -#pragma unroll - for (int i = 0; i < FS; ++i) { - filter[i] = inputFilter[i]; - } - - __shared__ scalar_t temp[SB + FS]; - zeroSharedMem(temp); - - const int numIterations = divUp(sequenceLength, SB); - - for (int i = 0; i < numIterations; ++i) { - // Read input into shared memory - const int inputOffset = i * SB; - - load_input_to_shared( - inputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - (numIterations == 1), - temp); - - __syncthreads(); - - scalar_t out = 0; -#pragma unroll - for (int j = 0; j < FS; ++j) { - out += filter[j] * temp[tid + j]; - } - - // Write output - const int outputOffset = inputOffset; - if ((outputOffset + tid) < sequenceLength) { - outputFeature[outputOffset + tid] = out; - } - - __syncthreads(); - } -} - -template -__global__ void lightconv_grad_wrt_input_kernel( - const scalar_t* input, - const scalar_t* filters, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - scalar_t* output) { - // input grad kernel is similar to forward kernel - const int tid = threadIdx.x; - const int batchIdx = blockIdx.x; - const int featureIdx = blockIdx.y; - const int filterIdx = featureIdx / numFiltersInBlock; - - const int IOOffset = - numFeatures * sequenceLength * batchIdx + featureIdx * sequenceLength; - const scalar_t* inputFeature = &input[IOOffset]; - scalar_t* outputFeature = &output[IOOffset]; - const scalar_t* inputFilter = &filters[filterIdx * FS]; - - assert(blockDim.x == SB); - - scalar_t filter[FS]; - -// The only change is loading the filter in reverse -#pragma unroll - for (int i = 0; i < FS; ++i) { - filter[i] = inputFilter[FS - i - 1]; - } - - __shared__ scalar_t temp[SB + FS]; - const int padding = FS - padding_l - 1; - zeroSharedMem(temp); - - __syncthreads(); - - const int numIterations = divUp(sequenceLength, SB); - - for (int i = 0; i < numIterations; ++i) { - // Read input into shared memory - const int inputOffset = i * SB; - - load_input_to_shared( - inputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - false, - temp); - - __syncthreads(); - - scalar_t out = 0; -#pragma unroll - for (int j = 0; j < FS; ++j) { - out += filter[j] * temp[tid + j]; - } - - // Write output - const int outputOffset = inputOffset; - if ((outputOffset + tid) < sequenceLength) { - outputFeature[outputOffset + tid] = out; - } - - __syncthreads(); - } -} - -// This is by far the most expensive kernel in terms of time taken. -// Can be 16x slower than the forward or grad_wrt_input when filter size is 31 -template -__global__ void lightconv_grad_wrt_weights_firstpass_short_kernel( - const scalar_t* input, - const scalar_t* gradInput, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - int numHeads, - float* output) { - const int tid = threadIdx.x; - const int batchIdx = blockIdx.x; - const int filterIdx = blockIdx.y; - - const int numIterations = divUp(sequenceLength, SB); - - float* tempOutputGradWeight = &output[filterIdx * FS * minibatch]; - - assert(blockDim.x == SB); - - __shared__ scalar_t tempInput[SB + FS]; - __shared__ scalar_t tempGradInput[SB + FS]; - - // local weight accumulation - float accumWeights[FS]; - - // Initialize memory - for (int i = 0; i < FS; ++i) { - accumWeights[i] = float(0.0); - } - - // loop over each sequence within filterblock - for (int idxInFilterBlock = 0; idxInFilterBlock < numFiltersInBlock; - ++idxInFilterBlock) { - const int featureOffset = batchIdx * numFeatures * sequenceLength + - (filterIdx * numFiltersInBlock + idxInFilterBlock) * sequenceLength; - const scalar_t* inputFeature = &input[featureOffset]; - const scalar_t* gradInputFeature = &gradInput[featureOffset]; - - zeroSharedMem(tempInput); - zeroSharedMem(tempGradInput); - __syncthreads(); - - for (int i = 0; i < numIterations; ++i) { - const int inputOffset = i * SB; - - load_input_to_shared( - inputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - false, - tempInput); - load_input_to_shared( - gradInputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - false, - tempGradInput); - - __syncthreads(); - - const int gradIndex = (FS / 2) + tid; - scalar_t tempGrad = tempGradInput[gradIndex]; - -#pragma unroll - for (int j = 0; j < FS; j++) { - const int inputIndex = tid + j; - accumWeights[j] += tempInput[inputIndex] * tempGrad; - } - - __syncthreads(); - } - } - - // Row-major sum - for (int filterWeightIdx = 0; filterWeightIdx < FS; ++filterWeightIdx) { - float temp; - if (tid < sequenceLength) { - temp = accumWeights[filterWeightIdx]; - } else { - temp = float(0.0); - } - - const int outputOffset = filterWeightIdx * minibatch + batchIdx; - - temp = blockReduce(temp); - - if (tid == 0) { - tempOutputGradWeight[outputOffset] = temp; - } - } -} - -template -__global__ void lightconv_grad_wrt_weights_secondpass_short_kernel( - const float* input, - const int minibatch, - const int numFiltersInBlock, - scalar_t* output) { - assert(blockDim.x == SB); - - const int tid = threadIdx.x; - - const int filterIdx = blockIdx.x; - const int filterWeightIdx = blockIdx.y; - - const int inputOffset = - filterIdx * FS * minibatch + filterWeightIdx * minibatch; - const float* tempInput = &input[inputOffset]; - - // read into shared memory for reduction - int readIndex = tid; - - float sum = 0.0; - while (readIndex < minibatch) { - sum += tempInput[readIndex]; - readIndex += SB; - } - - float temp = blockReduce(sum); - - if (tid == 0) { - output[blockIdx.x * FS + blockIdx.y] = temp; - } -} - -// This is by far the most expensive kernel in terms of time taken. -// Can be 16x slower than the forward or grad_wrt_input when filter size is 31 -template -__global__ void lightconv_grad_wrt_weights_firstpass_kernel( - const scalar_t* input, - const scalar_t* gradInput, - int minibatch, - int sequenceLength, - int numFeatures, - int numFiltersInBlock, - float* output) { - assert(blockDim.x == SB); - - const int tid = threadIdx.x; - const int batchIdx = blockIdx.x; - const int featureIdx = blockIdx.y; - const int filterIdx = featureIdx / numFiltersInBlock; - const int idxInFilterBlock = featureIdx % numFiltersInBlock; - - const int numIterations = divUp(sequenceLength, SB); - - float temp; - - __shared__ scalar_t tempInput[SB + FS]; - __shared__ scalar_t tempGradInput[SB + FS]; - zeroSharedMem(tempInput); - zeroSharedMem(tempGradInput); - __syncthreads(); - - float accumWeights[FS]; - - for (int i = 0; i < FS; ++i) { - accumWeights[i] = float(0.0); - } - - const int IOOffset = - batchIdx * numFeatures * sequenceLength + featureIdx * sequenceLength; - const scalar_t* inputFeature = &input[IOOffset]; - const scalar_t* gradInputFeature = &gradInput[IOOffset]; - float* tempOutputGradWeight = - &output[filterIdx * FS * minibatch * numFiltersInBlock]; - - for (int i = 0; i < numIterations; ++i) { - const int inputOffset = i * SB; - - load_input_to_shared( - inputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - false, - tempInput); - load_input_to_shared( - gradInputFeature, - inputOffset, - sequenceLength, - i, - numIterations, - false, - tempGradInput); - __syncthreads(); - -#pragma unroll - for (int j = 0; j < FS; ++j) { - accumWeights[j] += tempInput[tid + j] * tempGradInput[tid + (FS / 2)]; - } - - __syncthreads(); - } - - // Row-major sum - for (int filterWeightIdx = 0; filterWeightIdx < FS; ++filterWeightIdx) { - // Write to shared memory before reduction - if (tid < sequenceLength) { - temp = accumWeights[filterWeightIdx]; - } else { - temp = float(0.0); - } - - temp = blockReduce(temp); - - const int outputOffset = filterWeightIdx * minibatch * numFiltersInBlock + - batchIdx * numFiltersInBlock + idxInFilterBlock; - - if (tid == 0) { - tempOutputGradWeight[outputOffset] = temp; - } - } -} - -template -__global__ void lightconv_grad_wrt_weights_secondpass_kernel( - const float* input, - const int minibatch, - const int numFiltersInBlock, - scalar_t* output) { - assert(blockDim.x == SB); - const int tid = threadIdx.x; - - // What is the id within a minibatch - const int filterIdx = blockIdx.x; - const int filterWeightIdx = blockIdx.y; - - const int inputOffset = filterIdx * FS * minibatch * numFiltersInBlock + - filterWeightIdx * minibatch * numFiltersInBlock; - const float* tempInput = &input[inputOffset]; - - int readIndex = tid; - - float sum = float(0.0); - while (readIndex < (minibatch * numFiltersInBlock)) { - sum += tempInput[readIndex]; - readIndex += SB; - } - - float temp = blockReduce(sum); - - if (tid == 0) { - output[blockIdx.x * FS + blockIdx.y] = temp; - } -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_layer.py deleted file mode 100644 index e7e597f4749c591b057d776aacec39b44d99c037..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/lightconv_layer.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import lightconv_cuda -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from torch import nn -from torch.autograd import Function - - -class lightconvFunction(Function): - @staticmethod - def forward(ctx, x, weights, padding_l): - ctx.padding_l = padding_l - outputs = lightconv_cuda.forward(x, weights, padding_l) - variables = [x, weights] - ctx.save_for_backward(*variables) - return outputs[0] - - @staticmethod - def backward(ctx, grad_output): - outputs = lightconv_cuda.backward( - grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors - ) - grad_input, grad_weights = outputs - return grad_input, grad_weights, None - - -@with_incremental_state -class LightconvLayer(nn.Module): - def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - weight_softmax=False, - num_heads=1, - weight_dropout=0.0, - bias=False, - ): - super(LightconvLayer, self).__init__() - self.input_size = input_size - self.kernel_size = kernel_size - self.padding_l = padding_l - self.num_heads = num_heads - self.weight_softmax = weight_softmax - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - - self.weight = nn.Parameter(torch.Tensor(num_heads, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.bias = None - self.reset_parameters() - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - for k, v in state_dict.items(): - if k.endswith(prefix + "weight"): - if v.dim() == 3 and v.size(1) == 1: - state_dict[k] = v.squeeze(1) - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight) - if self.bias is not None: - nn.init.constant_(self.bias, 0.0) - - def forward(self, x, incremental_state=None): - - # during inference time, incremental BMM is faster - if incremental_state is not None: - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = x.new() - x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) - if self.kernel_size > 1: - self._set_input_buffer( - incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] - ) - x_unfold = x_unfold.view(T * B * H, R, -1) - - weight = self.weight - if self.weight_softmax: - weight = F.softmax(weight.float(), dim=1).type_as(weight) - - weight = weight[:, -x_unfold.size(2) :] - - K = weight.size(1) - - weight = ( - weight.view(1, H, K) - .expand(T * B, H, K) - .contiguous() - .view(T * B * H, K, 1) - ) - - weight = self.weight_dropout_module(weight) - output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 - output = output.view(T, B, C) - return output - - # during training time, use CUDA kernel - else: - x = x.permute(1, 2, 0).contiguous() - weight = self.weight - if self.weight_softmax: - weight = F.softmax(self.weight, -1) - if self.weight_dropout_module.p: - weight = self.weight_dropout_module(weight) - return lightconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1) - - def reorder_incremental_state(self, incremental_state, new_order): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(1, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - def half(self): - return self._apply(lambda t: t.half() if t.is_floating_point() else t) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/setup.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/setup.py deleted file mode 100644 index 2bb6483bf18a4efd5552038bc83694ee5c6c2ce4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightconv_layer/setup.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from setuptools import setup -from torch.utils.cpp_extension import BuildExtension, CUDAExtension - -setup( - name="lightconv_layer", - ext_modules=[ - CUDAExtension( - "lightconv_cuda", - [ - "lightconv_cuda.cpp", - "lightconv_cuda_kernel.cu", - ], - ), - ], - cmdclass={"build_ext": BuildExtension}, -) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightweight_convolution.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightweight_convolution.py deleted file mode 100644 index ec11a9507951c9e8f3564753841dd9c74a4900e0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lightweight_convolution.py +++ /dev/null @@ -1,310 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.unfold import unfold1d - - -def LightweightConv( - input_size, - kernel_size=1, - padding_l=None, - num_heads=1, - weight_dropout=0.0, - weight_softmax=False, - bias=False, -): - if torch.cuda.is_available(): - try: - from fairseq.modules.lightconv_layer import LightconvLayer - - return LightconvLayer( - input_size, - kernel_size=kernel_size, - padding_l=padding_l, - num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, - bias=bias, - ) - except ImportError as e: - print(e) - return LightweightConv1dTBC( - input_size, - kernel_size=kernel_size, - padding_l=padding_l, - num_heads=num_heads, - weight_dropout=weight_dropout, - weight_softmax=weight_softmax, - bias=bias, - ) - - -class LightweightConv1d(nn.Module): - """Lightweight Convolution assuming the input is BxCxT - This is just an example that explains LightConv clearer than the TBC version. - We don't use this module in the model. - - Args: - input_size: # of channels of the input and output - kernel_size: convolution channels - padding: padding - num_heads: number of heads used. The weight is of shape - `(num_heads, 1, kernel_size)` - weight_softmax: normalize the weight with softmax before the convolution - - Shape: - Input: BxCxT, i.e. (batch_size, input_size, timesteps) - Output: BxCxT, i.e. (batch_size, input_size, timesteps) - - Attributes: - weight: the learnable weights of the module of shape - `(num_heads, 1, kernel_size)` - bias: the learnable bias of the module of shape `(input_size)` - """ - - def __init__( - self, - input_size, - kernel_size=1, - padding=0, - num_heads=1, - weight_softmax=False, - bias=False, - weight_dropout=0.0, - ): - super().__init__() - self.input_size = input_size - self.kernel_size = kernel_size - self.num_heads = num_heads - self.padding = padding - self.weight_softmax = weight_softmax - self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) - - if bias: - self.bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.bias = None - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - self.reset_parameters() - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight) - if self.bias is not None: - nn.init.constant_(self.bias, 0.0) - - def forward(self, input): - """ - input size: B x C x T - output size: B x C x T - """ - B, C, T = input.size() - H = self.num_heads - - weight = self.weight - if self.weight_softmax: - weight = F.softmax(weight, dim=-1) - - weight = self.weight_dropout_module(weight) - # Merge every C/H entries into the batch dimension (C = self.input_size) - # B x C x T -> (B * C/H) x H x T - # One can also expand the weight to C x 1 x K by a factor of C/H - # and do not reshape the input instead, which is slow though - input = input.view(-1, H, T) - output = F.conv1d(input, weight, padding=self.padding, groups=self.num_heads) - output = output.view(B, C, T) - if self.bias is not None: - output = output + self.bias.view(1, -1, 1) - - return output - - -@with_incremental_state -class LightweightConv1dTBC(nn.Module): - """Lightweight Convolution assuming the input is TxBxC - Args: - input_size: # of channels of the input - kernel_size: convolution channels - padding_l: padding to the left when using "same" padding - num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size) - weight_dropout: the drop rate of the DropConnect to drop the weight - weight_softmax: normalize the weight with softmax before the convolution - bias: use bias - - Shape: - Input: TxBxC, i.e. (timesteps, batch_size, input_size) - Output: TxBxC, i.e. (timesteps, batch_size, input_size) - - Attributes: - weight: the learnable weights of the module of shape - `(num_heads, 1, kernel_size)` - bias: the learnable bias of the module of shape `(input_size)` - """ - - def __init__( - self, - input_size, - kernel_size=1, - padding_l=None, - num_heads=1, - weight_dropout=0.0, - weight_softmax=False, - bias=False, - ): - super().__init__() - self.input_size = input_size - self.kernel_size = kernel_size - self.padding_l = padding_l - self.num_heads = num_heads - self.weight_dropout_module = FairseqDropout( - weight_dropout, module_name=self.__class__.__name__ - ) - self.weight_softmax = weight_softmax - - self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size)) - if bias: - self.bias = nn.Parameter(torch.Tensor(input_size)) - else: - self.bias = None - - self.reset_parameters() - self.onnx_trace = False - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight) - if self.bias is not None: - nn.init.constant_(self.bias, 0.0) - - def forward(self, x, incremental_state=None, unfold=False): - """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C - args: - x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size) - incremental_state: A dict to keep the state - unfold: unfold the input or not. If not, we use the matrix trick instead - """ - unfold = unfold or (incremental_state is not None) - - if unfold: - output = self._forward_unfolded(x, incremental_state) - else: - output = self._forward_expanded(x, incremental_state) - - if self.bias is not None: - output = output + self.bias.view(1, 1, -1) - return output - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def _forward_unfolded(self, x, incremental_state): - """The conventional implementation of convolutions. - Unfolding the input by having a window shifting to the right.""" - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - - weight = self.weight.view(H, K) - if incremental_state is not None: - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = x.new() - x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3) - if self.kernel_size > 1: - self._set_input_buffer( - incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :] - ) - x_unfold = x_unfold.view(T * B * H, R, -1) - else: - # unfold the input: T x B x C --> T' x B x C x K - x_unfold = unfold1d(x, self.kernel_size, self.padding_l, 0) - x_unfold = x_unfold.view(T * B * H, R, K) - - if self.weight_softmax: - weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( - weight - ) - - if incremental_state is not None: - weight = weight[:, -x_unfold.size(2) :] - K = weight.size(1) - - weight = ( - weight.view(1, H, K).expand(T * B, H, K).contiguous().view(T * B * H, K, 1) - ) - - weight = self.weight_dropout_module(weight) - output = torch.bmm(x_unfold, weight) # T*B*H x R x 1 - output = output.view(T, B, C) - return output - - def _forward_expanded(self, x, incremental_state): - """Turn the convolution filters into band matrices and do matrix multiplication. - This is faster when the sequence is short, but less memory efficient. - This is not used in the decoder during inference. - """ - T, B, C = x.size() - K, H = self.kernel_size, self.num_heads - R = C // H - assert R * H == C == self.input_size - - weight = self.weight.view(H, K) - if self.weight_softmax: - weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as( - weight - ) - weight = weight.view(1, H, K).expand(T * B, H, K).contiguous() - weight = weight.view(T, B * H, K).transpose(0, 1) - - x = x.view(T, B * H, R).transpose(0, 1) - P = self.padding_l - if K > T and P == K - 1: - weight = weight.narrow(2, K - T, T) - K, P = T, T - 1 - # turn the convolution filters into band matrices - weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False) - weight_expanded.as_strided((B * H, T, K), (T * (T + K - 1), T + K, 1)).copy_( - weight - ) - weight_expanded = weight_expanded.narrow(2, P, T) - weight_expanded = self.weight_dropout_module(weight_expanded) - - output = torch.bmm(weight_expanded, x) - output = output.transpose(0, 1).contiguous().view(T, B, C) - return output - - def reorder_incremental_state(self, incremental_state, new_order): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(1, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - def _get_input_buffer(self, incremental_state): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - def _set_input_buffer(self, incremental_state, new_buffer): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - def extra_repr(self): - s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}".format( - self.input_size, - self.kernel_size, - self.padding_l, - self.num_heads, - self.weight_softmax, - self.bias is not None, - ) - if self.weight_dropout_module.p > 0.0: - s += ", weight_dropout={}".format(self.weight_dropout_module.p) - return s diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/linearized_convolution.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/linearized_convolution.py deleted file mode 100644 index 251bd95cc46ffade6fafea63cd3bc8a29aff6e81..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/linearized_convolution.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, Optional - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from torch import Tensor - -from .conv_tbc import ConvTBC - - -@with_incremental_state -class LinearizedConvolution(ConvTBC): - """An optimized version of nn.Conv1d. - - At training time, this module uses ConvTBC, which is an optimized version - of Conv1d. At inference time, it optimizes incremental generation (i.e., - one time step at a time) by replacing the convolutions with linear layers. - Note that the input order changes from training to inference. - """ - - def __init__(self, in_channels, out_channels, kernel_size, **kwargs): - super().__init__(in_channels, out_channels, kernel_size, **kwargs) - self._linearized_weight = None - self.register_backward_hook(self._clear_linearized_weight) - - def state_dict(self, destination=None, prefix="", keep_vars=False): - state = ConvTBC.state_dict(self, destination, prefix, keep_vars=keep_vars) - # don't store redundant _linearized_weight in checkpoints - if prefix + "_linearized_weight" in state: - del state[prefix + "_linearized_weight"] - return state - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - if prefix + "_linearized_weight" in state_dict: - del state_dict[prefix + "_linearized_weight"] - - @torch.jit.export - def forward( - self, - input, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - """ - Args: - incremental_state: Used to buffer signal; if not None, then input is - expected to contain a single frame. If the input order changes - between time steps, call reorder_incremental_state. - Input: - Time x Batch x Channel during training - Batch x Time x Channel during inference - """ - if incremental_state is None: - output = self.conv_tbc(input) - if self.kernel_size[0] > 1 and self.padding[0] > 0: - # remove future timesteps added by padding - output = output[: -self.padding[0], :, :] - return output - - # reshape weight - weight = self._get_linearized_weight() - kw = self.kernel_size[0] - - bsz = input.size(0) # input: bsz x len x dim - if kw > 1: - input = input.data - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is None: - input_buffer = input.new(bsz, kw, input.size(2)).zero_() - self._set_input_buffer(incremental_state, input_buffer) - else: - # shift buffer - input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone() - # append next input - input_buffer[:, -1, :] = input[:, -1, :] - input = input_buffer - with torch.no_grad(): - output = F.linear(input.view(bsz, -1), weight, self.bias) - return output.view(bsz, 1, -1) - - @torch.jit.unused - def reorder_incremental_state( - self, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], - new_order, - ): - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - input_buffer = input_buffer.index_select(0, new_order) - self._set_input_buffer(incremental_state, input_buffer) - - @torch.jit.unused - def _get_input_buffer( - self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] - ): - return utils.get_incremental_state(self, incremental_state, "input_buffer") - - @torch.jit.unused - def _set_input_buffer( - self, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], - new_buffer, - ): - return utils.set_incremental_state( - self, incremental_state, "input_buffer", new_buffer - ) - - @torch.jit.unused - def _get_linearized_weight(self): - if self._linearized_weight is None: - kw = self.kernel_size[0] - weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous() - assert weight.size() == (self.out_channels, kw, self.in_channels) - return weight.view(self.out_channels, -1) - return self._linearized_weight - - @torch.jit.unused - def _clear_linearized_weight(self, *args): - self._linearized_weight = None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/location_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/location_attention.py deleted file mode 100644 index f93af042001a6a0a59ddeda8a97ca2369c99281e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/location_attention.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class LocationAttention(nn.Module): - """ - Attention-Based Models for Speech Recognition - https://arxiv.org/pdf/1506.07503.pdf - - :param int encoder_dim: # projection-units of encoder - :param int decoder_dim: # units of decoder - :param int attn_dim: attention dimension - :param int conv_dim: # channels of attention convolution - :param int conv_kernel_size: filter size of attention convolution - """ - - def __init__( - self, - attn_dim, - encoder_dim, - decoder_dim, - attn_state_kernel_size, - conv_dim, - conv_kernel_size, - scaling=2.0, - ): - super(LocationAttention, self).__init__() - self.attn_dim = attn_dim - self.decoder_dim = decoder_dim - self.scaling = scaling - self.proj_enc = nn.Linear(encoder_dim, attn_dim) - self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False) - self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False) - self.conv = nn.Conv1d( - attn_state_kernel_size, - conv_dim, - 2 * conv_kernel_size + 1, - padding=conv_kernel_size, - bias=False, - ) - self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1)) - - self.proj_enc_out = None # cache - - def clear_cache(self): - self.proj_enc_out = None - - def forward(self, encoder_out, encoder_padding_mask, decoder_h, attn_state): - """ - :param torch.Tensor encoder_out: padded encoder hidden state B x T x D - :param torch.Tensor encoder_padding_mask: encoder padding mask - :param torch.Tensor decoder_h: decoder hidden state B x D - :param torch.Tensor attn_prev: previous attention weight B x K x T - :return: attention weighted encoder state (B, D) - :rtype: torch.Tensor - :return: previous attention weights (B x T) - :rtype: torch.Tensor - """ - bsz, seq_len, _ = encoder_out.size() - if self.proj_enc_out is None: - self.proj_enc_out = self.proj_enc(encoder_out) - - # B x K x T -> B x C x T - attn = self.conv(attn_state) - # B x C x T -> B x T x C -> B x T x D - attn = self.proj_attn(attn.transpose(1, 2)) - - if decoder_h is None: - decoder_h = encoder_out.new_zeros(bsz, self.decoder_dim) - dec_h = self.proj_dec(decoder_h).view(bsz, 1, self.attn_dim) - - out = self.proj_out(attn + self.proj_enc_out + dec_h).squeeze(2) - out.masked_fill_(encoder_padding_mask, -float("inf")) - - w = F.softmax(self.scaling * out, dim=1) - c = torch.sum(encoder_out * w.view(bsz, seq_len, 1), dim=1) - return c, w diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lstm_cell_with_zoneout.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lstm_cell_with_zoneout.py deleted file mode 100644 index 273308951f7b3d1924675fde4a62359602a11bca..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/lstm_cell_with_zoneout.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn - - -class LSTMCellWithZoneOut(nn.Module): - """ - Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations - https://arxiv.org/abs/1606.01305 - """ - - def __init__( - self, prob: float, input_size: int, hidden_size: int, bias: bool = True - ): - super(LSTMCellWithZoneOut, self).__init__() - self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias) - self.prob = prob - if prob > 1.0 or prob < 0.0: - raise ValueError( - "zoneout probability must be in the range from " "0.0 to 1.0." - ) - - def zoneout(self, h, next_h, prob): - if isinstance(h, tuple): - return tuple([self.zoneout(h[i], next_h[i], prob) for i in range(len(h))]) - - if self.training: - mask = h.new_zeros(*h.size()).bernoulli_(prob) - return mask * h + (1 - mask) * next_h - - return prob * h + (1 - prob) * next_h - - def forward(self, x, h): - return self.zoneout(h, self.lstm_cell(x, h), self.prob) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/multihead_attention.py deleted file mode 100644 index 431480601058691d6565a119732ccc9a9794b1f6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/multihead_attention.py +++ /dev/null @@ -1,908 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn.functional as F -from torch import Tensor, nn -from torch.nn import Parameter - -try: - from xformers.components.attention import build_attention - from xformers.components.attention.utils import maybe_merge_masks - - _xformers_available = True -except ImportError: - _xformers_available = False - -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise - - -# TODO: move this into xformers? -# TODO: uint8 input type should just output a bool -def _mask_for_xformers(mask: Tensor, to_dtype: Optional[torch.dtype] = None): - """ - call to pytorch multihead accepts three mask types: - - ByteTensor where non-zero means to mask - - FloatTensor which is an additive mask - - BoolTensor where True means to mask - xFormers currently accepts boolean and additive maks. For boolean masks - the values have opposite meaning. For a BoolTensor True mean to keep the value. - """ - float_types = [torch.float, torch.float16] - # If an input mask is a float it is an additive mask. Otherwise it is either uint8 or bool. - additive = mask.dtype in float_types - # If to_dype is not specified, keep same dtype as mask. - to_dtype = mask.dtype if to_dtype is None else to_dtype - to_additive = to_dtype in float_types - - if additive: - if to_additive: - return mask.to(to_dtype) - mask = mask < 0 - - if to_additive: - # return additive mask - new_mask = torch.zeros_like(mask, dtype=to_dtype) - new_mask = new_mask.masked_fill_(mask, -float("inf")) - return new_mask - - # In xFormers True is value to keep rather than value to mask - mask = ~mask.to(torch.bool) - mask = mask.to(to_dtype) - return mask - - -@with_incremental_state -class MultiheadAttention(nn.Module): - """Multi-headed attention. - - See "Attention Is All You Need" for more details. - """ - - def __init__( - self, - embed_dim, - num_heads, - kdim=None, - vdim=None, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - self_attention=False, - encoder_decoder_attention=False, - q_noise=0.0, - qn_block_size=8, - # TODO: pass in config rather than string. - # config defined in xformers.components.attention.AttentionConfig - xformers_att_config: Optional[str] = None, - xformers_blocksparse_layout: Optional[ - torch.Tensor - ] = None, # This should be part of the config - xformers_blocksparse_blocksize: Optional[ - int - ] = 16, # This should be part of the config - ): - super().__init__() - - xformers_att_config = utils.eval_str_dict(xformers_att_config) - self.use_xformers = xformers_att_config is not None - if self.use_xformers and not _xformers_available: - raise ImportError("\n\n Please install xFormers.") - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim - - self.num_heads = num_heads - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - - self.head_dim = embed_dim // num_heads - assert ( - self.head_dim * num_heads == self.embed_dim - ), "embed_dim must be divisible by num_heads" - self.scaling = self.head_dim**-0.5 - - self.self_attention = self_attention - self.encoder_decoder_attention = encoder_decoder_attention - - assert not self.self_attention or self.qkv_same_dim, ( - "Self-attention requires query, key and " "value to be of the same size" - ) - - self.k_proj = quant_noise( - nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size - ) - self.v_proj = quant_noise( - nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size - ) - self.q_proj = quant_noise( - nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size - ) - - self.out_proj = quant_noise( - nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size - ) - - if add_bias_kv: - self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) - self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) - else: - self.bias_k = self.bias_v = None - - self.add_zero_attn = add_zero_attn - self.beam_size = 1 - self.reset_parameters() - - if self.use_xformers: - xformers_att_config["dropout"] = xformers_att_config.get("dropout", dropout) - xformers_att_config["num_heads"] = xformers_att_config.get( - "num_heads", num_heads - ) - - if xformers_blocksparse_layout is not None: - # Could be part of a single config passed only once - xformers_att_config["block_size"] = xformers_blocksparse_blocksize - xformers_att_config["layout"] = xformers_blocksparse_layout - xformers_att_config["name"] = "blocksparse" - - self.attention = build_attention(xformers_att_config) - - self.onnx_trace = False - self.skip_embed_dim_check = False - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def reset_parameters(self): - if self.qkv_same_dim: - # Empirically observed the convergence to be much better with - # the scaled initialization - nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) - else: - nn.init.xavier_uniform_(self.k_proj.weight) - nn.init.xavier_uniform_(self.v_proj.weight) - nn.init.xavier_uniform_(self.q_proj.weight) - - nn.init.xavier_uniform_(self.out_proj.weight) - if self.out_proj.bias is not None: - nn.init.constant_(self.out_proj.bias, 0.0) - if self.bias_k is not None: - nn.init.xavier_normal_(self.bias_k) - if self.bias_v is not None: - nn.init.xavier_normal_(self.bias_v) - - def _get_reserve_head_index(self, num_heads_to_keep: int): - k_proj_heads_norm = [] - q_proj_heads_norm = [] - v_proj_heads_norm = [] - - for i in range(self.num_heads): - start_idx = i * self.head_dim - end_idx = (i + 1) * self.head_dim - k_proj_heads_norm.append( - torch.sum( - torch.abs( - self.k_proj.weight[ - start_idx:end_idx, - ] - ) - ).tolist() - + torch.sum(torch.abs(self.k_proj.bias[start_idx:end_idx])).tolist() - ) - q_proj_heads_norm.append( - torch.sum( - torch.abs( - self.q_proj.weight[ - start_idx:end_idx, - ] - ) - ).tolist() - + torch.sum(torch.abs(self.q_proj.bias[start_idx:end_idx])).tolist() - ) - v_proj_heads_norm.append( - torch.sum( - torch.abs( - self.v_proj.weight[ - start_idx:end_idx, - ] - ) - ).tolist() - + torch.sum(torch.abs(self.v_proj.bias[start_idx:end_idx])).tolist() - ) - - heads_norm = [] - for i in range(self.num_heads): - heads_norm.append( - k_proj_heads_norm[i] + q_proj_heads_norm[i] + v_proj_heads_norm[i] - ) - - sorted_head_index = sorted( - range(self.num_heads), key=lambda k: heads_norm[k], reverse=True - ) - reserve_head_index = [] - for i in range(num_heads_to_keep): - start = sorted_head_index[i] * self.head_dim - end = (sorted_head_index[i] + 1) * self.head_dim - reserve_head_index.append((start, end)) - return reserve_head_index - - def _adaptive_prune_heads(self, reserve_head_index: List[Tuple[int, int]]): - new_q_weight = [] - new_q_bias = [] - new_k_weight = [] - new_k_bias = [] - new_v_weight = [] - new_v_bias = [] - new_out_proj_weight = [] - - for ele in reserve_head_index: - start_idx, end_idx = ele - new_q_weight.append( - self.q_proj.weight[ - start_idx:end_idx, - ] - ) - new_q_bias.append(self.q_proj.bias[start_idx:end_idx]) - - new_k_weight.append( - self.k_proj.weight[ - start_idx:end_idx, - ] - ) - - new_k_bias.append(self.k_proj.bias[start_idx:end_idx]) - - new_v_weight.append( - self.v_proj.weight[ - start_idx:end_idx, - ] - ) - new_v_bias.append(self.v_proj.bias[start_idx:end_idx]) - - new_out_proj_weight.append(self.out_proj.weight[:, start_idx:end_idx]) - - new_q_weight = torch.cat(new_q_weight).detach() - new_k_weight = torch.cat(new_k_weight).detach() - new_v_weight = torch.cat(new_v_weight).detach() - new_out_proj_weight = torch.cat(new_out_proj_weight, dim=-1).detach() - new_q_weight.requires_grad = True - new_k_weight.requires_grad = True - new_v_weight.requires_grad = True - new_out_proj_weight.requires_grad = True - - new_q_bias = torch.cat(new_q_bias).detach() - new_q_bias.requires_grad = True - - new_k_bias = torch.cat(new_k_bias).detach() - new_k_bias.requires_grad = True - - new_v_bias = torch.cat(new_v_bias).detach() - new_v_bias.requires_grad = True - - self.q_proj.weight = torch.nn.Parameter(new_q_weight) - self.q_proj.bias = torch.nn.Parameter(new_q_bias) - - self.k_proj.weight = torch.nn.Parameter(new_k_weight) - self.k_proj.bias = torch.nn.Parameter(new_k_bias) - - self.v_proj.weight = torch.nn.Parameter(new_v_weight) - self.v_proj.bias = torch.nn.Parameter(new_v_bias) - - self.out_proj.weight = torch.nn.Parameter(new_out_proj_weight) - - self.num_heads = len(reserve_head_index) - self.embed_dim = self.head_dim * self.num_heads - self.q_proj.out_features = self.embed_dim - self.k_proj.out_features = self.embed_dim - self.v_proj.out_features = self.embed_dim - - def _set_skip_embed_dim_check(self): - self.skip_embed_dim_check = True - - def _pad_masks( - self, - key_padding_mask: Optional[Tensor], - attn_mask: Optional[Tensor], - ) -> Tuple[Optional[Tensor], Optional[Tensor]]: - if attn_mask is not None: - shape = attn_mask.size()[:-1] + torch.Size([1]) - attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(shape)], dim=-1) - if key_padding_mask is not None: - shape = key_padding_mask.size()[:-1] + torch.Size([1]) - key_padding_mask = torch.cat( - [ - key_padding_mask, - key_padding_mask.new_zeros(shape), - ], - dim=-1, - ) - return key_padding_mask, attn_mask - - def _add_bias( - self, - k: Tensor, - v: Tensor, - key_padding_mask: Optional[Tensor], - attn_mask: Optional[Tensor], - bsz: int, - ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]: - assert self.bias_k is not None - assert self.bias_v is not None - k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) - key_padding_mask, attn_mask = self._pad_masks( - key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - return k, v, key_padding_mask, attn_mask - - def _append_zero_attn( - self, - k: Tensor, - v: Tensor, - key_padding_mask: Optional[Tensor], - attn_mask: Optional[Tensor], - ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]: - zero_attn_shape = k.size()[:-2] + torch.Size([1]) + k.size()[-1:] - k = torch.cat( - [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=-2 - ) - v = torch.cat( - [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=-2 - ) - key_padding_mask, attn_mask = self._pad_masks( - key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - return k, v, key_padding_mask, attn_mask - - def _xformers_attn_forward( - self, - query, - key: Optional[Tensor], - value: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - ) -> Tuple[Tensor, Optional[Tensor]]: - - tgt_len, bsz, embed_dim = query.size() - - if key_padding_mask is not None: - assert key_padding_mask.size(0) == bsz - assert key_padding_mask.size(1) == tgt_len - - if self.self_attention: - key = query - value = query - elif self.encoder_decoder_attention: - value = key - - q = self.q_proj(query) - k = self.k_proj(key) - v = self.v_proj(value) - - if self.bias_k is not None: - assert self.bias_v is not None - k, v, attn_mask, key_padding_mask = self._add_bias( - k, v, attn_mask, key_padding_mask, bsz - ) - - def fold_heads(x): - return ( - x.contiguous() - .view(-1, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - def split_heads(x): - return ( - x.contiguous() - .view(-1, bsz, self.num_heads, self.head_dim) - .transpose(0, 1) - .transpose(1, 2) - ) - - massage = split_heads if self.attention.requires_head_dimension else fold_heads - q = massage(q) - if k is not None: - k = massage(k) - if v is not None: - v = massage(v) - - if self.add_zero_attn: - k, v, key_padding_mask, attn_mask = self._append_zero_attn( - k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - - kwargs = {} - - if attn_mask is not None and self.attention.supports_attention_mask: - attn_mask = _mask_for_xformers(attn_mask, to_dtype=q.dtype) - kwargs["att_mask"] = attn_mask - - if key_padding_mask is not None: - key_padding_mask = _mask_for_xformers(key_padding_mask, to_dtype=torch.bool) - if not self.attention.requires_separate_masks: - attn_mask = maybe_merge_masks( - attn_mask, - key_padding_mask, - batch_size=bsz, - src_len=k.size(-2), - tgt_len=q.size(-2), - num_heads=self.num_heads, - ) - key_padding_mask = None - kwargs["att_mask"] = attn_mask - if self.attention.supports_key_padding_mask: - kwargs["key_padding_mask"] = key_padding_mask - - y = self.attention(q, k, v, **kwargs) - - y = ( - y.view(bsz, self.num_heads, tgt_len, self.head_dim) - .transpose(1, 2) - .flatten(start_dim=2, end_dim=3) - .transpose(0, 1) - ) - assert list(y.size()) == [tgt_len, bsz, embed_dim] - - # Dropout not needed because already applied in attention. - # It is applied to the attention weights before matmul with v. - y = self.out_proj(y) - - # TODO: support returning attention weights if needed. - return y, None - - def forward( - self, - query, - key: Optional[Tensor], - value: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - need_weights: bool = True, - static_kv: bool = False, - attn_mask: Optional[Tensor] = None, - before_softmax: bool = False, - need_head_weights: bool = False, - ) -> Tuple[Tensor, Optional[Tensor]]: - """Input shape: Time x Batch x Channel - - Args: - key_padding_mask (ByteTensor, optional): mask to exclude - keys that are pads, of shape `(batch, src_len)`, where - padding elements are indicated by 1s. - need_weights (bool, optional): return the attention weights, - averaged over heads (default: False). - attn_mask (ByteTensor, optional): typically used to - implement causal attention, where the mask prevents the - attention from looking forward in time (default: None). - before_softmax (bool, optional): return the raw attention - weights and values before the attention softmax. - need_head_weights (bool, optional): return the attention - weights for each head. Implies *need_weights*. Default: - return the average attention weights over all heads. - """ - if need_head_weights: - need_weights = True - - is_tpu = query.device.type == "xla" - - tgt_len, bsz, embed_dim = query.size() - src_len = tgt_len - if not self.skip_embed_dim_check: - assert ( - embed_dim == self.embed_dim - ), f"query dim {embed_dim} != {self.embed_dim}" - assert list(query.size()) == [tgt_len, bsz, embed_dim] - if key is not None: - src_len, key_bsz, _ = key.size() - if not torch.jit.is_scripting(): - assert value is not None - assert src_len, key_bsz == value.shape[:2] - - if ( - not self.onnx_trace - and not is_tpu # don't use PyTorch version on TPUs - and incremental_state is None - and not static_kv - # A workaround for quantization to work. Otherwise JIT compilation - # treats bias in linear module as method. - and not torch.jit.is_scripting() - # The Multihead attention implemented in pytorch forces strong dimension check - # for input embedding dimention and K,Q,V projection dimension. - # Since pruning will break the dimension check and it is not easy to modify the pytorch API, - # it is preferred to bypass the pytorch MHA when we need to skip embed_dim_check - and not self.skip_embed_dim_check - ): - assert key is not None and value is not None - - if self.use_xformers: - return self._xformers_attn_forward( - query, key, value, key_padding_mask, need_weights, attn_mask - ) - - else: - return F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - torch.empty([0]), - torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout_module.p, - self.out_proj.weight, - self.out_proj.bias, - self.training or self.dropout_module.apply_during_inference, - key_padding_mask, - need_weights, - attn_mask, - use_separate_proj_weight=True, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - ) - - if incremental_state is not None: - saved_state = self._get_input_buffer(incremental_state) - if saved_state is not None and "prev_key" in saved_state: - # previous time steps are cached - no need to recompute - # key and value if they are static - if static_kv: - assert self.encoder_decoder_attention and not self.self_attention - key = value = None - else: - saved_state = None - - if self.self_attention: - q = self.q_proj(query) - k = self.k_proj(query) - v = self.v_proj(query) - elif self.encoder_decoder_attention: - # encoder-decoder attention - q = self.q_proj(query) - if key is None: - assert value is None - k = v = None - else: - if self.beam_size > 1 and bsz == key.size(1): - # key is [T, bsz*beam_size, C], reduce to [T, bsz, C] - key = key.view(key.size(0), -1, self.beam_size, key.size(2))[ - :, :, 0, : - ] - if key_padding_mask is not None: - key_padding_mask = key_padding_mask.view( - -1, self.beam_size, key_padding_mask.size(1) - )[:, 0, :] - k = self.k_proj(key) - v = self.v_proj(key) - - else: - assert key is not None and value is not None - q = self.q_proj(query) - k = self.k_proj(key) - v = self.v_proj(value) - q *= self.scaling - - if self.bias_k is not None: - assert self.bias_v is not None - k, v, attn_mask, key_padding_mask = self._add_bias( - k, v, attn_mask, key_padding_mask, bsz - ) - - q = ( - q.contiguous() - .view(tgt_len, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - kv_bsz = bsz # need default value for scripting - if k is not None: - kv_bsz = k.size(1) - k = ( - k.contiguous() - .view(-1, kv_bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if v is not None: - v = ( - v.contiguous() - .view(-1, kv_bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - if saved_state is not None: - # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) - if "prev_key" in saved_state: - _prev_key = saved_state["prev_key"] - assert _prev_key is not None - kv_bsz = _prev_key.size(0) - prev_key = _prev_key.view(kv_bsz * self.num_heads, -1, self.head_dim) - if static_kv: - k = prev_key - else: - assert k is not None - k = torch.cat([prev_key, k], dim=1) - src_len = k.size(1) - if "prev_value" in saved_state: - _prev_value = saved_state["prev_value"] - assert _prev_value is not None - assert kv_bsz == _prev_value.size(0) - prev_value = _prev_value.view( - kv_bsz * self.num_heads, -1, self.head_dim - ) - if static_kv: - v = prev_value - else: - assert v is not None - v = torch.cat([prev_value, v], dim=1) - prev_key_padding_mask: Optional[Tensor] = None - if "prev_key_padding_mask" in saved_state: - prev_key_padding_mask = saved_state["prev_key_padding_mask"] - assert k is not None and v is not None - key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( - key_padding_mask=key_padding_mask, - prev_key_padding_mask=prev_key_padding_mask, - batch_size=kv_bsz, - src_len=k.size(1), - static_kv=static_kv, - ) - - saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim) - saved_state["prev_value"] = v.view( - kv_bsz, self.num_heads, -1, self.head_dim - ) - saved_state["prev_key_padding_mask"] = key_padding_mask - # In this branch incremental_state is never None - assert incremental_state is not None - incremental_state = self._set_input_buffer(incremental_state, saved_state) - assert k is not None - assert k.size(1) == src_len - - # This is part of a workaround to get around fork/join parallelism - # not supporting Optional types. - if key_padding_mask is not None and key_padding_mask.dim() == 0: - key_padding_mask = None - - if key_padding_mask is not None: - assert key_padding_mask.size(0) == kv_bsz - assert key_padding_mask.size(1) == src_len - - if self.add_zero_attn: - assert v is not None - src_len += 1 - k, v, key_padding_mask, attn_mask = self._append_zero_attn( - k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - - if self.encoder_decoder_attention and bsz != kv_bsz: - attn_weights = torch.einsum( - "bxhtd,bhsd->bxhts", - q.view((kv_bsz, -1, self.num_heads) + q.size()[1:]), - k.view((kv_bsz, self.num_heads) + k.size()[1:]), - ) - attn_weights = attn_weights.reshape((-1,) + attn_weights.size()[-2:]) - else: - attn_weights = torch.bmm(q, k.transpose(1, 2)) - attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) - - assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] - - if attn_mask is not None: - attn_mask = attn_mask.unsqueeze(0) - if self.onnx_trace: - attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) - attn_weights += attn_mask - - if key_padding_mask is not None: - # don't attend to padding symbols - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - if not is_tpu: - attn_weights = attn_weights.view( - kv_bsz, -1, self.num_heads, tgt_len, src_len - ) - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .to(torch.bool), - float("-inf"), - ) - else: - attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) - attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - if before_softmax: - return attn_weights, v - - attn_weights_float = utils.softmax( - attn_weights, dim=-1, onnx_trace=self.onnx_trace - ) - attn_weights = attn_weights_float.type_as(attn_weights) - attn_probs = self.dropout_module(attn_weights) - - assert v is not None - if self.encoder_decoder_attention and bsz != kv_bsz: - attn = torch.einsum( - "bxhts,bhsd->bxhtd", - attn_probs.view( - ( - kv_bsz, - -1, - self.num_heads, - ) - + attn_probs.size()[1:] - ), - v.view( - ( - kv_bsz, - self.num_heads, - ) - + v.size()[1:] - ), - ) - attn = attn.reshape((-1,) + attn.size()[-2:]) - else: - attn = torch.bmm(attn_probs, v) - assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] - if self.onnx_trace and attn.size(1) == 1: - # when ONNX tracing a single decoder step (sequence length == 1) - # the transpose is a no-op copy before view, thus unnecessary - attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim) - else: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim) - attn = self.out_proj(attn) - attn_weights: Optional[Tensor] = None - if need_weights: - attn_weights = attn_weights_float.view( - bsz, self.num_heads, tgt_len, src_len - ).transpose(1, 0) - if not need_head_weights: - # average attention weights over heads - attn_weights = attn_weights.mean(dim=0) - - return attn, attn_weights - - @staticmethod - def _append_prev_key_padding_mask( - key_padding_mask: Optional[Tensor], - prev_key_padding_mask: Optional[Tensor], - batch_size: int, - src_len: int, - static_kv: bool, - ) -> Optional[Tensor]: - # saved key padding masks have shape (bsz, seq_len) - if prev_key_padding_mask is not None and static_kv: - new_key_padding_mask = prev_key_padding_mask - elif prev_key_padding_mask is not None and key_padding_mask is not None: - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 - ) - # During incremental decoding, as the padding token enters and - # leaves the frame, there will be a time when prev or current - # is None - elif prev_key_padding_mask is not None: - if src_len > prev_key_padding_mask.size(1): - filler = torch.zeros( - (batch_size, src_len - prev_key_padding_mask.size(1)), - device=prev_key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), filler.float()], dim=1 - ) - else: - new_key_padding_mask = prev_key_padding_mask.float() - elif key_padding_mask is not None: - if src_len > key_padding_mask.size(1): - filler = torch.zeros( - (batch_size, src_len - key_padding_mask.size(1)), - device=key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [filler.float(), key_padding_mask.float()], dim=1 - ) - else: - new_key_padding_mask = key_padding_mask.float() - else: - new_key_padding_mask = prev_key_padding_mask - return new_key_padding_mask - - @torch.jit.export - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Reorder buffered internal state (for incremental generation).""" - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - for k in input_buffer.keys(): - input_buffer_k = input_buffer[k] - if input_buffer_k is not None: - if self.encoder_decoder_attention: - if input_buffer_k.size(0) * self.beam_size == new_order.size(0): - return incremental_state - elif self.beam_size > 1: - input_buffer[k] = input_buffer_k.index_select( - 0, - new_order.reshape(-1, self.beam_size)[:, 0] - // self.beam_size, - ) - else: - input_buffer[k] = input_buffer_k.index_select(0, new_order) - else: - input_buffer[k] = input_buffer_k.index_select(0, new_order) - incremental_state = self._set_input_buffer(incremental_state, input_buffer) - return incremental_state - - def set_beam_size(self, beam_size): - """Used for effiecient beamable enc-dec attention""" - self.beam_size = beam_size - - def _get_input_buffer( - self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] - ) -> Dict[str, Optional[Tensor]]: - result = self.get_incremental_state(incremental_state, "attn_state") - if result is not None: - return result - else: - empty_result: Dict[str, Optional[Tensor]] = {} - return empty_result - - def _set_input_buffer( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - buffer: Dict[str, Optional[Tensor]], - ): - return self.set_incremental_state(incremental_state, "attn_state", buffer) - - def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): - return attn_weights - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - items_to_add = {} - keys_to_remove = [] - for k in state_dict.keys(): - if k.endswith(prefix + "in_proj_weight"): - # in_proj_weight used to be q + k + v with same dimensions - dim = int(state_dict[k].shape[0] / 3) - items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] - items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] - items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] - - keys_to_remove.append(k) - - k_bias = prefix + "in_proj_bias" - if k_bias in state_dict.keys(): - dim = int(state_dict[k].shape[0] / 3) - items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] - items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ - dim : 2 * dim - ] - items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] - - keys_to_remove.append(prefix + "in_proj_bias") - - for k in keys_to_remove: - del state_dict[k] - - for key, value in items_to_add.items(): - state_dict[key] = value diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/positional_embedding.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/positional_embedding.py deleted file mode 100644 index 97cd474b512f4ab851b4a135398a4d52a2b64533..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/positional_embedding.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn - -from .learned_positional_embedding import LearnedPositionalEmbedding -from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding - - -def PositionalEmbedding( - num_embeddings: int, - embedding_dim: int, - padding_idx: int, - learned: bool = False, -): - if learned: - # if padding_idx is specified then offset the embedding ids by - # this index and adjust num_embeddings appropriately - # TODO: The right place for this offset would be inside - # LearnedPositionalEmbedding. Move this there for a cleaner implementation. - if padding_idx is not None: - num_embeddings = num_embeddings + padding_idx + 1 - m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx) - nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5) - if padding_idx is not None: - nn.init.constant_(m.weight[padding_idx], 0) - else: - m = SinusoidalPositionalEmbedding( - embedding_dim, - padding_idx, - init_size=num_embeddings + padding_idx + 1, - ) - return m diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/positional_encoding.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/positional_encoding.py deleted file mode 100644 index cf6aba3fa2b1f89080ff7626dec2e56e743e23fb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/positional_encoding.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn as nn - - -class PositionalEncoding(nn.Module): - """Positional encoding. - - Args: - d_model: Embedding dimension. - dropout_rate: Dropout rate. - max_len: Maximum input length. - reverse: Whether to reverse the input position. - """ - - def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): - """Construct an PositionalEncoding object.""" - super(PositionalEncoding, self).__init__() - self.d_model = d_model - self.reverse = reverse - self.xscale = math.sqrt(self.d_model) - self.dropout = nn.Dropout(p=dropout_rate) - self.pe = None - self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - - def extend_pe(self, x): - """Reset the positional encodings.""" - if self.pe is not None: - if self.pe.size(1) >= x.size(1): - if self.pe.dtype != x.dtype or self.pe.device != x.device: - self.pe = self.pe.to(dtype=x.dtype, device=x.device) - return - pe = torch.zeros(x.size(1), self.d_model) - if self.reverse: - position = torch.arange( - x.size(1) - 1, -1, -1.0, dtype=torch.float32 - ).unsqueeze(1) - else: - position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, self.d_model, 2, dtype=torch.float32) - * -(math.log(10000.0) / self.d_model) - ) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) - self.pe = pe.to(device=x.device, dtype=x.dtype) - - def forward(self, x: torch.Tensor): - """Add positional encoding. - Args: - x (torch.Tensor): Input tensor B X T X C - Returns: - torch.Tensor: Encoded tensor B X T X C - """ - self.extend_pe(x) - x = x * self.xscale + self.pe[:, : x.size(1)] - return self.dropout(x) - - -class RelPositionalEncoding(nn.Module): - """Relative positional encoding module (new implementation). - - Args: - d_model: Embedding dimension. - dropout_rate: Dropout rate. - max_len: Maximum input length. - """ - - def __init__(self, max_len, d_model): - """Construct an PositionalEncoding object.""" - super(RelPositionalEncoding, self).__init__() - self.d_model = d_model - self.pe = None - self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - - def extend_pe(self, x): - """Reset the positional encodings.""" - if self.pe is not None: - # self.pe contains both positive and negative parts - # the length of self.pe is 2 * input_len - 1 - if self.pe.size(1) >= x.size(1) * 2 - 1: - if self.pe.dtype != x.dtype or self.pe.device != x.device: - self.pe = self.pe.to(dtype=x.dtype, device=x.device) - return - # Suppose `i` means to the position of query vecotr and `j` means the - # position of key vector. We use position relative positions when keys - # are to the left (i>j) and negative relative positions otherwise (i 0: - # given an empty cluster, find most populated cluster and split it into two - k = random.choice(list(empty_clusters)) - m = counts.most_common(1)[0][0] - e = torch.randn_like(self.centroids[m]) * self.eps - self.centroids[k] = self.centroids[m].clone() - self.centroids[k] += e - self.centroids[m] -= e - - # recompute assignments - distances = self.compute_distances() # (n_centroids x out_features) - self.assignments = torch.argmin(distances, dim=0) # (out_features) - - # check for empty clusters - counts = Counter(map(lambda x: x.item(), self.assignments)) - empty_clusters = set(range(self.n_centroids)) - set(counts.keys()) - - # increment tentatives - if tentatives == self.max_tentatives: - logging.info( - f"Could not resolve all empty clusters, {len(empty_clusters)} remaining" - ) - raise EmptyClusterResolveError - tentatives += 1 - - return n_empty_clusters - - def compute_distances(self): - """ - For every centroid m, computes - - ||M - m[None, :]||_2 - - Remarks: - - We rely on PyTorch's broadcasting to speed up computations - and reduce the memory overhead - - Without chunking, the sizes in the broadcasting are modified as: - (n_centroids x n_samples x out_features) -> (n_centroids x out_features) - - The broadcasting computation is automatically chunked so that - the tensors fit into the memory of the GPU - """ - - nb_centroids_chunks = 1 - - while True: - try: - return torch.cat( - [ - (self.W[None, :, :] - centroids_c[:, :, None]).norm(p=2, dim=1) - for centroids_c in self.centroids.chunk( - nb_centroids_chunks, dim=0 - ) - ], - dim=0, - ) - except RuntimeError: - nb_centroids_chunks *= 2 - - def assign(self): - """ - Assigns each column of W to its closest centroid, thus essentially - performing the E-step in train(). - - Remarks: - - The function must be called after train() or after loading - centroids using self.load(), otherwise it will return empty tensors - """ - - distances = self.compute_distances() # (n_centroids x out_features) - self.assignments = torch.argmin(distances, dim=0) # (out_features) - - def save(self, path, layer): - """ - Saves centroids and assignments. - - Args: - - path: folder used to save centroids and assignments - """ - - torch.save(self.centroids, os.path.join(path, "{}_centroids.pth".format(layer))) - torch.save( - self.assignments, os.path.join(path, "{}_assignments.pth".format(layer)) - ) - torch.save(self.objective, os.path.join(path, "{}_objective.pth".format(layer))) - - def load(self, path, layer): - """ - Loads centroids and assignments from a given path - - Args: - - path: folder use to load centroids and assignments - """ - - self.centroids = torch.load( - os.path.join(path, "{}_centroids.pth".format(layer)) - ) - self.assignments = torch.load( - os.path.join(path, "{}_assignments.pth".format(layer)) - ) - self.objective = torch.load( - os.path.join(path, "{}_objective.pth".format(layer)) - ) - - -class EmptyClusterResolveError(Exception): - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/__init__.py deleted file mode 100644 index b67c8e8ad691aa01e9e10e904d69d94595387668..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .qconv import PQConv2d # NOQA -from .qemb import PQEmbedding # NOQA -from .qlinear import PQLinear # NOQA diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qconv.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qconv.py deleted file mode 100644 index d15ec192e8cda6265a198e583a9bf7fb194dd129..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qconv.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.modules.utils import _pair - - -class PQConv2d(nn.Module): - """ - Quantized counterpart of nn.Conv2d module. Stores the centroid, the assignments - and the non-quantized biases. The full weight is re-instantiated at each forward - pass and autograd automatically computes the gradients with respect to the - centroids. - - Args: - - centroids: centroids of size n_centroids x block_size - - assignments: assignments of the centroids to the subvectors - of size self.out_channels x n_blocks - - bias: the non-quantized bias, must be either torch.Tensor or None - - Remarks: - - We refer the reader to the official documentation of the nn.Conv2d module - for the other arguments and the behavior of the module. - - Performance tests on GPU show that this implementation is 10% slower than - the non-quantized nn.Conv2d module for a standard training loop. - - During the backward, the gradients are averaged by cluster and not summed. - This explains the hook registered to the centroids. - """ - - def __init__( - self, - centroids, - assignments, - bias, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - padding_mode="zeros", - ): - super(PQConv2d, self).__init__() - self.block_size = centroids.size(1) - self.n_centroids = centroids.size(0) - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = _pair(stride) - self.padding = _pair(padding) - self.dilation = _pair(dilation) - self.groups = groups - self.padding_mode = padding_mode - # check compatibility - if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0: - raise ValueError("Wrong PQ sizes") - if len(assignments) % out_channels != 0: - raise ValueError("Wrong PQ sizes") - if in_channels % groups != 0: - raise ValueError("in_channels must be divisible by groups") - if out_channels % groups != 0: - raise ValueError("out_channels must be divisible by groups") - # define parameters - self.centroids = nn.Parameter(centroids, requires_grad=True) - self.register_buffer("assignments", assignments) - self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) - if bias is not None: - self.bias = nn.Parameter(bias) - else: - self.register_parameter("bias", None) - # register hook for averaging gradients per centroids instead of summing - self.centroids.register_hook(lambda x: x / self.counts[:, None]) - - @property - def weight(self): - return ( - self.centroids[self.assignments] - .reshape(-1, self.out_channels, self.block_size) - .permute(1, 0, 2) - .reshape( - self.out_channels, self.in_channels // self.groups, *self.kernel_size - ) - ) - - def forward(self, x): - return F.conv2d( - x, - self.weight, - self.bias, - self.stride, - self.padding, - self.dilation, - self.groups, - ) - - def extra_repr(self): - s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}" - if self.padding != (0,) * len(self.padding): - s += ", padding={padding}" - if self.dilation != (1,) * len(self.dilation): - s += ", dilation={dilation}" - if self.groups != 1: - s += ", groups={groups}" - if self.bias is None: - s += ", bias=False" - if self.padding_mode != "zeros": - s += ", padding_mode={padding_mode}" - s += ", n_centroids={n_centroids}, block_size={block_size}" - return s.format(**self.__dict__) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qemb.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qemb.py deleted file mode 100644 index 3a74ad3c4c7c9d3203d26e7885864ba578951bfe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qemb.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class PQEmbedding(nn.Module): - """ - Quantized counterpart of nn.Embedding module. Stores the centroids and - the assignments. The full weight is re-instantiated at each forward - pass. - - Args: - - centroids: centroids of size n_centroids x block_size - - assignments: assignments of the centroids to the subvectors - of size self.out_features x n_blocks - - bias: the non-quantized bias - - Remarks: - - We refer the reader to the official documentation of the nn.Embedding module - for the other arguments and the behavior of the module - - Performance tests on GPU show that this implementation is 10% slower than - the non-quantized nn.Embedding module for a standard training loop. - """ - - def __init__( - self, - centroids, - assignments, - num_embeddings, - embedding_dim, - padding_idx=None, - max_norm=None, - norm_type=2.0, - scale_grad_by_freq=False, - sparse=False, - _weight=None, - ): - super(PQEmbedding, self).__init__() - self.block_size = centroids.size(1) - self.n_centroids = centroids.size(0) - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - if padding_idx is not None: - if padding_idx > 0: - assert ( - padding_idx < self.num_embeddings - ), "Padding_idx must be within num_embeddings" - elif padding_idx < 0: - assert ( - padding_idx >= -self.num_embeddings - ), "Padding_idx must be within num_embeddings" - padding_idx = self.num_embeddings + padding_idx - self.padding_idx = padding_idx - self.max_norm = max_norm - self.norm_type = norm_type - self.scale_grad_by_freq = scale_grad_by_freq - self.sparse = sparse - # check compatibility - if self.embedding_dim % self.block_size != 0: - raise ValueError("Wrong PQ sizes") - if len(assignments) % self.num_embeddings != 0: - raise ValueError("Wrong PQ sizes") - # define parameters - self.centroids = nn.Parameter(centroids, requires_grad=True) - self.register_buffer("assignments", assignments) - self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) - - @property - def weight(self): - return ( - self.centroids[self.assignments] - .reshape(-1, self.num_embeddings, self.block_size) - .permute(1, 0, 2) - .flatten(1, 2) - ) - - def forward(self, input): - return F.embedding( - input, - self.weight, - self.padding_idx, - self.max_norm, - self.norm_type, - self.scale_grad_by_freq, - self.sparse, - ) - - def extra_repr(self): - s = "{num_embeddings}, {embedding_dim}" - if self.padding_idx is not None: - s += ", padding_idx={padding_idx}" - if self.max_norm is not None: - s += ", max_norm={max_norm}" - if self.norm_type != 2: - s += ", norm_type={norm_type}" - if self.scale_grad_by_freq is not False: - s += ", scale_grad_by_freq={scale_grad_by_freq}" - if self.sparse is not False: - s += ", sparse=True" - s += ", n_centroids={n_centroids}, block_size={block_size}" - - return s.format(**self.__dict__) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qlinear.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qlinear.py deleted file mode 100644 index 9bdd25a8685bb7c7b32e1f02372aaeb26d8ba53a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/modules/qlinear.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class PQLinear(nn.Module): - """ - Quantized counterpart of nn.Linear module. Stores the centroid, the assignments - and the non-quantized biases. The full weight is re-instantiated at each forward - pass. - - Args: - - centroids: centroids of size n_centroids x block_size - - assignments: assignments of the centroids to the subvectors - of size self.out_features x n_blocks - - bias: the non-quantized bias - - Remarks: - - We refer the reader to the official documentation of the nn.Linear module - for the other arguments and the behavior of the module - - Performance tests on GPU show that this implementation is 15% slower than - the non-quantized nn.Linear module for a standard training loop. - """ - - def __init__(self, centroids, assignments, bias, in_features, out_features): - super(PQLinear, self).__init__() - self.block_size = centroids.size(1) - self.n_centroids = centroids.size(0) - self.in_features = in_features - self.out_features = out_features - # check compatibility - if self.in_features % self.block_size != 0: - raise ValueError("Wrong PQ sizes") - if len(assignments) % self.out_features != 0: - raise ValueError("Wrong PQ sizes") - # define parameters - self.centroids = nn.Parameter(centroids, requires_grad=True) - self.register_buffer("assignments", assignments) - self.register_buffer("counts", torch.bincount(assignments).type_as(centroids)) - if bias is not None: - self.bias = nn.Parameter(bias) - else: - self.register_parameter("bias", None) - - @property - def weight(self): - return ( - self.centroids[self.assignments] - .reshape(-1, self.out_features, self.block_size) - .permute(1, 0, 2) - .flatten(1, 2) - ) - - def forward(self, x): - return F.linear( - x, - self.weight, - self.bias, - ) - - def extra_repr(self): - return f"in_features={self.in_features},\ - out_features={self.out_features},\ - n_centroids={self.n_centroids},\ - block_size={self.block_size},\ - bias={self.bias is not None}" diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/pq.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/pq.py deleted file mode 100644 index eddc2eb34602403f10979f54cd23a45bc2f104d5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/pq.py +++ /dev/null @@ -1,128 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .em import EM, EmptyClusterResolveError - - -class PQ(EM): - """ - Quantizes the layer weights W with the standard Product Quantization - technique. This learns a codebook of codewords or centroids of size - block_size from W. For further reference on using PQ to quantize - neural networks, see "And the Bit Goes Down: Revisiting the Quantization - of Neural Networks", Stock et al., ICLR 2020. - - PQ is performed in two steps: - (1) The matrix W (weights or fully-connected or convolutional layer) - is reshaped to (block_size, -1). - - If W is fully-connected (2D), its columns are split into - blocks of size block_size. - - If W is convolutional (4D), its filters are split along the - spatial dimension. - (2) We apply the standard EM/k-means algorithm to the resulting reshaped matrix. - - Args: - - W: weight matrix to quantize of size (in_features x out_features) - - block_size: size of the blocks (subvectors) - - n_centroids: number of centroids - - n_iter: number of k-means iterations - - eps: for cluster reassignment when an empty cluster is found - - max_tentatives for cluster reassignment when an empty cluster is found - - verbose: print information after each iteration - - Remarks: - - block_size be compatible with the shape of W - """ - - def __init__( - self, - W, - block_size, - n_centroids=256, - n_iter=20, - eps=1e-6, - max_tentatives=30, - verbose=True, - ): - self.block_size = block_size - W_reshaped = self._reshape(W) - super(PQ, self).__init__( - W_reshaped, - n_centroids=n_centroids, - n_iter=n_iter, - eps=eps, - max_tentatives=max_tentatives, - verbose=verbose, - ) - - def _reshape(self, W): - """ - Reshapes the matrix W as expained in step (1). - """ - - # fully connected: by convention the weight has size out_features x in_features - if len(W.size()) == 2: - self.out_features, self.in_features = W.size() - assert ( - self.in_features % self.block_size == 0 - ), "Linear: n_blocks must be a multiple of in_features" - return ( - W.reshape(self.out_features, -1, self.block_size) - .permute(2, 1, 0) - .flatten(1, 2) - ) - - # convolutional: we reshape along the spatial dimension - elif len(W.size()) == 4: - self.out_channels, self.in_channels, self.k_h, self.k_w = W.size() - assert ( - self.in_channels * self.k_h * self.k_w - ) % self.block_size == 0, ( - "Conv2d: n_blocks must be a multiple of in_channels * k_h * k_w" - ) - return ( - W.reshape(self.out_channels, -1, self.block_size) - .permute(2, 1, 0) - .flatten(1, 2) - ) - # not implemented - else: - raise NotImplementedError(W.size()) - - def encode(self): - """ - Performs self.n_iter EM steps. - """ - - self.initialize_centroids() - for i in range(self.n_iter): - try: - self.step(i) - except EmptyClusterResolveError: - break - - def decode(self): - """ - Returns the encoded full weight matrix. Must be called after - the encode function. - """ - - # fully connected case - if "k_h" not in self.__dict__: - return ( - self.centroids[self.assignments] - .reshape(-1, self.out_features, self.block_size) - .permute(1, 0, 2) - .flatten(1, 2) - ) - - # convolutional case - else: - return ( - self.centroids[self.assignments] - .reshape(-1, self.out_channels, self.block_size) - .permute(1, 0, 2) - .reshape(self.out_channels, self.in_channels, self.k_h, self.k_w) - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/utils.py deleted file mode 100644 index 98454f1bb2db9457b58be8bb4a1314e0dd5eb3a9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/pq/utils.py +++ /dev/null @@ -1,377 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import re -from operator import attrgetter, itemgetter - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn - -from .modules import PQConv2d, PQEmbedding, PQLinear -from .pq import PQ - - -def quantize_model_( - model, - size_tracker, - layers_to_quantize, - block_sizes_config, - n_centroids_config, - step=0, - n_iter=15, - eps=1e-6, - max_tentatives=100, - remove_weights=False, - verbose=True, - state_dict=None, -): - """ - Quantize a model in-place by stages. All the targeted - layers are replaced by their quantized counterpart, - and the model is ready for the finetuning of the - centroids in a standard training loop (no modifications - required). Note that we do not quantize biases. - - Args: - - model: a nn.Module - - size_tracker: useful for tracking quatization statistics - - layers_to_quantize: a list containing regexps for - filtering the layers to quantize at each stage according - to their name (as in model.named_parameters()) - - block_sizes_config: dict like - { - 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), - 'Linear': ('in_features', {'*': 8}) - } - For instance, all conv2d layers with kernel size 3x3 have - a block size of 9 and all Linear layers are quantized with - a block size of 8, irrespective of their size. - - n_centroids_config: dict like - { - 'Conv2d': ('kernel_size', {'*': 256}), - 'Linear': ('in_features', {'*': 256}) - } - For instance, all conv2d layers are quantized with 256 centroids - - step: the layers to quantize inplace corresponding - to layers_to_quantize[step] - """ - - quantized_layers = get_layers( - model, layers_to_quantize[step], remove_weights=remove_weights - ) - - for layer in quantized_layers: - - # book-keeping - is_master_process = (not dist.is_initialized()) or ( - dist.is_initialized() and dist.get_rank() == 0 - ) - verbose = verbose and is_master_process - - # get block size and centroids - module = attrgetter(layer)(model) - block_size = get_param(module, layer, block_sizes_config) - n_centroids = get_param(module, layer, n_centroids_config) - if verbose: - logging.info( - f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids" - ) - - # quantize layer - weight = module.weight.data.clone() - is_bias = "bias" in [x[0] for x in module.named_parameters()] - bias = module.bias.data.clone() if is_bias else None - quantizer = PQ( - weight, - block_size, - n_centroids=n_centroids, - n_iter=n_iter, - eps=eps, - max_tentatives=max_tentatives, - verbose=verbose, - ) - - # quantization performed on all GPUs with same seed - quantizer.encode() - centroids = quantizer.centroids.contiguous() - assignments = quantizer.assignments.contiguous() - - # If n_iter = 0 and state_dict is provided, then - # we initialize random assignments and centroids to - # random values of the appropriate dimensions - # because the quantized model parameters will - # overwritten by the state_dict later on. - if n_iter == 0 and state_dict: - # Initialize random centroids of the correct size - centroids = torch.rand(centroids.size()) - centroids.cuda() - # Get counts and assignment keys from layer in loaded checkpoint. - counts_key = layer + "." + "counts" - assignment_key = layer + "." + "assignments" - # Get number of different bins to include. - counts = list(state_dict[counts_key].shape)[0] - print(layer) - print(state_dict[counts_key]) - print(counts) - # Initialize random assignments of the correct size - # with an appropriate number of bins. - num_assignments = list(state_dict[assignment_key].shape)[0] - num_extra = num_assignments - counts - print(num_assignments) - print(num_extra) - assignments_bins = torch.arange(counts) - assignments_rand = torch.randint(0, counts - 1, (num_extra,)) - assignments = torch.cat((assignments_bins, assignments_rand), 0) - # assignments = assignments.type(torch.IntTensor) - assignments.cuda() - print("assignments") - print(assignments) - - # broadcast results to make sure weights are up-to-date - if dist.is_initialized(): - dist.broadcast(centroids, 0) - dist.broadcast(assignments, 0) - - # instantiate the quantized counterpart - if isinstance(module, nn.Linear): - out_features, in_features = map( - lambda k: module.__dict__[k], ["out_features", "in_features"] - ) - quantized_module = PQLinear( - centroids, assignments, bias, in_features, out_features - ) - elif isinstance(module, nn.Embedding): - num_embeddings, embedding_dim = map( - lambda k: module.__dict__[k], ["num_embeddings", "embedding_dim"] - ) - quantized_module = PQEmbedding( - centroids, assignments, num_embeddings, embedding_dim - ) - elif isinstance(module, nn.Conv2d): - out_channels, in_channels, kernel_size = map( - lambda k: module.__dict__[k], - ["out_channels", "in_channels", "kernel_size"], - ) - stride, padding, dilation, groups, padding_mode = map( - lambda k: module.__dict__[k], - ["stride", "padding", "dilation", "groups", "padding_mode"], - ) - - quantized_module = PQConv2d( - centroids, - assignments, - bias, - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - padding_mode=padding_mode, - ) - else: - raise ValueError(f"Module {module} not yet supported for quantization") - - # replace layer by its quantized counterpart - attrsetter(layer)(model, quantized_module) - - # update statistics - size_tracker.update(weight, block_size, n_centroids) - - # return name of quantized layers - return quantized_layers - - -def get_layers(model, filter_regexp, remove_weights=False): - """ - Filters out the layers according to a regexp. Note that - we omit biases. - - Args: - - model: a nn.Module - - filter_regexp: a regexp to filter the layers to keep - according to their name in model.named_parameters(). - For instance, the regexp: - - down_layers\\.[123456]\\.(conv[12]|identity\\.conv)) - - is keeping blocks down_layers from 1 to 6, and inside - each block is keeping conv1, conv2 and identity.conv. - - Remarks: - - We add (module\\.)? at the beginning of the regexp to - account for the possible use of nn.parallel.DataParallel - """ - - # get all parameter names - all_layers = map(itemgetter(0), model.named_parameters()) - - # remove biases - all_layers = filter(lambda x: "bias" not in x, all_layers) - - # remove .weight in all other names (or .weight_orig is spectral norm) - all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers) - # remove weights indicates whether the weights extension should be removed, in addition to - # weight_orig and weight extension on names - if remove_weights: - all_layers = map(lambda x: x.replace(".weights", ""), all_layers) - all_layers = map(lambda x: x.replace(".weight", ""), all_layers) - - # return filtered layers - filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")" - r = re.compile(filter_regexp) - - return list(filter(r.match, all_layers)) - - -def get_param(module, layer_name, param_config): - """ - Given a quantization configuration, get the right parameter - for the module to be quantized. - - Args: - - module: a nn.Module - - layer_name: the name of the layer - - param_config: a dict like - { - 'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}), - 'Linear': ('in_features', {'*': 8}) - } - For instance, all conv2d layers with kernel size 3x3 have - a block size of 9 and all Linear layers are quantized with - a block size of 8, irrespective of their size. - - Remarks: - - if 'fuzzy_name' is passed as a parameter, layers whose layer_name - include 'fuzzy_name' will be assigned the given parameter. - In the following example, conv.expand layers will have a block - size of 9 while conv.reduce will have a block size of 4 and all - other layers will have a block size of 2. - { - 'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}), - 'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4}) - } - - """ - - layer_type = module.__class__.__name__ - - if layer_type not in param_config: - raise KeyError(f"Layer type {layer_type} not in config for layer {module}") - - feature, params = param_config[module.__class__.__name__] - - if feature != "fuzzy_name": - feature_value = str(getattr(module, feature)) - if feature_value not in params: - if "*" in params: - feature_value = "*" - else: - raise KeyError( - f"{feature}={feature_value} not in config for layer {module}" - ) - else: - feature_values = [name for name in params if name in layer_name] - if len(feature_values) == 0: - if "*" in params: - feature_value = "*" - else: - raise KeyError(f"name={layer_name} not in config for {module}") - else: - feature_value = feature_values[0] - - return params[feature_value] - - -class SizeTracker(object): - """ - Class to keep track of the compressed network size with iPQ. - - Args: - - model: a nn.Module - - Remarks: - - The compressed size is the sum of three components - for each layer in the network: - (1) Storing the centroids given by iPQ in fp16 - (2) Storing the assignments of the blocks in int8 - (3) Storing all non-compressed elements such as biases - - This cost in only valid if we use 256 centroids (then - indexing can indeed by done with int8). - """ - - def __init__(self, model): - self.model = model - self.size_non_compressed_model = self.compute_size() - self.size_non_quantized = self.size_non_compressed_model - self.size_index = 0 - self.size_centroids = 0 - self.n_quantized_layers = 0 - - def compute_size(self): - """ - Computes the size of the model (in MB). - """ - - res = 0 - for _, p in self.model.named_parameters(): - res += p.numel() - return res * 4 / 1024 / 1024 - - def update(self, W, block_size, n_centroids): - """ - Updates the running statistics when quantizing a new layer. - """ - - # bits per weights - bits_per_weight = np.log2(n_centroids) / block_size - self.n_quantized_layers += 1 - - # size of indexing the subvectors of size block_size (in MB) - size_index_layer = bits_per_weight * W.numel() / 8 / 1024 / 1024 - self.size_index += size_index_layer - - # size of the centroids stored in float16 (in MB) - size_centroids_layer = n_centroids * block_size * 2 / 1024 / 1024 - self.size_centroids += size_centroids_layer - - # size of non-compressed layers, e.g. LayerNorms or biases (in MB) - size_uncompressed_layer = W.numel() * 4 / 1024 / 1024 - self.size_non_quantized -= size_uncompressed_layer - - def __repr__(self): - size_compressed = ( - self.size_index + self.size_centroids + self.size_non_quantized - ) - compression_ratio = self.size_non_compressed_model / size_compressed # NOQA - return ( - f"Non-compressed model size: {self.size_non_compressed_model:.2f} MB. " - f"After quantizing {self.n_quantized_layers} layers, size " - f"(indexing + centroids + other): {self.size_index:.2f} MB + " - f"{self.size_centroids:.2f} MB + {self.size_non_quantized:.2f} MB = " - f"{size_compressed:.2f} MB, compression ratio: {compression_ratio:.2f}x" - ) - - -def attrsetter(*items): - def resolve_attr(obj, attr): - attrs = attr.split(".") - head = attrs[:-1] - tail = attrs[-1] - - for name in head: - obj = getattr(obj, name) - return obj, tail - - def g(obj, val): - for attr in items: - resolved_obj, resolved_attr = resolve_attr(obj, attr) - setattr(resolved_obj, resolved_attr, val) - - return g diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/quantization_options.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/quantization_options.py deleted file mode 100644 index b46d682c0edaeaaf2a230e51d50da2a32d4bda98..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/quantization_options.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -def parse_config_yaml(yaml_data): - # Initialize to default options. - quantization_options = { - "n_centroids": { - "Linear": ["in_features", {"*": 256}], - "Embedding": ["embedding_dim", {"*": 256}], - }, - "block_sizes": { - "Linear": ["fuzzy_name", {"fc": 8, "attn": 4, "emb": 4}], - "Embedding": ["fuzzy_name", {"emb": 8}], - }, - "layers_to_quantize": [ - "decoder\\.layers\\.\\d+\\.fc[12]", - "decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01]", - "decoder\\.layers\\.\\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj)", - ], - } - - if "n_centroids" in yaml_data: - quantization_options["n_centroids"] = { - layer: convert_yaml_to_tuple(layer_data) - for layer, layer_data in yaml_data["n_centroids"].items() - } - if "block_sizes" in yaml_data: - quantization_options["block_sizes"] = { - layer: convert_yaml_to_tuple(layer_data) - for layer, layer_data in yaml_data["block_sizes"].items() - } - if "layers_to_quantize" in yaml_data: - quantization_options["layers_to_quantize"] = yaml_data["layers_to_quantize"] - - return quantization_options - - -def convert_yaml_to_tuple(yaml_dictionary): - """Converts a yaml dictionary with two keys: `key` and `value` into a two - argument tuple of those values.""" - return (yaml_dictionary["key"], yaml_dictionary["value"]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/__init__.py deleted file mode 100644 index 143834f3d036780eb6844c82f0c6f2d10cfe2f61..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .utils import quantize_model_ # NOQA diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/__init__.py deleted file mode 100644 index 8031d9cdb23f2bc72596f8bc9cfa4965f96e3e6c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .qact import ActivationQuantizer # NOQA -from .qconv import IntConv2d # NOQA -from .qemb import IntEmbedding # NOQA -from .qlinear import IntLinear # NOQA diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qact.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qact.py deleted file mode 100644 index b362c30dc7982e3717d1b764ae737ec1b24ea78e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qact.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from ..ops import emulate_int - - -class ActivationQuantizer: - """ - Fake scalar quantization of the activations using a forward hook. - - Args: - - module. a nn.Module for which we quantize the *post-activations* - - p: proportion of activations to quantize, set by default to 1 - - update_step: to recompute quantization parameters - - bits: number of bits for quantization - - method: choose among {"tensor", "histogram", "channel"} - - clamp_threshold: to prevent gradients overflow - - Remarks: - - Parameters scale and zero_point are recomputed every update_step - forward pass to reduce the overhead - - For the list of quantization methods and number of bits, see ops.py - - To remove the hook from the module, simply call self.handle.remove() - - At test time, the activations are fully quantized - - We use the straight-through estimator so that the gradients - back-propagate nicely in the network, this is implemented with - the detach() trick - - The activations are hard-clamped in [-clamp_threshold, clamp_threshold] - to prevent overflow during the backward pass - """ - - def __init__( - self, - module, - p=1, - update_step=1000, - bits=8, - method="histogram", - clamp_threshold=5, - ): - self.module = module - self.p = p - self.update_step = update_step - self.counter = 0 - self.bits = bits - self.method = method - self.clamp_threshold = clamp_threshold - self.handle = None - self.register_hook() - - def register_hook(self): - # forward hook - def quantize_hook(module, x, y): - - # update parameters every 1000 iterations - if self.counter % self.update_step == 0: - self.scale = None - self.zero_point = None - self.counter += 1 - - # train with QuantNoise and evaluate the fully quantized network - p = self.p if self.module.training else 1 - - # quantize activations - y_q, self.scale, self.zero_point = emulate_int( - y.detach(), - bits=self.bits, - method=self.method, - scale=self.scale, - zero_point=self.zero_point, - ) - - # mask to apply noise - mask = torch.zeros_like(y) - mask.bernoulli_(1 - p) - noise = (y_q - y).masked_fill(mask.bool(), 0) - - # using straight-through estimator (STE) - clamp_low = -self.scale * self.zero_point - clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) - return torch.clamp(y, clamp_low.item(), clamp_high.item()) + noise.detach() - - # register hook - self.handle = self.module.register_forward_hook(quantize_hook) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qconv.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qconv.py deleted file mode 100644 index 29744744ecffaec6187888e343f6d6031660292f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qconv.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -from torch.nn.modules.conv import _ConvNd -from torch.nn.modules.utils import _pair - -from ..ops import emulate_int - - -class IntConv2d(_ConvNd): - """ - Quantized counterpart of the nn.Conv2d module that applies QuantNoise during training. - - Args: - - standard nn.Conv2d parameters - - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights) - - bits: number of bits - - method: choose among {"tensor", "histogram", "channel"} - - update_step: recompute scale and zero_point every update_steps iterations - - Remarks: - - We use the straight-thgourh estimator so that the gradients - back-propagate nicely in the network, this is implemented with - the detach() trick - - Parameters scale and zero_point are recomputed every update_step - forward pass to reduce the overhead - - At test time, the weights are fully quantized - """ - - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True, - padding_mode="zeros", - p=0, - bits=8, - method="histogram", - update_step=1000, - ): - kernel_size = _pair(kernel_size) - stride = _pair(stride) - padding = _pair(padding) - dilation = _pair(dilation) - super(IntConv2d, self).__init__( - in_channels, - out_channels, - kernel_size, - stride, - padding, - dilation, - False, - _pair(0), - groups, - bias, - padding_mode, - ) - - # quantization parameters - self.p = p - self.bits = bits - self.method = method - self.update_step = update_step - self.counter = 0 - - def _conv_forward(self, input, weight): - if self.padding_mode != "zeros": - return F.conv2d( - F.pad(input, self._padding_repeated_twice, mode=self.padding_mode), - weight, - self.bias, - self.stride, - _pair(0), - self.dilation, - self.groups, - ) - return F.conv2d( - input, - weight, - self.bias, - self.stride, - self.padding, - self.dilation, - self.groups, - ) - - def forward(self, input): - # train with QuantNoise and evaluate the fully quantized network - p = self.p if self.training else 1 - - # update parameters every 100 iterations - if self.counter % self.update_step == 0: - self.scale = None - self.zero_point = None - self.counter += 1 - - # quantize weight - weight_quantized, self.scale, self.zero_point = emulate_int( - self.weight.detach(), - bits=self.bits, - method=self.method, - scale=self.scale, - zero_point=self.zero_point, - ) - - # mask to apply noise - mask = torch.zeros_like(self.weight) - mask.bernoulli_(1 - p) - noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) - - # using straight-through estimator (STE) - clamp_low = -self.scale * self.zero_point - clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) - weight = ( - torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) - + noise.detach() - ) - - # return output - output = self._conv_forward(input, weight) - return output - - def extra_repr(self): - return ( - "in_channels={}, out_channels={}, kernel_size={}, stride={}, " - "padding={}, dilation={}, groups={}, bias={}, quant_noise={}, " - "bits={}, method={}".format( - self.in_channels, - self.out_channels, - self.kernel_size, - self.stride, - self.padding, - self.dilation, - self.groups, - self.bias is not None, - self.p, - self.bits, - self.method, - ) - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qemb.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qemb.py deleted file mode 100644 index 3b293ac31e9ab683c47e05b763c077ec181e64f7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qemb.py +++ /dev/null @@ -1,147 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..ops import emulate_int - - -class IntEmbedding(nn.Module): - """ - Quantized counterpart of the nn.Embedding module that applies QuantNoise during training. - - Args: - - num_embeddings: number of tokens - - embedding_dim: embedding dimension - - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights) - - bits: number of bits - - method: choose among {"tensor", "histogram", "channel"} - - update_step: recompute scale and zero_point every update_steps iterations - - Remarks: - - We use the straight-through estimator so that the gradients - back-propagate nicely in the network, this is implemented with - the detach() trick - - Parameters scale and zero_point are recomputed every update_step - forward pass to reduce the overhead - - At test time, the weights are fully quantized - """ - - def __init__( - self, - num_embeddings, - embedding_dim, - padding_idx=None, - max_norm=None, - norm_type=2.0, - scale_grad_by_freq=False, - sparse=False, - _weight=None, - p=0, - update_step=1000, - bits=8, - method="histogram", - ): - super(IntEmbedding, self).__init__() - self.num_embeddings = num_embeddings - self.embedding_dim = embedding_dim - if padding_idx is not None: - if padding_idx > 0: - assert ( - padding_idx < self.num_embeddings - ), "Padding_idx must be within num_embeddings" - elif padding_idx < 0: - assert ( - padding_idx >= -self.num_embeddings - ), "Padding_idx must be within num_embeddings" - padding_idx = self.num_embeddings + padding_idx - self.padding_idx = padding_idx - self.max_norm = max_norm - self.norm_type = norm_type - self.scale_grad_by_freq = scale_grad_by_freq - if _weight is None: - self.weight = nn.Parameter(torch.Tensor(num_embeddings, embedding_dim)) - self.reset_parameters() - else: - assert list(_weight.shape) == [ - num_embeddings, - embedding_dim, - ], "Shape of weight does not match num_embeddings and embedding_dim" - self.weight = nn.Parameter(_weight) - self.sparse = sparse - - # quantization parameters - self.p = p - self.bits = bits - self.method = method - self.update_step = update_step - self.counter = 0 - - def reset_parameters(self): - nn.init.normal_(self.weight) - if self.padding_idx is not None: - with torch.no_grad(): - self.weight[self.padding_idx].fill_(0) - - def forward(self, input): - # train with QuantNoise and evaluate the fully quantized network - p = self.p if self.training else 1 - - # update parameters every 1000 iterations - if self.counter % self.update_step == 0: - self.scale = None - self.zero_point = None - self.counter += 1 - - # quantize weight - weight_quantized, self.scale, self.zero_point = emulate_int( - self.weight.detach(), - bits=self.bits, - method=self.method, - scale=self.scale, - zero_point=self.zero_point, - ) - - # mask to apply noise - mask = torch.zeros_like(self.weight) - mask.bernoulli_(1 - p) - noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) - - # using straight-through estimator (STE) - clamp_low = -self.scale * self.zero_point - clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) - weight = ( - torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) - + noise.detach() - ) - - # return output - output = F.embedding( - input, - weight, - self.padding_idx, - self.max_norm, - self.norm_type, - self.scale_grad_by_freq, - self.sparse, - ) - return output - - def extra_repr(self): - s = "{num_embeddings}, {embedding_dim}" - if self.padding_idx is not None: - s += ", padding_idx={padding_idx}" - if self.max_norm is not None: - s += ", max_norm={max_norm}" - if self.norm_type != 2: - s += ", norm_type={norm_type}" - if self.scale_grad_by_freq is not False: - s += ", scale_grad_by_freq={scale_grad_by_freq}" - if self.sparse is not False: - s += ", sparse=True" - s += "quant_noise={p}, bits={bits}, method={method}" - return s.format(**self.__dict__) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qlinear.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qlinear.py deleted file mode 100644 index 78606a25b98b69b42eef72742037616b21116133..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/modules/qlinear.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..ops import emulate_int - - -class IntLinear(nn.Module): - """ - Quantized counterpart of the nn.Linear module that applies QuantNoise during training. - - Args: - - in_features: input features - - out_features: output features - - bias: bias or not - - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights) - - bits: number of bits - - method: choose among {"tensor", "histogram", "channel"} - - update_step: recompute scale and zero_point every update_steps iterations - - Remarks: - - We use the straight-through estimator so that the gradients - back-propagate nicely in the network, this is implemented with - the detach() trick. - - Parameters scale and zero_point are recomputed every update_step - forward pass to reduce the overhead - - At test time, the weights are fully quantized - """ - - def __init__( - self, - in_features, - out_features, - bias=True, - p=0, - update_step=3000, - bits=8, - method="histogram", - ): - super(IntLinear, self).__init__() - self.in_features = int(in_features) - self.out_features = int(out_features) - self.weight = torch.nn.Parameter(torch.Tensor(out_features, in_features)) - self.chosen_bias = bias - if self.chosen_bias: - self.bias = torch.nn.Parameter(torch.Tensor(out_features)) - else: - self.register_parameter("bias", None) - self.reset_parameters() - - # quantization parameters - self.p = p - self.bits = bits - self.method = method - self.update_step = update_step - self.counter = 0 - - def reset_parameters(self): - nn.init.xavier_uniform_(self.weight) - if self.chosen_bias: - nn.init.constant_(self.bias, 0.0) - return - - def forward(self, input): - # train with QuantNoise and evaluate the fully quantized network - p = self.p if self.training else 1 - - # update parameters every 100 iterations - if self.counter % self.update_step == 0: - self.scale = None - self.zero_point = None - self.counter += 1 - - # quantize weight - weight_quantized, self.scale, self.zero_point = emulate_int( - self.weight.detach(), - bits=self.bits, - method=self.method, - scale=self.scale, - zero_point=self.zero_point, - ) - - # mask to apply noise - mask = torch.zeros_like(self.weight) - mask.bernoulli_(1 - p) - noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0) - - # using straight-through estimator (STE) - clamp_low = -self.scale * self.zero_point - clamp_high = self.scale * (2**self.bits - 1 - self.zero_point) - weight = ( - torch.clamp(self.weight, clamp_low.item(), clamp_high.item()) - + noise.detach() - ) - - # return output - output = F.linear(input, weight, self.bias) - return output - - def extra_repr(self): - return "in_features={}, out_features={}, bias={}, quant_noise={}, bits={}, method={}".format( - self.in_features, - self.out_features, - self.bias is not None, - self.p, - self.bits, - self.method, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/ops.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/ops.py deleted file mode 100644 index e0f9a0c1f83112e554cbbea37aafa7eea27900db..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/ops.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -try: - import torch.ao.quantization as quantization -except ImportError: - import torch.quantization as quantization - - -def emulate_int(w, bits, method, scale=None, zero_point=None): - q = globals()[f"emulate_int8_{method}"] - return q(w, scale=scale, zero_point=zero_point, bits=bits) - - -def quantize(w, scale, zero_point, bits=8): - # In the default behavior, max_val = 255. - max_val = 2**bits - 1 - return ( - torch.clamp(torch.round(w / scale + zero_point), 0, max_val) - zero_point - ) * scale - - -def emulate_int8_histogram(w, scale=None, zero_point=None, bits=8): - if scale is None: - obs = quantization.observer.HistogramObserver() - obs.to(device=w.device) - _ = obs(w.float()) - scale, zero_point = obs.calculate_qparams() - scale = scale.cuda().type_as(w) - zero_point = zero_point.cuda().type_as(w) - return quantize(w, scale, zero_point, bits=bits), scale, zero_point - - -def emulate_int8_channel(w, scale=None, zero_point=None, bits=8): - if scale is None: - obs = quantization.observer.PerChannelMinMaxObserver( - ch_axis=-1, qscheme=torch.per_channel_symmetric - ) - obs.to(device=w.device) - _ = obs(w) - scale, zero_point, ch_axis = obs.get_qparams() - scale = scale.cuda().type_as(w) - zero_point = zero_point.cuda().type_as(w) - return quantize(w, scale, zero_point, bits=bits), scale, zero_point - - -def emulate_int8_tensor(w, scale=None, zero_point=None, bits=8): - if scale is None: - obs = quantization.observer.MinMaxObserver() - obs.to(device=w.device) - _ = obs(w) - scale, zero_point = obs.calculate_qparams() - scale = scale.cuda().type_as(w) - zero_point = zero_point.cuda().type_as(w) - return quantize(w, scale, zero_point, bits=bits), scale, zero_point diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/utils.py deleted file mode 100644 index fbe59ba946ce5232bf37cba5441d07c7e33665db..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/quantization/scalar/utils.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from operator import attrgetter - -import torch.distributed as dist -import torch.nn as nn - -from ..pq.utils import attrsetter, get_layers -from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear - -MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d} - - -def quantize_model_( - model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False -): - """ - Replaces all modules with their scalar quantized counterpart and - registers hooks to quantize the post-ativations of those modules. - - Args: - - model: a nn.Module - - p: amount of noise (0 for no noise, 1 to quantize all the weights/activations) - - bits: number of bits - - update_step: update quantization parameters every update_step steps - """ - # quantize all layers - # remove weights indicates whether the weights extension should be removed, in addition to - # weight_orig and weight extension on names - quantized_layers = get_layers(model, "(.*?)", remove_weights=remove_weights) - - for layer in quantized_layers: - - # book-keeping - is_master_process = (not dist.is_initialized()) or ( - dist.is_initialized() and dist.get_rank() == 0 - ) - - # recover module - module = attrgetter(layer)(model) - if is_master_process: - logging.info( - f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}" - ) - - # quantization params - q_params = { - "p": p, - "update_step": update_step, - "bits": bits, - "method": method, - "counter": 0, - } - - # instantiate the quantized counterpart - if isinstance(module, tuple(MAPPING.keys())): - QuantizedModule = MAPPING[module.__class__] - quantized_module = QuantizedModule.__new__(QuantizedModule) - params = module.__dict__ - params.update(q_params) - quantized_module.__dict__.update(params) - - else: - if is_master_process: - logging.info(f"Module {module} not yet supported for quantization") - continue - - # activation quantization - a_q = ActivationQuantizer(quantized_module, p=0, bits=bits, method=method) - - # replace layer by its quantized counterpart - attrsetter(layer)(model, quantized_module) - - # return name of quantized layers - return quantized_layers diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/rotary_positional_embedding.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/rotary_positional_embedding.py deleted file mode 100644 index 84b88984ea2a3fb5af95a4362c1bded71a19df2e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/rotary_positional_embedding.py +++ /dev/null @@ -1,51 +0,0 @@ -import torch - - -class RotaryPositionalEmbedding(torch.nn.Module): - def __init__(self, dim, base=10000, precision=torch.half): - """Rotary positional embedding - Reference : https://blog.eleuther.ai/rotary-embeddings/ - Paper: https://arxiv.org/pdf/2104.09864.pdf - Args: - dim: Dimension of embedding - base: Base value for exponential - precision: precision to use for numerical values - """ - super().__init__() - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) - self.register_buffer("inv_freq", inv_freq) - self.seq_len_cached = None - self.cos_cached = None - self.sin_cached = None - self.precision = precision - - def forward(self, x, seq_len=None): - """ - Args: - x: Input x with T X B X C - seq_len: Sequence length of input x - """ - if seq_len != self.seq_len_cached: - self.seq_len_cached = seq_len - t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq) - freqs = torch.einsum("i,j->ij", t, self.inv_freq) - emb = torch.cat((freqs, freqs), dim=-1).to(x.device) - self.cos_cached = emb.cos()[:, None, None, :] - self.sin_cached = emb.sin()[:, None, None, :] - return self.cos_cached, self.sin_cached - - -# rotary pos emb helpers: -def rotate_half(x): - x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] - return torch.cat( - (-x2, x1), dim=x1.ndim - 1 - ) # dim=-1 triggers a bug in earlier torch versions - - -def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0): - cos, sin = ( - cos[offset : q.shape[0] + offset, ...], - sin[offset : q.shape[0] + offset, ...], - ) - return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/same_pad.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/same_pad.py deleted file mode 100644 index 4c04990ea6fdb291f162ee8ac3d17a92483daf8e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/same_pad.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from torch import nn - - -class SamePad(nn.Module): - def __init__(self, kernel_size, causal=False): - super().__init__() - if causal: - self.remove = kernel_size - 1 - else: - self.remove = 1 if kernel_size % 2 == 0 else 0 - - def forward(self, x): - if self.remove > 0: - x = x[:, :, : -self.remove] - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/scalar_bias.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/scalar_bias.py deleted file mode 100644 index c96247c75914fabb8a2b7ff731bb82b588f72690..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/scalar_bias.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch - - -class ScalarBias(torch.autograd.Function): - """ - Adds a vector of scalars, used in self-attention mechanism to allow - the model to optionally attend to this vector instead of the past - """ - - @staticmethod - def forward(ctx, input, dim, bias_init): - size = list(input.size()) - size[dim] += 1 - output = input.new(*size).fill_(bias_init) - output.narrow(dim, 1, size[dim] - 1).copy_(input) - ctx.dim = dim - return output - - @staticmethod - def backward(ctx, grad): - return grad.narrow(ctx.dim, 1, grad.size(ctx.dim) - 1), None, None - - -def scalar_bias(input, dim, bias_init=0): - return ScalarBias.apply(input, dim, bias_init) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sinusoidal_positional_embedding.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sinusoidal_positional_embedding.py deleted file mode 100644 index 4793ecfb522d0729fc2d24a3ddf0c6a774d67773..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sinusoidal_positional_embedding.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Any, Optional - -import torch -import torch.onnx.operators -from fairseq import utils -from torch import Tensor, nn - - -class SinusoidalPositionalEmbedding(nn.Module): - """This module produces sinusoidal positional embeddings of any length. - - Padding symbols are ignored. - """ - - def __init__(self, embedding_dim, padding_idx, init_size=1024): - super().__init__() - self.embedding_dim = embedding_dim - self.padding_idx = padding_idx if padding_idx is not None else 0 - self.weights = SinusoidalPositionalEmbedding.get_embedding( - init_size, embedding_dim, padding_idx - ) - self.onnx_trace = False - self.register_buffer("_float_tensor", torch.FloatTensor(1)) - self.max_positions = int(1e5) - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - @staticmethod - def get_embedding( - num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None - ): - """Build sinusoidal embeddings. - - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) - emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( - 1 - ) * emb.unsqueeze(0) - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( - num_embeddings, -1 - ) - if embedding_dim % 2 == 1: - # zero pad - emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) - if padding_idx is not None: - emb[padding_idx, :] = 0 - return emb - - def forward( - self, - input, - incremental_state: Optional[Any] = None, - timestep: Optional[Tensor] = None, - positions: Optional[Any] = None, - ): - """Input is expected to be of size [bsz x seqlen].""" - bspair = torch.onnx.operators.shape_as_tensor(input) - bsz, seq_len = bspair[0], bspair[1] - max_pos = self.padding_idx + 1 + seq_len - if self.weights is None or max_pos > self.weights.size(0): - # recompute/expand embeddings if needed - self.weights = SinusoidalPositionalEmbedding.get_embedding( - max_pos, self.embedding_dim, self.padding_idx - ) - self.weights = self.weights.to(self._float_tensor) - - if incremental_state is not None: - # positions is the same for every token when decoding a single step - pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len - if self.onnx_trace: - return ( - self.weights.index_select(index=self.padding_idx + pos, dim=0) - .unsqueeze(1) - .repeat(bsz, 1, 1) - ) - return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) - - positions = utils.make_positions( - input, self.padding_idx, onnx_trace=self.onnx_trace - ) - if self.onnx_trace: - flat_embeddings = self.weights.detach().index_select(0, positions.view(-1)) - embedding_shape = torch.cat( - (bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long)) - ) - embeddings = torch.onnx.operators.reshape_from_tensor_shape( - flat_embeddings, embedding_shape - ) - return embeddings - return ( - self.weights.index_select(0, positions.view(-1)) - .view(bsz, seq_len, -1) - .detach() - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_multihead_attention.py deleted file mode 100644 index 3cbd9d6785886e319aab0601517e27df733b6f97..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_multihead_attention.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch - -from .multihead_attention import MultiheadAttention - - -class SparseMultiheadAttention(MultiheadAttention): - """Sparse Multi-Headed Attention. - - "Generating Long Sequences with Sparse Transformers". Implements - fixed factorized self attention, where l=stride and c=expressivity. - A(1) includes all words in the stride window and A(2) takes a summary of c - words from the end of each stride window. - If is_bidirectional=False, we do not include any words past the current word, - as in the paper. - """ - - def __init__( - self, - embed_dim, - num_heads, - kdim=None, - vdim=None, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - self_attention=False, - encoder_decoder_attention=False, - stride=32, - expressivity=8, - is_bidirectional=True, - ): - - super().__init__( - embed_dim, - num_heads, - kdim, - vdim, - dropout, - bias, - add_bias_kv, - add_zero_attn, - self_attention, - encoder_decoder_attention, - ) - - self.is_bidirectional = is_bidirectional - self.stride = stride - self.expressivity = expressivity - assert self.stride > 0 and self.stride >= self.expressivity - - # Used for Ai(2) calculations - beginning of [l-c, l] range - def compute_checkpoint(self, word_index): - if word_index % self.stride == 0 and word_index != 0: - checkpoint_index = word_index - self.expressivity - else: - checkpoint_index = ( - math.floor(word_index / self.stride) * self.stride - + self.stride - - self.expressivity - ) - return checkpoint_index - - # Computes Ai(2) - def compute_subset_summaries(self, absolute_max): - checkpoint_index = self.compute_checkpoint(0) - subset_two = set() - while checkpoint_index <= absolute_max - 1: - summary = set( - range( - checkpoint_index, - min(checkpoint_index + self.expressivity + 1, absolute_max), - ) - ) - subset_two = subset_two.union(summary) - checkpoint_index = self.compute_checkpoint(checkpoint_index + self.stride) - return subset_two - - # Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf - def compute_fixed_attention_subset(self, word_index, tgt_len): - # +1s account for range function; [min, max) -> [min, max] - if not self.is_bidirectional: - absolute_max = word_index + 1 - else: - absolute_max = tgt_len - - # Subset 1 - whole window - rounded_index = ( - math.floor((word_index + self.stride) / self.stride) * self.stride - ) - if word_index % self.stride == 0 and word_index != 0: - subset_one = set( - range(word_index - self.stride, min(absolute_max, word_index + 1)) - ) - else: - subset_one = set( - range( - max(0, rounded_index - self.stride), - min(absolute_max, rounded_index + 1), - ) - ) - - # Subset 2 - summary per window - # If bidirectional, subset 2 is the same for every index - subset_two = set() - if not self.is_bidirectional: - subset_two = self.compute_subset_summaries(absolute_max) - - return subset_one.union(subset_two) - - # Compute sparse mask - if bidirectional, can pre-compute and store - def buffered_sparse_mask(self, tensor, tgt_len, src_len): - assert tgt_len > self.stride - sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float("-inf")) - - # If bidirectional, subset 2 is the same for every index - subset_summaries = set() - if self.is_bidirectional: - subset_summaries = self.compute_subset_summaries(tgt_len) - - for i in range(tgt_len): - fixed_attention_subset = self.compute_fixed_attention_subset(i, tgt_len) - fixed_attention_subset = fixed_attention_subset.union(subset_summaries) - included_word_indices = torch.LongTensor(list(fixed_attention_subset)) - sparse_mask[i].index_fill_(0, included_word_indices, 0) - return sparse_mask.type_as(tensor) - - def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): - sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len) - sparse_mask = sparse_mask.unsqueeze(0).expand( - bsz * self.num_heads, tgt_len, src_len - ) - attn_weights += sparse_mask diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_transformer_sentence_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_transformer_sentence_encoder.py deleted file mode 100644 index f41ec09327fe80b50d20674e7482794ce45c531c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_transformer_sentence_encoder.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn -from fairseq.modules import TransformerSentenceEncoder -from fairseq.modules.sparse_transformer_sentence_encoder_layer import ( - SparseTransformerSentenceEncoderLayer, -) - - -class SparseTransformerSentenceEncoder(TransformerSentenceEncoder): - """ - Sparse implementation of the TransformerSentenceEncoder - - see SparseMultiheadAttention - """ - - def __init__( - self, - padding_idx: int, - vocab_size: int, - num_encoder_layers: int = 6, - embedding_dim: int = 768, - ffn_embedding_dim: int = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - max_seq_len: int = 256, - num_segments: int = 2, - use_position_embeddings: bool = True, - offset_positions_by_padding: bool = True, - encoder_normalize_before: bool = False, - apply_bert_init: bool = False, - activation_fn: str = "relu", - learned_pos_embedding: bool = True, - embed_scale: float = None, - freeze_embeddings: bool = False, - n_trans_layers_to_freeze: int = 0, - export: bool = False, - is_bidirectional: bool = True, - stride: int = 32, - expressivity: int = 8, - ) -> None: - - super().__init__( - padding_idx, - vocab_size, - num_encoder_layers, - embedding_dim, - ffn_embedding_dim, - num_attention_heads, - dropout, - attention_dropout, - activation_dropout, - max_seq_len, - num_segments, - use_position_embeddings, - offset_positions_by_padding, - encoder_normalize_before, - apply_bert_init, - activation_fn, - learned_pos_embedding, - embed_scale, - freeze_embeddings, - n_trans_layers_to_freeze, - export, - ) - - self.layers = nn.ModuleList( - [ - SparseTransformerSentenceEncoderLayer( - embedding_dim=self.embedding_dim, - ffn_embedding_dim=ffn_embedding_dim, - num_attention_heads=num_attention_heads, - dropout=dropout, - attention_dropout=attention_dropout, - activation_dropout=activation_dropout, - activation_fn=activation_fn, - export=export, - is_bidirectional=is_bidirectional, - stride=stride, - expressivity=expressivity, - ) - for _ in range(num_encoder_layers) - ] - ) - - def freeze_module_params(m): - if m is not None: - for p in m.parameters(): - p.requires_grad = False - - for layer in range(n_trans_layers_to_freeze): - freeze_module_params(self.layers[layer]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_transformer_sentence_encoder_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_transformer_sentence_encoder_layer.py deleted file mode 100644 index d95da59c2471bfa858fd627605196d7f41f9ec12..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/sparse_transformer_sentence_encoder_layer.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.modules import TransformerSentenceEncoderLayer -from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention - - -class SparseTransformerSentenceEncoderLayer(TransformerSentenceEncoderLayer): - """ - Implements a Sprase Transformer Encoder Layer (see SparseMultiheadAttention) - """ - - def __init__( - self, - embedding_dim: int = 768, - ffn_embedding_dim: int = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - activation_fn: str = "relu", - export: bool = False, - is_bidirectional: bool = True, - stride: int = 32, - expressivity: int = 8, - ) -> None: - - super().__init__( - embedding_dim, - ffn_embedding_dim, - num_attention_heads, - dropout, - attention_dropout, - activation_dropout, - activation_fn, - export, - ) - - self.self_attn = SparseMultiheadAttention( - self.embedding_dim, - num_attention_heads, - dropout=attention_dropout, - add_bias_kv=False, - add_zero_attn=False, - self_attention=True, - is_bidirectional=is_bidirectional, - stride=stride, - expressivity=expressivity, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_layer.py deleted file mode 100644 index a530e54e5e255dddab171d56089706df29e5b09c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_layer.py +++ /dev/null @@ -1,563 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Dict, List, Optional - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.models.transformer import ( - TransformerConfig, -) -from fairseq.modules import LayerNorm, MultiheadAttention -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise -from torch import Tensor - - -class TransformerEncoderLayerBase(nn.Module): - """Encoder layer block. - - In the original paper each operation (multi-head attention or FFN) is - postprocessed with: `dropout -> add residual -> layernorm`. In the - tensor2tensor code they suggest that learning is more robust when - preprocessing each layer with layernorm and postprocessing with: - `dropout -> add residual`. We default to the approach in the paper, but the - tensor2tensor approach can be enabled by setting - *cfg.encoder.normalize_before* to ``True``. - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - - def __init__(self, cfg, return_fc=False): - super().__init__() - self.cfg = cfg - self.return_fc = return_fc - self.embed_dim = cfg.encoder.embed_dim - self.quant_noise = cfg.quant_noise.pq - self.quant_noise_block_size = cfg.quant_noise.pq_block_size - self.self_attn = self.build_self_attention(self.embed_dim, cfg) - self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) - self.dropout_module = FairseqDropout( - cfg.dropout, module_name=self.__class__.__name__ - ) - self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn) - activation_dropout_p = cfg.activation_dropout - if activation_dropout_p == 0: - # for backwards compatibility with models that use cfg.relu_dropout - activation_dropout_p = cfg.relu_dropout or 0 - self.activation_dropout_module = FairseqDropout( - float(activation_dropout_p), module_name=self.__class__.__name__ - ) - self.normalize_before = cfg.encoder.normalize_before - self.fc1 = self.build_fc1( - self.embed_dim, - cfg.encoder.ffn_embed_dim, - self.quant_noise, - self.quant_noise_block_size, - ) - self.fc2 = self.build_fc2( - cfg.encoder.ffn_embed_dim, - self.embed_dim, - self.quant_noise, - self.quant_noise_block_size, - ) - - self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) - - def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise( - nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size - ) - - def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise( - nn.Linear(input_dim, output_dim), p=q_noise, block_size=qn_block_size - ) - - def _get_fc_rank(self, remove_num: int) -> List[int]: - f1_filter_param = [] - for i in range(self.fc1.out_features): - f1_filter_param.append( - torch.sum(torch.abs(self.fc1.weight[i])) - + torch.sum(torch.abs(self.fc2.weight[:, i])) - + torch.abs(self.fc1.bias[i]) - ) - return sorted( - range(len(f1_filter_param)), key=lambda k: f1_filter_param[k], reverse=False - )[0:remove_num] - - def _prune_fc_layer(self, remove_index: List[int]): - new_fc1_weight = [] - new_fc1_bias = [] - for i in range(self.fc1.out_features): - if i not in remove_index: - new_fc1_weight.append(self.fc1.weight[i]) - new_fc1_bias.append(self.fc1.bias[i]) - - new_fc1_weight = torch.stack(new_fc1_weight).detach() - new_fc1_weight.requires_grad = True - - new_fc1_bias = torch.stack(new_fc1_bias).detach() - new_fc1_bias.requires_grad = True - - self.fc1 = quant_noise( - nn.Linear(self.fc1.in_features, self.fc1.out_features - len(remove_index)), - p=self.quant_noise, - block_size=self.quant_noise_block_size, - ) - self.fc1.weight = torch.nn.Parameter(new_fc1_weight) - self.fc1.bias = torch.nn.Parameter(new_fc1_bias) - - new_fc2_weight = [] - new_fc2_bias = [] - for i in range(self.fc2.in_features): - if i not in remove_index: - new_fc2_weight.append(self.fc2.weight[:, i]) - new_fc2_bias = self.fc2.bias.detach() - - new_fc2_weight = torch.stack(new_fc2_weight, dim=-1).detach() - new_fc2_weight.requires_grad = True - - new_fc2_bias = self.fc2.bias.detach() - new_fc2_bias.requires_grad = True - - self.fc2 = quant_noise( - nn.Linear(self.fc2.in_features - len(remove_index), self.fc2.out_features), - p=self.quant_noise, - block_size=self.quant_noise_block_size, - ) - self.fc2.weight = torch.nn.Parameter(new_fc2_weight) - self.fc2.bias = torch.nn.Parameter(new_fc2_bias) - - def build_self_attention(self, embed_dim, cfg): - return MultiheadAttention( - embed_dim, - cfg.encoder.attention_heads, - dropout=cfg.attention_dropout, - self_attention=True, - q_noise=self.quant_noise, - qn_block_size=self.quant_noise_block_size, - xformers_att_config=cfg.encoder.xformers_att_config, - ) - - def residual_connection(self, x, residual): - return residual + x - - def upgrade_state_dict_named(self, state_dict, name): - """ - Rename layer norm states from `...layer_norms.0.weight` to - `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to - `...final_layer_norm.weight` - """ - layer_norm_map = {"0": "self_attn_layer_norm", "1": "final_layer_norm"} - for old, new in layer_norm_map.items(): - for m in ("weight", "bias"): - k = "{}.layer_norms.{}.{}".format(name, old, m) - if k in state_dict: - state_dict["{}.{}.{}".format(name, new, m)] = state_dict[k] - del state_dict[k] - - def forward( - self, - x, - encoder_padding_mask: Optional[Tensor], - attn_mask: Optional[Tensor] = None, - ): - """ - Args: - x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` - encoder_padding_mask (ByteTensor): binary ByteTensor of shape - `(batch, seq_len)` where padding elements are indicated by ``1``. - attn_mask (ByteTensor): binary tensor of shape `(tgt_len, src_len)`, - where `tgt_len` is the length of output and `src_len` is the - length of input, though here both are equal to `seq_len`. - `attn_mask[tgt_i, src_j] = 1` means that when calculating the - embedding for `tgt_i`, we exclude (mask out) `src_j`. This is - useful for strided self-attention. - - Returns: - encoded output of shape `(seq_len, batch, embed_dim)` - """ - # anything in original attn_mask = 1, becomes -1e8 - # anything in original attn_mask = 0, becomes 0 - # Note that we cannot use -inf here, because at some edge cases, - # the attention weight (before softmax) for some padded element in query - # will become -inf, which results in NaN in model parameters - if attn_mask is not None: - attn_mask = attn_mask.masked_fill( - attn_mask.to(torch.bool), -1e8 if x.dtype == torch.float32 else -1e4 - ) - - residual = x - if self.normalize_before: - x = self.self_attn_layer_norm(x) - x, _ = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=encoder_padding_mask, - need_weights=False, - attn_mask=attn_mask, - ) - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.self_attn_layer_norm(x) - - residual = x - if self.normalize_before: - x = self.final_layer_norm(x) - x = self.activation_fn(self.fc1(x)) - x = self.activation_dropout_module(x) - x = self.fc2(x) - - fc_result = x - - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.final_layer_norm(x) - - if self.return_fc and not torch.jit.is_scripting(): - return x, fc_result - return x - - -# backward compatible with the legacy argparse format -class TransformerEncoderLayer(TransformerEncoderLayerBase): - def __init__(self, args): - super().__init__(TransformerConfig.from_namespace(args)) - self.args = args - - def build_self_attention(self, embed_dim, args): - return super().build_self_attention( - embed_dim, TransformerConfig.from_namespace(args) - ) - - -class TransformerDecoderLayerBase(nn.Module): - """Decoder layer block. - - In the original paper each operation (multi-head attention, encoder - attention or FFN) is postprocessed with: `dropout -> add residual -> - layernorm`. In the tensor2tensor code they suggest that learning is more - robust when preprocessing each layer with layernorm and postprocessing with: - `dropout -> add residual`. We default to the approach in the paper, but the - tensor2tensor approach can be enabled by setting - *cfg.decoder.normalize_before* to ``True``. - - Args: - args (argparse.Namespace): parsed command-line arguments - no_encoder_attn (bool, optional): whether to attend to encoder outputs - (default: False). - """ - - def __init__( - self, cfg, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False - ): - super().__init__() - self.embed_dim = cfg.decoder.embed_dim - self.dropout_module = FairseqDropout( - cfg.dropout, module_name=self.__class__.__name__ - ) - self.quant_noise = cfg.quant_noise.pq - self.quant_noise_block_size = cfg.quant_noise.pq_block_size - - self.cross_self_attention = cfg.cross_self_attention - - self.self_attn = self.build_self_attention( - self.embed_dim, - cfg, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - ) - self.attn_ln = ( - LayerNorm(self.embed_dim) - if utils.safe_getattr(cfg, "scale_attn", False) - else None - ) - self.nh = self.self_attn.num_heads - self.head_dim = self.self_attn.head_dim - scale_heads = utils.safe_getattr(cfg, "scale_heads", False) - self.c_attn = ( - nn.Parameter(torch.ones((self.nh,)), requires_grad=True) - if scale_heads - else None - ) - - self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn) - activation_dropout_p = cfg.activation_dropout - if activation_dropout_p == 0: - # for backwards compatibility with models that use cfg.relu_dropout - activation_dropout_p = cfg.relu_dropout or 0 - self.activation_dropout_module = FairseqDropout( - float(activation_dropout_p), module_name=self.__class__.__name__ - ) - self.normalize_before = cfg.decoder.normalize_before - - self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) - - if no_encoder_attn: - self.encoder_attn = None - self.encoder_attn_layer_norm = None - else: - self.encoder_attn = self.build_encoder_attention(self.embed_dim, cfg) - self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) - - self.ffn_layernorm = ( - LayerNorm(cfg.decoder.ffn_embed_dim) - if utils.safe_getattr(cfg, "scale_fc", False) - else None - ) - self.w_resid = ( - nn.Parameter( - torch.ones( - self.embed_dim, - ), - requires_grad=True, - ) - if utils.safe_getattr(cfg, "scale_resids", False) - else None - ) - - self.fc1 = self.build_fc1( - self.embed_dim, - cfg.decoder.ffn_embed_dim, - self.quant_noise, - self.quant_noise_block_size, - ) - self.fc2 = self.build_fc2( - cfg.decoder.ffn_embed_dim, - self.embed_dim, - self.quant_noise, - self.quant_noise_block_size, - ) - - self.final_layer_norm = LayerNorm(self.embed_dim, export=cfg.export) - self.need_attn = True - - self.onnx_trace = False - - def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) - - def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) - - def build_self_attention( - self, embed_dim, cfg, add_bias_kv=False, add_zero_attn=False - ): - return MultiheadAttention( - embed_dim, - cfg.decoder.attention_heads, - dropout=cfg.attention_dropout, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - self_attention=not cfg.cross_self_attention, - q_noise=self.quant_noise, - qn_block_size=self.quant_noise_block_size, - xformers_att_config=cfg.decoder.xformers_att_config, - ) - - def build_encoder_attention(self, embed_dim, cfg): - return MultiheadAttention( - embed_dim, - cfg.decoder.attention_heads, - kdim=cfg.encoder.embed_dim, - vdim=cfg.encoder.embed_dim, - dropout=cfg.attention_dropout, - encoder_decoder_attention=True, - q_noise=self.quant_noise, - qn_block_size=self.quant_noise_block_size, - xformers_att_config=cfg.encoder.xformers_att_config, - ) - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def residual_connection(self, x, residual): - return residual + x - - def forward( - self, - x, - encoder_out: Optional[torch.Tensor] = None, - encoder_padding_mask: Optional[torch.Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - prev_self_attn_state: Optional[List[torch.Tensor]] = None, - prev_attn_state: Optional[List[torch.Tensor]] = None, - self_attn_mask: Optional[torch.Tensor] = None, - self_attn_padding_mask: Optional[torch.Tensor] = None, - need_attn: bool = False, - need_head_weights: bool = False, - ): - """ - Args: - x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)` - encoder_padding_mask (ByteTensor, optional): binary - ByteTensor of shape `(batch, src_len)` where padding - elements are indicated by ``1``. - need_attn (bool, optional): return attention weights - need_head_weights (bool, optional): return attention weights - for each head (default: return average over heads). - - Returns: - encoded output of shape `(seq_len, batch, embed_dim)` - """ - if need_head_weights: - need_attn = True - - residual = x - if self.normalize_before: - x = self.self_attn_layer_norm(x) - if prev_self_attn_state is not None: - prev_key, prev_value = prev_self_attn_state[:2] - saved_state: Dict[str, Optional[Tensor]] = { - "prev_key": prev_key, - "prev_value": prev_value, - } - if len(prev_self_attn_state) >= 3: - saved_state["prev_key_padding_mask"] = prev_self_attn_state[2] - assert incremental_state is not None - self.self_attn._set_input_buffer(incremental_state, saved_state) - _self_attn_input_buffer = self.self_attn._get_input_buffer(incremental_state) - if self.cross_self_attention and not ( - incremental_state is not None - and _self_attn_input_buffer is not None - and "prev_key" in _self_attn_input_buffer - ): - if self_attn_mask is not None: - assert encoder_out is not None - self_attn_mask = torch.cat( - (x.new_zeros(x.size(0), encoder_out.size(0)), self_attn_mask), dim=1 - ) - if self_attn_padding_mask is not None: - if encoder_padding_mask is None: - assert encoder_out is not None - encoder_padding_mask = self_attn_padding_mask.new_zeros( - encoder_out.size(1), encoder_out.size(0) - ) - self_attn_padding_mask = torch.cat( - (encoder_padding_mask, self_attn_padding_mask), dim=1 - ) - assert encoder_out is not None - y = torch.cat((encoder_out, x), dim=0) - else: - y = x - - x, attn = self.self_attn( - query=x, - key=y, - value=y, - key_padding_mask=self_attn_padding_mask, - incremental_state=incremental_state, - need_weights=False, - attn_mask=self_attn_mask, - ) - if self.c_attn is not None: - tgt_len, bsz = x.size(0), x.size(1) - x = x.view(tgt_len, bsz, self.nh, self.head_dim) - x = torch.einsum("tbhd,h->tbhd", x, self.c_attn) - x = x.reshape(tgt_len, bsz, self.embed_dim) - if self.attn_ln is not None: - x = self.attn_ln(x) - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.self_attn_layer_norm(x) - - if self.encoder_attn is not None and encoder_out is not None: - residual = x - if self.normalize_before: - x = self.encoder_attn_layer_norm(x) - if prev_attn_state is not None: - prev_key, prev_value = prev_attn_state[:2] - saved_state: Dict[str, Optional[Tensor]] = { - "prev_key": prev_key, - "prev_value": prev_value, - } - if len(prev_attn_state) >= 3: - saved_state["prev_key_padding_mask"] = prev_attn_state[2] - assert incremental_state is not None - self.encoder_attn._set_input_buffer(incremental_state, saved_state) - - x, attn = self.encoder_attn( - query=x, - key=encoder_out, - value=encoder_out, - key_padding_mask=encoder_padding_mask, - incremental_state=incremental_state, - static_kv=True, - need_weights=need_attn or (not self.training and self.need_attn), - need_head_weights=need_head_weights, - ) - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.encoder_attn_layer_norm(x) - - residual = x - if self.normalize_before: - x = self.final_layer_norm(x) - - x = self.activation_fn(self.fc1(x)) - x = self.activation_dropout_module(x) - if self.ffn_layernorm is not None: - x = self.ffn_layernorm(x) - x = self.fc2(x) - x = self.dropout_module(x) - if self.w_resid is not None: - residual = torch.mul(self.w_resid, residual) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.final_layer_norm(x) - if self.onnx_trace and incremental_state is not None: - saved_state = self.self_attn._get_input_buffer(incremental_state) - assert saved_state is not None - if self_attn_padding_mask is not None: - self_attn_state = [ - saved_state["prev_key"], - saved_state["prev_value"], - saved_state["prev_key_padding_mask"], - ] - else: - self_attn_state = [saved_state["prev_key"], saved_state["prev_value"]] - return x, attn, self_attn_state - return x, attn, None - - def make_generation_fast_(self, need_attn: bool = False, **kwargs): - self.need_attn = need_attn - - -# backward compatible with the legacy argparse format -class TransformerDecoderLayer(TransformerDecoderLayerBase): - def __init__( - self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False - ): - super().__init__( - TransformerConfig.from_namespace(args), - no_encoder_attn=no_encoder_attn, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - ) - self.args = args - - def build_self_attention( - self, embed_dim, args, add_bias_kv=False, add_zero_attn=False - ): - return super().build_self_attention( - embed_dim, - TransformerConfig.from_namespace(args), - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - ) - - def build_encoder_attention(self, embed_dim, args): - return super().build_encoder_attention( - embed_dim, - TransformerConfig.from_namespace(args), - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_sentence_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_sentence_encoder.py deleted file mode 100644 index 5d2db91ad7b902386efa843f5ade0c0295758fa1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_sentence_encoder.py +++ /dev/null @@ -1,291 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Optional, Tuple - -import torch -import torch.nn as nn -from fairseq.modules import ( - FairseqDropout, - LayerDropModuleList, - LayerNorm, - MultiheadAttention, - PositionalEmbedding, - TransformerSentenceEncoderLayer, -) -from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_ - - -def init_bert_params(module): - """ - Initialize the weights specific to the BERT Model. - This overrides the default initializations depending on the specified arguments. - 1. If normal_init_linear_weights is set then weights of linear - layer will be initialized using the normal distribution and - bais will be set to the specified value. - 2. If normal_init_embed_weights is set then weights of embedding - layer will be initialized using the normal distribution. - 3. If normal_init_proj_weights is set then weights of - in_project_weight for MultiHeadAttention initialized using - the normal distribution (to be validated). - """ - - def normal_(data): - # with FSDP, module params will be on CUDA, so we cast them back to CPU - # so that the RNG is consistent with and without FSDP - data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device)) - - if isinstance(module, nn.Linear): - normal_(module.weight.data) - if module.bias is not None: - module.bias.data.zero_() - if isinstance(module, nn.Embedding): - normal_(module.weight.data) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - if isinstance(module, MultiheadAttention): - normal_(module.q_proj.weight.data) - normal_(module.k_proj.weight.data) - normal_(module.v_proj.weight.data) - - -class TransformerSentenceEncoder(nn.Module): - """ - Implementation for a Bi-directional Transformer based Sentence Encoder used - in BERT/XLM style pre-trained models. - - This first computes the token embedding using the token embedding matrix, - position embeddings (if specified) and segment embeddings - (if specified). After applying the specified number of - TransformerEncoderLayers, it outputs all the internal states of the - encoder as well as the final representation associated with the first - token (usually CLS token). - - Input: - - tokens: B x T matrix representing sentences - - segment_labels: B x T matrix representing segment label for tokens - - Output: - - a tuple of the following: - - a list of internal model states used to compute the - predictions where each tensor has shape T x B x C - - sentence representation associated with first input token - in format B x C. - """ - - def __init__( - self, - padding_idx: int, - vocab_size: int, - num_encoder_layers: int = 6, - embedding_dim: int = 768, - ffn_embedding_dim: int = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - layerdrop: float = 0.0, - max_seq_len: int = 256, - num_segments: int = 2, - use_position_embeddings: bool = True, - offset_positions_by_padding: bool = True, - encoder_normalize_before: bool = False, - apply_bert_init: bool = False, - activation_fn: str = "relu", - learned_pos_embedding: bool = True, - embed_scale: float = None, - freeze_embeddings: bool = False, - n_trans_layers_to_freeze: int = 0, - export: bool = False, - traceable: bool = False, - q_noise: float = 0.0, - qn_block_size: int = 8, - ) -> None: - - super().__init__() - self.padding_idx = padding_idx - self.vocab_size = vocab_size - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.layerdrop = layerdrop - self.max_seq_len = max_seq_len - self.embedding_dim = embedding_dim - self.num_segments = num_segments - self.use_position_embeddings = use_position_embeddings - self.apply_bert_init = apply_bert_init - self.learned_pos_embedding = learned_pos_embedding - self.traceable = traceable - - self.embed_tokens = self.build_embedding( - self.vocab_size, self.embedding_dim, self.padding_idx - ) - self.embed_scale = embed_scale - - if q_noise > 0: - self.quant_noise = apply_quant_noise_( - nn.Linear(self.embedding_dim, self.embedding_dim, bias=False), - q_noise, - qn_block_size, - ) - else: - self.quant_noise = None - - self.segment_embeddings = ( - nn.Embedding(self.num_segments, self.embedding_dim, padding_idx=None) - if self.num_segments > 0 - else None - ) - - self.embed_positions = ( - PositionalEmbedding( - self.max_seq_len, - self.embedding_dim, - padding_idx=(self.padding_idx if offset_positions_by_padding else None), - learned=self.learned_pos_embedding, - ) - if self.use_position_embeddings - else None - ) - - if encoder_normalize_before: - self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export) - else: - self.emb_layer_norm = None - - if self.layerdrop > 0.0: - self.layers = LayerDropModuleList(p=self.layerdrop) - else: - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - self.build_transformer_sentence_encoder_layer( - embedding_dim=self.embedding_dim, - ffn_embedding_dim=ffn_embedding_dim, - num_attention_heads=num_attention_heads, - dropout=self.dropout_module.p, - attention_dropout=attention_dropout, - activation_dropout=activation_dropout, - activation_fn=activation_fn, - export=export, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - for _ in range(num_encoder_layers) - ] - ) - - # Apply initialization of model params after building the model - if self.apply_bert_init: - self.apply(init_bert_params) - - def freeze_module_params(m): - if m is not None: - for p in m.parameters(): - p.requires_grad = False - - if freeze_embeddings: - freeze_module_params(self.embed_tokens) - freeze_module_params(self.segment_embeddings) - freeze_module_params(self.embed_positions) - freeze_module_params(self.emb_layer_norm) - - for layer in range(n_trans_layers_to_freeze): - freeze_module_params(self.layers[layer]) - - def build_embedding(self, vocab_size, embedding_dim, padding_idx): - return nn.Embedding(vocab_size, embedding_dim, padding_idx) - - def build_transformer_sentence_encoder_layer( - self, - embedding_dim, - ffn_embedding_dim, - num_attention_heads, - dropout, - attention_dropout, - activation_dropout, - activation_fn, - export, - q_noise, - qn_block_size, - ): - return TransformerSentenceEncoderLayer( - embedding_dim=embedding_dim, - ffn_embedding_dim=ffn_embedding_dim, - num_attention_heads=num_attention_heads, - dropout=dropout, - attention_dropout=attention_dropout, - activation_dropout=activation_dropout, - activation_fn=activation_fn, - export=export, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - - def forward( - self, - tokens: torch.Tensor, - segment_labels: torch.Tensor = None, - last_state_only: bool = False, - positions: Optional[torch.Tensor] = None, - token_embeddings: Optional[torch.Tensor] = None, - attn_mask: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - is_tpu = tokens.device.type == "xla" - - # compute padding mask. This is needed for multi-head attention - padding_mask = tokens.eq(self.padding_idx) - if not self.traceable and not is_tpu and not padding_mask.any(): - padding_mask = None - - if token_embeddings is not None: - x = token_embeddings - else: - x = self.embed_tokens(tokens) - - if self.embed_scale is not None: - x = x * self.embed_scale - - if self.embed_positions is not None: - x = x + self.embed_positions(tokens, positions=positions) - - if self.segment_embeddings is not None and segment_labels is not None: - x = x + self.segment_embeddings(segment_labels) - - if self.quant_noise is not None: - x = self.quant_noise(x) - - if self.emb_layer_norm is not None: - x = self.emb_layer_norm(x) - - x = self.dropout_module(x) - - # account for padding while computing the representation - if padding_mask is not None: - x = x * (1 - padding_mask.unsqueeze(-1).type_as(x)) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - inner_states = [] - if not last_state_only: - inner_states.append(x) - - for layer in self.layers: - x, _ = layer( - x, self_attn_padding_mask=padding_mask, self_attn_mask=attn_mask - ) - if not last_state_only: - inner_states.append(x) - - sentence_rep = x[0, :, :] - - if last_state_only: - inner_states = [x] - - if self.traceable: - return torch.stack(inner_states), sentence_rep - else: - return inner_states, sentence_rep diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_sentence_encoder_layer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_sentence_encoder_layer.py deleted file mode 100644 index f869c4b2f8fb15f96a292e39bd293df7898a4fce..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transformer_sentence_encoder_layer.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Callable, Optional - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.modules import LayerNorm, MultiheadAttention -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise - - -class TransformerSentenceEncoderLayer(nn.Module): - """ - Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained - models. - """ - - def __init__( - self, - embedding_dim: int = 768, - ffn_embedding_dim: int = 3072, - num_attention_heads: int = 8, - dropout: float = 0.1, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - activation_fn: str = "relu", - export: bool = False, - q_noise: float = 0.0, - qn_block_size: int = 8, - init_fn: Callable = None, - ) -> None: - super().__init__() - - if init_fn is not None: - init_fn() - - # Initialize parameters - self.embedding_dim = embedding_dim - self.num_attention_heads = num_attention_heads - self.attention_dropout = attention_dropout - self.q_noise = q_noise - self.qn_block_size = qn_block_size - - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - self.activation_dropout_module = FairseqDropout( - activation_dropout, module_name=self.__class__.__name__ - ) - - # Initialize blocks - self.activation_fn = utils.get_activation_fn(activation_fn) - self.self_attn = self.build_self_attention( - self.embedding_dim, - num_attention_heads, - dropout=attention_dropout, - self_attention=True, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - - # layer norm associated with the self attention layer - self.self_attn_layer_norm = LayerNorm(self.embedding_dim, export=export) - - self.fc1 = self.build_fc1( - self.embedding_dim, - ffn_embedding_dim, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - self.fc2 = self.build_fc2( - ffn_embedding_dim, - self.embedding_dim, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - - # layer norm associated with the position wise feed-forward NN - self.final_layer_norm = LayerNorm(self.embedding_dim, export=export) - - def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) - - def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size): - return quant_noise(nn.Linear(input_dim, output_dim), q_noise, qn_block_size) - - def build_self_attention( - self, - embed_dim, - num_attention_heads, - dropout, - self_attention, - q_noise, - qn_block_size, - ): - return MultiheadAttention( - embed_dim, - num_attention_heads, - dropout=dropout, - self_attention=True, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - - def forward( - self, - x: torch.Tensor, - self_attn_mask: Optional[torch.Tensor] = None, - self_attn_padding_mask: Optional[torch.Tensor] = None, - ): - """ - LayerNorm is applied either before or after the self-attention/ffn - modules similar to the original Transformer implementation. - """ - residual = x - x, attn = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=self_attn_padding_mask, - need_weights=False, - attn_mask=self_attn_mask, - ) - x = self.dropout_module(x) - x = residual + x - x = self.self_attn_layer_norm(x) - - residual = x - x = self.activation_fn(self.fc1(x)) - x = self.activation_dropout_module(x) - x = self.fc2(x) - x = self.dropout_module(x) - x = residual + x - x = self.final_layer_norm(x) - return x, attn diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transpose_last.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transpose_last.py deleted file mode 100644 index e578b3ec5097bfac5c976b207ea46bec1d9bd4f5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/transpose_last.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -transpose last 2 dimensions of the input -""" - -import torch.nn as nn - - -class TransposeLast(nn.Module): - def __init__(self, deconstruct_idx=None): - super().__init__() - self.deconstruct_idx = deconstruct_idx - - def forward(self, x): - if self.deconstruct_idx is not None: - x = x[self.deconstruct_idx] - return x.transpose(-2, -1) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/unfold.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/unfold.py deleted file mode 100644 index 138272f1ef4f673b29e36aed4531106f7ce95968..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/unfold.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn.functional as F - - -def unfold1d(x, kernel_size, padding_l, pad_value=0): - """unfold T x B x C to T x B x C x K""" - if kernel_size > 1: - T, B, C = x.size() - x = F.pad( - x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value - ) - x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C)) - else: - x = x.unsqueeze(3) - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/vggblock.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/vggblock.py deleted file mode 100644 index ee5ee19a34816c7350c21fba7c4907fec8ca7a61..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/modules/vggblock.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from __future__ import absolute_import, division, print_function, unicode_literals - -from collections.abc import Iterable -from itertools import repeat - -import torch -import torch.nn as nn - - -def _pair(v): - if isinstance(v, Iterable): - assert len(v) == 2, "len(v) != 2" - return v - return tuple(repeat(v, 2)) - - -def infer_conv_output_dim(conv_op, input_dim, sample_inchannel): - sample_seq_len = 200 - sample_bsz = 10 - x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim) - # N x C x H x W - # N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim - x = conv_op(x) - # N x C x H x W - x = x.transpose(1, 2) - # N x H x C x W - bsz, seq = x.size()[:2] - per_channel_dim = x.size()[3] - # bsz: N, seq: H, CxW the rest - return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim - - -class VGGBlock(torch.nn.Module): - """ - VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf - - Args: - in_channels: (int) number of input channels (typically 1) - out_channels: (int) number of output channels - conv_kernel_size: convolution channels - pooling_kernel_size: the size of the pooling window to take a max over - num_conv_layers: (int) number of convolution layers - input_dim: (int) input dimension - conv_stride: the stride of the convolving kernel. - Can be a single number or a tuple (sH, sW) Default: 1 - padding: implicit paddings on both sides of the input. - Can be a single number or a tuple (padH, padW). Default: None - layer_norm: (bool) if layer norm is going to be applied. Default: False - - Shape: - Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features) - Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features) - """ - - def __init__( - self, - in_channels, - out_channels, - conv_kernel_size, - pooling_kernel_size, - num_conv_layers, - input_dim, - conv_stride=1, - padding=None, - layer_norm=False, - ): - assert ( - input_dim is not None - ), "Need input_dim for LayerNorm and infer_conv_output_dim" - super(VGGBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.conv_kernel_size = _pair(conv_kernel_size) - self.pooling_kernel_size = _pair(pooling_kernel_size) - self.num_conv_layers = num_conv_layers - self.padding = ( - tuple(e // 2 for e in self.conv_kernel_size) - if padding is None - else _pair(padding) - ) - self.conv_stride = _pair(conv_stride) - - self.layers = nn.ModuleList() - for layer in range(num_conv_layers): - conv_op = nn.Conv2d( - in_channels if layer == 0 else out_channels, - out_channels, - self.conv_kernel_size, - stride=self.conv_stride, - padding=self.padding, - ) - self.layers.append(conv_op) - if layer_norm: - conv_output_dim, per_channel_dim = infer_conv_output_dim( - conv_op, input_dim, in_channels if layer == 0 else out_channels - ) - self.layers.append(nn.LayerNorm(per_channel_dim)) - input_dim = per_channel_dim - self.layers.append(nn.ReLU()) - - if self.pooling_kernel_size is not None: - pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True) - self.layers.append(pool_op) - self.total_output_dim, self.output_dim = infer_conv_output_dim( - pool_op, input_dim, out_channels - ) - - def forward(self, x): - for i, _ in enumerate(self.layers): - x = self.layers[i](x) - return x diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/nan_detector.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/nan_detector.py deleted file mode 100644 index 3406d86928e675f4dae6160048cdecda9f25d823..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/nan_detector.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import torch - -logger = logging.getLogger(__name__) - - -class NanDetector: - """ - Detects the first NaN or Inf in forward and/or backward pass and logs, together with the module name - """ - - def __init__(self, model, forward=True, backward=True): - self.bhooks = [] - self.fhooks = [] - self.forward = forward - self.backward = backward - self.named_parameters = list(model.named_parameters()) - self.reset() - - for name, mod in model.named_modules(): - mod.__module_name = name - self.add_hooks(mod) - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, exc_traceback): - # Dump out all model gnorms to enable better debugging - norm = {} - gradients = {} - for name, param in self.named_parameters: - if param.grad is not None: - grad_norm = torch.norm(param.grad.data.float(), p=2) - norm[name] = grad_norm.item() - if torch.isnan(grad_norm).any() or torch.isinf(grad_norm).any(): - gradients[name] = param.grad.data - if len(gradients) > 0: - logger.info("Detected nan/inf grad norm, dumping norms...") - logger.info(f"norms: {norm}") - logger.info(f"gradients: {gradients}") - - self.close() - - def add_hooks(self, module): - if self.forward: - self.fhooks.append(module.register_forward_hook(self.fhook_fn)) - if self.backward: - self.bhooks.append(module.register_backward_hook(self.bhook_fn)) - - def reset(self): - self.has_printed_f = False - self.has_printed_b = False - - def _detect(self, tensor, name, backward): - err = None - if ( - torch.is_floating_point(tensor) - # single value tensors (like the loss) will not provide much info - and tensor.numel() >= 2 - ): - with torch.no_grad(): - if torch.isnan(tensor).any(): - err = "NaN" - elif torch.isinf(tensor).any(): - err = "Inf" - if err is not None: - err = f"{err} detected in output of {name}, shape: {tensor.shape}, {'backward' if backward else 'forward'}" - return err - - def _apply(self, module, inp, x, backward): - if torch.is_tensor(x): - if isinstance(inp, tuple) and len(inp) > 0: - inp = inp[0] - err = self._detect(x, module.__module_name, backward) - if err is not None: - if torch.is_tensor(inp) and not backward: - err += ( - f" input max: {inp.max().item()}, input min: {inp.min().item()}" - ) - - has_printed_attr = "has_printed_b" if backward else "has_printed_f" - logger.warning(err) - setattr(self, has_printed_attr, True) - elif isinstance(x, dict): - for v in x.values(): - self._apply(module, inp, v, backward) - elif isinstance(x, list) or isinstance(x, tuple): - for v in x: - self._apply(module, inp, v, backward) - - def fhook_fn(self, module, inp, output): - if not self.has_printed_f: - self._apply(module, inp, output, backward=False) - - def bhook_fn(self, module, inp, output): - if not self.has_printed_b: - self._apply(module, inp, output, backward=True) - - def close(self): - for hook in self.fhooks + self.bhooks: - hook.remove() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/ngram_repeat_block.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/ngram_repeat_block.py deleted file mode 100644 index 4eb50303116671a47d03528fbe8a808647cbb116..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/ngram_repeat_block.py +++ /dev/null @@ -1,120 +0,0 @@ -# Originally from Microsoft Corporation. -# Licensed under the MIT License. - -""" Wrapper for ngram_repeat_block cuda extension """ -import math -import warnings -from typing import List - -import torch -from torch import nn - -try: - from fairseq import ngram_repeat_block_cuda - - EXTENSION_BUILT = True -except ImportError: - EXTENSION_BUILT = False - - -def is_cuda_extension_usable() -> bool: - """Check whether ngram_repeat_block_cuda is built properly""" - if not EXTENSION_BUILT or not torch.cuda.is_available(): - return False - bsz = 2 - tokens = torch.tensor([[4, 4, 3, 2], [1, 2, 3, 4]], dtype=torch.long, device="cuda") - lprobs = torch.rand((8, 12), device="cuda") - try: - outputs = ngram_repeat_block_cuda.forward(tokens, lprobs, bsz, 3, 4, 3) - outputs = outputs + 4 # This line breaks if the extension is built incorrectly. - return True - except RuntimeError: - warnings.warn( - "NGramRepeatBlock extension must be rebuilt." - 'Run TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0" python setup.py build_ext --inplace' - ) - return False - - -class NGramRepeatBlock(nn.Module): - """Wrapper class for calling ngram_repeat_block cuda extension""" - - def __init__(self, no_repeat_ngram_size: int, use_extension: bool = True): - super().__init__() - self.use_extension = is_cuda_extension_usable() if use_extension else False - self.no_repeat_ngram_size = no_repeat_ngram_size - - def reset_parameters(self): - pass - - @torch.jit.unused - def call_cuda_extension( - self, - tokens, - lprobs, - bsz: int, - beam_size: int, - step: int, - ): - return ngram_repeat_block_cuda.forward( - tokens, lprobs, bsz, step, beam_size, self.no_repeat_ngram_size - ) - - def forward( - self, - tokens, - lprobs, - bsz: int, - beam_size: int, - step: int, - ): - """ - Args: - tokens(Tensor): Input tokens(Bsz*beam, seq_len) - lprobs(Tensor): likelihood probability, - Expected to be updated in place.(Bsz*beam, vocab_size) - bsz(int): batch size - step(int): current step - beam_size(int): beam size - no_repeat_ngram_size(int): Ngram size - """ - msg = f"expected {bsz *beam_size} got" - assert tokens.size(0) == bsz * beam_size, f"{msg} {tokens.size(0)}" - assert lprobs.size(0) == bsz * beam_size, f"{msg} {lprobs.size(0)}" - if self.use_extension: - return self.call_cuda_extension(tokens, lprobs, bsz, beam_size, step) - - else: - return self._no_repeat_ngram( - tokens, - lprobs, - bsz, - beam_size, - step, - ) - - def _no_repeat_ngram(self, tokens, lprobs, bsz: int, beam_size: int, step: int): - """For each hypothesis generate a list of previous ngrams and set associated lprobs to -inf""" - banned_tokens = [ - torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size) - ] - if step + 2 - self.no_repeat_ngram_size >= 0: - cpu_tokens: List[List[int]] = tokens.cpu().tolist() - check_start_pos = step + 2 - self.no_repeat_ngram_size - for bbsz_idx in range(bsz * beam_size): - ngram_to_check = cpu_tokens[bbsz_idx][ - -(self.no_repeat_ngram_size - 1) : - ] - for i in range(check_start_pos): - if ( - ngram_to_check - == cpu_tokens[bbsz_idx][i : i + self.no_repeat_ngram_size - 1] - ): - banned_tokens[bbsz_idx].append( - cpu_tokens[bbsz_idx][i + self.no_repeat_ngram_size - 1] - ) - for bbsz_idx in range(bsz * beam_size): - lprobs[bbsz_idx][ - torch.tensor(banned_tokens[bbsz_idx], dtype=torch.int64) - ] = torch.tensor(-math.inf).to(lprobs) - return lprobs diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/__init__.py deleted file mode 100644 index be783be896396ff659c0bd173a7acebb8a2d165d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import importlib -import os - -from fairseq import registry -from fairseq.optim.bmuf import FairseqBMUF # noqa -from fairseq.optim.fairseq_optimizer import ( # noqa - FairseqOptimizer, - LegacyFairseqOptimizer, -) -from fairseq.optim.amp_optimizer import AMPOptimizer -from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer -from fairseq.optim.shard import shard_ -from omegaconf import DictConfig - -__all__ = [ - "AMPOptimizer", - "FairseqOptimizer", - "FP16Optimizer", - "MemoryEfficientFP16Optimizer", - "shard_", -] - -( - _build_optimizer, - register_optimizer, - OPTIMIZER_REGISTRY, - OPTIMIZER_DATACLASS_REGISTRY, -) = registry.setup_registry("--optimizer", base_class=FairseqOptimizer, required=True) - - -def build_optimizer(cfg: DictConfig, params, *extra_args, **extra_kwargs): - if all(isinstance(p, dict) for p in params): - params = [t for p in params for t in p.values()] - params = list(filter(lambda p: p.requires_grad, params)) - return _build_optimizer(cfg, params, *extra_args, **extra_kwargs) - - -# automatically import any Python files in the optim/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.optim." + file_name) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adadelta.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adadelta.py deleted file mode 100644 index f1a21549770f0904a6a40a42ff7eb52811f1bfbe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adadelta.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adadelta") -class Adadelta(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO', - help='coefficient used for computing a running average of squared gradients') - parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS', - help='term added to the denominator to improve numerical stability') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "rho": self.args.adadelta_rho, - "eps": self.args.adadelta_eps, - "weight_decay": self.args.weight_decay, - } - - @property - def supports_flat_params(self): - return True diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adafactor.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adafactor.py deleted file mode 100644 index 042ae926b0718d3ad66b28cddcc0f32293ad88a1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adafactor.py +++ /dev/null @@ -1,268 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adafactor") -class FairseqAdafactor(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = Adafactor(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--adafactor-eps', default='(1e-30, 1e-3)', metavar="E", - help='epsilons for Adafactor optimizer') - parser.add_argument('--clip-threshold', type=float, default=1.0, metavar="C", - help='threshold for clipping update root mean square') - parser.add_argument('--decay-rate', type=float, default=-0.8, metavar="D", - help='decay rate of the second moment estimator') - parser.add_argument('--beta1', type=float, default=None, metavar="B", - help='beta for first moment estimator. Optional') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - parser.add_argument('--scale-parameter', action='store_true', - help='scale learning rate by root mean square of parameter') - parser.add_argument('--relative-step', action='store_true', - help='set learning rate to inverse square root of timestep,' - 'otherwise use external learning rate') - parser.add_argument('--warmup-init', action='store_true', - help='use relative step for warm-up learning rate schedule') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - Note : Convergence issues empirically observed with fp16 on. - Might require search for appropriate configuration. - """ - return { - "lr": self.args.lr[0], - "eps": eval(self.args.adafactor_eps), - "clip_threshold": self.args.clip_threshold, - "decay_rate": self.args.decay_rate, - "beta1": self.args.beta1, - "weight_decay": self.args.weight_decay, - "scale_parameter": self.args.scale_parameter, # defaults to False - "relative_step": self.args.relative_step, # defaults to False - "warmup_init": self.args.warmup_init, - } - - -class Adafactor(torch.optim.Optimizer): - """Implements Adafactor algorithm. - - This implementation is based on: - `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost` - (see https://arxiv.org/abs/1804.04235) - - Note that this optimizer internally adjusts the learning rate - depending on the *scale_parameter*, *relative_step* and - *warmup_init* options. To use a manual (external) learning rate - schedule you should set `scale_parameter=False` and - `relative_step=False`. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): external learning rate (default: None) - eps (tuple[float, float]): regularization constans for square gradient - and parameter scale respectively (default: (1e-30, 1e-3)) - clip_threshold (float): threshold of root mean square of - final gradient update (default: 1.0) - decay_rate (float): coefficient used to compute running averages of square - gradient (default: -0.8) - beta1 (float): coefficient used for computing running averages of gradient - (default: None) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - scale_parameter (bool): if True, learning rate is scaled by root mean square of - parameter (default: True) - relative_step (bool): if True, time-dependent learning rate is computed - instead of external learning rate (default: True) - warmup_init (bool): time-dependent learning rate computation depends on - whether warm-up initialization is being used (default: False) - """ - - def __init__( - self, - params, - lr=None, - eps=(1e-30, 1e-3), - clip_threshold=1.0, - decay_rate=-0.8, - beta1=None, - weight_decay=0.0, - scale_parameter=True, - relative_step=True, - warmup_init=False, - ): - if lr is not None and relative_step: - raise ValueError("Cannot combine manual lr and relative_step options") - if warmup_init and not relative_step: - raise ValueError("warmup_init requires relative_step=True") - - defaults = dict( - lr=lr, - eps=eps, - clip_threshold=clip_threshold, - decay_rate=decay_rate, - beta1=beta1, - weight_decay=weight_decay, - scale_parameter=scale_parameter, - relative_step=relative_step, - warmup_init=warmup_init, - ) - super(Adafactor, self).__init__(params, defaults) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return False - - def _get_lr(self, param_group, param_state): - rel_step_sz = param_group["lr"] - if param_group["relative_step"]: - min_step = ( - 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 - ) - rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) - param_scale = 1.0 - if param_group["scale_parameter"]: - param_scale = max(param_group["eps"][1], param_state["RMS"]) - return param_scale * rel_step_sz - - def _get_options(self, param_group, param_shape): - factored = len(param_shape) >= 2 - use_first_moment = param_group["beta1"] is not None - return factored, use_first_moment - - def _rms(self, tensor): - return tensor.norm(2) / (tensor.numel() ** 0.5) - - def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col): - r_factor = ( - (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)) - .rsqrt_() - .unsqueeze(-1) - ) - c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() - return torch.mul(r_factor, c_factor) - - def step(self, closure=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad.data - if grad.dtype in {torch.float16, torch.bfloat16}: - grad = grad.float() - if grad.is_sparse: - raise RuntimeError("Adafactor does not support sparse gradients.") - - state = self.state[p] - grad_shape = grad.shape - - factored, use_first_moment = self._get_options(group, grad_shape) - # State Initialization - if len(state) == 0: - state["step"] = 0 - - if use_first_moment: - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(grad) - if factored: - state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) - state["exp_avg_sq_col"] = torch.zeros( - grad_shape[:-2] + grad_shape[-1:] - ).to(grad) - else: - state["exp_avg_sq"] = torch.zeros_like(grad) - - state["RMS"] = 0 - else: - if use_first_moment: - state["exp_avg"] = state["exp_avg"].to(grad) - if factored: - state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) - state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) - else: - state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) - - p_data_fp32 = p.data - if p.data.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - state["step"] += 1 - state["RMS"] = self._rms(p_data_fp32) - group["lr"] = self._get_lr(group, state) - - beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) - update = (grad**2) + group["eps"][0] - if factored: - exp_avg_sq_row = state["exp_avg_sq_row"] - exp_avg_sq_col = state["exp_avg_sq_col"] - - exp_avg_sq_row.mul_(beta2t).add_( - update.mean(dim=-1), alpha=1.0 - beta2t - ) - exp_avg_sq_col.mul_(beta2t).add_( - update.mean(dim=-2), alpha=1.0 - beta2t - ) - - # Approximation of exponential moving average of square of gradient - update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) - update.mul_(grad) - else: - exp_avg_sq = state["exp_avg_sq"] - - exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t) - update = exp_avg_sq.rsqrt().mul_(grad) - - update.div_( - (self._rms(update) / group["clip_threshold"]).clamp_(min=1.0) - ) - update.mul_(group["lr"]) - - if use_first_moment: - exp_avg = state["exp_avg"] - exp_avg.mul_(group["beta1"]).add_(update, alpha=1 - group["beta1"]) - update = exp_avg - - if group["weight_decay"] != 0: - p_data_fp32.add_( - p_data_fp32, alpha=-group["weight_decay"] * group["lr"] - ) - - p_data_fp32.add_(-update) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - return loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adagrad.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adagrad.py deleted file mode 100644 index 4f539541c1c91d8c822f7ce624fa6eabf744f60e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adagrad.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adagrad") -class Adagrad(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "weight_decay": self.args.weight_decay, - } - - @property - def supports_flat_params(self): - return False diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adam.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adam.py deleted file mode 100644 index dc9c438403f43f62fa35cdc37b71ca9c27bde79d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adam.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import math -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import Any, List - -import torch -import torch.distributed as dist -import torch.optim -from fairseq.dataclass import FairseqDataclass -from fairseq.optim import FairseqOptimizer, register_optimizer -from fairseq.optim.fused_adam import get_fused_adam_class -from omegaconf import II, OmegaConf - -logger = logging.getLogger(__name__) - - -@dataclass -class FairseqAdamConfig(FairseqDataclass): - adam_betas: Any = field( - default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"} - ) - adam_eps: float = field( - default=1e-8, metadata={"help": "epsilon for Adam optimizer"} - ) - weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) - use_old_adam: bool = field( - default=False, metadata={"help": "Use fairseq.optim.adam.Adam"} - ) - fp16_adam_stats: bool = field( - default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} - ) - # TODO common vars below in parent - tpu: bool = II("common.tpu") - lr: List[float] = II("optimization.lr") - - -@register_optimizer("adam", dataclass=FairseqAdamConfig) -class FairseqAdam(FairseqOptimizer): - """Adam optimizer for fairseq. - - Important note: this optimizer corresponds to the "AdamW" variant of - Adam in its weight decay behavior. As such, it is most closely - analogous to torch.optim.AdamW from PyTorch. - """ - - def __init__(self, cfg: FairseqAdamConfig, params): - super().__init__(cfg) - fused_adam_cls = get_fused_adam_class() - use_fused_adam = ( - not getattr(cfg, "use_old_adam", False) - and fused_adam_cls is not None - and torch.cuda.is_available() - ) - if getattr(cfg, "tpu", False): - if self.cfg.fp16_adam_stats: - raise NotImplementedError("--fp16-adam-stats is only supported on GPU") - # on TPUs we use the Adam defined here, since it - # automatically casts gradients to FP32 - self._optimizer = Adam(params, **self.optimizer_config) - elif use_fused_adam: - logger.info("using FusedAdam") - self._optimizer = fused_adam_cls( - params, use_fp16_stats=self.cfg.fp16_adam_stats, **self.optimizer_config - ) - else: - if self.cfg.fp16_adam_stats: - raise NotImplementedError( - "--fp16-adam-stats is only supported with FusedAdamV1" - ) - self._optimizer = Adam(params, **self.optimizer_config) - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.cfg.lr[0] - if isinstance(self.cfg.lr, Collection) - else self.cfg.lr, - "betas": eval(self.cfg.adam_betas) - if isinstance(self.cfg.adam_betas, str) - else OmegaConf.to_container(self.cfg.adam_betas), - "eps": self.cfg.adam_eps, - "weight_decay": self.cfg.weight_decay, - } - - def average_params(self): - """Reduce Params is only used during BMUF distributed training.""" - state_dict = self.optimizer.state_dict() - total_gpus = float(dist.get_world_size()) - - for _, value in state_dict["state"].items(): - value["exp_avg"] /= total_gpus - value["exp_avg_sq"] /= total_gpus - dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM) - dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM) - - -class Adam(torch.optim.Optimizer): - r"""Implements Adam algorithm. - - This implementation is modified from torch.optim.Adam based on: - `Fixed Weight Decay Regularization in Adam` - (see https://arxiv.org/abs/1711.05101) - - It has been proposed in `Adam: A Method for Stochastic Optimization`_. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - - .. _Adam\: A Method for Stochastic Optimization: - https://arxiv.org/abs/1412.6980 - .. _On the Convergence of Adam and Beyond: - https://openreview.net/forum?id=ryQu7f-RZ - """ - - def __init__( - self, - params, - lr=1e-3, - betas=(0.9, 0.999), - eps=1e-8, - weight_decay=0, - amsgrad=False, - ): - defaults = dict( - lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad - ) - super(Adam, self).__init__(params, defaults) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step(self, closure=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad.data - if grad.dtype in {torch.float16, torch.bfloat16}: - grad = grad.float() - if grad.is_sparse: - raise RuntimeError( - "Adam does not support sparse gradients, please consider SparseAdam instead" - ) - amsgrad = group.get("amsgrad", False) - - p_data_fp32 = p.data - if p.data.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - state = self.state[p] - - # State initialization - if len(state) == 0: - state["step"] = 0 - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p_data_fp32) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32) - else: - state["exp_avg"] = state["exp_avg"].to(p_data_fp32) - state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32) - if amsgrad: - state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to( - p_data_fp32 - ) - - exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] - if amsgrad: - max_exp_avg_sq = state["max_exp_avg_sq"] - beta1, beta2 = group["betas"] - - state["step"] += 1 - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - if amsgrad: - # Maintains the maximum of all 2nd moment running avg. till now - torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) - # Use the max. for normalizing running avg. of gradient - denom = max_exp_avg_sq.sqrt().add_(group["eps"]) - else: - denom = exp_avg_sq.sqrt().add_(group["eps"]) - - bias_correction1 = 1 - beta1 ** state["step"] - bias_correction2 = 1 - beta2 ** state["step"] - step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1 - - if group["weight_decay"] != 0: - p_data_fp32.add_( - p_data_fp32, alpha=-group["weight_decay"] * group["lr"] - ) - - p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - return loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adamax.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adamax.py deleted file mode 100644 index 98ff8ad7ad6c12ab5efc53ca76db2f1663be7906..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/adamax.py +++ /dev/null @@ -1,172 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("adamax") -class FairseqAdamax(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = Adamax(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--adamax-betas', default='(0.9, 0.999)', metavar='B', - help='betas for Adam optimizer') - parser.add_argument('--adamax-eps', type=float, default=1e-8, metavar='D', - help='epsilon for Adam optimizer') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - parser.add_argument('--no-bias-correction', default=False, action='store_true', - help='disable bias correction') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "betas": eval(self.args.adamax_betas), - "eps": self.args.adamax_eps, - "weight_decay": self.args.weight_decay, - "bias_correction": not self.args.no_bias_correction, - } - - -class Adamax(torch.optim.Optimizer): - """Implements Adamax algorithm (a variant of Adam based on infinity norm). - - It has been proposed in `Adam: A Method for Stochastic Optimization`__. - - Compared to the version in PyTorch, this version implements a fix for weight decay. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 2e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - bias_correction (bool, optional): enable bias correction (default: True) - - __ https://arxiv.org/abs/1412.6980 - """ - - def __init__( - self, - params, - lr=2e-3, - betas=(0.9, 0.999), - eps=1e-8, - weight_decay=0, - bias_correction=True, - ): - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict( - lr=lr, - betas=betas, - eps=eps, - weight_decay=weight_decay, - bias_correction=bias_correction, - ) - super(Adamax, self).__init__(params, defaults) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step(self, closure=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad.data.float() - if grad.is_sparse: - raise RuntimeError("Adamax does not support sparse gradients") - - p_data_fp32 = p.data - if p.data.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - state = self.state[p] - - # State initialization - if len(state) == 0: - state["step"] = 0 - state["exp_avg"] = torch.zeros_like(p_data_fp32) - state["exp_inf"] = torch.zeros_like(p_data_fp32) - else: - state["exp_avg"] = state["exp_avg"].to(p_data_fp32) - state["exp_inf"] = state["exp_inf"].to(p_data_fp32) - - exp_avg, exp_inf = state["exp_avg"], state["exp_inf"] - beta1, beta2 = group["betas"] - eps = group["eps"] - - state["step"] += 1 - - # Update biased first moment estimate. - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - - # Update the exponentially weighted infinity norm. - torch.max( - exp_inf.mul_(beta2), - grad.abs_(), - out=exp_inf, - ) - - step_size = group["lr"] - if group["bias_correction"]: - bias_correction = 1 - beta1 ** state["step"] - step_size /= bias_correction - - if group["weight_decay"] != 0: - p_data_fp32.add_( - p_data_fp32, alpha=-group["weight_decay"] * group["lr"] - ) - - p_data_fp32.addcdiv_(exp_avg, exp_inf.add(eps), value=-step_size) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - return loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/amp_optimizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/amp_optimizer.py deleted file mode 100644 index cfe57d07f9f7bc87a7ebb86b71ddb739e3496cf7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/amp_optimizer.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import torch -from fairseq import optim -from omegaconf import DictConfig - -logger = logging.getLogger(__name__) - - -class AMPOptimizer(optim.FairseqOptimizer): - """ - Wrap an *optimizer* to support AMP (automatic mixed precision) training. - """ - - def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs): - super().__init__(cfg.optimizer) - self.fp32_optimizer = fp32_optimizer - amp_kwargs = {"init_scale": cfg.common.fp16_init_scale} - if getattr(cfg.common, "amp_scale_window", None) is not None: - amp_kwargs["growth_interval"] = cfg.common.amp_init_scale - self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs) - self.min_loss_scale = cfg.common.min_loss_scale - - @classmethod - def build_optimizer(cls, cfg: DictConfig, params, **kwargs): - """ - Args: - cfg (omegaconf.DictConfig): fairseq args - params (iterable): iterable of parameters to optimize - """ - fp32_optimizer = optim.build_optimizer(cfg.optimizer, params) - return cls(cfg, params, fp32_optimizer, **kwargs) - - def backward(self, loss): - """Computes the sum of gradients of the given tensor w.r.t. graph leaves. - - Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this - function additionally dynamically scales the loss to avoid gradient - underflow. - """ - self._grad_scaler.scale(loss).backward() - - def step(self): - self.scaler.step(self.fp32_optimizer) - self.scaler.update() - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm.""" - self.scaler.unscale_(self.optimizer) - grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) - if not torch.isfinite(grad_norm).all(): - new_loss_scale = self.next_loss_scale - if new_loss_scale <= self.min_loss_scale: - raise FloatingPointError( - ( - "AMP: Minimum loss scale reached ({}). Your loss is probably exploding. " - "Try restarting training or use fp32. {}" - ).format(self.min_loss_scale, new_loss_scale) - ) - else: - logger.info( - "AMP: overflow detected, setting scale to " f"to {new_loss_scale}" - ) - return grad_norm - - @property - def scaler(self): - return self._grad_scaler - - @property - def next_loss_scale(self): - return self.scaler.get_scale() * self.scaler.get_backoff_factor() - - @property - def optimizer(self): - return self.fp32_optimizer.optimizer - - @optimizer.setter - def optimizer(self, optimizer): - self.fp32_optimizer.optimizer = optimizer - - @property - def lr_scheduler(self): - return getattr(self.fp32_optimizer, "lr_scheduler", None) - - @property - def optimizer_config(self): - return self.fp32_optimizer.optimizer_config - - def get_lr(self): - return self.fp32_optimizer.get_lr() - - def set_lr(self, lr): - self.fp32_optimizer.set_lr(lr) - - def all_reduce_grads(self, module): - self.fp32_optimizer.all_reduce_grads(module) - - @property - def supports_flat_params(self): - return self.fp32_optimizer.supports_flat_params diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/bmuf.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/bmuf.py deleted file mode 100644 index d6d0e04e86eb894efe59e13a78843d01ca9e651d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/bmuf.py +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field - -import torch -import torch.distributed as dist -from fairseq.dataclass.configs import FairseqBMUFConfig -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.optim.fairseq_optimizer import FairseqOptimizer - - -class FairseqBMUF(FairseqOptimizer): - """ - Implements incremental block distributed data parallelism similar to - https://ieeexplore.ieee.org/document/7472805 - - Paper title: Scalable training of deep learning machines by incremental - block training with intra-block parallel optimization and blockwise - model-update filtering - """ - - def __init__(self, cfg: FairseqBMUFConfig, optimizer): - super().__init__(cfg) - self._optimizer = optimizer - self._num_updates = 0 - self.sync_iter = cfg.global_sync_iter - self.block_momentum = cfg.block_momentum - self.block_lr = cfg.block_lr - self._reset_local_data() - self.warmup_iteration = cfg.warmup_iterations - self.use_nbm = cfg.use_nbm - self.initial_state = self._optimizer.state_dict() - self.average_sync = self.cfg.average_sync - self.world_size = self.cfg.distributed_world_size - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - gen_parser_from_dataclass(parser, FairseqBMUFConfig()) - - @property - def optimizer(self): - return self._optimizer.optimizer - - @property - def optimizer_config(self): - return self._optimizer.optimizer_config - - def get_lr(self): - return self._optimizer.get_lr() - - def set_lr(self, lr): - self._optimizer.set_lr(lr) - - def state_dict(self): - return self._optimizer.state_dict() - - def load_state_dict(self, state_dict, optimizer_overrides=None): - self._optimizer.load_state_dict(state_dict, optimizer_overrides) - self.initial_state = self._optimizer.state_dict() - - def multiply_grads(self, c): - """Multiplies grads by a constant *c*.""" - self._optimizer.multiply_grads(c) - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm.""" - return self._optimizer.clip_grad_norm(max_norm, aggregate_norm_fn) - - def average_params(self): - self._optimizer.average_params() - - def _block_sync(self): - if self.world_size <= 1: - return - # Update the global model using local models from all GPUs - # (Step-1) Calculate grad between previously synced model and - # currrent local model - if self.block_momentum != 0: - self._calc_grad() - - # (Step-2) Average gradient from all GPUs - self._avg_grad_from_all_gpus() - - # (Step-3) Calculate global momentum and update the global model - if self.block_momentum != 0: - self._update_global_model() - - # (Step-4) Average local optimizer params - if self.average_sync: - self.average_params() - - def _is_warmup_end(self): - # Check whether train iterations is equal to warmup iter - if self.get_num_updates() == self.warmup_iteration: - return True - return False - - def _is_bmuf_iter(self): - # Check whether train iterations is equal to bmuf sync iter - if (self.get_num_updates() > self.warmup_iteration) and ( - self.get_num_updates() % self.sync_iter == 0 - ): - return True - return False - - def _warmup_sync(self, root_rank=0): - if self.world_size <= 1: - return - # Broadcast the local model to all gpus - for param in self.params: - dist.broadcast(param.data, src=root_rank) - - # Update local optimizer state - if self.average_sync: - self._optimizer.average_params() - else: - self._optimizer.load_state_dict(self.initial_state) - - self._reset_local_data() - - def step(self, closure=None): - """Performs a single optimization step.""" - self._optimizer.step(closure) - self.set_num_updates(self.get_num_updates() + 1) - if self._is_warmup_end(): - self._warmup_sync() - elif self._is_bmuf_iter(): - self._block_sync() - - def zero_grad(self): - """Clears the gradients of all optimized parameters.""" - self._optimizer.zero_grad() - - def get_num_updates(self): - """Get the number of parameters updates.""" - return self._num_updates - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - self._num_updates = num_updates - - @torch.no_grad() - def _reset_local_data(self): - # (Step-0) Initialize global momentum parameters and store global copy on each gpu - self.global_params = [torch.zeros_like(p.data) for p in self.params] - self.smoothed_grads = [p.data.new_zeros(p.data.size()) for p in self.params] - self.grads = [p.data.new_zeros(p.data.size()) for p in self.params] - - # saving the global model locally for calculating gradient during bmuf sync - for param, global_param in zip(self.params, self.global_params): - global_param.copy_(param.data) - - @torch.no_grad() - def _calc_grad(self): - # global_params is basically the global copy from the previously finished - # synchronisation. param.data is local parameter after block_sync_freq - # for the local gpu. so grad is difference between previously synced - # model and currrent local model. - for index, (param, global_param) in enumerate( - zip(self.params, self.global_params) - ): - self.grads[index] = global_param - param.data - - def _avg_grad_from_all_gpus(self): - for index, param in enumerate(self.params): - sync_para = param.data if self.block_momentum == 0 else self.grads[index] - sync_para /= float(dist.get_world_size()) - dist.all_reduce(sync_para, op=dist.ReduceOp.SUM) - - @torch.no_grad() - def _update_global_model(self): - for index, (param, global_param, smoothed_grad, grad) in enumerate( - zip( - self.params, - self.global_params, - self.smoothed_grads, - # all gpus would share the same value of smoothed_grad, since it is - # always computed on synchronized gradients. - self.grads, - ) - ): - # global_param is basically last syncrhornized parameter. though - # smoothed_grad is local, all processes will have same value of - # smoothed_grad and hence param is globally synchronized copy. - # smoothed_grad(t) = BM * smoothed_grad(t-1) + BM_lr * grad(t) - smoothed_grad = self.block_momentum * smoothed_grad + self.block_lr * grad - param.data.copy_(global_param - smoothed_grad) - - # A Nesterov momentum here is to do a partial weight update before - # calculating the gradient - if self.use_nbm: - param.data.copy_(param.data - self.block_momentum * smoothed_grad) - - # backup for the next synchronization. - self.smoothed_grads[index] = smoothed_grad - global_param.copy_(param.data) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/composite.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/composite.py deleted file mode 100644 index db4f41e0e2b8f394986dfb06cdf4b70ced885251..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/composite.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from collections import defaultdict -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional - -import torch.optim -from fairseq.dataclass import FairseqDataclass -from fairseq.optim import FairseqOptimizer, _build_optimizer, register_optimizer -from fairseq.optim.lr_scheduler import FairseqLRScheduler, build_lr_scheduler -from omegaconf import II, open_dict - -logger = logging.getLogger(__name__) - - -@dataclass -class OptimizerAndSchedulerConfig(FairseqDataclass): - optimizer: Any = None - lr_scheduler: Optional[Any] = None - lr: List = II("optimization.lr") - lr_float: Optional[ - float - ] = None # this makes it easier to sweep on learning rate with auto sweepers - - -@dataclass -class CompositeOptimizerConfig(FairseqDataclass): - groups: Dict[str, Any] = field( - default_factory=lambda: {}, - metadata={ - "help": "optimizer name -> optimizer OptimizerAndSchedulerConfig. " - "Configures a different optimizer and (optionally) lr scheduler for each parameter group" - }, - ) - - -@register_optimizer("composite", dataclass=CompositeOptimizerConfig) -class FairseqCompositeOptimizer(FairseqOptimizer): - - optimizers: Dict[str, FairseqOptimizer] = {} - lr_schedulers: Dict[str, FairseqLRScheduler] = {} - lr_scheduler: FairseqLRScheduler = None - _optimizer: torch.optim.Optimizer - - def __init__(self, cfg: CompositeOptimizerConfig, params): - super().__init__(cfg) - - assert ( - len(params) > 1 - ), "Composite optimizer only works when there are multiple parameter groups (try fp16_no_flatten_grads: true)" - - groupped_params = defaultdict(list) - for p in params: - group = getattr(p, "param_group", "default") - groupped_params[group].append(p) - - assert groupped_params.keys() == cfg.groups.keys(), ( - f"Parameter groups {groupped_params.keys()} and optimizer groups {cfg.groups.keys()} are not the same! " - "Try setting 'param_group' on your parameters in the model." - ) - - for group, group_params in groupped_params.items(): - group_cfg = cfg.groups[group] - with open_dict(group_cfg): - if group_cfg.lr_float is not None: - group_cfg.optimizer.lr = [group_cfg.lr_float] - group_cfg.lr_scheduler.lr = [group_cfg.lr_float] - else: - group_cfg.optimizer.lr = group_cfg.lr - group_cfg.lr_scheduler.lr = group_cfg.lr - self.optimizers[group] = _build_optimizer(group_cfg.optimizer, group_params) - if group_cfg.lr_scheduler is not None: - self.lr_schedulers[group] = build_lr_scheduler( - group_cfg.lr_scheduler, self.optimizers[group] - ) - - if len(self.lr_schedulers) > 0: - assert len(self.lr_schedulers) == len(self.optimizers), ( - f"Please provide an lr scheduler for each optimizer to use pass_through scheduler. " - f"Optimizers: {self.optimizers}; Lr scheds: {self.lr_schedulers}" - ) - self.lr_scheduler = CompositeLRScheduler(self.lr_schedulers) - - self._optimizer = CompositeOptimizer(self.optimizers) - - @property - def supports_groups(self): - return True - - @property - def param_groups(self): - for opt in self.optimizers.values(): - for group in opt.param_groups: - yield group - - def get_lr(self): - """Return the current learning rate.""" - k = ( - "default" - if "default" in self.optimizers - else next(iter(self.optimizers.keys())) - ) - return self.optimizers[k].param_groups[0]["lr"] - - def state_dict(self): - """Return the LR scheduler state dict.""" - return {k: s.state_dict() for k, s in self.optimizers.items()} - - def load_state_dict(self, state_dict, optimizer_overrides=None): - """Load an LR scheduler state dict.""" - for k, state in state_dict.items(): - if k not in self.optimizers: - # skip extra keys like "loss_scale" added by fp16 optimizer - continue - - overrides = ( - optimizer_overrides[k] - if isinstance(optimizer_overrides, dict) and k in optimizer_overrides - else None - ) - self.optimizers[k].load_state_dict(state, optimizer_overrides=overrides) - - -class CompositeOptimizer(torch.optim.Optimizer): - def __init__(self, optimizers: Dict[str, FairseqOptimizer]): - self.optimizers = optimizers - - @property - def supports_memory_efficient_fp16(self): - return all(o.supports_memory_efficient_fp16 for o in self.optimizers.values()) - - @property - def supports_flat_params(self): - return all(o.supports_flat_params for o in self.optimizers.values()) - - def step(self, closure=None, groups=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for k, opt in self.optimizers.items(): - if groups is None or k in groups: - opt.step() - - return loss - - def zero_grad(self): - for opt in self.optimizers.values(): - opt.zero_grad() - - -class CompositeLRScheduler(FairseqLRScheduler): - def __init__(self, lr_schedulers): - super().__init__(None, None) - - self.lr_schedulers = lr_schedulers - - def state_dict(self): - """Return the LR scheduler state dict.""" - return {k: s.state_dict() for k, s in self.lr_schedulers.items()} - - def load_state_dict(self, state_dict): - """Load an LR scheduler state dict.""" - for k, state in state_dict.items(): - self.lr_schedulers[k].load_state_dict(state) - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - for s in self.lr_schedulers.values(): - s.step_begin_epoch(epoch) - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - for s in self.lr_schedulers.values(): - s.step(epoch) - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - return {k: s.step_update(num_updates) for k, s in self.lr_schedulers.items()} diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/cpu_adam.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/cpu_adam.py deleted file mode 100644 index 7143335a6e4fb2444f10b8aefa2388ae6d1bb7f3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/cpu_adam.py +++ /dev/null @@ -1,209 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import importlib -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import List - -import torch -from fairseq.dataclass import FairseqDataclass -from fairseq.optim import FairseqOptimizer, register_optimizer -from omegaconf import II, DictConfig - -try: - import deepspeed - - has_deepspeed = True -except ImportError as e: - has_deepspeed = False - - -def _get_cpu_adam(): - try: - from deepspeed.ops.op_builder import CPUAdamBuilder - - return CPUAdamBuilder().load() - except ImportError: - # fbcode - from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam - - return ds_opt_adam - - -@dataclass -class FairseqCPUAdamConfig(FairseqDataclass): - adam_betas: str = field( - default="(0.9, 0.999)", metadata={"help": "betas for Adam optimizer"} - ) - adam_eps: float = field( - default=1e-8, metadata={"help": "epsilon for Adam optimizer"} - ) - weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) - fp16_adam_stats: bool = field( - default=False, metadata={"help": "use FP16 stats (with automatic scaling)"} - ) - # TODO common vars below in parent - lr: List[float] = II("optimization.lr") - - -@register_optimizer("cpu_adam", dataclass=FairseqCPUAdamConfig) -class FairseqCPUAdam(FairseqOptimizer): - """Adam optimizer for fairseq, optimized for CPU tensors. - - Important note: this optimizer corresponds to the "AdamW" variant of - Adam in its weight decay behavior. As such, it is most closely - analogous to torch.optim.AdamW from PyTorch. - """ - - def __init__(self, cfg: DictConfig, params): - super().__init__(cfg) - self._optimizer = CPUAdam(params, **self.optimizer_config) - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.cfg.lr[0] - if isinstance(self.cfg.lr, Collection) - else self.cfg.lr, - "betas": eval(self.cfg.adam_betas), - "eps": self.cfg.adam_eps, - "weight_decay": self.cfg.weight_decay, - "use_fp16_stats": self.cfg.fp16_adam_stats, - } - - -class CPUAdam(torch.optim.Optimizer): - - optimizer_id = 0 - - def __init__( - self, - params, - lr=1e-3, - bias_correction=True, - betas=(0.9, 0.999), - eps=1e-8, - weight_decay=0, - use_fp16_stats=False, - ): - defaults = { - "lr": lr, - "bias_correction": bias_correction, - "betas": betas, - "eps": eps, - "weight_decay": weight_decay, - } - super().__init__(params, defaults) - - self.use_fp16_stats = use_fp16_stats - self.FLOAT16_MAX = 65504.0 - - if not has_deepspeed: - raise ImportError("Please install DeepSpeed: pip install deepspeed") - - self.opt_id = CPUAdam.optimizer_id - CPUAdam.optimizer_id = CPUAdam.optimizer_id + 1 - - self.ds_opt_adam = _get_cpu_adam() - adamw_mode = True - self.ds_opt_adam.create_adam( - self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode - ) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - @torch.no_grad() - def step(self, closure=None): - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - torch.cuda.synchronize() - - for group_id, group in enumerate(self.param_groups): - for param_id, p in enumerate(group["params"]): - if p.grad is None: - continue - - state = self.state[p] - if len(state) == 0: - state["step"] = 0 - dtype = torch.float16 if self.use_fp16_stats else p.data.dtype - # gradient momentums - state["exp_avg"] = torch.zeros_like( - p.data, dtype=dtype, device="cpu" - ) - # gradient variances - state["exp_avg_sq"] = torch.zeros_like( - p.data, dtype=dtype, device="cpu" - ) - if self.use_fp16_stats: - assert torch.is_floating_point(p.data) - state["exp_avg_scale"] = 1.0 - state["exp_avg_sq_scale"] = 1.0 - - exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] - - p_data_bak = p.data # backup of the original data pointer - - p.data = p.data.to(dtype=torch.float32, device="cpu") - p.grad.data = p.grad.data.to(dtype=torch.float32, device="cpu") - - if self.use_fp16_stats: - exp_avg = exp_avg.float() * state["exp_avg_scale"] - exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] - - state["step"] += 1 - beta1, beta2 = group["betas"] - - self.ds_opt_adam.adam_update( - self.opt_id, - state["step"], - group["lr"], - beta1, - beta2, - group["eps"], - group["weight_decay"], - group["bias_correction"], - p.data, - p.grad.data, - exp_avg, - exp_avg_sq, - ) - - if p_data_bak.data_ptr() != p.data.data_ptr(): - p_data_bak.copy_(p.data) - p.data = p_data_bak - - if self.use_fp16_stats: - - def inf_norm(t): - return torch.norm(t, float("inf")) - - # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py - state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( - 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, - 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, - ) - state["exp_avg"], state["exp_avg_sq"] = ( - (exp_avg / state["exp_avg_scale"]).half(), - (exp_avg_sq / state["exp_avg_sq_scale"]).half(), - ) - - return loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/dynamic_loss_scaler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/dynamic_loss_scaler.py deleted file mode 100644 index 60c47b8db0a4d907be966bf39fa24effa2c825db..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/dynamic_loss_scaler.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -class DynamicLossScaler(object): - def __init__( - self, - init_scale=2.0**15, - scale_factor=2.0, - scale_window=2000, - tolerance=0.0, - threshold=None, - min_loss_scale=1e-4, - ): - self.loss_scale = init_scale - self.scale_factor = scale_factor - self.scale_window = scale_window - self.tolerance = tolerance - self.threshold = threshold - self._iter = 0 - self._last_overflow_iter = -1 - self._last_rescale_iter = -1 - self._overflows_since_rescale = 0 - self.min_loss_scale = min_loss_scale - - def scale(self, outputs): - return self.loss_scale * outputs - - def update(self): - if (self._iter - self._last_overflow_iter) % self.scale_window == 0: - self.loss_scale *= self.scale_factor - self._last_rescale_iter = self._iter - self._iter += 1 - - def _decrease_loss_scale(self): - self.loss_scale /= self.scale_factor - if self.threshold is not None: - self.loss_scale = max(self.loss_scale, self.threshold) - - def check_overflow(self, grad_norm): - # detect inf and nan - if grad_norm == float("inf") or grad_norm != grad_norm: - # overflow has occured - prev_scale = self.loss_scale - iter_since_rescale = self._iter - self._last_rescale_iter - - self._last_overflow_iter = self._iter - self._overflows_since_rescale += 1 - pct_overflow = self._overflows_since_rescale / float(iter_since_rescale) - if pct_overflow >= self.tolerance: - self._decrease_loss_scale() - self._last_rescale_iter = self._iter - self._overflows_since_rescale = 0 - - if self.loss_scale <= self.min_loss_scale: - # Use FloatingPointError as an uncommon error that parent - # functions can safely catch to stop training. - self.loss_scale = prev_scale - raise FloatingPointError( - ( - "Minimum loss scale reached ({}). Your loss is probably exploding. " - "Try lowering the learning rate, using gradient clipping or " - "increasing the batch size." - ).format(self.min_loss_scale) - ) - - self._iter += 1 - raise OverflowError("setting loss scale to: " + str(self.loss_scale)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fairseq_optimizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fairseq_optimizer.py deleted file mode 100644 index 7e5411753a2ba94f3a7a68316131530b8b17d22a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fairseq_optimizer.py +++ /dev/null @@ -1,179 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from fairseq import utils -from fairseq.dataclass.utils import gen_parser_from_dataclass - - -class FairseqOptimizer(object): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - - @classmethod - def add_args(cls, parser): - """Add optimizer-specific arguments to the parser.""" - dc = getattr(cls, "__dataclass", None) - if dc is not None: - gen_parser_from_dataclass(parser, dc()) - - @property - def optimizer(self): - """Return a torch.optim.optimizer.Optimizer instance.""" - if not hasattr(self, "_optimizer"): - raise NotImplementedError - if not isinstance(self._optimizer, torch.optim.Optimizer): - raise ValueError("_optimizer must be an instance of torch.optim.Optimizer") - return self._optimizer - - @optimizer.setter - def optimizer(self, optimizer): - """Reset optimizer instance.""" - if not hasattr(self, "_optimizer"): - raise NotImplementedError - if not isinstance(self._optimizer, torch.optim.Optimizer): - raise ValueError("_optimizer must be an instance of torch.optim.Optimizer") - self._optimizer = optimizer - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - raise NotImplementedError - - @property - def params(self): - """Return an iterable of the parameters held by the optimizer.""" - for param_group in self.param_groups: - for p in param_group["params"]: - yield p - - @property - def param_groups(self): - return self.optimizer.param_groups - - def __getstate__(self): - return self._optimizer.__getstate__() - - def get_lr(self): - """Return the current learning rate.""" - return self.param_groups[0]["lr"] - - def set_lr(self, lr): - """Set the learning rate.""" - for param_group in self.param_groups: - param_group["lr"] = lr - - def state_dict(self): - """Return the optimizer's state dict.""" - return self.optimizer.state_dict() - - def load_state_dict(self, state_dict, optimizer_overrides=None): - """Load an optimizer state dict. - - In general we should prefer the configuration of the existing optimizer - instance (e.g., learning rate) over that found in the state_dict. This - allows us to resume training from a checkpoint using a new set of - optimizer args. - """ - self.optimizer.load_state_dict(state_dict) - - if optimizer_overrides is not None and len(optimizer_overrides) > 0: - # override learning rate, momentum, etc. with latest values - for group in self.param_groups: - group.update(optimizer_overrides) - - def backward(self, loss): - """Computes the sum of gradients of the given tensor w.r.t. graph leaves.""" - loss.backward() - - def all_reduce_grads(self, module): - """Manually all-reduce gradients (if required).""" - if hasattr(module, "all_reduce_grads"): - module.all_reduce_grads() - - def multiply_grads(self, c): - """Multiplies grads by a constant *c*.""" - for p in self.params: - if p.grad is not None: - if torch.is_tensor(c): - c = c.to(p.grad.device) - p.grad.data.mul_(c) - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm.""" - return utils.clip_grad_norm_(self.params, max_norm, aggregate_norm_fn) - - def step(self, closure=None, scale=1.0, groups=None): - """Performs a single optimization step.""" - if self.supports_step_with_scale: - if self.supports_groups: - self.optimizer.step(closure, scale=scale, groups=groups) - else: - self.optimizer.step(closure, scale=scale) - else: - if scale != 1.0: - self.multiply_grads(1.0 / scale) - if self.supports_groups: - self.optimizer.step(closure, groups=groups) - else: - self.optimizer.step(closure) - - def zero_grad(self): - """Clears the gradients of all optimized parameters.""" - for p in self.params: - p.grad = None - self.optimizer.zero_grad() - - @property - def supports_memory_efficient_fp16(self): - if hasattr(self.optimizer, "supports_memory_efficient_fp16"): - return self.optimizer.supports_memory_efficient_fp16 - return False - - @property - def supports_step_with_scale(self): - if hasattr(self.optimizer, "supports_step_with_scale"): - return self.optimizer.supports_step_with_scale - return False - - @property - def supports_groups(self): - if hasattr(self.optimizer, "supports_groups"): - return self.optimizer.supports_groups - return False - - @property - def supports_flat_params(self): - """ - Whether the optimizer supports collapsing of the model - parameters/gradients into a single contiguous Tensor. - """ - if hasattr(self.optimizer, "supports_flat_params"): - return self.optimizer.supports_flat_params - return False - - def average_params(self): - pass - - def broadcast_global_state_dict(self, state_dict): - """ - Broadcasts a global state dict to all ranks. - Useful for optimizers that shard state between ranks. - """ - if hasattr(self.optimizer, "broadcast_global_state_dict"): - return self.optimizer.broadcast_global_state_dict(state_dict) - else: - return state_dict - - -class LegacyFairseqOptimizer(FairseqOptimizer): - def __init__(self, args): - self.args = args diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fp16_optimizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fp16_optimizer.py deleted file mode 100644 index a3f4d04e7c27f341cc9328b65ab7360e6ba6a71e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fp16_optimizer.py +++ /dev/null @@ -1,552 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections import defaultdict -from itertools import chain - -import torch -from fairseq import optim -from omegaconf import DictConfig - -from .dynamic_loss_scaler import DynamicLossScaler - - -class _FP16OptimizerMixin(object): - def __init__(self, *args, **kwargs): - # forward __init__ call to the next class in mro(method resolution order) - super().__init__(*args, **kwargs) - self._multiply_factor = 1.0 - - @property - def has_flat_params(self): - return torch.is_tensor(self.fp32_params) or ( - isinstance(self.fp32_params, dict) - and all(torch.is_tensor(t) for t in self.fp32_params.values()) - ) - - @classmethod - def build_fp32_params(cls, args, params, flatten=True): - # create FP32 copy of parameters and grads - if flatten: - is_pipeline_parallel = getattr( - args, "pipeline_model_parallel", False - ) and getattr(args, "distributed_no_spawn", False) - total_param_size = sum(p.data.numel() for p in params) - devices = [torch.cuda.current_device()] - if is_pipeline_parallel: - devices = list(set(args.pipeline_devices)) - fp32_params = {} - for device in devices: - if is_pipeline_parallel: - device_param_size = sum( - p.data.numel() for p in params if p.device.index == device - ) - device_params = [p for p in params if p.device.index == device] - else: - device_param_size = total_param_size - device_params = params - fp32_params[device] = ( - device_params[0].new(0).float().new(device_param_size) - ) - offset = 0 - for p in device_params: - numel = p.data.numel() - fp32_params[device][offset : offset + numel].copy_(p.data.view(-1)) - offset += numel - fp32_params[device] = torch.nn.Parameter(fp32_params[device]) - fp32_params[device].grad = fp32_params[device].data.new( - device_param_size - ) - return fp32_params - else: - fp32_params = [] - for p in params: - p32 = torch.nn.Parameter(p.data.float()) - if hasattr(p, "expert"): - p32.expert = True - elif hasattr(p, "base_expert"): - p32.base_expert = True - p32.grad = torch.zeros_like(p32.data) - if hasattr(p, "param_group"): - p32.param_group = p.param_group - fp32_params.append(p32) - return fp32_params - - def state_dict(self): - """Return the optimizer's state dict.""" - state_dict = self.fp32_optimizer.state_dict() - if self.scaler is not None: - state_dict["loss_scale"] = self.scaler.loss_scale - return state_dict - - def load_state_dict(self, state_dict, optimizer_overrides=None): - """Load an optimizer state dict. - - In general we should prefer the configuration of the existing optimizer - instance (e.g., learning rate) over that found in the state_dict. This - allows us to resume training from a checkpoint using a new set of - optimizer args. - """ - if "loss_scale" in state_dict and self.scaler is not None: - self.scaler.loss_scale = state_dict["loss_scale"] - self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides) - - def backward(self, loss): - """Computes the sum of gradients of the given tensor w.r.t. graph leaves. - - Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this - function additionally dynamically scales the loss to avoid gradient - underflow. - """ - if self.scaler is not None: - loss = self.scaler.scale(loss) - loss.backward() - self._needs_sync = True - - def _sync_fp16_grads_to_fp32(self): - if self._needs_sync: - # copy FP16 grads to FP32 - if self.has_flat_params: - devices = list(self.fp32_params.keys()) - device_params_dict = defaultdict(list) - for p in self.fp16_params: - if p.requires_grad: - device_params_dict[p.device.index].append(p) - for device in devices: - device_params = device_params_dict[device] - offset = 0 - for p in device_params: - grad_data = ( - p.grad.data - if p.grad is not None - else p.data.new_zeros(p.data.shape) - ) - numel = grad_data.numel() - self.fp32_params[device].grad.data[ - offset : offset + numel - ].copy_(grad_data.view(-1)) - offset += numel - else: - for p, p32 in zip(self.fp16_params, self.fp32_params): - if not p.requires_grad: - continue - if p.grad is not None: - if p32.grad is None: - p32.grad = p.grad.data.float() - else: - p32.grad.data.copy_(p.grad.data) - else: - p32.grad = torch.zeros_like(p.data, dtype=torch.float) - - self._needs_sync = False - - def _sync_fp32_params_to_fp16(self): - # copy FP32 params back into FP16 model - if self.has_flat_params: - devices = list(self.fp32_params.keys()) - device_params_dict = defaultdict(list) - for p in self.fp16_params: - device_params_dict[p.device.index].append(p) - for device in devices: - device_params = device_params_dict[device] - offset = 0 - for p in device_params: - numel = p.data.numel() - p.data.copy_( - self.fp32_params[device] - .data[offset : offset + numel] - .view_as(p.data) - ) - offset += numel - else: - for p, p32 in zip(self.fp16_params, self.fp32_params): - if not p.requires_grad: - continue - p.data.copy_(p32.data) - - def _unscale_grads(self): - self._sync_fp16_grads_to_fp32() - if ( - # Skip the multiplication if it's a no-op (i.e., if _multiply_factor - # is 1.0). At the same time, we want to avoid the device-to-host - # transfer by comparing it to 1.0. Since _multiply_factor starts as - # a Python float, we roughly assume that if it's a tensor then it's - # probably not =1.0 anymore and we do the multiplication. Otherwise - # we can safely check the value without a D2H transfer. - torch.is_tensor(self._multiply_factor) - or self._multiply_factor != 1.0 - ): - self.fp32_optimizer.multiply_grads(self._multiply_factor) - self._multiply_factor = 1.0 - - def multiply_grads(self, c): - """Multiplies grads by a constant ``c``.""" - self._multiply_factor *= c - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm and updates dynamic loss scaler.""" - self._sync_fp16_grads_to_fp32() - - grad_norm = self._multiply_factor * self.fp32_optimizer.clip_grad_norm( - 0, aggregate_norm_fn - ) - - if self.scaler is not None: - if grad_norm > max_norm > 0.0: - self._multiply_factor *= max_norm / grad_norm - - self.scaler.check_overflow(grad_norm) - elif max_norm > 0.0: - clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1) - self._multiply_factor *= clip_coef - - return grad_norm - - def step(self, closure=None, groups=None): - """Performs a single optimization step.""" - self._sync_fp16_grads_to_fp32() - - if getattr(self, "supports_step_with_scale", False): - self.fp32_optimizer.step( - closure, scale=(1.0 / self._multiply_factor), groups=groups - ) - else: - self._unscale_grads() - self.fp32_optimizer.step(closure, groups=groups) - - if self.scaler is not None: - self.scaler.update() - - self._sync_fp32_params_to_fp16() - - def zero_grad(self): - """Clears the gradients of all optimized parameters.""" - for p in self.fp16_params: - p.grad = None - if self.has_flat_params: - if torch.is_tensor(self.fp32_params): - self.fp32_params.grad.zero_() - elif isinstance(self.fp32_params, dict): - for fp32_params in self.fp32_params.values(): - fp32_params.grad.zero_() - else: - raise RuntimeError("self.fp32_params must be a tensor or dict") - else: - for p32 in self.fp32_params: - if p32.grad is not None: - p32.grad.zero_() - self._needs_sync = False - - if self.scaler is not None: - self._multiply_factor = 1.0 / float(self.scaler.loss_scale) - - -class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer): - """ - Wrap an *optimizer* to support FP16 (mixed precision) training. - """ - - def __init__(self, cfg: DictConfig, params, fp32_optimizer, fp32_params, **kwargs): - super().__init__(cfg.optimizer) - self.fp16_params = params - self.fp32_optimizer = fp32_optimizer - self.fp32_params = fp32_params - - if getattr(cfg.common, "fp16_scale_window", None) is None: - if len(cfg.optimization.update_freq) > 1: - raise ValueError( - "--fp16-scale-window must be given explicitly when using a " - "custom --update-freq schedule" - ) - data_parallel_size = int( - cfg.distributed_training.distributed_world_size - / cfg.common.model_parallel_size - ) - scale_window = int( - 2**14 / data_parallel_size / cfg.optimization.update_freq[0] - ) - else: - scale_window = cfg.common.fp16_scale_window - - if not getattr(cfg.common, "bf16", False): - self.scaler = DynamicLossScaler( - init_scale=cfg.common.fp16_init_scale, - scale_window=scale_window, - tolerance=cfg.common.fp16_scale_tolerance, - threshold=cfg.common.threshold_loss_scale, - min_loss_scale=cfg.common.min_loss_scale, - ) - else: - # disable loss scaling for bfloat16 - self.scaler = None - - @classmethod - def build_optimizer(cls, cfg: DictConfig, params, **kwargs): - """ - Args: - cfg (omegaconf.DictConfig): fairseq args - params (iterable): iterable of parameters to optimize - """ - flatten = not getattr(cfg.common, "fp16_no_flatten_grads", False) - if getattr(cfg.common, "bf16", False): - flatten = False # mixed precision is faster on TPUs without flat grads - fp32_params = cls.build_fp32_params(cfg.optimizer, params, flatten=flatten) - if flatten: - fp32_optimizer = optim.build_optimizer(cfg.optimizer, [fp32_params]) - else: - fp32_optimizer = optim.build_optimizer(cfg.optimizer, fp32_params) - if flatten and not fp32_optimizer.supports_flat_params: - raise RuntimeError( - f"chosen optimizer {fp32_optimizer.__class__.__name__} does not support flat params, please set --fp16-no-flatten-grads" - ) - return cls(cfg, params, fp32_optimizer, fp32_params, **kwargs) - - @property - def optimizer(self): - return self.fp32_optimizer.optimizer - - @optimizer.setter - def optimizer(self, optimizer): - self.fp32_optimizer.optimizer = optimizer - - @property - def lr_scheduler(self): - return getattr(self.fp32_optimizer, "lr_scheduler", None) - - @property - def optimizer_config(self): - return self.fp32_optimizer.optimizer_config - - def get_lr(self): - return self.fp32_optimizer.get_lr() - - def set_lr(self, lr): - self.fp32_optimizer.set_lr(lr) - - def all_reduce_grads(self, module): - self.fp32_optimizer.all_reduce_grads(module) - - @property - def supports_flat_params(self): - return self.fp32_optimizer.supports_flat_params - - -class _MemoryEfficientFP16OptimizerMixin(object): - def __init__(self, *args, **kwargs): - # forward __init__ call to the next class in MRO (method resolution order) - super().__init__(*args, **kwargs) - self._multiply_factor = 1.0 - - @property - def has_flat_params(self): - return False - - def state_dict(self): - """Return the optimizer's state dict.""" - state_dict = self.wrapped_optimizer.state_dict() - if self.scaler is not None: - state_dict["loss_scale"] = self.scaler.loss_scale - return state_dict - - def load_state_dict(self, state_dict, optimizer_overrides=None): - """Load an optimizer state dict. - - In general we should prefer the configuration of the existing optimizer - instance (e.g., learning rate) over that found in the state_dict. This - allows us to resume training from a checkpoint using a new set of - optimizer args. - """ - if "loss_scale" in state_dict and self.scaler is not None: - self.scaler.loss_scale = state_dict["loss_scale"] - - self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides) - - # Hack: PyTorch automatically casts the optimizer state to match the - # type of the current parameters. But with --memory-efficient-fp16 the - # params are FP16 while the optimizer state is FP32 and we don't want - # to cast. A workaround is to manually copy back the original state - # after the optimizer has been loaded. - if not getattr(self.optimizer, "disable_mem_eff_fp16_loading_hack", False): - groups = self.optimizer.param_groups - saved_groups = state_dict["param_groups"] - id_map = { - old_id: p - for old_id, p in zip( - chain(*(g["params"] for g in saved_groups)), - chain(*(g["params"] for g in groups)), - ) - } - for k, v in state_dict["state"].items(): - if k in id_map: - param = id_map[k] - self.optimizer.state[param] = v - - def backward(self, loss): - """Computes the sum of gradients of the given tensor w.r.t. graph leaves. - - Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this - function additionally dynamically scales the loss to avoid gradient - underflow. - """ - if self.scaler is not None: - loss = self.scaler.scale(loss) - loss.backward() - - def _unscale_grads(self): - if ( - # Skip the multiplication if it's a no-op (i.e., if _multiply_factor - # is 1.0). At the same time, we want to avoid the device-to-host - # transfer by comparing it to 1.0. Since _multiply_factor starts as - # a Python float, we roughly assume that if it's a tensor then it's - # probably not =1.0 anymore and we do the multiplication. Otherwise - # we can safely check the value without a D2H transfer. - torch.is_tensor(self._multiply_factor) - or self._multiply_factor != 1.0 - ): - self.wrapped_optimizer.multiply_grads(self._multiply_factor) - self._multiply_factor = 1.0 - - def multiply_grads(self, c): - """Multiplies grads by a constant *c*.""" - self._multiply_factor *= c - - def clip_grad_norm(self, max_norm, aggregate_norm_fn=None): - """Clips gradient norm and updates dynamic loss scaler.""" - max_norm = float(max_norm) - grad_norm = self._multiply_factor * self.wrapped_optimizer.clip_grad_norm( - 0, aggregate_norm_fn - ) - - if self.scaler is not None: - grad_norm_cpu = float(grad_norm) - if grad_norm_cpu > max_norm > 0.0: - self._multiply_factor *= max_norm / grad_norm_cpu - - # detect overflow and adjust loss scale - self.scaler.check_overflow(grad_norm_cpu) - elif max_norm > 0.0: - clip_coef = (max_norm / (grad_norm + 1e-6)).clamp_(max=1) - self._multiply_factor *= clip_coef - - return grad_norm - - def step(self, closure=None, groups=None): - """Performs a single optimization step.""" - if getattr(self, "supports_step_with_scale", False): - # NOTE(msb) optimizer divides by scale factor - self.wrapped_optimizer.step( - closure, scale=(1.0 / self._multiply_factor), groups=groups - ) - else: - self._unscale_grads() - self.wrapped_optimizer.step(closure, groups=groups) - - if self.scaler is not None: - self.scaler.update() - - def zero_grad(self): - """Clears the gradients of all optimized parameters.""" - self.wrapped_optimizer.zero_grad() - if self.scaler is not None: - self._multiply_factor = 1.0 / float(self.scaler.loss_scale) - else: - self._multiply_factor = 1.0 - - @property - def supports_flat_params(self): - return self.wrapped_optimizer.supports_flat_params - - -class MemoryEfficientFP16Optimizer( - _MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer -): - """ - Wrap an *optimizer* to support FP16 (mixed precision) training. - - Compared to :class:`fairseq.optim.FP16Optimizer`, this version does not - maintain an FP32 copy of the model. We instead expect the optimizer to - convert the gradients to FP32 internally and sync the results back to the - FP16 model params. This significantly reduces memory usage but slightly - increases the time spent in the optimizer. - - Since this wrapper depends on specific functionality in the wrapped - optimizer (i.e., on-the-fly conversion of grads to FP32), only certain - optimizers can be wrapped. This is determined by the - *supports_memory_efficient_fp16* property. - """ - - def __init__( - self, cfg: DictConfig, params, optimizer, allow_unsupported=False, **kwargs - ): - if not allow_unsupported and not optimizer.supports_memory_efficient_fp16: - raise ValueError( - "Unsupported optimizer: {}".format(optimizer.__class__.__name__) - ) - - super().__init__(getattr(cfg, "optimizer", None)) - self.wrapped_optimizer = optimizer - - if getattr(cfg.common, "fp16_scale_window", None) is None: - if len(cfg.optimization.update_freq) > 1: - raise ValueError( - "--fp16-scale-window must be given explicitly when using a " - "custom --update-freq schedule" - ) - data_parallel_size = int( - cfg.distributed_training.distributed_world_size - / cfg.common.model_parallel_size - ) - scale_window = int( - 2**14 / data_parallel_size / cfg.optimization.update_freq[0] - ) - else: - scale_window = cfg.common.fp16_scale_window - - if not getattr(cfg.common, "bf16", False): - self.scaler = DynamicLossScaler( - init_scale=cfg.common.fp16_init_scale, - scale_window=scale_window, - tolerance=cfg.common.fp16_scale_tolerance, - threshold=cfg.common.threshold_loss_scale, - min_loss_scale=cfg.common.min_loss_scale, - ) - else: - # disable loss scaling for bfloat16 - self.scaler = None - - @classmethod - def build_optimizer(cls, cfg: DictConfig, params, **kwargs): - """ - Args: - args (argparse.Namespace): fairseq args - params (iterable): iterable of parameters to optimize - """ - fp16_optimizer = optim.build_optimizer(cfg.optimizer, params) - return cls(cfg, params, fp16_optimizer, **kwargs) - - @property - def optimizer(self): - return self.wrapped_optimizer.optimizer - - @optimizer.setter - def optimizer(self, optimizer): - self.wrapped_optimizer.optimizer = optimizer - - @property - def optimizer_config(self): - return self.wrapped_optimizer.optimizer_config - - @property - def lr_scheduler(self): - return getattr(self.wrapped_optimizer, "lr_scheduler", None) - - def get_lr(self): - return self.wrapped_optimizer.get_lr() - - def set_lr(self, lr): - self.wrapped_optimizer.set_lr(lr) - - def all_reduce_grads(self, module): - self.wrapped_optimizer.all_reduce_grads(module) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fused_adam.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fused_adam.py deleted file mode 100644 index 1290ecfdbf51657cc0ffd7944b5a5601337b0eb0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fused_adam.py +++ /dev/null @@ -1,386 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import types - -import torch - - -def get_fused_adam_class(): - """ - Look for the FusedAdam optimizer from apex. We first try to load the - "contrib" interface, which is a bit faster than the main interface, - but is technically deprecated. - """ - try: - # The "deprecated" interface in recent versions of apex is a bit - # faster than the main interface, since we don't use the apex - # optimizer. This can be installed by passing the - # `--deprecated_fused_adam` option when building apex. - global fused_adam_cuda - import importlib - - fused_adam_cuda = importlib.import_module("fused_adam_cuda") - return FusedAdamV1 - except ImportError: - try: - # fallback to the newer interface - from apex.multi_tensor_apply import multi_tensor_applier - from apex.optimizers import FusedAdam as _FusedAdam # noqa - - if multi_tensor_applier.available: - return FusedAdamV2 - except ImportError: - pass - return None - - -class FusedAdamV1(torch.optim.Optimizer): - """ - Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via - ``python setup.py install --cuda_ext --cpp_ext``. - - It has been proposed in `Adam: A Method for Stochastic Optimization`_. - - Compared to the original version in Apex, the fairseq version casts grads - and params to FP32 internally to support ``--memory-efficient-fp16``. - - Args: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups. - lr (float, optional): learning rate. (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square. (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability. (default: 1e-8) - weight_decay (float, optional): weight decay (L2 penalty) (default: 0) - amsgrad (boolean, optional): whether to use the AMSGrad variant of this - algorithm from the paper `On the Convergence of Adam and Beyond`_ - (default: False) NOT SUPPORTED in FusedAdam! - eps_inside_sqrt (boolean, optional): in the 'update parameters' step, - adds eps to the bias-corrected second moment estimate before - evaluating square root instead of adding it to the square root of - second moment estimate as in the original paper. (default: False) - .. _Adam: A Method for Stochastic Optimization: - https://arxiv.org/abs/1412.6980 - .. _On the Convergence of Adam and Beyond: - https://openreview.net/forum?id=ryQu7f-RZ - """ - - def __init__( - self, - params, - lr=1e-3, - bias_correction=True, - betas=(0.9, 0.999), - eps=1e-8, - eps_inside_sqrt=False, - weight_decay=0.0, - max_grad_norm=0.0, - amsgrad=False, - use_fp16_stats=False, - ): - global fused_adam_cuda - import importlib - - fused_adam_cuda = importlib.import_module("fused_adam_cuda") - - if amsgrad: - raise RuntimeError("FusedAdam does not support the AMSGrad variant.") - defaults = { - "lr": lr, - "bias_correction": bias_correction, - "betas": betas, - "eps": eps, - "weight_decay": weight_decay, - "max_grad_norm": max_grad_norm, - } - super().__init__(params, defaults) - self.eps_mode = 0 if eps_inside_sqrt else 1 - - self.use_fp16_stats = use_fp16_stats - self.FLOAT16_MAX = 65504.0 - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - @property - def supports_step_with_scale(self): - return True - - def step(self, closure=None, grads=None, scale=1.0, grad_norms=None): - """Performs a single optimization step. - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - grads (list of tensors, optional): weight gradient to use for the - optimizer update. If gradients have type torch.half, parameters - are expected to be in type torch.float. (default: None) - output params (list of tensors, optional): A reduced precision copy - of the updated weights written out in addition to the regular - updated weights. Have to be of same type as gradients. (default: None) - scale (float, optional): factor to divide gradient tensor values - by before applying to weights. (default: 1) - """ - loss = None - if closure is not None: - loss = closure() - - if grads is None: - grads_group = [None] * len(self.param_groups) - # backward compatibility - # assuming a list/generator of parameter means single group - elif isinstance(grads, types.GeneratorType): - grads_group = [grads] - elif type(grads[0]) != list: - grads_group = [grads] - else: - grads_group = grads - - if grad_norms is None: - grad_norms = [None] * len(self.param_groups) - - for group, grads_this_group, grad_norm in zip( - self.param_groups, grads_group, grad_norms - ): - if grads_this_group is None: - grads_this_group = [None] * len(group["params"]) - - # compute combined scale factor for this group - combined_scale = scale - if group.get("max_grad_norm", 0) > 0: - # norm is in fact norm*scale - clip = ((grad_norm / scale) + 1e-6) / group["max_grad_norm"] - if clip > 1: - combined_scale = clip * scale - - bias_correction = 1 if group.get("bias_correction", 1) else 0 - - for p, grad in zip(group["params"], grads_this_group): - # note: p.grad should not ever be set for correct - # operation of mixed precision optimizer that sometimes - # sends None gradients - if p.grad is None and grad is None: - continue - if grad is None: - grad = p.grad.data - if grad.is_sparse: - raise RuntimeError( - "FusedAdam does not support sparse gradients, " - "please consider SparseAdam instead" - ) - - if p.device.type == "cpu": - p_data_fp32 = p.data.cuda(non_blocking=True).float() - out_p = torch.tensor([], dtype=torch.float) - else: - p_data_fp32 = p.data.float() - out_p = p.data - - state = self.state[p] - - # State initialization - dtype = torch.float16 if self.use_fp16_stats else p_data_fp32.dtype - if len(state) == 0: - state["step"] = 0 - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p_data_fp32, dtype=dtype) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like(p_data_fp32, dtype=dtype) - if self.use_fp16_stats: - state["exp_avg_scale"] = 1.0 - state["exp_avg_sq_scale"] = 1.0 - else: - device = p_data_fp32.device - state["exp_avg"] = state["exp_avg"].to(device, dtype) - state["exp_avg_sq"] = state["exp_avg_sq"].to(device, dtype) - - exp_avg = state["exp_avg"] - exp_avg_sq = state["exp_avg_sq"] - if self.use_fp16_stats: - assert exp_avg.dtype == torch.float16 - exp_avg = exp_avg.float() * state["exp_avg_scale"] - exp_avg_sq = exp_avg_sq.float() * state["exp_avg_sq_scale"] - beta1, beta2 = group["betas"] - - state["step"] += 1 - - with torch.cuda.device(p_data_fp32.device): - fused_adam_cuda.adam( - p_data_fp32, - out_p, - exp_avg, - exp_avg_sq, - grad, - group["lr"], - beta1, - beta2, - group["eps"], - combined_scale, - state["step"], - self.eps_mode, - bias_correction, - group["weight_decay"], - ) - - if p.device.type == "cpu": - p.data.copy_(p_data_fp32, non_blocking=True) - - if self.use_fp16_stats: - - def inf_norm(t): - return torch.norm(t, float("inf")) - - # from github.com/openai/jukebox/blob/master/jukebox/utils/fp16.py - state["exp_avg_scale"], state["exp_avg_sq_scale"] = ( - 1e-8 + inf_norm(exp_avg) / self.FLOAT16_MAX, - 1e-8 + inf_norm(exp_avg_sq) / self.FLOAT16_MAX, - ) - state["exp_avg"], state["exp_avg_sq"] = ( - (exp_avg / state["exp_avg_scale"]).half(), - (exp_avg_sq / state["exp_avg_sq_scale"]).half(), - ) - - return loss - - -try: - from apex.multi_tensor_apply import multi_tensor_applier - from apex.optimizers import FusedAdam - - class FusedAdamV2(FusedAdam): - """ - Compared to the original version in Apex, the fairseq version casts grads - and params to FP32 internally to support ``--memory-efficient-fp16``. - """ - - def __init__(self, *args, use_fp16_stats=False, **kwargs): - if use_fp16_stats: - raise NotImplementedError( - "--fp16-adam-stats is only supported with FusedAdamV1" - ) - super().__init__(*args, **kwargs) - if not hasattr(self, "multi_tensor_adam"): - raise Exception( - "Apex installation is outdated. Please install an updated version of apex." - ) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step( - self, - closure=None, - grads=None, - output_params=None, - scale=None, - grad_norms=None, - ): - """Performs a single optimization step.""" - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - bias_correction = 1 if group["bias_correction"] else 0 - beta1, beta2 = group["betas"] - - # assume same step across group now to simplify things - # per parameter step can be easily support by making it tensor, or pass list into kernel - if "step" in group: - group["step"] += 1 - else: - group["step"] = 1 - - # create lists for multi-tensor apply - g_16, p_16, orig_p_16, m_16, v_16 = [], [], [], [], [] - g_32, p_32, m_32, v_32 = [], [], [], [] - - for p in group["params"]: - if p.grad is None: - continue - if p.grad.data.is_sparse: - raise RuntimeError( - "FusedAdam does not support sparse gradients, " - "please consider SparseAdam instead" - ) - - state = self.state[p] - # State initialization - if len(state) == 0: - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p.data, dtype=torch.float) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like( - p.data, dtype=torch.float - ) - else: - state["exp_avg"] = state["exp_avg"].to( - device=p.data.device, dtype=torch.float - ) - state["exp_avg_sq"] = state["exp_avg_sq"].to( - device=p.data.device, dtype=torch.float - ) - - if p.dtype == torch.float16: - g_16.append(p.grad.data.float()) - p_16.append(p.data.float()) - orig_p_16.append(p.data) - m_16.append(state["exp_avg"]) - v_16.append(state["exp_avg_sq"]) - elif p.dtype == torch.float32: - g_32.append(p.grad.data) - p_32.append(p.data) - m_32.append(state["exp_avg"]) - v_32.append(state["exp_avg_sq"]) - else: - raise RuntimeError("FusedAdam only support fp16 and fp32.") - - with torch.cuda.device(p.device): - if len(g_16) > 0: - multi_tensor_applier( - self.multi_tensor_adam, - self._dummy_overflow_buf, - [g_16, p_16, m_16, v_16], - group["lr"], - beta1, - beta2, - group["eps"], - group["step"], - self.adam_w_mode, - bias_correction, - group["weight_decay"], - ) - for orig_p, p in zip(orig_p_16, p_16): - orig_p.copy_(p.data) - if len(g_32) > 0: - multi_tensor_applier( - self.multi_tensor_adam, - self._dummy_overflow_buf, - [g_32, p_32, m_32, v_32], - group["lr"], - beta1, - beta2, - group["eps"], - group["step"], - self.adam_w_mode, - bias_correction, - group["weight_decay"], - ) - - return loss - -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fused_lamb.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fused_lamb.py deleted file mode 100644 index f4f2bdb0c6c65f7758509b6d4d2f2c48cb6e8b4f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/fused_lamb.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from fairseq.optim import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("lamb") -class FairseqLAMB(LegacyFairseqOptimizer): - """LAMB optimizer.""" - - def __init__(self, args, params): - super().__init__(args) - try: - from apex.optimizers import FusedLAMB - - self._optimizer = FusedLAMB(params, **self.optimizer_config) - except ImportError: - raise ImportError("Please install apex to use LAMB optimizer") - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--lamb-betas', default='(0.9, 0.999)', metavar='B', - help='betas for LAMB optimizer') - parser.add_argument('--lamb-eps', type=float, default=1e-8, metavar='D', - help='epsilon for LAMB optimizer') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "betas": eval(self.args.lamb_betas), - "eps": self.args.lamb_eps, - "weight_decay": self.args.weight_decay, - } - - @property - def supports_flat_params(self): - return False diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/__init__.py deleted file mode 100644 index 5b3dbc023aa4a6f7bfb8403b8204d71ca432f79c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import importlib -import os - -from fairseq import registry -from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa - FairseqLRScheduler, - LegacyFairseqLRScheduler, -) -from omegaconf import DictConfig - - -( - build_lr_scheduler_, - register_lr_scheduler, - LR_SCHEDULER_REGISTRY, - LR_SCHEDULER_DATACLASS_REGISTRY, -) = registry.setup_registry( - "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed" -) - - -def build_lr_scheduler(cfg: DictConfig, optimizer): - return build_lr_scheduler_(cfg, optimizer) - - -# automatically import any Python files in the optim/lr_scheduler/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.optim.lr_scheduler." + file_name) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py deleted file mode 100644 index 6a3b89730598446d42fb0d81cf926a5c78fab0c4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py +++ /dev/null @@ -1,146 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import List - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class CosineLRScheduleConfig(FairseqDataclass): - warmup_updates: int = field( - default=0, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - warmup_init_lr: float = field( - default=-1, - metadata={ - "help": "initial learning rate during warmup phase; default is cfg.lr" - }, - ) - lr: List[float] = field( - default=II("optimization.lr"), - metadata={"help": "max learning rate, must be more than cfg.min_lr"}, - ) - min_lr: float = field(default=0.0, metadata={"help": "min learning rate"}) - t_mult: float = field( - default=1.0, metadata={"help": "factor to grow the length of each period"} - ) - lr_period_updates: float = field( - default=-1, metadata={"help": "initial number of updates per period"} - ) - lr_shrink: float = field( - default=0.1, metadata={"help": "shrink factor for annealing"} - ) - # This is not required, but is for convenience in inferring lr_period_updates - max_update: int = II("optimization.max_update") - - -@register_lr_scheduler("cosine", dataclass=CosineLRScheduleConfig) -class CosineLRSchedule(FairseqLRScheduler): - """Assign LR based on a cyclical schedule that follows the cosine function. - - See https://arxiv.org/pdf/1608.03983.pdf for details. - - We also support a warmup phase where we linearly increase the learning rate - from some initial learning rate (``--warmup-init-lr``) until the configured - max learning rate (``--lr``). - - During warmup:: - - lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates) - lr = lrs[update_num] - - After warmup:: - - lr = cfg.min_lr + 0.5*(cfg.lr - cfg.min_lr)*(1 + cos(t_curr / t_i)) - - where ``t_curr`` is current percentage of updates within the current period - range and ``t_i`` is the current period range, which is scaled by ``t_mul`` - after every iteration. - """ - - def __init__(self, cfg: CosineLRScheduleConfig, fairseq_optimizer): - super().__init__(cfg, fairseq_optimizer) - if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: - raise ValueError( - "Cannot use a fixed learning rate schedule with cosine." - f" Consider --lr-scheduler=fixed instead. ({cfg.lr})" - ) - - self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr - assert ( - self.max_lr > cfg.min_lr - ), f"max_lr (={cfg.lr}) must be more than min_lr (={cfg.min_lr})" - - warmup_end_lr = self.max_lr - if cfg.warmup_init_lr < 0: - cfg.warmup_init_lr = cfg.min_lr - - self.t_mult = cfg.t_mult - self.period = cfg.lr_period_updates - - if self.period <= 0: - assert ( - cfg.max_update > 0 - ), "Either --max_update or --lr-period-updates must be set" - self.period = cfg.max_update - cfg.warmup_updates - - if cfg.warmup_updates > 0: - # linearly warmup for the first cfg.warmup_updates - self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates - else: - self.lr_step = 1 - - self.warmup_updates = cfg.warmup_updates - self.lr_shrink = cfg.lr_shrink - - # initial learning rate - self.lr = cfg.warmup_init_lr - self.optimizer.set_lr(self.lr) - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) - # we don't change the learning rate at epoch boundaries - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - if num_updates < self.cfg.warmup_updates: - self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step - else: - curr_updates = num_updates - self.cfg.warmup_updates - if self.t_mult != 1: - i = math.floor( - math.log( - 1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult - ) - ) - t_i = self.t_mult**i * self.period - t_curr = ( - curr_updates - - (1 - self.t_mult**i) / (1 - self.t_mult) * self.period - ) - else: - i = math.floor(curr_updates / self.period) - t_i = self.period - t_curr = curr_updates - (self.period * i) - - lr_shrink = self.lr_shrink**i - min_lr = self.cfg.min_lr * lr_shrink - max_lr = self.max_lr * lr_shrink - - self.lr = min_lr + 0.5 * (max_lr - min_lr) * ( - 1 + math.cos(math.pi * t_curr / t_i) - ) - - self.optimizer.set_lr(self.lr) - return self.lr diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py deleted file mode 100644 index 6c12fa56b825e81bcc3fc7a97d206777418260ef..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from argparse import Namespace - -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.optim import FairseqOptimizer - - -class FairseqLRScheduler(object): - def __init__(self, cfg, optimizer): - super().__init__() - if optimizer is not None and not isinstance(optimizer, FairseqOptimizer): - raise ValueError("optimizer must be an instance of FairseqOptimizer") - self.cfg = cfg - self.optimizer = optimizer - self.best = None - - @classmethod - def add_args(cls, parser): - """Add arguments to the parser for this LR scheduler.""" - dc = getattr(cls, "__dataclass", None) - if dc is not None: - gen_parser_from_dataclass(parser, dc()) - - def state_dict(self): - """Return the LR scheduler state dict.""" - return {"best": self.best} - - def load_state_dict(self, state_dict): - """Load an LR scheduler state dict.""" - self.best = state_dict["best"] - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - pass - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - if val_loss is not None: - if self.best is None: - self.best = val_loss - else: - self.best = min(self.best, val_loss) - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - return self.optimizer.get_lr() - - -class LegacyFairseqLRScheduler(FairseqLRScheduler): - def __init__(self, args: Namespace, optimizer): - if not isinstance(optimizer, FairseqOptimizer): - raise ValueError("optimizer must be an instance of FairseqOptimizer") - self.args = args - self.optimizer = optimizer - self.best = None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/fixed_schedule.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/fixed_schedule.py deleted file mode 100644 index a816a554f4caa645152669dab26c957a48ba7400..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/fixed_schedule.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field -from typing import List, Optional - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class FixedLRScheduleConfig(FairseqDataclass): - force_anneal: Optional[int] = field( - default=None, - metadata={"help": "force annealing at specified epoch"}, - ) - lr_shrink: float = field( - default=0.1, - metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"}, - ) - warmup_updates: int = field( - default=0, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - lr: List[float] = II("optimization.lr") - - -@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig) -class FixedLRSchedule(FairseqLRScheduler): - """Decay the LR on a fixed schedule.""" - - def __init__(self, cfg: FixedLRScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - - self.lr = cfg.lr[0] - if cfg.warmup_updates > 0: - self.warmup_factor = 1.0 / cfg.warmup_updates - else: - self.warmup_factor = 1 - - def state_dict(self): - return {"lr": self.lr} - - def load_state_dict(self, state_dict): - if "lr" in state_dict: - self.lr = state_dict["lr"] - - def get_next_lr(self, epoch): - lrs = self.cfg.lr - if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal: - # use fixed LR schedule - next_lr = lrs[min(epoch - 1, len(lrs) - 1)] - else: - # annneal based on lr_shrink - next_lr = lrs[-1] * self.cfg.lr_shrink ** ( - epoch + 1 - self.cfg.force_anneal - ) - return next_lr - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - self.lr = self.get_next_lr(epoch) - self.optimizer.set_lr(self.warmup_factor * self.lr) - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates: - self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates) - self.optimizer.set_lr(self.warmup_factor * self.lr) - else: - self.optimizer.set_lr(self.lr) - return self.optimizer.get_lr() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py deleted file mode 100644 index cf1ca1cf0a845543ac74d1e4c426c4cb1b960696..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import List - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class InverseSquareRootLRScheduleConfig(FairseqDataclass): - warmup_updates: int = field( - default=4000, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - warmup_init_lr: float = field( - default=-1, - metadata={ - "help": "initial learning rate during warmup phase; default is cfg.lr" - }, - ) - lr: List[float] = II("optimization.lr") - - -@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootLRScheduleConfig) -class InverseSquareRootSchedule(FairseqLRScheduler): - """Decay the LR based on the inverse square root of the update number. - - We also support a warmup phase where we linearly increase the learning rate - from some initial learning rate (``--warmup-init-lr``) until the configured - learning rate (``--lr``). Thereafter we decay proportional to the number of - updates, with a decay factor set to align with the configured learning rate. - - During warmup:: - - lrs = torch.linspace(cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates) - lr = lrs[update_num] - - After warmup:: - - decay_factor = cfg.lr * sqrt(cfg.warmup_updates) - lr = decay_factor / sqrt(update_num) - """ - - def __init__(self, cfg: InverseSquareRootLRScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: - raise ValueError( - "Cannot use a fixed learning rate schedule with inverse_sqrt." - " Consider --lr-scheduler=fixed instead." - ) - warmup_end_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr - if cfg.warmup_init_lr < 0: - cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr - - # linearly warmup for the first cfg.warmup_updates - self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates - - # then, decay prop. to the inverse square root of the update number - self.decay_factor = warmup_end_lr * cfg.warmup_updates**0.5 - - # initial learning rate - self.lr = cfg.warmup_init_lr - self.optimizer.set_lr(self.lr) - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) - # we don't change the learning rate at epoch boundaries - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - if num_updates < self.cfg.warmup_updates: - self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step - else: - self.lr = self.decay_factor * num_updates**-0.5 - self.optimizer.set_lr(self.lr) - return self.lr diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/manual_lr_scheduler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/manual_lr_scheduler.py deleted file mode 100644 index d6c531d8a9b6fd30bb6f7b36054dd02ff0d408f9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/manual_lr_scheduler.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import ast -import logging - -from . import LegacyFairseqLRScheduler, register_lr_scheduler - -logger = logging.getLogger(__name__) -logger.setLevel(logging.WARNING) - - -@register_lr_scheduler("manual") -class ManualSchedule(LegacyFairseqLRScheduler): - """Decay the LR on a manual schedule.""" - - def __init__(self, args, optimizer): - super().__init__(args, optimizer) - - self.epoch2lr = self.parse_manuallr_args(args.epoch2lr) - self.update2lr = self.parse_manuallr_args(args.update2lr) - logger.info("@@@ ManualSchedule epoch2lr={}".format(self.epoch2lr)) - logger.info("@@@ ManualSchedule update2lr={}".format(self.update2lr)) - - if 1 in self.epoch2lr: - self.lr = self.epoch2lr[1] - elif 1 in self.update2lr: - self.lr = self.update2lr[1] - else: - self.lr = args.lr[0] - self.optimizer.set_lr(self.lr) # Set the beginning of the epoch. - - def parse_manuallr_args(self, lr_args_str): - lr_dict = ast.literal_eval(lr_args_str.replace(" ", "")) - if not isinstance(lr_dict, dict): - raise ValueError("epoch2lr/update2lr must be abel to evaluated to a dict") - - lr_args = {} - logger.info("@@@ after parsing input dictionary lr_dict = {}".format(lr_dict)) - for key, val in lr_dict.items(): - if "," in key: - for k in key.split(","): - lr_args[int(k)] = float(val) - elif "-" in key: - s = int(key.split("-")[0]) - e = int(key.split("-")[1]) - for k in range(s, e + 1, 1): - lr_args[k] = float(val) - else: - lr_args[int(key)] = float(val) - - return lr_args - - @staticmethod - def add_args(parser): - """Add arguments to the parser for this LR scheduler.""" - # fmt: off - parser.add_argument( - "--epoch2lr", - type=str, - metavar="DICT", - default="{}", - help="a dictionary used to set lr for each epoch manually", - ) - parser.add_argument( - "--update2lr", - type=str, - metavar="DICT", - default="{}", - help="a dictionary used to set lr for each update manually", - ) - # fmt: on - - def state_dict(self): - return {"lr": self.lr} - - def load_state_dict(self, state_dict): - if "lr" in state_dict: - self.lr = state_dict["lr"] - - def get_next_lr(self, epoch): - manual_keys = [k for k in self.epoch2lr if k <= epoch] - if manual_keys: - manual_lr = self.epoch2lr[max(manual_keys)] - else: - logger.warning( - "@@@ epoch={} does not exist in manual lr input. epoch2lr={}...".format( - epoch, - list(self.epoch2lr.items())[ - : min(10, len(self.epoch2lr.keys()) - 1) - ], - ) - ) - manual_lr = self.optimizer.get_lr() - return manual_lr - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - self.lr = self.get_next_lr(epoch) - self.optimizer.set_lr(self.lr) - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - manual_keys = [k for k in self.update2lr if k <= num_updates] - if manual_keys: - manual_lr = self.update2lr[max(manual_keys)] - else: - logger.warning( - "epoch={} does not exist in manual lr input update2lr={}...".format( - num_updates, - list(self.update2lr.items())[ - : min(10, len(self.update2lr.keys()) - 1) - ], - ) - ) - manual_lr = self.optimizer.get_lr() - - self.optimizer.set_lr(manual_lr) - return self.optimizer.get_lr() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/pass_through.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/pass_through.py deleted file mode 100644 index 2f93db328c1de9b268e8ee1c0c1cad558fd089aa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/pass_through.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler - - -@dataclass -class PassThroughScheduleConfig(FairseqDataclass): - pass - - -@register_lr_scheduler("pass_through", dataclass=PassThroughScheduleConfig) -class PassThroughScheduleSchedule(FairseqLRScheduler): - """Delegate lr scheduling to the optimizer.""" - - def __init__(self, cfg: PassThroughScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - assert ( - hasattr(optimizer, "lr_scheduler") and optimizer.lr_scheduler is not None - ), "Pass-through schedule can only be used with optimizers with their own schedulers" - - def state_dict(self): - return self.optimizer.lr_scheduler.state_dict() - - def load_state_dict(self, state_dict): - self.optimizer.lr_scheduler.load_state_dict(state_dict) - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - return self.optimizer.lr_scheduler.step_begin_epoch(epoch) - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - return self.optimizer.lr_scheduler.step_update(num_updates) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py deleted file mode 100644 index 11be21a8c6e872294050881028c98650a570f979..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field -from typing import List, Optional - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class PolynomialDecayLRScheduleConfig(FairseqDataclass): - warmup_updates: int = field( - default=0, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - force_anneal: Optional[int] = field( - default=None, - metadata={"help": "force annealing at specified epoch"}, - ) - end_learning_rate: float = field( - default=0.0, - metadata={"help": "learning rate to decay to"}, - ) - power: float = field( - default=1.0, - metadata={"help": "decay exponent"}, - ) - total_num_update: float = field( - default=II("optimization.max_update"), - metadata={"help": "total number of updates over which to decay learning rate"}, - ) - lr: List[float] = II("optimization.lr") - - -@register_lr_scheduler("polynomial_decay", dataclass=PolynomialDecayLRScheduleConfig) -class PolynomialDecayLRSchedule(FairseqLRScheduler): - """Decay the LR on a fixed schedule.""" - - def __init__(self, cfg: PolynomialDecayLRScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - - assert cfg.total_num_update > 0 - - self.lr = cfg.lr[0] - if cfg.warmup_updates > 0: - self.warmup_factor = 1.0 / cfg.warmup_updates - else: - self.warmup_factor = 1 - self.end_learning_rate = cfg.end_learning_rate - self.total_num_update = cfg.total_num_update - self.power = cfg.power - self.optimizer.set_lr(self.warmup_factor * self.lr) - - def get_next_lr(self, epoch): - lrs = self.cfg.lr - if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal: - # use fixed LR schedule - next_lr = lrs[min(epoch, len(lrs) - 1)] - else: - # annneal based on lr_shrink - next_lr = self.optimizer.get_lr() - return next_lr - - def step_begin_epoch(self, epoch): - """Update the learning rate at the beginning of the given epoch.""" - self.lr = self.get_next_lr(epoch) - self.optimizer.set_lr(self.warmup_factor * self.lr) - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - if self.cfg.warmup_updates > 0 and num_updates <= self.cfg.warmup_updates: - self.warmup_factor = num_updates / float(self.cfg.warmup_updates) - lr = self.warmup_factor * self.lr - elif num_updates >= self.total_num_update: - lr = self.end_learning_rate - else: - warmup = self.cfg.warmup_updates - lr_range = self.lr - self.end_learning_rate - pct_remaining = 1 - (num_updates - warmup) / ( - self.total_num_update - warmup - ) - lr = lr_range * pct_remaining ** (self.power) + self.end_learning_rate - self.optimizer.set_lr(lr) - return self.optimizer.get_lr() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py deleted file mode 100644 index 9f75c5fd92e923b32060b5fc552009f01e4b698b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field -from typing import List - -import torch.optim.lr_scheduler -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class ReduceLROnPlateauLRScheduleConfig(FairseqDataclass): - lr_shrink: float = field( - default=0.1, metadata={"help": "shrink factor for annealing"} - ) - lr_threshold: float = field( - default=1e-4, - metadata={ - "help": ( - "threshold for measuring the new optimum, to only focus on " - "significant changes" - ) - }, - ) - lr_patience: int = field( - default=0, - metadata={ - "help": ( - "number of epochs with no improvement after which learning rate will " - "be reduced" - ) - }, - ) - warmup_updates: int = field( - default=0, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - warmup_init_lr: float = field( - default=-1, - metadata={ - "help": "initial learning rate during warmup phase; default is cfg.lr" - }, - ) - lr: List[float] = II("optimization.lr") - maximize_best_checkpoint_metric: bool = II( - "checkpoint.maximize_best_checkpoint_metric" - ) - - -@register_lr_scheduler( - "reduce_lr_on_plateau", dataclass=ReduceLROnPlateauLRScheduleConfig -) -class ReduceLROnPlateauLRSchedule(FairseqLRScheduler): - """ - Decay the LR by a factor every time the validation loss plateaus. - Also comes with optional warmup phase, where we linearly increase - the learning rate from some initial learning rate - (``--warmup-init-lr``) until the configured learning rate - (``--lr``). Thereafter the lr is adjusted according to original - reduce_on_plateau scheme. - - During warmup:: - - lrs = torch.linspace( - cfg.warmup_init_lr, cfg.lr, cfg.warmup_updates - ) - lr = lrs[update_num] - """ - - def __init__(self, cfg: ReduceLROnPlateauLRScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - if len(cfg.lr) > 1: - raise ValueError( - "Cannot use a fixed learning rate schedule with reduce_lr_on_plateau." - " Consider --lr-scheduler=fixed instead." - ) - self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.optimizer.optimizer, - patience=cfg.lr_patience, - factor=cfg.lr_shrink, - mode="max" if cfg.maximize_best_checkpoint_metric else "min", - threshold=cfg.lr_threshold, - ) - warmup_end_lr = cfg.lr[0] - # if no warm up, sets initial lr to be cfg.lr[0] - if cfg.warmup_init_lr < 0: - cfg.warmup_init_lr = 0 if cfg.warmup_updates > 0 else warmup_end_lr - - # linearly warmup for the first cfg.warmup_updates - if cfg.warmup_updates > 0: - self.lr_step = (warmup_end_lr - cfg.warmup_init_lr) / cfg.warmup_updates - - # this flag is either set from arg when no warm up, or set by - # step_update() when warmup finishes - self.warmup_end = True if cfg.warmup_updates <= 0 else False - - # initial learning rate - # this self.lr is used only during init and/or warm up period - self.lr = warmup_end_lr if self.warmup_end else cfg.warmup_init_lr - self.optimizer.set_lr(self.lr) - - def state_dict(self): - """Return the LR scheduler state dict.""" - return { - "best": self.lr_scheduler.best, - "last_epoch": self.lr_scheduler.last_epoch, - } - - def load_state_dict(self, state_dict): - """Load an LR scheduler state dict.""" - self.lr_scheduler.best = state_dict["best"] - if "last_epoch" in state_dict: - self.lr_scheduler.last_epoch = state_dict["last_epoch"] - - def step(self, epoch, val_loss=None): - """ - Update the learning rate at the end of the given epoch if warmup - finishes otherwise no update of lr on epoch boundaries - """ - if val_loss is not None and self.warmup_end is True: - self.lr_scheduler.step(val_loss) - else: - self.lr_scheduler.last_epoch = epoch - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """ - Update the learning rate after each update.""" - # if there is warmup - if self.cfg.warmup_updates > 0: - if num_updates <= self.cfg.warmup_updates: - self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step - self.optimizer.set_lr(self.lr) - else: - if self.warmup_end is False: - self.warmup_end = True - # else do nothing - return self.optimizer.get_lr() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/step_lr_scheduler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/step_lr_scheduler.py deleted file mode 100644 index 1fdbd78332a1b76bb4cafa93a64c85349a9716b1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/step_lr_scheduler.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import List - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class StepLRScheduleConfig(FairseqDataclass): - warmup_updates: int = field( - default=0, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - warmup_init_lr: float = field( - default=-1, - metadata={ - "help": "initial learning rate during warmup phase; default is cfg.lr" - }, - ) - lr: List[float] = field( - default=II("optimization.lr"), - metadata={"help": "max learning rate, must be more than cfg.min_lr"}, - ) - min_lr: float = field(default=0.0, metadata={"help": "min learning rate"}) - lr_deacy_period: int = field(default=25000, metadata={"help": "decay period"}) - lr_decay: float = field(default=0.5, metadata={"help": "decay factor"}) - - -@register_lr_scheduler("step", dataclass=StepLRScheduleConfig) -class StepLRSchedule(FairseqLRScheduler): - """Decay learning rate every k updates by a fixed factor""" - - def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer): - super().__init__(cfg, fairseq_optimizer) - self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr - self.min_lr = cfg.min_lr - self.lr_deacy_period = cfg.lr_deacy_period - self.lr_decay = cfg.lr_decay - self.warmup_updates = cfg.warmup_updates - self.warmup_init_lr = ( - cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr - ) - - assert self.lr_deacy_period > 0 - assert self.lr_decay <= 1 - assert self.min_lr >= 0 - assert self.max_lr > self.min_lr - - if cfg.warmup_updates > 0: - # linearly warmup for the first cfg.warmup_updates - self.warmup_lr_step = ( - self.max_lr - self.warmup_init_lr - ) / self.warmup_updates - else: - self.warmup_lr_step = 1 - - # initial learning rate - self.lr = self.warmup_init_lr - self.optimizer.set_lr(self.lr) - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) - # we don't change the learning rate at epoch boundaries - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - if num_updates < self.cfg.warmup_updates: - self.lr = self.warmup_init_lr + num_updates * self.warmup_lr_step - else: - curr_updates = num_updates - self.cfg.warmup_updates - lr_mult = self.lr_decay ** (curr_updates // self.lr_deacy_period) - self.lr = max(self.max_lr * lr_mult, self.min_lr) - - self.optimizer.set_lr(self.lr) - return self.lr diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py deleted file mode 100644 index f760f9e58239406b5d6cc6de304ecd5fcb6d1f14..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py +++ /dev/null @@ -1,175 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from dataclasses import dataclass, field -from typing import List, Optional, Tuple - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class TriStageLRScheduleConfig(FairseqDataclass): - warmup_steps: int = field( - default=0, - metadata={"help": "warmup the learning rate linearly for the first N updates"}, - ) - hold_steps: int = field( - default=0, - metadata={"help": "steps in hold stage"}, - ) - decay_steps: int = field( - default=0, - metadata={"help": "steps in decay stages"}, - ) - phase_ratio: Optional[Tuple[float, float, float]] = field( - default=None, - metadata={ - "help": ( - "if set, automatically sets warmup/hold/decay steps to the ratio " - "specified here from max_updates. the ratios must add up to 1.0" - ) - }, - ) - init_lr_scale: float = field( - default=0.01, - metadata={"help": "initial learning rate scale during warmup phase"}, - ) - final_lr_scale: float = field( - default=0.01, - metadata={"help": "final learning rate scale"}, - ) - max_update: float = II("optimization.max_update") - lr: List[float] = II("optimization.lr") - - -@register_lr_scheduler("tri_stage", dataclass=TriStageLRScheduleConfig) -class TriStageLRSchedule(FairseqLRScheduler): - """Tristage learning rate schedulr - - Implement the learning rate scheduler in https://arxiv.org/pdf/1904.08779.pdf - - Similar to inverse_squre_root scheduler, but tri_stage learning rate employs - three stages LR scheduling: - - - warmup stage, starting from `lr` * `init_lr_scale`, linearly - increased to `lr` in `warmup_steps` iterations - - - hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps` - iterations - - - decay stage, after hold stage, decay LR exponetially to - `lr` * `final_lr_scale` in `decay_steps`; - after that LR is keep as `final_lr_scale` * `lr` - - During warmup:: - - init_lr = cfg.init_lr_scale * cfg.lr - lrs = torch.linspace(init_lr, cfg.lr, cfg.warmup_steps) - lr = lrs[update_num] - - During hold:: - - lr = cfg.lr - - During decay:: - - decay_factor = - math.log(cfg.final_lr_scale) / cfg.decay_steps - lr = cfg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor) - - After that:: - - lr = cfg.lr * cfg.final_lr_scale - """ - - def __init__(self, cfg: TriStageLRScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - if len(cfg.lr) > 1: - raise ValueError( - "Cannot use a fixed learning rate schedule with tri-stage lr." - " Consider --lr-scheduler=fixed instead." - ) - - # calculate LR at each point - self.peak_lr = cfg.lr[0] - self.init_lr = cfg.init_lr_scale * cfg.lr[0] - self.final_lr = cfg.final_lr_scale * cfg.lr[0] - - if cfg.phase_ratio is not None: - assert cfg.max_update > 0 - assert sum(cfg.phase_ratio) == 1, "phase ratios must add up to 1" - self.warmup_steps = int(cfg.max_update * cfg.phase_ratio[0]) - self.hold_steps = int(cfg.max_update * cfg.phase_ratio[1]) - self.decay_steps = int(cfg.max_update * cfg.phase_ratio[2]) - else: - self.warmup_steps = cfg.warmup_steps - self.hold_steps = cfg.hold_steps - self.decay_steps = cfg.decay_steps - - assert ( - self.warmup_steps + self.hold_steps + self.decay_steps > 0 - ), "please specify steps or phase_ratio" - - self.warmup_rate = ( - (self.peak_lr - self.init_lr) / self.warmup_steps - if self.warmup_steps != 0 - else 0 - ) - self.decay_factor = -math.log(cfg.final_lr_scale) / self.decay_steps - - # initial learning rate - self.lr = self.init_lr - self.optimizer.set_lr(self.lr) - - def _decide_stage(self, update_step): - """ - return stage, and the corresponding steps within the current stage - """ - if update_step < self.warmup_steps: - # warmup state - return 0, update_step - - offset = self.warmup_steps - - if update_step < offset + self.hold_steps: - # hold stage - return 1, update_step - offset - - offset += self.hold_steps - - if update_step <= offset + self.decay_steps: - # decay stage - return 2, update_step - offset - - offset += self.decay_steps - - # still here ? constant lr stage - return 3, update_step - offset - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) - # we don't change the learning rate at epoch boundaries - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - stage, steps_in_stage = self._decide_stage(num_updates) - if stage == 0: - self.lr = self.init_lr + self.warmup_rate * steps_in_stage - elif stage == 1: - self.lr = self.peak_lr - elif stage == 2: - self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage) - elif stage == 3: - self.lr = self.final_lr - else: - raise ValueError("Undefined stage") - - self.optimizer.set_lr(self.lr) - - return self.lr diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py deleted file mode 100644 index 7d85442e465fe8b910c38b1f51c4a5a02574fafc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from dataclasses import dataclass, field -from typing import List - -from fairseq.dataclass import FairseqDataclass -from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler -from omegaconf import II - - -@dataclass -class TriangularLRScheduleConfig(FairseqDataclass): - max_lr: float = field( - default="???", metadata={"help": "max learning rate, must be more than cfg.lr"} - ) - lr_period_updates: float = field( - default=5000, - metadata={"help": "initial number of updates per period (cycle length)"}, - ) - lr_shrink: float = field( - default=0.1, metadata={"help": "shrink factor for annealing"} - ) - shrink_min: bool = field( - default=False, metadata={"help": "if set, also shrinks min lr"} - ) - lr: List[float] = II("optimization.lr") - - -@register_lr_scheduler("triangular", dataclass=TriangularLRScheduleConfig) -class TriangularLRSchedule(FairseqLRScheduler): - """Assign LR based on a triangular cyclical schedule. - - See https://arxiv.org/pdf/1506.01186.pdf for details. - """ - - def __init__(self, cfg: TriangularLRScheduleConfig, optimizer): - super().__init__(cfg, optimizer) - if len(cfg.lr) > 1: - raise ValueError( - "Cannot use a fixed learning rate schedule with triangular." - " Consider --lr-scheduler=fixed instead." - ) - - lr = cfg.lr[0] - - assert cfg.max_lr > lr, "max_lr must be more than lr" - self.min_lr = lr - self.max_lr = cfg.max_lr - self.stepsize = cfg.lr_period_updates // 2 - self.lr_shrink = cfg.lr_shrink - self.shrink_min = cfg.shrink_min - - # initial learning rate - self.lr = self.min_lr - self.optimizer.set_lr(self.lr) - - def step(self, epoch, val_loss=None): - """Update the learning rate at the end of the given epoch.""" - super().step(epoch, val_loss) - # we don't change the learning rate at epoch boundaries - return self.optimizer.get_lr() - - def step_update(self, num_updates): - """Update the learning rate after each update.""" - cycle = math.floor(num_updates / (2 * self.stepsize)) - - lr_shrink = self.lr_shrink**cycle - max_lr = self.max_lr * lr_shrink - if self.shrink_min: - min_lr = self.min_lr * lr_shrink - else: - min_lr = self.min_lr - - x = abs(num_updates / self.stepsize - 2 * (cycle + 1) + 1) - self.lr = min_lr + (max_lr - min_lr) * max(0, (1 - x)) - - self.optimizer.set_lr(self.lr) - return self.lr diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/nag.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/nag.py deleted file mode 100644 index c30a6c0fb1e8d5dc7edd5b53ba15a6acd46ecbff..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/nag.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections.abc import Collection -from dataclasses import dataclass, field -from typing import List - -import torch -from fairseq.dataclass import FairseqDataclass -from omegaconf import II, DictConfig -from torch.optim.optimizer import Optimizer, required - -from . import FairseqOptimizer, register_optimizer - - -@dataclass -class FairseqNAGConfig(FairseqDataclass): - momentum: float = field(default=0.99, metadata={"help": "momentum factor"}) - weight_decay: float = field(default=0.0, metadata={"help": "weight decay"}) - # TODO common vars in parent class - lr: List[float] = II("optimization.lr") - - -@register_optimizer("nag", dataclass=FairseqNAGConfig) -class FairseqNAG(FairseqOptimizer): - def __init__(self, cfg: DictConfig, params): - super().__init__(cfg) - self._optimizer = NAG(params, **self.optimizer_config) - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.cfg.lr[0] - if isinstance(self.cfg.lr, Collection) - else self.cfg.lr, - "momentum": self.cfg.momentum, - "weight_decay": self.cfg.weight_decay, - } - - -class NAG(Optimizer): - def __init__(self, params, lr=required, momentum=0, weight_decay=0): - defaults = dict(lr=lr, lr_old=lr, momentum=momentum, weight_decay=weight_decay) - super(NAG, self).__init__(params, defaults) - - @property - def supports_memory_efficient_fp16(self): - return True - - @property - def supports_flat_params(self): - return True - - def step(self, closure=None): - """Performs a single optimization step. - - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - weight_decay = group["weight_decay"] - momentum = group["momentum"] - lr = group["lr"] - lr_old = group.get("lr_old", lr) - lr_correct = lr / lr_old if lr_old > 0 else lr - - for p in group["params"]: - if p.grad is None: - continue - - p_data_fp32 = p.data - if p_data_fp32.dtype in {torch.float16, torch.bfloat16}: - p_data_fp32 = p_data_fp32.float() - - d_p = p.grad.data.float() - param_state = self.state[p] - if "momentum_buffer" not in param_state: - param_state["momentum_buffer"] = torch.zeros_like(d_p) - else: - param_state["momentum_buffer"] = param_state["momentum_buffer"].to( - d_p - ) - - buf = param_state["momentum_buffer"] - - if weight_decay != 0: - p_data_fp32.mul_(1 - lr * weight_decay) - p_data_fp32.add_(buf, alpha=momentum * momentum * lr_correct) - p_data_fp32.add_(d_p, alpha=-(1 + momentum) * lr) - - buf.mul_(momentum * lr_correct).add_(d_p, alpha=-lr) - - if p.data.dtype in {torch.float16, torch.bfloat16}: - p.data.copy_(p_data_fp32) - - group["lr_old"] = lr - - return loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/sgd.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/sgd.py deleted file mode 100644 index 8e34fb99a18fff12ab76be5894a84cbbb2f48176..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/sgd.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.optim - -from . import LegacyFairseqOptimizer, register_optimizer - - -@register_optimizer("sgd") -class SGD(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = torch.optim.SGD(params, **self.optimizer_config) - - @staticmethod - def add_args(parser): - """Add optimizer-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--momentum', default=0.0, type=float, metavar='M', - help='momentum factor') - parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', - help='weight decay') - # fmt: on - - @property - def optimizer_config(self): - """ - Return a kwarg dictionary that will be used to override optimizer - args stored in checkpoints. This allows us to load a checkpoint and - resume training using a different set of optimizer args, e.g., with a - different learning rate. - """ - return { - "lr": self.args.lr[0], - "momentum": self.args.momentum, - "weight_decay": self.args.weight_decay, - } - - @property - def supports_flat_params(self): - return True diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/shard.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/shard.py deleted file mode 100644 index 3d43f54d4fe997bf85db465dde446e96da4613b0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/optim/shard.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Any, Dict - -from fairseq.distributed import utils - -try: - from fairscale.optim import OSS - - _has_fairscale = True -except ImportError: - _has_fairscale = False - - -def shard_(optimizer, group): - if not _has_fairscale: - raise ImportError( - "\n\nPlease install the fairscale package:" "\n\n pip install fairscale" - ) - - class FairseqOSS(OSS): - @property - def disable_mem_eff_fp16_loading_hack(self): - return True - - def __getattr__(self, name): - if name.startswith("supports") and hasattr(self.optim, name): - return getattr(self.optim, name) - raise AttributeError( - "'FairseqOSS' object has no attribute {0!r}".format(name) - ) - - def broadcast_global_state_dict( - self, state_dict: Dict[str, Any] - ) -> Dict[str, Any]: - """ - Broadcasts the entire state_dict to all other ranks - each rank is responsible to load their own partition of data - """ - return utils.broadcast_object( - state_dict, - src_rank=0, - group=self.group, - ) - - torch_optimizer = optimizer.optimizer - optim_cls = type(torch_optimizer) - - optimizer.optimizer = FairseqOSS( - torch_optimizer.param_groups, - optim_cls, - group=group, - **optimizer.optimizer_config - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/options.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/options.py deleted file mode 100644 index c0394331e09a03593bcc75f6de20eae5ce078106..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/options.py +++ /dev/null @@ -1,413 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -from pathlib import Path -from typing import Callable, List, Optional, Union - -import torch -from fairseq import utils -from fairseq.data.indexed_dataset import get_available_dataset_impl -from fairseq.dataclass.configs import ( - CheckpointConfig, - CommonConfig, - CommonEvalConfig, - DatasetConfig, - DistributedTrainingConfig, - EMAConfig, - EvalLMConfig, - GenerationConfig, - InteractiveConfig, - OptimizationConfig, -) -from fairseq.dataclass.utils import gen_parser_from_dataclass - -# this import is for backward compatibility -from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list # noqa - - -def get_preprocessing_parser(default_task="translation"): - parser = get_parser("Preprocessing", default_task) - add_preprocess_args(parser) - return parser - - -def get_training_parser(default_task="translation"): - parser = get_parser("Trainer", default_task) - add_dataset_args(parser, train=True) - add_distributed_training_args(parser) - add_model_args(parser) - add_optimization_args(parser) - add_checkpoint_args(parser) - add_ema_args(parser) - return parser - - -def get_generation_parser(interactive=False, default_task="translation"): - parser = get_parser("Generation", default_task) - add_dataset_args(parser, gen=True) - add_distributed_training_args(parser, default_world_size=1) - add_generation_args(parser) - add_checkpoint_args(parser) - if interactive: - add_interactive_args(parser) - return parser - - -def get_speech_generation_parser(default_task="text_to_speech"): - parser = get_parser("Speech Generation", default_task) - add_dataset_args(parser, gen=True) - add_distributed_training_args(parser, default_world_size=1) - add_speech_generation_args(parser) - return parser - - -def get_interactive_generation_parser(default_task="translation"): - return get_generation_parser(interactive=True, default_task=default_task) - - -def get_eval_lm_parser(default_task="language_modeling"): - parser = get_parser("Evaluate Language Model", default_task) - add_dataset_args(parser, gen=True) - add_distributed_training_args(parser, default_world_size=1) - add_eval_lm_args(parser) - return parser - - -def get_validation_parser(default_task=None): - parser = get_parser("Validation", default_task) - add_dataset_args(parser, train=True) - add_distributed_training_args(parser, default_world_size=1) - group = parser.add_argument_group("Evaluation") - gen_parser_from_dataclass(group, CommonEvalConfig()) - return parser - - -def parse_args_and_arch( - parser: argparse.ArgumentParser, - input_args: List[str] = None, - parse_known: bool = False, - suppress_defaults: bool = False, - modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None, -): - """ - Args: - parser (ArgumentParser): the parser - input_args (List[str]): strings to parse, defaults to sys.argv - parse_known (bool): only parse known arguments, similar to - `ArgumentParser.parse_known_args` - suppress_defaults (bool): parse while ignoring all default values - modify_parser (Optional[Callable[[ArgumentParser], None]]): - function to modify the parser, e.g., to set default values - """ - if suppress_defaults: - # Parse args without any default values. This requires us to parse - # twice, once to identify all the necessary task/model args, and a second - # time with all defaults set to None. - args = parse_args_and_arch( - parser, - input_args=input_args, - parse_known=parse_known, - suppress_defaults=False, - ) - suppressed_parser = argparse.ArgumentParser(add_help=False, parents=[parser]) - suppressed_parser.set_defaults(**{k: None for k, v in vars(args).items()}) - args = suppressed_parser.parse_args(input_args) - return argparse.Namespace( - **{k: v for k, v in vars(args).items() if v is not None} - ) - - from fairseq.models import ARCH_CONFIG_REGISTRY, ARCH_MODEL_REGISTRY, MODEL_REGISTRY - - # Before creating the true parser, we need to import optional user module - # in order to eagerly import custom tasks, optimizers, architectures, etc. - usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) - usr_parser.add_argument("--user-dir", default=None) - usr_args, _ = usr_parser.parse_known_args(input_args) - utils.import_user_module(usr_args) - - if modify_parser is not None: - modify_parser(parser) - - # The parser doesn't know about model/criterion/optimizer-specific args, so - # we parse twice. First we parse the model/criterion/optimizer, then we - # parse a second time after adding the *-specific arguments. - # If input_args is given, we will parse those args instead of sys.argv. - args, _ = parser.parse_known_args(input_args) - - # Add model-specific args to parser. - if hasattr(args, "arch"): - model_specific_group = parser.add_argument_group( - "Model-specific configuration", - # Only include attributes which are explicitly given as command-line - # arguments or which have default values. - argument_default=argparse.SUPPRESS, - ) - if args.arch in ARCH_MODEL_REGISTRY: - ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group) - elif args.arch in MODEL_REGISTRY: - MODEL_REGISTRY[args.arch].add_args(model_specific_group) - else: - raise RuntimeError() - - if hasattr(args, "task"): - from fairseq.tasks import TASK_REGISTRY - - TASK_REGISTRY[args.task].add_args(parser) - if getattr(args, "use_bmuf", False): - # hack to support extra args for block distributed data parallelism - from fairseq.optim.bmuf import FairseqBMUF - - FairseqBMUF.add_args(parser) - - # Add *-specific args to parser. - from fairseq.registry import REGISTRIES - - for registry_name, REGISTRY in REGISTRIES.items(): - choice = getattr(args, registry_name, None) - if choice is not None: - cls = REGISTRY["registry"][choice] - if hasattr(cls, "add_args"): - cls.add_args(parser) - elif hasattr(cls, "__dataclass"): - gen_parser_from_dataclass(parser, cls.__dataclass()) - - # Modify the parser a second time, since defaults may have been reset - if modify_parser is not None: - modify_parser(parser) - - # Parse a second time. - if parse_known: - args, extra = parser.parse_known_args(input_args) - else: - args = parser.parse_args(input_args) - extra = None - # Post-process args. - if ( - hasattr(args, "batch_size_valid") and args.batch_size_valid is None - ) or not hasattr(args, "batch_size_valid"): - args.batch_size_valid = args.batch_size - if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None: - args.max_tokens_valid = args.max_tokens - if getattr(args, "memory_efficient_fp16", False): - args.fp16 = True - if getattr(args, "memory_efficient_bf16", False): - args.bf16 = True - args.tpu = getattr(args, "tpu", False) - args.bf16 = getattr(args, "bf16", False) - if args.bf16: - args.tpu = True - if args.tpu and args.fp16: - raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs") - - if getattr(args, "seed", None) is None: - args.seed = 1 # default seed for training - args.no_seed_provided = True - else: - args.no_seed_provided = False - - if getattr(args, "update_epoch_batch_itr", None) is None: - if hasattr(args, "grouped_shuffling"): - args.update_epoch_batch_itr = args.grouped_shuffling - else: - args.grouped_shuffling = False - args.update_epoch_batch_itr = False - - # Apply architecture configuration. - if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY: - ARCH_CONFIG_REGISTRY[args.arch](args) - - if parse_known: - return args, extra - else: - return args - - -def get_parser(desc, default_task="translation"): - # Before creating the true parser, we need to import optional user module - # in order to eagerly import custom tasks, optimizers, architectures, etc. - usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False) - usr_parser.add_argument("--user-dir", default=None) - usr_args, _ = usr_parser.parse_known_args() - utils.import_user_module(usr_args) - - parser = argparse.ArgumentParser(allow_abbrev=False) - gen_parser_from_dataclass(parser, CommonConfig()) - - from fairseq.registry import REGISTRIES - - for registry_name, REGISTRY in REGISTRIES.items(): - parser.add_argument( - "--" + registry_name.replace("_", "-"), - default=REGISTRY["default"], - choices=REGISTRY["registry"].keys(), - ) - - # Task definitions can be found under fairseq/tasks/ - from fairseq.tasks import TASK_REGISTRY - - parser.add_argument( - "--task", - metavar="TASK", - default=default_task, - choices=TASK_REGISTRY.keys(), - help="task", - ) - # fmt: on - return parser - - -def add_preprocess_args(parser): - group = parser.add_argument_group("Preprocessing") - # fmt: off - group.add_argument("-s", "--source-lang", default=None, metavar="SRC", - help="source language") - group.add_argument("-t", "--target-lang", default=None, metavar="TARGET", - help="target language") - group.add_argument("--trainpref", metavar="FP", default=None, - help="train file prefix (also used to build dictionaries)") - group.add_argument("--validpref", metavar="FP", default=None, - help="comma separated, valid file prefixes " - "(words missing from train set are replaced with )") - group.add_argument("--testpref", metavar="FP", default=None, - help="comma separated, test file prefixes " - "(words missing from train set are replaced with )") - group.add_argument("--align-suffix", metavar="FP", default=None, - help="alignment file suffix") - group.add_argument("--destdir", metavar="DIR", default="data-bin", - help="destination dir") - group.add_argument("--thresholdtgt", metavar="N", default=0, type=int, - help="map words appearing less than threshold times to unknown") - group.add_argument("--thresholdsrc", metavar="N", default=0, type=int, - help="map words appearing less than threshold times to unknown") - group.add_argument("--tgtdict", metavar="FP", - help="reuse given target dictionary") - group.add_argument("--srcdict", metavar="FP", - help="reuse given source dictionary") - group.add_argument("--nwordstgt", metavar="N", default=-1, type=int, - help="number of target words to retain") - group.add_argument("--nwordssrc", metavar="N", default=-1, type=int, - help="number of source words to retain") - group.add_argument("--alignfile", metavar="ALIGN", default=None, - help="an alignment file (optional)") - parser.add_argument('--dataset-impl', metavar='FORMAT', default='mmap', - choices=get_available_dataset_impl(), - help='output dataset implementation') - group.add_argument("--joined-dictionary", action="store_true", - help="Generate joined dictionary") - group.add_argument("--only-source", action="store_true", - help="Only process the source language") - group.add_argument("--padding-factor", metavar="N", default=8, type=int, - help="Pad dictionary size to be multiple of N") - group.add_argument("--workers", metavar="N", default=1, type=int, - help="number of parallel workers") - group.add_argument("--dict-only", action='store_true', - help="if true, only builds a dictionary and then exits") - # fmt: on - return parser - - -def add_dataset_args(parser, train=False, gen=False): - group = parser.add_argument_group("dataset_data_loading") - gen_parser_from_dataclass(group, DatasetConfig()) - # fmt: on - return group - - -def add_distributed_training_args(parser, default_world_size=None): - group = parser.add_argument_group("distributed_training") - if default_world_size is None: - default_world_size = max(1, torch.cuda.device_count()) - gen_parser_from_dataclass( - group, DistributedTrainingConfig(distributed_world_size=default_world_size) - ) - return group - - -def add_optimization_args(parser): - group = parser.add_argument_group("optimization") - # fmt: off - gen_parser_from_dataclass(group, OptimizationConfig()) - # fmt: on - return group - - -def add_checkpoint_args(parser): - group = parser.add_argument_group("checkpoint") - # fmt: off - gen_parser_from_dataclass(group, CheckpointConfig()) - # fmt: on - return group - - -def add_common_eval_args(group): - gen_parser_from_dataclass(group, CommonEvalConfig()) - - -def add_eval_lm_args(parser): - group = parser.add_argument_group("LM Evaluation") - add_common_eval_args(group) - gen_parser_from_dataclass(group, EvalLMConfig()) - - -def add_generation_args(parser): - group = parser.add_argument_group("Generation") - add_common_eval_args(group) - gen_parser_from_dataclass(group, GenerationConfig()) - return group - - -def add_speech_generation_args(parser): - group = parser.add_argument_group("Speech Generation") - add_common_eval_args(group) # NOTE: remove_bpe is not needed - # fmt: off - group.add_argument('--eos_prob_threshold', default=0.5, type=float, - help='terminate when eos probability exceeds this') - # fmt: on - return group - - -def add_interactive_args(parser): - group = parser.add_argument_group("Interactive") - gen_parser_from_dataclass(group, InteractiveConfig()) - - -def add_model_args(parser): - group = parser.add_argument_group("Model configuration") - # fmt: off - - # Model definitions can be found under fairseq/models/ - # - # The model architecture can be specified in several ways. - # In increasing order of priority: - # 1) model defaults (lowest priority) - # 2) --arch argument - # 3) --encoder/decoder-* arguments (highest priority) - from fairseq.models import ARCH_MODEL_REGISTRY - group.add_argument('--arch', '-a', metavar='ARCH', - choices=ARCH_MODEL_REGISTRY.keys(), - help='model architecture') - # fmt: on - return group - - -def get_args( - data: Union[str, Path], - task: str = "translation", - arch: str = "transformer", - **overrides -): - parser = get_training_parser(task) - args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch]) - - for k, v in overrides.items(): - setattr(args, k, v) - - return args - - -def add_ema_args(parser): - group = parser.add_argument_group("EMA configuration") - gen_parser_from_dataclass(group, EMAConfig()) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/pdb.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/pdb.py deleted file mode 100644 index 98390780e0a2154584912f7c45160621d0f175a4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/pdb.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import multiprocessing -import os -import pdb -import sys - -__all__ = ["set_trace"] - - -_stdin = [None] -_stdin_lock = multiprocessing.Lock() -try: - _stdin_fd = sys.stdin.fileno() -except Exception: - _stdin_fd = None - - -class MultiprocessingPdb(pdb.Pdb): - """A Pdb wrapper that works in a multiprocessing environment. - - Usage: `from fairseq import pdb; pdb.set_trace()` - """ - - def __init__(self): - pdb.Pdb.__init__(self, nosigint=True) - - def _cmdloop(self): - stdin_bak = sys.stdin - with _stdin_lock: - try: - if _stdin_fd is not None: - if not _stdin[0]: - _stdin[0] = os.fdopen(_stdin_fd) - sys.stdin = _stdin[0] - self.cmdloop() - finally: - sys.stdin = stdin_bak - - -def set_trace(): - pdb = MultiprocessingPdb() - pdb.set_trace(sys._getframe().f_back) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/quantization_utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/quantization_utils.py deleted file mode 100644 index 59992ea63ffecf79c4cc8bceaf4ae2c7cd139932..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/quantization_utils.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -from fairseq.modules.quantization import pq, quantization_options, scalar -from omegaconf import DictConfig - -logger = logging.getLogger(__name__) - - -def quantize_model_scalar(model, model_cfg: DictConfig): - quant_noise_scalar = getattr(model_cfg, "quant_noise_scalar", 0) or 0 - if quant_noise_scalar > 0: - # quantize_model edits the model in place - scalar.quantize_model_(model, p=quant_noise_scalar, bits=8, update_step=1000) - return model - - -class Quantizer(object): - def __init__(self, config_path, max_epoch, max_update): - try: - import yaml - except ImportError: - raise ImportError("Please install yaml with: pip install yaml") - - # parse config - if config_path: - with open(config_path) as config_file: - config = quantization_options.parse_config_yaml( - yaml.safe_load(config_file) - ) - else: - config = quantization_options.parse_config_yaml({}) - - self.n_centroids_config = config["n_centroids"] - self.block_sizes_config = config["block_sizes"] - self.layers_to_quantize = config["layers_to_quantize"] - - # We assume that training will run for a fixed number of epochs - # (or updates) and that we should train for equal durations - # between iterations of PQ. - num_iterations = len(self.layers_to_quantize) - if max_epoch > 0: - assert max_epoch % num_iterations == 0, ( - "for iterative PQ, --max-epoch (={}) must be evenly divisible by " - "len(layers_to_quantize) (={})".format(max_epoch, num_iterations) - ) - self.epoch_schedule = max_epoch // num_iterations - else: - self.epoch_schedule = None - if max_update > 0: - assert max_update % num_iterations == 0, ( - "for iterative PQ, --max-update (={}) must be evenly divisible by " - "len(layers_to_quantize) (={})".format(max_update, num_iterations) - ) - self.update_schedule = max_update // num_iterations - else: - self.update_schedule = None - assert (self.epoch_schedule is not None) ^ ( - self.update_schedule is not None - ), "for iterative PQ, cannot specify both --max-update and --max-epoch" - - # 0 is a special value for quantization step, which will force - # the first call to begin_epoch() to call step() - self.quantization_step = 0 - - def set_trainer(self, trainer): - self.trainer = trainer - self.size_tracker = pq.SizeTracker(self.trainer.get_model()) - - def step(self): - """Move to the next stage of quantization.""" - if self.quantization_step >= len(self.layers_to_quantize): - # Maybe we just finished the last training step or we loaded - # a checkpoint for an iterative PQ model which previously - # finished training. Either way, don't quantize again. - return - - logger.info( - "quantizing model (step={}; layers_to_quantize[step]={})".format( - self.quantization_step, self.layers_to_quantize[self.quantization_step] - ) - ) - quantized_layers = pq.quantize_model_( - self.trainer.get_model(), - self.size_tracker, - self.layers_to_quantize, - self.block_sizes_config, - self.n_centroids_config, - step=self.quantization_step, - ) - logger.info("quantized layers: {}".format(quantized_layers)) - logger.info(self.size_tracker) - - self.quantization_step += 1 - - # reintialize the Trainer since model parameters have changed - self.trainer.reinitialize() - - def begin_epoch(self, epoch): - """Called at the beginning of each epoch (epochs start at 1).""" - if ( - ( - self.epoch_schedule is not None - and epoch > 0 - and (epoch - 1) % self.epoch_schedule == 0 - ) - # we always step once in the beginning, even if using - # update-based quantization - or self.quantization_step == 0 - ): - self.step() - - def step_update(self, num_updates): - """Called at the end of each step.""" - if ( - self.update_schedule is not None - and num_updates > 0 - and num_updates % self.update_schedule == 0 - ): - self.step() - - def state_dict(self): - return { - "n_centroids_config": self.n_centroids_config, - "block_sizes_config": self.block_sizes_config, - "layers_to_quantize": self.layers_to_quantize, - "epoch_schedule": self.epoch_schedule, - "update_schedule": self.update_schedule, - "quantization_step": self.quantization_step, - } - - def load_state_dict(self, state_dict): - self.n_centroids_config = state_dict["n_centroids_config"] - self.block_sizes_config = state_dict["block_sizes_config"] - self.layers_to_quantize = state_dict["layers_to_quantize"] - self.epoch_schedule = state_dict["epoch_schedule"] - self.update_schedule = state_dict["update_schedule"] - self.quantization_step = state_dict["quantization_step"] diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/registry.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/registry.py deleted file mode 100644 index 0734f944692f6a13b1724f08ee8a34edd3372c4b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/registry.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from argparse import Namespace -from typing import Union - -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import merge_with_parent -from hydra.core.config_store import ConfigStore -from omegaconf import DictConfig - -REGISTRIES = {} - - -def setup_registry(registry_name: str, base_class=None, default=None, required=False): - assert registry_name.startswith("--") - registry_name = registry_name[2:].replace("-", "_") - - REGISTRY = {} - REGISTRY_CLASS_NAMES = set() - DATACLASS_REGISTRY = {} - - # maintain a registry of all registries - if registry_name in REGISTRIES: - return # registry already exists - REGISTRIES[registry_name] = { - "registry": REGISTRY, - "default": default, - "dataclass_registry": DATACLASS_REGISTRY, - } - - def build_x(cfg: Union[DictConfig, str, Namespace], *extra_args, **extra_kwargs): - if isinstance(cfg, DictConfig): - choice = cfg._name - - if choice and choice in DATACLASS_REGISTRY: - dc = DATACLASS_REGISTRY[choice] - cfg = merge_with_parent(dc(), cfg) - elif isinstance(cfg, str): - choice = cfg - if choice in DATACLASS_REGISTRY: - cfg = DATACLASS_REGISTRY[choice]() - else: - choice = getattr(cfg, registry_name, None) - if choice in DATACLASS_REGISTRY: - cfg = DATACLASS_REGISTRY[choice].from_namespace(cfg) - - if choice is None: - if required: - raise ValueError("{} is required!".format(registry_name)) - return None - - cls = REGISTRY[choice] - if hasattr(cls, "build_" + registry_name): - builder = getattr(cls, "build_" + registry_name) - else: - builder = cls - - return builder(cfg, *extra_args, **extra_kwargs) - - def register_x(name, dataclass=None): - def register_x_cls(cls): - if name in REGISTRY: - raise ValueError( - "Cannot register duplicate {} ({})".format(registry_name, name) - ) - if cls.__name__ in REGISTRY_CLASS_NAMES: - raise ValueError( - "Cannot register {} with duplicate class name ({})".format( - registry_name, cls.__name__ - ) - ) - if base_class is not None and not issubclass(cls, base_class): - raise ValueError( - "{} must extend {}".format(cls.__name__, base_class.__name__) - ) - - if dataclass is not None and not issubclass(dataclass, FairseqDataclass): - raise ValueError( - "Dataclass {} must extend FairseqDataclass".format(dataclass) - ) - - cls.__dataclass = dataclass - if cls.__dataclass is not None: - DATACLASS_REGISTRY[name] = cls.__dataclass - - cs = ConfigStore.instance() - node = dataclass() - node._name = name - cs.store(name=name, group=registry_name, node=node, provider="fairseq") - - REGISTRY[name] = cls - - return cls - - return register_x_cls - - return build_x, register_x, REGISTRY, DATACLASS_REGISTRY diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/__init__.py deleted file mode 100644 index 58f2f563e493327394dff1265030d18f0814b5a2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/__init__.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import importlib -import os -from abc import ABC, abstractmethod - -from fairseq import registry -from omegaconf import DictConfig - - -class BaseScorer(ABC): - def __init__(self, cfg): - self.cfg = cfg - self.ref = [] - self.pred = [] - - def add_string(self, ref, pred): - self.ref.append(ref) - self.pred.append(pred) - - @abstractmethod - def score(self) -> float: - pass - - @abstractmethod - def result_string(self) -> str: - pass - - -_build_scorer, register_scorer, SCORER_REGISTRY, _ = registry.setup_registry( - "--scoring", default="bleu" -) - - -def build_scorer(choice, tgt_dict): - _choice = choice._name if isinstance(choice, DictConfig) else choice - - if _choice == "bleu": - from fairseq.scoring import bleu - - return bleu.Scorer( - bleu.BleuConfig(pad=tgt_dict.pad(), eos=tgt_dict.eos(), unk=tgt_dict.unk()) - ) - return _build_scorer(choice) - - -# automatically import any Python files in the current directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - module = file[: file.find(".py")] - importlib.import_module("fairseq.scoring." + module) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/bertscore.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/bertscore.py deleted file mode 100644 index 7450104324d3a7239b3f382db1a4310866a5c55f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/bertscore.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field - -import numpy as np -from fairseq.dataclass import FairseqDataclass -from fairseq.scoring import BaseScorer, register_scorer - - -@dataclass -class BertScoreScorerConfig(FairseqDataclass): - bert_score_lang: str = field(default="en", metadata={"help": "BERTScore language"}) - - -@register_scorer("bert_score", dataclass=BertScoreScorerConfig) -class BertScoreScorer(BaseScorer): - def __init__(self, cfg): - super(BertScoreScorer, self).__init__(cfg) - try: - import bert_score as _bert_score - except ImportError: - raise ImportError("Please install BERTScore: pip install bert-score") - - self.cfg = cfg - self._bert_score = _bert_score - self.scores = None - - def add_string(self, ref, pred): - self.ref.append(ref) - self.pred.append(pred) - - def score(self, order=4): - _, _, self.scores = self._bert_score.score( - self.pred, self.ref, lang=self.cfg.bert_score_lang - ) - self.scores = self.scores.numpy() - return np.mean(self.scores) - - def result_string(self, order=4): - return f"BERTScore: {self.score():.4f}" diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/bleu.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/bleu.py deleted file mode 100644 index e55bd2f393e279d318e08d680c41cc20e8d49931..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/bleu.py +++ /dev/null @@ -1,168 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import ctypes -import math -import sys -from dataclasses import dataclass, field - -import torch -from fairseq.dataclass import FairseqDataclass -from fairseq.scoring import BaseScorer, register_scorer -from fairseq.scoring.tokenizer import EvaluationTokenizer - - -class BleuStat(ctypes.Structure): - _fields_ = [ - ("reflen", ctypes.c_size_t), - ("predlen", ctypes.c_size_t), - ("match1", ctypes.c_size_t), - ("count1", ctypes.c_size_t), - ("match2", ctypes.c_size_t), - ("count2", ctypes.c_size_t), - ("match3", ctypes.c_size_t), - ("count3", ctypes.c_size_t), - ("match4", ctypes.c_size_t), - ("count4", ctypes.c_size_t), - ] - - -@dataclass -class SacrebleuConfig(FairseqDataclass): - sacrebleu_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( - default="13a", metadata={"help": "tokenizer"} - ) - sacrebleu_lowercase: bool = field( - default=False, metadata={"help": "apply lowercasing"} - ) - sacrebleu_char_level: bool = field( - default=False, metadata={"help": "evaluate at character level"} - ) - - -@register_scorer("sacrebleu", dataclass=SacrebleuConfig) -class SacrebleuScorer(BaseScorer): - def __init__(self, cfg): - super(SacrebleuScorer, self).__init__(cfg) - import sacrebleu - - self.sacrebleu = sacrebleu - self.tokenizer = EvaluationTokenizer( - tokenizer_type=cfg.sacrebleu_tokenizer, - lowercase=cfg.sacrebleu_lowercase, - character_tokenization=cfg.sacrebleu_char_level, - ) - - def add_string(self, ref, pred): - self.ref.append(self.tokenizer.tokenize(ref)) - self.pred.append(self.tokenizer.tokenize(pred)) - - def _score(self, order=4): - if order != 4: - raise NotImplementedError - # tokenization and lowercasing are performed by self.tokenizer instead. - return self.sacrebleu.corpus_bleu(self.pred, [self.ref], tokenize="none") - - def score(self, order=4): - return self._score(order).score - - def result_string(self, order=4): - return self._score(order).format() - - -@dataclass -class BleuConfig(FairseqDataclass): - pad: int = field(default=1, metadata={"help": "padding index"}) - eos: int = field(default=2, metadata={"help": "eos index"}) - unk: int = field(default=3, metadata={"help": "unk index"}) - - -@register_scorer("bleu", dataclass=BleuConfig) -class Scorer(object): - def __init__(self, cfg): - self.stat = BleuStat() - self.pad = cfg.pad - self.eos = cfg.eos - self.unk = cfg.unk - - try: - from fairseq import libbleu - except ImportError as e: - sys.stderr.write( - "ERROR: missing libbleu.so. run `pip install --editable .`\n" - ) - raise e - - self.C = ctypes.cdll.LoadLibrary(libbleu.__file__) - - self.reset() - - def reset(self, one_init=False): - if one_init: - self.C.bleu_one_init(ctypes.byref(self.stat)) - else: - self.C.bleu_zero_init(ctypes.byref(self.stat)) - - def add(self, ref, pred): - if not isinstance(ref, torch.IntTensor): - raise TypeError("ref must be a torch.IntTensor (got {})".format(type(ref))) - if not isinstance(pred, torch.IntTensor): - raise TypeError("pred must be a torch.IntTensor(got {})".format(type(pred))) - - # don't match unknown words - rref = ref.clone() - assert not rref.lt(0).any() - rref[rref.eq(self.unk)] = -999 - - rref = rref.contiguous().view(-1) - pred = pred.contiguous().view(-1) - - self.C.bleu_add( - ctypes.byref(self.stat), - ctypes.c_size_t(rref.size(0)), - ctypes.c_void_p(rref.data_ptr()), - ctypes.c_size_t(pred.size(0)), - ctypes.c_void_p(pred.data_ptr()), - ctypes.c_int(self.pad), - ctypes.c_int(self.eos), - ) - - def score(self, order=4): - psum = sum( - math.log(p) if p > 0 else float("-Inf") for p in self.precision()[:order] - ) - return self.brevity() * math.exp(psum / order) * 100 - - def precision(self): - def ratio(a, b): - return a / b if b > 0 else 0 - - return [ - ratio(self.stat.match1, self.stat.count1), - ratio(self.stat.match2, self.stat.count2), - ratio(self.stat.match3, self.stat.count3), - ratio(self.stat.match4, self.stat.count4), - ] - - def brevity(self): - r = self.stat.reflen / self.stat.predlen - return min(1, math.exp(1 - r)) - - def result_string(self, order=4): - assert order <= 4, "BLEU scores for order > 4 aren't supported" - fmt = "BLEU{} = {:2.2f}, {:2.1f}" - for _ in range(1, order): - fmt += "/{:2.1f}" - fmt += " (BP={:.3f}, ratio={:.3f}, syslen={}, reflen={})" - bleup = [p * 100 for p in self.precision()[:order]] - return fmt.format( - order, - self.score(order=order), - *bleup, - self.brevity(), - self.stat.predlen / self.stat.reflen, - self.stat.predlen, - self.stat.reflen - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/chrf.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/chrf.py deleted file mode 100644 index 5df5a1c011243fe2e836c38a5f8459aeb824f0e7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/chrf.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -from dataclasses import dataclass - -from fairseq.dataclass import FairseqDataclass -from fairseq.scoring import BaseScorer, register_scorer - - -@dataclass -class ChrFScorerConfig(FairseqDataclass): - pass - - -@register_scorer("chrf", dataclass=ChrFScorerConfig) -class ChrFScorer(BaseScorer): - def __init__(self, args): - super(ChrFScorer, self).__init__(args) - import sacrebleu - - self.sacrebleu = sacrebleu - - def add_string(self, ref, pred): - self.ref.append(ref) - self.pred.append(pred) - - def score(self, order=4): - return self.result_string(order).score - - def result_string(self, order=4): - if order != 4: - raise NotImplementedError - return self.sacrebleu.corpus_chrf(self.pred, [self.ref]).format() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/meteor.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/meteor.py deleted file mode 100644 index b25c0b62793b793d029c56b873528fbb6a57e425..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/meteor.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass - -import numpy as np -from fairseq.dataclass import FairseqDataclass -from fairseq.scoring import BaseScorer, register_scorer - - -@dataclass -class MeteorScorerConfig(FairseqDataclass): - pass - - -@register_scorer("meteor", dataclass=MeteorScorerConfig) -class MeteorScorer(BaseScorer): - def __init__(self, args): - super(MeteorScorer, self).__init__(args) - try: - import nltk - except ImportError: - raise ImportError("Please install nltk to use METEOR scorer") - - self.nltk = nltk - self.scores = [] - - def add_string(self, ref, pred): - self.ref.append(ref) - self.pred.append(pred) - - def score(self, order=4): - self.scores = [ - self.nltk.translate.meteor_score.single_meteor_score(r, p) - for r, p in zip(self.ref, self.pred) - ] - return np.mean(self.scores) - - def result_string(self, order=4): - return f"METEOR: {self.score():.4f}" diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/tokenizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/tokenizer.py deleted file mode 100644 index 9a40c7b28320a5f22e858c0397548e249b3ad782..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/tokenizer.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unicodedata - -import sacrebleu as sb -from fairseq.dataclass import ChoiceEnum - -SACREBLEU_V2_ABOVE = int(sb.__version__[0]) >= 2 - - -class EvaluationTokenizer(object): - """A generic evaluation-time tokenizer, which leverages built-in tokenizers - in sacreBLEU (https://github.com/mjpost/sacrebleu). It additionally provides - lowercasing, punctuation removal and character tokenization, which are - applied after sacreBLEU tokenization. - - Args: - tokenizer_type (str): the type of sacreBLEU tokenizer to apply. - lowercase (bool): lowercase the text. - punctuation_removal (bool): remove punctuation (based on unicode - category) from text. - character_tokenization (bool): tokenize the text to characters. - """ - - SPACE = chr(32) - SPACE_ESCAPE = chr(9601) - _ALL_TOKENIZER_TYPES = ( - sb.BLEU.TOKENIZERS - if SACREBLEU_V2_ABOVE - else ["none", "13a", "intl", "zh", "ja-mecab"] - ) - ALL_TOKENIZER_TYPES = ChoiceEnum(_ALL_TOKENIZER_TYPES) - - def __init__( - self, - tokenizer_type: str = "13a", - lowercase: bool = False, - punctuation_removal: bool = False, - character_tokenization: bool = False, - ): - - assert ( - tokenizer_type in self._ALL_TOKENIZER_TYPES - ), f"{tokenizer_type}, {self._ALL_TOKENIZER_TYPES}" - self.lowercase = lowercase - self.punctuation_removal = punctuation_removal - self.character_tokenization = character_tokenization - if SACREBLEU_V2_ABOVE: - self.tokenizer = sb.BLEU(tokenize=str(tokenizer_type)).tokenizer - else: - self.tokenizer = sb.tokenizers.TOKENIZERS[tokenizer_type]() - - @classmethod - def remove_punctuation(cls, sent: str): - """Remove punctuation based on Unicode category.""" - return cls.SPACE.join( - t - for t in sent.split(cls.SPACE) - if not all(unicodedata.category(c)[0] == "P" for c in t) - ) - - def tokenize(self, sent: str): - tokenized = self.tokenizer(sent) - - if self.punctuation_removal: - tokenized = self.remove_punctuation(tokenized) - - if self.character_tokenization: - tokenized = self.SPACE.join( - list(tokenized.replace(self.SPACE, self.SPACE_ESCAPE)) - ) - - if self.lowercase: - tokenized = tokenized.lower() - - return tokenized diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/wer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/wer.py deleted file mode 100644 index 633dc47c247691c4c9e36cbdbab7d7cb74b38452..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/scoring/wer.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field - -from fairseq.dataclass import FairseqDataclass -from fairseq.scoring import BaseScorer, register_scorer -from fairseq.scoring.tokenizer import EvaluationTokenizer - - -@dataclass -class WerScorerConfig(FairseqDataclass): - wer_tokenizer: EvaluationTokenizer.ALL_TOKENIZER_TYPES = field( - default="none", metadata={"help": "sacreBLEU tokenizer to use for evaluation"} - ) - wer_remove_punct: bool = field( - default=False, metadata={"help": "remove punctuation"} - ) - wer_char_level: bool = field( - default=False, metadata={"help": "evaluate at character level"} - ) - wer_lowercase: bool = field(default=False, metadata={"help": "lowercasing"}) - - -@register_scorer("wer", dataclass=WerScorerConfig) -class WerScorer(BaseScorer): - def __init__(self, cfg): - super().__init__(cfg) - self.reset() - try: - import editdistance as ed - except ImportError: - raise ImportError("Please install editdistance to use WER scorer") - self.ed = ed - self.tokenizer = EvaluationTokenizer( - tokenizer_type=self.cfg.wer_tokenizer, - lowercase=self.cfg.wer_lowercase, - punctuation_removal=self.cfg.wer_remove_punct, - character_tokenization=self.cfg.wer_char_level, - ) - - def reset(self): - self.distance = 0 - self.ref_length = 0 - - def add_string(self, ref, pred): - ref_items = self.tokenizer.tokenize(ref).split() - pred_items = self.tokenizer.tokenize(pred).split() - self.distance += self.ed.eval(ref_items, pred_items) - self.ref_length += len(ref_items) - - def result_string(self): - return f"WER: {self.score():.2f}" - - def score(self): - return 100.0 * self.distance / self.ref_length if self.ref_length > 0 else 0 diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/search.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/search.py deleted file mode 100644 index a71e78019f0650826b4593daf2051d94834924f2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/search.py +++ /dev/null @@ -1,814 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import List, Optional - -import torch -import torch.nn as nn -from fairseq.token_generation_constraints import ( - ConstraintState, - OrderedConstraintState, - UnorderedConstraintState, -) -from torch import Tensor - - -class Search(nn.Module): - def __init__(self, tgt_dict): - super().__init__() - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.eos = tgt_dict.eos() - self.vocab_size = len(tgt_dict) - self.src_lengths = torch.tensor(-1) - self.supports_constraints = False - self.stop_on_max_len = False - - def step( - self, step, lprobs, scores, prev_output_tokens=None, original_batch_idxs=None - ): - """Take a single search step. - - Args: - step: the current search step, starting at 0 - lprobs: (bsz x input_beam_size x vocab_size) - the model's log-probabilities over the vocabulary at the current step - scores: (bsz x input_beam_size x step) - the historical model scores of each hypothesis up to this point - prev_output_tokens: (bsz x step) - the previously generated oputput tokens - original_batch_idxs: (bsz) - the tensor with the batch indices, in the range [0, bsz) - this is useful in case there has been applied a re-ordering - and we need to know the orignal indices - - Return: A tuple of (scores, indices, beams) where: - scores: (bsz x output_beam_size) - the scores of the chosen elements; output_beam_size can be - larger than input_beam_size, e.g., we may return - 2*input_beam_size to account for EOS - indices: (bsz x output_beam_size) - the indices of the chosen elements - beams: (bsz x output_beam_size) - the hypothesis ids of the chosen elements, in the range [0, input_beam_size) - """ - raise NotImplementedError - - @torch.jit.export - def set_src_lengths(self, src_lengths): - self.src_lengths = src_lengths - - @torch.jit.export - def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): - """Initialize constraint states for constrained decoding (if supported). - - Args: - batch_constraints: (torch.Tensor, optional) - the list of constraints, in packed form - beam_size: (int) - the beam size - Returns: - *encoder_out* rearranged according to *new_order* - """ - pass - - def prune_sentences(self, batch_idxs: Tensor): - """ - Removes constraint states for completed sentences (if supported). - This is called from sequence_generator._generate() when sentences are - deleted from the batch. - - Args: - batch_idxs: Indices of *sentences* whose constraint state should be *kept*. - """ - pass - - def update_constraints(self, active_hypos: Tensor): - """ - Updates the constraint states by selecting the beam items that are retained. - This is called at each time step of sequence_generator._generate() when - the set of 2 * {beam_size} candidate hypotheses are reduced to the beam size. - - Args: - active_hypos: (batch size, beam size) - list of integers denoting, for each sentence, which beam candidate items - should be kept. - """ - pass - - -class BeamSearch(Search): - def __init__(self, tgt_dict): - super().__init__(tgt_dict) - self.constraint_states = None - - @torch.jit.export - def step( - self, - step: int, - lprobs, - scores: Optional[Tensor], - prev_output_tokens: Optional[Tensor] = None, - original_batch_idxs: Optional[Tensor] = None, - ): - bsz, beam_size, vocab_size = lprobs.size() - - if step == 0: - # at the first step all hypotheses are equally likely, so use - # only the first beam - lprobs = lprobs[:, ::beam_size, :].contiguous() - else: - # make probs contain cumulative scores for each hypothesis - assert scores is not None - lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) - - top_prediction = torch.topk( - lprobs.view(bsz, -1), - k=min( - # Take the best 2 x beam_size predictions. We'll choose the first - # beam_size of these which don't predict eos to continue with. - beam_size * 2, - lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad - ), - ) - scores_buf = top_prediction[0] - indices_buf = top_prediction[1] - # Project back into relative indices and beams - beams_buf = torch.div(indices_buf, vocab_size, rounding_mode="trunc") - indices_buf = indices_buf.fmod(vocab_size) - - # At this point, beams_buf and indices_buf are single-dim and contain relative indices - return scores_buf, indices_buf, beams_buf - - -class PrefixConstrainedBeamSearch(Search): - def __init__(self, tgt_dict, prefix_allowed_tokens_fn): - super().__init__(tgt_dict) - self.prefix_allowed_tokens_fn = prefix_allowed_tokens_fn - self.stop_on_max_len = True - - @torch.jit.export - def apply_mask(self, x, prev_output_tokens, original_batch_idxs): - beam_size = x.shape[0] // original_batch_idxs.shape[0] - original_batch_idxs = ( - original_batch_idxs.unsqueeze(-1).repeat((1, beam_size)).flatten().tolist() - ) - - mask = torch.full_like(x, -math.inf) - for sent_i, (sent, batch_i) in enumerate( - zip(prev_output_tokens, original_batch_idxs) - ): - mask[sent_i, :, self.prefix_allowed_tokens_fn(batch_i, sent)] = 0 - - return mask - - @torch.jit.export - def step( - self, - step: int, - lprobs: Tensor, - scores: Tensor, - prev_output_tokens: Tensor, - original_batch_idxs: Tensor, - ): - bsz, beam_size, vocab_size = lprobs.size() - - lprobs += self.apply_mask( - lprobs.view(bsz * beam_size, 1, vocab_size), - prev_output_tokens, - original_batch_idxs, - ).view(bsz, beam_size, vocab_size) - - if step == 0: - # at the first step all hypotheses are equally likely, so use - # only the first beam - lprobs = lprobs[:, ::beam_size, :].contiguous() - else: - # make probs contain cumulative scores for each hypothesis - assert scores is not None - lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) - - top_prediction = torch.topk( - lprobs.view(bsz, -1), - k=min( - # Take the best beam_size predictions. We'll choose the first - # beam_size of these which don't predict eos to continue with. - beam_size, - lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad - ), - ) - scores_buf = top_prediction[0] - indices_buf = top_prediction[1] - beams_buf = indices_buf // vocab_size - indices_buf = indices_buf.fmod(vocab_size) - return scores_buf, indices_buf, beams_buf - - -class LexicallyConstrainedBeamSearch(Search): - """Implements lexically constrained beam search as described in - - Fast Lexically Constrained Decoding with Dynamic Beam - Allocation for Neural Machine Translation. Post & Vilar, - NAACL 2018. https://www.aclweb.org/anthology/N18-1119/ - - and - - Improved Lexically Constrained Decoding for Translation and - Monolingual Rewriting. Hu et al, NAACL - 2019. https://www.aclweb.org/anthology/N19-1090/ - - This is accomplished by maintaining, for each beam hypothesis, a - ConstraintState object (see constraints.py) that tracks which - constraints have been generated and using this information to - shape the beam for each input sentence. - """ - - def __init__(self, tgt_dict, representation): - super().__init__(tgt_dict) - self.representation = representation - self.vocab_size = len(tgt_dict) - self.num_cands = 0 - self.supports_constraints = True - - @torch.jit.export - def init_constraints(self, batch_constraints: Optional[Tensor], beam_size: int): - self.constraint_states = [] - for constraint_tensor in batch_constraints: - if self.representation == "ordered": - constraint_state = OrderedConstraintState.create(constraint_tensor) - elif self.representation == "unordered": - constraint_state = UnorderedConstraintState.create(constraint_tensor) - - self.constraint_states.append([constraint_state for i in range(beam_size)]) - - @torch.jit.export - def prune_sentences(self, batch_idxs: Tensor): - self.constraint_states = [ - self.constraint_states[i] for i in batch_idxs.tolist() - ] - - @torch.jit.export - def update_constraints(self, active_hypos: Tensor): - if self.constraint_states: - batch_size = active_hypos.size(0) - for sentid in range(batch_size): - self.constraint_states[sentid] = [ - self.constraint_states[sentid][i] for i in active_hypos[sentid] - ] - - @torch.jit.export - def step( - self, - step: int, - lprobs: Tensor, - scores: Optional[Tensor], - prev_output_tokens: Optional[Tensor] = None, - original_batch_idxs: Optional[Tensor] = None, - ): - """ - A constrained step builds a large candidates list from the following: - - the top 2 * {beam_size} items over the whole beam - - for each item in the beam - - the top {each_k} (default 1) - - all next constraints - We then compute the constrained state of each beam item, and assign - stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so - on. We then sort by (stripe, score), and truncate the list at - 2 * beam size. - - Args: - step: the decoder step - lprobs: (batch size, beam size, target vocab) - the target-vocab distributions for each item in the beam. - Retrun: A tuple of (scores, indices, beams, constraints) where: - scores: (batch, output beam size) - the scores of the chosen elements - indices: (batch, output beam size) - the target vocab indices of the chosen elements - beams: (batch, output beam size) - the 0-indexed hypothesis ids of the chosen elements - constraints: (batch, output beam size) - the new constraint states - """ - each_k = 1 - device = lprobs.device - - batch_size, beam_size, vocab_size = lprobs.size() - - self.num_cands = min( - # Just take the k-best. We'll get another k from the 1-best from each - # row, plus more from the constraints - beam_size * 2, - lprobs.view(batch_size, -1).size(1) - 1, # -1 so we never select pad - ) - - # STEP 0: Preliminary. Prevent EOS for unfinished hyps across all batch items - constraint_states = self.constraint_states - if constraint_states and step > 0: - not_finished_indices = [] - for sentno, sent_constraints in enumerate(constraint_states): - for beamno, state in enumerate(sent_constraints): - index = sentno * beam_size + beamno - if not state.finished: - not_finished_indices.append(index) - not_finished_indices = torch.tensor(not_finished_indices) - if not_finished_indices.numel() > 0: - lprobs.view(batch_size * beam_size, -1)[ - not_finished_indices, self.eos - ] = -math.inf - - if step == 0: - # at the first step all hypotheses are equally likely, so use - # only the first beam entry for each batch item - lprobs = lprobs[:, ::beam_size, :].contiguous() - else: - # make probs contain cumulative scores for each hypothesis - assert scores is not None - lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) - - top_prediction = torch.topk( - lprobs.view(batch_size, -1), - self.num_cands, - ) - scores_buf, indices_buf = top_prediction - # Project back into relative indices and beams - beams_buf = indices_buf // vocab_size - indices_buf = indices_buf.fmod(vocab_size) - - # Short circuit if there are no constraints in this batch - if not constraint_states: - return scores_buf, indices_buf, beams_buf - - # STEP 1: get top-1 from each hypothesis across all sentences in the batch - if step > 0: - top_scores, top_indices = torch.topk( - lprobs.view(batch_size * beam_size, -1), - k=each_k, - dim=1, - ) - top_scores = top_scores.view(batch_size, -1) - top_indices = top_indices.view(batch_size, -1) - scores_buf = torch.cat((scores_buf, top_scores), dim=1) - indices_buf = torch.cat((indices_buf, top_indices), dim=1) - new_beams = torch.arange(0, beam_size, device=device).repeat(batch_size, 1) - beams_buf = torch.cat((beams_buf, new_beams), dim=1) - - # Now, process sentences in the batch one by one. - new_scores_buf = torch.zeros((batch_size, 2 * beam_size), device=device) - new_indices_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long() - new_beams_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long() - for sentno, states in enumerate(constraint_states): - scores, indices, beams, new_states = self.step_sentence( - step, - sentno, - lprobs[sentno], - constraint_states[sentno], - beams_buf[sentno].clone(), - indices_buf[sentno].clone(), - scores_buf[sentno].clone(), - ) - new_scores_buf[sentno] = scores - new_indices_buf[sentno] = indices - new_beams_buf[sentno] = beams - self.constraint_states[sentno] = new_states - - return new_scores_buf, new_indices_buf, new_beams_buf - - @torch.jit.export - def step_sentence( - self, - step: int, - sentno: int, - lprobs: Tensor, - constraint_states: List[List[ConstraintState]], - beams_buf: Tensor, - indices_buf: Tensor, - scores_buf: Tensor, - ): - """Does per-sentence processing. Adds all constraints for each - hypothesis to the list of candidates; then removes duplicates, - sorts, and dynamically stripes across the banks. All tensor inputs - are collapsed to those pertaining to a single input sentence. - """ - device = lprobs.device - - # STEP 2: Add all constraints for each beam item - for beamno, state in enumerate(constraint_states): - next_tokens = torch.tensor(list(state.next_tokens()), device=device).long() - if next_tokens.numel() != 0: - indices_buf = torch.cat((indices_buf, next_tokens)) - next_beams = ( - torch.tensor(beamno, device=device) - .repeat(next_tokens.size(0)) - .long() - ) - beams_buf = torch.cat((beams_buf, next_beams)) - next_values = lprobs[beamno].take(next_tokens.view(-1)) - scores_buf = torch.cat((scores_buf, next_values)) - - # At the 0th time step, there is just one beam item - if step == 0: - break - - # STEP 3: Compute the "bank" for each candidate. This is the - # number of constraints it's generated. We need this so that - # we can do round-robin allocation of the beam across these - # banks. If C is the number of constraints, we select the best - # item in bank C, then the best in bank C-1, etc, followed by - # the 2nd-best in bank C, the 2nd-best in bank C-1, etc, and so - # on, until the maximum beam size. We accomplish this by - # creating a sort key and striping across the banks. - - # Compute the new states for all candidates - cands_size = indices_buf.size(0) - constraint_states = [ - constraint_states[beams_buf[i]].advance(indices_buf[i]) - for i in range(cands_size) - ] - - banks = torch.tensor([state.bank for state in constraint_states], device=device) - - # STEP 4: Sort - num_constraint_tokens = len(state.tokens) - - # Sort by keys (bank, score) (i.e., sort banks together, and scores - # within banks). AFAIK pytorch doesn't support either stable sort or - # multi-key sorting, so we have to hack this. - MAX_SCORE = -100 - sort_key = (num_constraint_tokens - banks) * MAX_SCORE + scores_buf - sort_values, sort_indices = sort_key.sort(dim=0, descending=True) - scores_buf = scores_buf[sort_indices] - indices_buf = indices_buf[sort_indices] - beams_buf = beams_buf[sort_indices] - banks = banks[sort_indices] - - # Sort the constraints to follow suit - constraint_states = [constraint_states[i] for i in sort_indices] - - # STEP 5: Remove duplicates. The topk calls (overall and - # per-row) plus the per-row generation of constraints will - # produce duplicates. Here we remove them. - - def roll(t): - """Rolls a 1d tensor left by 1. - - [0, 1, 2, 3, 4] becomes [4, 0, 1, 2, 3] - """ - return torch.cat((t[-1].unsqueeze(0), t[0:-1]), dim=0) - - # We map candidates (beam, token_id) to a single dimension. - # This is then shifted by 1. We can then easily identify - # duplicates and create a mask that identifies unique - # extensions. - uniques_mask = beams_buf * (self.vocab_size + 1) + indices_buf - uniques_mask = roll(uniques_mask) != uniques_mask - - # Use the mask to pare down the data structures - scores_buf = torch.masked_select(scores_buf, uniques_mask) - indices_buf = torch.masked_select(indices_buf, uniques_mask) - beams_buf = torch.masked_select(beams_buf, uniques_mask) - banks = torch.masked_select(banks, uniques_mask) - i = 1 - for mask in uniques_mask[1:]: - if not mask: - constraint_states.pop(i) - i += mask - - # STEP 6: Assign IDs round-robin across banks, sort, and - # truncate. Now that the candidates are sorted by (bank, - # score) and uniqed, we dynamically allocate the {beam_size} - # beam by striping across the candidates. These stripes will - # be used as sort keys to do round-robin selection. This is - # accomplished in a single pass with offsets. Sorting by - # highest-banks (furthest-along hypotheses) first ensures - # progress through the constraints. - # - # e.g., BANKS: 3 3 3 2 2 2 2 1 1 1 0 0 - # OLD STRIPES: 0 1 2 0 1 2 3 0 1 2 0 1 - # NEW STRIPES: 0 1+4 2+8 0+1 1+5 2+9 3+11 0+2 1+6 2+10 0+3 1+7 - # = 0 5 10 1 6 11 13 2 7 12 3 8 - # - # Sorting by this then gives the following banks: - # - # 3 2 1 0 3 2 1 0 3 2 1 2 - # - # We'll take the top {beam_size} of these. - stripe_offsets = [offset * (len(banks) + 1) for offset in range(len(banks) + 1)] - stripes = torch.zeros_like(banks) - cur_bank_count = -1 - cur_bank = banks[0] - for i, bank in enumerate(banks): - if bank != cur_bank: - cur_bank_count = 0 - cur_bank = bank - else: - cur_bank_count += 1 - stripes[i] = num_constraint_tokens - bank + stripe_offsets[cur_bank_count] - - # STEP 7: Sort by the stripes values - sort_values, sort_indices = stripes.sort(dim=0) - scores_buf = scores_buf[sort_indices] - indices_buf = indices_buf[sort_indices] - beams_buf = beams_buf[sort_indices] - constraint_states = [constraint_states[i] for i in sort_indices] - - # STEP 8: Truncate to the candidates size! - scores_buf = scores_buf[: self.num_cands] - indices_buf = indices_buf[: self.num_cands] - beams_buf = beams_buf[: self.num_cands] - - return scores_buf, indices_buf, beams_buf, constraint_states - - -class LengthConstrainedBeamSearch(Search): - def __init__(self, tgt_dict, min_len_a, min_len_b, max_len_a, max_len_b): - super().__init__(tgt_dict) - self.min_len_a = min_len_a - self.min_len_b = min_len_b - self.max_len_a = max_len_a - self.max_len_b = max_len_b - self.beam = BeamSearch(tgt_dict) - self.needs_src_lengths = True - - def step( - self, - step: int, - lprobs, - scores, - prev_output_tokens: Optional[Tensor] = None, - original_batch_idxs: Optional[Tensor] = None, - ): - min_lens = self.min_len_a * self.src_lengths + self.min_len_b - max_lens = self.max_len_a * self.src_lengths + self.max_len_b - lprobs[step < min_lens, :, self.eos] = -math.inf - lprobs[step >= max_lens, :, self.eos] = 0 - return self.beam.step(step, lprobs, scores) - - -class DiverseBeamSearch(Search): - """Diverse Beam Search. - - See "Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence - Models" for details. - - We only implement the Hamming Diversity penalty here, which performed best - in the original paper. - """ - - def __init__(self, tgt_dict, num_groups, diversity_strength): - super().__init__(tgt_dict) - self.num_groups = num_groups - self.diversity_strength = -diversity_strength - self.beam = BeamSearch(tgt_dict) - - @torch.jit.export - def step( - self, - step: int, - lprobs, - scores, - prev_output_tokens: Optional[Tensor] = None, - original_batch_idxs: Optional[Tensor] = None, - ): - bsz, beam_size, vocab_size = lprobs.size() - if beam_size % self.num_groups != 0: - raise ValueError( - "DiverseBeamSearch requires --beam to be divisible by the number of groups" - ) - - # initialize diversity penalty - diversity_buf = torch.zeros(lprobs[:, 0, :].size()).to(lprobs) - - scores_G, indices_G, beams_G = [], [], [] - for g in range(self.num_groups): - lprobs_g = lprobs[:, g :: self.num_groups, :] - scores_g = scores[:, g :: self.num_groups, :] if step > 0 else None - - # apply diversity penalty - if g > 0: - lprobs_g = torch.add( - lprobs_g, - other=diversity_buf.unsqueeze(1), - alpha=self.diversity_strength, - ) - else: - lprobs_g = lprobs_g.contiguous() - - scores_buf, indices_buf, beams_buf = self.beam.step( - step, lprobs_g, scores_g - ) - beams_buf.mul_(self.num_groups).add_(g) - - scores_G.append(scores_buf.clone()) - indices_G.append(indices_buf.clone()) - beams_G.append(beams_buf.clone()) - - # update diversity penalty - diversity_buf.scatter_add_( - 1, indices_buf, torch.ones(indices_buf.size()).to(diversity_buf) - ) - - # interleave results from different groups - scores_buf = torch.stack(scores_G, dim=2).view(bsz, -1) - indices_buf = torch.stack(indices_G, dim=2).view(bsz, -1) - beams_buf = torch.stack(beams_G, dim=2).view(bsz, -1) - return scores_buf, indices_buf, beams_buf - - -class Sampling(Search): - sampling_topk: int - sampling_topp: float - - def __init__(self, tgt_dict, sampling_topk=-1, sampling_topp=-1.0): - super().__init__(tgt_dict) - self.sampling_topk = sampling_topk - self.sampling_topp = sampling_topp - - def _sample_topp(self, lprobs): - """Sample among the smallest set of elements whose cumulative probability mass exceeds p. - - See `"The Curious Case of Neural Text Degeneration" - (Holtzman et al., 2019) `_. - - Args: - lprobs: (bsz x input_beam_size x vocab_size) - the model's log-probabilities over the vocabulary at the current step - - Return: A tuple of (trimed_probs, truncated_indices) where: - trimed_probs: (bsz x input_beam_size x ?) - the model's probabilities over the elements selected to sample from. The - width of the third dimension is determined by top-P. - truncated_indices: (bsz x input_beam_size x ?) - the indices of the chosen elements. - """ - probs = lprobs.exp_() - - # sort the last dimension (vocab dimension) in descending order - sorted_probs, sorted_indices = probs.sort(descending=True) - - # compute a mask to indicate the words to be included in the top-P set. - cumsum_probs = sorted_probs.cumsum(dim=2) - mask = cumsum_probs.lt(self.sampling_topp) - - # note that mask was computed by 'lt'. One more word needs to be included - # so that the cumulative probability mass can exceed p. - cumsum_mask = mask.cumsum(dim=2) - last_included = cumsum_mask[:, :, -1:] - last_included.clamp_(0, mask.size()[2] - 1) - mask = mask.scatter_(2, last_included, 1) - - # truncate unnecessary dims. - max_dim = last_included.max() - truncated_mask = mask[:, :, : max_dim + 1] - truncated_probs = sorted_probs[:, :, : max_dim + 1] - truncated_indices = sorted_indices[:, :, : max_dim + 1] - - # trim the words that are not in top-P by setting their probabilities - # to 0, so that they would not be sampled later. - trim_mask = ~truncated_mask - trimed_probs = truncated_probs.masked_fill_(trim_mask, 0) - return trimed_probs, truncated_indices - - @torch.jit.export - def step( - self, - step: int, - lprobs, - scores, - prev_output_tokens: Optional[Tensor] = None, - original_batch_idxs: Optional[Tensor] = None, - ): - bsz, beam_size, vocab_size = lprobs.size() - - if step == 0: - # at the first step all hypotheses are equally likely, so use - # only the first beam - lprobs = lprobs[:, ::beam_size, :].contiguous() - - if self.sampling_topp > 0: - # only sample from the smallest set of words whose cumulative probability mass exceeds p - probs, top_indices = self._sample_topp(lprobs) - elif self.sampling_topk > 0: - # only sample from top-k candidates - lprobs, top_indices = lprobs.topk(self.sampling_topk) - probs = lprobs.exp_() - else: - probs = lprobs.exp_() - - # dummy data to be consistent with true branch for type check - top_indices = torch.empty(0).to(probs) - # sample - if step == 0: - indices_buf = torch.multinomial( - probs.view(bsz, -1), - beam_size, - replacement=True, - ).view(bsz, beam_size) - else: - indices_buf = torch.multinomial( - probs.view(bsz * beam_size, -1), - 1, - replacement=True, - ).view(bsz, beam_size) - - if step == 0: - # expand to beam size - probs = probs.expand(bsz, beam_size, -1) - - # gather scores - scores_buf = torch.gather(probs, dim=2, index=indices_buf.unsqueeze(-1)) - scores_buf = scores_buf.log_().view(bsz, -1) - - # remap indices if using top-k or top-P sampling - if self.sampling_topk > 0 or self.sampling_topp > 0: - indices_buf = torch.gather( - top_indices.expand(bsz, beam_size, -1), - dim=2, - index=indices_buf.unsqueeze(-1), - ).squeeze(2) - - if step == 0: - beams_buf = indices_buf.new_zeros(bsz, beam_size) - else: - beams_buf = torch.arange(0, beam_size).to(indices_buf).repeat(bsz, 1) - # make scores cumulative - scores_buf.add_( - torch.gather(scores[:, :, step - 1], dim=1, index=beams_buf) - ) - - return scores_buf, indices_buf, beams_buf - - -class DiverseSiblingsSearch(Search): - """ - Beam search with diverse siblings. - - See "A Simple, Fast Diverse Decoding Algorithm for Neural Generation" for details. - https://arxiv.org/abs/1611.08562 - - 1/ Calculate hypotheses for each beam - 2/ Intra-sibling ordering - 3/ Rewrite scores - 4/ Choose top K hypotheses - - if diversity_rate == 0 is equivalent to BeamSearch - """ - - def __init__(self, tgt_dict, diversity_rate): - super().__init__(tgt_dict) - self.diversity_rate = diversity_rate - self.beam = BeamSearch(tgt_dict) - - def step( - self, - step: int, - lprobs, - scores, - prev_output_tokens: Optional[Tensor] = None, - original_batch_idxs: Optional[Tensor] = None, - ): - bsz, beam_size, vocab_size = lprobs.size() - k = min( - # Take the best 2 x beam_size predictions. We'll choose the first - # beam_size of these which don't predict eos to continue with. - beam_size * 2, - lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad - ) - s_list: List[Tensor] - i_list: List[Tensor] - s_list = [torch.empty(0).to(lprobs) for i in range(beam_size)] - i_list = [torch.LongTensor().to(device=lprobs.device) for i in range(beam_size)] - sibling_score = torch.arange(1, k + 1).to(lprobs) * self.diversity_rate - - if step == 0: - return self.beam.step(step, lprobs, scores) - lprobs.add_(scores[:, :, step - 1].unsqueeze(-1)) - - # 1/ Calculate hypotheses for each beam - for i in range(beam_size): - torch.topk(lprobs[:, i, :].view(bsz, -1), k, out=(s_list[i], i_list[i])) - i_list[i].fmod_(vocab_size) - - # 2/ Intra-sibling ordering by default from topk + 3/ Rewrite scores - s_list[i].sub_(sibling_score) - - # 4/ Choose top K hypotheses - indices = torch.stack(i_list, dim=1).view(bsz, -1) - - final_scores = torch.empty(0).to(lprobs) - final_indices = torch.LongTensor().to(device=lprobs.device) - final_beams = torch.LongTensor().to(device=lprobs.device) - (final_scores, final_indices) = torch.topk( - torch.stack(s_list, dim=1).view(bsz, -1), - k, - ) - - final_beams = final_indices // k - - for i in range(bsz): - final_indices[i] = indices[i][final_indices[i]] - - return final_scores, final_indices, final_beams diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/sequence_generator.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/sequence_generator.py deleted file mode 100644 index 168890fcf8a181a993f27acdc50943ddb9133139..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/sequence_generator.py +++ /dev/null @@ -1,996 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import sys -from typing import Dict, List, Optional - -import torch -import torch.nn as nn -from fairseq import search, utils -from fairseq.data import data_utils -from fairseq.models import FairseqIncrementalDecoder -from fairseq.ngram_repeat_block import NGramRepeatBlock -from torch import Tensor - - -class SequenceGenerator(nn.Module): - def __init__( - self, - models, - tgt_dict, - beam_size=1, - max_len_a=0, - max_len_b=200, - max_len=0, - min_len=1, - normalize_scores=True, - len_penalty=1.0, - unk_penalty=0.0, - temperature=1.0, - match_source_len=False, - no_repeat_ngram_size=0, - search_strategy=None, - eos=None, - symbols_to_strip_from_output=None, - lm_model=None, - lm_weight=1.0, - ): - """Generates translations of a given source sentence. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models, - currently support fairseq.models.TransformerModel for scripting - beam_size (int, optional): beam width (default: 1) - max_len_a/b (int, optional): generate sequences of maximum length - ax + b, where x is the source length - max_len (int, optional): the maximum length of the generated output - (not including end-of-sentence) - min_len (int, optional): the minimum length of the generated output - (not including end-of-sentence) - normalize_scores (bool, optional): normalize scores by the length - of the output (default: True) - len_penalty (float, optional): length penalty, where <1.0 favors - shorter, >1.0 favors longer sentences (default: 1.0) - unk_penalty (float, optional): unknown word penalty, where <0 - produces more unks, >0 produces fewer (default: 0.0) - temperature (float, optional): temperature, where values - >1.0 produce more uniform samples and values <1.0 produce - sharper samples (default: 1.0) - match_source_len (bool, optional): outputs should match the source - length (default: False) - """ - super().__init__() - if isinstance(models, EnsembleModel): - self.model = models - else: - self.model = EnsembleModel(models) - self.tgt_dict = tgt_dict - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.eos = tgt_dict.eos() if eos is None else eos - self.symbols_to_strip_from_output = ( - symbols_to_strip_from_output.union({self.eos}) - if symbols_to_strip_from_output is not None - else {self.eos} - ) - self.vocab_size = len(tgt_dict) - self.beam_size = beam_size - # the max beam size is the dictionary size - 1, since we never select pad - self.beam_size = min(beam_size, self.vocab_size - 1) - self.model.set_decoder_beam_size(self.beam_size) - self.max_len_a = max_len_a - self.max_len_b = max_len_b - self.min_len = min_len - self.max_len = max_len or self.model.max_decoder_positions() - - self.normalize_scores = normalize_scores - self.len_penalty = len_penalty - self.unk_penalty = unk_penalty - self.temperature = temperature - self.match_source_len = match_source_len - - if no_repeat_ngram_size > 0: - self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) - else: - self.repeat_ngram_blocker = None - - assert temperature > 0, "--temperature must be greater than 0" - - self.search = ( - search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy - ) - # We only need to set src_lengths in LengthConstrainedBeamSearch. - # As a module attribute, setting it would break in multithread - # settings when the model is shared. - self.should_set_src_lengths = ( - hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths - ) - - self.model.eval() - - self.lm_model = lm_model - self.lm_weight = lm_weight - if self.lm_model is not None: - self.lm_model.eval() - - def cuda(self): - self.model.cuda() - return self - - @torch.no_grad() - def forward( - self, - sample: Dict[str, Dict[str, Tensor]], - prefix_tokens: Optional[Tensor] = None, - bos_token: Optional[int] = None, - ): - """Generate a batch of translations. - - Args: - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - bos_token (int, optional): beginning of sentence token - (default: self.eos) - """ - return self._generate(sample, prefix_tokens, bos_token=bos_token) - - # TODO(myleott): unused, deprecate after pytorch-translate migration - def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None): - """Iterate over a batched dataset and yield individual translations. - Args: - cuda (bool, optional): use GPU for generation - timer (StopwatchMeter, optional): time generations - """ - for sample in data_itr: - s = utils.move_to_cuda(sample) if cuda else sample - if "net_input" not in s: - continue - input = s["net_input"] - # model.forward normally channels prev_output_tokens into the decoder - # separately, but SequenceGenerator directly calls model.encoder - encoder_input = { - k: v for k, v in input.items() if k != "prev_output_tokens" - } - if timer is not None: - timer.start() - with torch.no_grad(): - hypos = self.generate(encoder_input) - if timer is not None: - timer.stop(sum(len(h[0]["tokens"]) for h in hypos)) - for i, id in enumerate(s["id"].data): - # remove padding - src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad) - ref = ( - utils.strip_pad(s["target"].data[i, :], self.pad) - if s["target"] is not None - else None - ) - yield id, src, ref, hypos[i] - - @torch.no_grad() - def generate( - self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs - ) -> List[List[Dict[str, Tensor]]]: - """Generate translations. Match the api of other fairseq generators. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - constraints (torch.LongTensor, optional): force decoder to include - the list of constraints - bos_token (int, optional): beginning of sentence token - (default: self.eos) - """ - return self._generate(sample, **kwargs) - - def _generate( - self, - sample: Dict[str, Dict[str, Tensor]], - prefix_tokens: Optional[Tensor] = None, - constraints: Optional[Tensor] = None, - bos_token: Optional[int] = None, - ): - incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(self.model.models_size) - ], - ) - net_input = sample["net_input"] - - if "src_tokens" in net_input: - src_tokens = net_input["src_tokens"] - # length of the source text being the character length except EndOfSentence and pad - src_lengths = ( - (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) - ) - elif "source" in net_input: - src_tokens = net_input["source"] - src_lengths = ( - net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) - if net_input["padding_mask"] is not None - else torch.tensor(src_tokens.size(-1)).to(src_tokens) - ) - elif "features" in net_input: - src_tokens = net_input["features"] - src_lengths = ( - net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) - if net_input["padding_mask"] is not None - else torch.tensor(src_tokens.size(-1)).to(src_tokens) - ) - else: - raise Exception( - "expected src_tokens or source in net input. input keys: " - + str(net_input.keys()) - ) - - # bsz: total number of sentences in beam - # Note that src_tokens may have more than 2 dimensions (i.e. audio features) - bsz, src_len = src_tokens.size()[:2] - beam_size = self.beam_size - - if constraints is not None and not self.search.supports_constraints: - raise NotImplementedError( - "Target-side constraints were provided, but search method doesn't support them" - ) - - # Initialize constraints, when active - self.search.init_constraints(constraints, beam_size) - - max_len: int = -1 - if self.match_source_len: - max_len = src_lengths.max().item() - else: - max_len = min( - int(self.max_len_a * src_len + self.max_len_b), - self.max_len - 1, - ) - assert ( - self.min_len <= max_len - ), "min_len cannot be larger than max_len, please adjust these!" - # compute the encoder output for each beam - with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"): - encoder_outs = self.model.forward_encoder(net_input) - - # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores - new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) - new_order = new_order.to(src_tokens.device).long() - encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order) - # ensure encoder_outs is a List. - assert encoder_outs is not None - - # initialize buffers - scores = ( - torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() - ) # +1 for eos; pad is never chosen for scoring - tokens = ( - torch.zeros(bsz * beam_size, max_len + 2) - .to(src_tokens) - .long() - .fill_(self.pad) - ) # +2 for eos and pad - tokens[:, 0] = self.eos if bos_token is None else bos_token - attn: Optional[Tensor] = None - - # A list that indicates candidates that should be ignored. - # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then cands_to_ignore would mark 2 positions as being ignored, - # so that we only finalize the remaining 3 samples. - cands_to_ignore = ( - torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) - ) # forward and backward-compatible False mask - - # list of completed sentences - finalized = torch.jit.annotate( - List[List[Dict[str, Tensor]]], - [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], - ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step - - # a boolean array indicating if the sentence at the index is finished or not - finished = [False for i in range(bsz)] - num_remaining_sent = bsz # number of sentences remaining - - # number of candidate hypos per step - cand_size = 2 * beam_size # 2 x beam size in case half are EOS - - # offset arrays for converting between different indexing schemes - bbsz_offsets = ( - (torch.arange(0, bsz) * beam_size) - .unsqueeze(1) - .type_as(tokens) - .to(src_tokens.device) - ) - cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) - - reorder_state: Optional[Tensor] = None - batch_idxs: Optional[Tensor] = None - - original_batch_idxs: Optional[Tensor] = None - if "id" in sample and isinstance(sample["id"], Tensor): - original_batch_idxs = sample["id"] - else: - original_batch_idxs = torch.arange(0, bsz).type_as(tokens) - - for step in range(max_len + 1): # one extra step for EOS marker - # reorder decoder internal states based on the prev choice of beams - if reorder_state is not None: - if batch_idxs is not None: - # update beam indices to take into account removed sentences - corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( - batch_idxs - ) - reorder_state.view(-1, beam_size).add_( - corr.unsqueeze(-1) * beam_size - ) - original_batch_idxs = original_batch_idxs[batch_idxs] - self.model.reorder_incremental_state(incremental_states, reorder_state) - encoder_outs = self.model.reorder_encoder_out( - encoder_outs, reorder_state - ) - with torch.autograd.profiler.record_function( - "EnsembleModel: forward_decoder" - ): - lprobs, avg_attn_scores = self.model.forward_decoder( - tokens[:, : step + 1], - encoder_outs, - incremental_states, - self.temperature, - ) - - if self.lm_model is not None: - lm_out = self.lm_model(tokens[:, : step + 1]) - probs = self.lm_model.get_normalized_probs( - lm_out, log_probs=True, sample=None - ) - probs = probs[:, -1, :] * self.lm_weight - lprobs += probs - - lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) - - lprobs[:, self.pad] = -math.inf # never select pad - lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - - # handle max length constraint - if step >= max_len: - lprobs[:, : self.eos] = -math.inf - lprobs[:, self.eos + 1 :] = -math.inf - - # handle prefix tokens (possibly with different lengths) - if ( - prefix_tokens is not None - and step < prefix_tokens.size(1) - and step < max_len - ): - lprobs, tokens, scores = self._prefix_tokens( - step, lprobs, scores, tokens, prefix_tokens, beam_size - ) - elif step < self.min_len: - # minimum length constraint (does not apply if using prefix_tokens) - lprobs[:, self.eos] = -math.inf - - # Record attention scores, only support avg_attn_scores is a Tensor - if avg_attn_scores is not None: - if attn is None: - attn = torch.empty( - bsz * beam_size, avg_attn_scores.size(1), max_len + 2 - ).to(scores) - attn[:, :, step + 1].copy_(avg_attn_scores) - - scores = scores.type_as(lprobs) - eos_bbsz_idx = torch.empty(0).to( - tokens - ) # indices of hypothesis ending with eos (finished sentences) - eos_scores = torch.empty(0).to( - scores - ) # scores of hypothesis ending with eos (finished sentences) - - if self.should_set_src_lengths: - self.search.set_src_lengths(src_lengths) - - if self.repeat_ngram_blocker is not None: - lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) - - # Shape: (batch, cand_size) - cand_scores, cand_indices, cand_beams = self.search.step( - step, - lprobs.view(bsz, -1, self.vocab_size), - scores.view(bsz, beam_size, -1)[:, :, :step], - tokens[:, : step + 1], - original_batch_idxs, - ) - - # cand_bbsz_idx contains beam indices for the top candidate - # hypotheses, with a range of values: [0, bsz*beam_size), - # and dimensions: [bsz, cand_size] - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - # finalize hypotheses that end in eos - # Shape of eos_mask: (batch size, beam size) - eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) - eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) - - # only consider eos when it's among the top beam_size indices - # Now we know what beam item(s) to finish - # Shape: 1d list of absolute-numbered - eos_bbsz_idx = torch.masked_select( - cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents: List[int] = [] - if eos_bbsz_idx.numel() > 0: - eos_scores = torch.masked_select( - cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents = self.finalize_hypos( - step, - eos_bbsz_idx, - eos_scores, - tokens, - scores, - finalized, - finished, - beam_size, - attn, - src_lengths, - max_len, - ) - num_remaining_sent -= len(finalized_sents) - - assert num_remaining_sent >= 0 - if num_remaining_sent == 0: - break - if self.search.stop_on_max_len and step >= max_len: - break - assert step < max_len, f"{step} < {max_len}" - - # Remove finalized sentences (ones for which {beam_size} - # finished hypotheses have been generated) from the batch. - if len(finalized_sents) > 0: - new_bsz = bsz - len(finalized_sents) - - # construct batch_idxs which holds indices of batches to keep for the next pass - batch_mask = torch.ones( - bsz, dtype=torch.bool, device=cand_indices.device - ) - batch_mask[finalized_sents] = False - # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it - batch_idxs = torch.arange( - bsz, device=cand_indices.device - ).masked_select(batch_mask) - - # Choose the subset of the hypothesized constraints that will continue - self.search.prune_sentences(batch_idxs) - - eos_mask = eos_mask[batch_idxs] - cand_beams = cand_beams[batch_idxs] - bbsz_offsets.resize_(new_bsz, 1) - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - cand_scores = cand_scores[batch_idxs] - cand_indices = cand_indices[batch_idxs] - - if prefix_tokens is not None: - prefix_tokens = prefix_tokens[batch_idxs] - src_lengths = src_lengths[batch_idxs] - cands_to_ignore = cands_to_ignore[batch_idxs] - - scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - if attn is not None: - attn = attn.view(bsz, -1)[batch_idxs].view( - new_bsz * beam_size, attn.size(1), -1 - ) - bsz = new_bsz - else: - batch_idxs = None - - # Set active_mask so that values > cand_size indicate eos hypos - # and values < cand_size indicate candidate active hypos. - # After, the min values per row are the top candidate active hypos - - # Rewrite the operator since the element wise or is not supported in torchscript. - - eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) - active_mask = torch.add( - eos_mask.type_as(cand_offsets) * cand_size, - cand_offsets[: eos_mask.size(1)], - ) - - # get the top beam_size active hypotheses, which are just - # the hypos with the smallest values in active_mask. - # {active_hypos} indicates which {beam_size} hypotheses - # from the list of {2 * beam_size} candidates were - # selected. Shapes: (batch size, beam size) - new_cands_to_ignore, active_hypos = torch.topk( - active_mask, k=beam_size, dim=1, largest=False - ) - - # update cands_to_ignore to ignore any finalized hypos. - cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] - # Make sure there is at least one active item for each sentence in the batch. - assert (~cands_to_ignore).any(dim=1).all() - - # update cands_to_ignore to ignore any finalized hypos - - # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam - # can be selected more than once). - active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) - active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) - - active_bbsz_idx = active_bbsz_idx.view(-1) - active_scores = active_scores.view(-1) - - # copy tokens and scores for active hypotheses - - # Set the tokens for each beam (can select the same row more than once) - tokens[:, : step + 1] = torch.index_select( - tokens[:, : step + 1], dim=0, index=active_bbsz_idx - ) - # Select the next token for each of them - tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( - cand_indices, dim=1, index=active_hypos - ) - if step > 0: - scores[:, :step] = torch.index_select( - scores[:, :step], dim=0, index=active_bbsz_idx - ) - scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( - cand_scores, dim=1, index=active_hypos - ) - - # Update constraints based on which candidates were selected for the next beam - self.search.update_constraints(active_hypos) - - # copy attention for active hypotheses - if attn is not None: - attn[:, :, : step + 2] = torch.index_select( - attn[:, :, : step + 2], dim=0, index=active_bbsz_idx - ) - - # reorder incremental state in decoder - reorder_state = active_bbsz_idx - - # sort by score descending - for sent in range(len(finalized)): - scores = torch.tensor( - [float(elem["score"].item()) for elem in finalized[sent]] - ) - _, sorted_scores_indices = torch.sort(scores, descending=True) - finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] - finalized[sent] = torch.jit.annotate( - List[Dict[str, Tensor]], finalized[sent] - ) - return finalized - - def _prefix_tokens( - self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int - ): - """Handle prefix tokens""" - prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) - prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - prefix_mask = prefix_toks.ne(self.pad) - lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs) - lprobs[prefix_mask] = lprobs[prefix_mask].scatter( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] - ) - # if prefix includes eos, then we should make sure tokens and - # scores are the same across all beams - eos_mask = prefix_toks.eq(self.eos) - if eos_mask.any(): - # validate that the first beam matches the prefix - first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ - :, 0, 1 : step + 1 - ] - eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] - target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] - assert (first_beam == target_prefix).all() - - # copy tokens, scores and lprobs from the first beam to all beams - tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) - scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) - lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) - return lprobs, tokens, scores - - def replicate_first_beam(self, tensor, mask, beam_size: int): - tensor = tensor.view(-1, beam_size, tensor.size(-1)) - tensor[mask] = tensor[mask][:, :1, :] - return tensor.view(-1, tensor.size(-1)) - - def finalize_hypos( - self, - step: int, - bbsz_idx, - eos_scores, - tokens, - scores, - finalized: List[List[Dict[str, Tensor]]], - finished: List[bool], - beam_size: int, - attn: Optional[Tensor], - src_lengths, - max_len: int, - ): - """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. - A sentence is finalized when {beam_size} finished items have been collected for it. - - Returns number of sentences (not beam items) being finalized. - These will be removed from the batch and not processed further. - Args: - bbsz_idx (Tensor): - """ - assert bbsz_idx.numel() == eos_scores.numel() - - # clone relevant token and attention tensors. - # tokens is (batch * beam, max_len). So the index_select - # gets the newly EOS rows, then selects cols 1..{step + 2} - tokens_clone = tokens.index_select(0, bbsz_idx)[ - :, 1 : step + 2 - ] # skip the first index, which is EOS - - tokens_clone[:, step] = self.eos - attn_clone = ( - attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] - if attn is not None - else None - ) - - # compute scores per token position - pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] - pos_scores[:, step] = eos_scores - # convert from cumulative to per-position scores - pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] - - # normalize sentence-level scores - if self.normalize_scores: - eos_scores /= (step + 1) ** self.len_penalty - - # cum_unfin records which sentences in the batch are finished. - # It helps match indexing between (a) the original sentences - # in the batch and (b) the current, possibly-reduced set of - # sentences. - cum_unfin: List[int] = [] - prev = 0 - for f in finished: - if f: - prev += 1 - else: - cum_unfin.append(prev) - cum_fin_tensor = torch.tensor(cum_unfin, dtype=torch.int).to(bbsz_idx) - - unfin_idx = torch.div(bbsz_idx, beam_size, rounding_mode="trunc") - sent = unfin_idx + torch.index_select(cum_fin_tensor, 0, unfin_idx) - - # Create a set of "{sent}{unfin_idx}", where - # "unfin_idx" is the index in the current (possibly reduced) - # list of sentences, and "sent" is the index in the original, - # unreduced batch - # For every finished beam item - # sentence index in the current (possibly reduced) batch - seen = (sent << 32) + unfin_idx - unique_seen: List[int] = torch.unique(seen).tolist() - - if self.match_source_len: - condition = step > torch.index_select(src_lengths, 0, unfin_idx) - eos_scores = torch.where(condition, torch.tensor(-math.inf), eos_scores) - sent_list: List[int] = sent.tolist() - for i in range(bbsz_idx.size()[0]): - # An input sentence (among those in a batch) is finished when - # beam_size hypotheses have been collected for it - if len(finalized[sent_list[i]]) < beam_size: - if attn_clone is not None: - # remove padding tokens from attn scores - hypo_attn = attn_clone[i] - else: - hypo_attn = torch.empty(0) - - finalized[sent_list[i]].append( - { - "tokens": tokens_clone[i], - "score": eos_scores[i], - "attention": hypo_attn, # src_len x tgt_len - "alignment": torch.empty(0), - "positional_scores": pos_scores[i], - } - ) - - newly_finished: List[int] = [] - for unique_s in unique_seen: - # check termination conditions for this sentence - unique_sent: int = unique_s >> 32 - unique_unfin_idx: int = unique_s - (unique_sent << 32) - - if not finished[unique_sent] and self.is_finished( - step, unique_unfin_idx, max_len, len(finalized[unique_sent]), beam_size - ): - finished[unique_sent] = True - newly_finished.append(unique_unfin_idx) - - return newly_finished - - def is_finished( - self, - step: int, - unfin_idx: int, - max_len: int, - finalized_sent_len: int, - beam_size: int, - ): - """ - Check whether decoding for a sentence is finished, which - occurs when the list of finalized sentences has reached the - beam size, or when we reach the maximum length. - """ - assert finalized_sent_len <= beam_size - if finalized_sent_len == beam_size or step == max_len: - return True - return False - - -class EnsembleModel(nn.Module): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__() - self.models_size = len(models) - # method '__len__' is not supported in ModuleList for torch script - self.single_model = models[0] - self.models = nn.ModuleList(models) - - self.has_incremental: bool = False - if all( - hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) - for m in models - ): - self.has_incremental = True - - def forward(self): - pass - - def has_encoder(self): - return hasattr(self.single_model, "encoder") - - def has_incremental_states(self): - return self.has_incremental - - def max_decoder_positions(self): - return min( - [ - m.max_decoder_positions() - for m in self.models - if hasattr(m, "max_decoder_positions") - ] - + [sys.maxsize] - ) - - def set_decoder_beam_size(self, beam_size): - """Set beam size for efficient beamable enc-dec attention.""" - if beam_size > 1: - for model in self.models: - if hasattr(model, "set_beam_size"): - model.set_beam_size(beam_size) - - @torch.jit.export - def forward_encoder(self, net_input: Dict[str, Tensor]): - if not self.has_encoder(): - return None - return [model.encoder.forward_torchscript(net_input) for model in self.models] - - @torch.jit.export - def forward_decoder( - self, - tokens, - encoder_outs: List[Dict[str, List[Tensor]]], - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], - temperature: float = 1.0, - ): - log_probs = [] - avg_attn: Optional[Tensor] = None - encoder_out: Optional[Dict[str, List[Tensor]]] = None - for i, model in enumerate(self.models): - if self.has_encoder(): - encoder_out = encoder_outs[i] - # decode each model - if self.has_incremental_states(): - decoder_out = model.decoder.forward( - tokens, - encoder_out=encoder_out, - incremental_state=incremental_states[i], - ) - else: - if hasattr(model, "decoder"): - decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out) - else: - decoder_out = model.forward(tokens) - - attn: Optional[Tensor] = None - decoder_len = len(decoder_out) - if decoder_len > 1 and decoder_out[1] is not None: - if isinstance(decoder_out[1], Tensor): - attn = decoder_out[1] - else: - attn_holder = decoder_out[1]["attn"] - if isinstance(attn_holder, Tensor): - attn = attn_holder - elif attn_holder is not None: - attn = attn_holder[0] - if attn is not None: - attn = attn[:, -1, :] - - decoder_out_tuple = ( - decoder_out[0][:, -1:, :].div_(temperature), - None if decoder_len <= 1 else decoder_out[1], - ) - probs = model.get_normalized_probs( - decoder_out_tuple, log_probs=True, sample=None - ) - probs = probs[:, -1, :] - if self.models_size == 1: - return probs, attn - - log_probs.append(probs) - if attn is not None: - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - - avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( - self.models_size - ) - - if avg_attn is not None: - avg_attn.div_(self.models_size) - return avg_probs, avg_attn - - @torch.jit.export - def reorder_encoder_out( - self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order - ): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - new_outs: List[Dict[str, List[Tensor]]] = [] - if not self.has_encoder(): - return new_outs - for i, model in enumerate(self.models): - assert encoder_outs is not None - new_outs.append( - model.encoder.reorder_encoder_out(encoder_outs[i], new_order) - ) - return new_outs - - @torch.jit.export - def reorder_incremental_state( - self, - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], - new_order, - ): - if not self.has_incremental_states(): - return - for i, model in enumerate(self.models): - model.decoder.reorder_incremental_state_scripting( - incremental_states[i], new_order - ) - - -class SequenceGeneratorWithAlignment(SequenceGenerator): - def __init__( - self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs - ): - """Generates translations of a given source sentence. - - Produces alignments following "Jointly Learning to Align and - Translate with Transformer Models" (Garg et al., EMNLP 2019). - - Args: - left_pad_target (bool, optional): Whether or not the - hypothesis should be left padded or not when they are - teacher forced for generating alignments. - """ - super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs) - self.left_pad_target = left_pad_target - - if print_alignment == "hard": - self.extract_alignment = utils.extract_hard_alignment - elif print_alignment == "soft": - self.extract_alignment = utils.extract_soft_alignment - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - finalized = super()._generate(sample, **kwargs) - - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.shape[0] - beam_size = self.beam_size - ( - src_tokens, - src_lengths, - prev_output_tokens, - tgt_tokens, - ) = self._prepare_batch_for_alignment(sample, finalized) - if any(getattr(m, "full_context_alignment", False) for m in self.model.models): - attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens) - else: - attn = [ - finalized[i // beam_size][i % beam_size]["attention"].transpose(1, 0) - for i in range(bsz * beam_size) - ] - - if src_tokens.device != "cpu": - src_tokens = src_tokens.to("cpu") - tgt_tokens = tgt_tokens.to("cpu") - attn = [i.to("cpu") for i in attn] - - # Process the attn matrix to extract hard alignments. - for i in range(bsz * beam_size): - alignment = self.extract_alignment( - attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos - ) - finalized[i // beam_size][i % beam_size]["alignment"] = alignment - return finalized - - def _prepare_batch_for_alignment(self, sample, hypothesis): - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.shape[0] - src_tokens = ( - src_tokens[:, None, :] - .expand(-1, self.beam_size, -1) - .contiguous() - .view(bsz * self.beam_size, -1) - ) - src_lengths = sample["net_input"]["src_lengths"] - src_lengths = ( - src_lengths[:, None] - .expand(-1, self.beam_size) - .contiguous() - .view(bsz * self.beam_size) - ) - prev_output_tokens = data_utils.collate_tokens( - [beam["tokens"] for example in hypothesis for beam in example], - self.pad, - self.eos, - self.left_pad_target, - move_eos_to_beginning=True, - ) - tgt_tokens = data_utils.collate_tokens( - [beam["tokens"] for example in hypothesis for beam in example], - self.pad, - self.eos, - self.left_pad_target, - move_eos_to_beginning=False, - ) - return src_tokens, src_lengths, prev_output_tokens, tgt_tokens - - -class EnsembleModelWithAlignment(EnsembleModel): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__(models) - - def forward_align(self, src_tokens, src_lengths, prev_output_tokens): - avg_attn = None - for model in self.models: - decoder_out = model(src_tokens, src_lengths, prev_output_tokens) - attn = decoder_out[1]["attn"][0] - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - if len(self.models) > 1: - avg_attn.div_(len(self.models)) - return avg_attn diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/sequence_scorer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/sequence_scorer.py deleted file mode 100644 index 411d4df4445ef8dd3f1907ad56f9de6943d1fed8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/sequence_scorer.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import sys - -import torch -from fairseq import utils - - -class SequenceScorer(object): - """Scores the target for a given source sentence.""" - - def __init__( - self, - tgt_dict, - softmax_batch=None, - compute_alignment=False, - eos=None, - symbols_to_strip_from_output=None, - ): - self.pad = tgt_dict.pad() - self.eos = tgt_dict.eos() if eos is None else eos - self.softmax_batch = softmax_batch or sys.maxsize - assert self.softmax_batch > 0 - self.compute_alignment = compute_alignment - self.symbols_to_strip_from_output = ( - symbols_to_strip_from_output.union({self.eos}) - if symbols_to_strip_from_output is not None - else {self.eos} - ) - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - """Score a batch of translations.""" - net_input = sample["net_input"] - - def batch_for_softmax(dec_out, target): - # assumes decoder_out[0] is the only thing needed (may not be correct for future models!) - first, rest = dec_out[0], dec_out[1:] - bsz, tsz, dim = first.shape - if bsz * tsz < self.softmax_batch: - yield dec_out, target, True - else: - flat = first.contiguous().view(1, -1, dim) - flat_tgt = target.contiguous().view(flat.shape[:-1]) - s = 0 - while s < flat.size(1): - e = s + self.softmax_batch - yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False - s = e - - def gather_target_probs(probs, target): - probs = probs.gather( - dim=2, - index=target.unsqueeze(-1), - ) - return probs - - orig_target = sample["target"] - - # compute scores for each model in the ensemble - avg_probs = None - avg_attn = None - for model in models: - model.eval() - decoder_out = model(**net_input) - attn = decoder_out[1] if len(decoder_out) > 1 else None - if type(attn) is dict: - attn = attn.get("attn", None) - - batched = batch_for_softmax(decoder_out, orig_target) - probs, idx = None, 0 - for bd, tgt, is_single in batched: - sample["target"] = tgt - curr_prob = model.get_normalized_probs( - bd, log_probs=len(models) == 1, sample=sample - ).data - if is_single: - probs = gather_target_probs(curr_prob, orig_target) - else: - if probs is None: - probs = curr_prob.new(orig_target.numel()) - step = curr_prob.size(0) * curr_prob.size(1) - end = step + idx - tgt_probs = gather_target_probs( - curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt - ) - probs[idx:end] = tgt_probs.view(-1) - idx = end - sample["target"] = orig_target - - probs = probs.view(sample["target"].shape) - - if avg_probs is None: - avg_probs = probs - else: - avg_probs.add_(probs) - if attn is not None: - if torch.is_tensor(attn): - attn = attn.data - else: - attn = attn[0] - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - if len(models) > 1: - avg_probs.div_(len(models)) - avg_probs.log_() - if avg_attn is not None: - avg_attn.div_(len(models)) - - bsz = avg_probs.size(0) - hypos = [] - start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz - for i in range(bsz): - # remove padding from ref - ref = ( - utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad) - if sample["target"] is not None - else None - ) - tgt_len = ref.numel() - avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len] - score_i = avg_probs_i.sum() / tgt_len - if avg_attn is not None: - avg_attn_i = avg_attn[i] - if self.compute_alignment: - alignment = utils.extract_hard_alignment( - avg_attn_i, - sample["net_input"]["src_tokens"][i], - sample["target"][i], - self.pad, - self.eos, - ) - else: - alignment = None - else: - avg_attn_i = alignment = None - hypos.append( - [ - { - "tokens": ref, - "score": score_i, - "attention": avg_attn_i, - "alignment": alignment, - "positional_scores": avg_probs_i, - } - ] - ) - return hypos diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/speech_generator.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/speech_generator.py deleted file mode 100644 index 3a31979c1d73f68b16e8f696c94e68f72b1c9578..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/speech_generator.py +++ /dev/null @@ -1,230 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig - - -class SpeechGenerator(object): - def __init__(self, model, vocoder, data_cfg: S2TDataConfig): - self.model = model - self.vocoder = vocoder - stats_npz_path = data_cfg.global_cmvn_stats_npz - self.gcmvn_stats = None - if stats_npz_path is not None: - self.gcmvn_stats = np.load(stats_npz_path) - - def gcmvn_denormalize(self, x): - # x: B x T x C - if self.gcmvn_stats is None: - return x - mean = torch.from_numpy(self.gcmvn_stats["mean"]).to(x) - std = torch.from_numpy(self.gcmvn_stats["std"]).to(x) - assert len(x.shape) == 3 and mean.shape[0] == std.shape[0] == x.shape[2] - x = x * std.view(1, 1, -1).expand_as(x) - return x + mean.view(1, 1, -1).expand_as(x) - - def get_waveform(self, feat): - # T x C -> T - return None if self.vocoder is None else self.vocoder(feat).squeeze(0) - - -class AutoRegressiveSpeechGenerator(SpeechGenerator): - def __init__( - self, - model, - vocoder, - data_cfg, - max_iter: int = 6000, - eos_prob_threshold: float = 0.5, - ): - super().__init__(model, vocoder, data_cfg) - self.max_iter = max_iter - self.eos_prob_threshold = eos_prob_threshold - - @torch.no_grad() - def generate(self, model, sample, has_targ=False, **kwargs): - model.eval() - - src_tokens = sample["net_input"]["src_tokens"] - src_lengths = sample["net_input"]["src_lengths"] - bsz, src_len = src_tokens.size()[:2] - n_frames_per_step = model.decoder.n_frames_per_step - out_dim = model.decoder.out_dim - raw_dim = out_dim // n_frames_per_step - - # initialize - encoder_out = model.forward_encoder( - src_tokens, src_lengths, speaker=sample["speaker"] - ) - incremental_state = {} - feat, attn, eos_prob = [], [], [] - finished = src_tokens.new_zeros((bsz,)).bool() - out_lens = src_lengths.new_zeros((bsz,)).long().fill_(self.max_iter) - - prev_feat_out = encoder_out["encoder_out"][0].new_zeros(bsz, 1, out_dim) - for step in range(self.max_iter): - cur_out_lens = out_lens.clone() - cur_out_lens.masked_fill_(cur_out_lens.eq(self.max_iter), step + 1) - _, cur_eos_out, cur_extra = model.forward_decoder( - prev_feat_out, - encoder_out=encoder_out, - incremental_state=incremental_state, - target_lengths=cur_out_lens, - speaker=sample["speaker"], - **kwargs - ) - cur_eos_prob = torch.sigmoid(cur_eos_out).squeeze(2) - feat.append(cur_extra["feature_out"]) - attn.append(cur_extra["attn"]) - eos_prob.append(cur_eos_prob) - - cur_finished = cur_eos_prob.squeeze(1) > self.eos_prob_threshold - out_lens.masked_fill_((~finished) & cur_finished, step + 1) - finished = finished | cur_finished - if finished.sum().item() == bsz: - break - prev_feat_out = cur_extra["feature_out"] - - feat = torch.cat(feat, dim=1) - feat = model.decoder.postnet(feat) + feat - eos_prob = torch.cat(eos_prob, dim=1) - attn = torch.cat(attn, dim=2) - alignment = attn.max(dim=1)[1] - - feat = feat.reshape(bsz, -1, raw_dim) - feat = self.gcmvn_denormalize(feat) - - eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) - attn = attn.repeat_interleave(n_frames_per_step, dim=2) - alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) - out_lens = out_lens * n_frames_per_step - - finalized = [ - { - "feature": feat[b, :out_len], - "eos_prob": eos_prob[b, :out_len], - "attn": attn[b, :, :out_len], - "alignment": alignment[b, :out_len], - "waveform": self.get_waveform(feat[b, :out_len]), - } - for b, out_len in zip(range(bsz), out_lens) - ] - - if has_targ: - assert sample["target"].size(-1) == out_dim - tgt_feats = sample["target"].view(bsz, -1, raw_dim) - tgt_feats = self.gcmvn_denormalize(tgt_feats) - tgt_lens = sample["target_lengths"] * n_frames_per_step - for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): - finalized[b]["targ_feature"] = f[:l] - finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) - return finalized - - -class NonAutoregressiveSpeechGenerator(SpeechGenerator): - @torch.no_grad() - def generate(self, model, sample, has_targ=False, **kwargs): - model.eval() - - bsz, max_src_len = sample["net_input"]["src_tokens"].size() - n_frames_per_step = model.encoder.n_frames_per_step - out_dim = model.encoder.out_dim - raw_dim = out_dim // n_frames_per_step - - feat, feat_post, out_lens, log_dur_out, _, _ = model( - src_tokens=sample["net_input"]["src_tokens"], - src_lengths=sample["net_input"]["src_lengths"], - prev_output_tokens=sample["net_input"]["prev_output_tokens"], - incremental_state=None, - target_lengths=sample["target_lengths"], - speaker=sample["speaker"], - ) - if feat_post is not None: - feat = feat_post - - feat = feat.view(bsz, -1, raw_dim) - feat = self.gcmvn_denormalize(feat) - - dur_out = torch.clamp(torch.round(torch.exp(log_dur_out) - 1).long(), min=0) - - def get_dur_plot_data(d): - r = [] - for i, dd in enumerate(d): - r += [i + 1] * dd.item() - return r - - out_lens = out_lens * n_frames_per_step - finalized = [ - { - "feature": feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]), - "waveform": self.get_waveform( - feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]) - ), - "attn": feat.new_tensor(get_dur_plot_data(dur_out[b])), - } - for b, l in zip(range(bsz), out_lens) - ] - - if has_targ: - tgt_feats = sample["target"].view(bsz, -1, raw_dim) - tgt_feats = self.gcmvn_denormalize(tgt_feats) - tgt_lens = sample["target_lengths"] * n_frames_per_step - for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): - finalized[b]["targ_feature"] = f[:l] - finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) - return finalized - - -class TeacherForcingAutoRegressiveSpeechGenerator(AutoRegressiveSpeechGenerator): - @torch.no_grad() - def generate(self, model, sample, has_targ=False, **kwargs): - model.eval() - - src_tokens = sample["net_input"]["src_tokens"] - src_lens = sample["net_input"]["src_lengths"] - prev_out_tokens = sample["net_input"]["prev_output_tokens"] - tgt_lens = sample["target_lengths"] - n_frames_per_step = model.decoder.n_frames_per_step - raw_dim = model.decoder.out_dim // n_frames_per_step - bsz = src_tokens.shape[0] - - feat, eos_prob, extra = model( - src_tokens, - src_lens, - prev_out_tokens, - incremental_state=None, - target_lengths=tgt_lens, - speaker=sample["speaker"], - ) - - attn = extra["attn"] # B x T_s x T_t - alignment = attn.max(dim=1)[1] - feat = feat.reshape(bsz, -1, raw_dim) - feat = self.gcmvn_denormalize(feat) - eos_prob = eos_prob.repeat_interleave(n_frames_per_step, dim=1) - attn = attn.repeat_interleave(n_frames_per_step, dim=2) - alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) - tgt_lens = sample["target_lengths"] * n_frames_per_step - - finalized = [ - { - "feature": feat[b, :tgt_len], - "eos_prob": eos_prob[b, :tgt_len], - "attn": attn[b, :, :tgt_len], - "alignment": alignment[b, :tgt_len], - "waveform": self.get_waveform(feat[b, :tgt_len]), - } - for b, tgt_len in zip(range(bsz), tgt_lens) - ] - - if has_targ: - tgt_feats = sample["target"].view(bsz, -1, raw_dim) - tgt_feats = self.gcmvn_denormalize(tgt_feats) - for b, (f, l) in enumerate(zip(tgt_feats, tgt_lens)): - finalized[b]["targ_feature"] = f[:l] - finalized[b]["targ_waveform"] = self.get_waveform(f[:l]) - return finalized diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/__init__.py deleted file mode 100644 index 9a46b012c573a76e00e489307720fc3fa462c296..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/__init__.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import argparse -import importlib -import os - -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import merge_with_parent -from hydra.core.config_store import ConfigStore - -from .fairseq_task import FairseqTask, LegacyFairseqTask # noqa - - -# register dataclass -TASK_DATACLASS_REGISTRY = {} -TASK_REGISTRY = {} -TASK_CLASS_NAMES = set() - - -def setup_task(cfg: FairseqDataclass, **kwargs): - task = None - task_name = getattr(cfg, "task", None) - - if isinstance(task_name, str): - # legacy tasks - task = TASK_REGISTRY[task_name] - if task_name in TASK_DATACLASS_REGISTRY: - dc = TASK_DATACLASS_REGISTRY[task_name] - cfg = dc.from_namespace(cfg) - else: - task_name = getattr(cfg, "_name", None) - - if task_name and task_name in TASK_DATACLASS_REGISTRY: - dc = TASK_DATACLASS_REGISTRY[task_name] - cfg = merge_with_parent(dc(), cfg) - task = TASK_REGISTRY[task_name] - - assert ( - task is not None - ), f"Could not infer task type from {cfg}. Available argparse tasks: {TASK_REGISTRY.keys()}. Available hydra tasks: {TASK_DATACLASS_REGISTRY.keys()}" - - return task.setup_task(cfg, **kwargs) - - -def register_task(name, dataclass=None): - """ - New tasks can be added to fairseq with the - :func:`~fairseq.tasks.register_task` function decorator. - - For example:: - - @register_task('classification') - class ClassificationTask(FairseqTask): - (...) - - .. note:: - - All Tasks must implement the :class:`~fairseq.tasks.FairseqTask` - interface. - - Args: - name (str): the name of the task - """ - - def register_task_cls(cls): - if name in TASK_REGISTRY: - raise ValueError("Cannot register duplicate task ({})".format(name)) - if not issubclass(cls, FairseqTask): - raise ValueError( - "Task ({}: {}) must extend FairseqTask".format(name, cls.__name__) - ) - if cls.__name__ in TASK_CLASS_NAMES: - raise ValueError( - "Cannot register task with duplicate class name ({})".format( - cls.__name__ - ) - ) - TASK_REGISTRY[name] = cls - TASK_CLASS_NAMES.add(cls.__name__) - - if dataclass is not None and not issubclass(dataclass, FairseqDataclass): - raise ValueError( - "Dataclass {} must extend FairseqDataclass".format(dataclass) - ) - - cls.__dataclass = dataclass - if dataclass is not None: - TASK_DATACLASS_REGISTRY[name] = dataclass - - cs = ConfigStore.instance() - node = dataclass() - node._name = name - cs.store(name=name, group="task", node=node, provider="fairseq") - - return cls - - return register_task_cls - - -def get_task(name): - return TASK_REGISTRY[name] - - -def import_tasks(tasks_dir, namespace): - for file in os.listdir(tasks_dir): - path = os.path.join(tasks_dir, file) - if ( - not file.startswith("_") - and not file.startswith(".") - and (file.endswith(".py") or os.path.isdir(path)) - ): - task_name = file[: file.find(".py")] if file.endswith(".py") else file - importlib.import_module(namespace + "." + task_name) - - # expose `task_parser` for sphinx - if task_name in TASK_REGISTRY: - parser = argparse.ArgumentParser(add_help=False) - group_task = parser.add_argument_group("Task name") - # fmt: off - group_task.add_argument('--task', metavar=task_name, - help='Enable this task with: ``--task=' + task_name + '``') - # fmt: on - group_args = parser.add_argument_group( - "Additional command-line arguments" - ) - TASK_REGISTRY[task_name].add_args(group_args) - globals()[task_name + "_parser"] = parser - - -# automatically import any Python files in the tasks/ directory -tasks_dir = os.path.dirname(__file__) -import_tasks(tasks_dir, "fairseq.tasks") diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/audio_finetuning.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/audio_finetuning.py deleted file mode 100644 index 78e911c6dccf5f07231424eb5f5cc5743b063dd5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/audio_finetuning.py +++ /dev/null @@ -1,341 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -import json -import logging -import os -from argparse import Namespace -from dataclasses import dataclass, field -from typing import Any, Optional - -import torch -from fairseq.data import AddTargetDataset, Dictionary, encoders -from fairseq.data.text_compressor import TextCompressionLevel, TextCompressor -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.configs import GenerationConfig -from fairseq.tasks.audio_pretraining import AudioPretrainingConfig, AudioPretrainingTask - -from .. import utils -from ..logging import metrics -from . import register_task - -logger = logging.getLogger(__name__) - - -class LabelEncoder(object): - def __init__(self, dictionary): - self.dictionary = dictionary - - def __call__(self, label): - return self.dictionary.encode_line( - label, append_eos=False, add_if_not_exist=False - ) - - -def label_len_fn(label): - return len(label.split(" ")) - - -@dataclass -class AudioFinetuningConfig(AudioPretrainingConfig): - # Options for reporting WER metrics during validation. Only applicable to - # Seq2Seq models during fine-tuning - eval_wer: bool = field( - default=False, metadata={"help": "compute WER for Seq2Seq models"} - ) - eval_wer_config: GenerationConfig = field( - default_factory=lambda: GenerationConfig(), - metadata={"help": "beam search config for evaluating wer during training"}, - ) - eval_wer_tokenizer: Any = field( - default=None, - metadata={"help": "tokenizer config for evaluating wer during training"}, - ) - eval_wer_post_process: str = field( - default="letter", - metadata={ - "help": "remove BPE tokens before scoring (can be sentencepiece, letter, and more)" - }, - ) - eval_bleu: bool = field( - default=False, metadata={"help": "evaluation with BLEU scores"} - ) - eval_bleu_detok: Optional[str] = field( - default=None, - metadata={ - "help": "detokenize before computing BLEU (e.g., 'moses'); " - "required if using --eval-bleu; use 'space' to disable " - "detokenization; see fairseq.data.encoders for other options" - }, - ) - eval_bleu_detok_args: str = field( - default="{}", metadata={"help": "args for building the tokenizer, if needed"} - ) - eval_tokenized_bleu: bool = field( - default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"} - ) - eval_bleu_remove_bpe: Optional[str] = field( - default=None, metadata={"help": "remove BPE before computing BLEU"} - ) - eval_bleu_args: str = field( - default="{}", - metadata={ - "help": "generation args for BLUE scoring, e.g., " - '\'{"beam": 4, "lenpen": 0.6}\'' - }, - ) - eval_bleu_print_samples: bool = field( - default=False, metadata={"help": "print sample generations during validation"} - ) - autoregressive: bool = field( - default=False, - metadata={ - "help": "required for autoregressive decoders (like seq2seq models); " - "adds 'prev_output_tokens' to input and appends eos to target" - }, - ) - - -@register_task("audio_finetuning", dataclass=AudioFinetuningConfig) -class AudioFinetuningTask(AudioPretrainingTask): - """ """ - - cfg: AudioFinetuningConfig - - def __init__( - self, - cfg: AudioFinetuningConfig, - ): - super().__init__(cfg) - self.blank_symbol = "" - - self.state.add_factory("target_dictionary", self.load_target_dictionary) - - def load_target_dictionary(self): - if self.cfg.labels: - dict_path = os.path.join(self.cfg.data, f"dict.{self.cfg.labels}.txt") - return Dictionary.load(dict_path) - return None - - def load_dataset( - self, split: str, task_cfg: AudioFinetuningConfig = None, **kwargs - ): - super().load_dataset(split, task_cfg, **kwargs) - - task_cfg = task_cfg or self.cfg - assert task_cfg.labels is not None - text_compression_level = getattr( - TextCompressionLevel, str(self.cfg.text_compression_level) - ) - data_path = self.cfg.data - label_path = os.path.join(data_path, f"{split}.{task_cfg.labels}") - skipped_indices = getattr(self.datasets[split], "skipped_indices", set()) - text_compressor = TextCompressor(level=text_compression_level) - with open(label_path, "r") as f: - labels = [ - text_compressor.compress(l) - for i, l in enumerate(f) - if i not in skipped_indices - ] - - assert len(labels) == len(self.datasets[split]), ( - f"labels length ({len(labels)}) and dataset length " - f"({len(self.datasets[split])}) do not match" - ) - - process_label = LabelEncoder(self.target_dictionary) - - self.datasets[split] = AddTargetDataset( - self.datasets[split], - labels, - pad=self.target_dictionary.pad(), - eos=self.target_dictionary.eos(), - batch_targets=True, - process_label=process_label, - label_len_fn=label_len_fn, - add_to_input=task_cfg.get("autoregressive", False), - text_compression_level=text_compression_level, - ) - - @property - def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self.state.target_dictionary - - def valid_step(self, sample, model, criterion): - loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - if self.cfg.eval_wer and self.cfg.autoregressive: - metrics = self._inference_with_wer(self.sequence_generator, sample, model) - logging_output["_num_char_errors"] = metrics["num_char_errors"] - logging_output["_num_chars"] = metrics["num_chars"] - logging_output["_num_word_errors"] = metrics["num_word_errors"] - logging_output["_num_words"] = metrics["num_words"] - if self.cfg.eval_bleu and self.cfg.autoregressive: - metrics = self._inference_with_bleu(self.sequence_generator, sample, model) - logging_output["_bleu_sys_len"] = metrics.sys_len - logging_output["_bleu_ref_len"] = metrics.ref_len - # we split counts into separate entries so that they can be - # summed efficiently across workers using fast-stat-sync - assert len(metrics.counts) == 4 - for i in range(4): - logging_output[f"_bleu_counts_{i}"] = metrics.counts[i] - logging_output[f"_bleu_totals_{i}"] = metrics.totals[i] - return loss, sample_size, logging_output - - def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): - model = super().build_model(model_cfg, from_checkpoint) - - if self.cfg.eval_wer and self.cfg.autoregressive: - self.sequence_generator = self.build_generator( - [model], - self.cfg.eval_wer_config, - ) - if self.cfg.eval_wer_tokenizer: - self.tokenizer = encoders.build_tokenizer(self.cfg.eval_wer_tokenizer) - else: - self.tokenizer = None - if self.cfg.eval_bleu and self.cfg.autoregressive: - assert self.cfg.eval_bleu_detok is not None, ( - "--eval-bleu-detok is required if using --eval-bleu; " - "try --eval-bleu-detok=moses (or --eval-bleu-detok=space " - "to disable detokenization, e.g., when using sentencepiece)" - ) - detok_args = json.loads(self.cfg.eval_bleu_detok_args) - self.tokenizer = encoders.build_tokenizer( - Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) - ) - gen_args = json.loads(self.cfg.eval_bleu_args) - gen_args = Namespace(**gen_args) - self.sequence_generator = self.build_generator([model], gen_args) - - return model - - def _inference_with_wer(self, generator, sample, model): - import editdistance - - def decode(toks): - s = self.target_dictionary.string( - toks.int().cpu(), - self.cfg.eval_wer_post_process, - escape_unk=True, - ) - if self.tokenizer: - s = self.tokenizer.decode(s) - return s - - num_word_errors, num_char_errors = 0, 0 - num_chars, num_words = 0, 0 - gen_out = self.inference_step(generator, [model], sample, None) - for i in range(len(gen_out)): - hyp = decode(gen_out[i][0]["tokens"]) - ref = decode( - utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), - ) - num_char_errors += editdistance.eval(hyp, ref) - num_chars += len(ref) - hyp_words = hyp.split() - ref_words = ref.split() - num_word_errors += editdistance.eval(hyp_words, ref_words) - num_words += len(ref_words) - - return { - "num_char_errors": num_char_errors, - "num_chars": num_chars, - "num_word_errors": num_word_errors, - "num_words": num_words, - } - - def _inference_with_bleu(self, generator, sample, model): - import sacrebleu - - def decode(toks, is_ref): - s = self.target_dictionary.string( - toks.int().cpu(), - self.cfg.eval_bleu_remove_bpe, - # The default unknown string in fairseq is ``, but - # this is tokenized by sacrebleu as `< unk >`, inflating - # BLEU scores. Instead, we use a somewhat more verbose - # alternative that is unlikely to appear in the real - # reference, but doesn't get split into multiple tokens. - unk_string=("UNKNOWNTOKENINREF" if is_ref else "UNKNOWNTOKENINHYP"), - ) - if self.tokenizer: - s = self.tokenizer.decode(s) - return s - - gen_out = self.inference_step(generator, [model], sample) - hyps, refs = [], [] - for i in range(len(gen_out)): - hyps.append(decode(gen_out[i][0]["tokens"], is_ref=False)) - refs.append( - decode( - utils.strip_pad(sample["target"][i], self.target_dictionary.pad()), - is_ref=True, # don't count as matches to the hypo - ) - ) - if self.cfg.eval_bleu_print_samples: - logger.info("H-{} {}".format(sample["id"][0], hyps[0])) - logger.info("T-{} {}".format(sample["id"][0], refs[0])) - - eval_tokenization = "none" if self.cfg.eval_tokenized_bleu else "13a" - return sacrebleu.corpus_bleu(hyps, [refs], tokenize=eval_tokenization) - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - - if self.cfg.eval_wer: - zero = torch.scalar_tensor(0.0) - num_char_errors = sum( - log.get("_num_char_errors", zero) for log in logging_outputs - ) - num_chars = sum(log.get("_num_chars", zero) for log in logging_outputs) - num_word_errors = sum( - log.get("_num_word_errors", zero) for log in logging_outputs - ) - num_words = sum(log.get("_num_words", zero) for log in logging_outputs) - metrics.log_scalar("_num_char_errors", num_char_errors) - metrics.log_scalar("_num_chars", num_chars) - metrics.log_scalar("_num_word_errors", num_word_errors) - metrics.log_scalar("_num_words", num_words) - if num_chars > 0: - metrics.log_derived( - "uer", - lambda meters: meters["_num_char_errors"].sum - * 100.0 - / meters["_num_chars"].sum - if meters["_num_chars"].sum > 0 - else float("nan"), - ) - if num_words > 0: - metrics.log_derived( - "wer", - lambda meters: meters["_num_word_errors"].sum - * 100.0 - / meters["_num_words"].sum - if meters["_num_words"].sum > 0 - else float("nan"), - ) - if self.cfg.eval_bleu: - len_keys = ["_bleu_sys_len", "_bleu_ref_len"] - count_keys = [f"_bleu_counts_{i}" for i in range(4)] - total_keys = [f"_bleu_totals_{i}" for i in range(4)] - for k in len_keys + count_keys + total_keys: - metrics.log_scalar(k, sum(log.get(k, 0) for log in logging_outputs)) - - import sacrebleu - - metrics.log_derived( - "bleu", - lambda meters: sacrebleu.compute_bleu( - correct=[meters[k].sum for k in count_keys], - total=[meters[k].sum for k in total_keys], - sys_len=meters["_bleu_sys_len"].sum, - ref_len=meters["_bleu_ref_len"].sum, - smooth_method="exp", - ).score, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/audio_pretraining.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/audio_pretraining.py deleted file mode 100644 index 6fe1608519e7e7f11f6c1434374c47f4d19e5063..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/audio_pretraining.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -import logging -import os -import sys -from argparse import Namespace -from dataclasses import dataclass, field -from typing import Optional - -from fairseq.data import BinarizedAudioDataset, FileAudioDataset -from fairseq.data.text_compressor import TextCompressionLevel -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from omegaconf import II, MISSING, OmegaConf - -from . import FairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@dataclass -class InferredW2vConfig: - # The following are needed to precompute mask and mask channel indices - # before model's forward. - mask_length: Optional[int] = II("model.mask_length") - mask_prob: Optional[float] = II("model.mask_prob") - mask_selection: Optional[str] = II("model.mask_selection") - mask_other: Optional[float] = II("model.mask_other") - no_mask_overlap: Optional[bool] = II("model.no_mask_overlap") - mask_min_space: Optional[int] = II("model.mask_min_space") - mask_channel_length: Optional[int] = II("model.mask_channel_length") - mask_channel_prob: Optional[float] = II("model.mask_channel_prob") - mask_channel_selection: Optional[str] = II("model.mask_channel_selection") - mask_channel_other: Optional[float] = II("model.mask_channel_other") - no_mask_channel_overlap: Optional[bool] = II("model.no_mask_channel_overlap") - mask_channel_min_space: Optional[int] = II("model.mask_channel_min_space") - - conv_feature_layers: Optional[str] = II("model.conv_feature_layers") - encoder_embed_dim: Optional[int] = II("model.encoder_embed_dim") - - -@dataclass -class AudioPretrainingConfig(FairseqDataclass): - data: str = field(default=MISSING, metadata={"help": "path to data directory"}) - labels: Optional[str] = field( - default=None, - metadata={"help": "extension of the label file to load, used for fine-tuning"}, - ) - binarized_dataset: bool = field( - default=False, - metadata={ - "help": "if true, loads binarized dataset (useful for very large datasets). " - "See examples/wav2vec/scripts/binarize_manifest.sh" - }, - ) - sample_rate: int = field( - default=16_000, - metadata={ - "help": "target sample rate. audio files will be up/down sampled to this rate" - }, - ) - normalize: bool = field( - default=False, - metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, - ) - enable_padding: bool = field( - default=False, metadata={"help": "pad shorter samples instead of cropping"} - ) - max_sample_size: Optional[int] = field( - default=None, metadata={"help": "max sample size to crop to for batching"} - ) - min_sample_size: Optional[int] = field( - default=None, metadata={"help": "min sample size to skip small examples"} - ) - num_batch_buckets: int = field( - default=0, - metadata={"help": "number of buckets"}, - ) - precompute_mask_indices: bool = field( - default=False, - metadata={ - "help": "flag to compute mask indices in data preparation.", - }, - ) - - inferred_w2v_config: Optional[InferredW2vConfig] = field( - default=None, - metadata={ - "help": "wav2vec 2.0 masking arguments used to pre-compute masks (required for TPU)", - }, - ) - - tpu: bool = II("common.tpu") - text_compression_level: ChoiceEnum([x.name for x in TextCompressionLevel]) = field( - default="none", - metadata={ - "help": "compression level for texts (e.g. audio filenames, " - "target texts): none/low/high (default: none). " - }, - ) - - -@register_task("audio_pretraining", dataclass=AudioPretrainingConfig) -class AudioPretrainingTask(FairseqTask): - """ """ - - cfg: AudioPretrainingConfig - - @classmethod - def setup_task(cls, cfg: AudioPretrainingConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - cfg (AudioPretrainingConfig): configuration of this task - """ - - return cls(cfg) - - def _get_mask_precompute_kwargs(self, cfg): - if self.cfg.precompute_mask_indices or self.cfg.tpu: - assert ( - cfg.inferred_w2v_config is not None - ), "inferred_w2v_config must be set" - return OmegaConf.to_container( - cfg.inferred_w2v_config, resolve=True, enum_to_str=True - ) - else: - return {} - - def load_dataset(self, split: str, task_cfg: FairseqDataclass = None, **kwargs): - data_path = self.cfg.data - task_cfg = task_cfg or self.cfg - - # upgrade old task - if isinstance(task_cfg, Namespace): - if not hasattr(task_cfg, "autoregressive"): - task_cfg.autoregressive = not task_cfg.criterion == "ctc" - - text_compression_level = getattr( - TextCompressionLevel, str(self.cfg.text_compression_level) - ) - if getattr(task_cfg, "binarized_dataset", False): - self.datasets[split] = BinarizedAudioDataset( - data_path, - split=split, - sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), - max_sample_size=self.cfg.max_sample_size, - min_sample_size=self.cfg.min_sample_size, - pad=task_cfg.labels is not None or task_cfg.enable_padding, - normalize=task_cfg.normalize, - num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), - compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu), - **self._get_mask_precompute_kwargs(task_cfg), - ) - else: - manifest_path = os.path.join(data_path, "{}.tsv".format(split)) - - self.datasets[split] = FileAudioDataset( - manifest_path=manifest_path, - sample_rate=task_cfg.get("sample_rate", self.cfg.sample_rate), - max_sample_size=self.cfg.max_sample_size, - min_sample_size=self.cfg.min_sample_size, - pad=task_cfg.labels is not None or task_cfg.enable_padding, - normalize=task_cfg.normalize, - num_buckets=self.cfg.num_batch_buckets or int(self.cfg.tpu), - compute_mask_indices=(self.cfg.precompute_mask_indices or self.cfg.tpu), - text_compression_level=text_compression_level, - **self._get_mask_precompute_kwargs(task_cfg), - ) - - if self.cfg.tpu and task_cfg.inferred_w2v_config.mask_channel_prob == 0.0: - logger.info( - "Pretraining on TPUs may suffer convergence " - "issues when training with `mask_channel_prob` value of " - "0. You may want to set this to a low value close to 0." - ) - - @property - def source_dictionary(self): - return None - - @property - def target_dictionary(self): - return None - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return sys.maxsize, sys.maxsize - - def build_model(self, model_cfg: FairseqDataclass, from_checkpoint=False): - model = super().build_model(model_cfg, from_checkpoint) - - actualized_cfg = getattr(model, "cfg", None) - if actualized_cfg is not None: - # if "w2v_args" in actualized_cfg: - if hasattr(actualized_cfg, "w2v_args"): - model_cfg.w2v_args = actualized_cfg.w2v_args - - return model diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/cross_lingual_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/cross_lingual_lm.py deleted file mode 100644 index 950097c2ebfe361a407e44a32bfd0aa2f0f5cfc1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/cross_lingual_lm.py +++ /dev/null @@ -1,190 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import itertools -import logging -import os -from collections import OrderedDict - -import numpy as np -from fairseq import tokenizer, utils -from fairseq.data import ConcatDataset, Dictionary, TokenBlockDataset, data_utils -from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset -from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary -from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("cross_lingual_lm") -class CrossLingualLMTask(LegacyFairseqTask): - """ - Task for training cross-lingual language models. - - For more details look at: https://arxiv.org/pdf/1901.07291.pdf - - Args: - dictionary (Dictionary): the dictionary for the input of the task - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "data", - help="colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner", - ) - parser.add_argument( - "--tokens-per-sample", - default=512, - type=int, - help="max number of total tokens over all segments" " per sample", - ) - parser.add_argument( - "--monolingual-langs", - default="en", - type=str, - help="comma separated list of languages for which we" - " want to train XLM on", - ) - parser.add_argument( - "--shuffle", - action="store_true", - help="shuffle each monolingual dataset while" " training", - ) - - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed - self.distributed_world_size = args.distributed_world_size - self.langs2id = self._lang_to_id(args.monolingual_langs) - - def _lang_to_id(self, languages: str): - """ - Build a map from languages to ids. These ids are used as segment labels - for cross-lingual LM training. - """ - lang2id = {} - langs = [l.strip() for l in languages.split(",")] - for id, lang in enumerate(langs): - lang2id[lang] = id - return lang2id - - @classmethod - def load_dictionary(cls, filename): - return MaskedLMDictionary.load(filename) - - @classmethod - def build_dictionary( - cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 - ): - d = MaskedLMDictionary() - for filename in filenames: - Dictionary.add_file_to_dictionary( - filename, d, tokenizer.tokenize_line, workers - ) - d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) - return d - - @property - def target_dictionary(self): - return self.dictionary - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task.""" - dictionary = MaskedLMDictionary.load(os.path.join(args.data, "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - return cls(args, dictionary) - - def _load_single_lang_dataset(self, split, epoch): - loaded_datasets = [] - - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - for k in itertools.count(): - split_k = split + (str(k) if k > 0 else "") - path = os.path.join(data_path, split_k) - - ds = data_utils.load_indexed_dataset( - path, self.dictionary, self.args.dataset_impl - ) - if ds is None: - if k > 0: - break - else: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, data_path) - ) - - # Since we append each block with the classification_token, - # we need to effectively create blocks of length - # tokens_per_sample-1 - loaded_datasets.append( - TokenBlockDataset( - ds, - ds.sizes, - self.args.tokens_per_sample - 1, - pad=self.dictionary.pad(), - eos=self.dictionary.eos(), - ) - ) - - logger.info( - "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) - ) - - if len(loaded_datasets) == 1: - dataset = loaded_datasets[0] - sizes = dataset.sizes - else: - dataset = ConcatDataset(loaded_datasets) - sizes = np.concatenate([ds.sizes for ds in loaded_datasets]) - - return dataset, sizes - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - dataset_map = OrderedDict() - - for lang in self.langs2id.keys(): - # Datasets are expected to be in "split.lang" format (Eg: train.en) - language_split = "{}.{}".format(split, lang) - - block_dataset, sizes = self._load_single_lang_dataset( - split=language_split, epoch=epoch - ) - - dataset_map[lang] = MaskedLMDataset( - dataset=block_dataset, - sizes=sizes, - vocab=self.dictionary, - pad_idx=self.dictionary.pad(), - mask_idx=self.dictionary.mask(), - classif_token_idx=self.dictionary.eos(), - sep_token_idx=self.dictionary.eos(), - shuffle=getattr(self.args, "shuffle", False), - has_pairs=False, - segment_id=self.langs2id[lang], - seed=self.seed, - ) - - self.datasets[split] = MultiCorpusSampledDataset(dataset_map) - logger.info( - "{} {} {} examples".format( - utils.split_paths(self.args.data)[epoch - 1], - split, - len(self.datasets[split]), - ) - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/denoising.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/denoising.py deleted file mode 100644 index 49ba692b2b01b1da9fd991b4f724ad88d57aed3a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/denoising.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os - -import numpy as np -from fairseq import utils -from fairseq.data import ( - AppendTokenDataset, - DenoisingDataset, - Dictionary, - IdDataset, - NestedDictionaryDataset, - NumelDataset, - PadDataset, - PrependTokenDataset, - StripTokenDataset, - TokenBlockDataset, - data_utils, -) -from fairseq.data.encoders.utils import get_whole_word_mask -from fairseq.data.shorten_dataset import maybe_shorten_dataset -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("denoising") -class DenoisingTask(LegacyFairseqTask): - """ - Denoising task for applying sequence to sequence denoising. (ie. BART) - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument("data", help="path to data directory") - parser.add_argument( - "--tokens-per-sample", - default=512, - type=int, - help="max number of total tokens over all segments" - " per sample for dataset", - ) - parser.add_argument( - "--sample-break-mode", - default="complete_doc", - type=str, - help="mode for breaking sentence", - ) - parser.add_argument( - "--mask", - default=0.0, - type=float, - help="fraction of words/subwords that will be masked", - ) - parser.add_argument( - "--mask-random", - default=0.0, - type=float, - help="instead of using [MASK], use random token this often", - ) - parser.add_argument( - "--insert", - default=0.0, - type=float, - help="insert this percentage of additional random tokens", - ) - parser.add_argument( - "--permute", - default=0.0, - type=float, - help="take this proportion of subwords and permute them", - ) - parser.add_argument( - "--rotate", - default=0.5, - type=float, - help="rotate this proportion of inputs", - ) - parser.add_argument( - "--poisson-lambda", - default=3.0, - type=float, - help="randomly shuffle sentences for this proportion of inputs", - ) - parser.add_argument( - "--permute-sentences", - default=0.0, - type=float, - help="shuffle this proportion of sentences in all inputs", - ) - parser.add_argument( - "--mask-length", - default="subword", - type=str, - choices=["subword", "word", "span-poisson"], - help="mask length to choose", - ) - parser.add_argument( - "--replace-length", - default=-1, - type=int, - help="when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)", - ) - parser.add_argument( - "--max-source-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the source sequence", - ) - parser.add_argument( - "--max-target-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the target sequence", - ) - - parser.add_argument( - "--shorten-method", - default="none", - choices=["none", "truncate", "random_crop"], - help="if not none, shorten sequences that exceed --tokens-per-sample", - ) - parser.add_argument( - "--shorten-data-split-list", - default="", - help="comma-separated list of dataset splits to apply shortening to, " - 'e.g., "train,valid" (default: all dataset splits)', - ) - - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed - - # add mask token - self.mask_idx = self.dictionary.add_symbol("") - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task.""" - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - if not hasattr(args, "shuffle_instance"): - args.shuffle_instance = False - return cls(args, dictionary) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - split_path = os.path.join(data_path, split) - - dataset = data_utils.load_indexed_dataset( - split_path, - self.dictionary, - self.args.dataset_impl, - combine=combine, - ) - if dataset is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - dataset = StripTokenDataset(dataset, self.dictionary.eos()) - - dataset = maybe_shorten_dataset( - dataset, - split, - self.args.shorten_data_split_list, - self.args.shorten_method, - self.args.tokens_per_sample, - self.args.seed, - ) - - # create continuous blocks of tokens - dataset = TokenBlockDataset( - dataset, - dataset.sizes, - self.args.tokens_per_sample - 2, # one less for and one for - pad=self.dictionary.pad(), - eos=self.dictionary.eos(), - break_mode=self.args.sample_break_mode, - document_sep_len=0, - ) - logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) - - # prepend beginning-of-sentence token (, equiv. to [CLS] in BERT) - dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) - dataset = AppendTokenDataset(dataset, self.source_dictionary.eos()) - - mask_whole_words = ( - get_whole_word_mask(self.args, self.source_dictionary) - if self.args.mask_length != "subword" - else None - ) - - self.datasets[split] = DenoisingDataset( - dataset, - dataset.sizes, - self.dictionary, - self.mask_idx, - mask_whole_words, - shuffle=self.args.shuffle_instance, - seed=self.seed, - args=self.args, - ) - logger.info( - "Split: {0}, Loaded {1} samples of denoising_dataset".format( - split, - len(self.datasets[split]), - ) - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): - """ - Generate batches for inference. We assume that the input begins with a - bos symbol (``) and ends with an eos symbol (``). - """ - pad = self.source_dictionary.pad() - eos = self.source_dictionary.eos() - src_dataset = TokenBlockDataset( - src_tokens, - src_lengths, - block_size=self.args.tokens_per_sample - 2, # for and - pad=pad, - eos=eos, - break_mode=self.args.sample_break_mode, - document_sep_len=0, - ) - prev_output_tokens = PrependTokenDataset( - StripTokenDataset(src_dataset, eos), eos - ) - src_dataset = PadDataset(src_dataset, pad_idx=pad, left_pad=False) - return NestedDictionaryDataset( - { - "id": IdDataset(), - "net_input": { - "src_tokens": src_dataset, - "src_lengths": NumelDataset(src_dataset, reduce=False), - "prev_output_tokens": PadDataset( - prev_output_tokens, pad_idx=pad, left_pad=False - ), - }, - "target": src_dataset, - }, - sizes=[np.array(src_lengths)], - ) - - def max_positions(self): - """Return the max sentence length allowed by the task.""" - return (self.args.max_source_positions, self.args.max_target_positions) - - @property - def source_dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary`.""" - return self.dictionary - - @property - def target_dictionary(self): - """Return the target :class:`~fairseq.data.Dictionary`.""" - return self.dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/fairseq_task.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/fairseq_task.py deleted file mode 100644 index 1b17e7ba7715e0ccf9595b9fdf13dc71627263a4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/fairseq_task.py +++ /dev/null @@ -1,687 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import warnings -from argparse import Namespace -from typing import Any, Callable, Dict, List - -import torch -from fairseq import metrics, search, tokenizer, utils -from fairseq.data import Dictionary, FairseqDataset, data_utils, encoders, iterators -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import gen_parser_from_dataclass -from fairseq.optim.amp_optimizer import AMPOptimizer -from omegaconf import DictConfig - -logger = logging.getLogger(__name__) - - -class StatefulContainer(object): - def __init__(self): - self._state = dict() - self._factories = dict() - - def add_factory(self, name, factory: Callable[[], Any]): - self._factories[name] = factory - - def merge_state_dict(self, state_dict: Dict[str, Any]): - self._state.update(state_dict) - - @property - def state_dict(self) -> Dict[str, Any]: - return self._state - - def __getattr__(self, name): - if name not in self._state and name in self._factories: - self._state[name] = self._factories[name]() - - if name in self._state: - return self._state[name] - - raise AttributeError(f"Task state has no factory for attribute {name}") - - -class FairseqTask(object): - """ - Tasks store dictionaries and provide helpers for loading/iterating over - Datasets, initializing the Model/Criterion and calculating the loss. - - Tasks have limited statefulness. In particular, state that needs to be - saved to/loaded from checkpoints needs to be stored in the `self.state` - :class:`StatefulContainer` object. For example:: - - self.state.add_factory("dictionary", self.load_dictionary) - print(self.state.dictionary) # calls self.load_dictionary() - - This is necessary so that when loading checkpoints, we can properly - recreate the task state after initializing the task instance. - """ - - @classmethod - def add_args(cls, parser): - """Add task-specific arguments to the parser.""" - dc = getattr(cls, "__dataclass", None) - if dc is not None: - gen_parser_from_dataclass(parser, dc()) - - @staticmethod - def logging_outputs_can_be_summed(criterion) -> bool: - """ - Whether the logging outputs returned by `train_step` and `valid_step` can - be summed across workers prior to calling `aggregate_logging_outputs`. - Setting this to True will improves distributed training speed. - """ - return criterion.logging_outputs_can_be_summed() - - def __init__(self, cfg: FairseqDataclass, **kwargs): - self.cfg = cfg - self.datasets = dict() - self.dataset_to_epoch_iter = dict() - self.state = StatefulContainer() - - @classmethod - def load_dictionary(cls, filename): - """Load the dictionary from the filename - - Args: - filename (str): the filename - """ - return Dictionary.load(filename) - - @classmethod - def build_dictionary( - cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 - ): - """Build the dictionary - - Args: - filenames (list): list of filenames - workers (int): number of concurrent workers - threshold (int): defines the minimum word count - nwords (int): defines the total number of words in the final dictionary, - including special symbols - padding_factor (int): can be used to pad the dictionary size to be a - multiple of 8, which is important on some hardware (e.g., Nvidia - Tensor Cores). - """ - d = Dictionary() - for filename in filenames: - Dictionary.add_file_to_dictionary( - filename, d, tokenizer.tokenize_line, workers - ) - d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) - return d - - @classmethod - def setup_task(cls, cfg: DictConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - cfg (omegaconf.DictConfig): parsed command-line arguments - """ - return cls(cfg, **kwargs) - - def has_sharded_data(self, split): - return os.pathsep in getattr(self.cfg, "data", "") - - def load_dataset( - self, - split: str, - combine: bool = False, - task_cfg: FairseqDataclass = None, - **kwargs, - ): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - combine (bool): combines a split segmented into pieces into one dataset - task_cfg (FairseqDataclass): optional task configuration stored in the checkpoint that can be used - to load datasets - """ - raise NotImplementedError - - def dataset(self, split): - """ - Return a loaded dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - - Returns: - a :class:`~fairseq.data.FairseqDataset` corresponding to *split* - """ - from fairseq.data import FairseqDataset - - if split not in self.datasets: - raise KeyError("Dataset not loaded: " + split) - if not isinstance(self.datasets[split], FairseqDataset): - raise TypeError("Datasets are expected to be of type FairseqDataset") - return self.datasets[split] - - def filter_indices_by_size( - self, indices, dataset, max_positions=None, ignore_invalid_inputs=False - ): - """ - Filter examples that are too large - - Args: - indices (np.array): original array of sample indices - dataset (~fairseq.data.FairseqDataset): dataset to batch - max_positions (optional): max sentence length supported by the - model (default: None). - ignore_invalid_inputs (bool, optional): don't raise Exception for - sentences that are too long (default: False). - Returns: - np.array: array of filtered sample indices - """ - indices, ignored = dataset.filter_indices_by_size(indices, max_positions) - if len(ignored) > 0: - if not ignore_invalid_inputs: - raise Exception( - ( - "Size of sample #{} is invalid (={}) since max_positions={}, " - "skip this example with --skip-invalid-size-inputs-valid-test" - ).format(ignored[0], dataset.size(ignored[0]), max_positions) - ) - logger.warning( - ( - "{:,} samples have invalid sizes and will be skipped, " - "max_positions={}, first few sample ids={}" - ).format(len(ignored), max_positions, ignored[:10]) - ) - return indices - - def can_reuse_epoch_itr(self, dataset): - # We can reuse the epoch iterator across epochs as long as the dataset - # hasn't disabled it. We default to ``False`` here, although in practice - # this will be ``True`` for most datasets that inherit from - # ``FairseqDataset`` due to the base implementation there. - return getattr(dataset, "can_reuse_epoch_itr_across_epochs", False) - - def get_batch_iterator( - self, - dataset, - max_tokens=None, - max_sentences=None, - max_positions=None, - ignore_invalid_inputs=False, - required_batch_size_multiple=1, - seed=1, - num_shards=1, - shard_id=0, - num_workers=0, - epoch=1, - data_buffer_size=0, - disable_iterator_cache=False, - skip_remainder_batch=False, - grouped_shuffling=False, - update_epoch_batch_itr=False, - ): - """ - Get an iterator that yields batches of data from the given dataset. - - Args: - dataset (~fairseq.data.FairseqDataset): dataset to batch - max_tokens (int, optional): max number of tokens in each batch - (default: None). - max_sentences (int, optional): max number of sentences in each - batch (default: None). - max_positions (optional): max sentence length supported by the - model (default: None). - ignore_invalid_inputs (bool, optional): don't raise Exception for - sentences that are too long (default: False). - required_batch_size_multiple (int, optional): require batch size to - be a multiple of N (default: 1). - seed (int, optional): seed for random number generator for - reproducibility (default: 1). - num_shards (int, optional): shard the data iterator into N - shards (default: 1). - shard_id (int, optional): which shard of the data iterator to - return (default: 0). - num_workers (int, optional): how many subprocesses to use for data - loading. 0 means the data will be loaded in the main process - (default: 0). - epoch (int, optional): the epoch to start the iterator from - (default: 1). - data_buffer_size (int, optional): number of batches to - preload (default: 0). - disable_iterator_cache (bool, optional): don't cache the - EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) - (default: False). - skip_remainder_batch (bool, optional): if set, discard the last - batch in each training epoch, as the last batch is often smaller than - local_batch_size * distributed_word_size (default: ``True``). - grouped_shuffling (bool, optional): group batches with each groups - containing num_shards batches and shuffle groups. Reduces difference - between sequence lengths among workers for batches sorted by length. - update_epoch_batch_itr (bool optional): if true then donot use the cached - batch iterator for the epoch - - Returns: - ~fairseq.iterators.EpochBatchIterator: a batched iterator over the - given dataset split - """ - can_reuse_epoch_itr = ( - not disable_iterator_cache - and not update_epoch_batch_itr - and self.can_reuse_epoch_itr(dataset) - ) - if can_reuse_epoch_itr and dataset in self.dataset_to_epoch_iter: - logger.debug("reusing EpochBatchIterator for epoch {}".format(epoch)) - return self.dataset_to_epoch_iter[dataset] - - assert isinstance(dataset, FairseqDataset) - - # initialize the dataset with the correct starting epoch - dataset.set_epoch(epoch) - - # get indices ordered by example size - with data_utils.numpy_seed(seed): - indices = dataset.ordered_indices() - - # filter examples that are too large - if max_positions is not None: - indices = self.filter_indices_by_size( - indices, dataset, max_positions, ignore_invalid_inputs - ) - - # create mini-batches with given size constraints - batch_sampler = dataset.batch_by_size( - indices, - max_tokens=max_tokens, - max_sentences=max_sentences, - required_batch_size_multiple=required_batch_size_multiple, - ) - - # return a reusable, sharded iterator - epoch_iter = iterators.EpochBatchIterator( - dataset=dataset, - collate_fn=dataset.collater, - batch_sampler=batch_sampler, - seed=seed, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - epoch=epoch, - buffer_size=data_buffer_size, - skip_remainder_batch=skip_remainder_batch, - grouped_shuffling=grouped_shuffling, - ) - - if can_reuse_epoch_itr: - self.dataset_to_epoch_iter[dataset] = epoch_iter - - return epoch_iter - - def build_model(self, cfg: FairseqDataclass, from_checkpoint=False): - """ - Build the :class:`~fairseq.models.BaseFairseqModel` instance for this - task. - - Args: - cfg (FairseqDataclass): configuration object - - Returns: - a :class:`~fairseq.models.BaseFairseqModel` instance - """ - from fairseq import models, quantization_utils - - model = models.build_model(cfg, self, from_checkpoint) - model = quantization_utils.quantize_model_scalar(model, cfg) - return model - - def build_criterion(self, cfg: DictConfig): - """ - Build the :class:`~fairseq.criterions.FairseqCriterion` instance for - this task. - - Args: - cfg (omegaconf.DictConfig): configration object - - Returns: - a :class:`~fairseq.criterions.FairseqCriterion` instance - """ - from fairseq import criterions - - return criterions.build_criterion(cfg, self) - - def build_generator( - self, - models, - args, - seq_gen_cls=None, - extra_gen_cls_kwargs=None, - prefix_allowed_tokens_fn=None, - ): - """ - Build a :class:`~fairseq.SequenceGenerator` instance for this - task. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - args (fairseq.dataclass.configs.GenerationConfig): - configuration object (dataclass) for generation - extra_gen_cls_kwargs (Dict[str, Any]): extra options to pass - through to SequenceGenerator - prefix_allowed_tokens_fn (Callable[[int, torch.Tensor], List[int]]): - If provided, this function constrains the beam search to - allowed tokens only at each step. The provided function - should take 2 arguments: the batch ID (`batch_id: int`) - and a unidimensional tensor of token ids (`inputs_ids: - torch.Tensor`). It has to return a `List[int]` with the - allowed tokens for the next generation step conditioned - on the previously generated tokens (`inputs_ids`) and - the batch ID (`batch_id`). This argument is useful for - constrained generation conditioned on the prefix, as - described in "Autoregressive Entity Retrieval" - (https://arxiv.org/abs/2010.00904) and - https://github.com/facebookresearch/GENRE. - """ - if getattr(args, "score_reference", False): - from fairseq.sequence_scorer import SequenceScorer - - return SequenceScorer( - self.target_dictionary, - compute_alignment=getattr(args, "print_alignment", False), - ) - - from fairseq.sequence_generator import ( - SequenceGenerator, - SequenceGeneratorWithAlignment, - ) - - # Choose search strategy. Defaults to Beam Search. - sampling = getattr(args, "sampling", False) - sampling_topk = getattr(args, "sampling_topk", -1) - sampling_topp = getattr(args, "sampling_topp", -1.0) - diverse_beam_groups = getattr(args, "diverse_beam_groups", -1) - diverse_beam_strength = getattr(args, "diverse_beam_strength", 0.5) - match_source_len = getattr(args, "match_source_len", False) - diversity_rate = getattr(args, "diversity_rate", -1) - constrained = getattr(args, "constraints", False) - if prefix_allowed_tokens_fn is None: - prefix_allowed_tokens_fn = getattr(args, "prefix_allowed_tokens_fn", None) - if ( - sum( - int(cond) - for cond in [ - sampling, - diverse_beam_groups > 0, - match_source_len, - diversity_rate > 0, - ] - ) - > 1 - ): - raise ValueError("Provided Search parameters are mutually exclusive.") - assert sampling_topk < 0 or sampling, "--sampling-topk requires --sampling" - assert sampling_topp < 0 or sampling, "--sampling-topp requires --sampling" - - if sampling: - search_strategy = search.Sampling( - self.target_dictionary, sampling_topk, sampling_topp - ) - elif diverse_beam_groups > 0: - search_strategy = search.DiverseBeamSearch( - self.target_dictionary, diverse_beam_groups, diverse_beam_strength - ) - elif match_source_len: - # this is useful for tagging applications where the output - # length should match the input length, so we hardcode the - # length constraints for simplicity - search_strategy = search.LengthConstrainedBeamSearch( - self.target_dictionary, - min_len_a=1, - min_len_b=0, - max_len_a=1, - max_len_b=0, - ) - elif diversity_rate > -1: - search_strategy = search.DiverseSiblingsSearch( - self.target_dictionary, diversity_rate - ) - elif constrained: - search_strategy = search.LexicallyConstrainedBeamSearch( - self.target_dictionary, args.constraints - ) - elif prefix_allowed_tokens_fn: - search_strategy = search.PrefixConstrainedBeamSearch( - self.target_dictionary, prefix_allowed_tokens_fn - ) - else: - search_strategy = search.BeamSearch(self.target_dictionary) - - extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} - if seq_gen_cls is None: - if getattr(args, "print_alignment", False): - seq_gen_cls = SequenceGeneratorWithAlignment - extra_gen_cls_kwargs["print_alignment"] = args.print_alignment - else: - seq_gen_cls = SequenceGenerator - - return seq_gen_cls( - models, - self.target_dictionary, - beam_size=getattr(args, "beam", 5), - max_len_a=getattr(args, "max_len_a", 0), - max_len_b=getattr(args, "max_len_b", 200), - min_len=getattr(args, "min_len", 1), - normalize_scores=(not getattr(args, "unnormalized", False)), - len_penalty=getattr(args, "lenpen", 1), - unk_penalty=getattr(args, "unkpen", 0), - temperature=getattr(args, "temperature", 1.0), - match_source_len=getattr(args, "match_source_len", False), - no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), - search_strategy=search_strategy, - **extra_gen_cls_kwargs, - ) - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - """ - Do forward and backward, and return the loss as computed by *criterion* - for the given *model* and *sample*. - - Args: - sample (dict): the mini-batch. The format is defined by the - :class:`~fairseq.data.FairseqDataset`. - model (~fairseq.models.BaseFairseqModel): the model - criterion (~fairseq.criterions.FairseqCriterion): the criterion - optimizer (~fairseq.optim.FairseqOptimizer): the optimizer - update_num (int): the current update - ignore_grad (bool): multiply loss by 0 if this is set to True - - Returns: - tuple: - - the loss - - the sample size, which is used as the denominator for the - gradient - - logging outputs to display while training - """ - model.train() - model.set_num_updates(update_num) - with torch.autograd.profiler.record_function("forward"): - with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))): - loss, sample_size, logging_output = criterion(model, sample) - if ignore_grad: - loss *= 0 - with torch.autograd.profiler.record_function("backward"): - optimizer.backward(loss) - return loss, sample_size, logging_output - - def valid_step(self, sample, model, criterion): - model.eval() - with torch.no_grad(): - loss, sample_size, logging_output = criterion(model, sample) - return loss, sample_size, logging_output - - def optimizer_step(self, optimizer, model, update_num): - optimizer.step() - - def build_dataset_for_inference( - self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs - ) -> torch.utils.data.Dataset: - raise NotImplementedError - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - return generator.generate( - models, sample, prefix_tokens=prefix_tokens, constraints=constraints - ) - - def begin_epoch(self, epoch, model): - """Hook function called before the start of each epoch.""" - pass - - def begin_valid_epoch(self, epoch, model): - """Hook function called before the start of each validation epoch.""" - pass - - def aggregate_logging_outputs(self, logging_outputs, criterion): - """[deprecated] Aggregate logging outputs from data parallel training.""" - utils.deprecation_warning( - "The aggregate_logging_outputs API is deprecated. " - "Please use the reduce_metrics API instead." - ) - with metrics.aggregate() as agg: - self.reduce_metrics(logging_outputs, criterion) - return agg.get_smoothed_values() - - def reduce_metrics(self, logging_outputs, criterion): - """Aggregate logging outputs from data parallel training.""" - # backward compatibility for tasks that override aggregate_logging_outputs - base_func = FairseqTask.aggregate_logging_outputs - self_func = getattr(self, "aggregate_logging_outputs").__func__ - if self_func is not base_func: - utils.deprecation_warning( - "Tasks should implement the reduce_metrics API. " - "Falling back to deprecated aggregate_logging_outputs API." - ) - agg_logging_outputs = self.aggregate_logging_outputs( - logging_outputs, criterion - ) - for k, v in agg_logging_outputs.items(): - metrics.log_scalar(k, v) - return - - if not any("ntokens" in log for log in logging_outputs): - warnings.warn( - "ntokens not found in Criterion logging outputs, cannot log wpb or wps" - ) - else: - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - metrics.log_scalar("wpb", ntokens, priority=180, round=1) - metrics.log_speed("wps", ntokens, priority=90, round=1) - - if not any("nsentences" in log for log in logging_outputs): - warnings.warn( - "nsentences not found in Criterion logging outputs, cannot log bsz" - ) - else: - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - metrics.log_scalar("bsz", nsentences, priority=190, round=1) - - criterion.__class__.reduce_metrics(logging_outputs) - - def state_dict(self): - if self.state is not None: - return self.state.state_dict - return {} - - def load_state_dict(self, state_dict: Dict[str, Any]): - if self.state is not None: - self.state.merge_state_dict(state_dict) - - def max_positions(self): - """Return the max input length allowed by the task.""" - return None - - @property - def source_dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary` (if applicable - for this task).""" - raise NotImplementedError - - @property - def target_dictionary(self): - """Return the target :class:`~fairseq.data.Dictionary` (if applicable - for this task).""" - raise NotImplementedError - - def build_tokenizer(self, args): - """Build the pre-tokenizer for this task.""" - return encoders.build_tokenizer(args) - - def build_bpe(self, args): - """Build the tokenizer for this task.""" - return encoders.build_bpe(args) - - def get_interactive_tokens_and_lengths(self, lines, encode_fn): - tokens = [ - self.source_dictionary.encode_line( - encode_fn(src_str), add_if_not_exist=False - ).long() - for src_str in lines - ] - lengths = [t.numel() for t in tokens] - return tokens, lengths - - -class LegacyFairseqTask(FairseqTask): - def __init__(self, args: Namespace): - super().__init__(None) - self.args = args - self.datasets = {} - self.dataset_to_epoch_iter = {} - - @classmethod - def setup_task(cls, args: Namespace, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - return cls(args, **kwargs) - - def has_sharded_data(self, split): - return os.pathsep in getattr(self.args, "data", "") - - def build_model(self, args: Namespace, from_checkpoint=False): - """ - Build the :class:`~fairseq.models.BaseFairseqModel` instance for this - task. - - Args: - args (argparse.Namespace): parsed command-line arguments - - Returns: - a :class:`~fairseq.models.BaseFairseqModel` instance - """ - from fairseq import models, quantization_utils - - model = models.build_model(args, self, from_checkpoint) - model = quantization_utils.quantize_model_scalar(model, args) - return model - - def build_criterion(self, args: Namespace): - """ - Build the :class:`~fairseq.criterions.FairseqCriterion` instance for - this task. - - Args: - args (argparse.Namespace): parsed command-line arguments - - Returns: - a :class:`~fairseq.criterions.FairseqCriterion` instance - """ - from fairseq import criterions - - return criterions.build_criterion(args, self) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/frm_text_to_speech.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/frm_text_to_speech.py deleted file mode 100644 index e21e5fbbdd0f8d606f9216b199ca420f2c9be68e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/frm_text_to_speech.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -from fairseq.data.audio.frm_text_to_speech_dataset import FrmTextToSpeechDatasetCreator -from fairseq.tasks import register_task -from fairseq.tasks.text_to_speech import TextToSpeechTask - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=logging.INFO, -) -logger = logging.getLogger(__name__) - - -@register_task("frm_text_to_speech") -class FrmTextToSpeechTask(TextToSpeechTask): - @staticmethod - def add_args(parser): - TextToSpeechTask.add_args(parser) - parser.add_argument("--do_chunk", action="store_true", help="train on chunks") - parser.add_argument("--chunk_bound", default=-1, type=int) - parser.add_argument("--chunk_init", default=50, type=int) - parser.add_argument("--chunk_incr", default=5, type=int) - parser.add_argument("--add_eos", action="store_true") - parser.add_argument("--dedup", action="store_true") - parser.add_argument("--ref_fpu", default=-1, type=float) - - def load_dataset(self, split, **unused_kwargs): - is_train_split = split.startswith("train") - pre_tokenizer = self.build_tokenizer(self.args) - bpe_tokenizer = self.build_bpe(self.args) - self.datasets[split] = FrmTextToSpeechDatasetCreator.from_tsv( - self.args.data, - self.data_cfg, - split, - self.src_dict, - pre_tokenizer, - bpe_tokenizer, - is_train_split=is_train_split, - n_frames_per_step=self.args.n_frames_per_step, - speaker_to_id=self.speaker_to_id, - do_chunk=self.args.do_chunk, - chunk_bound=self.args.chunk_bound, - chunk_init=self.args.chunk_init, - chunk_incr=self.args.chunk_incr, - add_eos=self.args.add_eos, - dedup=self.args.dedup, - ref_fpu=self.args.ref_fpu, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/hubert_pretraining.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/hubert_pretraining.py deleted file mode 100644 index 0fa911901965cc846cca8486bab94ae591d256ae..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/hubert_pretraining.py +++ /dev/null @@ -1,190 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -import logging -import os -import sys -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple - -import numpy as np -from fairseq.data import Dictionary, HubertDataset -from fairseq.dataclass.configs import FairseqDataclass -from fairseq.tasks import register_task -from fairseq.tasks.fairseq_task import FairseqTask -from omegaconf import MISSING - -logger = logging.getLogger(__name__) - - -class LabelEncoder(object): - def __init__(self, dictionary: Dictionary) -> None: - self.dictionary = dictionary - - def __call__(self, label: str) -> List[str]: - return self.dictionary.encode_line( - label, - append_eos=False, - add_if_not_exist=False, - ) - - -@dataclass -class HubertPretrainingConfig(FairseqDataclass): - data: str = field(default=MISSING, metadata={"help": "path to data directory"}) - fine_tuning: bool = field( - default=False, metadata={"help": "set to true if fine-tuning Hubert"} - ) - labels: List[str] = field( - default_factory=lambda: ["ltr"], - metadata={ - "help": ( - "extension of the label files to load, frame-level labels for" - " pre-training, and sequence-level label for fine-tuning" - ) - }, - ) - label_dir: Optional[str] = field( - default=None, - metadata={ - "help": "if set, looks for labels in this directory instead", - }, - ) - label_rate: float = field( - default=-1.0, - metadata={"help": "label frame rate. -1.0 for sequence label"}, - ) - sample_rate: int = field( - default=16_000, - metadata={ - "help": "target sample rate. audio files will be up/down " - "sampled to this rate" - }, - ) - normalize: bool = field( - default=False, - metadata={"help": "if set, normalizes input to have 0 mean and unit variance"}, - ) - enable_padding: bool = field( - default=False, - metadata={"help": "pad shorter samples instead of cropping"}, - ) - max_keep_size: Optional[int] = field( - default=None, - metadata={"help": "exclude sample longer than this"}, - ) - max_sample_size: Optional[int] = field( - default=None, - metadata={"help": "max sample size to crop to for batching"}, - ) - min_sample_size: Optional[int] = field( - default=None, - metadata={"help": "min sample size to crop to for batching"}, - ) - single_target: Optional[bool] = field( - default=False, - metadata={ - "help": "if set, AddTargetDatasets outputs same keys " "as AddTargetDataset" - }, - ) - random_crop: Optional[bool] = field( - default=True, - metadata={"help": "always crop from the beginning if false"}, - ) - pad_audio: Optional[bool] = field( - default=False, - metadata={"help": "pad audio to the longest one in the batch if true"}, - ) - - -@register_task("hubert_pretraining", dataclass=HubertPretrainingConfig) -class HubertPretrainingTask(FairseqTask): - - cfg: HubertPretrainingConfig - - def __init__( - self, - cfg: HubertPretrainingConfig, - ) -> None: - super().__init__(cfg) - - logger.info(f"current directory is {os.getcwd()}") - logger.info(f"HubertPretrainingTask Config {cfg}") - - self.cfg = cfg - self.fine_tuning = cfg.fine_tuning - - if cfg.fine_tuning: - self.state.add_factory("target_dictionary", self.load_dictionaries) - else: - self.state.add_factory("dictionaries", self.load_dictionaries) - - self.blank_symbol = "" - - @property - def source_dictionary(self) -> Optional[Dictionary]: - return None - - @property - def target_dictionary(self) -> Optional[Dictionary]: - return self.state.target_dictionary - - @property - def dictionaries(self) -> List[Dictionary]: - return self.state.dictionaries - - @classmethod - def setup_task( - cls, cfg: HubertPretrainingConfig, **kwargs - ) -> "HubertPretrainingTask": - return cls(cfg) - - def load_dictionaries(self): - label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir - dictionaries = [ - Dictionary.load(f"{label_dir}/dict.{label}.txt") - for label in self.cfg.labels - ] - return dictionaries[0] if self.cfg.fine_tuning else dictionaries - - def get_label_dir(self) -> str: - if self.cfg.label_dir is None: - return self.cfg.data - return self.cfg.label_dir - - def load_dataset(self, split: str, **kwargs) -> None: - manifest = f"{self.cfg.data}/{split}.tsv" - dicts = [self.target_dictionary] if self.cfg.fine_tuning else self.dictionaries - pad_list = [dict.pad() for dict in dicts] - eos_list = [dict.eos() for dict in dicts] - procs = [LabelEncoder(dict) for dict in dicts] - paths = [f"{self.get_label_dir()}/{split}.{l}" for l in self.cfg.labels] - - # hubert v1: pad_audio=True, random_crop=False; - self.datasets[split] = HubertDataset( - manifest, - sample_rate=self.cfg.sample_rate, - label_paths=paths, - label_rates=self.cfg.label_rate, - pad_list=pad_list, - eos_list=eos_list, - label_processors=procs, - max_keep_sample_size=self.cfg.max_keep_size, - min_keep_sample_size=self.cfg.min_sample_size, - max_sample_size=self.cfg.max_sample_size, - pad_audio=self.cfg.pad_audio, - normalize=self.cfg.normalize, - store_labels=False, - random_crop=self.cfg.random_crop, - single_target=self.cfg.single_target, - ) - - def max_positions(self) -> Tuple[int, int]: - return (sys.maxsize, sys.maxsize) - - def filter_indices_by_size(self, indices: np.array, *args, **kwargs) -> np.array: - return indices diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/language_modeling.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/language_modeling.py deleted file mode 100644 index 42d592deb36982a8da8264bd38890e0d58a3722f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/language_modeling.py +++ /dev/null @@ -1,382 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -from dataclasses import dataclass, field -from typing import Optional - -import numpy as np -import torch -from fairseq import utils -from fairseq.data import ( - AppendTokenDataset, - Dictionary, - IdDataset, - LMContextWindowDataset, - MonolingualDataset, - NestedDictionaryDataset, - NumelDataset, - PadDataset, - PrependTokenDataset, - StripTokenDataset, - TokenBlockDataset, - TruncatedDictionary, - data_utils, -) -from fairseq.data.indexed_dataset import get_available_dataset_impl -from fairseq.data.shorten_dataset import maybe_shorten_dataset -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.tasks import LegacyFairseqTask, register_task -from omegaconf import II - -SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) -SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) -logger = logging.getLogger(__name__) - - -@dataclass -class LanguageModelingConfig(FairseqDataclass): - data: Optional[str] = field( - default=None, metadata={"help": "path to data directory"} - ) - sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( - default="none", - metadata={ - "help": 'If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - "of sentence, but may include multiple sentences per sample. " - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.' - }, - ) - tokens_per_sample: int = field( - default=1024, - metadata={"help": "max number of tokens per sample for LM dataset"}, - ) - output_dictionary_size: int = field( - default=-1, metadata={"help": "limit the size of output dictionary"} - ) - self_target: bool = field(default=False, metadata={"help": "include self target"}) - future_target: bool = field( - default=False, metadata={"help": "include future target"} - ) - past_target: bool = field(default=False, metadata={"help": "include past target"}) - add_bos_token: bool = field( - default=False, metadata={"help": "prepend beginning of sentence token ()"} - ) - max_target_positions: Optional[int] = field( - default=None, metadata={"help": "max number of tokens in the target sequence"} - ) - shorten_method: SHORTEN_METHOD_CHOICES = field( - default="none", - metadata={ - "help": "if not none, shorten sequences that exceed --tokens-per-sample" - }, - ) - shorten_data_split_list: str = field( - default="", - metadata={ - "help": "comma-separated list of dataset splits to apply shortening to, " - 'e.g., "train,valid" (default: all dataset splits)' - }, - ) - pad_to_fixed_length: Optional[bool] = field( - default=False, - metadata={"help": "pad to fixed length"}, - ) - pad_to_fixed_bsz: Optional[bool] = field( - default=False, - metadata={"help": "boolean to pad to fixed batch size"}, - ) - - # TODO common vars below add to parent - seed: int = II("common.seed") - batch_size: Optional[int] = II("dataset.batch_size") - batch_size_valid: Optional[int] = II("dataset.batch_size_valid") - dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( - "dataset.dataset_impl" - ) - data_buffer_size: int = II("dataset.data_buffer_size") - tpu: bool = II("common.tpu") - use_plasma_view: bool = II("common.use_plasma_view") - plasma_path: str = II("common.plasma_path") - - -@register_task("language_modeling", dataclass=LanguageModelingConfig) -class LanguageModelingTask(LegacyFairseqTask): - """ - Train a language model. - - Args: - dictionary (~fairseq.data.Dictionary): the dictionary for the input of - the language model - output_dictionary (~fairseq.data.Dictionary): the dictionary for the - output of the language model. In most cases it will be the same as - *dictionary*, but could possibly be a more limited version of the - dictionary (if ``--output-dictionary-size`` is used). - targets (List[str]): list of the target types that the language model - should predict. Can be one of "self", "future", and "past". - Defaults to "future". - - .. note:: - - The language modeling task is compatible with :mod:`fairseq-train`, - :mod:`fairseq-generate`, :mod:`fairseq-interactive` and - :mod:`fairseq-eval-lm`. - - The language modeling task provides the following additional command-line - arguments: - - .. argparse:: - :ref: fairseq.tasks.language_modeling_parser - :prog: - """ - - def __init__(self, args, dictionary, output_dictionary=None, targets=None): - super().__init__(args) - self.dictionary = dictionary - self.output_dictionary = output_dictionary or dictionary - - if targets is None: - targets = ["future"] - self.targets = targets - - @classmethod - def setup_dictionary(cls, args, **kwargs): - dictionary = None - output_dictionary = None - if args.data: - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - output_dictionary = dictionary - if args.output_dictionary_size >= 0: - output_dictionary = TruncatedDictionary( - dictionary, args.output_dictionary_size - ) - return (dictionary, output_dictionary) - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - dictionary, output_dictionary = cls.setup_dictionary(args, **kwargs) - - # upgrade old checkpoints - if getattr(args, "exclude_self_target", False): - args.self_target = False - - targets = [] - if getattr(args, "self_target", False): - targets.append("self") - if getattr(args, "future_target", False): - targets.append("future") - if getattr(args, "past_target", False): - targets.append("past") - if len(targets) == 0: - # standard language modeling - targets = ["future"] - - return cls(args, dictionary, output_dictionary, targets=targets) - - def build_model(self, args, from_checkpoint=False): - model = super().build_model(args, from_checkpoint) - for target in self.targets: - if target not in model.supported_targets: - raise ValueError( - "Unsupported language modeling target: {}".format(target) - ) - - return model - - def load_dataset( - self, split: str, epoch=1, combine=False, **kwargs - ) -> MonolingualDataset: - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, valid1, test) - """ - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - - data_path = paths[(epoch - 1) % len(paths)] - split_path = os.path.join(data_path, split) - - # each process has its own copy of the raw data (likely to be an np.memmap) - dataset = data_utils.load_indexed_dataset( - split_path, self.dictionary, self.args.dataset_impl, combine=combine - ) - if dataset is None: - raise FileNotFoundError(f"Dataset not found: {split} ({split_path})") - - dataset = maybe_shorten_dataset( - dataset, - split, - self.args.shorten_data_split_list, - self.args.shorten_method, - self.args.tokens_per_sample, - self.args.seed, - ) - dataset = TokenBlockDataset( - dataset, - dataset.sizes, - self.args.tokens_per_sample, - pad=self.dictionary.pad(), - eos=self.dictionary.eos(), - break_mode=self.args.sample_break_mode, - include_targets=True, - use_plasma_view=self.args.use_plasma_view, - split_path=split_path, - plasma_path=self.args.plasma_path, - ) - - add_eos_for_other_targets = ( - self.args.sample_break_mode is not None - and self.args.sample_break_mode != "none" - ) - fixed_pad_length = None - if self.args.pad_to_fixed_length: - fixed_pad_length = self.args.tokens_per_sample - - pad_to_bsz = None - if self.args.pad_to_fixed_bsz: - pad_to_bsz = ( - self.args.batch_size_valid if "valid" in split else self.args.batch_size - ) - - self.datasets[split] = MonolingualDataset( - dataset=dataset, - sizes=dataset.sizes, - src_vocab=self.dictionary, - tgt_vocab=self.output_dictionary, - add_eos_for_other_targets=add_eos_for_other_targets, - shuffle=True, - targets=self.targets, - add_bos_token=self.args.add_bos_token, - fixed_pad_length=fixed_pad_length, - pad_to_bsz=pad_to_bsz, - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): - """ - Generate batches for inference. We prepend an eos token to src_tokens - (or bos if `--add-bos-token` is set) and we append a to target. - This is convenient both for generation with a prefix and LM scoring. - """ - dataset = StripTokenDataset( - TokenBlockDataset( - src_tokens, - src_lengths, - block_size=None, # ignored for "eos" break mode - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode="eos", - ), - # remove eos from (end of) target sequence - self.source_dictionary.eos(), - ) - src_dataset = PrependTokenDataset( - dataset, - token=( - self.source_dictionary.bos() - if getattr(self.args, "add_bos_token", False) - else self.source_dictionary.eos() - ), - ) - tgt_dataset = AppendTokenDataset(dataset, token=self.source_dictionary.pad()) - return NestedDictionaryDataset( - { - "id": IdDataset(), - "net_input": { - "src_tokens": PadDataset( - src_dataset, - pad_idx=self.source_dictionary.pad(), - left_pad=False, - ), - "src_lengths": NumelDataset(src_dataset, reduce=False), - }, - "target": PadDataset( - tgt_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False - ), - }, - sizes=[np.array(src_lengths)], - ) - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - # Generation will always be conditioned on bos_token - if getattr(self.args, "add_bos_token", False): - bos_token = self.source_dictionary.bos() - else: - bos_token = self.source_dictionary.eos() - - if constraints is not None: - raise NotImplementedError( - "Constrained decoding with the language_modeling task is not supported" - ) - - # SequenceGenerator doesn't use src_tokens directly, we need to - # pass the `prefix_tokens` argument instead - if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): - prefix_tokens = sample["net_input"]["src_tokens"] - if prefix_tokens[:, 0].eq(bos_token).all(): - prefix_tokens = prefix_tokens[:, 1:] - - return generator.generate( - models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token - ) - - def eval_lm_dataloader( - self, - dataset, - max_tokens: Optional[int] = 36000, - batch_size: Optional[int] = None, - max_positions: Optional[int] = None, - num_shards: int = 1, - shard_id: int = 0, - num_workers: int = 1, - data_buffer_size: int = 10, - # ensures that every evaluated token has access to a context of at least - # this size, if possible - context_window: int = 0, - ): - if context_window > 0: - dataset = LMContextWindowDataset( - dataset=dataset, - tokens_per_sample=self.args.tokens_per_sample, - context_window=context_window, - pad_idx=self.source_dictionary.pad(), - ) - return self.get_batch_iterator( - dataset=dataset, - max_tokens=max_tokens, - max_sentences=batch_size, - max_positions=max_positions, - ignore_invalid_inputs=True, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - data_buffer_size=data_buffer_size, - ).next_epoch_itr(shuffle=False) - - @property - def source_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self.dictionary - - @property - def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self.output_dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/legacy_masked_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/legacy_masked_lm.py deleted file mode 100644 index e0285319c63fc01f21c9f0f1a6e1534c5992fdd8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/legacy_masked_lm.py +++ /dev/null @@ -1,151 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import itertools -import logging -import os - -import numpy as np -from fairseq import tokenizer, utils -from fairseq.data import ConcatDataset, Dictionary, data_utils, indexed_dataset -from fairseq.data.legacy.block_pair_dataset import BlockPairDataset -from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset -from fairseq.data.legacy.masked_lm_dictionary import BertDictionary -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("legacy_masked_lm") -class LegacyMaskedLMTask(LegacyFairseqTask): - """ - Task for training Masked LM (BERT) model. - Args: - dictionary (Dictionary): the dictionary for the input of the task - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "data", - help="colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner", - ) - parser.add_argument( - "--tokens-per-sample", - default=512, - type=int, - help="max number of total tokens over all segments" - " per sample for BERT dataset", - ) - parser.add_argument( - "--break-mode", default="doc", type=str, help="mode for breaking sentence" - ) - parser.add_argument("--shuffle-dataset", action="store_true", default=False) - - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed - - @classmethod - def load_dictionary(cls, filename): - return BertDictionary.load(filename) - - @classmethod - def build_dictionary( - cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8 - ): - d = BertDictionary() - for filename in filenames: - Dictionary.add_file_to_dictionary( - filename, d, tokenizer.tokenize_line, workers - ) - d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor) - return d - - @property - def target_dictionary(self): - return self.dictionary - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task.""" - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dictionary = BertDictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - - return cls(args, dictionary) - - def load_dataset(self, split, epoch=1, combine=False): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - loaded_datasets = [] - - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - logger.info("data_path", data_path) - - for k in itertools.count(): - split_k = split + (str(k) if k > 0 else "") - path = os.path.join(data_path, split_k) - ds = indexed_dataset.make_dataset( - path, - impl=self.args.dataset_impl, - fix_lua_indexing=True, - dictionary=self.dictionary, - ) - - if ds is None: - if k > 0: - break - else: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, data_path) - ) - - with data_utils.numpy_seed(self.seed + k): - loaded_datasets.append( - BlockPairDataset( - ds, - self.dictionary, - ds.sizes, - self.args.tokens_per_sample, - break_mode=self.args.break_mode, - doc_break_size=1, - ) - ) - - logger.info( - "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1])) - ) - - if not combine: - break - - if len(loaded_datasets) == 1: - dataset = loaded_datasets[0] - sizes = dataset.sizes - else: - dataset = ConcatDataset(loaded_datasets) - sizes = np.concatenate([ds.sizes for ds in loaded_datasets]) - - self.datasets[split] = MaskedLMDataset( - dataset=dataset, - sizes=sizes, - vocab=self.dictionary, - pad_idx=self.dictionary.pad(), - mask_idx=self.dictionary.mask(), - classif_token_idx=self.dictionary.cls(), - sep_token_idx=self.dictionary.sep(), - shuffle=self.args.shuffle_dataset, - seed=self.seed, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/masked_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/masked_lm.py deleted file mode 100644 index 678e234eaac493da3f217b4da8c76e4fb5bc8208..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/masked_lm.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -from dataclasses import dataclass, field - -import numpy as np -from fairseq import utils -from fairseq.data import ( - Dictionary, - IdDataset, - MaskTokensDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - PrependTokenDataset, - RightPadDataset, - SortDataset, - TokenBlockDataset, - data_utils, -) -from fairseq.data.encoders.utils import get_whole_word_mask -from fairseq.data.shorten_dataset import maybe_shorten_dataset -from fairseq.dataclass import FairseqDataclass -from fairseq.tasks import FairseqTask, register_task -from omegaconf import II, MISSING, OmegaConf - -from .language_modeling import SAMPLE_BREAK_MODE_CHOICES, SHORTEN_METHOD_CHOICES - -logger = logging.getLogger(__name__) - - -@dataclass -class MaskedLMConfig(FairseqDataclass): - data: str = field( - default=MISSING, - metadata={ - "help": "colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner" - }, - ) - sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( - default="none", - metadata={ - "help": 'If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - "of sentence, but may include multiple sentences per sample. " - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.' - }, - ) - tokens_per_sample: int = field( - default=1024, - metadata={"help": "max number of tokens per sample for LM dataset"}, - ) - mask_prob: float = field( - default=0.15, - metadata={"help": "probability of replacing a token with mask"}, - ) - leave_unmasked_prob: float = field( - default=0.1, - metadata={"help": "probability that a masked token is unmasked"}, - ) - random_token_prob: float = field( - default=0.1, - metadata={"help": "probability of replacing a token with a random token"}, - ) - freq_weighted_replacement: bool = field( - default=False, - metadata={"help": "sample random replacement words based on word frequencies"}, - ) - mask_whole_words: bool = field( - default=False, - metadata={"help": "mask whole words; you may also want to set --bpe"}, - ) - mask_multiple_length: int = field( - default=1, - metadata={"help": "repeat the mask indices multiple times"}, - ) - mask_stdev: float = field( - default=0.0, - metadata={"help": "stdev of the mask length"}, - ) - shorten_method: SHORTEN_METHOD_CHOICES = field( - default="none", - metadata={ - "help": "if not none, shorten sequences that exceed --tokens-per-sample" - }, - ) - shorten_data_split_list: str = field( - default="", - metadata={ - "help": "comma-separated list of dataset splits to apply shortening to, " - 'e.g., "train,valid" (default: all dataset splits)' - }, - ) - seed: int = II("common.seed") - - include_target_tokens: bool = field( - default=False, - metadata={ - "help": "include target tokens in model input. this is used for data2vec" - }, - ) - - -@register_task("masked_lm", dataclass=MaskedLMConfig) -class MaskedLMTask(FairseqTask): - - cfg: MaskedLMConfig - - """Task for training masked language models (e.g., BERT, RoBERTa).""" - - def __init__(self, cfg: MaskedLMConfig, dictionary): - super().__init__(cfg) - self.dictionary = dictionary - - # add mask token - self.mask_idx = dictionary.add_symbol("") - - @classmethod - def setup_task(cls, cfg: MaskedLMConfig, **kwargs): - paths = utils.split_paths(cfg.data) - assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - return cls(cfg, dictionary) - - def _load_dataset_split(self, split, epoch, combine): - paths = utils.split_paths(self.cfg.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - split_path = os.path.join(data_path, split) - - dataset = data_utils.load_indexed_dataset( - split_path, - self.source_dictionary, - combine=combine, - ) - if dataset is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - dataset = maybe_shorten_dataset( - dataset, - split, - self.cfg.shorten_data_split_list, - self.cfg.shorten_method, - self.cfg.tokens_per_sample, - self.cfg.seed, - ) - - # create continuous blocks of tokens - dataset = TokenBlockDataset( - dataset, - dataset.sizes, - self.cfg.tokens_per_sample - 1, # one less for - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode=self.cfg.sample_break_mode, - ) - logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) - - # prepend beginning-of-sentence token (, equiv. to [CLS] in BERT) - return PrependTokenDataset(dataset, self.source_dictionary.bos()) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - dataset = self._load_dataset_split(split, epoch, combine) - - # create masked input and targets - mask_whole_words = ( - get_whole_word_mask(self.args, self.source_dictionary) - if self.cfg.mask_whole_words - else None - ) - - src_dataset, tgt_dataset = MaskTokensDataset.apply_mask( - dataset, - self.source_dictionary, - pad_idx=self.source_dictionary.pad(), - mask_idx=self.mask_idx, - seed=self.cfg.seed, - mask_prob=self.cfg.mask_prob, - leave_unmasked_prob=self.cfg.leave_unmasked_prob, - random_token_prob=self.cfg.random_token_prob, - freq_weighted_replacement=self.cfg.freq_weighted_replacement, - mask_whole_words=mask_whole_words, - mask_multiple_length=self.cfg.mask_multiple_length, - mask_stdev=self.cfg.mask_stdev, - ) - - with data_utils.numpy_seed(self.cfg.seed): - shuffle = np.random.permutation(len(src_dataset)) - - target_dataset = RightPadDataset( - tgt_dataset, - pad_idx=self.source_dictionary.pad(), - ) - - input_dict = { - "src_tokens": RightPadDataset( - src_dataset, - pad_idx=self.source_dictionary.pad(), - ), - "src_lengths": NumelDataset(src_dataset, reduce=False), - } - if self.cfg.include_target_tokens: - input_dict["target_tokens"] = target_dataset - - self.datasets[split] = SortDataset( - NestedDictionaryDataset( - { - "id": IdDataset(), - "net_input": input_dict, - "target": target_dataset, - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_dataset, reduce=True), - }, - sizes=[src_dataset.sizes], - ), - sort_order=[ - shuffle, - src_dataset.sizes, - ], - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): - src_dataset = RightPadDataset( - TokenBlockDataset( - src_tokens, - src_lengths, - self.cfg.tokens_per_sample - 1, # one less for - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode="eos", - ), - pad_idx=self.source_dictionary.pad(), - ) - src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos()) - src_dataset = NestedDictionaryDataset( - { - "id": IdDataset(), - "net_input": { - "src_tokens": src_dataset, - "src_lengths": NumelDataset(src_dataset, reduce=False), - }, - }, - sizes=src_lengths, - ) - if sort: - src_dataset = SortDataset(src_dataset, sort_order=[src_lengths]) - return src_dataset - - @property - def source_dictionary(self): - return self.dictionary - - @property - def target_dictionary(self): - return self.dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_denoising.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_denoising.py deleted file mode 100644 index 2250906fb10edb736c31d5d71dd2527f4e720507..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_denoising.py +++ /dev/null @@ -1,253 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os - -import numpy as np -from fairseq.data import ( - AppendTokenDataset, - ConcatDataset, - DenoisingDataset, - Dictionary, - PrependTokenDataset, - ResamplingDataset, - SortDataset, - TokenBlockDataset, - data_utils, -) -from fairseq.data.encoders.utils import get_whole_word_mask -from fairseq.tasks import register_task - -from .denoising import DenoisingTask - -logger = logging.getLogger(__name__) - - -@register_task("multilingual_denoising") -class MultilingualDenoisingTask(DenoisingTask): - @staticmethod - def add_args(parser): - DenoisingTask.add_args(parser) - parser.add_argument( - "--multilang-sampling-alpha", - type=float, - default=1.0, - help="smoothing alpha for sample ratios across multiple datasets", - ) - parser.add_argument("--add-lang-token", default=False, action="store_true") - parser.add_argument( - "--langs", type=str, help="language ids we are considering", default=None - ) - parser.add_argument( - "--no-whole-word-mask-langs", - type=str, - default="", - metavar="N", - help="languages without spacing between words dont support whole word masking", - ) - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task.""" - paths = args.data.split(":") - assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - - data_path = paths[0] - if args.langs is None: - languages = sorted( - [ - name - for name in os.listdir(data_path) - if os.path.isdir(os.path.join(data_path, name)) - ] - ) - else: - languages = args.langs.split(",") - - if args.add_lang_token: - for lang in languages: - dictionary.add_symbol("[{}]".format(lang)) - - logger.info("dictionary: {} types".format(len(dictionary))) - if not hasattr(args, "shuffle_instance"): - args.shuffle_instance = False - return cls(args, dictionary) - - def __init__(self, args, dictionary): - super().__init__(args, dictionary) - self.dictionary = dictionary - self.seed = args.seed - - # add mask token - self.mask_idx = self.dictionary.add_symbol("") - self.langs = args.langs - self.args = args - - def _get_sample_prob(self, dataset_lens): - """ - Get smoothed sampling porbability by languages. This helps low resource - languages by upsampling them. - """ - prob = dataset_lens / dataset_lens.sum() - smoothed_prob = prob**self.args.multilang_sampling_alpha - smoothed_prob = smoothed_prob / smoothed_prob.sum() - return smoothed_prob - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - paths = self.args.data.split(":") - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - split_path = os.path.join(data_path, split) - - if self.langs is None: - languages = sorted( - [ - name - for name in os.listdir(data_path) - if os.path.isdir(os.path.join(data_path, name)) - ] - ) - else: - languages = self.langs.split(",") - for name in languages: - p = os.path.join(data_path, name) - assert os.path.exists(p), "data not found: {}".format(p) - - logger.info("Training on {0} languages: {1}".format(len(languages), languages)) - logger.info( - "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)} - ) - - mask_whole_words = get_whole_word_mask(self.args, self.dictionary) - language_without_segmentations = self.args.no_whole_word_mask_langs.split(",") - lang_datasets = [] - for language in languages: - split_path = os.path.join(data_path, language, split) - - dataset = data_utils.load_indexed_dataset( - split_path, - self.source_dictionary, - self.args.dataset_impl, - combine=combine, - ) - if dataset is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - end_token = ( - self.source_dictionary.index("[{}]".format(language)) - if self.args.add_lang_token - else self.source_dictionary.eos() - ) - - # create continuous blocks of tokens - dataset = TokenBlockDataset( - dataset, - dataset.sizes, - self.args.tokens_per_sample - 2, # one less for - pad=self.source_dictionary.pad(), - eos=end_token, - break_mode=self.args.sample_break_mode, - ) - logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) - - # prepend beginning-of-sentence token (, equiv. to [CLS] in BERT) - dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) - dataset = AppendTokenDataset(dataset, end_token) - - lang_mask_whole_words = ( - mask_whole_words - if language not in language_without_segmentations - else None - ) - lang_dataset = DenoisingDataset( - dataset, - dataset.sizes, - self.dictionary, - self.mask_idx, - lang_mask_whole_words, - shuffle=self.args.shuffle_instance, - seed=self.seed, - args=self.args, - eos=None - if not self.args.add_lang_token - else self.source_dictionary.index("[{}]".format(language)), - ) - lang_datasets.append(lang_dataset) - - dataset_lengths = np.array( - [len(d) for d in lang_datasets], - dtype=float, - ) - logger.info( - "loaded total {} blocks for all languages".format( - int(dataset_lengths.sum()), - ) - ) - if split == self.args.train_subset: - # For train subset, additionally up or down sample languages. - sample_probs = self._get_sample_prob(dataset_lengths) - logger.info( - "Sample probability by language: {}".format( - { - lang: "{0:.4f}".format(sample_probs[id]) - for id, lang in enumerate(languages) - } - ) - ) - size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths - logger.info( - "Up/Down Sampling ratio by language: {}".format( - { - lang: "{0:.2f}".format(size_ratio[id]) - for id, lang in enumerate(languages) - } - ) - ) - - resampled_lang_datasets = [ - ResamplingDataset( - lang_datasets[i], - size_ratio=size_ratio[i], - seed=self.args.seed, - epoch=epoch, - replace=size_ratio[i] >= 1.0, - ) - for i, d in enumerate(lang_datasets) - ] - dataset = ConcatDataset( - resampled_lang_datasets, - ) - else: - dataset = ConcatDataset(lang_datasets) - lang_splits = [split] - for lang_id, lang_dataset in enumerate(lang_datasets): - split_name = split + "_" + languages[lang_id] - lang_splits.append(split_name) - self.datasets[split_name] = lang_dataset - - if split in self.args.valid_subset: - self.args.valid_subset = self.args.valid_subset.replace( - split, ",".join(lang_splits) - ) - - with data_utils.numpy_seed(self.args.seed + epoch): - shuffle = np.random.permutation(len(dataset)) - - self.datasets[split] = SortDataset( - dataset, - sort_order=[ - shuffle, - dataset.sizes, - ], - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_language_modeling.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_language_modeling.py deleted file mode 100644 index f3e97d3137f601a0c0be903e107dfb74ee95dc18..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_language_modeling.py +++ /dev/null @@ -1,626 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -from dataclasses import dataclass, field -from typing import Optional - -import numpy as np -import torch -from fairseq import utils -from fairseq.data import ( - AppendTokenDataset, - ConcatDataset, - Dictionary, - IdDataset, - LMContextWindowDataset, - MonolingualDataset, - NestedDictionaryDataset, - NumelDataset, - PadDataset, - PrependTokenDataset, - ResamplingDataset, - SortDataset, - StripTokenDataset, - TokenBlockDataset, - TruncatedDictionary, - data_utils, -) -from fairseq.data.indexed_dataset import get_available_dataset_impl -from fairseq.data.shorten_dataset import maybe_shorten_dataset -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.tasks import LegacyFairseqTask, register_task -from omegaconf import II - -SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"]) -SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) -logger = logging.getLogger(__name__) - - -def lang_token(lang): - return f"<{lang}>" - - -@dataclass -class MultilingualLanguageModelingConfig(FairseqDataclass): - # TODO common var add to parent - data: Optional[str] = field( - default=None, metadata={"help": "path to data directory"} - ) - sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field( - default="none", - metadata={ - "help": 'If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - "of sentence, but may include multiple sentences per sample. " - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.' - }, - ) - tokens_per_sample: int = field( - default=1024, - metadata={"help": "max number of tokens per sample for LM dataset"}, - ) - output_dictionary_size: int = field( - default=-1, metadata={"help": "limit the size of output dictionary"} - ) - self_target: bool = field(default=False, metadata={"help": "include self target"}) - future_target: bool = field( - default=False, metadata={"help": "include future target"} - ) - past_target: bool = field(default=False, metadata={"help": "include past target"}) - add_bos_token: bool = field( - default=False, metadata={"help": "prepend lang id token "} - ) - max_source_positions: Optional[int] = field( - default=None, metadata={"help": "max number of tokens in the source sequence"} - ) - max_target_positions: Optional[int] = field( - default=None, metadata={"help": "max number of tokens in the target sequence"} - ) - pad_to_fixed_length: Optional[bool] = field( - default=False, metadata={"help": "pad to fixed length"} - ) - pad_to_fixed_bsz: Optional[bool] = field( - default=False, metadata={"help": "boolean to pad to fixed batch size"} - ) - - multilang_sampling_alpha: Optional[float] = field( - default=1.0, - metadata={ - "help": "smoothing alpha for sample rations across multiple datasets" - }, - ) - - shorten_method: SHORTEN_METHOD_CHOICES = field( - default="none", - metadata={ - "help": "if not none, shorten sequences that exceed --tokens-per-sample" - }, - ) - shorten_data_split_list: str = field( - default="", - metadata={ - "help": "comma-separated list of dataset splits to apply shortening to, " - 'e.g., "train,valid" (default: all dataset splits)' - }, - ) - - langs: str = field( - default="", - metadata={ - "help": "comma-separated list of languages (default: all directories in data path)" - }, - ) - baseline_model_langs: str = field( - default="", - metadata={ - "help": "comma-separated list of languages in the baseline model (default: none)" - }, - ) - # TODO: legacy parameter kept for compatibility - baseline_model: str = field( - default="", - metadata={"help": "path to the baseline model (default: none)"}, - ) - - lang_to_offline_shard_ratio: str = field( - default="", - metadata={ - "help": "absolute path of tsv file location to indicate lang to offline shard ratio.", - }, - ) - # TODO common vars below add to parent - seed: int = II("common.seed") - dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( - "dataset.dataset_impl" - ) - data_buffer_size: int = II("dataset.data_buffer_size") - tpu: bool = II("common.tpu") - batch_size: Optional[int] = II("dataset.batch_size") - batch_size_valid: Optional[int] = II("dataset.batch_size_valid") - train_subset: str = II("common.train_subset") - valid_subset: str = II("common.valid_subset") - - -@register_task( - "multilingual_language_modeling", dataclass=MultilingualLanguageModelingConfig -) -class MultilingualLanguageModelingTask(LegacyFairseqTask): - """ - Train a language model. - - Args: - dictionary (~fairseq.data.Dictionary): the dictionary for the input of - the language model - output_dictionary (~fairseq.data.Dictionary): the dictionary for the - output of the language model. In most cases it will be the same as - *dictionary*, but could possibly be a more limited version of the - dictionary (if ``--output-dictionary-size`` is used). - targets (List[str]): list of the target types that the language model - should predict. Can be one of "self", "future", and "past". - Defaults to "future". - - .. note:: - - The language modeling task is compatible with :mod:`fairseq-train`, - :mod:`fairseq-generate`, :mod:`fairseq-interactive` and - :mod:`fairseq-eval-lm`. - - The language modeling task provides the following additional command-line - arguments: - - .. argparse:: - :ref: fairseq.tasks.language_modeling_parser - :prog: - """ - - def __init__(self, args, dictionary, output_dictionary=None, targets=None): - super().__init__(args) - self.dictionary = dictionary - self.output_dictionary = output_dictionary or dictionary - - if targets is None: - targets = ["future"] - self.targets = targets - - @staticmethod - def _get_langs(args, epoch=1): - paths = utils.split_paths(args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - languages = sorted( - name - for name in os.listdir(data_path) - if os.path.isdir(os.path.join(data_path, name)) - ) - if args.langs: - keep_langs = set(args.langs.split(",")) - languages = [lang for lang in languages if lang in keep_langs] - assert len(languages) == len(keep_langs) - - return languages, data_path - - @classmethod - def setup_dictionary(cls, args, **kwargs): - dictionary = None - output_dictionary = None - if args.data: - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - if args.add_bos_token: - languages, _ = cls._get_langs(args) - logger.info("----------------") - for lang in languages: - dictionary.add_symbol(lang_token(lang)) - logger.info(f"add language token: {lang_token(lang)}") - logger.info("----------------") - - logger.info("dictionary: {} types".format(len(dictionary))) - output_dictionary = dictionary - if args.output_dictionary_size >= 0: - output_dictionary = TruncatedDictionary( - dictionary, args.output_dictionary_size - ) - return (dictionary, output_dictionary) - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - dictionary, output_dictionary = cls.setup_dictionary(args, **kwargs) - - # upgrade old checkpoints - if hasattr(args, "exclude_self_target"): - args.self_target = not args.exclude_self_target - - targets = [] - if getattr(args, "self_target", False): - targets.append("self") - if getattr(args, "future_target", False): - targets.append("future") - if getattr(args, "past_target", False): - targets.append("past") - if len(targets) == 0: - # standard language modeling - targets = ["future"] - - return cls(args, dictionary, output_dictionary, targets=targets) - - def build_model(self, args, from_checkpoint=False): - model = super().build_model(args, from_checkpoint) - for target in self.targets: - if target not in model.supported_targets: - raise ValueError( - f"Unsupported language modeling target: {target} not in {model.supported_targets}" - ) - - return model - - def _get_sample_prob(self, dataset_lens): - """ - Get smoothed sampling porbability by languages. This helps low resource - languages by upsampling them. - """ - prob = dataset_lens / dataset_lens.sum() - smoothed_prob = prob**self.args.multilang_sampling_alpha - smoothed_prob = smoothed_prob / smoothed_prob.sum() - return smoothed_prob - - def load_dataset(self, split: str, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - languages, data_path = MultilingualLanguageModelingTask._get_langs( - self.args, epoch - ) - lang_to_offline_shard_ratio = None - if self.args.lang_to_offline_shard_ratio != "": - lang_to_offline_shard_ratio = {} - assert os.path.exists( - self.args.lang_to_offline_shard_ratio - ), "provided offline shard ratio file doesn't exist: {0}".format( - self.args.lang_to_offline_shard_ratio - ) - with open(self.args.lang_to_offline_shard_ratio) as fin: - for line in fin: - lang, ratio = line.strip().split("\t") - ratio = float(ratio) - lang_to_offline_shard_ratio[lang] = ratio - - logger.info( - "Found offline sharded ratio: %s", - lang_to_offline_shard_ratio, - ) - - if split == self.args.train_subset: - logger.info( - "Training on {0} languages: {1}".format(len(languages), languages) - ) - else: - logger.info( - "Evaluating on {0} languages: {1}".format(len(languages), languages) - ) - - tokens_per_sample = self.args.tokens_per_sample - int(self.args.add_bos_token) - - fixed_pad_length = None - if self.args.pad_to_fixed_length: - fixed_pad_length = self.args.tokens_per_sample - - pad_to_bsz = None - if self.args.pad_to_fixed_bsz: - pad_to_bsz = ( - self.args.batch_size_valid if "valid" in split else self.args.batch_size - ) - - lang_datasets = [] - for lang_id, language in enumerate(languages): - split_path = os.path.join(data_path, language, split) - dataset = data_utils.load_indexed_dataset( - split_path, self.dictionary, self.args.dataset_impl, combine=combine - ) - # print('len(dataset) =', len(dataset)) - if dataset is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - dataset = maybe_shorten_dataset( - dataset, - split, - self.args.shorten_data_split_list, - self.args.shorten_method, - tokens_per_sample, - self.args.seed, - ) - - dataset = TokenBlockDataset( - dataset, - dataset.sizes, - tokens_per_sample, - pad=self.dictionary.pad(), - eos=self.dictionary.eos(), - break_mode=self.args.sample_break_mode, - include_targets=True, - ) - - add_eos_for_other_targets = ( - self.args.sample_break_mode is not None - and self.args.sample_break_mode != "none" - ) - src_lang_idx, tgt_lang_idx = None, None - if self.args.add_bos_token: - src_lang_idx = self.dictionary.index(lang_token(language)) - tgt_lang_idx = self.output_dictionary.index(lang_token(language)) - - lang_datasets.append( - MonolingualDataset( - dataset=dataset, - sizes=dataset.sizes, - src_vocab=self.dictionary, - tgt_vocab=self.output_dictionary, - add_eos_for_other_targets=add_eos_for_other_targets, - shuffle=True, - targets=self.targets, - fixed_pad_length=fixed_pad_length, - pad_to_bsz=pad_to_bsz, - add_bos_token=self.args.add_bos_token, - src_lang_idx=src_lang_idx, - tgt_lang_idx=tgt_lang_idx, - ) - ) - - dataset_lengths = np.array( - [len(d) for d in lang_datasets], - dtype=float, - ) - logger.info( - "loaded total {} blocks for all languages".format( - dataset_lengths.sum(), - ) - ) - if split == self.args.train_subset: - dataset_lengths_ratio_multiplier = np.ones(len(dataset_lengths)) - if lang_to_offline_shard_ratio is not None: - dataset_lengths_ratio_multiplier = [] - for lang in languages: - assert ( - lang in lang_to_offline_shard_ratio - ), "Lang: {0} missing in offline shard ratio file: {1}".format( - lang, - self.args.lang_to_offline_shard_ratio, - ) - dataset_lengths_ratio_multiplier.append( - lang_to_offline_shard_ratio[lang] - ) - dataset_lengths_ratio_multiplier = np.array( - dataset_lengths_ratio_multiplier - ) - true_dataset_lengths = ( - dataset_lengths * dataset_lengths_ratio_multiplier - ) - else: - true_dataset_lengths = dataset_lengths - # For train subset, additionally up or down sample languages. - sample_probs = self._get_sample_prob(true_dataset_lengths) - - logger.info( - "Sample probability by language: %s", - { - lang: "{0:.4f}".format(sample_probs[id]) - for id, lang in enumerate(languages) - }, - ) - size_ratio = (sample_probs * true_dataset_lengths.sum()) / dataset_lengths - # TODO: add an option for shrinking all size ratios to below 1 - # if self.args.multilang_sampling_alpha != 1: - # size_ratio /= size_ratio.max() - - # Fix numeric errors in size ratio computation - # 0.999999999999999999 -> 1 - # 1.000000000000000002 -> 1 - for i in range(len(size_ratio)): - size_ratio[i] = round(size_ratio[i], 8) - - logger.info( - "Up/Down Sampling ratio by language: %s", - { - lang: "{0:.2f}".format(size_ratio[id]) - for id, lang in enumerate(languages) - }, - ) - logger.info( - "Actual dataset size by language: %s", - { - lang: "{0:.2f}".format(len(lang_datasets[id])) - for id, lang in enumerate(languages) - }, - ) - resampled_lang_datasets = [ - ResamplingDataset( - lang_datasets[i], - size_ratio=size_ratio[i], - seed=self.args.seed, - epoch=epoch, - replace=size_ratio[i] > 1.0, - ) - for i, d in enumerate(lang_datasets) - ] - logger.info( - "Resampled dataset size by language: %s", - { - lang: "{0:.2f}".format(len(resampled_lang_datasets[id])) - for id, lang in enumerate(languages) - }, - ) - dataset = ConcatDataset(resampled_lang_datasets) - else: - dataset = ConcatDataset(lang_datasets) - lang_splits = [split] - for lang_id, lang_dataset in enumerate(lang_datasets): - split_name = split + "_" + languages[lang_id] - lang_splits.append(split_name) - self.datasets[split_name] = lang_dataset - - # [TODO]: This is hacky for now to print validation ppl for each - # language individually. Maybe need task API changes to allow it - # in more generic ways. - if split in self.args.valid_subset: - self.args.valid_subset = self.args.valid_subset.replace( - split, ",".join(lang_splits) - ) - - with data_utils.numpy_seed(self.args.seed + epoch): - shuffle = np.random.permutation(len(dataset)) - - self.datasets[split] = SortDataset( - dataset, - sort_order=[ - shuffle, - dataset.sizes, - ], - ) - - def build_dataset_for_inference( - self, src_tokens, src_lengths, language="en_XX", **kwargs - ): - """ - Generate batches for inference. We prepend an eos token to src_tokens - (or bos if `--add-bos-token` is set) and we append a to target. - This is convenient both for generation with a prefix and LM scoring. - """ - dataset = StripTokenDataset( - TokenBlockDataset( - src_tokens, - src_lengths, - block_size=None, # ignored for "eos" break mode - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode="eos", - ), - # remove eos from (end of) target sequence - self.source_dictionary.eos(), - ) - - src_lang_idx = self.dictionary.index(lang_token(language)) - src_dataset = PrependTokenDataset( - dataset, - token=( - (src_lang_idx or self.source_dictionary.bos()) - if getattr(self.args, "add_bos_token", False) - else self.source_dictionary.eos() - ), - ) - - max_seq_len = max(src_lengths) + 1 - tgt_dataset = AppendTokenDataset(dataset, token=self.source_dictionary.pad()) - return NestedDictionaryDataset( - { - "id": IdDataset(), - "net_input": { - "src_tokens": PadDataset( - src_dataset, - pad_idx=self.source_dictionary.pad(), - left_pad=False, - pad_length=max_seq_len, - ), - "src_lengths": NumelDataset(src_dataset, reduce=False), - }, - "target": PadDataset( - tgt_dataset, - pad_idx=self.source_dictionary.pad(), - left_pad=False, - pad_length=max_seq_len, - ), - }, - sizes=[np.array(src_lengths)], - ) - - @torch.no_grad() - def inference_step( - self, - generator, - models, - sample, - language="en_XX", - prefix_tokens=None, - constraints=None, - ): - # Generation will always be conditioned on bos_token - if getattr(self.args, "add_bos_token", False): - src_lang_idx = self.dictionary.index(lang_token(language)) - bos_token = src_lang_idx or self.source_dictionary.bos() - else: - bos_token = self.source_dictionary.eos() - - if constraints is not None: - raise NotImplementedError( - "Constrained decoding with the language_modeling task is not supported" - ) - - # SequenceGenerator doesn't use src_tokens directly, we need to - # pass the `prefix_tokens` argument instead - if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement(): - prefix_tokens = sample["net_input"]["src_tokens"] - if prefix_tokens[:, 0].eq(bos_token).all(): - prefix_tokens = prefix_tokens[:, 1:] - - return generator.generate( - models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token - ) - - def eval_lm_dataloader( - self, - dataset, - max_tokens: Optional[int] = 36000, - batch_size: Optional[int] = None, - max_positions: Optional[int] = None, - num_shards: int = 1, - shard_id: int = 0, - num_workers: int = 1, - data_buffer_size: int = 10, - # ensures that every evaluated token has access to a context of at least - # this size, if possible - context_window: int = 0, - ): - if context_window > 0: - dataset = LMContextWindowDataset( - dataset=dataset, - tokens_per_sample=self.args.tokens_per_sample, - context_window=context_window, - pad_idx=self.source_dictionary.pad(), - ) - return self.get_batch_iterator( - dataset=dataset, - max_tokens=max_tokens, - max_sentences=batch_size, - max_positions=max_positions, - ignore_invalid_inputs=True, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - data_buffer_size=data_buffer_size, - ) - - @property - def source_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self.dictionary - - @property - def target_dictionary(self): - """Return the :class:`~fairseq.data.Dictionary` for the language - model.""" - return self.output_dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_masked_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_masked_lm.py deleted file mode 100644 index 774bacd37252761e4f3a5c0c184b27bbb173db84..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_masked_lm.py +++ /dev/null @@ -1,337 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os - -import numpy as np -import torch -from fairseq import utils -from fairseq.data import ( - ConcatDataset, - Dictionary, - IdDataset, - MaskTokensDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - PadDataset, - PrependTokenDataset, - RawLabelDataset, - ResamplingDataset, - SortDataset, - TokenBlockDataset, - data_utils, - encoders, -) -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("multilingual_masked_lm") -class MultiLingualMaskedLMTask(LegacyFairseqTask): - """Task for training masked language models (e.g., BERT, RoBERTa).""" - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "data", - help="colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner", - ) - parser.add_argument( - "--sample-break-mode", - default="complete", - choices=["none", "complete", "complete_doc", "eos"], - help='If omitted or "none", fills each sample with tokens-per-sample ' - 'tokens. If set to "complete", splits samples only at the end ' - "of sentence, but may include multiple sentences per sample. " - '"complete_doc" is similar but respects doc boundaries. ' - 'If set to "eos", includes only one sentence per sample.', - ) - parser.add_argument( - "--tokens-per-sample", - default=512, - type=int, - help="max number of total tokens over all segments " - "per sample for BERT dataset", - ) - parser.add_argument( - "--mask-prob", - default=0.15, - type=float, - help="probability of replacing a token with mask", - ) - parser.add_argument( - "--leave-unmasked-prob", - default=0.1, - type=float, - help="probability that a masked token is unmasked", - ) - parser.add_argument( - "--random-token-prob", - default=0.1, - type=float, - help="probability of replacing a token with a random token", - ) - parser.add_argument( - "--freq-weighted-replacement", - action="store_true", - help="sample random replacement words based on word frequencies", - ) - parser.add_argument( - "--mask-whole-words", - default=False, - action="store_true", - help="mask whole words; you may also want to set --bpe", - ) - parser.add_argument( - "--multilang-sampling-alpha", - type=float, - default=1.0, - help="smoothing alpha for sample rations across multiple datasets", - ) - - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - self.seed = args.seed - - # add mask token - self.mask_idx = dictionary.add_symbol("") - - @classmethod - def setup_task(cls, args, **kwargs): - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(dictionary))) - return cls(args, dictionary) - - def _get_whole_word_mask(self): - # create masked input and targets - if self.args.mask_whole_words: - bpe = encoders.build_bpe(self.args) - if bpe is not None: - - def is_beginning_of_word(i): - if i < self.source_dictionary.nspecial: - # special elements are always considered beginnings - return True - tok = self.source_dictionary[i] - if tok.startswith("madeupword"): - return True - try: - return bpe.is_beginning_of_word(tok) - except ValueError: - return True - - mask_whole_words = torch.ByteTensor( - list(map(is_beginning_of_word, range(len(self.source_dictionary)))) - ) - else: - mask_whole_words = None - return mask_whole_words - - def _get_sample_prob(self, dataset_lens): - """ - Get smoothed sampling porbability by languages. This helps low resource - languages by upsampling them. - """ - prob = dataset_lens / dataset_lens.sum() - smoothed_prob = prob**self.args.multilang_sampling_alpha - smoothed_prob = smoothed_prob / smoothed_prob.sum() - return smoothed_prob - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - languages = sorted( - name - for name in os.listdir(data_path) - if os.path.isdir(os.path.join(data_path, name)) - ) - - logger.info("Training on {0} languages: {1}".format(len(languages), languages)) - logger.info( - "Language to id mapping: ", {lang: id for id, lang in enumerate(languages)} - ) - - mask_whole_words = self._get_whole_word_mask() - lang_datasets = [] - for lang_id, language in enumerate(languages): - split_path = os.path.join(data_path, language, split) - - dataset = data_utils.load_indexed_dataset( - split_path, - self.source_dictionary, - self.args.dataset_impl, - combine=combine, - ) - if dataset is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - # create continuous blocks of tokens - dataset = TokenBlockDataset( - dataset, - dataset.sizes, - self.args.tokens_per_sample - 1, # one less for - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode=self.args.sample_break_mode, - ) - logger.info("loaded {} blocks from: {}".format(len(dataset), split_path)) - - # prepend beginning-of-sentence token (, equiv. to [CLS] in BERT) - dataset = PrependTokenDataset(dataset, self.source_dictionary.bos()) - - src_dataset, tgt_dataset = MaskTokensDataset.apply_mask( - dataset, - self.source_dictionary, - pad_idx=self.source_dictionary.pad(), - mask_idx=self.mask_idx, - seed=self.args.seed, - mask_prob=self.args.mask_prob, - leave_unmasked_prob=self.args.leave_unmasked_prob, - random_token_prob=self.args.random_token_prob, - freq_weighted_replacement=self.args.freq_weighted_replacement, - mask_whole_words=mask_whole_words, - ) - - lang_dataset = NestedDictionaryDataset( - { - "net_input": { - "src_tokens": PadDataset( - src_dataset, - pad_idx=self.source_dictionary.pad(), - left_pad=False, - ), - "src_lengths": NumelDataset(src_dataset, reduce=False), - }, - "target": PadDataset( - tgt_dataset, - pad_idx=self.source_dictionary.pad(), - left_pad=False, - ), - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_dataset, reduce=True), - "lang_id": RawLabelDataset([lang_id] * src_dataset.sizes.shape[0]), - }, - sizes=[src_dataset.sizes], - ) - lang_datasets.append(lang_dataset) - - dataset_lengths = np.array( - [len(d) for d in lang_datasets], - dtype=float, - ) - logger.info( - "loaded total {} blocks for all languages".format( - dataset_lengths.sum(), - ) - ) - if split == self.args.train_subset: - # For train subset, additionally up or down sample languages. - sample_probs = self._get_sample_prob(dataset_lengths) - logger.info( - "Sample probability by language: ", - { - lang: "{0:.4f}".format(sample_probs[id]) - for id, lang in enumerate(languages) - }, - ) - size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths - logger.info( - "Up/Down Sampling ratio by language: ", - { - lang: "{0:.2f}".format(size_ratio[id]) - for id, lang in enumerate(languages) - }, - ) - - resampled_lang_datasets = [ - ResamplingDataset( - lang_datasets[i], - size_ratio=size_ratio[i], - seed=self.args.seed, - epoch=epoch, - replace=size_ratio[i] >= 1.0, - ) - for i, d in enumerate(lang_datasets) - ] - dataset = ConcatDataset(resampled_lang_datasets) - else: - dataset = ConcatDataset(lang_datasets) - lang_splits = [split] - for lang_id, lang_dataset in enumerate(lang_datasets): - split_name = split + "_" + languages[lang_id] - lang_splits.append(split_name) - self.datasets[split_name] = lang_dataset - - # [TODO]: This is hacky for now to print validation ppl for each - # language individually. Maybe need task API changes to allow it - # in more generic ways. - if split in self.args.valid_subset: - self.args.valid_subset = self.args.valid_subset.replace( - split, ",".join(lang_splits) - ) - - with data_utils.numpy_seed(self.args.seed + epoch): - shuffle = np.random.permutation(len(dataset)) - - self.datasets[split] = SortDataset( - dataset, - sort_order=[ - shuffle, - dataset.sizes, - ], - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True): - src_dataset = PadDataset( - TokenBlockDataset( - src_tokens, - src_lengths, - self.args.tokens_per_sample - 1, # one less for - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode="eos", - ), - pad_idx=self.source_dictionary.pad(), - left_pad=False, - ) - src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos()) - src_dataset = NestedDictionaryDataset( - { - "id": IdDataset(), - "net_input": { - "src_tokens": src_dataset, - "src_lengths": NumelDataset(src_dataset, reduce=False), - }, - }, - sizes=src_lengths, - ) - if sort: - src_dataset = SortDataset(src_dataset, sort_order=[src_lengths]) - return src_dataset - - @property - def source_dictionary(self): - return self.dictionary - - @property - def target_dictionary(self): - return self.dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_translation.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_translation.py deleted file mode 100644 index 0aea986e04b6cd9d82c4afee811867bc105eb988..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/multilingual_translation.py +++ /dev/null @@ -1,461 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import logging -import os -from argparse import ArgumentError -from collections import OrderedDict - -import torch -from fairseq import metrics, options, utils -from fairseq.data import ( - Dictionary, - LanguagePairDataset, - RoundRobinZipDatasets, - TransformEosLangPairDataset, -) -from fairseq.models import FairseqMultiModel -from fairseq.tasks.translation import load_langpair_dataset - -from . import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -def _lang_token(lang: str): - return "__{}__".format(lang) - - -def _lang_token_index(dic: Dictionary, lang: str): - """Return language token index.""" - idx = dic.index(_lang_token(lang)) - assert idx != dic.unk_index, "cannot find language token for lang {}".format(lang) - return idx - - -@register_task("multilingual_translation") -class MultilingualTranslationTask(LegacyFairseqTask): - """A task for training multiple translation models simultaneously. - - We iterate round-robin over batches from multiple language pairs, ordered - according to the `--lang-pairs` argument. - - The training loop is roughly: - - for i in range(len(epoch)): - for lang_pair in args.lang_pairs: - batch = next_batch_for_lang_pair(lang_pair) - loss = criterion(model_for_lang_pair(lang_pair), batch) - loss.backward() - optimizer.step() - - In practice, `next_batch_for_lang_pair` is abstracted in a FairseqDataset - (e.g., `RoundRobinZipDatasets`) and `model_for_lang_pair` is a model that - implements the `FairseqMultiModel` interface. - - During inference it is required to specify a single `--source-lang` and - `--target-lang`, which indicates the inference langauge direction. - `--lang-pairs`, `--encoder-langtok`, `--decoder-langtok` have to be set to - the same value as training. - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - parser.add_argument('data', metavar='DIR', help='path to data directory') - parser.add_argument('--lang-pairs', default=None, metavar='PAIRS', - help='comma-separated list of language pairs (in training order): en-de,en-fr,de-fr') - parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', - help='source language (only needed for inference)') - parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', - help='target language (only needed for inference)') - parser.add_argument('--left-pad-source', default='True', type=str, metavar='BOOL', - help='pad the source on the left (default: True)') - parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', - help='pad the target on the left (default: False)') - try: - parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the source sequence') - parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the target sequence') - except ArgumentError: - # this might have already been defined. Once we transition this to hydra it should be fine to add it here. - pass - parser.add_argument('--upsample-primary', default=1, type=int, - help='amount to upsample primary dataset') - parser.add_argument('--encoder-langtok', default=None, type=str, choices=['src', 'tgt'], - metavar='SRCTGT', - help='replace beginning-of-sentence in source sentence with source or target ' - 'language token. (src/tgt)') - parser.add_argument('--decoder-langtok', action='store_true', - help='replace beginning-of-sentence in target sentence with target language token') - # fmt: on - - def __init__(self, args, dicts, training): - super().__init__(args) - self.dicts = dicts - self.training = training - if training: - self.lang_pairs = args.lang_pairs - else: - self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] - # eval_lang_pairs for multilingual translation is usually all of the - # lang_pairs. However for other multitask settings or when we want to - # optimize for certain languages we want to use a different subset. Thus - # the eval_lang_pairs class variable is provided for classes that extend - # this class. - self.eval_lang_pairs = self.lang_pairs - # model_lang_pairs will be used to build encoder-decoder model pairs in - # models.build_model(). This allows multitask type of sub-class can - # build models other than the input lang_pairs - self.model_lang_pairs = self.lang_pairs - self.langs = list(dicts.keys()) - - @classmethod - def setup_task(cls, args, **kwargs): - dicts, training = cls.prepare(args, **kwargs) - return cls(args, dicts, training) - - @classmethod - def update_args(cls, args): - args.left_pad_source = utils.eval_bool(args.left_pad_source) - args.left_pad_target = utils.eval_bool(args.left_pad_target) - - if args.lang_pairs is None: - raise ValueError( - "--lang-pairs is required. List all the language pairs in the training objective." - ) - if isinstance(args.lang_pairs, str): - args.lang_pairs = args.lang_pairs.split(",") - - @classmethod - def prepare(cls, args, **kargs): - cls.update_args(args) - sorted_langs = sorted( - list({x for lang_pair in args.lang_pairs for x in lang_pair.split("-")}) - ) - if args.source_lang is not None or args.target_lang is not None: - training = False - else: - training = True - - # load dictionaries - dicts = OrderedDict() - for lang in sorted_langs: - paths = utils.split_paths(args.data) - assert len(paths) > 0 - dicts[lang] = cls.load_dictionary( - os.path.join(paths[0], "dict.{}.txt".format(lang)) - ) - if len(dicts) > 0: - assert dicts[lang].pad() == dicts[sorted_langs[0]].pad() - assert dicts[lang].eos() == dicts[sorted_langs[0]].eos() - assert dicts[lang].unk() == dicts[sorted_langs[0]].unk() - if args.encoder_langtok is not None or args.decoder_langtok: - for lang_to_add in sorted_langs: - dicts[lang].add_symbol(_lang_token(lang_to_add)) - logger.info("[{}] dictionary: {} types".format(lang, len(dicts[lang]))) - return dicts, training - - def get_encoder_langtok(self, src_lang, tgt_lang): - if self.args.encoder_langtok is None: - return self.dicts[src_lang].eos() - if self.args.encoder_langtok == "src": - return _lang_token_index(self.dicts[src_lang], src_lang) - else: - return _lang_token_index(self.dicts[src_lang], tgt_lang) - - def get_decoder_langtok(self, tgt_lang): - if not self.args.decoder_langtok: - return self.dicts[tgt_lang].eos() - return _lang_token_index(self.dicts[tgt_lang], tgt_lang) - - def alter_dataset_langtok( - self, - lang_pair_dataset, - src_eos=None, - src_lang=None, - tgt_eos=None, - tgt_lang=None, - ): - if self.args.encoder_langtok is None and not self.args.decoder_langtok: - return lang_pair_dataset - - new_src_eos = None - if ( - self.args.encoder_langtok is not None - and src_eos is not None - and src_lang is not None - and tgt_lang is not None - ): - new_src_eos = self.get_encoder_langtok(src_lang, tgt_lang) - else: - src_eos = None - - new_tgt_bos = None - if self.args.decoder_langtok and tgt_eos is not None and tgt_lang is not None: - new_tgt_bos = self.get_decoder_langtok(tgt_lang) - else: - tgt_eos = None - - return TransformEosLangPairDataset( - lang_pair_dataset, - src_eos=src_eos, - new_src_eos=new_src_eos, - tgt_bos=tgt_eos, - new_tgt_bos=new_tgt_bos, - ) - - def load_dataset(self, split, epoch=1, **kwargs): - """Load a dataset split.""" - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - def language_pair_dataset(lang_pair): - src, tgt = lang_pair.split("-") - langpair_dataset = load_langpair_dataset( - data_path, - split, - src, - self.dicts[src], - tgt, - self.dicts[tgt], - combine=True, - dataset_impl=self.args.dataset_impl, - upsample_primary=self.args.upsample_primary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - max_source_positions=self.args.max_source_positions, - max_target_positions=self.args.max_target_positions, - ) - return self.alter_dataset_langtok( - langpair_dataset, - src_eos=self.dicts[src].eos(), - src_lang=src, - tgt_eos=self.dicts[tgt].eos(), - tgt_lang=tgt, - ) - - self.datasets[split] = RoundRobinZipDatasets( - OrderedDict( - [ - (lang_pair, language_pair_dataset(lang_pair)) - for lang_pair in self.lang_pairs - ] - ), - eval_key=None - if self.training - else "%s-%s" % (self.args.source_lang, self.args.target_lang), - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): - if constraints is not None: - raise NotImplementedError( - "Constrained decoding with the multilingual_translation task is not supported" - ) - - lang_pair = "%s-%s" % (self.args.source_lang, self.args.target_lang) - return RoundRobinZipDatasets( - OrderedDict( - [ - ( - lang_pair, - self.alter_dataset_langtok( - LanguagePairDataset( - src_tokens, src_lengths, self.source_dictionary - ), - src_eos=self.source_dictionary.eos(), - src_lang=self.args.source_lang, - tgt_eos=self.target_dictionary.eos(), - tgt_lang=self.args.target_lang, - ), - ) - ] - ), - eval_key=lang_pair, - ) - - def build_model(self, args, from_checkpoint=False): - def check_args(): - messages = [] - if ( - len(set(self.args.lang_pairs).symmetric_difference(args.lang_pairs)) - != 0 - ): - messages.append( - "--lang-pairs should include all the language pairs {}.".format( - args.lang_pairs - ) - ) - if self.args.encoder_langtok != args.encoder_langtok: - messages.append( - "--encoder-langtok should be {}.".format(args.encoder_langtok) - ) - if self.args.decoder_langtok != args.decoder_langtok: - messages.append( - "--decoder-langtok should {} be set.".format( - "" if args.decoder_langtok else "not" - ) - ) - - if len(messages) > 0: - raise ValueError(" ".join(messages)) - - # Update args -> the fact that the constructor here - # changes the args object doesn't mean you get the same one here - self.update_args(args) - - # Check if task args are consistant with model args - check_args() - - from fairseq import models - - model = models.build_model(args, self, from_checkpoint) - if not isinstance(model, FairseqMultiModel): - raise ValueError( - "MultilingualTranslationTask requires a FairseqMultiModel architecture" - ) - return model - - def _per_lang_pair_train_loss( - self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad - ): - loss, sample_size, logging_output = criterion( - model.models[lang_pair], sample[lang_pair] - ) - if ignore_grad: - loss *= 0 - optimizer.backward(loss) - return loss, sample_size, logging_output - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - model.train() - from collections import defaultdict - - agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, defaultdict(float) - curr_lang_pairs = [ - lang_pair - for lang_pair in self.model_lang_pairs - if sample[lang_pair] is not None and len(sample[lang_pair]) != 0 - ] - - for idx, lang_pair in enumerate(curr_lang_pairs): - - def maybe_no_sync(): - if ( - self.args.distributed_world_size > 1 - and hasattr(model, "no_sync") - and idx < len(curr_lang_pairs) - 1 - ): - return model.no_sync() - else: - return contextlib.ExitStack() # dummy contextmanager - - with maybe_no_sync(): - loss, sample_size, logging_output = self._per_lang_pair_train_loss( - lang_pair, - model, - update_num, - criterion, - sample, - optimizer, - ignore_grad, - ) - agg_loss += loss.detach().item() - # TODO make summing of the sample sizes configurable - agg_sample_size += sample_size - for k in logging_output: - agg_logging_output[k] += logging_output[k] - agg_logging_output[f"{lang_pair}:{k}"] += logging_output[k] - return agg_loss, agg_sample_size, agg_logging_output - - def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample): - return criterion(model.models[lang_pair], sample[lang_pair]) - - def valid_step(self, sample, model, criterion): - model.eval() - with torch.no_grad(): - from collections import defaultdict - - agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, defaultdict(float) - for lang_pair in self.eval_lang_pairs: - if ( - lang_pair not in sample - or sample[lang_pair] is None - or len(sample[lang_pair]) == 0 - ): - continue - loss, sample_size, logging_output = self._per_lang_pair_valid_loss( - lang_pair, model, criterion, sample - ) - agg_loss += loss.data.item() - # TODO make summing of the sample sizes configurable - agg_sample_size += sample_size - for k in logging_output: - agg_logging_output[k] += logging_output[k] - agg_logging_output[f"{lang_pair}:{k}"] += logging_output[k] - return agg_loss, agg_sample_size, agg_logging_output - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - if self.args.decoder_langtok: - bos_token = _lang_token_index( - self.target_dictionary, self.args.target_lang - ) - else: - bos_token = self.target_dictionary.eos() - return generator.generate( - models, - sample, - prefix_tokens=prefix_tokens, - constraints=constraints, - bos_token=bos_token, - ) - - def reduce_metrics(self, logging_outputs, criterion): - with metrics.aggregate(): - # pass 'sample_size', 'nsentences', 'ntokens' stats to fairseq_task - super().reduce_metrics(logging_outputs, criterion) - for k in ["sample_size", "nsentences", "ntokens"]: - metrics.log_scalar(k, sum(l[k] for l in logging_outputs)) - - @property - def source_dictionary(self): - if self.training: - return next(iter(self.dicts.values())) - else: - return self.dicts[self.args.source_lang] - - @property - def target_dictionary(self): - if self.training: - return next(iter(self.dicts.values())) - else: - return self.dicts[self.args.target_lang] - - def max_positions(self): - """Return the max sentence length allowed by the task.""" - if len(self.datasets.values()) == 0: - return { - "%s-%s" - % (self.args.source_lang, self.args.target_lang): ( - self.args.max_source_positions, - self.args.max_target_positions, - ) - } - return OrderedDict( - [ - (key, (self.args.max_source_positions, self.args.max_target_positions)) - for split in self.datasets.keys() - for key in self.datasets[split].datasets.keys() - ] - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/online_backtranslation.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/online_backtranslation.py deleted file mode 100644 index bf1335912670a25e34ddb0a0f34311b92a8234ea..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/online_backtranslation.py +++ /dev/null @@ -1,680 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import json -import logging -import math -import os -from argparse import ArgumentError, Namespace -from collections import OrderedDict, defaultdict -from pathlib import Path -from typing import Dict, Sequence, Tuple - -import fairseq -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq import metrics, options, utils -from fairseq.data import ( - FairseqDataset, - LanguagePairDataset, - NoisingDataset, - PrependTokenDataset, - RoundRobinZipDatasets, - TransformEosLangPairDataset, - data_utils, - encoders, -) -from fairseq.sequence_generator import SequenceGenerator -from fairseq.tasks import register_task -from fairseq.tasks.translation import TranslationTask, load_langpair_dataset - -logger = logging.getLogger(__name__) - - -class PiecewiseLinearFn: - """Piecewise linear function. Can be configured with a string.""" - - def __init__(self, pieces: Sequence[Tuple[int, float]]): - assert pieces == sorted( - pieces - ), f"PiecewiseLinearFn configuration should be sorted, received: {pieces}" - - self.pieces = pieces - - def __call__(self, x: int) -> float: - for i, (x_a, y_a) in enumerate(self.pieces[:-1]): - x_b, y_b = self.pieces[i + 1] - if x_a <= x <= x_b: - return y_a + (x - x_a) * (y_b - y_a) / (x_b - x_a) - - return self.pieces[-1][1] - - @staticmethod - def from_string(configuration: str) -> "PiecewiseLinearFn": - """ - Parse the configuration of lambda coefficient (for scheduling). - x = "3" # lambda will be a constant equal to x - x = "0:1,1000:0" # lambda will start from 1 and linearly decrease - # to 0 during the first 1000 iterations - x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 - # iterations, then will linearly increase to 1 until iteration 2000 - """ - if isinstance(configuration, float): - return PiecewiseLinearFn([(0, configuration)]) - - try: - parts = configuration.split(",") - if len(parts) == 1: - v = float(configuration) - return PiecewiseLinearFn([(0, v)]) - - split = [s.split(":") for s in parts] - pieces = [(int(t), float(v)) for t, v in split] - return PiecewiseLinearFn(pieces) - except Exception: - raise ValueError( - f"Invalid PiecewiseLinearFn configuration: {configuration!r}" - ) - - @staticmethod - def one() -> "PiecewiseLinearFn": - return PiecewiseLinearFn([(0, 1.0)]) - - -@register_task("online_backtranslation") -class OnlineBackTranslationTask(TranslationTask): - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - # Generic translation args - parser.add_argument('data', help='colon separated path to data directories list, \ - will be iterated upon during epochs in round-robin manner; \ - however, valid and test data are always in the first directory to \ - avoid the need for repeating them in all directories') - parser.add_argument('--mono-langs', metavar='MONO_LANGS', - help='monolingual languages for training') - parser.add_argument('--valid-lang-pairs', default=None, metavar='VALID_LANG_PAIRS', - help='language pairs for validation') - parser.add_argument('--load-alignments', action='store_true', - help='load the binarized alignments') - parser.add_argument('--left-pad-source', default='False', type=str, metavar='BOOL', - help='pad the source on the left') - parser.add_argument('--left-pad-target', default='False', type=str, metavar='BOOL', - help='pad the target on the left') - parser.add_argument('--upsample-primary', default=1, type=int, - help='amount to upsample primary dataset') - try: - parser.add_argument('--max-source-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the source sequence') - parser.add_argument('--max-target-positions', default=1024, type=int, metavar='N', - help='max number of tokens in the target sequence') - except ArgumentError: - # this might have already been defined. Once we transition this to hydra it should be fine to add it here. - pass - parser.add_argument('--truncate-source', action='store_true', default=False, - help='truncate source to max-source-positions') - parser.add_argument('--num-batch-buckets', default=0, type=int, metavar='N', - help='if >0, then bucket source and target lengths into N ' - 'buckets and pad accordingly; this is useful on TPUs ' - 'to minimize the number of compilations') - - # Denoising args - parser.add_argument('--max-word-shuffle-distance', default=3.0, type=float, metavar='N', - help='maximum word shuffle distance for denoising autoencoding data generation') - parser.add_argument('--word-dropout-prob', default=0.1, type=float, metavar='N', - help='word dropout probability for denoising autoencoding data generation') - parser.add_argument('--word-blanking-prob', default=0.2, type=float, metavar='N', - help='word blanking probability for denoising autoencoding data generation') - - # Backtranslation args - parser.add_argument('--lambda-bt', default="1.0", type=str, metavar='N', - help='back-translation weight') - parser.add_argument('--lambda-dae', default="1.0", type=str, metavar='N', - help='denoising auto-encoder weight') - - # Evaluation args - parser.add_argument('--generate-one-by-one', action='store_true', - help='generate one sentence at a time for backtranslation') - - parser.add_argument('--eval-bleu', action='store_true', - help='evaluation with BLEU scores') - parser.add_argument('--eval-bleu-detok', type=str, default="space", - help='detokenize before computing BLEU (e.g., "moses"); ' - 'required if using --eval-bleu; use "space" to ' - 'disable detokenization; see fairseq.data.encoders ' - 'for other options') - parser.add_argument('--eval-bleu-detok-args', type=str, metavar='JSON', - help='args for building the tokenizer, if needed') - parser.add_argument('--eval-tokenized-bleu', action='store_true', default=False, - help='compute tokenized BLEU instead of sacrebleu') - parser.add_argument('--eval-bleu-remove-bpe', nargs='?', const='@@ ', default=None, - help='remove BPE before computing BLEU') - parser.add_argument('--eval-bleu-args', type=str, metavar='JSON', - help='generation args for BLUE scoring, ' - 'e.g., \'{"beam": 4, "lenpen": 0.6}\'') - parser.add_argument('--eval-bleu-print-samples', action='store_true', - help='print sample generations during validation') - # fmt: on - - def __init__(self, args, common_dict, mono_langs, valid_lang_pairs): - super().__init__(args, common_dict, common_dict) - self.common_dict = common_dict - self.mono_langs = mono_langs - self.valid_lang_pairs = valid_lang_pairs - - self.SHOW_SAMPLES_INTERVAL = 1000 - # Start by showing samples - self._show_samples_ctr = self.SHOW_SAMPLES_INTERVAL - self.SHOW_SAMPLES_NUMBER = 5 - self.lambda_bt = PiecewiseLinearFn.from_string(args.lambda_bt) - self.lambda_dae = PiecewiseLinearFn.from_string(args.lambda_dae) - - self.args = args - self.data = utils.split_paths(self.args.data) - if len(self.data) == 1: - shards = list(Path(self.data[0]).glob("shard*")) - if len(shards) > 0: - # keep this as strings, since it can also be a manifold path - old_data = self.data - self.data = [str(shard) for shard in shards] - logging.warning(f"Expanded data directory {old_data} to {self.data}") - - @classmethod - def setup_task(cls, args, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - args.left_pad_source = options.eval_bool(args.left_pad_source) - args.left_pad_target = options.eval_bool(args.left_pad_target) - - paths = utils.split_paths(args.data) - assert len(paths) > 0 - assert args.mono_langs is not None - - mono_langs = args.mono_langs.split(",") - valid_lang_pairs = args.valid_lang_pairs.split(",") - - # load dictionary - dict_path = os.path.join(paths[0], "dict.txt") - common_dict = cls.load_dictionary(dict_path) - - return cls(args, common_dict, mono_langs, valid_lang_pairs) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs) -> FairseqDataset: - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - if split == "train": - data_path = self.data[(epoch - 1) % len(self.data)] - dataset = self.load_train_dataset(data_path) - else: - # valid/test should always be the same. - dataset = self.load_translation_dataset(split, self.data[0]) - - self.datasets[split] = dataset - return dataset - - def load_train_dataset(self, data_path: str) -> FairseqDataset: - """The training dataset is made of backtranslation dataset and denoising dataset.""" - data = [] - for lang in self.mono_langs: - train_path = os.path.join(data_path, lang, "train") - # TODO: could we do the BT using denoise sample ? - # this would half the data loading work - data.append((f"{lang}-BT", self.load_bt_dataset(train_path, lang))) - data.append( - (f"{lang}-DENOISE", self.load_denoise_dataset(train_path, lang)) - ) - - return RoundRobinZipDatasets(OrderedDict(data)) - - def _langpair_dataset( - self, src: FairseqDataset, tgt: FairseqDataset - ) -> LanguagePairDataset: - return LanguagePairDataset( - src, - src.sizes, - self.dictionary, - tgt=tgt, - tgt_sizes=tgt.sizes, - tgt_dict=self.dictionary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - # TODO: should we shuffle ? we are already sorting batch by sizes so ? - # shuffle=True, - ) - - def _prepend_lang_bos_to_target( - self, dataset: LanguagePairDataset, lang: str - ) -> LanguagePairDataset: - bos = _lang_token_index(self.dictionary, lang) - return TransformEosLangPairDataset( - dataset, - src_eos=self.dictionary.eos(), - new_src_eos=self.dictionary.eos(), - tgt_bos=self.dictionary.eos(), - new_tgt_bos=bos, - ) - - def load_bt_dataset(self, data_path: str, lang: str) -> FairseqDataset: - """The BT dataset is generated with (tgt, tgt) pairs. - The actual translation to a (generated_src, tgt) pair - is done on the fly during training. - """ - mono_dataset = data_utils.load_indexed_dataset( - data_path, self.common_dict, self.args.dataset_impl - ) - assert mono_dataset is not None, f"No dataset found for {lang}" - - mono_dataset_src = PrependTokenDataset( - mono_dataset, _lang_token_index(self.dictionary, lang) - ) - - mono_dataset_bt = self._langpair_dataset(mono_dataset_src, mono_dataset) - logger.info( - f"mono_lang = {lang} " - f"lang token index = {_lang_token_index(self.dictionary, lang)} " - f"lang token = {_lang_token(lang)}" - ) - - mono_dataset_bt = self._prepend_lang_bos_to_target(mono_dataset_bt, lang) - return mono_dataset_bt - - def load_denoise_dataset(self, data_path: str, lang: str) -> FairseqDataset: - """Classic denoising dataset""" - dataset = data_utils.load_indexed_dataset( - data_path, self.common_dict, self.args.dataset_impl - ) - noisy_dataset = NoisingDataset( - dataset, - self.dictionary, - seed=1, - max_word_shuffle_distance=self.args.max_word_shuffle_distance, - word_dropout_prob=self.args.word_dropout_prob, - word_blanking_prob=self.args.word_blanking_prob, - ) - noisy_dataset = PrependTokenDataset( - noisy_dataset, _lang_token_index(self.dictionary, lang) - ) - - clean_dataset = data_utils.load_indexed_dataset( - data_path, self.common_dict, self.args.dataset_impl - ) - denoising_dataset = self._langpair_dataset(noisy_dataset, clean_dataset) - denoising_dataset = self._prepend_lang_bos_to_target(denoising_dataset, lang) - return denoising_dataset - - def load_translation_dataset( - self, split: str, data_path: str, combine: bool = False - ): - # only judging with one language pair for the moment, - # since ConcatDataset doesn't work as expected - assert len(self.valid_lang_pairs) == 1, "For now..." - valid_lang_pair = self.valid_lang_pairs[0] - src, tgt = valid_lang_pair.split("-") - - # use the same function than TranslationTask - src_tgt_dt = load_langpair_dataset( - data_path, - split, - src, - self.common_dict, - tgt, - self.common_dict, - combine=combine, - dataset_impl=self.args.dataset_impl, - upsample_primary=self.args.upsample_primary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - max_source_positions=self.args.max_source_positions, - max_target_positions=self.args.max_target_positions, - load_alignments=self.args.load_alignments, - truncate_source=self.args.truncate_source, - num_buckets=self.args.num_batch_buckets, - shuffle=(split != "test"), - prepend_bos_src=_lang_token_index(self.dictionary, src), - ) - - src_tgt_eos_dt = self._prepend_lang_bos_to_target(src_tgt_dt, tgt) - src_tgt_eos_dt.args = self.args - return src_tgt_eos_dt - - def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): - raise NotImplementedError - - def build_model(self, args, from_checkpoint=False): - # torch.autograd.set_detect_anomaly(True) - model = super().build_model(args, from_checkpoint) - - add_secial_tokens_to_dict_and_model(self.common_dict, model, self.mono_langs) - - self.sequence_generators = {} - for mono_lang in self.mono_langs: - self.sequence_generators[mono_lang] = SequenceGenerator( - [model], - tgt_dict=self.dictionary, - beam_size=1, - max_len_a=1.3, - max_len_b=5, - min_len=5, - # keep 1 to be able to prepend bos - max_len=model.max_decoder_positions() - 1, - ) - - if getattr(args, "eval_bleu", False): - assert getattr(args, "eval_bleu_detok", None) is not None, ( - "--eval-bleu-detok is required if using --eval-bleu; " - "try --eval-bleu-detok=moses (or --eval-bleu-detok=space " - "to disable detokenization, e.g., when using sentencepiece)" - ) - detok_args = json.loads(getattr(args, "eval_bleu_detok_args", "{}") or "{}") - self.tokenizer = encoders.build_tokenizer( - Namespace( - tokenizer=getattr(args, "eval_bleu_detok", None), **detok_args - ) - ) - - gen_args = json.loads(getattr(args, "eval_bleu_args", "{}") or "{}") - self.bleu_sequence_generator = self.build_generator( - [model], Namespace(**gen_args) - ) - - return model - - def max_positions(self): - """Return the max sentence length allowed by the task.""" - return (self.args.max_source_positions, self.args.max_target_positions) - - @property - def dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary`.""" - return self.common_dict - - def display_samples_once_in_a_while(self, smp, mono_lang, other_lang): - self._show_samples_ctr += 1 - if self._show_samples_ctr < self.SHOW_SAMPLES_INTERVAL: - return - self._show_samples_ctr = 0 - - ln = smp["net_input"]["src_tokens"].shape[0] - - logger.info( - f"(r:{self.args.distributed_rank}) : " - f"{other_lang} ---> {mono_lang} " - f"({other_lang} was generated by back-translation.) {ln} samples" - ) - - for i in range(min(ln, self.SHOW_SAMPLES_NUMBER)): - src_tokens = smp["net_input"]["src_tokens"][i] - tgt_tokens = smp["target"][i] - - src_str = self.dictionary.string(src_tokens, "sentencepiece") - tgt_str = self.dictionary.string(tgt_tokens, "sentencepiece") - logger.info( - f"\n{i}\t\t[{other_lang} generated] {src_str}\n" - f"\t\t[{mono_lang} original ] {tgt_str}\n" - f"\t\t[ src tokens] {src_tokens}\n" - ) - - def backtranslate_sample(self, smp, orig_lang, other_lang) -> None: - """ - * WARNING: smp is modified in place. - * At the start of this function, `smp` has the same input and target: - |--------------------------------------------------------| - | smp['net_input']['src_tokens'] | smp['target'] | - | (from data) __en__ hello world | __en__ hello world | - |--------------------------------------------------------| - - * We call generator.generate(smp, bos_token = token("ro")), - and copy the result as input - * At the end, `smp` has the translation to other language. - |--------------------------------------------------------| - | smp['net_input']['src_tokens'] | smp['target'] | - | (generated) __ro__ salut lume | __en__ hello world | - |--------------------------------------------------------| - - """ - bos_token = _lang_token_index(self.dictionary, other_lang) - generated = self.sequence_generators[orig_lang].generate( - models=[], sample=smp, bos_token=bos_token - ) - - max_lngth = max([gn[0]["tokens"].size(0) for gn in generated]) - net_input = smp["net_input"] - n_src_tokens = torch.empty( - size=(len(generated), max_lngth + 1), dtype=net_input["src_tokens"].dtype - ) - n_src_lengths = torch.empty( - len(generated), dtype=net_input["src_lengths"].dtype - ) - - for i, gn in enumerate(generated): - tokens = gn[0]["tokens"] - tokens_size = tokens.size(0) - padding_needed = max_lngth - tokens_size - tokens = torch.cat([tokens.new([bos_token]), tokens]) - tokens = F.pad(tokens, (0, padding_needed), value=self.dictionary.pad()) - n_src_tokens[i] = tokens - n_src_lengths[i] = tokens_size + 1 - - device = net_input["src_tokens"].device - # This seems to be important - del net_input["src_tokens"] - del net_input["src_lengths"] - net_input["src_tokens"] = n_src_tokens.to(device) - net_input["src_lengths"] = n_src_lengths.to(device) - - def generate(self, smp, model): - model.eval() - orig_lang = ( - self.dictionary[smp["net_input"]["src_tokens"][0][0]] - .replace(" ", "") - .replace("_", "") - ) - bos_token = smp["net_input"]["prev_output_tokens"][0][0] - with torch.no_grad(): - generated = self.sequence_generators[orig_lang].generate( - models=[model], sample=smp, bos_token=bos_token - ) - return generated - - def get_other_lang(self, lang): - # TODO: allow more complex mapping - if lang != self.mono_langs[0]: - return self.mono_langs[0] - if len(self.mono_langs) == 2: - return self.mono_langs[1] - return self.mono_langs[np.random.randint(1, len(self.mono_langs))] - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - - model.train() - model.set_num_updates(update_num) - - agg_loss, agg_sample_size = 0.0, 0.0 - agg_logging_output: Dict[str, float] = defaultdict(float) - - dataset_keys = self.datasets["train"].datasets.keys() - - weights = { - "BT": self.lambda_bt(update_num), - "DENOISE": self.lambda_dae(update_num), - } - log_keys = {"BT": "bt_", "DENOISE": "dae_"} - - for dataset_key in dataset_keys: - smp = sample[dataset_key] - mono_lang, task_subtype = dataset_key.split("-") - if weights[task_subtype] == 0: - continue - - if task_subtype == "BT": - with torch.autograd.profiler.record_function("backtranslation"): - model.eval() - # TODO: Could we translate to several language at once ? - # this would allow to share encoder_out and maximize GPU usage. - other_lang = self.get_other_lang(mono_lang) - self.backtranslate_sample(smp, mono_lang, other_lang) - self.display_samples_once_in_a_while(smp, mono_lang, other_lang) - model.train() - - # Like in FairseqTask.train_step - with torch.autograd.profiler.record_function("forward"): - loss, sample_size, logging_output = criterion(model, smp) - loss *= weights[task_subtype] - if ignore_grad: - loss *= 0 - with torch.autograd.profiler.record_function("backward"): - optimizer.backward(loss) - - agg_loss += loss.item() - agg_sample_size += sample_size - for k in logging_output: - agg_logging_output[log_keys[task_subtype] + k] += logging_output[k] - agg_logging_output[k] += logging_output[k] - - return agg_loss, agg_sample_size, agg_logging_output - - def get_bos_token_from_sample(self, sample): - net_input = sample["net_input"] - source_lang_token_id = torch.unique(net_input["src_tokens"][:, 0]).item() - source_lang_token = self.dictionary[source_lang_token_id].replace("_", "") - target_lang_token_id = _lang_token_index( - self.dictionary, self.get_other_lang(source_lang_token) - ) - - return target_lang_token_id - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - bt_sample_size = sum(x.get("bt_sample_size", 0) for x in logging_outputs) - if bt_sample_size: - bt_loss_sum = sum(x.get("bt_loss", 0) for x in logging_outputs) - bt_loss_sum *= 1 / bt_sample_size / math.log(2) - metrics.log_scalar("bt_loss", bt_loss_sum, bt_sample_size, round=3) - - bt_nll_loss_sum = sum(x.get("bt_nll_loss", 0) for x in logging_outputs) - bt_ntokens = sum(x.get("bt_ntokens", 0) for x in logging_outputs) - bt_nll_loss_sum *= 1 / bt_ntokens / math.log(2) - metrics.log_scalar("bt_nll_loss", bt_nll_loss_sum, bt_ntokens, round=3) - metrics.log_derived( - "bt_ppl", lambda meters: utils.get_perplexity(meters["bt_nll_loss"].avg) - ) - - dae_sample_size = sum(x.get("dae_sample_size", 0) for x in logging_outputs) - if dae_sample_size: - dae_loss_sum = sum(x.get("dae_loss", 0) for x in logging_outputs) - dae_loss_sum *= 1 / dae_sample_size / math.log(2) - metrics.log_scalar("dae_loss", dae_loss_sum, dae_sample_size, round=3) - - dae_nll_loss_sum = sum(x.get("dae_nll_loss", 0) for x in logging_outputs) - dae_ntokens = sum(x.get("dae_ntokens", 0) for x in logging_outputs) - dae_nll_loss_sum *= 1 / dae_ntokens / math.log(2) - metrics.log_scalar("dae_nll_loss", dae_nll_loss_sum, dae_ntokens, round=3) - metrics.log_derived( - "dae_ppl", - lambda meters: utils.get_perplexity(meters["dae_nll_loss"].avg), - ) - - -@torch.no_grad() -def extend_embedding( - emb: nn.Module, new_vocab_size: int, copy_from_token_id: int -) -> None: - old_emb_data = emb.weight.data - (old_vocab_size, dim) = old_emb_data.shape - assert new_vocab_size >= old_vocab_size - - if new_vocab_size > old_vocab_size: - emb.weight.data = torch.zeros((new_vocab_size, dim)) - emb.weight.data[:old_vocab_size, :] = old_emb_data - # initialize new embeddings - emb.weight.data[old_vocab_size:, :] = old_emb_data[copy_from_token_id] - if hasattr(emb, "num_embeddings"): - emb.num_embeddings = new_vocab_size - if hasattr(emb, "out_features"): - emb.out_features = new_vocab_size - - if getattr(emb, "bias", None) is None: - return - - # Fix the bias. - # Bias shape can be different from the previous vocab size - # if the weight matrix was shared and alread extended but not the bias. - (old_vocab_size,) = emb.bias.shape - assert new_vocab_size >= old_vocab_size - if new_vocab_size > old_vocab_size: - old_bias = emb.bias.data - new_bias = torch.zeros( - (new_vocab_size,), dtype=old_bias.dtype, device=old_bias.device - ) - new_bias[:old_vocab_size] = old_bias - emb.bias.data = new_bias - - -def add_secial_tokens_to_dict_and_model( - dictionary: "fairseq.data.Dictionary", - model: nn.Module, - mono_langs: Sequence[str], -) -> None: - embs = model.encoder.embed_tokens - vocab_size, embedding_dim = embs.weight.shape - - # The model may or may not have a '' embedding yet - assert ( - len(dictionary) <= vocab_size <= len(dictionary) + 1 - ), f"Dictionary len ({len(dictionary)}) doesn't match embs shape ({embs.weight.shape})" - # TODO: we should reuse the pretrained model dict which already has - dictionary.add_symbol("") - - for lang in mono_langs: - lang_token = _lang_token(lang) - dictionary.add_symbol(lang_token) - logger.info( - f"dictionary: {len(dictionary)} -> {vocab_size} tokens " - f"after adding {len(mono_langs)} lang tokens." - ) - - if len(dictionary) <= vocab_size: - return - - extend_embedding(embs, len(dictionary), dictionary.bos()) - dec_embs = model.decoder.embed_tokens - extend_embedding(dec_embs, len(dictionary), dictionary.bos()) - lm_head = model.decoder.output_projection - extend_embedding(lm_head, len(dictionary), dictionary.bos()) - assert lm_head.weight.shape == (len(dictionary), embedding_dim) - - -def _lang_token(lang: str) -> str: - return f"__{lang}__" - - -def _lang_token_index(dictionary, lang: str) -> int: - return dictionary.index(_lang_token(lang)) - - -@contextlib.contextmanager -def assert_weights_have_changed(model: nn.Module): - def checksum(model: nn.Module) -> float: - return sum(p.sum().item() for p in model.parameters()) - - initial_checksum = checksum(model) - yield model - final_checksum = checksum(model) - logger.info( - f"initial_checksum={initial_checksum} -> final_checksum={final_checksum}" - ) - assert initial_checksum != final_checksum, "Model hasn't changed !" diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/semisupervised_translation.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/semisupervised_translation.py deleted file mode 100644 index 769f05b536d087e125faf1ded7711c8a0c64012a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/semisupervised_translation.py +++ /dev/null @@ -1,484 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -from collections import OrderedDict - -from fairseq import utils -from fairseq.data import ( - BacktranslationDataset, - IndexedCachedDataset, - IndexedDataset, - IndexedRawTextDataset, - LanguagePairDataset, - NoisingDataset, - RoundRobinZipDatasets, - data_utils, - indexed_dataset, -) -from fairseq.models import FairseqMultiModel -from fairseq.sequence_generator import SequenceGenerator - -from . import register_task -from .multilingual_translation import MultilingualTranslationTask - -logger = logging.getLogger(__name__) - - -def _get_bt_dataset_key(lang_pair): - return "bt:" + lang_pair - - -def _get_denoising_dataset_key(lang_pair): - return "denoising:" + lang_pair - - -# ported from UnsupervisedMT -def parse_lambda_config(x): - """ - Parse the configuration of lambda coefficient (for scheduling). - x = "3" # lambda will be a constant equal to x - x = "0:1,1000:0" # lambda will start from 1 and linearly decrease - # to 0 during the first 1000 iterations - x = "0:0,1000:0,2000:1" # lambda will be equal to 0 for the first 1000 - # iterations, then will linearly increase to 1 until iteration 2000 - """ - split = x.split(",") - if len(split) == 1: - return float(x), None - else: - split = [s.split(os.pathsep) for s in split] - assert all(len(s) == 2 for s in split) - assert all(k.isdigit() for k, _ in split) - assert all( - int(split[i][0]) < int(split[i + 1][0]) for i in range(len(split) - 1) - ) - return float(split[0][1]), [(int(k), float(v)) for k, v in split] - - -@register_task("semisupervised_translation") -class SemisupervisedTranslationTask(MultilingualTranslationTask): - """A task for training multiple translation models simultaneously. - - We iterate round-robin over batches from multiple language pairs, ordered - according to the `--lang-pairs` argument. - - The training loop is roughly: - - for i in range(len(epoch)): - for lang_pair in args.lang_pairs: - batch = next_batch_for_lang_pair(lang_pair) - loss = criterion(model_for_lang_pair(lang_pair), batch) - loss.backward() - optimizer.step() - - In practice, `next_batch_for_lang_pair` is abstracted in a FairseqDataset - (e.g., `RoundRobinZipDatasets`) and `model_for_lang_pair` is a model that - implements the `FairseqMultiModel` interface. - - During inference it is required to specify a single `--source-lang` and - `--target-lang`, instead of `--lang-pairs`. - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - MultilingualTranslationTask.add_args(parser) - parser.add_argument('--lambda-parallel-config', default="1.0", type=str, metavar='CONFIG', - help='cross-entropy reconstruction coefficient (parallel data). ' - 'use fixed weight during training if set to floating point number. ' - 'use piecewise linear function over number of updates to schedule the ' - 'weight with the format: w0:step0,w1:step1,...') - parser.add_argument('--lambda-denoising-config', default="0.0", type=str, metavar='CONFIG', - help='Cross-entropy reconstruction coefficient (denoising autoencoding)' - 'use fixed weight during training if set to floating point number. ' - 'use piecewise linear function over number of updates to schedule the ' - 'weight with the format: w0:step0,w1:step1,...') - parser.add_argument('--lambda-otf-bt-config', default="0.0", type=str, metavar='CONFIG', - help='cross-entropy reconstruction coefficient (on-the-fly back-translation parallel data)' - 'use fixed weight during training if set to floating point number. ' - 'use piecewise linear function over number of updates to schedule the ' - 'weight with the format: w0:step0,w1:step1,...') - parser.add_argument('--bt-max-len-a', default=1.1, type=float, metavar='N', - help='generate back-translated sequences of maximum length ax + b, where x is the ' - 'source length') - parser.add_argument('--bt-max-len-b', default=10.0, type=float, metavar='N', - help='generate back-translated sequences of maximum length ax + b, where x is the ' - 'source length') - parser.add_argument('--bt-beam-size', default=1, type=int, metavar='N', - help='beam size used in beam search of online back-translation') - parser.add_argument('--max-word-shuffle-distance', default=3.0, type=float, metavar='N', - help='maximum word shuffle distance for denoising autoencoding data generation') - parser.add_argument('--word-dropout-prob', default=0.1, type=float, metavar='N', - help='word dropout probability for denoising autoencoding data generation') - parser.add_argument('--word-blanking-prob', default=0.2, type=float, metavar='N', - help='word blanking probability for denoising autoencoding data generation') - # fmt: on - - def __init__(self, args, dicts, training): - super().__init__(args, dicts, training) - self.lambda_parallel, self.lambda_parallel_steps = parse_lambda_config( - args.lambda_parallel_config - ) - self.lambda_otf_bt, self.lambda_otf_bt_steps = parse_lambda_config( - args.lambda_otf_bt_config - ) - self.lambda_denoising, self.lambda_denoising_steps = parse_lambda_config( - args.lambda_denoising_config - ) - if self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None: - denoising_lang_pairs = [ - "%s-%s" % (tgt, tgt) - for tgt in {lang_pair.split("-")[1] for lang_pair in args.lang_pairs} - ] - self.model_lang_pairs = self.model_lang_pairs + denoising_lang_pairs - self.backtranslate_datasets = {} - self.backtranslators = {} - - @classmethod - def setup_task(cls, args, **kwargs): - dicts, training = MultilingualTranslationTask.prepare(args, **kwargs) - return cls(args, dicts, training) - - def load_dataset(self, split, epoch=1, **kwargs): - """Load a dataset split.""" - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - def split_exists(split, src, tgt, lang): - if src is not None: - filename = os.path.join( - data_path, "{}.{}-{}.{}".format(split, src, tgt, lang) - ) - else: - filename = os.path.join( - data_path, "{}.{}-None.{}".format(split, src, tgt) - ) - return indexed_dataset.dataset_exists(filename, impl=self.args.dataset_impl) - - def load_indexed_dataset(path, dictionary): - return data_utils.load_indexed_dataset( - path, dictionary, self.args.dataset_impl - ) - - # load parallel datasets - src_datasets, tgt_datasets = {}, {} - if ( - self.lambda_parallel > 0.0 - or self.lambda_parallel_steps is not None - or not split.startswith("train") - ): - for lang_pair in self.lang_pairs: - src, tgt = lang_pair.split("-") - if split_exists(split, src, tgt, src): - prefix = os.path.join( - data_path, "{}.{}-{}.".format(split, src, tgt) - ) - elif split_exists(split, tgt, src, src): - prefix = os.path.join( - data_path, "{}.{}-{}.".format(split, tgt, src) - ) - else: - continue - src_datasets[lang_pair] = load_indexed_dataset( - prefix + src, self.dicts[src] - ) - tgt_datasets[lang_pair] = load_indexed_dataset( - prefix + tgt, self.dicts[tgt] - ) - logger.info( - "parallel-{} {} {} examples".format( - data_path, split, len(src_datasets[lang_pair]) - ) - ) - if len(src_datasets) == 0: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, data_path) - ) - - # back translation datasets - backtranslate_datasets = {} - if ( - self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None - ) and split.startswith("train"): - for lang_pair in self.lang_pairs: - src, tgt = lang_pair.split("-") - if not split_exists(split, tgt, None, tgt): - raise FileNotFoundError( - "Dataset not found: backtranslation {} ({})".format( - split, data_path - ) - ) - filename = os.path.join( - data_path, "{}.{}-None.{}".format(split, tgt, tgt) - ) - dataset = load_indexed_dataset(filename, self.dicts[tgt]) - lang_pair_dataset_tgt = LanguagePairDataset( - dataset, - dataset.sizes, - self.dicts[tgt], - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - ) - lang_pair_dataset = LanguagePairDataset( - dataset, - dataset.sizes, - src_dict=self.dicts[src], - tgt=dataset, - tgt_sizes=dataset.sizes, - tgt_dict=self.dicts[tgt], - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - ) - backtranslate_datasets[lang_pair] = BacktranslationDataset( - tgt_dataset=self.alter_dataset_langtok( - lang_pair_dataset_tgt, - src_eos=self.dicts[tgt].eos(), - src_lang=tgt, - tgt_lang=src, - ), - backtranslation_fn=self.backtranslators[lang_pair], - src_dict=self.dicts[src], - tgt_dict=self.dicts[tgt], - output_collater=self.alter_dataset_langtok( - lang_pair_dataset=lang_pair_dataset, - src_eos=self.dicts[src].eos(), - src_lang=src, - tgt_eos=self.dicts[tgt].eos(), - tgt_lang=tgt, - ).collater, - ) - logger.info( - "backtranslate-{}: {} {} {} examples".format( - tgt, - data_path, - split, - len(backtranslate_datasets[lang_pair]), - ) - ) - self.backtranslate_datasets[lang_pair] = backtranslate_datasets[ - lang_pair - ] - - # denoising autoencoder - noising_datasets = {} - if ( - self.lambda_denoising > 0.0 or self.lambda_denoising_steps is not None - ) and split.startswith("train"): - for lang_pair in self.lang_pairs: - _, tgt = lang_pair.split("-") - if not split_exists(split, tgt, None, tgt): - continue - filename = os.path.join( - data_path, "{}.{}-None.{}".format(split, tgt, tgt) - ) - tgt_dataset1 = load_indexed_dataset(filename, self.dicts[tgt]) - tgt_dataset2 = load_indexed_dataset(filename, self.dicts[tgt]) - noising_dataset = NoisingDataset( - tgt_dataset1, - self.dicts[tgt], - seed=1, - max_word_shuffle_distance=self.args.max_word_shuffle_distance, - word_dropout_prob=self.args.word_dropout_prob, - word_blanking_prob=self.args.word_blanking_prob, - ) - noising_datasets[lang_pair] = self.alter_dataset_langtok( - LanguagePairDataset( - noising_dataset, - tgt_dataset1.sizes, - self.dicts[tgt], - tgt_dataset2, - tgt_dataset2.sizes, - self.dicts[tgt], - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - ), - src_eos=self.dicts[tgt].eos(), - src_lang=tgt, - tgt_eos=self.dicts[tgt].eos(), - tgt_lang=tgt, - ) - logger.info( - "denoising-{}: {} {} {} examples".format( - tgt, - data_path, - split, - len(noising_datasets[lang_pair]), - ) - ) - - def language_pair_dataset(lang_pair): - src, tgt = lang_pair.split("-") - src_dataset, tgt_dataset = src_datasets[lang_pair], tgt_datasets[lang_pair] - return self.alter_dataset_langtok( - LanguagePairDataset( - src_dataset, - src_dataset.sizes, - self.dicts[src], - tgt_dataset, - tgt_dataset.sizes, - self.dicts[tgt], - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - ), - self.dicts[src].eos(), - src, - self.dicts[tgt].eos(), - tgt, - ) - - self.datasets[split] = RoundRobinZipDatasets( - OrderedDict( - [ - (lang_pair, language_pair_dataset(lang_pair)) - for lang_pair in src_datasets.keys() - ] - + [ - (_get_bt_dataset_key(lang_pair), dataset) - for lang_pair, dataset in backtranslate_datasets.items() - ] - + [ - (_get_denoising_dataset_key(lang_pair), dataset) - for lang_pair, dataset in noising_datasets.items() - ] - ), - eval_key=None - if self.training - else "%s-%s" % (self.args.source_lang, self.args.target_lang), - ) - - def build_model(self, args, from_checkpoint=False): - from fairseq import models - - model = models.build_model(args, self, from_checkpoint) - if not isinstance(model, FairseqMultiModel): - raise ValueError( - "SemisupervisedTranslationTask requires a FairseqMultiModel architecture" - ) - - # create SequenceGenerator for each model that has backtranslation dependency on it - self.sequence_generators = {} - if ( - self.lambda_otf_bt > 0.0 or self.lambda_otf_bt_steps is not None - ) and self.training: - for lang_pair in self.lang_pairs: - src, tgt = lang_pair.split("-") - key = "{}-{}".format(tgt, src) - self.sequence_generators[key] = SequenceGenerator( - [model.models[key]], - tgt_dict=self.dicts[src], - beam_size=args.bt_beam_size, - max_len_a=args.bt_max_len_a, - max_len_b=args.bt_max_len_b, - ) - decoder_lang_tok_idx = self.get_decoder_langtok(src) - - def backtranslate_fn( - sample, - model=model.models[key], - bos_token=decoder_lang_tok_idx, - sequence_generator=self.sequence_generators[key], - ): - return sequence_generator.generate( - [model], - sample, - bos_token=bos_token, - ) - - self.backtranslators[lang_pair] = backtranslate_fn - - return model - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - model.train() - - if update_num > 0: - self.update_step(update_num) - - agg_loss, agg_sample_size, agg_logging_output = 0.0, 0.0, {} - - def forward_backward(model, samples, logging_output_key, weight): - nonlocal agg_loss, agg_sample_size, agg_logging_output - if samples is None or len(samples) == 0: - return - loss, sample_size, logging_output = criterion(model, samples) - if ignore_grad: - loss *= 0 - else: - loss *= weight - optimizer.backward(loss) - agg_loss += loss.detach().item() - # TODO make summing of the sample sizes configurable - agg_sample_size += sample_size - for k in logging_output: - agg_logging_output[k] += logging_output[k] - agg_logging_output[logging_output_key] += logging_output[k] - - if self.lambda_parallel > 0.0: - for lang_pair in self.lang_pairs: - forward_backward( - model.models[lang_pair], - sample[lang_pair], - lang_pair, - self.lambda_parallel, - ) - - if self.lambda_otf_bt > 0.0: - for lang_pair in self.lang_pairs: - sample_key = _get_bt_dataset_key(lang_pair) - forward_backward( - model.models[lang_pair], - sample[sample_key], - sample_key, - self.lambda_otf_bt, - ) - - if self.lambda_denoising > 0.0: - for lang_pair in self.lang_pairs: - _, tgt = lang_pair.split("-") - sample_key = _get_denoising_dataset_key(lang_pair) - forward_backward( - model.models["{0}-{0}".format(tgt)], - sample[sample_key], - sample_key, - self.lambda_denoising, - ) - - return agg_loss, agg_sample_size, agg_logging_output - - def update_step(self, num_updates): - def lambda_step_func(config, n_iter): - """ - Update a lambda value according to its schedule configuration. - """ - ranges = [ - i - for i in range(len(config) - 1) - if config[i][0] <= n_iter < config[i + 1][0] - ] - if len(ranges) == 0: - assert n_iter >= config[-1][0] - return config[-1][1] - assert len(ranges) == 1 - i = ranges[0] - x_a, y_a = config[i] - x_b, y_b = config[i + 1] - return y_a + (n_iter - x_a) * float(y_b - y_a) / float(x_b - x_a) - - if self.lambda_parallel_steps is not None: - self.lambda_parallel = lambda_step_func( - self.lambda_parallel_steps, num_updates - ) - if self.lambda_denoising_steps is not None: - self.lambda_denoising = lambda_step_func( - self.lambda_denoising_steps, num_updates - ) - if self.lambda_otf_bt_steps is not None: - self.lambda_otf_bt = lambda_step_func(self.lambda_otf_bt_steps, num_updates) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_prediction.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_prediction.py deleted file mode 100644 index 10b863af2dcb8bc1cf935a24dfb9b614bd9b3f5d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_prediction.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import logging -import os -from dataclasses import dataclass, field -from typing import Optional - -import numpy as np -from fairseq.data import ( - ConcatSentencesDataset, - Dictionary, - IdDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - OffsetTokensDataset, - PrependTokenDataset, - RawLabelDataset, - RightPadDataset, - RollDataset, - SortDataset, - StripTokenDataset, - data_utils, -) -from fairseq.data.shorten_dataset import maybe_shorten_dataset -from fairseq.dataclass import ChoiceEnum -from fairseq.tasks import FairseqDataclass, FairseqTask, register_task -from omegaconf import II, MISSING, OmegaConf, open_dict - -logger = logging.getLogger(__name__) -SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"]) - - -@dataclass -class SentencePredictionConfig(FairseqDataclass): - data: str = field(default=MISSING, metadata={"help": "path to data directory"}) - num_classes: int = field( - default=-1, - metadata={"help": "number of classes or regression targets"}, - ) - init_token: Optional[int] = field( - default=None, - metadata={"help": "add token at the beginning of each batch item"}, - ) - separator_token: Optional[int] = field( - default=None, - metadata={"help": "add separator token between inputs"}, - ) - no_shuffle: bool = field( - default=False, - ) - shorten_method: SHORTEN_METHOD_CHOICES = field( - default="none", - metadata={ - "help": "if not none, shorten sequences that exceed tokens_per_sample" - }, - ) - shorten_data_split_list: str = field( - default="", - metadata={ - "help": "comma-separated list of dataset splits to apply shortening to, " - 'e.g., "train,valid" (default: all dataset splits)' - }, - ) - add_prev_output_tokens: bool = field( - default=False, - metadata={ - "help": "add prev_output_tokens to sample, used for encoder-decoder arch" - }, - ) - max_positions: int = field( - default=512, - metadata={"help": "max tokens per example"}, - ) - - regression_target: bool = II("criterion.regression_target") - classification_head_name: str = II("criterion.classification_head_name") - seed: int = II("common.seed") - - -@register_task("sentence_prediction", dataclass=SentencePredictionConfig) -class SentencePredictionTask(FairseqTask): - """ - Sentence (or sentence pair) prediction (classification or regression) task. - - Args: - dictionary (Dictionary): the dictionary for the input of the task - """ - - def __init__(self, cfg, data_dictionary, label_dictionary): - super().__init__(cfg) - self.dictionary = data_dictionary - self._label_dictionary = label_dictionary - - @classmethod - def load_dictionary(cls, filename): - """Load the dictionary from the filename - - Args: - filename (str): the filename - """ - dictionary = Dictionary.load(filename) - dictionary.add_symbol("") - return dictionary - - @classmethod - def setup_task(cls, cfg, **kwargs): - assert cfg.num_classes > 0, "Must set task.num_classes" - - # load data dictionary - data_dict = cls.load_dictionary( - os.path.join(cfg.data, "input0", "dict.txt"), - ) - logger.info("[input] dictionary: {} types".format(len(data_dict))) - - # load label dictionary - if not cfg.regression_target: - label_dict = cls.load_dictionary( - os.path.join(cfg.data, "label", "dict.txt"), - ) - logger.info("[label] dictionary: {} types".format(len(label_dict))) - else: - label_dict = data_dict - return cls(cfg, data_dict, label_dict) - - def load_dataset(self, split, combine=False, **kwargs): - """Load a given dataset split (e.g., train, valid, test).""" - - def get_path(key, split): - return os.path.join(self.cfg.data, key, split) - - def make_dataset(key, dictionary): - split_path = get_path(key, split) - - try: - dataset = data_utils.load_indexed_dataset( - split_path, - dictionary, - combine=combine, - ) - except Exception as e: - if "StorageException: [404] Path not found" in str(e): - logger.warning(f"dataset {e} not found") - dataset = None - else: - raise e - return dataset - - input0 = make_dataset("input0", self.source_dictionary) - assert input0 is not None, "could not find dataset: {}".format( - get_path("input0", split) - ) - input1 = make_dataset("input1", self.source_dictionary) - - if self.cfg.init_token is not None: - input0 = PrependTokenDataset(input0, self.cfg.init_token) - - if input1 is None: - src_tokens = input0 - else: - if self.cfg.separator_token is not None: - input1 = PrependTokenDataset(input1, self.cfg.separator_token) - - src_tokens = ConcatSentencesDataset(input0, input1) - - with data_utils.numpy_seed(self.cfg.seed): - shuffle = np.random.permutation(len(src_tokens)) - - src_tokens = maybe_shorten_dataset( - src_tokens, - split, - self.cfg.shorten_data_split_list, - self.cfg.shorten_method, - self.max_positions(), - self.cfg.seed, - ) - - dataset = { - "id": IdDataset(), - "net_input": { - "src_tokens": RightPadDataset( - src_tokens, - pad_idx=self.source_dictionary.pad(), - ), - "src_lengths": NumelDataset(src_tokens, reduce=False), - }, - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_tokens, reduce=True), - } - - if self.cfg.add_prev_output_tokens: - prev_tokens_dataset = RightPadDataset( - RollDataset(src_tokens, 1), - pad_idx=self.dictionary.pad(), - ) - dataset["net_input"].update( - prev_output_tokens=prev_tokens_dataset, - ) - - if not self.cfg.regression_target: - label_dataset = make_dataset("label", self.label_dictionary) - if label_dataset is not None: - dataset.update( - target=OffsetTokensDataset( - StripTokenDataset( - label_dataset, - id_to_strip=self.label_dictionary.eos(), - ), - offset=-self.label_dictionary.nspecial, - ) - ) - else: - label_path = "{0}.label".format(get_path("label", split)) - if os.path.exists(label_path): - - def parse_regression_target(i, line): - values = line.split() - assert ( - len(values) == self.cfg.num_classes - ), f'expected num_classes={self.cfg.num_classes} regression target values on line {i}, found: "{line}"' - return [float(x) for x in values] - - with open(label_path) as h: - dataset.update( - target=RawLabelDataset( - [ - parse_regression_target(i, line.strip()) - for i, line in enumerate(h.readlines()) - ] - ) - ) - - nested_dataset = NestedDictionaryDataset( - dataset, - sizes=[src_tokens.sizes], - ) - - if self.cfg.no_shuffle: - dataset = nested_dataset - else: - dataset = SortDataset( - nested_dataset, - # shuffle - sort_order=[shuffle], - ) - - logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset))) - - self.datasets[split] = dataset - return self.datasets[split] - - def build_model(self, cfg, from_checkpoint=False): - from fairseq import models - - with open_dict(cfg) if OmegaConf.is_config(cfg) else contextlib.ExitStack(): - cfg.max_positions = self.cfg.max_positions - - model = models.build_model(cfg, self, from_checkpoint) - - model.register_classification_head( - self.cfg.classification_head_name, - num_classes=self.cfg.num_classes, - ) - - return model - - def max_positions(self): - return self.cfg.max_positions - - @property - def source_dictionary(self): - return self.dictionary - - @property - def target_dictionary(self): - return self.dictionary - - @property - def label_dictionary(self): - return self._label_dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_prediction_adapters.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_prediction_adapters.py deleted file mode 100644 index 75fb06f710ef633999992ce6232593199d39524b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_prediction_adapters.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import logging - -from fairseq.tasks import register_task -from fairseq.tasks.sentence_prediction import ( - SentencePredictionConfig, - SentencePredictionTask, -) -from omegaconf import OmegaConf, open_dict - -logger = logging.getLogger(__name__) - - -@register_task("sentence_prediction_adapters", dataclass=SentencePredictionConfig) -class SentencePredictionAdapterTask(SentencePredictionTask): - def build_model(self, cfg): - from fairseq import models - - with open_dict(cfg) if OmegaConf.is_config(cfg) else contextlib.ExitStack(): - cfg.max_positions = self.cfg.max_positions - - model = models.build_model(cfg, self) - - model.register_classification_head( - self.cfg.classification_head_name, - num_classes=self.cfg.num_classes, - ) - - logger.info("Freezing Embedding Parameters") - for parameter in model.encoder.sentence_encoder.embed_positions.parameters(): - parameter.requires_grad = False - for ( - parameter - ) in model.encoder.sentence_encoder.layernorm_embedding.parameters(): - parameter.requires_grad = False - for parameter in model.encoder.sentence_encoder.embed_tokens.parameters(): - parameter.requires_grad = False - - logger.info("Freezing Adapters") - for k, v in model.encoder.sentence_encoder.layers._modules.items(): - logger.info("Freezing Adapters in Layer " + str(k)) - if hasattr(v, "adapter_layer_norm"): - logger.info("Freezing Adapter LN") - for parameter in v.adapter_layer_norm.parameters(): - parameter.requires_grad = False - for parameter in v.adapter_modules.parameters(): - parameter.requires_grad = False - - return model diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_ranking.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_ranking.py deleted file mode 100644 index 8845d6011a403c3478ec97884b81da407d694f6c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/sentence_ranking.py +++ /dev/null @@ -1,218 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os - -import numpy as np -from fairseq import utils -from fairseq.data import ( - ConcatSentencesDataset, - Dictionary, - IdDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - PrependTokenDataset, - RawLabelDataset, - RightPadDataset, - SortDataset, - TruncateDataset, - data_utils, -) -from fairseq.data.shorten_dataset import maybe_shorten_dataset -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("sentence_ranking") -class SentenceRankingTask(LegacyFairseqTask): - """ - Ranking task on multiple sentences. - - Args: - dictionary (Dictionary): the dictionary for the input of the task - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument("data", metavar="FILE", help="file prefix for data") - parser.add_argument( - "--num-classes", type=int, help="number of sentences to be ranked" - ) - parser.add_argument( - "--init-token", - type=int, - help="add token at the beginning of each batch item", - ) - parser.add_argument( - "--separator-token", type=int, help="add separator token between inputs" - ) - parser.add_argument("--no-shuffle", action="store_true") - parser.add_argument( - "--shorten-method", - default="none", - choices=["none", "truncate", "random_crop"], - help="if not none, shorten sequences that exceed --tokens-per-sample", - ) - parser.add_argument( - "--shorten-data-split-list", - default="", - help="comma-separated list of dataset splits to apply shortening to, " - 'e.g., "train,valid" (default: all dataset splits)', - ) - parser.add_argument( - "--max-option-length", type=int, help="max length for each option" - ) - - def __init__(self, args, dictionary): - super().__init__(args) - self.dictionary = dictionary - - @classmethod - def load_dictionary(cls, args, filename, source=True): - """Load the dictionary from the filename - - Args: - filename (str): the filename - """ - dictionary = Dictionary.load(filename) - dictionary.add_symbol("") - return dictionary - - @classmethod - def setup_task(cls, args, **kwargs): - assert ( - args.criterion == "sentence_ranking" - ), "Must set --criterion=sentence_ranking" - - # load data dictionary - data_dict = cls.load_dictionary( - args, - os.path.join(args.data, "input0", "dict.txt"), - source=True, - ) - logger.info("[input] dictionary: {} types".format(len(data_dict))) - return SentenceRankingTask(args, data_dict) - - def load_dataset(self, split, combine=False, **kwargs): - """Load a given dataset split (e.g., train, valid, test).""" - - def get_path(type, split): - return os.path.join(self.args.data, type, split) - - def make_dataset(type, dictionary): - split_path = get_path(type, split) - - dataset = data_utils.load_indexed_dataset( - split_path, - self.source_dictionary, - self.args.dataset_impl, - combine=combine, - ) - return dataset - - input0 = make_dataset("input0", self.source_dictionary) - input_options = [ - make_dataset("input{idx}".format(idx=idx + 1), self.source_dictionary) - for idx in range(self.args.num_classes) - ] - - if self.args.separator_token is not None: - input0 = PrependTokenDataset(input0, self.args.separator_token) - - src_tokens = [] - for input_option in input_options: - if self.args.init_token is not None: - input_option = PrependTokenDataset(input_option, self.args.init_token) - if self.args.max_option_length is not None: - input_option = TruncateDataset( - input_option, self.args.max_option_length - ) - src_token = ConcatSentencesDataset(input_option, input0) - src_token = maybe_shorten_dataset( - src_token, - split, - self.args.shorten_data_split_list, - self.args.shorten_method, - self.args.max_positions, - self.args.seed, - ) - src_tokens.append(src_token) - - with data_utils.numpy_seed(self.args.seed): - shuffle = np.random.permutation(len(src_tokens[0])) - - dataset = { - "id": IdDataset(), - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_tokens[0], reduce=True), - } - - for src_token_idx in range(len(src_tokens)): - dataset.update( - { - "net_input{idx}".format(idx=src_token_idx + 1): { - "src_tokens": RightPadDataset( - src_tokens[src_token_idx], - pad_idx=self.source_dictionary.pad(), - ), - "src_lengths": NumelDataset( - src_tokens[src_token_idx], reduce=False - ), - } - } - ) - - label_path = "{}.label".format(get_path("label", split)) - if os.path.exists(label_path): - with open(label_path) as h: - dataset.update( - target=RawLabelDataset([int(x.strip()) for x in h.readlines()]) - ) - - nested_dataset = NestedDictionaryDataset( - dataset, - sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])], - ) - - if self.args.no_shuffle: - dataset = nested_dataset - else: - dataset = SortDataset( - nested_dataset, - # shuffle - sort_order=[shuffle], - ) - - logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset))) - - self.datasets[split] = dataset - return self.datasets[split] - - def build_model(self, args, from_checkpoint=False): - from fairseq import models - - model = models.build_model(args, self, from_checkpoint) - - model.register_classification_head( - getattr(args, "ranking_head_name", "sentence_classification_head"), - num_classes=1, - ) - - return model - - def max_positions(self): - return self.args.max_positions - - @property - def source_dictionary(self): - return self.dictionary - - @property - def target_dictionary(self): - return self.dictionary diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/simultaneous_translation.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/simultaneous_translation.py deleted file mode 100644 index 5950415cbee4723a4f8cfd6df8e60e2e6619bca8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/simultaneous_translation.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -from fairseq.tasks import register_task -from fairseq.tasks.speech_to_text import SpeechToTextTask -from fairseq.tasks.translation import TranslationConfig, TranslationTask - -try: - import examples.simultaneous_translation # noqa - - import_successful = True -except BaseException: - import_successful = False - - -logger = logging.getLogger(__name__) - - -def check_import(flag): - if not flag: - raise ImportError( - "'examples.simultaneous_translation' is not correctly imported. " - "Please considering `pip install -e $FAIRSEQ_DIR`." - ) - - -@register_task("simul_speech_to_text") -class SimulSpeechToTextTask(SpeechToTextTask): - def __init__(self, args, tgt_dict): - check_import(import_successful) - super().__init__(args, tgt_dict) - - -@register_task("simul_text_to_text", dataclass=TranslationConfig) -class SimulTextToTextTask(TranslationTask): - def __init__(self, cfg, src_dict, tgt_dict): - check_import(import_successful) - super().__init__(cfg, src_dict, tgt_dict) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_to_speech.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_to_speech.py deleted file mode 100644 index 71fe3eb4f2f2203fca824c4465d4bd9b42d6b8b2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_to_speech.py +++ /dev/null @@ -1,519 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import logging -import math -from argparse import Namespace -from pathlib import Path - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.data import Dictionary -from fairseq.data.audio.data_cfg import MultitaskConfig, S2SDataConfig -from fairseq.data.audio.speech_to_speech_dataset import SpeechToSpeechDatasetCreator -from fairseq.data.audio.speech_to_text_dataset import SpeechToTextDataset -from fairseq.tasks import LegacyFairseqTask, register_task -from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion - -logger = logging.getLogger(__name__) - - -class StackUnitSequenceGenerator(nn.Module): - def __init__(self, tgt_dict, vocab_size): - super().__init__() - self.pad = tgt_dict.pad() - self.eos = tgt_dict.eos() - self.unk = tgt_dict.unk() - self.offset = len(tgt_dict) - vocab_size - self.vocab_size = vocab_size - - def pack_units(self, input: torch.Tensor, n_frames_per_step) -> torch.Tensor: - if n_frames_per_step <= 1: - return input - - bsz, _, n = input.shape - assert n == n_frames_per_step - - scale = [ - pow(self.vocab_size, n_frames_per_step - 1 - i) - for i in range(n_frames_per_step) - ] - scale = torch.LongTensor(scale).squeeze(0).to(input.device) - mask = input >= self.offset - res = ((input - self.offset) * scale * mask).sum(dim=2) + self.offset - return res - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - # currently only support viterbi search for stacked units - model = models[0] - model.eval() - - max_len = model.max_decoder_positions() - # TODO: incorporate max_len_a and max_len_b - - src_tokens = sample["net_input"]["src_tokens"] - src_lengths = sample["net_input"]["src_lengths"] - bsz, src_len, _ = src_tokens.size() - n_frames_per_step = model.decoder.n_frames_per_step - - # initialize - encoder_out = model.forward_encoder( - src_tokens, src_lengths, speaker=sample["speaker"] - ) - incremental_state = {} - pred_out, attn, scores = [], [], [] - finished = src_tokens.new_zeros((bsz,)).bool() - - prev_output_tokens = src_lengths.new_zeros((bsz, 1)).long().fill_(self.eos) - for _ in range(max_len): - cur_out, cur_extra = model.forward_decoder( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - ) - - lprobs = model.get_normalized_probs([cur_out], log_probs=True) - # never select pad, unk - lprobs[:, :, self.pad] = -math.inf - lprobs[:, :, self.unk] = -math.inf - - cur_pred_lprob, cur_pred_out = torch.max(lprobs, dim=2) - scores.append(cur_pred_lprob) - pred_out.append(cur_pred_out) - - prev_output_tokens = torch.cat( - ( - prev_output_tokens, - self.pack_units( - cur_pred_out.view(bsz, 1, n_frames_per_step), n_frames_per_step - ), - ), - dim=1, - ) - - attn.append(cur_extra["attn"][0]) - - cur_finished = torch.any(cur_pred_out.squeeze(1) == self.eos, dim=1) - finished = finished | cur_finished - if finished.sum().item() == bsz: - break - - pred_out = torch.cat(pred_out, dim=1).view(bsz, -1) - attn = torch.cat(attn, dim=2) - alignment = attn.max(dim=1)[1] - attn = attn.repeat_interleave(n_frames_per_step, dim=2) - alignment = alignment.repeat_interleave(n_frames_per_step, dim=1) - scores = torch.cat(scores, dim=1) - eos_idx = (pred_out == self.eos).nonzero(as_tuple=True) - out_lens = src_lengths.new_zeros((bsz,)).long().fill_(max_len) - for b, l in zip(eos_idx[0], eos_idx[1]): - out_lens[b] = min(l, out_lens[b]) - - hypos = [ - [ - { - "tokens": pred_out[b, :out_len], - "attn": attn[b, :, :out_len], - "alignment": alignment[b, :out_len], - "positional_scores": scores[b, :out_len], - "score": utils.item(scores[b, :out_len].sum().data), - } - ] - for b, out_len in zip(range(bsz), out_lens) - ] - - return hypos - - -@register_task("speech_to_speech") -class SpeechToSpeechTask(LegacyFairseqTask): - @classmethod - def add_args(cls, parser): - parser.add_argument("data", help="manifest root path") - parser.add_argument( - "--config-yaml", - type=str, - default="config.yaml", - help="Configuration YAML filename (under manifest root)", - ) - parser.add_argument( - "--max-source-positions", - default=6000, - type=int, - metavar="N", - help="max number of tokens in the source sequence", - ) - parser.add_argument( - "--max-target-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the target sequence", - ) - parser.add_argument( - "--target-is-code", - action="store_true", - help="set if target is discrete unit instead of spectrogram", - ) - parser.add_argument( - "--target-code-size", type=int, default=None, help="# discrete units" - ) - parser.add_argument( - "--n-frames-per-step", - type=int, - default=1, - help="# stacked frames, use 0 for reduced discrete unit sequence", - ) - parser.add_argument( - "--multitask-config-yaml", - type=str, - default=None, - help="Configuration YAML filename for the multitasks (under manifest root)", - ) - parser.add_argument("--eval-inference", action="store_true") - parser.add_argument( - "--eval-args", - type=str, - default="{}", - help='generation args for speech-to-unit model , e.g., \'{"beam": 5, "max_len_a": 1}\', as JSON string', - ) - parser.add_argument("--eos-prob-threshold", type=float, default=0.5) - parser.add_argument( - "--mcd-normalize-type", - type=str, - default="targ", - choices=["targ", "pred", "path"], - ) - parser.add_argument( - "--vocoder", - type=str, - default="griffin_lim", - choices=["griffin_lim", "hifigan", "code_hifigan"], - ) - parser.add_argument("--spec-bwd-max-iter", type=int, default=8) - parser.add_argument( - "--infer-target-lang", - type=str, - default="", - help="target language for inference", - ) - - def __init__(self, args, tgt_dict, infer_tgt_lang_id=None): - super().__init__(args) - self.tgt_dict = tgt_dict - self.data_cfg = S2SDataConfig(Path(args.data) / args.config_yaml) - self.multitask_tasks = {} - if getattr(args, "multitask_config_yaml", None) is not None: - multitask_cfg = MultitaskConfig( - Path(args.data) / args.multitask_config_yaml - ) - for task_name, task_config in multitask_cfg.get_all_tasks().items(): - self.multitask_tasks[task_name] = DummyMultiTask( - task_config, task_config.tgt_dict - ) - self._infer_tgt_lang_id = infer_tgt_lang_id - - @classmethod - def setup_task(cls, args, **kwargs): - data_cfg = data_cfg = S2SDataConfig(Path(args.data) / args.config_yaml) - tgt_dict = None - infer_tgt_lang_id = None - if args.target_is_code: - if data_cfg.prepend_tgt_lang_tag_as_bos: - # dictionary with language tags - dict_path = Path(args.data) / data_cfg.vocab_filename - if not dict_path.is_file(): - raise FileNotFoundError( - f"Dict has to be provided when setting prepend_tgt_lang_tag_as_bos: true, but dict not found: {dict_path}" - ) - tgt_dict = Dictionary.load(dict_path.as_posix()) - - # target langauge for inference - if args.infer_target_lang != "": - tgt_lang_tag = SpeechToTextDataset.LANG_TAG_TEMPLATE.format( - args.infer_target_lang - ) - infer_tgt_lang_id = tgt_dict.index(tgt_lang_tag) - assert infer_tgt_lang_id != tgt_dict.unk() - else: - assert args.target_code_size is not None - - tgt_dict = Dictionary() - for i in range(args.target_code_size): - tgt_dict.add_symbol(str(i)) - logger.info(f"dictionary size: " f"{len(tgt_dict):,}") - - if getattr(args, "train_subset", None) is not None: - if not all(s.startswith("train") for s in args.train_subset.split(",")): - raise ValueError('Train splits should be named like "train*".') - - assert args.n_frames_per_step >= 1 - assert ( - not args.eval_inference - or (args.target_is_code and args.vocoder == "code_hifigan") - or (not args.target_is_code and args.vocoder != "code_hifigan") - ) - - return cls(args, tgt_dict, infer_tgt_lang_id=infer_tgt_lang_id) - - def build_criterion(self, args): - from fairseq import criterions - - if len(self.multitask_tasks) > 0: - if self.args.target_is_code and args._name != "speech_to_unit": - raise ValueError( - "set --criterion speech_to_unit for speech-to-unit loss with multitask" - ) - elif not self.args.target_is_code and args._name != "speech_to_spectrogram": - raise ValueError( - "set --criterion speech_to_spectrogram for speech-to-spectrogram loss with multitask" - ) - - return criterions.build_criterion(args, self) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - self.datasets[split] = SpeechToSpeechDatasetCreator.from_tsv( - self.args.data, - self.data_cfg, - split, - is_train_split=split.startswith("train"), - epoch=epoch, - seed=self.args.seed, - target_is_code=self.args.target_is_code, - target_dictionary=self.target_dictionary, - n_frames_per_step=self.args.n_frames_per_step, - multitask=self.multitask_tasks, - ) - - @property - def target_dictionary(self): - return self.tgt_dict - - @property - def source_dictionary(self): - return None - - def max_positions(self): - return self.args.max_source_positions, self.args.max_target_positions - - def build_model(self, args, from_checkpoint=False): - args.input_feat_per_channel = self.data_cfg.input_feat_per_channel - args.input_channels = self.data_cfg.input_transformed_channels - args.target_speaker_embed = self.data_cfg.target_speaker_embed is not None - args.n_frames_per_step = self.args.n_frames_per_step - - model = super().build_model(args, from_checkpoint) - - if len(self.multitask_tasks) > 0: - from fairseq.models.speech_to_speech.s2s_transformer import ( - S2STransformerMultitaskModelBase, - ) - - assert isinstance(model, S2STransformerMultitaskModelBase) - - if self.args.eval_inference: - self.eval_gen_args = json.loads(self.args.eval_args) - self.generator = self.build_generator( - [model], Namespace(**self.eval_gen_args) - ) - - return model - - def build_generator( - self, - models, - args, - seq_gen_cls=None, - extra_gen_cls_kwargs=None, - ): - - if not self.args.target_is_code or self.args.eval_inference: - from fairseq.models.text_to_speech.vocoder import get_vocoder - - self.vocoder = get_vocoder(self.args, self.data_cfg) - self.vocoder = ( - self.vocoder.cuda() - if torch.cuda.is_available() and not self.args.cpu - else self.vocoder.cpu() - ) - - if self.args.target_is_code: - if self.args.n_frames_per_step == 1: - seq_generator = super().build_generator( - models, - args, - seq_gen_cls=None, - extra_gen_cls_kwargs=extra_gen_cls_kwargs, - ) - else: - assert ( - getattr(args, "beam", 1) == 1 and getattr(args, "nbest", 1) == 1 - ), "only support viterbi search for stacked units" - seq_generator = StackUnitSequenceGenerator( - self.tgt_dict, - self.args.target_code_size, - ) - else: - if getattr(args, "teacher_forcing", False): - from fairseq.speech_generator import ( - TeacherForcingAutoRegressiveSpeechGenerator, - ) - - generator = TeacherForcingAutoRegressiveSpeechGenerator - logger.info("Teacher forcing mode for generation") - else: - from fairseq.speech_generator import AutoRegressiveSpeechGenerator - - generator = AutoRegressiveSpeechGenerator - seq_generator = generator( - models[0], - self.vocoder, - self.data_cfg, - max_iter=self.args.max_target_positions, - eos_prob_threshold=self.args.eos_prob_threshold, - ) - - return seq_generator - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - for task_name, task_obj in self.multitask_tasks.items(): - criterion.set_multitask_loss_weight( - task_name, task_obj.args.get_loss_weight(update_num) - ) - - loss, sample_size, logging_output = super().train_step( - sample, model, criterion, optimizer, update_num, ignore_grad - ) - return loss, sample_size, logging_output - - def valid_step(self, sample, model, criterion): - loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - - if self.args.eval_inference: - hypos, inference_losses = self.valid_step_with_inference( - sample, model, self.generator - ) - for k, v in inference_losses.items(): - assert k not in logging_output - logging_output[k] = v - - return loss, sample_size, logging_output - - def valid_step_with_inference(self, sample, model, generator): - if self.args.target_is_code: - hypos = generator.generate([model], sample) - tgt_lens = ( - sample["target_lengths"] - 1 - ) * self.args.n_frames_per_step # strip - for b, (f, l) in enumerate(zip(sample["target"], tgt_lens)): - hypos[b][0]["targ_waveform"] = self.vocoder( - {"code": f[:l] - 4}, # remove , , , - dur_prediction=self.eval_gen_args.get("dur_prediction", False), - ) - if len(hypos[b][0]["tokens"]) > 0: - hypos[b][0]["waveform"] = self.vocoder( - {"code": hypos[b][0]["tokens"] - 4}, - dur_prediction=self.eval_gen_args.get("dur_prediction", False), - ) - else: - hypos[b][0]["waveform"] = torch.flip( - hypos[b][0]["targ_waveform"], dims=[0] - ) - else: - hypos = [ - [hypo] for hypo in generator.generate(model, sample, has_targ=True) - ] - - losses = { - "mcd_loss": 0.0, - "targ_frames": 0.0, - "pred_frames": 0.0, - "path_frames": 0.0, - "nins": 0.0, - "ndel": 0.0, - } - rets = batch_mel_cepstral_distortion( - [hypo[0]["targ_waveform"] for hypo in hypos], - [hypo[0]["waveform"] for hypo in hypos], - self.data_cfg.output_sample_rate, - normalize_type=None, - ) - for d, extra in rets: - pathmap = extra[-1] - losses["mcd_loss"] += d.item() - losses["targ_frames"] += pathmap.size(0) - losses["pred_frames"] += pathmap.size(1) - losses["path_frames"] += pathmap.sum().item() - losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item() - losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item() - losses["norm_frames"] = losses[ - f"{getattr(self.args, 'mcd_normalize_type', 'targ')}_frames" - ] - - return hypos, losses - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - if self._infer_tgt_lang_id is not None: - return generator.generate( - models, - sample, - prefix_tokens=prefix_tokens, - constraints=constraints, - bos_token=self._infer_tgt_lang_id, - ) - else: - return super().inference_step( - generator, - models, - sample, - prefix_tokens=prefix_tokens, - constraints=constraints, - ) - - -class DummyMultiTask(LegacyFairseqTask): - def __init__(self, args, tgt_dict): - super().__init__(args) - self.tgt_dict = tgt_dict - - @property - def target_dictionary(self): - return self.tgt_dict - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - if self.args.decoder_type == "ctc": - model = models[0] # only support single model - encoder_out = model(**sample) - if hasattr(model, "get_logits"): - emissions = model.get_logits( - encoder_out - ) # no need to normalize emissions - else: - emissions = model.get_normalized_probs(encoder_out, log_probs=True) - return generator.decode( - emissions.transpose(0, 1).float().cpu().contiguous() - ) - else: - raise NotImplementedError("only ctc decoder is supported at the moment") - - def build_generator( - self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None - ): - if self.args.decoder_type == "ctc": - from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder - - return W2lViterbiDecoder(args, self.tgt_dict) - else: - raise NotImplementedError("only ctc decoder is supported at the moment") diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_to_text.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_to_text.py deleted file mode 100644 index 9b80e8736a217c47afc179dd5682a3ce867cfb33..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_to_text.py +++ /dev/null @@ -1,185 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from argparse import Namespace -from pathlib import Path - -from fairseq.data import Dictionary, encoders -from fairseq.data.audio.speech_to_text_dataset import ( - S2TDataConfig, - SpeechToTextDataset, - SpeechToTextDatasetCreator, - get_features_or_waveform, -) -from fairseq.tasks import LegacyFairseqTask, register_task - -logger = logging.getLogger(__name__) - - -@register_task("speech_to_text") -class SpeechToTextTask(LegacyFairseqTask): - @classmethod - def add_args(cls, parser): - parser.add_argument("data", help="manifest root path") - parser.add_argument( - "--config-yaml", - type=str, - default="config.yaml", - help="Configuration YAML filename (under manifest root)", - ) - parser.add_argument( - "--max-source-positions", - default=6000, - type=int, - metavar="N", - help="max number of tokens in the source sequence", - ) - parser.add_argument( - "--max-target-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the target sequence", - ) - - def __init__(self, args, tgt_dict): - super().__init__(args) - self.tgt_dict = tgt_dict - self.data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml) - self.speaker_to_id = self._get_speaker_to_id() - if ( - self.data_cfg.prepend_tgt_lang_tag - and self.data_cfg.prepend_bos_and_append_tgt_lang_tag - ): - raise ValueError( - "Please set only one of the two options to avoid adding target token multiple times" - ) - - def _get_speaker_to_id(self): - speaker_to_id = None - speaker_set_filename = self.data_cfg.config.get("speaker_set_filename") - if speaker_set_filename is not None: - speaker_set_path = Path(self.args.data) / speaker_set_filename - with open(speaker_set_path) as f: - speaker_to_id = {r.strip(): i for i, r in enumerate(f)} - return speaker_to_id - - @classmethod - def setup_task(cls, args, **kwargs): - data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml) - dict_path = Path(args.data) / data_cfg.vocab_filename - if not dict_path.is_file(): - raise FileNotFoundError(f"Dict not found: {dict_path.as_posix()}") - tgt_dict = Dictionary.load(dict_path.as_posix()) - logger.info( - f"dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}" - ) - - if getattr(args, "train_subset", None) is not None: - if not all(s.startswith("train") for s in args.train_subset.split(",")): - raise ValueError('Train splits should be named like "train*".') - return cls(args, tgt_dict) - - def build_criterion(self, args): - from fairseq import criterions - - if self.data_cfg.prepend_tgt_lang_tag and args.ignore_prefix_size != 1: - raise ValueError( - 'Please set "--ignore-prefix-size 1" since ' - "target language ID token is prepended as BOS." - ) - return criterions.build_criterion(args, self) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - is_train_split = split.startswith("train") - pre_tokenizer = self.build_tokenizer(self.args) - bpe_tokenizer = self.build_bpe(self.args) - self.datasets[split] = SpeechToTextDatasetCreator.from_tsv( - self.args.data, - self.data_cfg, - split, - self.tgt_dict, - pre_tokenizer, - bpe_tokenizer, - is_train_split=is_train_split, - epoch=epoch, - seed=self.args.seed, - speaker_to_id=self.speaker_to_id, - ) - - @property - def target_dictionary(self): - return self.tgt_dict - - @property - def source_dictionary(self): - return None - - def max_positions(self): - return self.args.max_source_positions, self.args.max_target_positions - - def build_model(self, args, from_checkpoint=False): - args.input_feat_per_channel = self.data_cfg.input_feat_per_channel - args.input_channels = self.data_cfg.input_channels - args.speaker_to_id = self.speaker_to_id - return super(SpeechToTextTask, self).build_model(args, from_checkpoint) - - def build_generator( - self, - models, - args, - seq_gen_cls=None, - extra_gen_cls_kwargs=None, - ): - if self.data_cfg.prepend_tgt_lang_tag and args.prefix_size != 1: - raise ValueError( - 'Please set "--prefix-size 1" since ' - "target language ID token is prepended as BOS." - ) - lang_token_ids = { - i - for s, i in self.tgt_dict.indices.items() - if SpeechToTextDataset.is_lang_tag(s) - } - - if extra_gen_cls_kwargs is None: - extra_gen_cls_kwargs = {} - extra_gen_cls_kwargs["symbols_to_strip_from_output"] = lang_token_ids - - eos_token = ( - args.eos_token - if "eos_token" in args and args.eos_token is not None - else self.data_cfg.config.get("eos_token", None) - ) - - if self.data_cfg.prepend_bos_and_append_tgt_lang_tag and not eos_token: - raise Warning( - "Please provide --eos_token to replace eos in sequence generator" - ) - - eos_id = self.tgt_dict.index(eos_token) if eos_token else None - extra_gen_cls_kwargs["eos"] = eos_id - - return super().build_generator( - models, args, seq_gen_cls=None, extra_gen_cls_kwargs=extra_gen_cls_kwargs - ) - - def build_tokenizer(self, args): - logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}") - return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer)) - - def build_bpe(self, args): - logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}") - return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer)) - - def get_interactive_tokens_and_lengths(self, lines, encode_fn): - n_frames = [get_features_or_waveform(p).shape[0] for p in lines] - return lines, n_frames - - def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): - return SpeechToTextDataset( - "interactive", False, self.data_cfg, src_tokens, src_lengths - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_ulm_task.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_ulm_task.py deleted file mode 100644 index 8ab11fdbc0ba43e399aa46745f5cc206d093d36b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/speech_ulm_task.py +++ /dev/null @@ -1,223 +0,0 @@ -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - -import logging -import sys -from dataclasses import dataclass, field -from typing import List, Optional, Tuple - -import torch -from fairseq.data import Dictionary -from fairseq.data.codedataset import CodeDataset, ExpressiveCodeDataConfig -from fairseq.dataclass.configs import FairseqDataclass -from fairseq.tasks import register_task -from fairseq.tasks.fairseq_task import FairseqTask -from omegaconf import MISSING, DictConfig - -logger = logging.getLogger(__name__) - - -class UnitDictionary(Dictionary): - """ - A fixed-sized Dictionary that operates on integer-valued tokens - wth a trivial (identity) token <-> id mapping. - Special symbols (bos, eos, ...) have ids above n_units. - """ - - def __init__( - self, - *, # begin keyword-only arguments - n_units, - bos="", - pad="", - eos="", - unk="", - extra_special_symbols=None, - clip=False, - ): - self.n_units = n_units - self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos - self.clip = clip - - self.symbols = [] - self.count = [] - self.indices = {} - for i in range(n_units): - self.add_symbol(str(i)) - - self.bos_index = self.add_symbol(bos) - self.pad_index = self.add_symbol(pad) - self.eos_index = self.add_symbol(eos) - self.unk_index = self.add_symbol(unk) - - if extra_special_symbols: - for s in extra_special_symbols: - self.add_symbol(s) - self.nspecial = len(self.symbols) - - def encode_line(self, line, append_eos=True, prepend_bos=False) -> torch.IntTensor: - words = [int(x) for x in line.split()] - if self.clip: - words = [min(self.n_units - 1, word) for word in words] - if prepend_bos: - words = [self.bos_index] + words - if append_eos: - words.append(self.eos_index) - ids = torch.IntTensor(words) - return ids - - -@dataclass -class SpeechUnitModelingConfig(FairseqDataclass): - data: str = field(default=MISSING, metadata={"help": "Path to data config.json"}) - max_token_duration: int = field( - default=20, metadata={"help": "all token durations are capped to this value"} - ) - tokens_per_sample: int = field( - default=1024, metadata={"help": "tokens in a sample"} - ) - max_target_positions: int = field( - default=1024, metadata={"help": "max target positions"} - ) - - # duration modeling - ignore_duration_input: bool = field( - default=False, metadata={"help": "whether token durations should be zeroed out"} - ) - discrete_duration: bool = field( - default=False, metadata={"help": "treat duration as discrete variable"} - ) - # F0 modeling - ignore_f0_input: bool = field( - default=False, metadata={"help": "whether F0 should be zeroed out"} - ) - discrete_f0: bool = field( - default=False, metadata={"help": "load quantized f0. get bin from config"} - ) - log_f0: bool = field( - default=False, metadata={"help": "whether f0 should be modeled in log space"} - ) - normalize_f0_mean: bool = field( - default=False, metadata={"help": "whether normalize f0 by speaker mean"} - ) - normalize_f0_std: bool = field( - default=False, metadata={"help": "whether normalize f0 by speaker stddev"} - ) - interpolate_f0: bool = field( - default=False, - metadata={"help": "whether interpolate f0 for non-voiced segments"}, - ) - - # input/output streams - stream_shifts: str = field( - default="0,0", - metadata={ - "help": ( - "comma-separated integer list denoting right-shift for " - "duration and pitch streams" - ) - }, - ) - - -@register_task("speech_unit_modeling", dataclass=SpeechUnitModelingConfig) -class SpeechUnitLanguageModelingTask(FairseqTask): - def __init__(self, cfg: SpeechUnitModelingConfig) -> None: - super().__init__(cfg) - assert not self.cfg.normalize_f0_std or self.cfg.normalize_f0_mean - - self.data_config = ExpressiveCodeDataConfig(cfg.data) - self._source_dictionary = self._target_dictionary = UnitDictionary( - n_units=self.data_config.n_units - ) - self._source_duration_dictionary = self._target_duration_dictionary = ( - UnitDictionary(n_units=self.cfg.max_token_duration + 1, clip=True) - if self.cfg.discrete_duration - else None - ) - self._source_f0_dictionary = self._target_f0_dictionary = ( - UnitDictionary(n_units=self.data_config.f0_vq_n_units) - if self.cfg.discrete_f0 - else None - ) - - self._channel_names = ["token", "duration", "f0"] - self._channel_sizes = [ - len(self.target_dictionary), - len(self.target_duration_dictionary) if self.cfg.discrete_duration else 1, - len(self.target_f0_dictionary) if self.cfg.discrete_f0 else 1, - ] - - @property - def source_dictionary(self) -> Optional[Dictionary]: - return self._source_dictionary - - @property - def source_duration_dictionary(self) -> Optional[Dictionary]: - return self._source_duration_dictionary - - @property - def source_f0_dictionary(self) -> Optional[Dictionary]: - return self._source_f0_dictionary - - @property - def channel_names(self) -> List[str]: - return self._channel_names - - @property - def channel_sizes(self) -> List[int]: - return self._channel_sizes - - @property - def dictionary(self) -> Optional[Dictionary]: - return self._source_dictionary - - @property - def target_dictionary(self) -> Optional[Dictionary]: - return self._target_dictionary - - @property - def target_duration_dictionary(self) -> Optional[Dictionary]: - return self._target_duration_dictionary - - @property - def target_f0_dictionary(self) -> Optional[Dictionary]: - return self._target_f0_dictionary - - @property - def dictionaries(self) -> List[Dictionary]: - return [self._dictionaries[l] for l in self.cfg.labels] - - @classmethod - def setup_task( - cls, cfg: SpeechUnitModelingConfig, **kwargs - ) -> "SpeechUnitLanguageModelingTask": - return cls(cfg) - - def load_dataset(self, split: str, **kwargs) -> None: - self.datasets[split] = CodeDataset( - manifest=self.data_config.manifests[split], - dictionary=self.source_dictionary, - dur_dictionary=self.source_duration_dictionary, - f0_dictionary=self.source_f0_dictionary, - config=self.data_config, - discrete_dur=self.cfg.discrete_duration, - discrete_f0=self.cfg.discrete_f0, - log_f0=self.cfg.log_f0, - normalize_f0_mean=self.cfg.normalize_f0_mean, - normalize_f0_std=self.cfg.normalize_f0_std, - interpolate_f0=self.cfg.interpolate_f0, - shifts=self.cfg.stream_shifts, - ) - - def max_positions(self) -> Tuple[int, int]: - return (sys.maxsize, sys.maxsize) - - def build_criterion(self, cfg: DictConfig): - import fairseq.criterions - - return fairseq.criterions.build_criterion(cfg, self) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/text_to_speech.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/text_to_speech.py deleted file mode 100644 index d0de33d11d96847fae827a730de367782a5be462..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/text_to_speech.py +++ /dev/null @@ -1,500 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import os.path as op - -import numpy as np -import torch -import torch.nn.functional as F -from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDatasetCreator -from fairseq.speech_generator import ( - AutoRegressiveSpeechGenerator, - NonAutoregressiveSpeechGenerator, - TeacherForcingAutoRegressiveSpeechGenerator, -) -from fairseq.tasks import register_task -from fairseq.tasks.speech_to_text import SpeechToTextTask - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=logging.INFO, -) -logger = logging.getLogger(__name__) - - -try: - from tensorboardX import SummaryWriter -except ImportError: - logger.info("Please install tensorboardX: pip install tensorboardX") - SummaryWriter = None - - -@register_task("text_to_speech") -class TextToSpeechTask(SpeechToTextTask): - @staticmethod - def add_args(parser): - parser.add_argument("data", help="manifest root path") - parser.add_argument( - "--config-yaml", - type=str, - default="config.yaml", - help="Configuration YAML filename (under manifest root)", - ) - parser.add_argument( - "--max-source-positions", - default=1024, - type=int, - metavar="N", - help="max number of tokens in the source sequence", - ) - parser.add_argument( - "--max-target-positions", - default=1200, - type=int, - metavar="N", - help="max number of tokens in the target sequence", - ) - parser.add_argument("--n-frames-per-step", type=int, default=1) - parser.add_argument("--eos-prob-threshold", type=float, default=0.5) - parser.add_argument("--eval-inference", action="store_true") - parser.add_argument("--eval-tb-nsample", type=int, default=8) - parser.add_argument("--vocoder", type=str, default="griffin_lim") - parser.add_argument("--spec-bwd-max-iter", type=int, default=8) - - def __init__(self, args, src_dict): - super().__init__(args, src_dict) - self.src_dict = src_dict - self.sr = self.data_cfg.config.get("features").get("sample_rate") - - self.tensorboard_writer = None - self.tensorboard_dir = "" - if args.tensorboard_logdir and SummaryWriter is not None: - self.tensorboard_dir = os.path.join(args.tensorboard_logdir, "valid_extra") - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - is_train_split = split.startswith("train") - pre_tokenizer = self.build_tokenizer(self.args) - bpe_tokenizer = self.build_bpe(self.args) - self.datasets[split] = TextToSpeechDatasetCreator.from_tsv( - self.args.data, - self.data_cfg, - split, - self.src_dict, - pre_tokenizer, - bpe_tokenizer, - is_train_split=is_train_split, - epoch=epoch, - seed=self.args.seed, - n_frames_per_step=self.args.n_frames_per_step, - speaker_to_id=self.speaker_to_id, - ) - - @property - def target_dictionary(self): - return None - - @property - def source_dictionary(self): - return self.src_dict - - def get_speaker_embeddings_path(self): - speaker_emb_path = None - if self.data_cfg.config.get("speaker_emb_filename") is not None: - speaker_emb_path = op.join( - self.args.data, self.data_cfg.config.get("speaker_emb_filename") - ) - return speaker_emb_path - - @classmethod - def get_speaker_embeddings(cls, args): - embed_speaker = None - if args.speaker_to_id is not None: - if args.speaker_emb_path is None: - embed_speaker = torch.nn.Embedding( - len(args.speaker_to_id), args.speaker_embed_dim - ) - else: - speaker_emb_mat = np.load(args.speaker_emb_path) - assert speaker_emb_mat.shape[1] == args.speaker_embed_dim - embed_speaker = torch.nn.Embedding.from_pretrained( - torch.from_numpy(speaker_emb_mat), - freeze=True, - ) - logger.info( - f"load speaker embeddings from {args.speaker_emb_path}. " - f"train embedding? {embed_speaker.weight.requires_grad}\n" - f"embeddings:\n{speaker_emb_mat}" - ) - return embed_speaker - - def build_model(self, cfg, from_checkpoint=False): - cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None) - cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None) - cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None) - cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None) - cfg.speaker_emb_path = self.get_speaker_embeddings_path() - model = super().build_model(cfg, from_checkpoint) - self.generator = None - if getattr(cfg, "eval_inference", False): - self.generator = self.build_generator([model], cfg) - return model - - def build_generator(self, models, cfg, vocoder=None, **unused): - if vocoder is None: - vocoder = self.build_default_vocoder() - model = models[0] - if getattr(model, "NON_AUTOREGRESSIVE", False): - return NonAutoregressiveSpeechGenerator(model, vocoder, self.data_cfg) - else: - generator = AutoRegressiveSpeechGenerator - if getattr(cfg, "teacher_forcing", False): - generator = TeacherForcingAutoRegressiveSpeechGenerator - logger.info("Teacher forcing mode for generation") - return generator( - model, - vocoder, - self.data_cfg, - max_iter=self.args.max_target_positions, - eos_prob_threshold=self.args.eos_prob_threshold, - ) - - def build_default_vocoder(self): - from fairseq.models.text_to_speech.vocoder import get_vocoder - - vocoder = get_vocoder(self.args, self.data_cfg) - if torch.cuda.is_available() and not self.args.cpu: - vocoder = vocoder.cuda() - else: - vocoder = vocoder.cpu() - return vocoder - - def valid_step(self, sample, model, criterion): - loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - - if getattr(self.args, "eval_inference", False): - hypos, inference_losses = self.valid_step_with_inference( - sample, model, self.generator - ) - for k, v in inference_losses.items(): - assert k not in logging_output - logging_output[k] = v - - picked_id = 0 - if self.tensorboard_dir and (sample["id"] == picked_id).any(): - self.log_tensorboard( - sample, - hypos[: self.args.eval_tb_nsample], - model._num_updates, - is_na_model=getattr(model, "NON_AUTOREGRESSIVE", False), - ) - return loss, sample_size, logging_output - - def valid_step_with_inference(self, sample, model, generator): - hypos = generator.generate(model, sample, has_targ=True) - - losses = { - "mcd_loss": 0.0, - "targ_frames": 0.0, - "pred_frames": 0.0, - "nins": 0.0, - "ndel": 0.0, - } - rets = batch_mel_cepstral_distortion( - [hypo["targ_waveform"] for hypo in hypos], - [hypo["waveform"] for hypo in hypos], - self.sr, - normalize_type=None, - ) - for d, extra in rets: - pathmap = extra[-1] - losses["mcd_loss"] += d.item() - losses["targ_frames"] += pathmap.size(0) - losses["pred_frames"] += pathmap.size(1) - losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item() - losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item() - - return hypos, losses - - def log_tensorboard(self, sample, hypos, num_updates, is_na_model=False): - if self.tensorboard_writer is None: - self.tensorboard_writer = SummaryWriter(self.tensorboard_dir) - tb_writer = self.tensorboard_writer - for b in range(len(hypos)): - idx = sample["id"][b] - text = sample["src_texts"][b] - targ = hypos[b]["targ_feature"] - pred = hypos[b]["feature"] - attn = hypos[b]["attn"] - - if is_na_model: - data = plot_tts_output( - [targ.transpose(0, 1), pred.transpose(0, 1)], - [f"target (idx={idx})", "output"], - attn, - "alignment", - ret_np=True, - suptitle=text, - ) - else: - eos_prob = hypos[b]["eos_prob"] - data = plot_tts_output( - [targ.transpose(0, 1), pred.transpose(0, 1), attn], - [f"target (idx={idx})", "output", "alignment"], - eos_prob, - "eos prob", - ret_np=True, - suptitle=text, - ) - - tb_writer.add_image( - f"inference_sample_{b}", data, num_updates, dataformats="HWC" - ) - - if hypos[b]["waveform"] is not None: - targ_wave = hypos[b]["targ_waveform"].detach().cpu().float() - pred_wave = hypos[b]["waveform"].detach().cpu().float() - tb_writer.add_audio( - f"inference_targ_{b}", targ_wave, num_updates, sample_rate=self.sr - ) - tb_writer.add_audio( - f"inference_pred_{b}", pred_wave, num_updates, sample_rate=self.sr - ) - - -def save_figure_to_numpy(fig): - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - return data - - -DEFAULT_V_MIN = np.log(1e-5) - - -def plot_tts_output( - data_2d, - title_2d, - data_1d, - title_1d, - figsize=(24, 4), - v_min=DEFAULT_V_MIN, - v_max=3, - ret_np=False, - suptitle="", -): - try: - import matplotlib.pyplot as plt - from mpl_toolkits.axes_grid1 import make_axes_locatable - except ImportError: - raise ImportError("Please install Matplotlib: pip install matplotlib") - - data_2d = [ - x.detach().cpu().float().numpy() if isinstance(x, torch.Tensor) else x - for x in data_2d - ] - fig, axes = plt.subplots(1, len(data_2d) + 1, figsize=figsize) - if suptitle: - fig.suptitle(suptitle[:400]) # capped at 400 chars - axes = [axes] if len(data_2d) == 0 else axes - for ax, x, name in zip(axes, data_2d, title_2d): - ax.set_title(name) - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - im = ax.imshow( - x, - origin="lower", - aspect="auto", - vmin=max(x.min(), v_min), - vmax=min(x.max(), v_max), - ) - fig.colorbar(im, cax=cax, orientation="vertical") - - if isinstance(data_1d, torch.Tensor): - data_1d = data_1d.detach().cpu().numpy() - axes[-1].plot(data_1d) - axes[-1].set_title(title_1d) - plt.tight_layout() - - if ret_np: - fig.canvas.draw() - data = save_figure_to_numpy(fig) - plt.close(fig) - return data - - -def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None): - """ - for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs - - offset=2 (1, 1), - offset=3 (2, 1), (1, 2) - offset=4 (2, 2), (1, 3) - offset=5 (2, 3) - - constraints: - i + j = offset - min_j <= j < max_j - min_i <= offset - j < max_i - """ - if max_i is None: - max_i = offset + 1 - if max_j is None: - max_j = offset + 1 - min_j = max(min_j, offset - max_i + 1, 0) - max_j = min(max_j, offset - min_i + 1, offset + 1) - j = torch.arange(min_j, max_j) - i = offset - j - return torch.stack([i, j]) - - -def batch_dynamic_time_warping(distance, shapes=None): - """full batched DTW without any constraints - - distance: (batchsize, max_M, max_N) matrix - shapes: (batchsize,) vector specifying (M, N) for each entry - """ - # ptr: 0=left, 1=up-left, 2=up - ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)} - - bsz, m, n = distance.size() - cumdist = torch.zeros_like(distance) - backptr = torch.zeros_like(distance).type(torch.int32) - 1 - - # initialize - cumdist[:, 0, :] = distance[:, 0, :].cumsum(dim=-1) - cumdist[:, :, 0] = distance[:, :, 0].cumsum(dim=-1) - backptr[:, 0, :] = 0 - backptr[:, :, 0] = 2 - - # DP with optimized anti-diagonal parallelization, O(M+N) steps - for offset in range(2, m + n - 1): - ind = antidiag_indices(offset, 1, m, 1, n) - c = torch.stack( - [ - cumdist[:, ind[0], ind[1] - 1], - cumdist[:, ind[0] - 1, ind[1] - 1], - cumdist[:, ind[0] - 1, ind[1]], - ], - dim=2, - ) - v, b = c.min(axis=-1) - backptr[:, ind[0], ind[1]] = b.int() - cumdist[:, ind[0], ind[1]] = v + distance[:, ind[0], ind[1]] - - # backtrace - pathmap = torch.zeros_like(backptr) - for b in range(bsz): - i = m - 1 if shapes is None else (shapes[b][0] - 1).item() - j = n - 1 if shapes is None else (shapes[b][1] - 1).item() - dtwpath = [(i, j)] - while (i != 0 or j != 0) and len(dtwpath) < 10000: - assert i >= 0 and j >= 0 - di, dj = ptr2dij[backptr[b, i, j].item()] - i, j = i + di, j + dj - dtwpath.append((i, j)) - dtwpath = dtwpath[::-1] - indices = torch.from_numpy(np.array(dtwpath)) - pathmap[b, indices[:, 0], indices[:, 1]] = 1 - - return cumdist, backptr, pathmap - - -def compute_l2_dist(x1, x2): - """compute an (m, n) L2 distance matrix from (m, d) and (n, d) matrices""" - return torch.cdist(x1.unsqueeze(0), x2.unsqueeze(0), p=2).squeeze(0).pow(2) - - -def compute_rms_dist(x1, x2): - l2_dist = compute_l2_dist(x1, x2) - return (l2_dist / x1.size(1)).pow(0.5) - - -def get_divisor(pathmap, normalize_type): - if normalize_type is None: - return 1 - elif normalize_type == "len1": - return pathmap.size(0) - elif normalize_type == "len2": - return pathmap.size(1) - elif normalize_type == "path": - return pathmap.sum().item() - else: - raise ValueError(f"normalize_type {normalize_type} not supported") - - -def batch_compute_distortion(y1, y2, sr, feat_fn, dist_fn, normalize_type): - d, s, x1, x2 = [], [], [], [] - for cur_y1, cur_y2 in zip(y1, y2): - assert cur_y1.ndim == 1 and cur_y2.ndim == 1 - cur_x1 = feat_fn(cur_y1) - cur_x2 = feat_fn(cur_y2) - x1.append(cur_x1) - x2.append(cur_x2) - - cur_d = dist_fn(cur_x1, cur_x2) - d.append(cur_d) - s.append(d[-1].size()) - max_m = max(ss[0] for ss in s) - max_n = max(ss[1] for ss in s) - d = torch.stack( - [F.pad(dd, (0, max_n - dd.size(1), 0, max_m - dd.size(0))) for dd in d] - ) - s = torch.LongTensor(s).to(d.device) - cumdists, backptrs, pathmaps = batch_dynamic_time_warping(d, s) - - rets = [] - itr = zip(s, x1, x2, d, cumdists, backptrs, pathmaps) - for (m, n), cur_x1, cur_x2, dist, cumdist, backptr, pathmap in itr: - cumdist = cumdist[:m, :n] - backptr = backptr[:m, :n] - pathmap = pathmap[:m, :n] - divisor = get_divisor(pathmap, normalize_type) - - distortion = cumdist[-1, -1] / divisor - ret = distortion, (cur_x1, cur_x2, dist, cumdist, backptr, pathmap) - rets.append(ret) - return rets - - -def batch_mel_cepstral_distortion(y1, y2, sr, normalize_type="path", mfcc_fn=None): - """ - https://arxiv.org/pdf/2011.03568.pdf - - The root mean squared error computed on 13-dimensional MFCC using DTW for - alignment. MFCC features are computed from an 80-channel log-mel - spectrogram using a 50ms Hann window and hop of 12.5ms. - - y1: list of waveforms - y2: list of waveforms - sr: sampling rate - """ - - try: - import torchaudio - except ImportError: - raise ImportError("Please install torchaudio: pip install torchaudio") - - if mfcc_fn is None or mfcc_fn.sample_rate != sr: - melkwargs = { - "n_fft": int(0.05 * sr), - "win_length": int(0.05 * sr), - "hop_length": int(0.0125 * sr), - "f_min": 20, - "n_mels": 80, - "window_fn": torch.hann_window, - } - mfcc_fn = torchaudio.transforms.MFCC( - sr, n_mfcc=13, log_mels=True, melkwargs=melkwargs - ).to(y1[0].device) - return batch_compute_distortion( - y1, - y2, - sr, - lambda y: mfcc_fn(y).transpose(-1, -2), - compute_rms_dist, - normalize_type, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation.py deleted file mode 100644 index efe3369bf151680497e9906b229563db0b25a429..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation.py +++ /dev/null @@ -1,496 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import itertools -import json -import logging -import os -from argparse import Namespace -from dataclasses import dataclass, field -from typing import Optional - -import numpy as np -from fairseq import metrics, utils -from fairseq.data import ( - AppendTokenDataset, - ConcatDataset, - LanguagePairDataset, - PrependTokenDataset, - StripTokenDataset, - TruncateDataset, - data_utils, - encoders, - indexed_dataset, -) -from fairseq.data.indexed_dataset import get_available_dataset_impl -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.tasks import FairseqTask, register_task -from omegaconf import II - -EVAL_BLEU_ORDER = 4 - - -logger = logging.getLogger(__name__) - - -def load_langpair_dataset( - data_path, - split, - src, - src_dict, - tgt, - tgt_dict, - combine, - dataset_impl, - upsample_primary, - left_pad_source, - left_pad_target, - max_source_positions, - max_target_positions, - prepend_bos=False, - load_alignments=False, - truncate_source=False, - append_source_id=False, - num_buckets=0, - shuffle=True, - pad_to_multiple=1, - prepend_bos_src=None, -): - def split_exists(split, src, tgt, lang, data_path): - filename = os.path.join(data_path, "{}.{}-{}.{}".format(split, src, tgt, lang)) - return indexed_dataset.dataset_exists(filename, impl=dataset_impl) - - src_datasets = [] - tgt_datasets = [] - - for k in itertools.count(): - split_k = split + (str(k) if k > 0 else "") - - # infer langcode - if split_exists(split_k, src, tgt, src, data_path): - prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, src, tgt)) - elif split_exists(split_k, tgt, src, src, data_path): - prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, tgt, src)) - else: - if k > 0: - break - else: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, data_path) - ) - - src_dataset = data_utils.load_indexed_dataset( - prefix + src, src_dict, dataset_impl - ) - if truncate_source: - src_dataset = AppendTokenDataset( - TruncateDataset( - StripTokenDataset(src_dataset, src_dict.eos()), - max_source_positions - 1, - ), - src_dict.eos(), - ) - src_datasets.append(src_dataset) - - tgt_dataset = data_utils.load_indexed_dataset( - prefix + tgt, tgt_dict, dataset_impl - ) - if tgt_dataset is not None: - tgt_datasets.append(tgt_dataset) - - logger.info( - "{} {} {}-{} {} examples".format( - data_path, split_k, src, tgt, len(src_datasets[-1]) - ) - ) - - if not combine: - break - - assert len(src_datasets) == len(tgt_datasets) or len(tgt_datasets) == 0 - - if len(src_datasets) == 1: - src_dataset = src_datasets[0] - tgt_dataset = tgt_datasets[0] if len(tgt_datasets) > 0 else None - else: - sample_ratios = [1] * len(src_datasets) - sample_ratios[0] = upsample_primary - src_dataset = ConcatDataset(src_datasets, sample_ratios) - if len(tgt_datasets) > 0: - tgt_dataset = ConcatDataset(tgt_datasets, sample_ratios) - else: - tgt_dataset = None - - if prepend_bos: - assert hasattr(src_dict, "bos_index") and hasattr(tgt_dict, "bos_index") - src_dataset = PrependTokenDataset(src_dataset, src_dict.bos()) - if tgt_dataset is not None: - tgt_dataset = PrependTokenDataset(tgt_dataset, tgt_dict.bos()) - elif prepend_bos_src is not None: - logger.info(f"prepending src bos: {prepend_bos_src}") - src_dataset = PrependTokenDataset(src_dataset, prepend_bos_src) - - eos = None - if append_source_id: - src_dataset = AppendTokenDataset( - src_dataset, src_dict.index("[{}]".format(src)) - ) - if tgt_dataset is not None: - tgt_dataset = AppendTokenDataset( - tgt_dataset, tgt_dict.index("[{}]".format(tgt)) - ) - eos = tgt_dict.index("[{}]".format(tgt)) - - align_dataset = None - if load_alignments: - align_path = os.path.join(data_path, "{}.align.{}-{}".format(split, src, tgt)) - if indexed_dataset.dataset_exists(align_path, impl=dataset_impl): - align_dataset = data_utils.load_indexed_dataset( - align_path, None, dataset_impl - ) - - tgt_dataset_sizes = tgt_dataset.sizes if tgt_dataset is not None else None - return LanguagePairDataset( - src_dataset, - src_dataset.sizes, - src_dict, - tgt_dataset, - tgt_dataset_sizes, - tgt_dict, - left_pad_source=left_pad_source, - left_pad_target=left_pad_target, - align_dataset=align_dataset, - eos=eos, - num_buckets=num_buckets, - shuffle=shuffle, - pad_to_multiple=pad_to_multiple, - ) - - -@dataclass -class TranslationConfig(FairseqDataclass): - data: Optional[str] = field( - default=None, - metadata={ - "help": "colon separated path to data directories list, will be iterated upon during epochs " - "in round-robin manner; however, valid and test data are always in the first directory " - "to avoid the need for repeating them in all directories" - }, - ) - source_lang: Optional[str] = field( - default=None, - metadata={ - "help": "source language", - "argparse_alias": "-s", - }, - ) - target_lang: Optional[str] = field( - default=None, - metadata={ - "help": "target language", - "argparse_alias": "-t", - }, - ) - load_alignments: bool = field( - default=False, metadata={"help": "load the binarized alignments"} - ) - left_pad_source: bool = field( - default=True, metadata={"help": "pad the source on the left"} - ) - left_pad_target: bool = field( - default=False, metadata={"help": "pad the target on the left"} - ) - max_source_positions: int = field( - default=1024, metadata={"help": "max number of tokens in the source sequence"} - ) - max_target_positions: int = field( - default=1024, metadata={"help": "max number of tokens in the target sequence"} - ) - upsample_primary: int = field( - default=-1, metadata={"help": "the amount of upsample primary dataset"} - ) - truncate_source: bool = field( - default=False, metadata={"help": "truncate source to max-source-positions"} - ) - num_batch_buckets: int = field( - default=0, - metadata={ - "help": "if >0, then bucket source and target lengths into " - "N buckets and pad accordingly; this is useful on TPUs to minimize the number of compilations" - }, - ) - train_subset: str = II("dataset.train_subset") - dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II( - "dataset.dataset_impl" - ) - required_seq_len_multiple: int = II("dataset.required_seq_len_multiple") - - # options for reporting BLEU during validation - eval_bleu: bool = field( - default=False, metadata={"help": "evaluation with BLEU scores"} - ) - eval_bleu_args: Optional[str] = field( - default="{}", - metadata={ - "help": 'generation args for BLUE scoring, e.g., \'{"beam": 4, "lenpen": 0.6}\', as JSON string' - }, - ) - eval_bleu_detok: str = field( - default="space", - metadata={ - "help": "detokenize before computing BLEU (e.g., 'moses'); required if using --eval-bleu; " - "use 'space' to disable detokenization; see fairseq.data.encoders for other options" - }, - ) - eval_bleu_detok_args: Optional[str] = field( - default="{}", - metadata={"help": "args for building the tokenizer, if needed, as JSON string"}, - ) - eval_tokenized_bleu: bool = field( - default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"} - ) - eval_bleu_remove_bpe: Optional[str] = field( - default=None, - metadata={ - "help": "remove BPE before computing BLEU", - "argparse_const": "@@ ", - }, - ) - eval_bleu_print_samples: bool = field( - default=False, metadata={"help": "print sample generations during validation"} - ) - - -@register_task("translation", dataclass=TranslationConfig) -class TranslationTask(FairseqTask): - """ - Translate from one (source) language to another (target) language. - - Args: - src_dict (~fairseq.data.Dictionary): dictionary for the source language - tgt_dict (~fairseq.data.Dictionary): dictionary for the target language - - .. note:: - - The translation task is compatible with :mod:`fairseq-train`, - :mod:`fairseq-generate` and :mod:`fairseq-interactive`. - """ - - cfg: TranslationConfig - - def __init__(self, cfg: TranslationConfig, src_dict, tgt_dict): - super().__init__(cfg) - self.src_dict = src_dict - self.tgt_dict = tgt_dict - - @classmethod - def setup_task(cls, cfg: TranslationConfig, **kwargs): - """Setup the task (e.g., load dictionaries). - - Args: - args (argparse.Namespace): parsed command-line arguments - """ - - paths = utils.split_paths(cfg.data) - assert len(paths) > 0 - # find language pair automatically - if cfg.source_lang is None or cfg.target_lang is None: - cfg.source_lang, cfg.target_lang = data_utils.infer_language_pair(paths[0]) - if cfg.source_lang is None or cfg.target_lang is None: - raise Exception( - "Could not infer language pair, please provide it explicitly" - ) - - # load dictionaries - src_dict = cls.load_dictionary( - os.path.join(paths[0], "dict.{}.txt".format(cfg.source_lang)) - ) - tgt_dict = cls.load_dictionary( - os.path.join(paths[0], "dict.{}.txt".format(cfg.target_lang)) - ) - assert src_dict.pad() == tgt_dict.pad() - assert src_dict.eos() == tgt_dict.eos() - assert src_dict.unk() == tgt_dict.unk() - logger.info("[{}] dictionary: {} types".format(cfg.source_lang, len(src_dict))) - logger.info("[{}] dictionary: {} types".format(cfg.target_lang, len(tgt_dict))) - - return cls(cfg, src_dict, tgt_dict) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - paths = utils.split_paths(self.cfg.data) - assert len(paths) > 0 - if split != self.cfg.train_subset: - # if not training data set, use the first shard for valid and test - paths = paths[:1] - data_path = paths[(epoch - 1) % len(paths)] - - # infer langcode - src, tgt = self.cfg.source_lang, self.cfg.target_lang - - self.datasets[split] = load_langpair_dataset( - data_path, - split, - src, - self.src_dict, - tgt, - self.tgt_dict, - combine=combine, - dataset_impl=self.cfg.dataset_impl, - upsample_primary=self.cfg.upsample_primary, - left_pad_source=self.cfg.left_pad_source, - left_pad_target=self.cfg.left_pad_target, - max_source_positions=self.cfg.max_source_positions, - max_target_positions=self.cfg.max_target_positions, - load_alignments=self.cfg.load_alignments, - truncate_source=self.cfg.truncate_source, - num_buckets=self.cfg.num_batch_buckets, - shuffle=(split != "test"), - pad_to_multiple=self.cfg.required_seq_len_multiple, - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): - return LanguagePairDataset( - src_tokens, - src_lengths, - self.source_dictionary, - tgt_dict=self.target_dictionary, - constraints=constraints, - ) - - def build_model(self, cfg, from_checkpoint=False): - model = super().build_model(cfg, from_checkpoint) - if self.cfg.eval_bleu: - detok_args = json.loads(self.cfg.eval_bleu_detok_args) - self.tokenizer = encoders.build_tokenizer( - Namespace(tokenizer=self.cfg.eval_bleu_detok, **detok_args) - ) - - gen_args = json.loads(self.cfg.eval_bleu_args) - self.sequence_generator = self.build_generator( - [model], Namespace(**gen_args) - ) - return model - - def valid_step(self, sample, model, criterion): - loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - if self.cfg.eval_bleu: - bleu = self._inference_with_bleu(self.sequence_generator, sample, model) - logging_output["_bleu_sys_len"] = bleu.sys_len - logging_output["_bleu_ref_len"] = bleu.ref_len - # we split counts into separate entries so that they can be - # summed efficiently across workers using fast-stat-sync - assert len(bleu.counts) == EVAL_BLEU_ORDER - for i in range(EVAL_BLEU_ORDER): - logging_output["_bleu_counts_" + str(i)] = bleu.counts[i] - logging_output["_bleu_totals_" + str(i)] = bleu.totals[i] - return loss, sample_size, logging_output - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - if self.cfg.eval_bleu: - - def sum_logs(key): - import torch - - result = sum(log.get(key, 0) for log in logging_outputs) - if torch.is_tensor(result): - result = result.cpu() - return result - - counts, totals = [], [] - for i in range(EVAL_BLEU_ORDER): - counts.append(sum_logs("_bleu_counts_" + str(i))) - totals.append(sum_logs("_bleu_totals_" + str(i))) - - if max(totals) > 0: - # log counts as numpy arrays -- log_scalar will sum them correctly - metrics.log_scalar("_bleu_counts", np.array(counts)) - metrics.log_scalar("_bleu_totals", np.array(totals)) - metrics.log_scalar("_bleu_sys_len", sum_logs("_bleu_sys_len")) - metrics.log_scalar("_bleu_ref_len", sum_logs("_bleu_ref_len")) - - def compute_bleu(meters): - import inspect - - try: - from sacrebleu.metrics import BLEU - - comp_bleu = BLEU.compute_bleu - except ImportError: - # compatibility API for sacrebleu 1.x - import sacrebleu - - comp_bleu = sacrebleu.compute_bleu - - fn_sig = inspect.getfullargspec(comp_bleu)[0] - if "smooth_method" in fn_sig: - smooth = {"smooth_method": "exp"} - else: - smooth = {"smooth": "exp"} - bleu = comp_bleu( - correct=meters["_bleu_counts"].sum, - total=meters["_bleu_totals"].sum, - sys_len=meters["_bleu_sys_len"].sum, - ref_len=meters["_bleu_ref_len"].sum, - **smooth, - ) - return round(bleu.score, 2) - - metrics.log_derived("bleu", compute_bleu) - - def max_positions(self): - """Return the max sentence length allowed by the task.""" - return (self.cfg.max_source_positions, self.cfg.max_target_positions) - - @property - def source_dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary`.""" - return self.src_dict - - @property - def target_dictionary(self): - """Return the target :class:`~fairseq.data.Dictionary`.""" - return self.tgt_dict - - def _inference_with_bleu(self, generator, sample, model): - import sacrebleu - - def decode(toks, escape_unk=False): - s = self.tgt_dict.string( - toks.int().cpu(), - self.cfg.eval_bleu_remove_bpe, - # The default unknown string in fairseq is ``, but - # this is tokenized by sacrebleu as `< unk >`, inflating - # BLEU scores. Instead, we use a somewhat more verbose - # alternative that is unlikely to appear in the real - # reference, but doesn't get split into multiple tokens. - unk_string=("UNKNOWNTOKENINREF" if escape_unk else "UNKNOWNTOKENINHYP"), - ) - if self.tokenizer: - s = self.tokenizer.decode(s) - return s - - gen_out = self.inference_step(generator, [model], sample, prefix_tokens=None) - hyps, refs = [], [] - for i in range(len(gen_out)): - hyps.append(decode(gen_out[i][0]["tokens"])) - refs.append( - decode( - utils.strip_pad(sample["target"][i], self.tgt_dict.pad()), - escape_unk=True, # don't count as matches to the hypo - ) - ) - if self.cfg.eval_bleu_print_samples: - logger.info("example hypothesis: " + hyps[0]) - logger.info("example reference: " + refs[0]) - if self.cfg.eval_tokenized_bleu: - return sacrebleu.corpus_bleu(hyps, [refs], tokenize="none") - else: - return sacrebleu.corpus_bleu(hyps, [refs]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_from_pretrained_bart.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_from_pretrained_bart.py deleted file mode 100644 index 0fd7a5b29f0e34699b5d5ef7574bc39b8c6052c9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_from_pretrained_bart.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from fairseq import utils -from fairseq.data import LanguagePairDataset - -from . import register_task -from .translation import TranslationTask, load_langpair_dataset - - -@register_task("translation_from_pretrained_bart") -class TranslationFromPretrainedBARTTask(TranslationTask): - """ - Translate from source language to target language with a model initialized with a multilingual pretrain. - - Args: - src_dict (~fairseq.data.Dictionary): dictionary for the source language - tgt_dict (~fairseq.data.Dictionary): dictionary for the target language - - .. note:: - - The translation task is compatible with :mod:`fairseq-train`, - :mod:`fairseq-generate` and :mod:`fairseq-interactive`. - - The translation task provides the following additional command-line - arguments: - - .. argparse:: - :ref: fairseq.tasks.translation_parser - :prog: - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - TranslationTask.add_args(parser) - parser.add_argument('--langs', type=str, metavar='LANG', - help='comma-separated list of monolingual language, ' - 'for example, "en,de,fr". These should match the ' - 'langs from pretraining (and be in the same order). ' - 'You should always add all pretraining language idx ' - 'during finetuning.') - parser.add_argument('--prepend-bos', action='store_true', - help='prepend bos token to each sentence, which matches ' - 'mBART pretraining') - # fmt: on - - def __init__(self, args, src_dict, tgt_dict): - super().__init__(args, src_dict, tgt_dict) - self.langs = args.langs.split(",") - for d in [src_dict, tgt_dict]: - for l in self.langs: - d.add_symbol("[{}]".format(l)) - d.add_symbol("") - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - paths = utils.split_paths(self.args.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - # infer langcode - src, tgt = self.args.source_lang, self.args.target_lang - - self.datasets[split] = load_langpair_dataset( - data_path, - split, - src, - self.src_dict, - tgt, - self.tgt_dict, - combine=combine, - dataset_impl=self.args.dataset_impl, - upsample_primary=self.args.upsample_primary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - max_source_positions=getattr(self.args, "max_source_positions", 1024), - max_target_positions=getattr(self.args, "max_target_positions", 1024), - load_alignments=self.args.load_alignments, - prepend_bos=getattr(self.args, "prepend_bos", False), - append_source_id=True, - ) - - def build_generator(self, models, args, **unused): - if getattr(args, "score_reference", False): - from fairseq.sequence_scorer import SequenceScorer - - return SequenceScorer( - self.target_dictionary, - eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)), - ) - else: - from fairseq.sequence_generator import SequenceGenerator - - return SequenceGenerator( - models, - self.target_dictionary, - beam_size=getattr(args, "beam", 5), - max_len_a=getattr(args, "max_len_a", 0), - max_len_b=getattr(args, "max_len_b", 200), - min_len=getattr(args, "min_len", 1), - normalize_scores=(not getattr(args, "unnormalized", False)), - len_penalty=getattr(args, "lenpen", 1), - unk_penalty=getattr(args, "unkpen", 0), - temperature=getattr(args, "temperature", 1.0), - match_source_len=getattr(args, "match_source_len", False), - no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0), - eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)), - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): - src_lang_id = self.source_dictionary.index("[{}]".format(self.args.source_lang)) - source_tokens = [] - for s_t in src_tokens: - s_t = torch.cat([s_t, s_t.new(1).fill_(src_lang_id)]) - source_tokens.append(s_t) - dataset = LanguagePairDataset( - source_tokens, - src_lengths, - self.source_dictionary, - tgt_dict=self.target_dictionary, - constraints=constraints, - ) - return dataset diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_from_pretrained_xlm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_from_pretrained_xlm.py deleted file mode 100644 index a3fc5472a73ff926898273bf3a74c97e2fd56de6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_from_pretrained_xlm.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass - -from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary -from fairseq.tasks.translation import TranslationConfig, TranslationTask - -from . import register_task - - -@dataclass -class TranslationFromPretrainedXLMConfig(TranslationConfig): - pass - - -@register_task( - "translation_from_pretrained_xlm", dataclass=TranslationFromPretrainedXLMConfig -) -class TranslationFromPretrainedXLMTask(TranslationTask): - """ - Same as TranslationTask except use the MaskedLMDictionary class so that - we can load data that was binarized with the MaskedLMDictionary class. - - This task should be used for the entire training pipeline when we want to - train an NMT model from a pretrained XLM checkpoint: binarizing NMT data, - training NMT with the pretrained XLM checkpoint, and subsequent evaluation - of that trained model. - """ - - @classmethod - def load_dictionary(cls, filename): - """Load the masked LM dictionary from the filename - - Args: - filename (str): the filename - """ - return MaskedLMDictionary.load(filename) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_lev.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_lev.py deleted file mode 100644 index 7666f804d50779606db681e33344bd1538623eba..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_lev.py +++ /dev/null @@ -1,195 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from dataclasses import dataclass, field - -import torch -from fairseq import utils -from fairseq.data import LanguagePairDataset -from fairseq.dataclass import ChoiceEnum -from fairseq.tasks import register_task -from fairseq.tasks.translation import ( - TranslationConfig, - TranslationTask, - load_langpair_dataset, -) -from fairseq.utils import new_arange - -NOISE_CHOICES = ChoiceEnum(["random_delete", "random_mask", "no_noise", "full_mask"]) - - -@dataclass -class TranslationLevenshteinConfig(TranslationConfig): - noise: NOISE_CHOICES = field( - default="random_delete", - metadata={"help": "type of noise"}, - ) - - -@register_task("translation_lev", dataclass=TranslationLevenshteinConfig) -class TranslationLevenshteinTask(TranslationTask): - """ - Translation (Sequence Generation) task for Levenshtein Transformer - See `"Levenshtein Transformer" `_. - """ - - cfg: TranslationLevenshteinConfig - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - paths = utils.split_paths(self.cfg.data) - assert len(paths) > 0 - data_path = paths[(epoch - 1) % len(paths)] - - # infer langcode - src, tgt = self.cfg.source_lang, self.cfg.target_lang - - self.datasets[split] = load_langpair_dataset( - data_path, - split, - src, - self.src_dict, - tgt, - self.tgt_dict, - combine=combine, - dataset_impl=self.cfg.dataset_impl, - upsample_primary=self.cfg.upsample_primary, - left_pad_source=self.cfg.left_pad_source, - left_pad_target=self.cfg.left_pad_target, - max_source_positions=self.cfg.max_source_positions, - max_target_positions=self.cfg.max_target_positions, - prepend_bos=True, - ) - - def inject_noise(self, target_tokens): - def _random_delete(target_tokens): - pad = self.tgt_dict.pad() - bos = self.tgt_dict.bos() - eos = self.tgt_dict.eos() - - max_len = target_tokens.size(1) - target_mask = target_tokens.eq(pad) - target_score = target_tokens.clone().float().uniform_() - target_score.masked_fill_( - target_tokens.eq(bos) | target_tokens.eq(eos), 0.0 - ) - target_score.masked_fill_(target_mask, 1) - target_score, target_rank = target_score.sort(1) - target_length = target_mask.size(1) - target_mask.float().sum( - 1, keepdim=True - ) - - # do not delete and (we assign 0 score for them) - target_cutoff = ( - 2 - + ( - (target_length - 2) - * target_score.new_zeros(target_score.size(0), 1).uniform_() - ).long() - ) - target_cutoff = target_score.sort(1)[1] >= target_cutoff - - prev_target_tokens = ( - target_tokens.gather(1, target_rank) - .masked_fill_(target_cutoff, pad) - .gather(1, target_rank.masked_fill_(target_cutoff, max_len).sort(1)[1]) - ) - prev_target_tokens = prev_target_tokens[ - :, : prev_target_tokens.ne(pad).sum(1).max() - ] - - return prev_target_tokens - - def _random_mask(target_tokens): - pad = self.tgt_dict.pad() - bos = self.tgt_dict.bos() - eos = self.tgt_dict.eos() - unk = self.tgt_dict.unk() - - target_masks = ( - target_tokens.ne(pad) & target_tokens.ne(bos) & target_tokens.ne(eos) - ) - target_score = target_tokens.clone().float().uniform_() - target_score.masked_fill_(~target_masks, 2.0) - target_length = target_masks.sum(1).float() - target_length = target_length * target_length.clone().uniform_() - target_length = target_length + 1 # make sure to mask at least one token. - - _, target_rank = target_score.sort(1) - target_cutoff = new_arange(target_rank) < target_length[:, None].long() - prev_target_tokens = target_tokens.masked_fill( - target_cutoff.scatter(1, target_rank, target_cutoff), unk - ) - return prev_target_tokens - - def _full_mask(target_tokens): - pad = self.tgt_dict.pad() - bos = self.tgt_dict.bos() - eos = self.tgt_dict.eos() - unk = self.tgt_dict.unk() - - target_mask = ( - target_tokens.eq(bos) | target_tokens.eq(eos) | target_tokens.eq(pad) - ) - return target_tokens.masked_fill(~target_mask, unk) - - if self.cfg.noise == "random_delete": - return _random_delete(target_tokens) - elif self.cfg.noise == "random_mask": - return _random_mask(target_tokens) - elif self.cfg.noise == "full_mask": - return _full_mask(target_tokens) - elif self.cfg.noise == "no_noise": - return target_tokens - else: - raise NotImplementedError - - def build_generator(self, models, args, **unused): - # add models input to match the API for SequenceGenerator - from fairseq.iterative_refinement_generator import IterativeRefinementGenerator - - return IterativeRefinementGenerator( - self.target_dictionary, - eos_penalty=getattr(args, "iter_decode_eos_penalty", 0.0), - max_iter=getattr(args, "iter_decode_max_iter", 10), - beam_size=getattr(args, "iter_decode_with_beam", 1), - reranking=getattr(args, "iter_decode_with_external_reranker", False), - decoding_format=getattr(args, "decoding_format", None), - adaptive=not getattr(args, "iter_decode_force_max_iter", False), - retain_history=getattr(args, "retain_iter_history", False), - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): - if constraints is not None: - # Though see Susanto et al. (ACL 2020): https://www.aclweb.org/anthology/2020.acl-main.325/ - raise NotImplementedError( - "Constrained decoding with the translation_lev task is not supported" - ) - - return LanguagePairDataset( - src_tokens, src_lengths, self.source_dictionary, append_bos=True - ) - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - model.train() - sample["prev_target"] = self.inject_noise(sample["target"]) - loss, sample_size, logging_output = criterion(model, sample) - if ignore_grad: - loss *= 0 - optimizer.backward(loss) - return loss, sample_size, logging_output - - def valid_step(self, sample, model, criterion): - model.eval() - with torch.no_grad(): - sample["prev_target"] = self.inject_noise(sample["target"]) - loss, sample_size, logging_output = criterion(model, sample) - return loss, sample_size, logging_output diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_multi_simple_epoch.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_multi_simple_epoch.py deleted file mode 100644 index 5db36a7c79ab291319339f5df15b70234154eda2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tasks/translation_multi_simple_epoch.py +++ /dev/null @@ -1,441 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import datetime -import logging -import time - -import torch -from fairseq.data import ( - FairseqDataset, - LanguagePairDataset, - ListDataset, - data_utils, - iterators, -) -from fairseq.data.multilingual.multilingual_data_manager import ( - MultilingualDatasetManager, -) -from fairseq.data.multilingual.sampling_method import SamplingMethod -from fairseq.tasks import LegacyFairseqTask, register_task -from fairseq.utils import FileContentsAction - - -### -def get_time_gap(s, e): - return ( - datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s) - ).__str__() - - -### - - -logger = logging.getLogger(__name__) - - -@register_task("translation_multi_simple_epoch") -class TranslationMultiSimpleEpochTask(LegacyFairseqTask): - """ - Translate from one (source) language to another (target) language. - - Args: - langs (List[str]): a list of languages that are being supported - dicts (Dict[str, fairseq.data.Dictionary]): mapping from supported languages to their dictionaries - training (bool): whether the task should be configured for training or not - - .. note:: - - The translation task is compatible with :mod:`fairseq-train`, - :mod:`fairseq-generate` and :mod:`fairseq-interactive`. - - The translation task provides the following additional command-line - arguments: - - .. argparse:: - :ref: fairseq.tasks.translation_parser - :prog: - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - parser.add_argument('-s', '--source-lang', default=None, metavar='SRC', - help='inference source language') - parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET', - help='inference target language') - parser.add_argument('--lang-pairs', default=None, metavar='PAIRS', - help='comma-separated list of language pairs (in training order): en-de,en-fr,de-fr', - action=FileContentsAction) - parser.add_argument('--keep-inference-langtok', action='store_true', - help='keep language tokens in inference output (e.g. for analysis or debugging)') - - SamplingMethod.add_arguments(parser) - MultilingualDatasetManager.add_args(parser) - # fmt: on - - def __init__(self, args, langs, dicts, training): - super().__init__(args) - self.langs = langs - self.dicts = dicts - self.training = training - if training: - self.lang_pairs = args.lang_pairs - else: - self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] - # eval_lang_pairs for multilingual translation is usually all of the - # lang_pairs. However for other multitask settings or when we want to - # optimize for certain languages we want to use a different subset. Thus - # the eval_lang_pairs class variable is provided for classes that extend - # this class. - self.eval_lang_pairs = self.lang_pairs - # model_lang_pairs will be used to build encoder-decoder model pairs in - # models.build_model(). This allows multitask type of sub-class can - # build models other than the input lang_pairs - self.model_lang_pairs = self.lang_pairs - self.source_langs = [d.split("-")[0] for d in self.lang_pairs] - self.target_langs = [d.split("-")[1] for d in self.lang_pairs] - self.check_dicts(self.dicts, self.source_langs, self.target_langs) - - self.sampling_method = SamplingMethod.build_sampler(args, self) - self.data_manager = MultilingualDatasetManager.setup_data_manager( - args, self.lang_pairs, langs, dicts, self.sampling_method - ) - - def check_dicts(self, dicts, source_langs, target_langs): - if self.args.source_dict is not None or self.args.target_dict is not None: - # no need to check whether the source side and target side are sharing dictionaries - return - src_dict = dicts[source_langs[0]] - tgt_dict = dicts[target_langs[0]] - for src_lang in source_langs: - assert ( - src_dict == dicts[src_lang] - ), "Diffrent dictionary are specified for different source languages; " - "TranslationMultiSimpleEpochTask only supports one shared dictionary across all source languages" - for tgt_lang in target_langs: - assert ( - tgt_dict == dicts[tgt_lang] - ), "Diffrent dictionary are specified for different target languages; " - "TranslationMultiSimpleEpochTask only supports one shared dictionary across all target languages" - - @classmethod - def setup_task(cls, args, **kwargs): - langs, dicts, training = MultilingualDatasetManager.prepare( - cls.load_dictionary, args, **kwargs - ) - return cls(args, langs, dicts, training) - - def has_sharded_data(self, split): - return self.data_manager.has_sharded_data(split) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - if split in self.datasets: - dataset = self.datasets[split] - if self.has_sharded_data(split): - if self.args.virtual_epoch_size is not None: - if dataset.load_next_shard: - shard_epoch = dataset.shard_epoch - else: - # no need to load next shard so skip loading - # also this avoid always loading from beginning of the data - return - else: - shard_epoch = epoch - else: - # estimate the shard epoch from virtual data size and virtual epoch size - shard_epoch = self.data_manager.estimate_global_pass_epoch(epoch) - logger.info(f"loading data for {split} epoch={epoch}/{shard_epoch}") - logger.info(f"mem usage: {data_utils.get_mem_usage()}") - if split in self.datasets: - del self.datasets[split] - logger.info("old dataset deleted manually") - logger.info(f"mem usage: {data_utils.get_mem_usage()}") - self.datasets[split] = self.data_manager.load_dataset( - split, - self.training, - epoch=epoch, - combine=combine, - shard_epoch=shard_epoch, - **kwargs, - ) - - def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None): - if constraints is not None: - raise NotImplementedError( - "Constrained decoding with the multilingual_translation task is not supported" - ) - - src_data = ListDataset(src_tokens, src_lengths) - dataset = LanguagePairDataset(src_data, src_lengths, self.source_dictionary) - src_langtok_spec, tgt_langtok_spec = self.args.langtoks["main"] - if self.args.lang_tok_replacing_bos_eos: - dataset = self.data_manager.alter_dataset_langtok( - dataset, - src_eos=self.source_dictionary.eos(), - src_lang=self.args.source_lang, - tgt_eos=self.target_dictionary.eos(), - tgt_lang=self.args.target_lang, - src_langtok_spec=src_langtok_spec, - tgt_langtok_spec=tgt_langtok_spec, - ) - else: - dataset.src = self.data_manager.src_dataset_tranform_func( - self.args.source_lang, - self.args.target_lang, - dataset=dataset.src, - spec=src_langtok_spec, - ) - return dataset - - def build_generator( - self, - models, - args, - seq_gen_cls=None, - extra_gen_cls_kwargs=None, - ): - if not getattr(args, "keep_inference_langtok", False): - _, tgt_langtok_spec = self.args.langtoks["main"] - if tgt_langtok_spec: - tgt_lang_tok = self.data_manager.get_decoder_langtok( - self.args.target_lang, tgt_langtok_spec - ) - extra_gen_cls_kwargs = extra_gen_cls_kwargs or {} - extra_gen_cls_kwargs["symbols_to_strip_from_output"] = {tgt_lang_tok} - - return super().build_generator( - models, args, seq_gen_cls=None, extra_gen_cls_kwargs=extra_gen_cls_kwargs - ) - - def build_model(self, args, from_checkpoint=False): - return super().build_model(args, from_checkpoint) - - def valid_step(self, sample, model, criterion): - loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - return loss, sample_size, logging_output - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - _, tgt_langtok_spec = self.args.langtoks["main"] - if not self.args.lang_tok_replacing_bos_eos: - if prefix_tokens is None and tgt_langtok_spec: - tgt_lang_tok = self.data_manager.get_decoder_langtok( - self.args.target_lang, tgt_langtok_spec - ) - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.size(0) - prefix_tokens = ( - torch.LongTensor([[tgt_lang_tok]]).expand(bsz, 1).to(src_tokens) - ) - return generator.generate( - models, - sample, - prefix_tokens=prefix_tokens, - constraints=constraints, - ) - else: - return generator.generate( - models, - sample, - prefix_tokens=prefix_tokens, - bos_token=self.data_manager.get_decoder_langtok( - self.args.target_lang, tgt_langtok_spec - ) - if tgt_langtok_spec - else self.target_dictionary.eos(), - ) - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - - def max_positions(self): - """Return the max sentence length allowed by the task.""" - return (self.args.max_source_positions, self.args.max_target_positions) - - @property - def source_dictionary(self): - return self.data_manager.get_source_dictionary(self.source_langs[0]) - - @property - def target_dictionary(self): - return self.data_manager.get_target_dictionary(self.target_langs[0]) - - def create_batch_sampler_func( - self, - max_positions, - ignore_invalid_inputs, - max_tokens, - max_sentences, - required_batch_size_multiple=1, - seed=1, - ): - def construct_batch_sampler(dataset, epoch): - splits = [ - s for s, _ in self.datasets.items() if self.datasets[s] == dataset - ] - split = splits[0] if len(splits) > 0 else None - # NEW implementation - if epoch is not None: - # initialize the dataset with the correct starting epoch - dataset.set_epoch(epoch) - - # get indices ordered by example size - start_time = time.time() - logger.info(f"start batch sampler: mem usage: {data_utils.get_mem_usage()}") - - with data_utils.numpy_seed(seed): - indices = dataset.ordered_indices() - logger.info( - f"[{split}] @batch_sampler order indices time: {get_time_gap(start_time, time.time())}" - ) - logger.info(f"mem usage: {data_utils.get_mem_usage()}") - - # filter examples that are too large - if max_positions is not None: - my_time = time.time() - indices = self.filter_indices_by_size( - indices, dataset, max_positions, ignore_invalid_inputs - ) - logger.info( - f"[{split}] @batch_sampler filter_by_size time: {get_time_gap(my_time, time.time())}" - ) - logger.info(f"mem usage: {data_utils.get_mem_usage()}") - - # create mini-batches with given size constraints - my_time = time.time() - batch_sampler = dataset.batch_by_size( - indices, - max_tokens=max_tokens, - max_sentences=max_sentences, - required_batch_size_multiple=required_batch_size_multiple, - ) - - logger.info( - f"[{split}] @batch_sampler batch_by_size time: {get_time_gap(my_time, time.time())}" - ) - logger.info( - f"[{split}] per epoch batch_sampler set-up time: {get_time_gap(start_time, time.time())}" - ) - logger.info(f"mem usage: {data_utils.get_mem_usage()}") - - return batch_sampler - - return construct_batch_sampler - - # we need to override get_batch_iterator because we want to reset the epoch iterator each time - def get_batch_iterator( - self, - dataset, - max_tokens=None, - max_sentences=None, - max_positions=None, - ignore_invalid_inputs=False, - required_batch_size_multiple=1, - seed=1, - num_shards=1, - shard_id=0, - num_workers=0, - epoch=1, - data_buffer_size=0, - disable_iterator_cache=False, - skip_remainder_batch=False, - grouped_shuffling=False, - update_epoch_batch_itr=False, - ): - """ - Get an iterator that yields batches of data from the given dataset. - - Args: - dataset (~fairseq.data.FairseqDataset): dataset to batch - max_tokens (int, optional): max number of tokens in each batch - (default: None). - max_sentences (int, optional): max number of sentences in each - batch (default: None). - max_positions (optional): max sentence length supported by the - model (default: None). - ignore_invalid_inputs (bool, optional): don't raise Exception for - sentences that are too long (default: False). - required_batch_size_multiple (int, optional): require batch size to - be a multiple of N (default: 1). - seed (int, optional): seed for random number generator for - reproducibility (default: 1). - num_shards (int, optional): shard the data iterator into N - shards (default: 1). - shard_id (int, optional): which shard of the data iterator to - return (default: 0). - num_workers (int, optional): how many subprocesses to use for data - loading. 0 means the data will be loaded in the main process - (default: 0). - epoch (int, optional): the epoch to start the iterator from - (default: 0). - data_buffer_size (int, optional): number of batches to - preload (default: 0). - disable_iterator_cache (bool, optional): don't cache the - EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`) - (default: False). - grouped_shuffling (bool, optional): group batches with each groups - containing num_shards batches and shuffle groups. Reduces difference - between sequence lengths among workers for batches sorted by length. - update_epoch_batch_itr (bool optional): if true then donot use the cached - batch iterator for the epoch - - Returns: - ~fairseq.iterators.EpochBatchIterator: a batched iterator over the - given dataset split - """ - # initialize the dataset with the correct starting epoch - assert isinstance(dataset, FairseqDataset) - if dataset in self.dataset_to_epoch_iter: - return self.dataset_to_epoch_iter[dataset] - if self.args.sampling_method == "RoundRobin": - batch_iter = super().get_batch_iterator( - dataset, - max_tokens=max_tokens, - max_sentences=max_sentences, - max_positions=max_positions, - ignore_invalid_inputs=ignore_invalid_inputs, - required_batch_size_multiple=required_batch_size_multiple, - seed=seed, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - epoch=epoch, - data_buffer_size=data_buffer_size, - disable_iterator_cache=disable_iterator_cache, - skip_remainder_batch=skip_remainder_batch, - update_epoch_batch_itr=update_epoch_batch_itr, - ) - self.dataset_to_epoch_iter[dataset] = batch_iter - return batch_iter - - construct_batch_sampler = self.create_batch_sampler_func( - max_positions, - ignore_invalid_inputs, - max_tokens, - max_sentences, - required_batch_size_multiple=required_batch_size_multiple, - seed=seed, - ) - - epoch_iter = iterators.EpochBatchIterator( - dataset=dataset, - collate_fn=dataset.collater, - batch_sampler=construct_batch_sampler, - seed=seed, - num_shards=num_shards, - shard_id=shard_id, - num_workers=num_workers, - epoch=epoch, - ) - return epoch_iter diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/token_generation_constraints.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/token_generation_constraints.py deleted file mode 100644 index e708dc51bcb0ffb7b411496239c74d5e6f3c2448..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/token_generation_constraints.py +++ /dev/null @@ -1,506 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -"""Implements tracking of constraints for a beam item. - -A list of constraints is given as a list of one or more token -sequences, each of length at least one token. For example, for an input sentence - -> Die maschinelle Übersetzung ist schwer zu kontrollieren. - -We could have the constraints: -* to influence -* hard - -There are two implementations: -* OrderedConstraintState: Tracks progress through an ordered list of multitoken constraints. -* UnorderedConstraintState: Tracks progress through an unordered list of multitoken constraints. - -The difference is that in the first, the constraints are assumed to be -in order; the algorithm will permit zero or more tokens between them. -In the second, the constraints are not ordered, so many orderings will -be explored. - -The same sequence can be present any number of times, and will appear -that many times in the output. -""" - -from collections import Counter -from typing import List, Optional, Set, Tuple - -import torch - - -class ConstraintState: - def __init__(self): - pass - - -def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor: - """Takes a list of list of constraints in tensor form (a list of - tensor constraints for each sentence) and transforms it into a - packed Tensor. For example, here is a batch of size 3 with 3, 0, - and 1 constraints: - - [ [ [3 1 2], [3], [4 5 6 7], ] - [], - [ [1 8 9 10 1 4 11 12], ] - ] - - Its corresponding packed structure is: - - [ [ 3 3 1 2 0 3 0 4 5 6 7 0], - [ 0 0 0 0 0 0 0 0 0 0 0 0], - [ 1 1 8 9 10 1 4 11 12 0 0 0] ] - - The packed tensor has shape (batch size, maxlen), where - maxlen is defined below. Each row contains concatenated - constraint tokens for that sentence, with 0 appended after - each constraint. The first item in each row is the number - of constraints for that sentence. So maxlen is the maximum - of - - (number of constraints) + (sum length of constraints) + 1. - - across all sentences in the batch. - """ - # The maximum word length of concatenated constraints for any sentence - max_constraints_len = 1 - for sentence_constraints in batch_constraints: - if len(sentence_constraints): - # number of constraints, plus sum of constrain lens, plus a zero after each - constraints_len = ( - 1 - + sum([c.size(0) for c in sentence_constraints]) - + len(sentence_constraints) - ) - max_constraints_len = max(max_constraints_len, constraints_len) - - batch_size = len(batch_constraints) - constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long() - for i, sentence_constraints in enumerate(batch_constraints): - constraints_tensor[i, 0] = len(sentence_constraints) - offset = 1 - for j, constraint in enumerate(sentence_constraints): - this_len = constraint.size(0) - constraints_tensor[i, offset : offset + this_len] = constraint - offset += this_len + 1 - - return constraints_tensor.long() - - -def unpack_constraints(constraint_tensor: torch.Tensor) -> List[torch.Tensor]: - """ - Transforms *one row* of a packed constraint tensor (e.g., for one - sentence in the batch) into a list of constraint tensors. - """ - constraint_list = [] - num_constraints = constraint_tensor[0] - constraints = constraint_tensor.tolist() - offset = 1 - for i in range(num_constraints): - where = constraints.index(0, offset) - constraint_list.append(constraint_tensor[offset:where]) - offset = where + 1 - - return constraint_list - - -class ConstraintNode: - """ - Represents a node in a trie managing unordered constraints. - """ - - def __init__(self, token: int = None, parent=None): - # The token associate with this node (None for the root) - self.token = int(token) if token is not None else None - # The parent (None at the root) - self.parent = parent - # Whether this node is a completed constraint - self.terminal = 0 - # List of child nodes - self.children = {} - - # The cumulative number of constraints from this point in the - # trie forward - self.num_constraints = 0 - - @property - def id(self): - return self.token - - def __str__(self): - term = self.terminal != 0 - return f"[{self.token}].{term}#{self.num_constraints}" - - def __getitem__(self, key: int): - return self.children.get(key, None) - - def next_tokens(self) -> Set[int]: - """The set of child labels.""" - return set(self.children.keys()) - - @staticmethod - def create(constraints: List[List[int]]): - root = ConstraintNode() - for sequence in constraints: - root.add_sequence(sequence) - - return root - - @staticmethod - def print_graph(node: "ConstraintNode"): - if len(node.children) == 0: - return str(node) - else: - s = f"({node}" - for child in node.children.values(): - s += " " + ConstraintNode.print_graph(child) - s += ")" - return s - - def token_counts(self) -> Counter: - """Returns a counter of the number of times each token is used - in a constraint. - """ - token_counts = Counter() - kids = list(self.children.values()) - while len(kids) > 0: - kid = kids.pop() - token_counts[kid.id] += kid.num_constraints - kids += list(kid.children.values()) - - return token_counts - - def tokens(self) -> Set[int]: - """Returns the set of tokens in constraints.""" - return set(self.token_counts().keys()) - - def add_sequence(self, sequence: List[int]): - """Adds a constraint, represented as a list of integers, to - the trie.""" - assert len(sequence) > 0 - - token = int(sequence[0]) - if token not in self.children: - self.children[token] = ConstraintNode(token, parent=self) - - node = self.children[token] - if len(sequence) == 1: - node.terminal += 1 - node.num_constraints += 1 - parent = node.parent - while parent is not None: - parent.num_constraints += 1 - parent = parent.parent - else: - node.add_sequence(sequence[1:]) - - -class UnorderedConstraintState(ConstraintState): - """ - Records progress through the set of constraints for each item in the beam - using a trie. - """ - - def __init__(self, node: ConstraintNode, copy_from: "ConstraintState" = None): - self.node = node - - if copy_from is None: - # The root node - self.root = node - # The set of states in the graph that have been completed - self.completed = Counter() - # The... - self.generated = Counter() - # The list of tokens we need to generate - self.needed_tokens = self.root.tokens() - else: - self.completed = Counter(copy_from.completed) - self.generated = Counter(copy_from.generated) - self.root = copy_from.root - - # Mark the node as generated - if self.node != self.root: - self.generated[node] += 1 - - @staticmethod - def create(constraint_tensor: torch.Tensor): - constraint_list = unpack_constraints(constraint_tensor) - constraint_trie_root = ConstraintNode.create(constraint_list) - return UnorderedConstraintState(constraint_trie_root) - - def __str__(self): - gen_str = ",".join([str(node) for node in self.generated]) - return f"{self.name}/{self.bank}({gen_str})x{self.num_completed}" - - def __copy__(self): - copied_state = UnorderedConstraintState(self.node, copy_from=self) - return copied_state - - def copy(self): - return self.__copy__() - - @property - def name(self): - if self.node.id is None: - return "ROOT" - else: - return str(self.node.id) - - @property - def is_root(self): - return self.node == self.root - - @property - def bank(self): - return sum(self.generated.values()) - - @property - def num_completed(self): - """The number of constraints (not constraint tokens) that are completed. - In addition to the already-completed states, we need to account for the - current state, which might get marked as completed when another token - is generated. - """ - in_final = self.node.terminal and self.completed[self.node] < self.node.terminal - return sum(self.completed.values()) + in_final - - @property - def finished(self): - return self.root.num_constraints - self.num_completed == 0 - - @property - def token_counts(self): - return self.root.token_counts() - - @property - def tokens(self): - return self.root.tokens() - - @property - def num_constraint_tokens(self): - return sum(self.token_counts.values()) - - def next_tokens(self) -> Set[int]: - """Returns the list of tokens that could come next. - These are (a) all tokens extending the root state and, for - non-root states, additionally all tokens extending the current - state.""" - - if self.node != self.root: - return self.root.next_tokens().union(self.node.next_tokens()) - else: - return self.root.next_tokens() - - def advance(self, token: int): - """Reads in a token and advances the state. Here's how it works. - - We can advance to the next state if: - - there is a matching child - - its path isn't blocked - - A path is blocked when all constraints that are descendants of - that node have already been generated, in the current state. - - If we are not able to advance from the current state, we "fall - off the graph" and return to the root state. There, we again - try to advance, checking the same criteria. - - In any case, when falling off the graph, we need to do some - bookkeeping. We: - - check whether any constraints were met (all prefixes of - current state) - - if one is found, mark it as completed - - adjust visited nodes accordingly - """ - token = int(token) - - next_state = None - child = self.node[token] - if child is not None and self.generated[child] < child.num_constraints: - next_state = UnorderedConstraintState(child, copy_from=self) - - def rewind(): - """If we're mid-trie and an "illegal" token is chosen next, we need - to reset our state to the root state. However, along the way, we need - to check whether a prefix of the current trie state represents a state - we could mark as completed. - """ - node = self.node - while node != self.root: - if node.terminal and self.completed[node] < node.terminal: - next_state.completed[node] += 1 - return - - next_state.generated[node] -= 1 - node = node.parent - - # Fall off the graph, check the root - if next_state is None and token in self.root.next_tokens(): - child = self.root[token] - # We can only traverse this edge if it's not saturated - if self.generated[child] < child.num_constraints: - next_state = UnorderedConstraintState(child, copy_from=self) - else: - next_state = UnorderedConstraintState(self.root, copy_from=self) - - # Rewind - rewind() - - elif next_state is None: - next_state = UnorderedConstraintState(self.root, copy_from=self) - # Rewind - rewind() - - return next_state - - -class ConstraintSequence: - def __init__(self, sequences: List[List[int]]): - """Represents a set of possibly multitoken constraints by - concatenating them and internally recording the end points. - """ - self.sequences = [] - self.endpoints = [] - self.num_tokens = 0 - self.tokens = set() - for sequence in sequences: - for token in sequence: - self.tokens.add(token) - self.num_tokens += len(sequence) - self.endpoints += [False for x in range(len(sequence) - 1)] + [True] - self.sequences += sequence - - def __getitem__(self, key: int): - return self.sequences[key] - - def __len__(self): - return len(self.sequences) - - def __str__(self): - return str(self.sequences) - - -class OrderedConstraintState(ConstraintState): - """ - Records progress through the set of linear nonbranching constraints with gaps. - """ - - def __init__(self, sequence: ConstraintSequence, state: int = -1): - self.sequence = sequence - self.state = state - - @staticmethod - def create(constraint_tensor: torch.Tensor): - constraint_list = unpack_constraints(constraint_tensor) - return OrderedConstraintState(ConstraintSequence(constraint_list), -1) - - def __str__(self): - return f"{self.state}/{self.bank}x{self.num_completed}" - - def __copy__(self): - return OrderedConstraintState(self.sequence, self.state) - - def copy(self): - return self.__copy__() - - @property - def num_completed(self): - if self.state == -1: - return 0 - count = len( - list(filter(lambda x: x, self.sequence.endpoints[0 : self.state + 1])) - ) - return count - - @property - def is_root(self): - return self.state == -1 - - @property - def name(self): - if self.state == -1: - return "ROOT" - else: - return str(self.sequence[self.state]) - - @property - def bank(self) -> int: - return self.state + 1 - - @property - def finished(self): - return self.state + 1 == len(self.sequence) - - @property - def token_counts(self): - return self.sequence.token_counts() - - @property - def tokens(self): - return self.sequence.tokens - - @property - def num_constraint_tokens(self): - return sum(self.token_counts.values()) - - def next_tokens(self) -> Set[int]: - """Returns the list of tokens that could come next. - These are (a) all tokens extending the root state and, for - non-root states, additionally all tokens extending the current - state.""" - - tokens = set() - if self.state > 0: - tokens.add(self.sequence[0]) - if not self.finished: - tokens.add(self.sequence[self.state + 1]) - return tokens - - def advance(self, token: int): - """Reads in a token and advances the state. Here's how it works. - - We can advance to the next state if: - - there is a matching child - - its path isn't blocked - - A path is blocked when all constraints that are descendants of - that node have already been generated, in the current state. - - If we are not able to advance from the current state, we "fall - off the graph" and return to the root state. There, we again - try to advance, checking the same criteria. - - In any case, when falling off the graph, we need to do some - bookkeeping. We: - - check whether any constraints were met (all prefixes of - current state) - - if one is found, mark it as completed - - adjust visited nodes accordingly - """ - token = int(token) - # print(f"{self} ADVANCE({token}) {self.sequence} -> ", end="") - - if self.finished: - # Accept anything - next_state = self.copy() - - elif self.sequence[self.state + 1] == token: - # Advance to the next token - next_state = OrderedConstraintState(self.sequence, self.state + 1) - - elif self.sequence.endpoints[self.state]: - # Accept anything between constraints (*) - next_state = self.copy() - - elif token == self.sequence[0]: - # Start over having generated the first token - next_state = OrderedConstraintState(self.sequence, 0) - else: - # Start over from the root - next_state = OrderedConstraintState(self.sequence, -1) - - return next_state diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tokenizer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/tokenizer.py deleted file mode 100644 index 8c4d694aa08be0e73179290785c565419247618b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/tokenizer.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import re - -SPACE_NORMALIZER = re.compile(r"\s+") - - -def tokenize_line(line): - line = SPACE_NORMALIZER.sub(" ", line) - line = line.strip() - return line.split() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/trainer.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/trainer.py deleted file mode 100644 index 2ef4d57a9fdf632922b8b46bc05a417291cde3b5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/trainer.py +++ /dev/null @@ -1,1589 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -Train a network across multiple GPUs. -""" - -import contextlib -import logging -import os -import sys -import time -from argparse import Namespace -from itertools import chain -from typing import Any, Dict, List - -import torch -from fairseq import checkpoint_utils, models, optim, utils -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.distributed import utils as distributed_utils -from fairseq.file_io import PathManager -from fairseq.logging import meters, metrics -from fairseq.models.ema import build_ema -from fairseq.nan_detector import NanDetector -from fairseq.optim import lr_scheduler -from fairseq.utils import safe_hasattr -from omegaconf import OmegaConf - -logger = logging.getLogger(__name__) - - -class Trainer(object): - """Main class for data parallel training. - - This class supports synchronous distributed data parallel training, - where multiple workers each have a full model replica and gradients - are accumulated across workers before each update. We use - :class:`~torch.nn.parallel.DistributedDataParallel` to handle - communication of the gradients across workers. - """ - - def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None): - - if isinstance(cfg, Namespace): - logger.warning( - "argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf" - ) - cfg = convert_namespace_to_omegaconf(cfg) - - self.cfg = cfg - self.task = task - - # catalog shared parameters - shared_params = _catalog_shared_params(model) - self.tpu = cfg.common.tpu - self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu - if self.cuda: - self.device = torch.device("cuda") - elif self.tpu: - self.device = utils.get_tpu_device() - else: - self.device = torch.device("cpu") - - if self.is_fsdp: - import fairscale - - if self.cfg.common.bf16: - raise ValueError( - "FullyShardedDataParallel is not compatible with --bf16 or " - "--memory-efficient-bf16" - ) - if self.cfg.distributed_training.zero_sharding != "none": - raise ValueError( - "FullyShardedDataParallel is not compatible with --zero-sharding " - "option (it's already built in)" - ) - if ( - max(self.cfg.optimization.update_freq) > 1 - and fairscale.__version__ < "0.4.0" - ): - raise RuntimeError( - "Please update to fairscale 0.4.0 or newer when combining " - "--update-freq with FullyShardedDataParallel" - ) - else: - if ( - hasattr(self.cfg.distributed_training, "cpu_offload") - and self.cfg.distributed_training.cpu_offload - ): - raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded") - - # copy model and criterion to current device/dtype - self._criterion = criterion - self._model = model - if not self.is_fsdp: - if cfg.common.fp16: - assert not cfg.common.amp, "Cannot use fp16 and AMP together" - self._criterion = self._criterion.half() - self._model = self._model.half() - elif cfg.common.bf16: - self._criterion = self._criterion.to(dtype=torch.bfloat16) - self._model = self._model.to(dtype=torch.bfloat16) - elif cfg.common.amp: - self._amp_retries = 0 - if ( - not cfg.distributed_training.pipeline_model_parallel - # the DistributedFairseqModel wrapper will handle moving to device, - # so only handle cases which don't use the wrapper - and not self.use_distributed_wrapper - ): - self._criterion = self._criterion.to(device=self.device) - self._model = self._model.to(device=self.device) - self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel - self.last_device = None - if self.cuda and self.pipeline_model_parallel: - self.last_device = torch.device( - cfg.distributed_training.pipeline_devices[-1] - ) - - # check that shared parameters are preserved after device transfer - for shared_param in shared_params: - ref = _get_module_by_path(self._model, shared_param[0]) - for path in shared_param[1:]: - logger.info( - "detected shared parameter: {} <- {}".format(shared_param[0], path) - ) - _set_module_by_path(self._model, path, ref) - - self._dummy_batch = None # indicates we don't have a dummy batch at first - self._lr_scheduler = None - self._num_updates = 0 - self._num_xla_compiles = 0 # for TPUs - self._optim_history = None - self._optimizer = None - self._warn_once = set() - self._wrapped_criterion = None - self._wrapped_model = None - self._ema = None - - # TODO(myleott): support tpu - if self.cuda and self.data_parallel_world_size > 1: - self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size) - else: - self._grad_norm_buf = None - - self.quantizer = quantizer - if self.quantizer is not None: - self.quantizer.set_trainer(self) - - # get detailed cuda environment - if self.cuda: - self.cuda_env = utils.CudaEnvironment() - if self.data_parallel_world_size > 1: - self.cuda_env_arr = distributed_utils.all_gather_list( - self.cuda_env, group=distributed_utils.get_global_group() - ) - else: - self.cuda_env_arr = [self.cuda_env] - if self.data_parallel_rank == 0: - utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr) - else: - self.cuda_env = None - self.cuda_env_arr = None - - metrics.log_start_time("wall", priority=790, round=0) - - self._start_time = time.time() - self._previous_training_time = 0 - self._cumulative_training_time = None - - def reinitialize(self): - """Reinitialize the Trainer, typically after model params change.""" - self._lr_scheduler = None - self._optimizer = None - self._wrapped_criterion = None - self._wrapped_model = None - - @property - def data_parallel_world_size(self): - if self.cfg.distributed_training.distributed_world_size == 1: - return 1 - return distributed_utils.get_data_parallel_world_size() - - @property - def data_parallel_process_group(self): - return distributed_utils.get_data_parallel_group() - - @property - def data_parallel_rank(self): - if self.cfg.distributed_training.distributed_world_size == 1: - return 0 - return distributed_utils.get_data_parallel_rank() - - @property - def is_data_parallel_master(self): - # NOTE: this returns true for all model parallel replicas with data - # parallel rank 0 - return self.data_parallel_rank == 0 - - @property - def use_distributed_wrapper(self) -> bool: - return ( - self.data_parallel_world_size > 1 and not self.cfg.optimization.use_bmuf - ) or (self.is_fsdp and self.cfg.distributed_training.cpu_offload) - - @property - def should_save_checkpoint_on_current_rank(self) -> bool: - """Indicates whether to save checkpoints on the current DDP rank.""" - if ( - self.is_fsdp and self.cfg.distributed_training.use_sharded_state - ) or getattr(self.cfg.model, "base_layers", 0) > 0: - return True - else: - return self.is_data_parallel_master - - @property - def always_call_state_dict_during_save_checkpoint(self) -> bool: - if self.is_fsdp and not self.cfg.distributed_training.use_sharded_state: - # FSDP calls communication collective when consolidating checkpoints - return True - else: - return False - - @property - def checkpoint_suffix(self) -> str: - """Suffix to add to the checkpoint file name.""" - if self.is_fsdp and self.cfg.distributed_training.use_sharded_state: - return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format( - self.data_parallel_rank - ) - else: - return self.cfg.checkpoint.checkpoint_suffix or "" - - @property - def criterion(self): - if self._wrapped_criterion is None: - if utils.has_parameters(self._criterion) and self.use_distributed_wrapper: - self._wrapped_criterion = models.DistributedFairseqModel( - self.cfg.distributed_training, - self._criterion, - process_group=self.data_parallel_process_group, - device=self.device, - ) - else: - self._wrapped_criterion = self._criterion - return self._wrapped_criterion - - @property - def model(self): - if self._wrapped_model is None: - if self.use_distributed_wrapper: - self._wrapped_model = models.DistributedFairseqModel( - self.cfg.distributed_training, - self._model, - process_group=self.data_parallel_process_group, - device=self.device, - ) - else: - self._wrapped_model = self._model - return self._wrapped_model - - @property - def ema(self): - if self._ema is None: - self._build_ema() - return self._ema - - def _build_ema(self): - if self.cfg.ema.store_ema: - self._ema = build_ema(self._model, self.cfg.ema, self.device) - logger.info("Exponential Moving Average Shadow Model is initialized.") - - @property - def optimizer(self): - if self._optimizer is None: - self._build_optimizer() - return self._optimizer - - @property - def lr_scheduler(self): - if self._lr_scheduler is None: - self._build_optimizer() # this will initialize self._lr_scheduler - return self._lr_scheduler - - def _build_optimizer(self): - params = list( - filter( - lambda p: p.requires_grad, - chain(self.model.parameters(), self.criterion.parameters()), - ) - ) - - if self.is_fsdp and self.cfg.common.fp16: - # FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper, - # mostly for the grad scaling. But if we don't have the - # --memory-efficient-fp16 flag set, then we're effectively doing - # regular --fp16 and can allow the use of optimizers that would - # otherwise be unsupported by MemoryEfficientFP16Optimizer. - allow_unsupported = not self.cfg.common.memory_efficient_fp16 - self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( - self.cfg, params, allow_unsupported=allow_unsupported - ) - elif self.cfg.common.fp16 or self.cfg.common.bf16 or self.cfg.common.amp: - if self.cuda and torch.cuda.get_device_capability(0)[0] < 7: - logger.info( - "NOTE: your device does NOT support faster training with --fp16 or --amp, " - "please switch to FP32 which is likely to be faster" - ) - if ( - self.cfg.common.memory_efficient_fp16 - or self.cfg.common.memory_efficient_bf16 - ): - self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( - self.cfg, params - ) - elif self.cfg.common.amp: - self._optimizer = optim.AMPOptimizer.build_optimizer(self.cfg, params) - else: - self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params) - else: - if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7: - logger.info( - "NOTE: your device may support faster training with --fp16 or --amp" - ) - self._optimizer = optim.build_optimizer(self.cfg.optimizer, params) - - if self.is_fsdp: - assert ( - not self.cfg.optimization.use_bmuf - ), "--ddp-backend=fully_sharded is not compatible with BMUF" - assert self._optimizer.supports_flat_params, ( - "--ddp-backend=fully_sharded is only compatible with pointwise " - "optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). " - "However, the sharding will result in slightly different results when " - "using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)" - ) - - if self.cfg.optimization.use_bmuf: - self._optimizer = optim.FairseqBMUF( - self.cfg.bmuf, - self._optimizer, - ) - - if self.cfg.distributed_training.zero_sharding == "os": - if ( - self.cfg.common.fp16 - and not self.cfg.common.memory_efficient_fp16 - and not self.cfg.common.memory_efficient_bf16 - ) and not self.cfg.common.fp16_no_flatten_grads: - raise ValueError( - "ZeRO is incomptabile with fp16 and flattened grads. " - "Please use --fp16-no-flatten-grads" - ) - else: - optim.shard_(self._optimizer, self.data_parallel_process_group) - - # We should initialize the learning rate scheduler immediately after - # building the optimizer, so that the initial learning rate is set. - self._lr_scheduler = lr_scheduler.build_lr_scheduler( - self.cfg.lr_scheduler, - self.optimizer, - ) - self._lr_scheduler.step_update(0) - - @property - def is_fsdp(self): - return self.cfg.distributed_training.ddp_backend == "fully_sharded" - - def consolidate_optimizer(self): - """For OSS, we need to consolidate the state dict.""" - if self.cfg.checkpoint.no_save_optimizer_state: - return - self._gathered_optim_state = None - if hasattr(self.optimizer.optimizer, "consolidate_state_dict"): - self.optimizer.optimizer.consolidate_state_dict() - elif self.is_fsdp and not self.model.use_sharded_state: - st = self.model.gather_full_optim_state_dict( - self.optimizer - ) # only returns on rank 0 - self._gathered_optim_state = st - - def state_dict(self): - state_dict = { - "args": None, # legacy - "cfg": ( - OmegaConf.to_container(self.cfg, resolve=True, enum_to_str=True) - if OmegaConf.is_config(self.cfg) - else self.cfg - ), - "model": self.model.state_dict(), - "criterion": ( - self.criterion.state_dict() - if utils.has_parameters(self.criterion) - else None - ), - "optimizer_history": (self._optim_history or []) - + [ - { - "criterion_name": self.get_criterion().__class__.__name__, - "optimizer_name": self.optimizer.__class__.__name__, - "lr_scheduler_state": self.lr_scheduler.state_dict(), - "num_updates": self.get_num_updates(), - } - ], - "task_state": self.task.state_dict() if self.task is not None else {}, - "extra_state": { - "metrics": metrics.state_dict(), - "previous_training_time": self.cumulative_training_time(), - }, - } - if self.cfg.ema.store_ema: - # Save EMA model state as extra state - state_dict["extra_state"]["ema"] = self.ema.get_model().state_dict() - if self.cfg.ema.ema_fp32: - # Save EMA params in fp32 - state_dict["extra_state"]["ema_fp32_params"] = self.ema.fp32_params - if not self.cfg.checkpoint.no_save_optimizer_state: - if self._gathered_optim_state is not None: - state_dict["last_optimizer_state"] = self._gathered_optim_state - self._gathered_optim_state = None - else: - state_dict["last_optimizer_state"] = self.optimizer.state_dict() - if self.is_fsdp: - # save meta data for recombining checkpoint upon loading - state_dict["fsdp_metadata"] = self.model.local_metadata_dict() - return state_dict - - def save_checkpoint(self, filename, extra_state): - """Save all training state in a checkpoint file.""" - - logger.info(f"Saving checkpoint to {os.path.abspath(filename)}") - # call state_dict on all ranks in case it needs internal communication - state_dict = utils.move_to_cpu(self.state_dict()) - state_dict["extra_state"].update(extra_state) - if self.should_save_checkpoint_on_current_rank: - checkpoint_utils.torch_persistent_save( - state_dict, - filename, - async_write=self.cfg.checkpoint.write_checkpoints_asynchronously, - ) - logger.info(f"Finished saving checkpoint to {os.path.abspath(filename)}") - - def load_checkpoint( - self, - filename, - reset_optimizer=False, - reset_lr_scheduler=False, - optimizer_overrides=None, - reset_meters=False, - ): - """ - Load all training state from a checkpoint file. - rank = 0 will load the checkpoint, and then broadcast it to all - other ranks. - """ - extra_state, self._optim_history, last_optim_state = None, [], None - - logger.info(f"Preparing to load checkpoint {filename}") - is_distributed = self.data_parallel_world_size > 1 - bexists = PathManager.isfile(filename) - if bexists: - load_on_all_ranks = ( - self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks - # TPUs don't support broadcast yet, so load checkpoints - # on every worker for now - or self.tpu - # FSDP requires loading checkpoint shards on all ranks - or (self.is_fsdp and self.cfg.distributed_training.use_sharded_state) - or getattr(self.cfg.model, "base_layers", 0) > 0 - ) - - if load_on_all_ranks or self.data_parallel_rank == 0: - state = checkpoint_utils.load_checkpoint_to_cpu( - filename, load_on_all_ranks=load_on_all_ranks - ) - last_optim_state = state.get("last_optimizer_state", None) - - # If doing zero_sharding, do not broadcast global optimizer - # state. Later we will broadcast sharded states to each rank - # to avoid memory from exploding. - if ( - not load_on_all_ranks - and self.cfg.distributed_training.zero_sharding == "os" - and "last_optimizer_state" in state - and is_distributed - ): - state["last_optimizer_state"] = "SHARDED" - else: - last_optim_state = None - state = None - - if is_distributed and not load_on_all_ranks: - state = distributed_utils.broadcast_object( - state, - src_rank=0, - group=self.data_parallel_process_group, - dist_device=self.device, - ) - if self.data_parallel_rank > 0: - last_optim_state = state.get("last_optimizer_state", None) - - # load model parameters - try: - if ( - "optimizer_history" in state - and len(state["optimizer_history"]) > 0 - and "num_updates" in state["optimizer_history"][-1] - ): - self.model.set_num_updates( - state["optimizer_history"][-1]["num_updates"] - ) - - # this is the code related to AdaPrune - # In short, it removes redundant heads in multi-head attention module based on heads importance provided - # For more info, please refer to the paper: https://openreview.net/forum?id=_CMSV7FTzGI - # The idea of prune in mha can be summarized as - # Fine tune model (e.g. roberta encoder) on a certain datasets with regularization - # After the model is trained. User could use get_reserve_head_index and _adaptive_prune_heads functions to get the top X heads with most importance. - # Then user uses the rank to prune a new roberta encoder and save the pruned ckpt manually. - # User will fine tune the the new roberta encoder via the ckpt saved above - # To get rid of registering different pruned version of Roberta, I use the argument --mha-heads-to-keep to prune the Roberta model into a pruned version which matches the pruned ckpt. - if ( - safe_hasattr(self.model, "args") - and safe_hasattr(self.model.args, "mha_heads_to_keep") - and self.model.args.mha_heads_to_keep != -1 - ): - logger.info( - f"Prune model: keep {self.model.args.mha_heads_to_keep} heads for each multihead attention module" - ) - for layer in self.model.encoder.sentence_encoder.layers: - reserve_head_index = layer.self_attn._get_reserve_head_index( - num_heads_to_keep=self.model.args.mha_heads_to_keep - ) - layer.self_attn._adaptive_prune_heads( - reserve_head_index=reserve_head_index - ) - layer.self_attn._set_skip_embed_dim_check() - logger.info(self.model) - # this is the code related to AdaPrune - # In short, it removes redundant units in feedforward layer in each transformer layer based on importance - # For more info, please refer to the paper: https://openreview.net/forum?id=_CMSV7FTzGI - # The idea of prune in ffn can be summarized as - # Fine tune model (e.g. roberta encoder) on a certain datasets with regularization - # After the model is trained. User could use _get_fc_rank and _prune_fc_layer functions to get the top X units with most importance. - # Then user uses the rank to prune a new roberta encoder and save the pruned ckpt manually. - # User will fine tune the the new roberta encoder via the ckpt saved above - # To get rid of registering different pruned version of Roberta, I use the argument --ffn-blocks-to-remove to prune the Roberta model into a pruned version which matches the pruned ckpt. - if ( - safe_hasattr(self.model, "args") - and safe_hasattr(self.model.args, "ffn_blocks_to_remove") - and self.model.args.ffn_blocks_to_remove != -1 - ): - logger.info( - f"Prune model: remove {self.model.args.ffn_blocks_to_remove} ffn blocks for each transformer layer" - ) - for layer in self.model.encoder.sentence_encoder.layers: - remove_index = layer._get_fc_rank( - remove_num=self.model.args.ffn_blocks_to_remove - ) - layer._prune_fc_layer(remove_index=remove_index) - logger.info(self.model) - - self.model.load_state_dict( - state["model"], strict=True, model_cfg=self.cfg.model - ) - # save memory for later steps - del state["model"] - if utils.has_parameters(self.get_criterion()): - self.get_criterion().load_state_dict( - state["criterion"], strict=True - ) - del state["criterion"] - - except Exception: - raise Exception( - "Cannot load model parameters from checkpoint {}; " - "please ensure that the architectures match.".format(filename) - ) - extra_state = state["extra_state"] - self._optim_history = state["optimizer_history"] - - if last_optim_state is not None and not reset_optimizer: - # rebuild optimizer after loading model, since params may have changed - self._build_optimizer() - - # only reload optimizer and lr_scheduler if they match - last_optim = self._optim_history[-1] - assert ( - last_optim["criterion_name"] == self.get_criterion().__class__.__name__ - ), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim['criterion_name']} vs {self.get_criterion().__class__.__name__}" - assert ( - last_optim["optimizer_name"] == self.optimizer.__class__.__name__ - ), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim['optimizer_name']} vs {self.optimizer.__class__.__name__}" - - if not reset_lr_scheduler: - self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"]) - - if self.is_fsdp and not self.model.use_sharded_state: - # if use_sharded_state, the last_optim_state is already sharded, skip this - last_optim_state = self.model.get_shard_from_optim_state_dict( - last_optim_state - ) - elif not load_on_all_ranks and is_distributed: - last_optim_state = self.optimizer.broadcast_global_state_dict( - last_optim_state - ) - - self.optimizer.load_state_dict(last_optim_state, optimizer_overrides) - - self.set_num_updates(last_optim["num_updates"]) - - if extra_state is not None: - itr_state = extra_state["train_iterator"] - epoch = itr_state["epoch"] - - if "previous_training_time" in extra_state: - self._previous_training_time = extra_state["previous_training_time"] - self._start_time = time.time() - - self.lr_step(epoch) - - if ( - itr_state.get("version", 1) >= 2 - and itr_state["iterations_in_epoch"] == 0 - ): - # reset meters at start of epoch - reset_meters = True - - if "metrics" in extra_state and not reset_meters: - metrics.load_state_dict(extra_state["metrics"]) - - # reset TimeMeters, since their start times don't make sense anymore - for meter in metrics.get_meters("default"): - if isinstance(meter, meters.TimeMeter): - meter.reset() - - if self.cfg.ema.store_ema: - if "ema" not in extra_state: - logger.warn( - "EMA not found in checkpoint. But store_ema is True. " - "EMA is re-initialized from checkpoint." - ) - self.ema.restore( - state["model"], build_fp32_params=self.cfg.ema.ema_fp32 - ) - else: - logger.info("Loading EMA from checkpoint") - self.ema.restore(extra_state["ema"], build_fp32_params=False) - - if self.cfg.ema.ema_fp32: - if "ema_fp32_params" in extra_state: - logger.info("Loading EMA fp32 params from checkpoint") - self.ema.build_fp32_params(extra_state["ema_fp32_params"]) - else: - logger.info( - "Building EMA fp32 params from EMA model in checkpoint" - ) - self.ema.build_fp32_params() - - logger.info( - "Loaded checkpoint {} (epoch {} @ {} updates)".format( - filename, epoch, self.get_num_updates() - ) - ) - - else: - logger.info("No existing checkpoint found {}".format(filename)) - - return extra_state - - def get_train_iterator( - self, - epoch, - combine=True, - load_dataset=True, - data_selector=None, - shard_batch_itr=True, - disable_iterator_cache=False, - ): - """Return an EpochBatchIterator over the training set for a given epoch.""" - if load_dataset: - logger.info("loading train data for epoch {}".format(epoch)) - self.task.load_dataset( - self.cfg.dataset.train_subset, - epoch=epoch, - combine=combine, - data_selector=data_selector, - tpu=self.tpu, - ) - batch_iterator = self.task.get_batch_iterator( - dataset=self.task.dataset(self.cfg.dataset.train_subset), - max_tokens=self.cfg.dataset.max_tokens, - max_sentences=self.cfg.dataset.batch_size, - max_positions=utils.resolve_max_positions( - self.task.max_positions(), - self.model.max_positions(), - self.cfg.dataset.max_tokens, - ), - ignore_invalid_inputs=True, - required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, - seed=(self.cfg.common.seed + epoch) - if self.cfg.dataset.update_ordered_indices_seed - else self.cfg.common.seed, - num_shards=self.data_parallel_world_size if shard_batch_itr else 1, - shard_id=self.data_parallel_rank if shard_batch_itr else 0, - num_workers=self.cfg.dataset.num_workers, - epoch=epoch, - data_buffer_size=self.cfg.dataset.data_buffer_size, - disable_iterator_cache=disable_iterator_cache, - skip_remainder_batch=self.cfg.optimization.skip_remainder_batch, - grouped_shuffling=self.cfg.dataset.grouped_shuffling, - update_epoch_batch_itr=self.cfg.dataset.update_epoch_batch_itr, - ) - self.reset_dummy_batch(batch_iterator.first_batch) - return batch_iterator - - def get_valid_iterator( - self, - subset, - disable_iterator_cache=False, - ): - """Return an EpochBatchIterator over given validation subset for a given epoch.""" - batch_iterator = self.task.get_batch_iterator( - dataset=self.task.dataset(subset), - max_tokens=self.cfg.dataset.max_tokens_valid, - max_sentences=self.cfg.dataset.batch_size_valid, - max_positions=utils.resolve_max_positions( - self.task.max_positions(), - self.model.max_positions(), - ), - ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, - seed=self.cfg.common.seed, - num_shards=self.data_parallel_world_size, - shard_id=self.data_parallel_rank, - num_workers=self.cfg.dataset.num_workers, - # always pass a fixed "epoch" to keep validation data consistent - # across training epochs - epoch=1, - data_buffer_size=self.cfg.dataset.data_buffer_size, - disable_iterator_cache=disable_iterator_cache, - skip_remainder_batch=False, - ) - self.reset_dummy_batch(batch_iterator.first_batch) - return batch_iterator - - def begin_epoch(self, epoch): - """Called at the beginning of each epoch.""" - logger.info("begin training epoch {}".format(epoch)) - - self.lr_step_begin_epoch(epoch) - - if self.quantizer is not None: - self.quantizer.begin_epoch(epoch) - - # task specific setup per epoch - self.task.begin_epoch(epoch, self.get_model()) - - if self.tpu: - import torch_xla.core.xla_model as xm - - xm.rendezvous("begin_epoch") # wait for all workers - xm.mark_step() - - def begin_valid_epoch(self, epoch): - """Called at the beginning of each validation epoch.""" - - # task specific setup per validation epoch - self.task.begin_valid_epoch(epoch, self.get_model()) - - def reset_dummy_batch(self, batch): - self._dummy_batch = batch - - @metrics.aggregate("train") - def train_step(self, samples, raise_oom=False): - """Do forward, backward and parameter update.""" - self._set_seed() - self.model.train() - self.criterion.train() - self.zero_grad() - - metrics.log_start_time("train_wall", priority=800, round=0) - - # If EMA is enabled through store_ema=True - # and task.uses_ema is True, pass the EMA model as a keyword - # argument to the task. - extra_kwargs = {} - if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False): - extra_kwargs["ema_model"] = self.ema.get_model() - - # forward and backward pass - logging_outputs, sample_size, ooms = [], 0, 0 - for i, sample in enumerate(samples): # delayed update loop - sample, is_dummy_batch = self._prepare_sample(sample) - - def maybe_no_sync(): - """ - Whenever *samples* contains more than one mini-batch, we - want to accumulate gradients locally and only call - all-reduce in the last backwards pass. - """ - if ( - self.data_parallel_world_size > 1 - and hasattr(self.model, "no_sync") - and i < len(samples) - 1 - # The no_sync context manager results in increased memory - # usage with FSDP, since full-size gradients will be - # accumulated on each GPU. It's typically a better tradeoff - # to do the extra communication with FSDP. - and not self.is_fsdp - ): - return self.model.no_sync() - else: - return contextlib.ExitStack() # dummy contextmanager - - try: - with maybe_no_sync(): - # forward and backward - loss, sample_size_i, logging_output = self.task.train_step( - sample=sample, - model=self.model, - criterion=self.criterion, - optimizer=self.optimizer, - update_num=self.get_num_updates(), - ignore_grad=is_dummy_batch, - **extra_kwargs, - ) - del loss - - logging_outputs.append(logging_output) - sample_size += sample_size_i - - # emptying the CUDA cache after the first step can - # reduce the chance of OOM - if self.cuda and self.get_num_updates() == 0: - torch.cuda.empty_cache() - except RuntimeError as e: - if "out of memory" in str(e): - self._log_oom(e) - if raise_oom: - raise e - logger.warning( - "attempting to recover from OOM in forward/backward pass" - ) - ooms += 1 - self.zero_grad() - if self.cuda: - torch.cuda.empty_cache() - if self.cfg.distributed_training.distributed_world_size == 1: - return None - else: - raise e - except Exception: - self.consolidate_optimizer() - self.save_checkpoint( - os.path.join(self.cfg.checkpoint.save_dir, "crash.pt"), {} - ) - raise - - if self.tpu and i < len(samples) - 1: - # tpu-comment: every XLA operation before marking step is - # appended to the IR graph, and processing too many batches - # before marking step can lead to OOM errors. - # To handle gradient accumulation use case, we explicitly - # mark step here for every forward pass without a backward pass - self._xla_markstep_and_send_to_cpu() - - if is_dummy_batch: - if torch.is_tensor(sample_size): - sample_size.zero_() - else: - sample_size *= 0.0 - - if torch.is_tensor(sample_size): - sample_size = sample_size.float() - else: - sample_size = float(sample_size) - - # gather logging outputs from all replicas - if self._sync_stats(): - train_time = self._local_cumulative_training_time() - ( - logging_outputs, - ( - sample_size, - ooms, - total_train_time, - ), - ) = self._aggregate_logging_outputs( - logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch - ) - self._cumulative_training_time = ( - total_train_time / self.data_parallel_world_size - ) - - overflow = False - try: - with torch.autograd.profiler.record_function("reduce-grads"): - # reduce gradients across workers - self.optimizer.all_reduce_grads(self.model) - if utils.has_parameters(self.criterion): - self.optimizer.all_reduce_grads(self.criterion) - - with torch.autograd.profiler.record_function("multiply-grads"): - # multiply gradients by (data_parallel_size / sample_size) since - # DDP normalizes by the number of data parallel workers for - # improved fp16 precision. - # Thus we get (sum_of_gradients / sample_size) at the end. - # In case of fp16, this step also undoes loss scaling. - # (Debugging note: Some optimizers perform this scaling on the - # fly, so inspecting model.parameters() or optimizer.params may - # still show the original, unscaled gradients.) - numer = ( - self.data_parallel_world_size - if not self.cfg.optimization.use_bmuf or self._sync_stats() - else 1 - ) - self.optimizer.multiply_grads(numer / (sample_size or 1.0)) - # Note: (sample_size or 1.0) handles the case of a zero gradient, in a - # way that avoids CPU/device transfers in case sample_size is a GPU or - # TPU object. The assumption is that the gradient itself is also 0. - - with torch.autograd.profiler.record_function("clip-grads"): - # clip grads - grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm) - - # check that grad norms are consistent across workers - # on tpu check tensor is slow - if not self.tpu: - if ( - not self.cfg.optimization.use_bmuf - and self.cfg.distributed_training.ddp_backend != "slowmo" - ): - self._check_grad_norms(grad_norm) - if not torch.isfinite(grad_norm).all(): - # in case of AMP, if gradients are Nan/Inf then - # optimizer step is still required - if self.cfg.common.amp: - overflow = True - else: - # check local gradnorm single GPU case, trigger NanDetector - raise FloatingPointError("gradients are Nan/Inf") - - with torch.autograd.profiler.record_function("optimizer"): - # take an optimization step - self.task.optimizer_step( - self.optimizer, model=self.model, update_num=self.get_num_updates() - ) - if self.cfg.common.amp and overflow: - if self._amp_retries == self.cfg.common.amp_batch_retries: - logger.info("AMP: skipping this batch.") - self._amp_retries = 0 - else: - self._amp_retries += 1 - return self.train_step( - samples, raise_oom - ) # recursion to feed in same batch - - except FloatingPointError: - - self.consolidate_optimizer() - self.save_checkpoint( - os.path.join(self.cfg.checkpoint.save_dir, "crash.pt"), {} - ) - - # re-run the forward and backward pass with hooks attached to print - # out where it fails - self.zero_grad() - with NanDetector(self.get_model()): - for _, sample in enumerate(samples): - sample, _ = self._prepare_sample(sample) - self.task.train_step( - sample, - self.model, - self.criterion, - self.optimizer, - self.get_num_updates(), - ignore_grad=False, - **extra_kwargs, - ) - raise - except OverflowError as e: - overflow = True - logger.info( - f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}" - ) - grad_norm = torch.tensor(0.0).cuda() - self.zero_grad() - except RuntimeError as e: - if "out of memory" in str(e): - self._log_oom(e) - logger.error("OOM during optimization, irrecoverable") - raise e - - # Some distributed wrappers (e.g., SlowMo) need access to the optimizer - # after the step - if hasattr(self.model, "perform_slowmo"): - self.model.perform_slowmo( - self.optimizer.optimizer, getattr(self.optimizer, "fp32_params", None) - ) - - logging_output = None - if not overflow or self.cfg.distributed_training.ddp_backend == "slowmo": - self.set_num_updates(self.get_num_updates() + 1) - - if self.cfg.ema.store_ema: - # Step EMA forward with new model. - self.ema.step( - self.get_model(), - self.get_num_updates(), - ) - metrics.log_scalar( - "ema_decay", - self.ema.get_decay(), - priority=10000, - round=5, - weight=0, - ) - - if self.tpu: - import torch_xla.core.xla_model as xm - - # mark step on TPUs - self._xla_markstep_and_send_to_cpu() - - # only log stats every log_interval steps - # this causes wps to be misreported when log_interval > 1 - logging_output = {} - if self.get_num_updates() % self.cfg.common.log_interval == 0: - # log memory usage - mem_info = xm.get_memory_info(self.device) - gb_free = mem_info["kb_free"] / 1024 / 1024 - gb_total = mem_info["kb_total"] / 1024 / 1024 - metrics.log_scalar( - "gb_free", gb_free, priority=1500, round=1, weight=0 - ) - metrics.log_scalar( - "gb_total", gb_total, priority=1600, round=1, weight=0 - ) - logging_outputs = self._xla_markstep_and_send_to_cpu( - logging_outputs - ) - logging_output = self._reduce_and_log_stats( - logging_outputs, sample_size, grad_norm - ) - - # log whenever there's an XLA compilation, since these - # slow down training and may indicate opportunities for - # optimization - self._check_xla_compilation() - else: - if self.cuda and self.cuda_env is not None: - # log minimum free memory over the iteration - gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024 - torch.cuda.reset_peak_memory_stats() - gb_free = self.cuda_env.total_memory_in_GB - gb_used - metrics.log_scalar( - "gb_free", gb_free, priority=1500, round=1, weight=0 - ) - - # log stats - logging_output = self._reduce_and_log_stats( - logging_outputs, sample_size, grad_norm - ) - - # clear CUDA cache to reduce memory fragmentation - if ( - self.cuda - and self.cfg.common.empty_cache_freq > 0 - and ( - (self.get_num_updates() + self.cfg.common.empty_cache_freq - 1) - % self.cfg.common.empty_cache_freq - ) - == 0 - ): - torch.cuda.empty_cache() - - if self.cfg.common.fp16 or self.cfg.common.amp: - metrics.log_scalar( - "loss_scale", - ( - self.optimizer.scaler.loss_scale - if self.cfg.common.fp16 - else self.optimizer.scaler.get_scale() - ), - priority=700, - round=4, - weight=0, - ) - - metrics.log_stop_time("train_wall") - return logging_output - - @metrics.aggregate("valid") - def valid_step(self, sample, raise_oom=False): - """Do forward pass in evaluation mode.""" - if self.tpu: - import torch_xla.core.xla_model as xm - - xm.rendezvous("valid_step") # wait for all workers - - # If EMA is enabled through store_ema=True - # and task.uses_ema is True, pass the EMA model as a keyword - # argument to the task. - extra_kwargs = {} - if self.cfg.ema.store_ema and getattr(self.task, "uses_ema", False): - extra_kwargs["ema_model"] = self.ema.get_model() - - with torch.no_grad(): - self.model.eval() - self.criterion.eval() - - sample, is_dummy_batch = self._prepare_sample(sample) - - try: - _loss, sample_size, logging_output = self.task.valid_step( - sample, self.model, self.criterion, **extra_kwargs - ) - except RuntimeError as e: - if "out of memory" in str(e): - self._log_oom(e) - if not raise_oom: - logger.warning( - "ran out of memory in validation step, retrying batch" - ) - for p in self.model.parameters(): - if p.grad is not None: - p.grad = None # free some memory - if self.cuda: - torch.cuda.empty_cache() - return self.valid_step(sample, raise_oom=True) - raise e - - logging_outputs = [logging_output] - if is_dummy_batch: - if torch.is_tensor(sample_size): - sample_size.zero_() - else: - sample_size *= 0.0 - - # gather logging outputs from all replicas - if self.data_parallel_world_size > 1: - logging_outputs, (sample_size,) = self._aggregate_logging_outputs( - logging_outputs, - sample_size, - ignore=is_dummy_batch, - ) - - # log validation stats - if self.tpu: - logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs) - logging_output = self._reduce_and_log_stats(logging_outputs, sample_size) - - return logging_output - - def zero_grad(self): - self.optimizer.zero_grad() - - def lr_step_begin_epoch(self, epoch): - """Adjust the learning rate at the beginning of the epoch.""" - self.lr_scheduler.step_begin_epoch(epoch) - # prefer updating the LR based on the number of steps - return self.lr_step_update() - - def lr_step(self, epoch, val_loss=None): - """Adjust the learning rate at the end of the epoch.""" - self.lr_scheduler.step(epoch, val_loss) - # prefer updating the LR based on the number of steps - return self.lr_step_update() - - def lr_step_update(self): - """Update the learning rate after each update.""" - new_lr = self.lr_scheduler.step_update(self.get_num_updates()) - if isinstance(new_lr, dict): - for k, v in new_lr.items(): - metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300) - new_lr = new_lr.get("default", next(iter(new_lr.values()))) - else: - metrics.log_scalar("lr", new_lr, weight=0, priority=300) - return new_lr - - def get_lr(self): - """Get the current learning rate.""" - return self.optimizer.get_lr() - - def get_model(self): - """Get the (non-wrapped) model instance.""" - return self._model - - def get_criterion(self): - """Get the (non-wrapped) criterion instance.""" - return self._criterion - - def get_meter(self, name): - """[deprecated] Get a specific meter by name.""" - from fairseq import meters - - if "get_meter" not in self._warn_once: - self._warn_once.add("get_meter") - utils.deprecation_warning( - "Trainer.get_meter is deprecated. Please use fairseq.metrics instead." - ) - - train_meters = metrics.get_meters("train") - if train_meters is None: - train_meters = {} - - if name == "train_loss" and "loss" in train_meters: - return train_meters["loss"] - elif name == "train_nll_loss": - # support for legacy train.py, which assumed this meter is - # always initialized - m = train_meters.get("nll_loss", None) - return m or meters.AverageMeter() - elif name == "wall": - # support for legacy train.py, which assumed this meter is - # always initialized - m = metrics.get_meter("default", "wall") - return m or meters.TimeMeter() - elif name == "wps": - m = metrics.get_meter("train", "wps") - return m or meters.TimeMeter() - elif name in {"valid_loss", "valid_nll_loss"}: - # support for legacy train.py, which assumed these meters - # are always initialized - k = name[len("valid_") :] - m = metrics.get_meter("valid", k) - return m or meters.AverageMeter() - elif name == "oom": - return meters.AverageMeter() - elif name in train_meters: - return train_meters[name] - return None - - def get_num_updates(self): - """Get the number of parameters updates.""" - return self._num_updates - - def set_num_updates(self, num_updates): - """Set the number of parameters updates.""" - self._num_updates = num_updates - self.lr_step_update() - if self.quantizer: - self.quantizer.step_update(self._num_updates) - metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200) - - def clip_grad_norm(self, clip_norm): - def agg_norm_fn(total_norm): - total_norm = total_norm.cuda().float() ** 2 - total_norm = distributed_utils.all_reduce( - total_norm, group=self.data_parallel_process_group - ) - return total_norm**0.5 - - should_agg_norm = self.is_fsdp and ( - self.data_parallel_process_group is not None - or torch.distributed.is_initialized() - ) - return self.optimizer.clip_grad_norm( - clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None - ) - - def cumulative_training_time(self): - if self._cumulative_training_time is None: - # single GPU - return self._local_cumulative_training_time() - else: - return self._cumulative_training_time - - def _local_cumulative_training_time(self): - """Aggregate training time in seconds.""" - return time.time() - self._start_time + self._previous_training_time - - def _fp_convert_sample(self, sample): - def apply_half(t): - if t.dtype is torch.float32: - return t.to(dtype=torch.half) - return t - - def apply_bfloat16(t): - if t.dtype is torch.float32: - return t.to(dtype=torch.bfloat16) - return t - - if self.cfg.common.fp16: - sample = utils.apply_to_sample(apply_half, sample) - - if self.cfg.common.bf16: - sample = utils.apply_to_sample(apply_bfloat16, sample) - - return sample - - def _prepare_sample(self, sample, is_dummy=False): - if sample == "DUMMY": - raise Exception( - "Trying to use an uninitialized 'dummy' batch. This usually indicates " - "that the total number of batches is smaller than the number of " - "participating GPUs. Try reducing the batch size or using fewer GPUs." - ) - - if sample is None or len(sample) == 0: - assert ( - self._dummy_batch is not None and len(self._dummy_batch) > 0 - ), "Invalid dummy batch: {}".format(self._dummy_batch) - sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True) - return sample, True - - # Given that PCIe/NVLink bandwidth is significantly smaller than DRAM bandwidth - # it makes sense to do the format conversion on the CPU and then transfer - # a smaller buffer to the device. This also saves GPU memory capacity. - - if self.cfg.common.on_cpu_convert_precision: - sample = self._fp_convert_sample(sample) - - if self.cuda: - if self.pipeline_model_parallel: - if "target" in sample: - sample["target"] = utils.move_to_cuda( - sample["target"], device=self.last_device - ) - else: - sample = utils.move_to_cuda(sample) - elif self.tpu and is_dummy: - # the dummy batch may not be on the appropriate device - sample = utils.move_to_cuda(sample, device=self.device) - - if not self.cfg.common.on_cpu_convert_precision: - sample = self._fp_convert_sample(sample) - - if self._dummy_batch == "DUMMY": - self._dummy_batch = sample - - return sample, False - - def _set_seed(self): - # Set seed based on args.seed and the update number so that we get - # reproducible results when resuming from checkpoints - seed = self.cfg.common.seed + self.get_num_updates() - utils.set_torch_seed(seed) - - def _sync_stats(self): - # Return True if it's using multiple GPUs and DDP or multiple GPUs with - # BMUF and it's a bmuf sync with warmup iterations completed before. - if self.data_parallel_world_size == 1: - return False - elif self.cfg.optimization.use_bmuf: - return ( - self.get_num_updates() + 1 - ) % self.cfg.bmuf.global_sync_iter == 0 and ( - self.get_num_updates() + 1 - ) > self.cfg.bmuf.warmup_iterations - else: - return True - - def _log_oom(self, exc): - msg = "OOM: Ran out of memory with exception: {}".format(exc) - logger.warning(msg) - if torch.cuda.is_available() and hasattr(torch.cuda, "memory_summary"): - for device_idx in range(torch.cuda.device_count()): - logger.warning(torch.cuda.memory_summary(device=device_idx)) - sys.stderr.flush() - - def _aggregate_logging_outputs( - self, - logging_outputs: List[Dict[str, Any]], - *extra_stats_to_sum, - ignore=False, - ): - if self.task.__class__.logging_outputs_can_be_summed(self.get_criterion()): - return self._fast_stat_sync_sum( - logging_outputs, *extra_stats_to_sum, ignore=ignore - ) - else: - return self._all_gather_list_sync( - logging_outputs, *extra_stats_to_sum, ignore=ignore - ) - - def _all_gather_list_sync( - self, - logging_outputs: List[Dict[str, Any]], - *extra_stats_to_sum, - ignore=False, - ): - """ - Sync logging outputs across workers. all_gather_list_sync is - suitable when logging outputs are complex types. - """ - if self.tpu: - raise NotImplementedError - if ignore: - logging_outputs = [] - results = list( - zip( - *distributed_utils.all_gather_list( - [logging_outputs] + list(extra_stats_to_sum), - max_size=getattr(self.cfg.common, "all_gather_list_size", 16384), - group=self.data_parallel_process_group, - ) - ) - ) - logging_outputs, extra_stats_to_sum = results[0], results[1:] - logging_outputs = list(chain.from_iterable(logging_outputs)) - extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum] - return logging_outputs, extra_stats_to_sum - - def _fast_stat_sync_sum( - self, - logging_outputs: List[Dict[str, Any]], - *extra_stats_to_sum, - ignore=False, - ): - """ - Sync logging outputs across workers. fast_stat_sync_sum is - faster than all_gather_list_sync, but is only suitable when - logging outputs are scalars and can be summed. Note that - *logging_outputs* cannot contain any nested dicts/lists. - """ - data = {} - for i, stat in enumerate(extra_stats_to_sum): - data["extra_stats_" + str(i)] = stat - if len(logging_outputs) > 0: - log_keys = list(logging_outputs[0].keys()) - for k in log_keys: - if not ignore: - v = sum(log[k] for log in logging_outputs if k in log) - else: - v = logging_outputs[0][k] - v = torch.zeros_like(v) if torch.is_tensor(v) else 0 - data["logging_outputs_" + k] = v - else: - log_keys = None - - data = distributed_utils.all_reduce_dict( - data, device=self.device, group=self.data_parallel_process_group - ) - - extra_stats_to_sum = [ - data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum)) - ] - if log_keys is not None: - logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}] - else: - logging_outputs = [] - return logging_outputs, extra_stats_to_sum - - def _check_grad_norms(self, grad_norm): - """Check that grad norms are consistent across workers.""" - if self._grad_norm_buf is not None: - self._grad_norm_buf.zero_() - self._grad_norm_buf[self.data_parallel_rank] = grad_norm - distributed_utils.all_reduce( - self._grad_norm_buf, group=self.data_parallel_process_group - ) - - def is_consistent(tensor): - max_abs_diff = torch.max(torch.abs(tensor - tensor[0])) - return ( - ( - torch.isfinite(tensor).all() - and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all() - ) - or (self.cfg.common.amp and not torch.isfinite(tensor).all()) - # in case of amp non-finite grads are fine - ) - - if not is_consistent(self._grad_norm_buf): - pretty_detail = "\n".join( - "rank {:3d} = {:.8f}".format(r, n) - for r, n in enumerate(self._grad_norm_buf.tolist()) - ) - error_detail = "grad_norm across the workers:\n{}\n".format( - pretty_detail - ) - # use FloatingPointError to trigger NanDetector - raise FloatingPointError( - "Fatal error: gradients are inconsistent between workers. " - "Try --ddp-backend=legacy_ddp. " - "Or are you mixing up different generation of GPUs in training?" - + "\n" - + "-" * 80 - + "\n{}\n".format(error_detail) - + "-" * 80 - ) - - def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None): - if grad_norm is not None and ( - not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm) - ): - metrics.log_speed("ups", 1.0, priority=100, round=2) - metrics.log_scalar("gnorm", grad_norm, priority=400, round=3) - if self.cfg.optimization.clip_norm > 0: - metrics.log_scalar( - "clip", - torch.where( - grad_norm > self.cfg.optimization.clip_norm, - grad_norm.new_tensor(100), - grad_norm.new_tensor(0), - ), - priority=500, - round=1, - ) - - with metrics.aggregate() as agg: - if logging_outputs is not None: - self.task.reduce_metrics(logging_outputs, self.get_criterion()) - del logging_outputs - - # extra warning for criterions that don't properly log a loss value - if "loss" not in agg: - if "loss" not in self._warn_once: - self._warn_once.add("loss") - logger.warning( - "Criterion.reduce_metrics did not log a 'loss' value, " - "which may break some functionality" - ) - metrics.log_scalar("loss", -1) - - # support legacy interface - if self.tpu: - logging_output = {} - else: - logging_output = agg.get_smoothed_values() - logging_output["sample_size"] = sample_size - for key_to_delete in ["ppl", "wps", "wpb", "bsz"]: - if key_to_delete in logging_output: - del logging_output[key_to_delete] - return logging_output - - def _check_xla_compilation(self): - import torch_xla.debug.metrics as met - - compile_stats = met.metric_data("CompileTime") - if compile_stats is None: - return - num_xla_compiles = compile_stats[0] - if num_xla_compiles > self._num_xla_compiles: - logger.warning( - "XLA compilation detected on device #{}; too many of these can lead " - "to slow training, but we expect a few in the beginning".format( - self.cfg.distributed_training.distributed_rank - ) - ) - self._num_xla_compiles = num_xla_compiles - - def _xla_markstep_and_send_to_cpu(self, data=None): - import torch_xla.core.xla_model as xm - - xm.mark_step() - if data is not None: - from fairseq.utils import xla_device_to_cpu - - return xla_device_to_cpu(data) - - -def _catalog_shared_params(module, memo=None, prefix=""): - if memo is None: - first_call = True - memo = {} - else: - first_call = False - for name, param in module._parameters.items(): - param_prefix = prefix + ("." if prefix else "") + name - if param not in memo: - memo[param] = [] - memo[param].append(param_prefix) - for name, m in module._modules.items(): - if m is None: - continue - submodule_prefix = prefix + ("." if prefix else "") + name - _catalog_shared_params(m, memo, submodule_prefix) - if first_call: - return [x for x in memo.values() if len(x) > 1] - - -def _get_module_by_path(module, path): - path = path.split(".") - for name in path: - module = getattr(module, name) - return module - - -def _set_module_by_path(module, path, value): - path = path.split(".") - for name in path[:-1]: - module = getattr(module, name) - setattr(module, path[-1], value) diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/utils.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/utils.py deleted file mode 100644 index 7e5cb5e1fa414e84edb23d24bd8fdfcae864d3d1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/utils.py +++ /dev/null @@ -1,841 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import collections -import contextlib -import copy -import importlib -import logging -import os -import sys -import warnings -from itertools import accumulate -from typing import TYPE_CHECKING, Callable, Dict, List, Optional - -import torch -import torch.nn.functional as F -from torch import Tensor - -if TYPE_CHECKING: - from fairseq.modules.multihead_attention import MultiheadAttention - -try: - from amp_C import multi_tensor_l2norm - - multi_tensor_l2norm_available = True -except ImportError: - multi_tensor_l2norm_available = False - -try: - import torch_xla.core.xla_model as xm -except ImportError: - xm = None - - -logger = logging.getLogger(__name__) - - -MANIFOLD_PATH_SEP = "|" - - -class FileContentsAction(argparse.Action): - def __init__(self, option_strings, dest, nargs=None, **kwargs): - if nargs is not None: - raise ValueError("nargs not allowed") - super(FileContentsAction, self).__init__(option_strings, dest, **kwargs) - - def __call__(self, parser, namespace, values, option_string=None): - from fairseq.file_io import PathManager - - if PathManager.isfile(values): - with PathManager.open(values) as f: - argument = f.read().strip() - else: - argument = values - setattr(namespace, self.dest, argument) - - -def split_paths(paths: str, separator=os.pathsep) -> List[str]: - return ( - paths.split(separator) if "://" not in paths else paths.split(MANIFOLD_PATH_SEP) - ) - - -def load_ensemble_for_inference(filenames, task, model_arg_overrides=None): - from fairseq import checkpoint_utils - - deprecation_warning( - "utils.load_ensemble_for_inference is deprecated. " - "Please use checkpoint_utils.load_model_ensemble instead." - ) - return checkpoint_utils.load_model_ensemble( - filenames, arg_overrides=model_arg_overrides, task=task - ) - - -def apply_to_sample(f, sample): - if hasattr(sample, "__len__") and len(sample) == 0: - return {} - - def _apply(x): - if torch.is_tensor(x): - return f(x) - elif isinstance(x, collections.OrderedDict): - # OrderedDict has attributes that needs to be preserved - od = collections.OrderedDict( - (key, _apply(value)) for key, value in x.items() - ) - od.__dict__ = x.__dict__ - return od - elif isinstance(x, dict): - return {key: _apply(value) for key, value in x.items()} - elif isinstance(x, list): - return [_apply(x) for x in x] - elif isinstance(x, tuple): - return tuple(_apply(x) for x in x) - elif isinstance(x, set): - return {_apply(x) for x in x} - else: - return x - - return _apply(sample) - - -def move_to_cuda(sample, device=None): - device = device or torch.cuda.current_device() - - def _move_to_cuda(tensor): - # non_blocking is ignored if tensor is not pinned, so we can always set - # to True (see github.com/PyTorchLightning/pytorch-lightning/issues/620) - return tensor.to(device=device, non_blocking=True) - - return apply_to_sample(_move_to_cuda, sample) - - -def move_to_cpu(sample): - def _move_to_cpu(tensor): - # PyTorch has poor support for half tensors (float16) on CPU. - # Move any such tensors to float32. - if tensor.dtype in {torch.bfloat16, torch.float16}: - tensor = tensor.to(dtype=torch.float32) - return tensor.cpu() - - return apply_to_sample(_move_to_cpu, sample) - - -def move_to_tpu(sample): - - import torch_xla.core.xla_model as xm - - device = xm.xla_device() - - def _move_to_tpu(tensor): - return tensor.to(device) - - return apply_to_sample(_move_to_tpu, sample) - - -def get_incremental_state( - module: "MultiheadAttention", - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], - key: str, -) -> Optional[Dict[str, Optional[Tensor]]]: - """Helper for getting incremental state for an nn.Module.""" - return module.get_incremental_state(incremental_state, key) - - -def set_incremental_state( - module: "MultiheadAttention", - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], - key: str, - value: Dict[str, Optional[Tensor]], -) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]: - """Helper for setting incremental state for an nn.Module.""" - if incremental_state is not None: - result = module.set_incremental_state(incremental_state, key, value) - if result is not None: - incremental_state = result - return incremental_state - - -def load_align_dict(replace_unk): - if replace_unk is None: - align_dict = None - elif isinstance(replace_unk, str) and len(replace_unk) > 0: - # Load alignment dictionary for unknown word replacement if it was passed as an argument. - align_dict = {} - with open(replace_unk, "r") as f: - for line in f: - cols = line.split() - align_dict[cols[0]] = cols[1] - else: - # No alignment dictionary provided but we still want to perform unknown word replacement by copying the - # original source word. - align_dict = {} - return align_dict - - -def print_embed_overlap(embed_dict, vocab_dict): - embed_keys = set(embed_dict.keys()) - vocab_keys = set(vocab_dict.symbols) - overlap = len(embed_keys & vocab_keys) - logger.info("found {}/{} types in embedding file".format(overlap, len(vocab_dict))) - - -def parse_embedding(embed_path): - """Parse embedding text file into a dictionary of word and embedding tensors. - - The first line can have vocabulary size and dimension. The following lines - should contain word and embedding separated by spaces. - - Example: - 2 5 - the -0.0230 -0.0264 0.0287 0.0171 0.1403 - at -0.0395 -0.1286 0.0275 0.0254 -0.0932 - """ - embed_dict = {} - with open(embed_path) as f_embed: - next(f_embed) # skip header - for line in f_embed: - pieces = line.rstrip().split(" ") - embed_dict[pieces[0]] = torch.Tensor( - [float(weight) for weight in pieces[1:]] - ) - return embed_dict - - -def load_embedding(embed_dict, vocab, embedding): - for idx in range(len(vocab)): - token = vocab[idx] - if token in embed_dict: - embedding.weight.data[idx] = embed_dict[token] - return embedding - - -def replace_unk(hypo_str, src_str, alignment, align_dict, unk): - from fairseq import tokenizer - - # Tokens are strings here - hypo_tokens = tokenizer.tokenize_line(hypo_str) - # TODO: Very rare cases where the replacement is '' should be handled gracefully - src_tokens = tokenizer.tokenize_line(src_str) + [""] - for i, ht in enumerate(hypo_tokens): - if ht == unk: - src_token = src_tokens[alignment[i]] - # Either take the corresponding value in the aligned dictionary or just copy the original value. - hypo_tokens[i] = align_dict.get(src_token, src_token) - return " ".join(hypo_tokens) - - -def post_process_prediction( - hypo_tokens, - src_str, - alignment, - align_dict, - tgt_dict, - remove_bpe=None, - extra_symbols_to_ignore=None, -): - hypo_str = tgt_dict.string( - hypo_tokens, remove_bpe, extra_symbols_to_ignore=extra_symbols_to_ignore - ) - if align_dict is not None: - hypo_str = replace_unk( - hypo_str, src_str, alignment, align_dict, tgt_dict.unk_string() - ) - if align_dict is not None or remove_bpe is not None: - # Convert back to tokens for evaluating with unk replacement or without BPE - # Note that the dictionary can be modified inside the method. - hypo_tokens = tgt_dict.encode_line(hypo_str, add_if_not_exist=True) - return hypo_tokens, hypo_str, alignment - - -def make_positions(tensor, padding_idx: int, onnx_trace: bool = False): - """Replace non-padding symbols with their position numbers. - - Position numbers begin at padding_idx+1. Padding symbols are ignored. - """ - # The series of casts and type-conversions here are carefully - # balanced to both work with ONNX export and XLA. In particular XLA - # prefers ints, cumsum defaults to output longs, and ONNX doesn't know - # how to handle the dtype kwarg in cumsum. - mask = tensor.ne(padding_idx).int() - return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx - - -def strip_pad(tensor, pad): - return tensor[tensor.ne(pad)] - - -def buffered_arange(max): - if not hasattr(buffered_arange, "buf"): - buffered_arange.buf = torch.LongTensor() - if max > buffered_arange.buf.numel(): - buffered_arange.buf.resize_(max) - torch.arange(max, out=buffered_arange.buf) - return buffered_arange.buf[:max] - - -def convert_padding_direction( - src_tokens, padding_idx, right_to_left: bool = False, left_to_right: bool = False -): - assert right_to_left ^ left_to_right - pad_mask = src_tokens.eq(padding_idx) - if not pad_mask.any(): - # no padding, return early - return src_tokens - if left_to_right and not pad_mask[:, 0].any(): - # already right padded - return src_tokens - if right_to_left and not pad_mask[:, -1].any(): - # already left padded - return src_tokens - max_len = src_tokens.size(1) - buffered = torch.empty(0).long() - if max_len > 0: - torch.arange(max_len, out=buffered) - range = buffered.type_as(src_tokens).expand_as(src_tokens) - num_pads = pad_mask.long().sum(dim=1, keepdim=True) - if right_to_left: - index = torch.remainder(range - num_pads, max_len) - else: - index = torch.remainder(range + num_pads, max_len) - return src_tokens.gather(1, index) - - -def item(tensor): - # tpu-comment: making this a no-op for xla devices. - if torch.is_tensor(tensor) and tensor.device.type == "xla": - return tensor.detach() - if hasattr(tensor, "item"): - return tensor.item() - if hasattr(tensor, "__getitem__"): - return tensor[0] - return tensor - - -def multi_tensor_total_norm(grads, chunk_size=2048 * 32) -> torch.Tensor: - per_device_grads = {} - norms = [] - for grad in grads: - device = grad.device - cur_device_grads = per_device_grads.get(device) - if cur_device_grads is None: - cur_device_grads = [] - per_device_grads[device] = cur_device_grads - cur_device_grads.append(grad) - for device in per_device_grads.keys(): - cur_device_grads = per_device_grads[device] - if device.type == "cuda": - # TODO(msb) return has_inf - has_inf = torch.zeros((1, 1), dtype=torch.int, device=device) - with torch.cuda.device(device): - norm = multi_tensor_l2norm( - chunk_size, has_inf, [cur_device_grads], False - ) - norms.append(norm[0].to(torch.cuda.current_device())) - else: - norms += [torch.norm(g, p=2, dtype=torch.float32) for g in cur_device_grads] - total_norm = torch.norm(torch.stack(norms)) - return total_norm - - -@torch.no_grad() -def clip_grad_norm_(params, max_norm, aggregate_norm_fn=None) -> torch.Tensor: - def grad_exists(p): - return p is not None and getattr(p, "grad", None) is not None - - if isinstance(params, torch.Tensor): - params = [params] - params = list(params) - grads = [ - p.grad.detach() for p in params if grad_exists(p) and not hasattr(p, "expert") - ] - expert_grads = [ - p.grad.detach() for p in params if grad_exists(p) and hasattr(p, "expert") - ] - - if len(grads) == 0: - if len(params) > 0: - return params[0].new_tensor(0.0) - else: - return torch.tensor(0.0) - - if len(grads) == 1: - total_norm = torch.norm(grads[0], p=2, dtype=torch.float32) - else: - if multi_tensor_l2norm_available: - total_norm = multi_tensor_total_norm(grads) - else: - if torch.cuda.is_available(): - warnings.warn( - "amp_C fused kernels unavailable, disabling multi_tensor_l2norm; " - "you may get better performance by installing NVIDIA's apex library" - ) - device = torch.cuda.current_device() - elif grads[0].device.type == "xla": - device = grads[0].device - else: - device = torch.device("cpu") - total_norm = torch.norm( - torch.stack( - [torch.norm(g, p=2, dtype=torch.float32).to(device) for g in grads] - ) - ) - - if aggregate_norm_fn is not None: - total_norm = aggregate_norm_fn(total_norm) - - if max_norm > 0: - max_norm = float(max_norm) - clip_coef = (max_norm / (total_norm + 1e-6)).clamp_(max=1) - for g in grads + expert_grads: - g.mul_(clip_coef) - return total_norm - - -def fill_with_neg_inf(t): - """FP16-compatible function that fills a tensor with -inf.""" - return t.float().fill_(float("-inf")).type_as(t) - - -def _match_types(arg1, arg2): - """Convert the numerical argument to the same type as the other argument""" - - def upgrade(arg_number, arg_structure): - if isinstance(arg_structure, tuple): - return tuple([arg_number] * len(arg_structure)) - elif isinstance(arg_structure, dict): - arg = copy.deepcopy(arg_structure) - for k in arg: - arg[k] = upgrade(arg_number, arg_structure[k]) - return arg - else: - return arg_number - - if isinstance(arg1, float) or isinstance(arg1, int): - return upgrade(arg1, arg2), arg2 - elif isinstance(arg2, float) or isinstance(arg2, int): - return arg1, upgrade(arg2, arg1) - - return arg1, arg2 - - -def resolve_max_positions(*args): - """Resolve max position constraints from multiple sources.""" - - def map_value_update(d1, d2): - updated_value = copy.deepcopy(d1) - for key in d2: - if key not in updated_value: - updated_value[key] = d2[key] - else: - updated_value[key] = min(d1[key], d2[key]) - return updated_value - - def nullsafe_min(l): - minim = None - for item in l: - if minim is None: - minim = item - elif item is not None and item < minim: - minim = item - return minim - - max_positions = None - for arg in args: - if max_positions is None: - max_positions = arg - elif arg is not None: - max_positions, arg = _match_types(max_positions, arg) - if isinstance(arg, float) or isinstance(arg, int): - max_positions = min(max_positions, arg) - elif isinstance(arg, dict): - max_positions = map_value_update(max_positions, arg) - else: - max_positions = tuple(map(nullsafe_min, zip(max_positions, arg))) - - return max_positions - - -def import_user_module(args): - module_path = getattr(args, "user_dir", None) - if module_path is not None: - module_path = os.path.abspath(args.user_dir) - if not os.path.exists(module_path) and not os.path.isfile( - os.path.dirname(module_path) - ): - fairseq_rel_path = os.path.join(os.path.dirname(__file__), args.user_dir) - if os.path.exists(fairseq_rel_path): - module_path = fairseq_rel_path - else: - fairseq_rel_path = os.path.join( - os.path.dirname(__file__), "..", args.user_dir - ) - if os.path.exists(fairseq_rel_path): - module_path = fairseq_rel_path - else: - raise FileNotFoundError(module_path) - - # ensure that user modules are only imported once - import_user_module.memo = getattr(import_user_module, "memo", set()) - if module_path not in import_user_module.memo: - import_user_module.memo.add(module_path) - - module_parent, module_name = os.path.split(module_path) - if module_name not in sys.modules: - sys.path.insert(0, module_parent) - importlib.import_module(module_name) - - tasks_path = os.path.join(module_path, "tasks") - if os.path.exists(tasks_path): - from fairseq.tasks import import_tasks - - import_tasks(tasks_path, f"{module_name}.tasks") - - models_path = os.path.join(module_path, "models") - if os.path.exists(models_path): - from fairseq.models import import_models - - import_models(models_path, f"{module_name}.models") - elif module_path in sys.modules[module_name].__path__: - logger.info(f"--user-dir={module_path} has already been imported.") - else: - raise ImportError( - "Failed to import --user-dir={} because the corresponding module name " - "({}) is not globally unique. Please rename the directory to " - "something unique and try again.".format(module_path, module_name) - ) - - -def softmax(x, dim: int, onnx_trace: bool = False): - if onnx_trace: - return F.softmax(x.float(), dim=dim) - else: - return F.softmax(x, dim=dim, dtype=torch.float32) - - -def log_softmax(x, dim: int, onnx_trace: bool = False): - if onnx_trace: - return F.log_softmax(x.float(), dim=dim) - else: - return F.log_softmax(x, dim=dim, dtype=torch.float32) - - -def get_perplexity(loss, round=2, base=2): - from fairseq.logging.meters import safe_round - - if loss is None: - return 0.0 - try: - return safe_round(base**loss, round) - except OverflowError: - return float("inf") - - -def deprecation_warning(message, stacklevel=3): - # don't use DeprecationWarning, since it's ignored by default - warnings.warn(message, stacklevel=stacklevel) - - -def relu_squared(x: torch.Tensor): - return F.relu(x).pow(2) - - -def get_activation_fn(activation: str) -> Callable: - """Returns the activation function corresponding to `activation`""" - from fairseq.modules import gelu, gelu_accurate - - if activation == "relu": - return F.relu - elif activation == "relu_squared": - return relu_squared - elif activation == "gelu": - return gelu - elif activation == "gelu_fast": - deprecation_warning( - "--activation-fn=gelu_fast has been renamed to gelu_accurate" - ) - return gelu_accurate - elif activation == "gelu_accurate": - return gelu_accurate - elif activation == "tanh": - return torch.tanh - elif activation == "linear": - return lambda x: x - elif activation == "swish": - return torch.nn.SiLU - else: - raise RuntimeError("--activation-fn {} not supported".format(activation)) - - -def get_available_activation_fns() -> List: - return [ - "relu", - "gelu", - "gelu_fast", # deprecated - "gelu_accurate", - "tanh", - "linear", - ] - - -@contextlib.contextmanager -def model_eval(model): - is_training = model.training - model.eval() - yield - model.train(is_training) - - -def has_parameters(module): - try: - next(module.parameters()) - return True - except StopIteration: - return False - - -def get_rng_state(): - state = {"torch_rng_state": torch.get_rng_state()} - if xm is not None: - state["xla_rng_state"] = xm.get_rng_state() - if torch.cuda.is_available(): - state["cuda_rng_state"] = torch.cuda.get_rng_state() - return state - - -def set_rng_state(state): - torch.set_rng_state(state["torch_rng_state"]) - if xm is not None: - xm.set_rng_state(state["xla_rng_state"]) - if torch.cuda.is_available(): - torch.cuda.set_rng_state(state["cuda_rng_state"]) - - -class set_torch_seed(object): - def __init__(self, seed): - assert isinstance(seed, int) - self.rng_state = get_rng_state() - - torch.manual_seed(seed) - if xm is not None: - xm.set_rng_state(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed(seed) - - def __enter__(self): - return self - - def __exit__(self, *exc): - set_rng_state(self.rng_state) - - -def parse_alignment(line): - """ - Parses a single line from the alingment file. - - Args: - line (str): String containing the alignment of the format: - - - .. - -. All indices are 0 indexed. - - Returns: - torch.IntTensor: packed alignments of shape (2 * m). - """ - alignments = line.strip().split() - parsed_alignment = torch.IntTensor(2 * len(alignments)) - for idx, alignment in enumerate(alignments): - src_idx, tgt_idx = alignment.split("-") - parsed_alignment[2 * idx] = int(src_idx) - parsed_alignment[2 * idx + 1] = int(tgt_idx) - return parsed_alignment - - -def get_token_to_word_mapping(tokens, exclude_list): - n = len(tokens) - word_start = [int(token not in exclude_list) for token in tokens] - word_idx = list(accumulate(word_start)) - token_to_word = {i: word_idx[i] for i in range(n)} - return token_to_word - - -def extract_hard_alignment(attn, src_sent, tgt_sent, pad, eos): - tgt_valid = ( - ((tgt_sent != pad) & (tgt_sent != eos)).nonzero(as_tuple=False).squeeze(dim=-1) - ) - src_invalid = ( - ((src_sent == pad) | (src_sent == eos)).nonzero(as_tuple=False).squeeze(dim=-1) - ) - src_token_to_word = get_token_to_word_mapping(src_sent, [eos, pad]) - tgt_token_to_word = get_token_to_word_mapping(tgt_sent, [eos, pad]) - alignment = [] - if len(tgt_valid) != 0 and len(src_invalid) < len(src_sent): - attn_valid = attn[tgt_valid] - attn_valid[:, src_invalid] = float("-inf") - _, src_indices = attn_valid.max(dim=1) - for tgt_idx, src_idx in zip(tgt_valid, src_indices): - alignment.append( - ( - src_token_to_word[src_idx.item()] - 1, - tgt_token_to_word[tgt_idx.item()] - 1, - ) - ) - return alignment - - -def extract_soft_alignment(attn, src_sent, tgt_sent, pad, eos): - tgt_valid = ((tgt_sent != pad)).nonzero(as_tuple=False) - src_valid = ((src_sent != pad)).nonzero(as_tuple=False).squeeze(dim=-1) - alignment = [] - if len(tgt_valid) != 0 and len(src_valid) != 0: - attn_valid = attn[tgt_valid, src_valid] - alignment = [ - ["{:.6f}".format(p) for p in src_probs.tolist()] for src_probs in attn_valid - ] - return alignment - - -def new_arange(x, *size): - """ - Return a Tensor of `size` filled with a range function on the device of x. - If size is empty, using the size of the variable x. - """ - if len(size) == 0: - size = x.size() - return torch.arange(size[-1], device=x.device).expand(*size).contiguous() - - -def get_tpu_device(): - return xm.xla_device() - - -def tpu_data_loader(itr): - import torch_xla.core.xla_model as xm - import torch_xla.distributed.parallel_loader as pl - from fairseq.data import iterators - - xm.rendezvous("tpu_data_loader") # wait for all workers - xm.mark_step() - device = xm.xla_device() - return iterators.CountingIterator( - pl.ParallelLoader(itr, [device]).per_device_loader(device), - start=getattr(itr, "n", 0), - total=len(itr), - ) - - -def is_xla_tensor(tensor): - return torch.is_tensor(tensor) and tensor.device.type == "xla" - - -def index_put(tensor, indices, value): - if is_xla_tensor(tensor): - for _ in range(indices.dim(), tensor.dim()): - indices = indices.unsqueeze(-1) - if indices.size(-1) < tensor.size(-1): - indices = indices.expand_as(tensor) - tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices) - else: - tensor[indices] = value - return tensor - - -def xla_device_to_cpu(dat): - import torch_xla.core.xla_model as xm - - return xm._maybe_convert_to_cpu(dat) - - -class CudaEnvironment(object): - def __init__(self): - cur_device = torch.cuda.current_device() - prop = torch.cuda.get_device_properties("cuda:{}".format(cur_device)) - self.name = prop.name - self.major = prop.major - self.minor = prop.minor - self.total_memory_in_GB = prop.total_memory / 1024 / 1024 / 1024 - - @staticmethod - def pretty_print_cuda_env_list(cuda_env_list): - """ - Given a list of CudaEnviorments, pretty print them - """ - num_workers = len(cuda_env_list) - center = "CUDA enviroments for all {} workers".format(num_workers) - banner_len = 40 - len(center) // 2 - first_line = "*" * banner_len + center + "*" * banner_len - logger.info(first_line) - for r, env in enumerate(cuda_env_list): - logger.info( - "rank {:3d}: ".format(r) - + "capabilities = {:2d}.{:<2d} ; ".format(env.major, env.minor) - + "total memory = {:.3f} GB ; ".format(env.total_memory_in_GB) - + "name = {:40s}".format(env.name) - ) - logger.info(first_line) - - -def csv_str_list(x): - return x.split(",") - - -def eval_str_list(x, type=float): - if x is None: - return None - if isinstance(x, str): - x = eval(x) - try: - return list(map(type, x)) - except TypeError: - return [type(x)] - - -def eval_str_dict(x, type=dict): - if x is None: - return None - if isinstance(x, str): - x = eval(x) - return x - - -def eval_bool(x, default=False): - if x is None: - return default - try: - return bool(eval(x)) - except TypeError: - return default - - -def reset_logging(): - root = logging.getLogger() - for handler in root.handlers: - root.removeHandler(handler) - root.setLevel(os.environ.get("LOGLEVEL", "INFO").upper()) - handler = logging.StreamHandler(sys.stdout) - handler.setFormatter( - logging.Formatter( - fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - ) - ) - root.addHandler(handler) - - -def safe_getattr(obj, k, default=None): - """Returns obj[k] if it exists and is not None, otherwise returns default.""" - from omegaconf import OmegaConf - - if OmegaConf.is_config(obj): - return obj[k] if k in obj and obj[k] is not None else default - - return getattr(obj, k, default) - - -def safe_hasattr(obj, k): - """Returns True if the given key exists and is not None.""" - return getattr(obj, k, None) is not None diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/version.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq/version.py deleted file mode 100644 index def467e0715133c0902a139775faf5dc4ea29710..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.12.1" diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq/version.txt b/audio/safeear/safeear_repo/fairseq_ours/fairseq/version.txt deleted file mode 100644 index aac2dacab4fe202f940d8d2746e4efcbb7341bc1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq/version.txt +++ /dev/null @@ -1 +0,0 @@ -0.12.1 \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/eval_lm.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/eval_lm.py deleted file mode 100644 index 601d44adcc5c5b977a7e7d0b4fc04507145f7527..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/eval_lm.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -Evaluate the perplexity of a trained language model. -""" - -import logging -import math -import os -import sys -from argparse import Namespace -from typing import Iterable, List, Optional - -import fairseq -import torch -from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.logging import progress_bar -from fairseq.logging.meters import StopwatchMeter -from fairseq.sequence_scorer import SequenceScorer -from omegaconf import DictConfig - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.eval_lm") - - -def eval_lm( - models: List[fairseq.models.FairseqModel], - source_dictionary: fairseq.data.Dictionary, - batch_iterator: Iterable, - post_process: Optional[str] = None, - output_word_probs: bool = False, - output_word_stats: bool = False, - target_dictionary: Optional[fairseq.data.Dictionary] = None, - softmax_batch: int = 0, - remove_bos_token: bool = False, - device: Optional[torch.device] = None, -): - """ - Args: - models (List[~fairseq.models.FairseqModel]): list of models to - evaluate. Models are essentially `nn.Module` instances, but - must be compatible with fairseq's `SequenceScorer`. - source_dictionary (~fairseq.data.Dictionary): dictionary for - applying any relevant post processing or outputing word - probs/stats. - batch_iterator (Iterable): yield batches of data - post_process (Optional[str]): post-process text by removing BPE, - letter segmentation, etc. Valid options can be found in - fairseq.data.utils.post_process, although not all options - are implemented here. - output_word_probs (Optional[bool]): output words and their - predicted log probabilities - output_word_stats (Optional[bool]): output word statistics such - as word count and average probability - target_dictionary (Optional[~fairseq.data.Dictionary]): output - dictionary (defaults to *source_dictionary*) - softmax_batch (Optional[bool]): if BxT is more than this, will - batch the softmax over vocab to this amount of tokens, in - order to fit into GPU memory - remove_bos_token (Optional[bool]): if True, confirm that the - first token is the beginning-of-sentence symbol (according - to the relevant dictionary) and remove it from the output - device (Optional[torch.device]): device to use for evaluation - (defaults to device of first model parameter) - """ - if target_dictionary is None: - target_dictionary = source_dictionary - if device is None: - device = next(models[0].parameters()).device - - gen_timer = StopwatchMeter() - scorer = SequenceScorer(target_dictionary, softmax_batch) - - score_sum = 0.0 - count = 0 - - if post_process is not None: - if post_process in {"subword_nmt", "@@ "}: - bpe_cont = post_process.rstrip() - bpe_toks = { - i - for i in range(len(source_dictionary)) - if source_dictionary[i].endswith(bpe_cont) - } - else: - raise NotImplementedError( - f"--post-process={post_process} is not implemented" - ) - bpe_len = len(bpe_cont) - else: - bpe_toks = None - bpe_len = 0 - - word_stats = dict() - - for sample in batch_iterator: - if "net_input" not in sample: - continue - - sample = utils.move_to_cuda(sample, device=device) - - gen_timer.start() - hypos = scorer.generate(models, sample) - gen_timer.stop(sample["ntokens"]) - - for i, hypos_i in enumerate(hypos): - hypo = hypos_i[0] - sample_id = sample["id"][i] - - tokens = hypo["tokens"] - tgt_len = tokens.numel() - pos_scores = hypo["positional_scores"].float() - - if remove_bos_token: - assert hypo["tokens"][0].item() == target_dictionary.bos() - tokens = tokens[1:] - pos_scores = pos_scores[1:] - - skipped_toks = 0 - if bpe_toks is not None: - for i in range(tgt_len - 1): - if tokens[i].item() in bpe_toks: - skipped_toks += 1 - pos_scores[i + 1] += pos_scores[i] - pos_scores[i] = 0 - - inf_scores = pos_scores.eq(float("inf")) | pos_scores.eq(float("-inf")) - if inf_scores.any(): - logger.info( - "skipping tokens with inf scores:", - target_dictionary.string(tokens[inf_scores.nonzero()]), - ) - pos_scores = pos_scores[(~inf_scores).nonzero()] - score_sum += pos_scores.sum().cpu() - count += pos_scores.numel() - skipped_toks - - if output_word_probs or output_word_stats: - w = "" - word_prob = [] - is_bpe = False - for i in range(len(tokens)): - w_ind = tokens[i].item() - w += source_dictionary[w_ind] - if bpe_toks is not None and w_ind in bpe_toks: - w = w[:-bpe_len] - is_bpe = True - else: - word_prob.append((w, pos_scores[i].item())) - - next_prob = None - ind = i + 1 - while ind < len(tokens): - if pos_scores[ind].item() != 0: - next_prob = pos_scores[ind] - break - ind += 1 - - word_stats.setdefault(w, WordStat(w, is_bpe)).add( - pos_scores[i].item(), next_prob - ) - is_bpe = False - w = "" - if output_word_probs: - logger.info( - str(int(sample_id)) - + " " - + ( - "\t".join( - "{} [{:2f}]".format(x[0], x[1]) for x in word_prob - ) - ) - ) - - avg_nll_loss = ( - -score_sum / count / math.log(2) if count > 0 else 0 - ) # convert to base 2 - logger.info( - "Evaluated {:,} tokens in {:.1f}s ({:.2f} tokens/s)".format( - gen_timer.n, gen_timer.sum, 1.0 / gen_timer.avg if gen_timer.avg > 0 else 0 - ) - ) - - if output_word_stats: - for ws in sorted(word_stats.values(), key=lambda x: x.count, reverse=True): - logger.info(ws) - - return { - "loss": avg_nll_loss, - "perplexity": 2**avg_nll_loss, - } - - -class WordStat(object): - def __init__(self, word, is_bpe): - self.word = word - self.is_bpe = is_bpe - self.log_prob = 0 - self.next_word_prob = 0 - self.count = 0 - self.missing_next_words = 0 - - def add(self, log_prob, next_word_prob): - """increments counters for the sum of log probs of current word and next - word (given context ending at current word). Since the next word might be at the end of the example, - or it might be not counted because it is not an ending subword unit, - also keeps track of how many of those we have seen""" - if next_word_prob is not None: - self.next_word_prob += next_word_prob - else: - self.missing_next_words += 1 - self.log_prob += log_prob - self.count += 1 - - def __str__(self): - return "{}\t{}\t{}\t{}\t{}\t{}".format( - self.word, - self.count, - self.log_prob, - self.is_bpe, - self.next_word_prob, - self.count - self.missing_next_words, - ) - - -def main(cfg: DictConfig, **unused_kwargs): - if isinstance(cfg, Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - utils.import_user_module(cfg.common) - - logger.info(cfg) - - if cfg.eval_lm.context_window > 0: - # reduce tokens per sample by the required context window size - cfg.task.tokens_per_sample -= cfg.eval_lm.context_window - - # Initialize the task using the current *cfg* - task = tasks.setup_task(cfg.task) - - # Load ensemble - logger.info("loading model(s) from {}".format(cfg.common_eval.path)) - models, model_args, task = checkpoint_utils.load_model_ensemble_and_task( - [cfg.common_eval.path], - arg_overrides=eval(cfg.common_eval.model_overrides), - suffix=cfg.checkpoint.checkpoint_suffix, - strict=(cfg.checkpoint.checkpoint_shard_count == 1), - num_shards=cfg.checkpoint.checkpoint_shard_count, - task=task, - ) - - use_fp16 = cfg.common.fp16 - use_cuda = torch.cuda.is_available() and not cfg.common.cpu - if use_cuda: - torch.cuda.set_device(cfg.distributed_training.device_id) - - # Optimize ensemble for generation and set the source and dest dicts on the model - # (required by scorer) - for model in models: - if use_fp16: - model.half() - if use_cuda and not cfg.distributed_training.pipeline_model_parallel: - model.cuda() - model.prepare_for_inference_(cfg) - - assert len(models) > 0 - - logger.info( - "num. model params: {:,}".format(sum(p.numel() for p in models[0].parameters())) - ) - - # Load dataset splits - task.load_dataset(cfg.dataset.gen_subset) - dataset = task.dataset(cfg.dataset.gen_subset) - logger.info( - "{} {} {:,} examples".format( - cfg.task.data, cfg.dataset.gen_subset, len(dataset) - ) - ) - - itr = task.eval_lm_dataloader( - dataset=dataset, - max_tokens=cfg.dataset.max_tokens or 36000, - batch_size=cfg.dataset.batch_size, - max_positions=utils.resolve_max_positions( - *[model.max_positions() for model in models] - ), - num_shards=max( - cfg.dataset.num_shards, - cfg.distributed_training.distributed_world_size, - ), - shard_id=max( - cfg.dataset.shard_id, - cfg.distributed_training.distributed_rank, - ), - num_workers=cfg.dataset.num_workers, - data_buffer_size=cfg.dataset.data_buffer_size, - context_window=cfg.eval_lm.context_window, - ) - - itr = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_interval=cfg.common.log_interval, - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - ) - - results = eval_lm( - models=models, - source_dictionary=task.source_dictionary, - batch_iterator=itr, - post_process=cfg.common_eval.post_process, - output_word_probs=cfg.eval_lm.output_word_probs, - output_word_stats=cfg.eval_lm.output_word_stats, - target_dictionary=task.target_dictionary, - softmax_batch=cfg.eval_lm.softmax_batch, - remove_bos_token=getattr(cfg.task, "add_bos_token", False), - ) - - logger.info( - "Loss (base 2): {:.4f}, Perplexity: {:.2f}".format( - results["loss"], results["perplexity"] - ) - ) - - return results - - -def cli_main(): - parser = options.get_eval_lm_parser() - args = options.parse_args_and_arch(parser) - - distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/generate.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/generate.py deleted file mode 100644 index 3bd044fc2bc39f6b23c08086dab4209e80fa5b90..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/generate.py +++ /dev/null @@ -1,416 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Translate pre-processed data with a trained model. -""" - -import ast -import logging -import math -import os -import sys -from argparse import Namespace -from itertools import chain - -import numpy as np -import torch -from fairseq import checkpoint_utils, options, scoring, tasks, utils -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.logging import progress_bar -from fairseq.logging.meters import StopwatchMeter, TimeMeter -from omegaconf import DictConfig - - -def main(cfg: DictConfig): - - if isinstance(cfg, Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - assert cfg.common_eval.path is not None, "--path required for generation!" - assert ( - not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam - ), "--sampling requires --nbest to be equal to --beam" - assert ( - cfg.generation.replace_unk is None or cfg.dataset.dataset_impl == "raw" - ), "--replace-unk requires a raw text dataset (--dataset-impl=raw)" - - if cfg.common_eval.results_path is not None: - os.makedirs(cfg.common_eval.results_path, exist_ok=True) - output_path = os.path.join( - cfg.common_eval.results_path, - "generate-{}.txt".format(cfg.dataset.gen_subset), - ) - with open(output_path, "w", buffering=1, encoding="utf-8") as h: - return _main(cfg, h) - else: - return _main(cfg, sys.stdout) - - -def get_symbols_to_strip_from_output(generator): - if hasattr(generator, "symbols_to_strip_from_output"): - return generator.symbols_to_strip_from_output - else: - return {generator.eos} - - -def _main(cfg: DictConfig, output_file): - logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=output_file, - ) - logger = logging.getLogger("fairseq_cli.generate") - - utils.import_user_module(cfg.common) - - if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: - cfg.dataset.max_tokens = 12000 - logger.info(cfg) - - # Fix seed for stochastic decoding - if cfg.common.seed is not None and not cfg.generation.no_seed_provided: - np.random.seed(cfg.common.seed) - utils.set_torch_seed(cfg.common.seed) - - use_cuda = torch.cuda.is_available() and not cfg.common.cpu - - # Load dataset splits - task = tasks.setup_task(cfg.task) - - # Set dictionaries - try: - src_dict = getattr(task, "source_dictionary", None) - except NotImplementedError: - src_dict = None - tgt_dict = task.target_dictionary - - overrides = ast.literal_eval(cfg.common_eval.model_overrides) - - # Load ensemble - logger.info("loading model(s) from {}".format(cfg.common_eval.path)) - models, saved_cfg = checkpoint_utils.load_model_ensemble( - utils.split_paths(cfg.common_eval.path), - arg_overrides=overrides, - task=task, - suffix=cfg.checkpoint.checkpoint_suffix, - strict=(cfg.checkpoint.checkpoint_shard_count == 1), - num_shards=cfg.checkpoint.checkpoint_shard_count, - ) - - # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config - task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task) - - if cfg.generation.lm_path is not None: - overrides["data"] = cfg.task.data - - try: - lms, _ = checkpoint_utils.load_model_ensemble( - [cfg.generation.lm_path], arg_overrides=overrides, task=None - ) - except: - logger.warning( - f"Failed to load language model! Please make sure that the language model dict is the same " - f"as target dict and is located in the data dir ({cfg.task.data})" - ) - raise - - assert len(lms) == 1 - else: - lms = [None] - - # Optimize ensemble for generation - for model in chain(models, lms): - if model is None: - continue - if cfg.common.fp16: - model.half() - if use_cuda and not cfg.distributed_training.pipeline_model_parallel: - model.cuda() - model.prepare_for_inference_(cfg) - - # Load alignment dictionary for unknown word replacement - # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(cfg.generation.replace_unk) - - # Load dataset (possibly sharded) - itr = task.get_batch_iterator( - dataset=task.dataset(cfg.dataset.gen_subset), - max_tokens=cfg.dataset.max_tokens, - max_sentences=cfg.dataset.batch_size, - max_positions=utils.resolve_max_positions( - task.max_positions(), *[m.max_positions() for m in models] - ), - ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, - seed=cfg.common.seed, - num_shards=cfg.distributed_training.distributed_world_size, - shard_id=cfg.distributed_training.distributed_rank, - num_workers=cfg.dataset.num_workers, - data_buffer_size=cfg.dataset.data_buffer_size, - ).next_epoch_itr(shuffle=False) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_interval=cfg.common.log_interval, - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - ) - - # Initialize generator - gen_timer = StopwatchMeter() - - extra_gen_cls_kwargs = {"lm_model": lms[0], "lm_weight": cfg.generation.lm_weight} - generator = task.build_generator( - models, cfg.generation, extra_gen_cls_kwargs=extra_gen_cls_kwargs - ) - - # Handle tokenization and BPE - tokenizer = task.build_tokenizer(cfg.tokenizer) - bpe = task.build_bpe(cfg.bpe) - - def decode_fn(x): - if bpe is not None: - x = bpe.decode(x) - if tokenizer is not None: - x = tokenizer.decode(x) - return x - - scorer = scoring.build_scorer(cfg.scoring, tgt_dict) - - num_sentences = 0 - has_target = True - wps_meter = TimeMeter() - for sample in progress: - sample = utils.move_to_cuda(sample) if use_cuda else sample - if "net_input" not in sample: - continue - - prefix_tokens = None - if cfg.generation.prefix_size > 0: - prefix_tokens = sample["target"][:, : cfg.generation.prefix_size] - - constraints = None - if "constraints" in sample: - constraints = sample["constraints"] - - gen_timer.start() - hypos = task.inference_step( - generator, - models, - sample, - prefix_tokens=prefix_tokens, - constraints=constraints, - ) - num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) - gen_timer.stop(num_generated_tokens) - - for i, sample_id in enumerate(sample["id"].tolist()): - has_target = sample["target"] is not None - - # Remove padding - if "src_tokens" in sample["net_input"]: - src_tokens = utils.strip_pad( - sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() - ) - else: - src_tokens = None - - target_tokens = None - if has_target: - target_tokens = ( - utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu() - ) - - # Either retrieve the original sentences or regenerate them from tokens. - if align_dict is not None: - src_str = task.dataset(cfg.dataset.gen_subset).src.get_original_text( - sample_id - ) - target_str = task.dataset(cfg.dataset.gen_subset).tgt.get_original_text( - sample_id - ) - else: - if src_dict is not None: - src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) - else: - src_str = "" - if has_target: - target_str = tgt_dict.string( - target_tokens, - cfg.common_eval.post_process, - escape_unk=True, - extra_symbols_to_ignore=get_symbols_to_strip_from_output( - generator - ), - ) - - src_str = decode_fn(src_str) - if has_target: - target_str = decode_fn(target_str) - - if not cfg.common_eval.quiet: - if src_dict is not None: - print("S-{}\t{}".format(sample_id, src_str), file=output_file) - if has_target: - print("T-{}\t{}".format(sample_id, target_str), file=output_file) - - # Process top predictions - for j, hypo in enumerate(hypos[i][: cfg.generation.nbest]): - hypo_tokens, hypo_str, alignment = utils.post_process_prediction( - hypo_tokens=hypo["tokens"].int().cpu(), - src_str=src_str, - alignment=hypo["alignment"], - align_dict=align_dict, - tgt_dict=tgt_dict, - remove_bpe=cfg.common_eval.post_process, - extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), - ) - detok_hypo_str = decode_fn(hypo_str) - if not cfg.common_eval.quiet: - score = hypo["score"] / math.log(2) # convert to base 2 - # original hypothesis (after tokenization and BPE) - print( - "H-{}\t{}\t{}".format(sample_id, score, hypo_str), - file=output_file, - ) - # detokenized hypothesis - print( - "D-{}\t{}\t{}".format(sample_id, score, detok_hypo_str), - file=output_file, - ) - print( - "P-{}\t{}".format( - sample_id, - " ".join( - map( - lambda x: "{:.4f}".format(x), - # convert from base e to base 2 - hypo["positional_scores"] - .div_(math.log(2)) - .tolist(), - ) - ), - ), - file=output_file, - ) - - if cfg.generation.print_alignment == "hard": - print( - "A-{}\t{}".format( - sample_id, - " ".join( - [ - "{}-{}".format(src_idx, tgt_idx) - for src_idx, tgt_idx in alignment - ] - ), - ), - file=output_file, - ) - if cfg.generation.print_alignment == "soft": - print( - "A-{}\t{}".format( - sample_id, - " ".join( - [",".join(src_probs) for src_probs in alignment] - ), - ), - file=output_file, - ) - - if cfg.generation.print_step: - print( - "I-{}\t{}".format(sample_id, hypo["steps"]), - file=output_file, - ) - - if cfg.generation.retain_iter_history: - for step, h in enumerate(hypo["history"]): - _, h_str, _ = utils.post_process_prediction( - hypo_tokens=h["tokens"].int().cpu(), - src_str=src_str, - alignment=None, - align_dict=None, - tgt_dict=tgt_dict, - remove_bpe=None, - ) - print( - "E-{}_{}\t{}".format(sample_id, step, h_str), - file=output_file, - ) - - # Score only the top hypothesis - if has_target and j == 0: - if ( - align_dict is not None - or cfg.common_eval.post_process is not None - ): - # Convert back to tokens for evaluation with unk replacement and/or without BPE - target_tokens = tgt_dict.encode_line( - target_str, add_if_not_exist=True - ) - hypo_tokens = tgt_dict.encode_line( - detok_hypo_str, add_if_not_exist=True - ) - if hasattr(scorer, "add_string"): - scorer.add_string(target_str, detok_hypo_str) - else: - scorer.add(target_tokens, hypo_tokens) - - wps_meter.update(num_generated_tokens) - progress.log({"wps": round(wps_meter.avg)}) - num_sentences += ( - sample["nsentences"] if "nsentences" in sample else sample["id"].numel() - ) - - logger.info("NOTE: hypothesis and token scores are output in base 2") - logger.info( - "Translated {:,} sentences ({:,} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)".format( - num_sentences, - gen_timer.n, - gen_timer.sum, - num_sentences / gen_timer.sum, - 1.0 / gen_timer.avg, - ) - ) - if has_target: - if cfg.bpe and not cfg.generation.sacrebleu: - if cfg.common_eval.post_process: - logger.warning( - "BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization" - ) - else: - logger.warning( - "If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words. Use --sacrebleu for standard 13a BLEU tokenization" - ) - # use print to be consistent with other main outputs: S-, H-, T-, D- and so on - print( - "Generate {} with beam={}: {}".format( - cfg.dataset.gen_subset, cfg.generation.beam, scorer.result_string() - ), - file=output_file, - ) - - return scorer - - -def cli_main(): - parser = options.get_generation_parser() - # TODO: replace this workaround with refactoring of `AudioPretraining` - parser.add_argument( - "--arch", - "-a", - metavar="ARCH", - default="wav2vec2", - help="Model architecture. For constructing tasks that rely on " - "model args (e.g. `AudioPretraining`)", - ) - args = options.parse_args_and_arch(parser) - main(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/hydra_train.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/hydra_train.py deleted file mode 100644 index e94fb37a9350b558af20a1c875c4d1317f8fdc72..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/hydra_train.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os - -import hydra -import torch -from fairseq import distributed_utils, metrics -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.initialize import add_defaults, hydra_init -from fairseq.dataclass.utils import omegaconf_no_object_check -from fairseq.utils import reset_logging -from fairseq_cli.train import main as pre_main -from hydra.core.hydra_config import HydraConfig -from omegaconf import OmegaConf, open_dict - -logger = logging.getLogger("fairseq_cli.hydra_train") - - -@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config") -def hydra_main(cfg: FairseqConfig) -> float: - _hydra_main(cfg) - - -def _hydra_main(cfg: FairseqConfig, **kwargs) -> float: - add_defaults(cfg) - - if cfg.common.reset_logging: - reset_logging() # Hydra hijacks logging, fix that - else: - # check if directly called or called through hydra_main - if HydraConfig.initialized(): - with open_dict(cfg): - # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) - cfg.job_logging_cfg = OmegaConf.to_container( - HydraConfig.get().job_logging, resolve=True - ) - - with omegaconf_no_object_check(): - cfg = OmegaConf.create( - OmegaConf.to_container(cfg, resolve=True, enum_to_str=True) - ) - OmegaConf.set_struct(cfg, True) - - try: - if cfg.common.profile: - with torch.cuda.profiler.profile(): - with torch.autograd.profiler.emit_nvtx(): - distributed_utils.call_main(cfg, pre_main, **kwargs) - else: - distributed_utils.call_main(cfg, pre_main, **kwargs) - except BaseException as e: - if not cfg.common.suppress_crashes: - raise - else: - logger.error("Crashed! " + str(e)) - - # get best val and return - useful for sweepers - try: - best_val = metrics.get_smoothed_value( - "valid", cfg.checkpoint.best_checkpoint_metric - ) - except: - best_val = None - - if best_val is None: - best_val = float("inf") - - return best_val - - -def cli_main(): - try: - from hydra._internal.utils import get_args - - cfg_name = get_args().config_name or "config" - except: - logger.warning("Failed to get config name from hydra args") - cfg_name = "config" - - hydra_init(cfg_name) - hydra_main() - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/interactive.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/interactive.py deleted file mode 100644 index f1cd6a13f1705443254c2030b2a37685ed099e67..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/interactive.py +++ /dev/null @@ -1,316 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Translate raw text with a trained model. Batches data on-the-fly. -""" - -import ast -import fileinput -import logging -import math -import os -import sys -import time -from argparse import Namespace -from collections import namedtuple - -import numpy as np -import torch -from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.token_generation_constraints import pack_constraints, unpack_constraints -from fairseq_cli.generate import get_symbols_to_strip_from_output - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.interactive") - - -Batch = namedtuple("Batch", "ids src_tokens src_lengths constraints") -Translation = namedtuple("Translation", "src_str hypos pos_scores alignments") - - -def buffered_read(input, buffer_size): - buffer = [] - with fileinput.input(files=[input], openhook=fileinput.hook_encoded("utf-8")) as h: - for src_str in h: - buffer.append(src_str.strip()) - if len(buffer) >= buffer_size: - yield buffer - buffer = [] - - if len(buffer) > 0: - yield buffer - - -def make_batches(lines, cfg, task, max_positions, encode_fn): - def encode_fn_target(x): - return encode_fn(x) - - if cfg.generation.constraints: - # Strip (tab-delimited) contraints, if present, from input lines, - # store them in batch_constraints - batch_constraints = [list() for _ in lines] - for i, line in enumerate(lines): - if "\t" in line: - lines[i], *batch_constraints[i] = line.split("\t") - - # Convert each List[str] to List[Tensor] - for i, constraint_list in enumerate(batch_constraints): - batch_constraints[i] = [ - task.target_dictionary.encode_line( - encode_fn_target(constraint), - append_eos=False, - add_if_not_exist=False, - ) - for constraint in constraint_list - ] - - if cfg.generation.constraints: - constraints_tensor = pack_constraints(batch_constraints) - else: - constraints_tensor = None - - tokens, lengths = task.get_interactive_tokens_and_lengths(lines, encode_fn) - - itr = task.get_batch_iterator( - dataset=task.build_dataset_for_inference( - tokens, lengths, constraints=constraints_tensor - ), - max_tokens=cfg.dataset.max_tokens, - max_sentences=cfg.dataset.batch_size, - max_positions=max_positions, - ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, - ).next_epoch_itr(shuffle=False) - for batch in itr: - ids = batch["id"] - src_tokens = batch["net_input"]["src_tokens"] - src_lengths = batch["net_input"]["src_lengths"] - constraints = batch.get("constraints", None) - - yield Batch( - ids=ids, - src_tokens=src_tokens, - src_lengths=src_lengths, - constraints=constraints, - ) - - -def main(cfg: FairseqConfig): - if isinstance(cfg, Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - start_time = time.time() - total_translate_time = 0 - - utils.import_user_module(cfg.common) - - if cfg.interactive.buffer_size < 1: - cfg.interactive.buffer_size = 1 - if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None: - cfg.dataset.batch_size = 1 - - assert ( - not cfg.generation.sampling or cfg.generation.nbest == cfg.generation.beam - ), "--sampling requires --nbest to be equal to --beam" - assert ( - not cfg.dataset.batch_size - or cfg.dataset.batch_size <= cfg.interactive.buffer_size - ), "--batch-size cannot be larger than --buffer-size" - - logger.info(cfg) - - # Fix seed for stochastic decoding - if cfg.common.seed is not None and not cfg.generation.no_seed_provided: - np.random.seed(cfg.common.seed) - utils.set_torch_seed(cfg.common.seed) - - use_cuda = torch.cuda.is_available() and not cfg.common.cpu - - # Setup task, e.g., translation - task = tasks.setup_task(cfg.task) - - # Load ensemble - overrides = ast.literal_eval(cfg.common_eval.model_overrides) - logger.info("loading model(s) from {}".format(cfg.common_eval.path)) - models, _model_args = checkpoint_utils.load_model_ensemble( - utils.split_paths(cfg.common_eval.path), - arg_overrides=overrides, - task=task, - suffix=cfg.checkpoint.checkpoint_suffix, - strict=(cfg.checkpoint.checkpoint_shard_count == 1), - num_shards=cfg.checkpoint.checkpoint_shard_count, - ) - - # Set dictionaries - src_dict = task.source_dictionary - tgt_dict = task.target_dictionary - - # Optimize ensemble for generation - for model in models: - if model is None: - continue - if cfg.common.fp16: - model.half() - if use_cuda and not cfg.distributed_training.pipeline_model_parallel: - model.cuda() - model.prepare_for_inference_(cfg) - - # Initialize generator - generator = task.build_generator(models, cfg.generation) - - # Handle tokenization and BPE - tokenizer = task.build_tokenizer(cfg.tokenizer) - bpe = task.build_bpe(cfg.bpe) - - def encode_fn(x): - if tokenizer is not None: - x = tokenizer.encode(x) - if bpe is not None: - x = bpe.encode(x) - return x - - def decode_fn(x): - if bpe is not None: - x = bpe.decode(x) - if tokenizer is not None: - x = tokenizer.decode(x) - return x - - # Load alignment dictionary for unknown word replacement - # (None if no unknown word replacement, empty if no path to align dictionary) - align_dict = utils.load_align_dict(cfg.generation.replace_unk) - - max_positions = utils.resolve_max_positions( - task.max_positions(), *[model.max_positions() for model in models] - ) - - if cfg.generation.constraints: - logger.warning( - "NOTE: Constrained decoding currently assumes a shared subword vocabulary." - ) - - if cfg.interactive.buffer_size > 1: - logger.info("Sentence buffer size: %s", cfg.interactive.buffer_size) - logger.info("NOTE: hypothesis and token scores are output in base 2") - logger.info("Type the input sentence and press return:") - start_id = 0 - for inputs in buffered_read(cfg.interactive.input, cfg.interactive.buffer_size): - results = [] - for batch in make_batches(inputs, cfg, task, max_positions, encode_fn): - bsz = batch.src_tokens.size(0) - src_tokens = batch.src_tokens - src_lengths = batch.src_lengths - constraints = batch.constraints - if use_cuda: - src_tokens = src_tokens.cuda() - src_lengths = src_lengths.cuda() - if constraints is not None: - constraints = constraints.cuda() - - sample = { - "net_input": { - "src_tokens": src_tokens, - "src_lengths": src_lengths, - }, - } - translate_start_time = time.time() - translations = task.inference_step( - generator, models, sample, constraints=constraints - ) - translate_time = time.time() - translate_start_time - total_translate_time += translate_time - list_constraints = [[] for _ in range(bsz)] - if cfg.generation.constraints: - list_constraints = [unpack_constraints(c) for c in constraints] - for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)): - src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad()) - constraints = list_constraints[i] - results.append( - ( - start_id + id, - src_tokens_i, - hypos, - { - "constraints": constraints, - "time": translate_time / len(translations), - }, - ) - ) - - # sort output to match input order - for id_, src_tokens, hypos, info in sorted(results, key=lambda x: x[0]): - src_str = "" - if src_dict is not None: - src_str = src_dict.string(src_tokens, cfg.common_eval.post_process) - print("S-{}\t{}".format(id_, src_str)) - print("W-{}\t{:.3f}\tseconds".format(id_, info["time"])) - for constraint in info["constraints"]: - print( - "C-{}\t{}".format( - id_, - tgt_dict.string(constraint, cfg.common_eval.post_process), - ) - ) - - # Process top predictions - for hypo in hypos[: min(len(hypos), cfg.generation.nbest)]: - hypo_tokens, hypo_str, alignment = utils.post_process_prediction( - hypo_tokens=hypo["tokens"].int().cpu(), - src_str=src_str, - alignment=hypo["alignment"], - align_dict=align_dict, - tgt_dict=tgt_dict, - remove_bpe=cfg.common_eval.post_process, - extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator), - ) - detok_hypo_str = decode_fn(hypo_str) - score = hypo["score"] / math.log(2) # convert to base 2 - # original hypothesis (after tokenization and BPE) - print("H-{}\t{}\t{}".format(id_, score, hypo_str)) - # detokenized hypothesis - print("D-{}\t{}\t{}".format(id_, score, detok_hypo_str)) - print( - "P-{}\t{}".format( - id_, - " ".join( - map( - lambda x: "{:.4f}".format(x), - # convert from base e to base 2 - hypo["positional_scores"].div_(math.log(2)).tolist(), - ) - ), - ) - ) - if cfg.generation.print_alignment: - alignment_str = " ".join( - ["{}-{}".format(src, tgt) for src, tgt in alignment] - ) - print("A-{}\t{}".format(id_, alignment_str)) - - # update running id_ counter - start_id += len(inputs) - - logger.info( - "Total time: {:.3f} seconds; translation time: {:.3f}".format( - time.time() - start_time, total_translate_time - ) - ) - - -def cli_main(): - parser = options.get_interactive_generation_parser() - args = options.parse_args_and_arch(parser) - distributed_utils.call_main(convert_namespace_to_omegaconf(args), main) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/preprocess.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/preprocess.py deleted file mode 100644 index 2ba9e09338abd3b04b5d2497ce3f97613802bd6f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/preprocess.py +++ /dev/null @@ -1,393 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Data pre-processing: build vocabularies and binarize training data. -""" - -import logging -import os -import shutil -import sys -import typing as tp -from argparse import Namespace -from itertools import zip_longest - -from fairseq import options, tasks, utils -from fairseq.binarizer import ( - AlignmentDatasetBinarizer, - FileBinarizer, - VocabularyDatasetBinarizer, -) -from fairseq.data import Dictionary - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.preprocess") - -##################################################################### -# file name tools -##################################################################### - - -def _train_path(lang, trainpref): - return "{}{}".format(trainpref, ("." + lang) if lang else "") - - -def _file_name(prefix, lang): - fname = prefix - if lang is not None: - fname += ".{lang}".format(lang=lang) - return fname - - -def _dest_path(prefix, lang, destdir): - return os.path.join(destdir, _file_name(prefix, lang)) - - -def _dict_path(lang, destdir): - return _dest_path("dict", lang, destdir) + ".txt" - - -def dataset_dest_prefix(args, output_prefix, lang): - base = os.path.join(args.destdir, output_prefix) - if lang is not None: - lang_part = f".{args.source_lang}-{args.target_lang}.{lang}" - elif args.only_source: - lang_part = "" - else: - lang_part = f".{args.source_lang}-{args.target_lang}" - - return "{}{}".format(base, lang_part) - - -def dataset_dest_file(args, output_prefix, lang, extension): - return "{}.{}".format(dataset_dest_prefix(args, output_prefix, lang), extension) - - -##################################################################### -# dictionary tools -##################################################################### - - -def _build_dictionary( - filenames, - task, - args, - src=False, - tgt=False, -): - assert src ^ tgt - return task.build_dictionary( - filenames, - workers=args.workers, - threshold=args.thresholdsrc if src else args.thresholdtgt, - nwords=args.nwordssrc if src else args.nwordstgt, - padding_factor=args.padding_factor, - ) - - -##################################################################### -# bin file creation logic -##################################################################### - - -def _make_binary_dataset( - vocab: Dictionary, - input_prefix: str, - output_prefix: str, - lang: tp.Optional[str], - num_workers: int, - args: Namespace, -): - logger.info("[{}] Dictionary: {} types".format(lang, len(vocab))) - - binarizer = VocabularyDatasetBinarizer( - vocab, - append_eos=True, - ) - - input_file = "{}{}".format(input_prefix, ("." + lang) if lang is not None else "") - full_output_prefix = dataset_dest_prefix(args, output_prefix, lang) - - final_summary = FileBinarizer.multiprocess_dataset( - input_file, - args.dataset_impl, - binarizer, - full_output_prefix, - vocab_size=len(vocab), - num_workers=num_workers, - ) - - logger.info(f"[{lang}] {input_file}: {final_summary} (by {vocab.unk_word})") - - -def _make_binary_alignment_dataset( - input_prefix: str, output_prefix: str, num_workers: int, args: Namespace -): - - binarizer = AlignmentDatasetBinarizer(utils.parse_alignment) - - input_file = input_prefix - full_output_prefix = dataset_dest_prefix(args, output_prefix, lang=None) - - final_summary = FileBinarizer.multiprocess_dataset( - input_file, - args.dataset_impl, - binarizer, - full_output_prefix, - vocab_size=None, - num_workers=num_workers, - ) - - logger.info( - "[alignments] {}: parsed {} alignments".format( - input_file, final_summary.num_seq - ) - ) - - -##################################################################### -# routing logic -##################################################################### - - -def _make_dataset( - vocab: Dictionary, - input_prefix: str, - output_prefix: str, - lang: tp.Optional[str], - args: Namespace, - num_workers: int, -): - if args.dataset_impl == "raw": - # Copy original text file to destination folder - output_text_file = _dest_path( - output_prefix + ".{}-{}".format(args.source_lang, args.target_lang), - lang, - args.destdir, - ) - shutil.copyfile(_file_name(input_prefix, lang), output_text_file) - else: - _make_binary_dataset( - vocab, input_prefix, output_prefix, lang, num_workers, args - ) - - -def _make_all(lang, vocab, args): - if args.trainpref: - _make_dataset( - vocab, args.trainpref, "train", lang, args=args, num_workers=args.workers - ) - if args.validpref: - for k, validpref in enumerate(args.validpref.split(",")): - outprefix = "valid{}".format(k) if k > 0 else "valid" - _make_dataset( - vocab, validpref, outprefix, lang, args=args, num_workers=args.workers - ) - if args.testpref: - for k, testpref in enumerate(args.testpref.split(",")): - outprefix = "test{}".format(k) if k > 0 else "test" - _make_dataset( - vocab, testpref, outprefix, lang, args=args, num_workers=args.workers - ) - - -def _make_all_alignments(args): - if args.trainpref and os.path.exists(args.trainpref + "." + args.align_suffix): - _make_binary_alignment_dataset( - args.trainpref + "." + args.align_suffix, - "train.align", - num_workers=args.workers, - args=args, - ) - if args.validpref and os.path.exists(args.validpref + "." + args.align_suffix): - _make_binary_alignment_dataset( - args.validpref + "." + args.align_suffix, - "valid.align", - num_workers=args.workers, - args=args, - ) - if args.testpref and os.path.exists(args.testpref + "." + args.align_suffix): - _make_binary_alignment_dataset( - args.testpref + "." + args.align_suffix, - "test.align", - num_workers=args.workers, - args=args, - ) - - -##################################################################### -# align -##################################################################### - - -def _align_files(args, src_dict, tgt_dict): - assert args.trainpref, "--trainpref must be set if --alignfile is specified" - src_file_name = _train_path(args.source_lang, args.trainpref) - tgt_file_name = _train_path(args.target_lang, args.trainpref) - freq_map = {} - with open(args.alignfile, "r", encoding="utf-8") as align_file: - with open(src_file_name, "r", encoding="utf-8") as src_file: - with open(tgt_file_name, "r", encoding="utf-8") as tgt_file: - for a, s, t in zip_longest(align_file, src_file, tgt_file): - si = src_dict.encode_line(s, add_if_not_exist=False) - ti = tgt_dict.encode_line(t, add_if_not_exist=False) - ai = list(map(lambda x: tuple(x.split("-")), a.split())) - for sai, tai in ai: - srcidx = si[int(sai)] - tgtidx = ti[int(tai)] - if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk(): - assert srcidx != src_dict.pad() - assert srcidx != src_dict.eos() - assert tgtidx != tgt_dict.pad() - assert tgtidx != tgt_dict.eos() - if srcidx not in freq_map: - freq_map[srcidx] = {} - if tgtidx not in freq_map[srcidx]: - freq_map[srcidx][tgtidx] = 1 - else: - freq_map[srcidx][tgtidx] += 1 - align_dict = {} - for srcidx in freq_map.keys(): - align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get) - with open( - os.path.join( - args.destdir, - "alignment.{}-{}.txt".format(args.source_lang, args.target_lang), - ), - "w", - encoding="utf-8", - ) as f: - for k, v in align_dict.items(): - print("{} {}".format(src_dict[k], tgt_dict[v]), file=f) - - -##################################################################### -# MAIN -##################################################################### - - -def main(args): - # setup some basic things - utils.import_user_module(args) - - os.makedirs(args.destdir, exist_ok=True) - - logger.addHandler( - logging.FileHandler( - filename=os.path.join(args.destdir, "preprocess.log"), - ) - ) - logger.info(args) - - assert ( - args.dataset_impl != "huffman" - ), "preprocessing.py doesn't support Huffman yet, use HuffmanCodeBuilder directly." - - # build dictionaries - - target = not args.only_source - - if not args.srcdict and os.path.exists(_dict_path(args.source_lang, args.destdir)): - raise FileExistsError(_dict_path(args.source_lang, args.destdir)) - - if ( - target - and not args.tgtdict - and os.path.exists(_dict_path(args.target_lang, args.destdir)) - ): - raise FileExistsError(_dict_path(args.target_lang, args.destdir)) - - task = tasks.get_task(args.task) - - if args.joined_dictionary: - assert ( - not args.srcdict or not args.tgtdict - ), "cannot use both --srcdict and --tgtdict with --joined-dictionary" - - if args.srcdict: - src_dict = task.load_dictionary(args.srcdict) - elif args.tgtdict: - src_dict = task.load_dictionary(args.tgtdict) - else: - assert ( - args.trainpref - ), "--trainpref must be set if --srcdict is not specified" - src_dict = _build_dictionary( - { - _train_path(lang, args.trainpref) - for lang in [args.source_lang, args.target_lang] - }, - task=task, - args=args, - src=True, - ) - tgt_dict = src_dict - else: - if args.srcdict: - src_dict = task.load_dictionary(args.srcdict) - else: - assert ( - args.trainpref - ), "--trainpref must be set if --srcdict is not specified" - src_dict = _build_dictionary( - [_train_path(args.source_lang, args.trainpref)], - task=task, - args=args, - src=True, - ) - - if target: - if args.tgtdict: - tgt_dict = task.load_dictionary(args.tgtdict) - else: - assert ( - args.trainpref - ), "--trainpref must be set if --tgtdict is not specified" - tgt_dict = _build_dictionary( - [_train_path(args.target_lang, args.trainpref)], - task=task, - args=args, - tgt=True, - ) - else: - tgt_dict = None - - # save dictionaries - - src_dict.save(_dict_path(args.source_lang, args.destdir)) - if target and tgt_dict is not None: - tgt_dict.save(_dict_path(args.target_lang, args.destdir)) - - if args.dict_only: - return - - _make_all(args.source_lang, src_dict, args) - if target: - _make_all(args.target_lang, tgt_dict, args) - - # align the datasets if needed - if args.align_suffix: - _make_all_alignments(args) - - logger.info("Wrote preprocessed data to {}".format(args.destdir)) - - if args.alignfile: - _align_files(args, src_dict=src_dict, tgt_dict=tgt_dict) - - -def cli_main(): - parser = options.get_preprocessing_parser() - args = parser.parse_args() - main(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/score.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/score.py deleted file mode 100644 index 0b207be959d55f6a56d8c5eb7db3dbe0c1ac977e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/score.py +++ /dev/null @@ -1,102 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -BLEU scoring of generated translations against reference translations. -""" - -import argparse -import os -import sys - -from fairseq.data import dictionary -from fairseq.scoring import bleu - - -def get_parser(): - parser = argparse.ArgumentParser( - description="Command-line script for BLEU scoring." - ) - # fmt: off - parser.add_argument('-s', '--sys', default='-', help='system output') - parser.add_argument('-r', '--ref', required=True, help='references') - parser.add_argument('-o', '--order', default=4, metavar='N', - type=int, help='consider ngrams up to this order') - parser.add_argument('--ignore-case', action='store_true', - help='case-insensitive scoring') - parser.add_argument('--sacrebleu', action='store_true', - help='score with sacrebleu') - parser.add_argument('--sentence-bleu', action='store_true', - help='report sentence-level BLEUs (i.e., with +1 smoothing)') - # fmt: on - return parser - - -def cli_main(): - parser = get_parser() - args = parser.parse_args() - print(args) - - assert args.sys == "-" or os.path.exists( - args.sys - ), "System output file {} does not exist".format(args.sys) - assert os.path.exists(args.ref), "Reference file {} does not exist".format(args.ref) - - dict = dictionary.Dictionary() - - def readlines(fd): - for line in fd.readlines(): - if args.ignore_case: - yield line.lower() - else: - yield line - - if args.sacrebleu: - import sacrebleu - - def score(fdsys): - with open(args.ref) as fdref: - print(sacrebleu.corpus_bleu(fdsys, [fdref]).format()) - - elif args.sentence_bleu: - - def score(fdsys): - with open(args.ref) as fdref: - scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk()) - for i, (sys_tok, ref_tok) in enumerate( - zip(readlines(fdsys), readlines(fdref)) - ): - scorer.reset(one_init=True) - sys_tok = dict.encode_line(sys_tok) - ref_tok = dict.encode_line(ref_tok) - scorer.add(ref_tok, sys_tok) - print(i, scorer.result_string(args.order)) - - else: - - def score(fdsys): - with open(args.ref) as fdref: - scorer = bleu.Scorer( - bleu.BleuConfig( - pad=dict.pad(), - eos=dict.eos(), - unk=dict.unk(), - ) - ) - for sys_tok, ref_tok in zip(readlines(fdsys), readlines(fdref)): - sys_tok = dict.encode_line(sys_tok) - ref_tok = dict.encode_line(ref_tok) - scorer.add(ref_tok, sys_tok) - print(scorer.result_string(args.order)) - - if args.sys == "-": - score(sys.stdin) - else: - with open(args.sys, "r") as f: - score(f) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/train.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/train.py deleted file mode 100644 index e4bf7ca974cd732fe3fa32fa9a5fa462fd6c75e3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/train.py +++ /dev/null @@ -1,563 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Train a new model on one or across multiple GPUs. -""" - -import argparse -import logging -import math -import os -import sys -from typing import Any, Callable, Dict, List, Optional, Tuple - -# We need to setup root logger before importing any fairseq libraries. -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.train") - -import numpy as np -import torch -from fairseq import checkpoint_utils, options, quantization_utils, tasks, utils -from fairseq.data import data_utils, iterators -from fairseq.data.plasma_utils import PlasmaStore -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.initialize import add_defaults -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap -from fairseq.distributed import utils as distributed_utils -from fairseq.file_io import PathManager -from fairseq.logging import meters, metrics, progress_bar -from fairseq.model_parallel.megatron_trainer import MegatronTrainer -from fairseq.trainer import Trainer -from omegaconf import DictConfig, OmegaConf - - -def main(cfg: FairseqConfig) -> None: - if isinstance(cfg, argparse.Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - utils.import_user_module(cfg.common) - add_defaults(cfg) - - if ( - distributed_utils.is_master(cfg.distributed_training) - and "job_logging_cfg" in cfg - ): - # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) - logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg)) - - assert ( - cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None - ), "Must specify batch size either with --max-tokens or --batch-size" - metrics.reset() - - if cfg.common.log_file is not None: - handler = logging.FileHandler(filename=cfg.common.log_file) - logger.addHandler(handler) - - np.random.seed(cfg.common.seed) - utils.set_torch_seed(cfg.common.seed) - - if distributed_utils.is_master(cfg.distributed_training): - checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir) - - # Print args - logger.info(cfg) - - if cfg.checkpoint.write_checkpoints_asynchronously: - try: - import iopath # noqa: F401 - except ImportError: - logging.exception( - "Asynchronous checkpoint writing is specified but iopath is " - "not installed: `pip install iopath`" - ) - return - - # Setup task, e.g., translation, language modeling, etc. - task = tasks.setup_task(cfg.task) - - assert cfg.criterion, "Please specify criterion to train a model" - - # Build model and criterion - if cfg.distributed_training.ddp_backend == "fully_sharded": - with fsdp_enable_wrap(cfg.distributed_training): - model = fsdp_wrap(task.build_model(cfg.model)) - else: - model = task.build_model(cfg.model) - criterion = task.build_criterion(cfg.criterion) - logger.info(model) - logger.info("task: {}".format(task.__class__.__name__)) - logger.info("model: {}".format(model.__class__.__name__)) - logger.info("criterion: {}".format(criterion.__class__.__name__)) - logger.info( - "num. shared model params: {:,} (num. trained: {:,})".format( - sum( - p.numel() for p in model.parameters() if not getattr(p, "expert", False) - ), - sum( - p.numel() - for p in model.parameters() - if not getattr(p, "expert", False) and p.requires_grad - ), - ) - ) - - logger.info( - "num. expert model params: {} (num. trained: {})".format( - sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)), - sum( - p.numel() - for p in model.parameters() - if getattr(p, "expert", False) and p.requires_grad - ), - ) - ) - - # Load valid dataset (we load training data below, based on the latest checkpoint) - # We load the valid dataset AFTER building the model - data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg) - if cfg.dataset.combine_valid_subsets: - task.load_dataset("valid", combine=True, epoch=1) - else: - for valid_sub_split in cfg.dataset.valid_subset.split(","): - task.load_dataset(valid_sub_split, combine=False, epoch=1) - - # (optionally) Configure quantization - if cfg.common.quantization_config_path is not None: - quantizer = quantization_utils.Quantizer( - config_path=cfg.common.quantization_config_path, - max_epoch=cfg.optimization.max_epoch, - max_update=cfg.optimization.max_update, - ) - else: - quantizer = None - - # Build trainer - if cfg.common.model_parallel_size == 1: - trainer = Trainer(cfg, task, model, criterion, quantizer) - else: - trainer = MegatronTrainer(cfg, task, model, criterion) - logger.info( - "training on {} devices (GPUs/TPUs)".format( - cfg.distributed_training.distributed_world_size - ) - ) - logger.info( - "max tokens per device = {} and max sentences per device = {}".format( - cfg.dataset.max_tokens, - cfg.dataset.batch_size, - ) - ) - - # Load the latest checkpoint if one is available and restore the - # corresponding train iterator - extra_state, epoch_itr = checkpoint_utils.load_checkpoint( - cfg.checkpoint, - trainer, - # don't cache epoch iterators for sharded datasets - disable_iterator_cache=task.has_sharded_data("train"), - ) - if cfg.common.tpu: - import torch_xla.core.xla_model as xm - - xm.rendezvous("load_checkpoint") # wait for all workers - - max_epoch = cfg.optimization.max_epoch or math.inf - lr = trainer.get_lr() - - train_meter = meters.StopwatchMeter() - train_meter.start() - while epoch_itr.next_epoch_idx <= max_epoch: - if lr <= cfg.optimization.stop_min_lr: - logger.info( - f"stopping training because current learning rate ({lr}) is smaller " - "than or equal to minimum learning rate " - f"(--stop-min-lr={cfg.optimization.stop_min_lr})" - ) - break - - # train for one epoch - valid_losses, should_stop = train(cfg, trainer, task, epoch_itr) - if should_stop: - break - - # only use first validation loss to update the learning rate - lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0]) - - epoch_itr = trainer.get_train_iterator( - epoch_itr.next_epoch_idx, - # sharded data: get train iterator for next epoch - load_dataset=task.has_sharded_data("train"), - # don't cache epoch iterators for sharded datasets - disable_iterator_cache=task.has_sharded_data("train"), - ) - train_meter.stop() - logger.info("done training in {:.1f} seconds".format(train_meter.sum)) - - # ioPath implementation to wait for all asynchronous file writes to complete. - if cfg.checkpoint.write_checkpoints_asynchronously: - logger.info( - "ioPath PathManager waiting for all asynchronous checkpoint " - "writes to finish." - ) - PathManager.async_close() - logger.info("ioPath PathManager finished waiting.") - - -def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool: - # skip check if no validation was done in the current epoch - if valid_loss is None: - return False - if cfg.checkpoint.patience <= 0: - return False - - def is_better(a, b): - return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b - - prev_best = getattr(should_stop_early, "best", None) - if prev_best is None or is_better(valid_loss, prev_best): - should_stop_early.best = valid_loss - should_stop_early.num_runs = 0 - return False - else: - should_stop_early.num_runs += 1 - if should_stop_early.num_runs >= cfg.checkpoint.patience: - logger.info( - "early stop since valid performance hasn't improved for last {} runs".format( - cfg.checkpoint.patience - ) - ) - return True - else: - return False - - -@metrics.aggregate("train") -def train( - cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr -) -> Tuple[List[Optional[float]], bool]: - """Train the model for one epoch and return validation losses.""" - # Initialize data iterator - itr = epoch_itr.next_epoch_itr( - fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus, - shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum), - ) - update_freq = ( - cfg.optimization.update_freq[epoch_itr.epoch - 1] - if epoch_itr.epoch <= len(cfg.optimization.update_freq) - else cfg.optimization.update_freq[-1] - ) - itr = iterators.GroupedIterator( - itr, - update_freq, - skip_remainder_batch=cfg.optimization.skip_remainder_batch, - ) - if cfg.common.tpu: - itr = utils.tpu_data_loader(itr) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_file=cfg.common.log_file, - log_interval=cfg.common.log_interval, - epoch=epoch_itr.epoch, - aim_repo=( - cfg.common.aim_repo - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - aim_run_hash=( - cfg.common.aim_run_hash - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - aim_param_checkpoint_dir=cfg.checkpoint.save_dir, - tensorboard_logdir=( - cfg.common.tensorboard_logdir - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - wandb_project=( - cfg.common.wandb_project - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - wandb_run_name=os.environ.get( - "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) - ), - azureml_logging=( - cfg.common.azureml_logging - if distributed_utils.is_master(cfg.distributed_training) - else False - ), - ) - progress.update_config(_flatten_config(cfg)) - - trainer.begin_epoch(epoch_itr.epoch) - - valid_subsets = cfg.dataset.valid_subset.split(",") - should_stop = False - num_updates = trainer.get_num_updates() - logger.info("Start iterating over samples") - for i, samples in enumerate(progress): - with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( - "train_step-%d" % i - ): - log_output = trainer.train_step(samples) - - if log_output is not None: # not OOM, overflow, ... - # log mid-epoch stats - num_updates = trainer.get_num_updates() - if num_updates % cfg.common.log_interval == 0: - stats = get_training_stats(metrics.get_smoothed_values("train_inner")) - progress.log(stats, tag="train_inner", step=num_updates) - - # reset mid-epoch stats after each log interval - # the end-of-epoch stats will still be preserved - metrics.reset_meters("train_inner") - - end_of_epoch = not itr.has_next() - valid_losses, should_stop = validate_and_save( - cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch - ) - - if should_stop: - break - - # log end-of-epoch stats - logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) - stats = get_training_stats(metrics.get_smoothed_values("train")) - progress.print(stats, tag="train", step=num_updates) - - # reset epoch-level meters - metrics.reset_meters("train") - return valid_losses, should_stop - - -def _flatten_config(cfg: DictConfig): - config = OmegaConf.to_container(cfg) - # remove any legacy Namespaces and replace with a single "args" - namespace = None - for k, v in list(config.items()): - if isinstance(v, argparse.Namespace): - namespace = v - del config[k] - if namespace is not None: - config["args"] = vars(namespace) - return config - - -def validate_and_save( - cfg: DictConfig, - trainer: Trainer, - task: tasks.FairseqTask, - epoch_itr, - valid_subsets: List[str], - end_of_epoch: bool, -) -> Tuple[List[Optional[float]], bool]: - num_updates = trainer.get_num_updates() - max_update = cfg.optimization.max_update or math.inf - - # Stopping conditions (and an additional one based on validation loss later - # on) - should_stop = False - if num_updates >= max_update: - should_stop = True - logger.info( - f"Stopping training due to " - f"num_updates: {num_updates} >= max_update: {max_update}" - ) - - training_time_hours = trainer.cumulative_training_time() / (60 * 60) - if ( - cfg.optimization.stop_time_hours > 0 - and training_time_hours > cfg.optimization.stop_time_hours - ): - should_stop = True - logger.info( - f"Stopping training due to " - f"cumulative_training_time: {training_time_hours} > " - f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)" - ) - - do_save = ( - (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0) - or should_stop - or ( - cfg.checkpoint.save_interval_updates > 0 - and num_updates > 0 - and num_updates % cfg.checkpoint.save_interval_updates == 0 - and num_updates >= cfg.dataset.validate_after_updates - ) - ) - do_validate = ( - ( - (not end_of_epoch and do_save) # validate during mid-epoch saves - or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0) - or should_stop - or ( - cfg.dataset.validate_interval_updates > 0 - and num_updates > 0 - and num_updates % cfg.dataset.validate_interval_updates == 0 - ) - ) - and not cfg.dataset.disable_validation - and num_updates >= cfg.dataset.validate_after_updates - ) - - # Validate - valid_losses = [None] - if do_validate: - valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets) - - should_stop |= should_stop_early(cfg, valid_losses[0]) - - # Save checkpoint - if do_save or should_stop: - checkpoint_utils.save_checkpoint( - cfg.checkpoint, trainer, epoch_itr, valid_losses[0] - ) - - return valid_losses, should_stop - - -def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]: - stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) - return stats - - -def validate( - cfg: DictConfig, - trainer: Trainer, - task: tasks.FairseqTask, - epoch_itr, - subsets: List[str], -) -> List[Optional[float]]: - """Evaluate the model on the validation set(s) and return the losses.""" - - if cfg.dataset.fixed_validation_seed is not None: - # set fixed seed for every validation - utils.set_torch_seed(cfg.dataset.fixed_validation_seed) - - trainer.begin_valid_epoch(epoch_itr.epoch) - valid_losses = [] - for subset_idx, subset in enumerate(subsets): - logger.info('begin validation on "{}" subset'.format(subset)) - - # Initialize data iterator - itr = trainer.get_valid_iterator(subset).next_epoch_itr( - shuffle=False, set_dataset_epoch=False # use a fixed valid set - ) - if cfg.common.tpu: - itr = utils.tpu_data_loader(itr) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_interval=cfg.common.log_interval, - epoch=epoch_itr.epoch, - prefix=f"valid on '{subset}' subset", - aim_repo=( - cfg.common.aim_repo - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - aim_run_hash=( - cfg.common.aim_run_hash - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - aim_param_checkpoint_dir=cfg.checkpoint.save_dir, - tensorboard_logdir=( - cfg.common.tensorboard_logdir - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - wandb_project=( - cfg.common.wandb_project - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - wandb_run_name=os.environ.get( - "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) - ), - ) - - # create a new root metrics aggregator so validation metrics - # don't pollute other aggregators (e.g., train meters) - with metrics.aggregate(new_root=True) as agg: - for i, sample in enumerate(progress): - if ( - cfg.dataset.max_valid_steps is not None - and i > cfg.dataset.max_valid_steps - ): - break - trainer.valid_step(sample) - - # log validation stats - # only tracking the best metric on the 1st validation subset - tracking_best = subset_idx == 0 - stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values(), tracking_best) - - if hasattr(task, "post_validate"): - task.post_validate(trainer.get_model(), stats, agg) - - progress.print(stats, tag=subset, step=trainer.get_num_updates()) - - valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric]) - return valid_losses - - -def get_valid_stats( - cfg: DictConfig, - trainer: Trainer, - stats: Dict[str, Any], - tracking_best: bool, -) -> Dict[str, Any]: - stats["num_updates"] = trainer.get_num_updates() - if tracking_best and hasattr(checkpoint_utils.save_checkpoint, "best"): - key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric) - best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min - stats[key] = best_function( - checkpoint_utils.save_checkpoint.best, - stats[cfg.checkpoint.best_checkpoint_metric], - ) - return stats - - -def cli_main( - modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None -) -> None: - parser = options.get_training_parser() - args = options.parse_args_and_arch(parser, modify_parser=modify_parser) - - cfg = convert_namespace_to_omegaconf(args) - - if cfg.common.use_plasma_view: - server = PlasmaStore(path=cfg.common.plasma_path) - logger.info( - f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}" - ) - - if args.profile: - with torch.cuda.profiler.profile(): - with torch.autograd.profiler.emit_nvtx(): - distributed_utils.call_main(cfg, main) - else: - distributed_utils.call_main(cfg, main) - - # if cfg.common.use_plasma_view: - # server.server.kill() - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/validate.py b/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/validate.py deleted file mode 100644 index b10758cf746b7ef060d0e1d6349d4cbdf226d291..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/fairseq_cli/validate.py +++ /dev/null @@ -1,152 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import sys -from argparse import Namespace -from itertools import chain - -import torch -from fairseq import checkpoint_utils, distributed_utils, options, utils -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.logging import metrics, progress_bar -from fairseq.utils import reset_logging -from omegaconf import DictConfig - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.validate") - - -def main(cfg: DictConfig, override_args=None): - if isinstance(cfg, Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - utils.import_user_module(cfg.common) - - reset_logging() - - assert ( - cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None - ), "Must specify batch size either with --max-tokens or --batch-size" - - use_fp16 = cfg.common.fp16 - use_cuda = torch.cuda.is_available() and not cfg.common.cpu - - if use_cuda: - torch.cuda.set_device(cfg.distributed_training.device_id) - - if cfg.distributed_training.distributed_world_size > 1: - data_parallel_world_size = distributed_utils.get_data_parallel_world_size() - data_parallel_rank = distributed_utils.get_data_parallel_rank() - else: - data_parallel_world_size = 1 - data_parallel_rank = 0 - - if override_args is not None: - overrides = vars(override_args) - overrides.update(eval(getattr(override_args, "model_overrides", "{}"))) - else: - overrides = None - - # Load ensemble - logger.info("loading model(s) from {}".format(cfg.common_eval.path)) - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - [cfg.common_eval.path], - arg_overrides=overrides, - suffix=cfg.checkpoint.checkpoint_suffix, - ) - model = models[0] - - # Move models to GPU - for model in models: - model.eval() - if use_fp16: - model.half() - if use_cuda: - model.cuda() - - # Print args - logger.info(saved_cfg) - - # Build criterion - criterion = task.build_criterion(saved_cfg.criterion) - criterion.eval() - - for subset in cfg.dataset.valid_subset.split(","): - try: - task.load_dataset(subset, combine=False, epoch=1, task_cfg=saved_cfg.task) - dataset = task.dataset(subset) - except KeyError: - raise Exception("Cannot find dataset: " + subset) - - # Initialize data iterator - itr = task.get_batch_iterator( - dataset=dataset, - max_tokens=cfg.dataset.max_tokens, - max_sentences=cfg.dataset.batch_size, - max_positions=utils.resolve_max_positions( - task.max_positions(), - *[m.max_positions() for m in models], - ), - ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=cfg.dataset.required_batch_size_multiple, - seed=cfg.common.seed, - num_shards=data_parallel_world_size, - shard_id=data_parallel_rank, - num_workers=cfg.dataset.num_workers, - data_buffer_size=cfg.dataset.data_buffer_size, - ).next_epoch_itr(shuffle=False) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_interval=cfg.common.log_interval, - prefix=f"valid on '{subset}' subset", - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - ) - - log_outputs = [] - for i, sample in enumerate(progress): - sample = utils.move_to_cuda(sample) if use_cuda else sample - _loss, _sample_size, log_output = task.valid_step(sample, model, criterion) - progress.log(log_output, step=i) - log_outputs.append(log_output) - - if data_parallel_world_size > 1: - log_outputs = distributed_utils.all_gather_list( - log_outputs, - max_size=cfg.common.all_gather_list_size, - group=distributed_utils.get_data_parallel_group(), - ) - log_outputs = list(chain.from_iterable(log_outputs)) - - with metrics.aggregate() as agg: - task.reduce_metrics(log_outputs, criterion) - log_output = agg.get_smoothed_values() - - progress.print(log_output, tag=subset, step=i) - - -def cli_main(): - parser = options.get_validation_parser() - args = options.parse_args_and_arch(parser) - - # only override args that are explicitly given on the command line - override_parser = options.get_validation_parser() - override_args = options.parse_args_and_arch(override_parser, suppress_defaults=True) - - distributed_utils.call_main( - convert_namespace_to_omegaconf(args), main, override_args=override_args - ) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/hubconf.py b/audio/safeear/safeear_repo/fairseq_ours/hubconf.py deleted file mode 100644 index 5949e274edd02e86cb323331211641ce0d0b9b93..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/hubconf.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import functools -import importlib - - -dependencies = [ - "dataclasses", - "hydra", - "numpy", - "omegaconf", - "regex", - "requests", - "torch", -] - - -# Check for required dependencies and raise a RuntimeError if any are missing. -missing_deps = [] -for dep in dependencies: - try: - importlib.import_module(dep) - except ImportError: - # Hack: the hydra package is provided under the "hydra-core" name in - # pypi. We don't want the user mistakenly calling `pip install hydra` - # since that will install an unrelated package. - if dep == "hydra": - dep = "hydra-core" - missing_deps.append(dep) -if len(missing_deps) > 0: - raise RuntimeError("Missing dependencies: {}".format(", ".join(missing_deps))) - - -# only do fairseq imports after checking for dependencies -from fairseq.hub_utils import ( # noqa; noqa - BPEHubInterface as bpe, - TokenizerHubInterface as tokenizer, -) -from fairseq.models import MODEL_REGISTRY # noqa - - -# torch.hub doesn't build Cython components, so if they are not found then try -# to build them here -try: - import fairseq.data.token_block_utils_fast # noqa -except ImportError: - try: - import cython # noqa - import os - from setuptools import sandbox - - sandbox.run_setup( - os.path.join(os.path.dirname(__file__), "setup.py"), - ["build_ext", "--inplace"], - ) - except ImportError: - print( - "Unable to build Cython components. Please make sure Cython is " - "installed if the torch.hub model you are loading depends on it." - ) - - -# automatically expose models defined in FairseqModel::hub_models -for _model_type, _cls in MODEL_REGISTRY.items(): - for model_name in _cls.hub_models().keys(): - globals()[model_name] = functools.partial( - _cls.from_pretrained, - model_name, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/pyproject.toml b/audio/safeear/safeear_repo/fairseq_ours/pyproject.toml deleted file mode 100644 index 6d1b4c5b6fb56a63069147e3a1de922ce71a45d8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/pyproject.toml +++ /dev/null @@ -1,3 +0,0 @@ -[build-system] -requires = ["setuptools", "wheel", "cython"] -build-backend = "setuptools.build_meta" diff --git a/audio/safeear/safeear_repo/fairseq_ours/release_utils.py b/audio/safeear/safeear_repo/fairseq_ours/release_utils.py deleted file mode 100644 index 69a5e8dda3122643124b1e97d55b4efe7d0e0bc6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/release_utils.py +++ /dev/null @@ -1,72 +0,0 @@ -import argparse -from typing import Tuple - - -def get_next_version(release_type) -> Tuple[Tuple[int, int, int], str, str]: - current_ver = find_version("fairseq/version.txt") - version_list = [int(x) for x in current_ver.strip("'").split(".")] - major, minor, patch = version_list[0], version_list[1], version_list[2] - if release_type == "patch": - patch += 1 - elif release_type == "minor": - minor += 1 - patch = 0 - elif release_type == "major": - major += 1 - minor = patch = 0 - else: - raise ValueError( - "Incorrect release type specified. Acceptable types are major, minor and patch." - ) - - new_version_tuple = (major, minor, patch) - new_version_str = ".".join([str(x) for x in new_version_tuple]) - new_tag_str = "v" + new_version_str - return new_version_tuple, new_version_str, new_tag_str - - -def find_version(version_file_path) -> str: - with open(version_file_path) as f: - version = f.read().strip() - return version - - -def update_version(new_version_str) -> None: - """ - given the current version, update the version to the - next version depending on the type of release. - """ - - with open("fairseq/version.txt", "w") as writer: - writer.write(new_version_str) - - -def main(args): - if args.release_type in ["major", "minor", "patch"]: - new_version_tuple, new_version, new_tag = get_next_version(args.release_type) - else: - raise ValueError("Incorrect release type specified") - - if args.update_version: - update_version(new_version) - - print(new_version, new_tag) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Versioning utils") - parser.add_argument( - "--release-type", - type=str, - required=True, - help="type of release = major/minor/patch", - ) - parser.add_argument( - "--update-version", - action="store_true", - required=False, - help="updates the version in fairseq/version.txt", - ) - - args = parser.parse_args() - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/average_checkpoints.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/average_checkpoints.py deleted file mode 100644 index a4711e4840a45118c9e28d0258f89fe64e964cf3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/average_checkpoints.py +++ /dev/null @@ -1,160 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import collections -import os -import re - -import torch -from fairseq.file_io import PathManager - - -def average_checkpoints(inputs): - """Loads checkpoints from inputs and returns a model with averaged weights. - - Args: - inputs: An iterable of string paths of checkpoints to load from. - - Returns: - A dict of string keys mapping to various values. The 'model' key - from the returned dict should correspond to an OrderedDict mapping - string parameter names to torch Tensors. - """ - params_dict = collections.OrderedDict() - params_keys = None - new_state = None - num_models = len(inputs) - - for fpath in inputs: - with PathManager.open(fpath, "rb") as f: - state = torch.load( - f, - map_location=( - lambda s, _: torch.serialization.default_restore_location(s, "cpu") - ), - ) - # Copies over the settings from the first checkpoint - if new_state is None: - new_state = state - - model_params = state["model"] - - model_params_keys = list(model_params.keys()) - if params_keys is None: - params_keys = model_params_keys - elif params_keys != model_params_keys: - raise KeyError( - "For checkpoint {}, expected list of params: {}, " - "but found: {}".format(f, params_keys, model_params_keys) - ) - - for k in params_keys: - p = model_params[k] - if isinstance(p, torch.HalfTensor): - p = p.float() - if k not in params_dict: - params_dict[k] = p.clone() - # NOTE: clone() is needed in case of p is a shared parameter - else: - params_dict[k] += p - - averaged_params = collections.OrderedDict() - for k, v in params_dict.items(): - averaged_params[k] = v - if averaged_params[k].is_floating_point(): - averaged_params[k].div_(num_models) - else: - averaged_params[k] //= num_models - new_state["model"] = averaged_params - return new_state - - -def last_n_checkpoints(paths, n, update_based, upper_bound=None): - assert len(paths) == 1 - path = paths[0] - if update_based: - pt_regexp = re.compile(r"checkpoint_\d+_(\d+)\.pt") - else: - pt_regexp = re.compile(r"checkpoint(\d+)\.pt") - files = PathManager.ls(path) - - entries = [] - for f in files: - m = pt_regexp.fullmatch(f) - if m is not None: - sort_key = int(m.group(1)) - if upper_bound is None or sort_key <= upper_bound: - entries.append((sort_key, m.group(0))) - if len(entries) < n: - raise Exception( - "Found {} checkpoint files but need at least {}", len(entries), n - ) - return [os.path.join(path, x[1]) for x in sorted(entries, reverse=True)[:n]] - - -def main(): - parser = argparse.ArgumentParser( - description="Tool to average the params of input checkpoints to " - "produce a new checkpoint", - ) - # fmt: off - parser.add_argument('--inputs', required=True, nargs='+', - help='Input checkpoint file paths.') - parser.add_argument('--output', required=True, metavar='FILE', - help='Write the new checkpoint containing the averaged weights to this path.') - num_group = parser.add_mutually_exclusive_group() - num_group.add_argument('--num-epoch-checkpoints', type=int, - help='if set, will try to find checkpoints with names checkpoint_xx.pt in the ' - 'path specified by input, and average last this many of them.') - num_group.add_argument('--num-update-checkpoints', type=int, - help='if set, will try to find checkpoints with names checkpoint_ee_xx.pt in the path specified by' - ' input, and average last this many of them.') - parser.add_argument('--checkpoint-upper-bound', type=int, - help='when using --num-epoch-checkpoints, this will set an upper bound on which epoch to use, ' - 'when using --num-update-checkpoints, this will set an upper bound on which update to use' - 'e.g., with --num-epoch-checkpoints=10 --checkpoint-upper-bound=50, checkpoints 41-50 would be' - ' averaged.' - 'e.g., with --num-update-checkpoints=10 --checkpoint-upper-bound=50000, checkpoints 40500-50000 would' - ' be averaged assuming --save-interval-updates 500' - ) - # fmt: on - args = parser.parse_args() - print(args) - - num = None - is_update_based = False - if args.num_update_checkpoints is not None: - num = args.num_update_checkpoints - is_update_based = True - elif args.num_epoch_checkpoints is not None: - num = args.num_epoch_checkpoints - - assert args.checkpoint_upper_bound is None or ( - args.num_epoch_checkpoints is not None - or args.num_update_checkpoints is not None - ), "--checkpoint-upper-bound requires --num-epoch-checkpoints or --num-update-checkpoints" - assert ( - args.num_epoch_checkpoints is None or args.num_update_checkpoints is None - ), "Cannot combine --num-epoch-checkpoints and --num-update-checkpoints" - - if num is not None: - args.inputs = last_n_checkpoints( - args.inputs, - num, - is_update_based, - upper_bound=args.checkpoint_upper_bound, - ) - print("averaging checkpoints: ", args.inputs) - - new_state = average_checkpoints(args.inputs) - with PathManager.open(args.output, "wb") as f: - torch.save(new_state, f) - print("Finished writing averaged checkpoint to {}".format(args.output)) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/build_sym_alignment.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/build_sym_alignment.py deleted file mode 100644 index 0ca5c18f7bd4b0fbf58b203793506ca395466129..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/build_sym_alignment.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Use this script in order to build symmetric alignments for your translation -dataset. -This script depends on fast_align and mosesdecoder tools. You will need to -build those before running the script. -fast_align: - github: http://github.com/clab/fast_align - instructions: follow the instructions in README.md -mosesdecoder: - github: http://github.com/moses-smt/mosesdecoder - instructions: http://www.statmt.org/moses/?n=Development.GetStarted -The script produces the following files under --output_dir: - text.joined - concatenation of lines from the source_file and the - target_file. - align.forward - forward pass of fast_align. - align.backward - backward pass of fast_align. - aligned.sym_heuristic - symmetrized alignment. -""" - -import argparse -import os -from itertools import zip_longest - - -def main(): - parser = argparse.ArgumentParser(description="symmetric alignment builer") - # fmt: off - parser.add_argument('--fast_align_dir', - help='path to fast_align build directory') - parser.add_argument('--mosesdecoder_dir', - help='path to mosesdecoder root directory') - parser.add_argument('--sym_heuristic', - help='heuristic to use for symmetrization', - default='grow-diag-final-and') - parser.add_argument('--source_file', - help='path to a file with sentences ' - 'in the source language') - parser.add_argument('--target_file', - help='path to a file with sentences ' - 'in the target language') - parser.add_argument('--output_dir', - help='output directory') - # fmt: on - args = parser.parse_args() - - fast_align_bin = os.path.join(args.fast_align_dir, "fast_align") - symal_bin = os.path.join(args.mosesdecoder_dir, "bin", "symal") - sym_fast_align_bin = os.path.join( - args.mosesdecoder_dir, "scripts", "ems", "support", "symmetrize-fast-align.perl" - ) - - # create joined file - joined_file = os.path.join(args.output_dir, "text.joined") - with open(args.source_file, "r", encoding="utf-8") as src, open( - args.target_file, "r", encoding="utf-8" - ) as tgt: - with open(joined_file, "w", encoding="utf-8") as joined: - for s, t in zip_longest(src, tgt): - print("{} ||| {}".format(s.strip(), t.strip()), file=joined) - - bwd_align_file = os.path.join(args.output_dir, "align.backward") - - # run forward alignment - fwd_align_file = os.path.join(args.output_dir, "align.forward") - fwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v > {FWD}".format( - FASTALIGN=fast_align_bin, JOINED=joined_file, FWD=fwd_align_file - ) - assert os.system(fwd_fast_align_cmd) == 0 - - # run backward alignment - bwd_align_file = os.path.join(args.output_dir, "align.backward") - bwd_fast_align_cmd = "{FASTALIGN} -i {JOINED} -d -o -v -r > {BWD}".format( - FASTALIGN=fast_align_bin, JOINED=joined_file, BWD=bwd_align_file - ) - assert os.system(bwd_fast_align_cmd) == 0 - - # run symmetrization - sym_out_file = os.path.join(args.output_dir, "aligned") - sym_cmd = "{SYMFASTALIGN} {FWD} {BWD} {SRC} {TGT} {OUT} {HEURISTIC} {SYMAL}".format( - SYMFASTALIGN=sym_fast_align_bin, - FWD=fwd_align_file, - BWD=bwd_align_file, - SRC=args.source_file, - TGT=args.target_file, - OUT=sym_out_file, - HEURISTIC=args.sym_heuristic, - SYMAL=symal_bin, - ) - assert os.system(sym_cmd) == 0 - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/compare_namespaces.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/compare_namespaces.py deleted file mode 100644 index bc24db624f8db36f546c263ba3a806dae6d466bf..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/compare_namespaces.py +++ /dev/null @@ -1,46 +0,0 @@ -#!/usr/bin/env python -"""Helper script to compare two argparse.Namespace objects.""" - -from argparse import Namespace # noqa - - -def main(): - - ns1 = eval(input("Namespace 1: ")) - ns2 = eval(input("Namespace 2: ")) - - def keys(ns): - ks = set() - for k in dir(ns): - if not k.startswith("_"): - ks.add(k) - return ks - - k1 = keys(ns1) - k2 = keys(ns2) - - def print_keys(ks, ns1, ns2=None): - for k in ks: - if ns2 is None: - print("{}\t{}".format(k, getattr(ns1, k, None))) - else: - print( - "{}\t{}\t{}".format(k, getattr(ns1, k, None), getattr(ns2, k, None)) - ) - - print("Keys unique to namespace 1:") - print_keys(k1 - k2, ns1) - print() - - print("Keys unique to namespace 2:") - print_keys(k2 - k1, ns2) - print() - - print("Overlapping keys with different values:") - ks = [k for k in k1 & k2 if getattr(ns1, k, "None") != getattr(ns2, k, "None")] - print_keys(ks, ns1, ns2) - print() - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/compound_split_bleu.sh b/audio/safeear/safeear_repo/fairseq_ours/scripts/compound_split_bleu.sh deleted file mode 100644 index 1972fddcebff9a43a70bcf14c287175c68f60e3f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/compound_split_bleu.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -if [ $# -ne 1 ]; then - echo "usage: $0 GENERATE_PY_OUTPUT" - exit 1 -fi - -GEN=$1 - -SYS=$GEN.sys -REF=$GEN.ref - -if [ $(tail -n 1 $GEN | grep BLEU | wc -l) -ne 1 ]; then - echo "not done generating" - exit -fi - -grep ^H $GEN | awk -F '\t' '{print $NF}' | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $SYS -grep ^T $GEN | cut -f2- | perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' > $REF -fairseq-score --sys $SYS --ref $REF diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/constraints/extract.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/constraints/extract.py deleted file mode 100644 index 437b373856966e568ca93c13ebbd1417291e49da..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/constraints/extract.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -"""Extracts random constraints from reference files.""" - -import argparse -import random -import sys - - -def get_phrase(words, index, length): - assert index < len(words) - length + 1 - phr = " ".join(words[index : index + length]) - for i in range(index, index + length): - words.pop(index) - return phr - - -def main(args): - - if args.seed: - random.seed(args.seed) - - for line in sys.stdin: - constraints = [] - - def add_constraint(constraint): - constraints.append(constraint) - - source = line.rstrip() - if "\t" in line: - source, target = line.split("\t") - if args.add_sos: - target = f" {target}" - if args.add_eos: - target = f"{target} " - - if len(target.split()) >= args.len: - words = [target] - - num = args.number - - choices = {} - for i in range(num): - if len(words) == 0: - break - segmentno = random.choice(range(len(words))) - segment = words.pop(segmentno) - tokens = segment.split() - phrase_index = random.choice(range(len(tokens))) - choice = " ".join( - tokens[phrase_index : min(len(tokens), phrase_index + args.len)] - ) - for j in range( - phrase_index, min(len(tokens), phrase_index + args.len) - ): - tokens.pop(phrase_index) - if phrase_index > 0: - words.append(" ".join(tokens[0:phrase_index])) - if phrase_index + 1 < len(tokens): - words.append(" ".join(tokens[phrase_index:])) - choices[target.find(choice)] = choice - - # mask out with spaces - target = target.replace(choice, " " * len(choice), 1) - - for key in sorted(choices.keys()): - add_constraint(choices[key]) - - print(source, *constraints, sep="\t") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--number", "-n", type=int, default=1, help="number of phrases") - parser.add_argument("--len", "-l", type=int, default=1, help="phrase length") - parser.add_argument( - "--add-sos", default=False, action="store_true", help="add token" - ) - parser.add_argument( - "--add-eos", default=False, action="store_true", help="add token" - ) - parser.add_argument("--seed", "-s", default=0, type=int) - args = parser.parse_args() - - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/constraints/validate.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/constraints/validate.py deleted file mode 100644 index 6d1a4a0885dc91f0270cf29e6e4f59c4be6168bd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/constraints/validate.py +++ /dev/null @@ -1,33 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import sys - -"""Reads in a fairseq output file, and verifies that the constraints -(C- lines) are present in the output (the first H- line). Assumes that -constraints are listed prior to the first hypothesis. -""" - -constraints = [] -found = 0 -total = 0 -for line in sys.stdin: - if line.startswith("C-"): - constraints.append(line.rstrip().split("\t")[1]) - elif line.startswith("H-"): - text = line.split("\t")[2] - - for constraint in constraints: - total += 1 - if constraint in text: - found += 1 - else: - print(f"No {constraint} in {text}", file=sys.stderr) - - constraints = [] - -print(f"Found {found} / {total} = {100 * found / total:.1f}%") diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/convert_dictionary.lua b/audio/safeear/safeear_repo/fairseq_ours/scripts/convert_dictionary.lua deleted file mode 100644 index 14ee8c997f642c8ff196617c2dcd0584037a60c4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/convert_dictionary.lua +++ /dev/null @@ -1,34 +0,0 @@ --- Copyright (c) Facebook, Inc. and its affiliates. --- --- This source code is licensed under the MIT license found in the --- LICENSE file in the root directory of this source tree. --- --- Usage: convert_dictionary.lua -require 'fairseq' -require 'torch' -require 'paths' - -if #arg < 1 then - print('usage: convert_dictionary.lua ') - os.exit(1) -end -if not paths.filep(arg[1]) then - print('error: file does not exit: ' .. arg[1]) - os.exit(1) -end - -dict = torch.load(arg[1]) -dst = paths.basename(arg[1]):gsub('.th7', '.txt') -assert(dst:match('.txt$')) - -f = io.open(dst, 'w') -for idx, symbol in ipairs(dict.index_to_symbol) do - if idx > dict.cutoff then - break - end - f:write(symbol) - f:write(' ') - f:write(dict.index_to_freq[idx]) - f:write('\n') -end -f:close() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/convert_model.lua b/audio/safeear/safeear_repo/fairseq_ours/scripts/convert_model.lua deleted file mode 100644 index 61b92139294fb90a25989ebd2ee52a765fb278a2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/convert_model.lua +++ /dev/null @@ -1,108 +0,0 @@ --- Copyright (c) Facebook, Inc. and its affiliates. --- --- This source code is licensed under the MIT license found in the --- LICENSE file in the root directory of this source tree. --- --- Usage: convert_model.lua -require 'torch' -local fairseq = require 'fairseq' - -model = torch.load(arg[1]) - -function find_weight_norm(container, module) - for _, wn in ipairs(container:listModules()) do - if torch.type(wn) == 'nn.WeightNorm' and wn.modules[1] == module then - return wn - end - end -end - -function push_state(dict, key, module) - if torch.type(module) == 'nn.Linear' then - local wn = find_weight_norm(model.module, module) - assert(wn) - dict[key .. '.weight_v'] = wn.v:float() - dict[key .. '.weight_g'] = wn.g:float() - elseif torch.type(module) == 'nn.TemporalConvolutionTBC' then - local wn = find_weight_norm(model.module, module) - assert(wn) - local v = wn.v:float():view(wn.viewOut):transpose(2, 3) - dict[key .. '.weight_v'] = v - dict[key .. '.weight_g'] = wn.g:float():view(module.weight:size(3), 1, 1) - else - dict[key .. '.weight'] = module.weight:float() - end - if module.bias then - dict[key .. '.bias'] = module.bias:float() - end -end - -encoder_dict = {} -decoder_dict = {} -combined_dict = {} - -function encoder_state(encoder) - luts = encoder:findModules('nn.LookupTable') - push_state(encoder_dict, 'embed_tokens', luts[1]) - push_state(encoder_dict, 'embed_positions', luts[2]) - - fcs = encoder:findModules('nn.Linear') - assert(#fcs >= 2) - local nInputPlane = fcs[1].weight:size(1) - push_state(encoder_dict, 'fc1', table.remove(fcs, 1)) - push_state(encoder_dict, 'fc2', table.remove(fcs, #fcs)) - - for i, module in ipairs(encoder:findModules('nn.TemporalConvolutionTBC')) do - push_state(encoder_dict, 'convolutions.' .. tostring(i - 1), module) - if nInputPlane ~= module.weight:size(3) / 2 then - push_state(encoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1)) - end - nInputPlane = module.weight:size(3) / 2 - end - assert(#fcs == 0) -end - -function decoder_state(decoder) - luts = decoder:findModules('nn.LookupTable') - push_state(decoder_dict, 'embed_tokens', luts[1]) - push_state(decoder_dict, 'embed_positions', luts[2]) - - fcs = decoder:findModules('nn.Linear') - local nInputPlane = fcs[1].weight:size(1) - push_state(decoder_dict, 'fc1', table.remove(fcs, 1)) - push_state(decoder_dict, 'fc2', fcs[#fcs - 1]) - push_state(decoder_dict, 'fc3', fcs[#fcs]) - - table.remove(fcs, #fcs) - table.remove(fcs, #fcs) - - for i, module in ipairs(decoder:findModules('nn.TemporalConvolutionTBC')) do - if nInputPlane ~= module.weight:size(3) / 2 then - push_state(decoder_dict, 'projections.' .. tostring(i - 1), table.remove(fcs, 1)) - end - nInputPlane = module.weight:size(3) / 2 - - local prefix = 'attention.' .. tostring(i - 1) - push_state(decoder_dict, prefix .. '.in_projection', table.remove(fcs, 1)) - push_state(decoder_dict, prefix .. '.out_projection', table.remove(fcs, 1)) - push_state(decoder_dict, 'convolutions.' .. tostring(i - 1), module) - end - assert(#fcs == 0) -end - - -_encoder = model.module.modules[2] -_decoder = model.module.modules[3] - -encoder_state(_encoder) -decoder_state(_decoder) - -for k, v in pairs(encoder_dict) do - combined_dict['encoder.' .. k] = v -end -for k, v in pairs(decoder_dict) do - combined_dict['decoder.' .. k] = v -end - - -torch.save('state_dict.t7', combined_dict) diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/count_docs.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/count_docs.py deleted file mode 100644 index 58d85af85e91377a34dbd01f7674436152fd08e8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/count_docs.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Count the number of documents and average number of lines and tokens per -document in a large file. Documents should be separated by a single empty line. -""" - -import argparse -import gzip -import sys - -import numpy as np - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("input") - parser.add_argument("--gzip", action="store_true") - args = parser.parse_args() - - def gopen(): - if args.gzip: - return gzip.open(args.input, "r") - else: - return open(args.input, "r", encoding="utf-8") - - num_lines = [] - num_toks = [] - with gopen() as h: - num_docs = 1 - num_lines_in_doc = 0 - num_toks_in_doc = 0 - for i, line in enumerate(h): - if len(line.strip()) == 0: # empty line indicates new document - num_docs += 1 - num_lines.append(num_lines_in_doc) - num_toks.append(num_toks_in_doc) - num_lines_in_doc = 0 - num_toks_in_doc = 0 - else: - num_lines_in_doc += 1 - num_toks_in_doc += len(line.rstrip().split()) - if i % 1000000 == 0: - print(i, file=sys.stderr, end="", flush=True) - elif i % 100000 == 0: - print(".", file=sys.stderr, end="", flush=True) - print(file=sys.stderr, flush=True) - - print("found {} docs".format(num_docs)) - print("average num lines per doc: {}".format(np.mean(num_lines))) - print("average num toks per doc: {}".format(np.mean(num_toks))) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/read_binarized.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/read_binarized.py deleted file mode 100644 index a414095d03fb022a6753e816fc8bfd80e11db24d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/read_binarized.py +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse - -from fairseq.data import Dictionary, data_utils, indexed_dataset - - -def get_parser(): - parser = argparse.ArgumentParser( - description="writes text from binarized file to stdout" - ) - # fmt: off - parser.add_argument('--dataset-impl', help='dataset implementation', - choices=indexed_dataset.get_available_dataset_impl()) - parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None) - parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read') - # fmt: on - - return parser - - -def main(): - parser = get_parser() - args = parser.parse_args() - - dictionary = Dictionary.load(args.dict) if args.dict is not None else None - dataset = data_utils.load_indexed_dataset( - args.input, - dictionary, - dataset_impl=args.dataset_impl, - default="lazy", - ) - - for tensor_line in dataset: - if dictionary is None: - line = " ".join([str(int(x)) for x in tensor_line]) - else: - line = dictionary.string(tensor_line) - - print(line) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/rm_pt.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/rm_pt.py deleted file mode 100644 index ad5ad86a6b3b6e7b83b8968912b355df7d6ff65d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/rm_pt.py +++ /dev/null @@ -1,140 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import os -import re -import shutil -import sys - -pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt") -pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt") -pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt") - - -def parse_checkpoints(files): - entries = [] - for f in files: - m = pt_regexp_epoch_based.fullmatch(f) - if m is not None: - entries.append((int(m.group(1)), m.group(0))) - else: - m = pt_regexp_update_based.fullmatch(f) - if m is not None: - entries.append((int(m.group(1)), m.group(0))) - return entries - - -def last_n_checkpoints(files, n): - entries = parse_checkpoints(files) - return [x[1] for x in sorted(entries, reverse=True)[:n]] - - -def every_n_checkpoints(files, n): - entries = parse_checkpoints(files) - return [x[1] for x in sorted(sorted(entries)[::-n])] - - -def main(): - parser = argparse.ArgumentParser( - description=( - "Recursively delete checkpoint files from `root_dir`, " - "but preserve checkpoint_best.pt and checkpoint_last.pt" - ) - ) - parser.add_argument("root_dirs", nargs="*") - parser.add_argument( - "--save-last", type=int, default=0, help="number of last checkpoints to save" - ) - parser.add_argument( - "--save-every", type=int, default=0, help="interval of checkpoints to save" - ) - parser.add_argument( - "--preserve-test", - action="store_true", - help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)", - ) - parser.add_argument( - "--delete-best", action="store_true", help="delete checkpoint_best.pt" - ) - parser.add_argument( - "--delete-last", action="store_true", help="delete checkpoint_last.pt" - ) - parser.add_argument( - "--no-dereference", action="store_true", help="don't dereference symlinks" - ) - args = parser.parse_args() - - files_to_desymlink = [] - files_to_preserve = [] - files_to_delete = [] - for root_dir in args.root_dirs: - for root, _subdirs, files in os.walk(root_dir): - if args.save_last > 0: - to_save = last_n_checkpoints(files, args.save_last) - else: - to_save = [] - if args.save_every > 0: - to_save += every_n_checkpoints(files, args.save_every) - for file in files: - if not pt_regexp.fullmatch(file): - continue - full_path = os.path.join(root, file) - if ( - not os.path.basename(root).startswith("test_") or args.preserve_test - ) and ( - (file == "checkpoint_last.pt" and not args.delete_last) - or (file == "checkpoint_best.pt" and not args.delete_best) - or file in to_save - ): - if os.path.islink(full_path) and not args.no_dereference: - files_to_desymlink.append(full_path) - else: - files_to_preserve.append(full_path) - else: - files_to_delete.append(full_path) - - if len(files_to_desymlink) == 0 and len(files_to_delete) == 0: - print("Nothing to do.") - sys.exit(0) - - files_to_desymlink = sorted(files_to_desymlink) - files_to_preserve = sorted(files_to_preserve) - files_to_delete = sorted(files_to_delete) - - print("Operations to perform (in order):") - if len(files_to_desymlink) > 0: - for file in files_to_desymlink: - print(" - preserve (and dereference symlink): " + file) - if len(files_to_preserve) > 0: - for file in files_to_preserve: - print(" - preserve: " + file) - if len(files_to_delete) > 0: - for file in files_to_delete: - print(" - delete: " + file) - while True: - resp = input("Continue? (Y/N): ") - if resp.strip().lower() == "y": - break - elif resp.strip().lower() == "n": - sys.exit(0) - - print("Executing...") - if len(files_to_desymlink) > 0: - for file in files_to_desymlink: - realpath = os.path.realpath(file) - print("rm " + file) - os.remove(file) - print("cp {} {}".format(realpath, file)) - shutil.copyfile(realpath, file) - if len(files_to_delete) > 0: - for file in files_to_delete: - print("rm " + file) - os.remove(file) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/sacrebleu.sh b/audio/safeear/safeear_repo/fairseq_ours/scripts/sacrebleu.sh deleted file mode 100644 index c10bf2b76ea032deabab6f5c9d8a3e1e884f1642..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/sacrebleu.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash - -if [ $# -ne 4 ]; then - echo "usage: $0 TESTSET SRCLANG TGTLANG GEN" - exit 1 -fi - -TESTSET=$1 -SRCLANG=$2 -TGTLANG=$3 - -GEN=$4 - -if ! command -v sacremoses &> /dev/null -then - echo "sacremoses could not be found, please install with: pip install sacremoses" - exit -fi - -grep ^H $GEN \ -| sed 's/^H\-//' \ -| sort -n -k 1 \ -| cut -f 3 \ -| sacremoses detokenize \ -> $GEN.sorted.detok - -sacrebleu --test-set $TESTSET --language-pair "${SRCLANG}-${TGTLANG}" < $GEN.sorted.detok diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/shard_docs.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/shard_docs.py deleted file mode 100644 index 97232c3c845ee01dc5ab627388934cc0f9588280..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/shard_docs.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Split a large file into shards while respecting document boundaries. Documents -should be separated by a single empty line. -""" - -import argparse -import contextlib - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("input") - parser.add_argument("--num-shards", type=int) - args = parser.parse_args() - - assert args.num_shards is not None and args.num_shards > 1 - - with open(args.input, "r", encoding="utf-8") as h: - with contextlib.ExitStack() as stack: - outputs = [ - stack.enter_context( - open(args.input + ".shard" + str(i), "w", encoding="utf-8") - ) - for i in range(args.num_shards) - ] - - doc = [] - first_doc = [True] * args.num_shards - - def output_doc(i): - if not first_doc[i]: - outputs[i].write("\n") - first_doc[i] = False - for line in doc: - outputs[i].write(line) - doc.clear() - - num_docs = 0 - for line in h: - if line.strip() == "": # empty line indicates new document - output_doc(num_docs % args.num_shards) - num_docs += 1 - else: - doc.append(line) - output_doc(num_docs % args.num_shards) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/split_train_valid_docs.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/split_train_valid_docs.py deleted file mode 100644 index ff159785284a13b44626b207d84430c592acaf8f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/split_train_valid_docs.py +++ /dev/null @@ -1,86 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Split a large file into a train and valid set while respecting document -boundaries. Documents should be separated by a single empty line. -""" - -import argparse -import random -import sys - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("input") - parser.add_argument("sample_output", help="train output file") - parser.add_argument("remainder_output", help="valid output file") - parser.add_argument("-k", type=int, help="remainder size") - parser.add_argument( - "--lines", action="store_true", help="split lines instead of docs" - ) - args = parser.parse_args() - - assert args.k is not None - - sample = [] - remainder = [] - num_docs = [0] - - def update_sample(doc): - if len(sample) < args.k: - sample.append(doc.copy()) - else: - i = num_docs[0] - j = random.randrange(i + 1) - if j < args.k: - remainder.append(sample[j]) - sample[j] = doc.copy() - else: - remainder.append(doc.copy()) - num_docs[0] += 1 - doc.clear() - - with open(args.input, "r", encoding="utf-8") as h: - doc = [] - for i, line in enumerate(h): - if line.strip() == "": # empty line indicates new document - update_sample(doc) - else: - doc.append(line) - if args.lines: - update_sample(doc) - if i % 1000000 == 0: - print(i, file=sys.stderr, end="", flush=True) - elif i % 100000 == 0: - print(".", file=sys.stderr, end="", flush=True) - if len(doc) > 0: - update_sample(doc) - print(file=sys.stderr, flush=True) - - assert len(sample) == args.k - - with open(args.sample_output, "w", encoding="utf-8") as out: - first = True - for doc in sample: - if not first and not args.lines: - out.write("\n") - first = False - for line in doc: - out.write(line) - - with open(args.remainder_output, "w", encoding="utf-8") as out: - first = True - for doc in remainder: - if not first and not args.lines: - out.write("\n") - first = False - for line in doc: - out.write(line) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_decode.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_decode.py deleted file mode 100644 index 7d7b68b240265924601ca6a738ed3d7b4b8e9cda..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_decode.py +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from __future__ import absolute_import, division, print_function, unicode_literals - -import argparse - -import sentencepiece as spm - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--model", required=True, help="sentencepiece model to use for decoding" - ) - parser.add_argument("--input", required=True, help="input file to decode") - parser.add_argument("--input_format", choices=["piece", "id"], default="piece") - args = parser.parse_args() - - sp = spm.SentencePieceProcessor() - sp.Load(args.model) - - if args.input_format == "piece": - - def decode(input): - return "".join(sp.DecodePieces(input)) - - elif args.input_format == "id": - - def decode(input): - return "".join(sp.DecodeIds(input)) - - else: - raise NotImplementedError - - def tok2int(tok): - # remap reference-side (represented as <>) to 0 - return int(tok) if tok != "<>" else 0 - - with open(args.input, "r", encoding="utf-8") as h: - for line in h: - if args.input_format == "id": - print(decode(list(map(tok2int, line.rstrip().split())))) - elif args.input_format == "piece": - print(decode(line.rstrip().split())) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_encode.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_encode.py deleted file mode 100644 index f91e0bb728a33448c1415aee6036ac9d0feac11f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_encode.py +++ /dev/null @@ -1,119 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from __future__ import absolute_import, division, print_function, unicode_literals - -import argparse -import contextlib -import sys - -import sentencepiece as spm - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--model", required=True, help="sentencepiece model to use for encoding" - ) - parser.add_argument( - "--inputs", nargs="+", default=["-"], help="input files to filter/encode" - ) - parser.add_argument( - "--outputs", nargs="+", default=["-"], help="path to save encoded outputs" - ) - parser.add_argument("--output_format", choices=["piece", "id"], default="piece") - parser.add_argument( - "--min-len", - type=int, - metavar="N", - help="filter sentence pairs with fewer than N tokens", - ) - parser.add_argument( - "--max-len", - type=int, - metavar="N", - help="filter sentence pairs with more than N tokens", - ) - args = parser.parse_args() - - assert len(args.inputs) == len( - args.outputs - ), "number of input and output paths should match" - - sp = spm.SentencePieceProcessor() - sp.Load(args.model) - - if args.output_format == "piece": - - def encode(input): - return sp.EncodeAsPieces(input) - - elif args.output_format == "id": - - def encode(input): - return list(map(str, sp.EncodeAsIds(input))) - - else: - raise NotImplementedError - - if args.min_len is not None or args.max_len is not None: - - def valid(line): - return (args.min_len is None or len(line) >= args.min_len) and ( - args.max_len is None or len(line) <= args.max_len - ) - - else: - - def valid(lines): - return True - - with contextlib.ExitStack() as stack: - inputs = [ - stack.enter_context(open(input, "r", encoding="utf-8")) - if input != "-" - else sys.stdin - for input in args.inputs - ] - outputs = [ - stack.enter_context(open(output, "w", encoding="utf-8")) - if output != "-" - else sys.stdout - for output in args.outputs - ] - - stats = { - "num_empty": 0, - "num_filtered": 0, - } - - def encode_line(line): - line = line.strip() - if len(line) > 0: - line = encode(line) - if valid(line): - return line - else: - stats["num_filtered"] += 1 - else: - stats["num_empty"] += 1 - return None - - for i, lines in enumerate(zip(*inputs), start=1): - enc_lines = list(map(encode_line, lines)) - if not any(enc_line is None for enc_line in enc_lines): - for enc_line, output_h in zip(enc_lines, outputs): - print(" ".join(enc_line), file=output_h) - if i % 10000 == 0: - print("processed {} lines".format(i), file=sys.stderr) - - print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr) - print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_train.py b/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_train.py deleted file mode 100644 index c6b678725757c81d55f8e5692ae0dc396bf4bd50..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/spm_train.py +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from __future__ import absolute_import, division, print_function, unicode_literals - -import sys - -import sentencepiece as spm - -if __name__ == "__main__": - spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:])) diff --git a/audio/safeear/safeear_repo/fairseq_ours/scripts/test_fsdp.sh b/audio/safeear/safeear_repo/fairseq_ours/scripts/test_fsdp.sh deleted file mode 100644 index 1f428a035e4474427ded991f8e8307ea59f61f69..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/scripts/test_fsdp.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/usr/bin/env bash -rm -rf fsdp_dummy -mkdir -p fsdp_dummy -CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \ - --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ - --cpu-offload --checkpoint-activations \ - --task language_modeling --tokens-per-sample 256 --batch-size 8 \ - --arch transformer_lm_gpt2_tiny \ - --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ - --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ - --max-update 5 --log-format json --log-interval 1 \ - --save-interval-updates 5 --save-dir fsdp_dummy --disable-validation \ - --restore-file x.pt "$@" - -# Now we try to load the checkpoint -CUDA_VISIBLE_DEVICES=0,1 fairseq-train /private/home/sshleifer/data-bin/stories_mmap \ - --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ - --cpu-offload --checkpoint-activations \ - --task language_modeling --tokens-per-sample 256 --batch-size 8 \ - --arch transformer_lm_gpt2_tiny \ - --optimizer cpu_adam --adam-betas "(0.9,0.98)" \ - --lr 0.0001 --lr-scheduler polynomial_decay --warmup-updates 5 --total-num-update 10 \ - --max-update 2 --log-format json --log-interval 1 \ - --save-interval-updates 2 --save-dir fsdp_dummy diff --git a/audio/safeear/safeear_repo/fairseq_ours/setup.cfg b/audio/safeear/safeear_repo/fairseq_ours/setup.cfg deleted file mode 100644 index 3fa679ddf1dd8dc4aa4045e0ac1f0a8bdc842e27..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/setup.cfg +++ /dev/null @@ -1,4 +0,0 @@ -[flake8] -max-line-length = 127 -extend-ignore = E203, W503 -extend-exclude = fairseq/model_parallel/megatron diff --git a/audio/safeear/safeear_repo/fairseq_ours/setup.py b/audio/safeear/safeear_repo/fairseq_ours/setup.py deleted file mode 100644 index a7ce61a8969454a4e7f4d877c206bcf913ca5213..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/setup.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import subprocess -import sys - -from setuptools import Extension, find_packages, setup - -if sys.version_info < (3, 6): - sys.exit("Sorry, Python >= 3.6 is required for fairseq.") - - -def write_version_py(): - with open(os.path.join("fairseq", "version.txt")) as f: - version = f.read().strip() - - # write version info to fairseq/version.py - with open(os.path.join("fairseq", "version.py"), "w") as f: - f.write('__version__ = "{}"\n'.format(version)) - return version - - -version = write_version_py() - - -with open("README.md") as f: - readme = f.read() - - -if sys.platform == "darwin": - extra_compile_args = ["-stdlib=libc++", "-O3"] -else: - extra_compile_args = ["-std=c++11", "-O3"] - - -class NumpyExtension(Extension): - """Source: https://stackoverflow.com/a/54128391""" - - def __init__(self, *args, **kwargs): - self.__include_dirs = [] - super().__init__(*args, **kwargs) - - @property - def include_dirs(self): - import numpy - - return self.__include_dirs + [numpy.get_include()] - - @include_dirs.setter - def include_dirs(self, dirs): - self.__include_dirs = dirs - - -extensions = [ - Extension( - "fairseq.libbleu", - sources=[ - "fairseq/clib/libbleu/libbleu.cpp", - "fairseq/clib/libbleu/module.cpp", - ], - extra_compile_args=extra_compile_args, - ), - NumpyExtension( - "fairseq.data.data_utils_fast", - sources=["fairseq/data/data_utils_fast.pyx"], - language="c++", - extra_compile_args=extra_compile_args, - ), - NumpyExtension( - "fairseq.data.token_block_utils_fast", - sources=["fairseq/data/token_block_utils_fast.pyx"], - language="c++", - extra_compile_args=extra_compile_args, - ), -] - - -cmdclass = {} - - -try: - # torch is not available when generating docs - from torch.utils import cpp_extension - - extensions.extend( - [ - cpp_extension.CppExtension( - "fairseq.libbase", - sources=[ - "fairseq/clib/libbase/balanced_assignment.cpp", - ], - ) - ] - ) - - extensions.extend( - [ - cpp_extension.CppExtension( - "fairseq.libnat", - sources=[ - "fairseq/clib/libnat/edit_dist.cpp", - ], - ), - cpp_extension.CppExtension( - "alignment_train_cpu_binding", - sources=[ - "examples/operators/alignment_train_cpu.cpp", - ], - ), - ] - ) - if "CUDA_HOME" in os.environ: - extensions.extend( - [ - cpp_extension.CppExtension( - "fairseq.libnat_cuda", - sources=[ - "fairseq/clib/libnat_cuda/edit_dist.cu", - "fairseq/clib/libnat_cuda/binding.cpp", - ], - ), - cpp_extension.CppExtension( - "fairseq.ngram_repeat_block_cuda", - sources=[ - "fairseq/clib/cuda/ngram_repeat_block_cuda.cpp", - "fairseq/clib/cuda/ngram_repeat_block_cuda_kernel.cu", - ], - ), - cpp_extension.CppExtension( - "alignment_train_cuda_binding", - sources=[ - "examples/operators/alignment_train_kernel.cu", - "examples/operators/alignment_train_cuda.cpp", - ], - ), - ] - ) - cmdclass["build_ext"] = cpp_extension.BuildExtension - -except ImportError: - pass - - -if "READTHEDOCS" in os.environ: - # don't build extensions when generating docs - extensions = [] - if "build_ext" in cmdclass: - del cmdclass["build_ext"] - - # use CPU build of PyTorch - dependency_links = [ - "https://download.pytorch.org/whl/cpu/torch-1.7.0%2Bcpu-cp36-cp36m-linux_x86_64.whl" - ] -else: - dependency_links = [] - - -if "clean" in sys.argv[1:]: - # Source: https://bit.ly/2NLVsgE - print("deleting Cython files...") - - subprocess.run( - ["rm -f fairseq/*.so fairseq/**/*.so fairseq/*.pyd fairseq/**/*.pyd"], - shell=True, - ) - - -extra_packages = [] -if os.path.exists(os.path.join("fairseq", "model_parallel", "megatron", "mpu")): - extra_packages.append("fairseq.model_parallel.megatron.mpu") - - -def do_setup(package_data): - setup( - name="fairseq", - version=version, - description="Facebook AI Research Sequence-to-Sequence Toolkit", - url="https://github.com/pytorch/fairseq", - classifiers=[ - "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - ], - long_description=readme, - long_description_content_type="text/markdown", - setup_requires=[ - "cython", - 'numpy<1.20.0; python_version<"3.7"', - 'numpy; python_version>="3.7"', - "setuptools>=18.0", - ], - install_requires=[ - "cffi", - "cython", - 'dataclasses; python_version<"3.7"', - "hydra-core>=1.0.7,<1.1", - "omegaconf<2.1", - 'numpy<1.20.0; python_version<"3.7"', - 'numpy; python_version>="3.7"', - "regex", - "sacrebleu>=1.4.12", - "torch", - "tqdm", - "bitarray", - "torchaudio>=0.8.0", - ], - dependency_links=dependency_links, - packages=find_packages( - exclude=[ - "examples", - "examples.*", - "scripts", - "scripts.*", - "tests", - "tests.*", - ] - ) - + extra_packages, - package_data=package_data, - ext_modules=extensions, - test_suite="tests", - entry_points={ - "console_scripts": [ - "fairseq-eval-lm = fairseq_cli.eval_lm:cli_main", - "fairseq-generate = fairseq_cli.generate:cli_main", - "fairseq-hydra-train = fairseq_cli.hydra_train:cli_main", - "fairseq-interactive = fairseq_cli.interactive:cli_main", - "fairseq-preprocess = fairseq_cli.preprocess:cli_main", - "fairseq-score = fairseq_cli.score:cli_main", - "fairseq-train = fairseq_cli.train:cli_main", - "fairseq-validate = fairseq_cli.validate:cli_main", - ], - }, - cmdclass=cmdclass, - zip_safe=False, - ) - - -def get_files(path, relative_to="fairseq"): - all_files = [] - for root, _dirs, files in os.walk(path, followlinks=True): - root = os.path.relpath(root, relative_to) - for file in files: - if file.endswith(".pyc"): - continue - all_files.append(os.path.join(root, file)) - return all_files - - -if __name__ == "__main__": - try: - # symlink examples into fairseq package so package_data accepts them - fairseq_examples = os.path.join("fairseq", "examples") - if "build_ext" not in sys.argv[1:] and not os.path.exists(fairseq_examples): - os.symlink(os.path.join("..", "examples"), fairseq_examples) - - package_data = { - "fairseq": ( - get_files(fairseq_examples) - + get_files(os.path.join("fairseq", "config")) - ) - } - do_setup(package_data) - finally: - if "build_ext" not in sys.argv[1:] and os.path.islink(fairseq_examples): - os.unlink(fairseq_examples) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_bmuf.py b/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_bmuf.py deleted file mode 100644 index c4e6c8741985109347d431982850e72245067969..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_bmuf.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import functools -import random -import unittest -from multiprocessing import Manager - -import torch -import torch.nn as nn -from fairseq import optim -from fairseq.distributed import utils as distributed_utils -from omegaconf import OmegaConf - - -class Model(nn.Module): - def __init__(self, input_size, output_size): - super(Model, self).__init__() - self.fc = nn.Linear(input_size, output_size) - - def forward(self, input): - output = self.fc(input) - return output - - -def setup_model_loss_criterion(cfg, args, rank, is_cuda): - """ - setup model, criterion and optimizer based on input args - """ - args.distributed_rank = rank - cfg.distributed_training.distributed_rank = args.distributed_rank - if cfg.distributed_training.distributed_world_size > 1: - distributed_utils.distributed_init(cfg) - torch.manual_seed(1) - model = Model(args.input_size, args.nb_classes) - loss_fn = nn.CrossEntropyLoss() - if is_cuda: - model = model.cuda() - loss_fn = loss_fn.cuda() - - optimizer = optim.sgd.SGD(args, model.parameters()) - optimizer = optim.FairseqBMUF(cfg=cfg.bmuf, optimizer=optimizer) - - return model, loss_fn, optimizer - - -def train_step(input, target, model, loss_fn, optimizer, **unused): - """Do forward, backward and parameter update.""" - model.train() - output = model(input) - loss = loss_fn(output, target) - optimizer.backward(loss) - optimizer.step() - - -def single_gpu_training(cfg, args, rank, iterations, shared_results): - - is_cuda = torch.cuda.is_available() - if is_cuda: - torch.cuda.set_device(rank) - - model, loss_fn, optimizer = setup_model_loss_criterion(cfg, args, rank, is_cuda) - - for _ in range(iterations): - input = torch.randn(1, args.input_size) - target = torch.empty(args.batch_size, dtype=torch.long).random_(args.nb_classes) - - if is_cuda: - input = input.cuda() - target = target.cuda() - train_step(input, target, model, loss_fn, optimizer) - - results = [] - for param in model.parameters(): - if len(results) == 0: - results = param.flatten().cpu().data - else: - results = torch.cat((results, param.flatten().cpu().data), 0) - - shared_results[rank] = results - - -def setup_args(): - args = argparse.Namespace() - args.global_sync_iter = 20 - args.block_momentum = 0.875 - args.block_lr = 0.5 - args.input_size = 5 - args.nb_classes = 2 - args.batch_size = 1 - args.lr = [1e-3] - args.momentum = 0 - args.weight_decay = 0 - args.warmup_iterations = 0 - args.use_nbm = True - args.average_sync = True - args.global_sync_iter = 1 - args.model_parallel_size = 1 - args.distributed_backend = "gloo" - - args.distributed_world_size = 2 - port = random.randint(10000, 20000) - args.distributed_init_method = "tcp://localhost:{port}".format(port=port) - args.distributed_init_host = "localhost" - args.distributed_port = port + 1 - args.local_world_size = args.distributed_world_size - - cfg = OmegaConf.create() - cfg.optimization = OmegaConf.create() - cfg.common = OmegaConf.create() - cfg.distributed_training = OmegaConf.create() - cfg.dataset = OmegaConf.create() - cfg.bmuf = OmegaConf.create() - cfg.optimizer = OmegaConf.create() - - cfg.bmuf.global_sync_iter = args.global_sync_iter - cfg.bmuf.block_momentum = args.block_momentum - cfg.bmuf.block_lr = args.block_lr - cfg.dataset.batch_size = args.batch_size - cfg.optimization.lr = args.lr - cfg.optimizer.momentum = args.momentum - cfg.optimizer.weight_decay = args.weight_decay - cfg.bmuf.warmup_iterations = args.warmup_iterations - cfg.bmuf.use_nbm = args.use_nbm - cfg.bmuf.average_sync = args.average_sync - cfg.common.model_parallel_size = args.model_parallel_size - cfg.distributed_training.distributed_backend = args.distributed_backend - cfg.distributed_training.distributed_world_size = args.distributed_world_size - cfg.bmuf.distributed_world_size = args.distributed_world_size - cfg.distributed_training.distributed_init_method = args.distributed_init_method - cfg.distributed_training.distributed_port = args.distributed_port - - return cfg, args - - -@unittest.skipIf(torch.cuda.device_count() < 2, "test requires 2 GPUs") -class TestBMUF(unittest.TestCase): - def bmuf_process(self, cfg, args, iterations): - results = Manager().dict() - torch.multiprocessing.spawn( - fn=functools.partial(single_gpu_training, cfg, args), - args=(iterations, results), - nprocs=args.distributed_world_size, - join=True, - ) - return results - - def test_bmuf_sync(self): - # Train model for 1 iteration and do bmuf sync without doing warmup - cfg, args = setup_args() - iterations = 1 - results = self.bmuf_process(cfg, args, iterations) - # Make sure params in both machines are same - assert len(results) == 2 - self.assertAlmostEqual(results[0], results[1]) - - def test_warmup_sync(self): - # Train model for 20 iteration and do warmup sync without doing bmuf sync - cfg, args = setup_args() - args.warmup_iterations = 20 - cfg.bmuf.warmup_iterations = args.warmup_iterations - iterations = 20 - results = self.bmuf_process(cfg, args, iterations) - # Make sure params in both machines are same - assert len(results) == 2 - self.assertAlmostEqual(results[0], results[1]) - - def test_warmup_sync_bmuf_sync(self): - # Train model for 25 iteration and do warmup sync after 20 iteration - # and bmuf sync after 25 iteration - cfg, args = setup_args() - args.warmup_iterations = 20 - args.global_sync_iter = 5 - cfg.bmuf.warmup_iterations = args.warmup_iterations - cfg.bmuf.global_sync_iter = args.global_sync_iter - iterations = 25 - results = self.bmuf_process(cfg, args, iterations) - # Make sure params in both machines are same - assert len(results) == 2 - self.assertAlmostEqual(results[0], results[1]) - - def test_single_gpu_bmuf(self): - # Train model for 5 iterations and use GPU 1 - cfg, args = setup_args() - args.distributed_world_size = 1 - args.warmup_iterations = 5 - cfg.distributed_training.distributed_world_size = args.distributed_world_size - cfg.bmuf.distributed_world_size = args.distributed_world_size - cfg.bmuf.warmup_iterations = args.warmup_iterations - iterations = 20 - results = self.bmuf_process(cfg, args, iterations) - assert len(results) == 1 - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-4) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_distributed_timeout_wrapper.py b/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_distributed_timeout_wrapper.py deleted file mode 100644 index 42cda4e3699e976f7fd5ea986d2945ff54738189..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_distributed_timeout_wrapper.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import signal -import time -import unittest - -import torch -from fairseq.distributed import DistributedTimeoutWrapper -from torch import nn - - -class ModuleWithDelay(nn.Module): - def __init__(self, delay): - super().__init__() - self.delay = delay - - def forward(self, x): - time.sleep(self.delay) - return x - - -class TestDistributedTimeoutWrapper(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_no_timeout(self): - module = DistributedTimeoutWrapper(ModuleWithDelay(1), 0, signal.SIGINT) - module(torch.rand(5)) - module.stop_timeout() - - def test_timeout_safe(self): - module = DistributedTimeoutWrapper(ModuleWithDelay(1), 10, signal.SIGINT) - module(torch.rand(5)) - module.stop_timeout() - - def test_timeout_killed(self): - with self.assertRaises(KeyboardInterrupt): - module = DistributedTimeoutWrapper(ModuleWithDelay(5), 1, signal.SIGINT) - module(torch.rand(5)) - module.stop_timeout() - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_module_proxy_wrapper.py b/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_module_proxy_wrapper.py deleted file mode 100644 index daaa9d16ccccf7c8f25a5f38a5179762301209d8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_module_proxy_wrapper.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.distributed import ModuleProxyWrapper -from torch import nn - -from .utils import objects_are_equal - - -class MockDDPWrapper(nn.Module): - """A simple wrapper with an interface similar to DistributedDataParallel.""" - - def __init__(self, module): - super().__init__() - self.module = module - - def forward(self, x): - return self.module(x) - - -class Model(nn.Module): - def __init__(self): - super().__init__() - self.linear = nn.Linear(5, 10) - self.xyz = "hello" - - def forward(self, x): - return self.linear(x) - - def get_xyz(self): - return self.xyz - - -class TestModuleProxyWrapper(unittest.TestCase): - def _get_module(self): - module = Model() - wrapped_module = MockDDPWrapper(module) - wrapped_module = ModuleProxyWrapper(wrapped_module) - return wrapped_module, module - - def test_getattr_forwarding(self): - wrapped_module, module = self._get_module() - assert module.xyz == "hello" - assert module.get_xyz() == "hello" - assert wrapped_module.xyz == "hello" - - wrapped_module.xyz = "world" - assert wrapped_module.xyz == "world" - assert module.get_xyz() == "hello" - - def test_state_dict(self): - wrapped_module, module = self._get_module() - assert objects_are_equal(wrapped_module.state_dict(), module.state_dict()) - - def test_load_state_dict(self): - wrapped_module, module = self._get_module() - wrapped_module.load_state_dict(module.state_dict()) - input = torch.rand(4, 5) - torch.testing.assert_allclose(wrapped_module(input), module(input)) - - def test_forward(self): - wrapped_module, module = self._get_module() - input = torch.rand(4, 5) - torch.testing.assert_allclose(wrapped_module(input), module(input)) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_utils.py deleted file mode 100644 index e5b82b75e3d2471184a681702a8cbc74b5c733d8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/test_utils.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import functools -import sys -import unittest - -import torch -from fairseq.distributed import utils as dist_utils - -from .utils import objects_are_equal, spawn_and_init - - -class DistributedTest(unittest.TestCase): - def setUp(self): - if not torch.cuda.is_available(): - raise unittest.SkipTest("CUDA not available, skipping test") - if sys.platform == "win32": - raise unittest.SkipTest("NCCL doesn't support Windows, skipping test") - if torch.cuda.device_count() < 2: - raise unittest.SkipTest("distributed tests require 2+ GPUs, skipping") - - -class TestBroadcastObject(DistributedTest): - def test_str(self): - spawn_and_init( - functools.partial( - TestBroadcastObject._test_broadcast_object, "hello world" - ), - world_size=2, - ) - - def test_tensor(self): - spawn_and_init( - functools.partial( - TestBroadcastObject._test_broadcast_object, - torch.rand(5), - ), - world_size=2, - ) - - def test_complex(self): - spawn_and_init( - functools.partial( - TestBroadcastObject._test_broadcast_object, - { - "a": "1", - "b": [2, torch.rand(2, 3), 3], - "c": (torch.rand(2, 3), 4), - "d": {5, torch.rand(5)}, - "e": torch.rand(5), - "f": torch.rand(5).int().cuda(), - }, - ), - world_size=2, - ) - - @staticmethod - def _test_broadcast_object(ref_obj, rank, group): - obj = dist_utils.broadcast_object( - ref_obj if rank == 0 else None, src_rank=0, group=group - ) - assert objects_are_equal(ref_obj, obj) - - -class TestAllGatherList(DistributedTest): - def test_str_equality(self): - spawn_and_init( - functools.partial( - TestAllGatherList._test_all_gather_list_equality, - "hello world", - ), - world_size=2, - ) - - def test_tensor_equality(self): - spawn_and_init( - functools.partial( - TestAllGatherList._test_all_gather_list_equality, - torch.rand(5), - ), - world_size=2, - ) - - def test_complex_equality(self): - spawn_and_init( - functools.partial( - TestAllGatherList._test_all_gather_list_equality, - { - "a": "1", - "b": [2, torch.rand(2, 3), 3], - "c": (torch.rand(2, 3), 4), - "d": {5, torch.rand(5)}, - "e": torch.rand(5), - "f": torch.rand(5).int(), - }, - ), - world_size=2, - ) - - @staticmethod - def _test_all_gather_list_equality(ref_obj, rank, group): - objs = dist_utils.all_gather_list(ref_obj, group) - for obj in objs: - assert objects_are_equal(ref_obj, obj) - - def test_rank_tensor(self): - spawn_and_init( - TestAllGatherList._test_all_gather_list_rank_tensor, world_size=2 - ) - - @staticmethod - def _test_all_gather_list_rank_tensor(rank, group): - obj = torch.tensor([rank]) - objs = dist_utils.all_gather_list(obj, group) - for i, obj in enumerate(objs): - assert obj.item() == i - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/utils.py deleted file mode 100644 index be4e19cd1e2182903e9dad6b0e52bf33b9968c9d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/distributed/utils.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import functools -import tempfile - -import torch - - -def spawn_and_init(fn, world_size, args=None): - if args is None: - args = () - with tempfile.NamedTemporaryFile(delete=False) as tmp_file: - torch.multiprocessing.spawn( - fn=functools.partial(init_and_run, fn, args), - args=( - world_size, - tmp_file.name, - ), - nprocs=world_size, - join=True, - ) - - -def distributed_init(rank, world_size, tmp_file): - torch.distributed.init_process_group( - backend="nccl", - init_method="file://{}".format(tmp_file), - world_size=world_size, - rank=rank, - ) - torch.cuda.set_device(rank) - - -def init_and_run(fn, args, rank, world_size, tmp_file): - distributed_init(rank, world_size, tmp_file) - group = torch.distributed.new_group() - fn(rank, group, *args) - - -def objects_are_equal(a, b) -> bool: - if type(a) is not type(b): - return False - if isinstance(a, dict): - if set(a.keys()) != set(b.keys()): - return False - for k in a.keys(): - if not objects_are_equal(a[k], b[k]): - return False - return True - elif isinstance(a, (list, tuple, set)): - if len(a) != len(b): - return False - return all(objects_are_equal(x, y) for x, y in zip(a, b)) - elif torch.is_tensor(a): - return ( - a.size() == b.size() - and a.dtype == b.dtype - and a.device == b.device - and torch.all(a == b) - ) - else: - return a == b diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/test_binaries_gpu.py b/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/test_binaries_gpu.py deleted file mode 100644 index 267eadcb305203323bcc1e9f1996520f5e977365..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/test_binaries_gpu.py +++ /dev/null @@ -1,590 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import json -import logging -import os -import tempfile -import unittest -from io import StringIO - -import torch -from fairseq import options -from fairseq_cli import train - -from tests.utils import ( - create_dummy_data, - generate_main, - preprocess_lm_data, - preprocess_translation_data, - train_language_model, - train_translation_model, -) - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestMultiGPU(unittest.TestCase): - @staticmethod - def parse_logs(logfile): - logs = [] - for ln in open(logfile, "r").readlines(): - try: - logs.append(json.loads(ln)) - except json.JSONDecodeError: - continue - return logs - - @property - def world_size(self): - return torch.cuda.device_count() - - def train_flags(self, mu): - return [ - "--memory-efficient-fp16", - "--update-freq", - "1", - "--seed", - "1", - "--log-format", - "json", - "--max-update", - str(mu), - "--tokens-per-sample", - "20", - "--batch-size", - "2", - "--share-decoder-input-output-embed", - "--optimizer", - "adam", - "--max-valid-steps", - "1", - "--pad-to-fixed-length", - "--sample-break-mode", - "none", - ] - - def _test_resume_multilingual_training( - self, extra_clargs, arch="transformer_lm_gpt2_tiny" - ): - languages = ["en_XX", "fr_XX", "zh_CN"] - save_interval = 5 - mu = 10 - flags = ( - self.train_flags(mu) - + ["--save-interval-updates", str(save_interval), "--log-interval", "1"] - + extra_clargs - ) - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fp16") as data_dir: - log = os.path.join(data_dir, "train.log") - create_dummy_data( - data_dir, - num_examples=int( - mu * 20 * self.world_size * 1.5 - ), # make sure enough data for max updates - languages=languages, - ) - preprocess_lm_data(data_dir, languages) - train_language_model( - data_dir, - arch, - flags + ["--log-file", log], - task="multilingual_language_modeling", - world_size=self.world_size, - ) - log2 = os.path.join(data_dir, "resume.log") - ckpt_name = f"checkpoint_1_{save_interval}.pt" - restore_file = os.path.join(data_dir, ckpt_name) - train_language_model( - data_dir, - arch, - flags - + ["--log-file", log2, "--restore-file", restore_file, "--no-save"], - task="multilingual_language_modeling", - world_size=self.world_size, - ) - - l1 = self.parse_logs(log) - assert ( - int(l1[-1]["train_num_updates"]) == mu - ), f"The first run did not complete {mu} updates. Add more data" - l2 = self.parse_logs(log2) - - if int(l2[0]["num_updates"]) != save_interval + 1: - all_ckpt_files = [ - x for x in os.listdir(data_dir) if x.endswith(".pt") - ] - import shutil - - shutil.move(data_dir, "last_failed_resume") - raise AssertionError( - f"Likely failed to load {ckpt_name}. {all_ckpt_files} \n LOGS: {l1} \n\n {l2}. " - ) - for k in [ - "train_loss", - "train_num_updates", - "train_ppl", - "train_gnorm", - ]: - from_scratch, resumed = float(l1[-1][k]), float(l2[-1][k]) - # This fails without rounding! - assert ( - from_scratch == resumed - ), f"difference at {k} {from_scratch} != {resumed}" - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestTranslationGPU(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_fp16_multigpu(self): - self._test_multigpu("test_fp16", ["--fp16"]) - - def test_slowmo_multigpu(self): - self._test_multigpu( - "test_slowmo", ["--ddp-backend", "slowmo", "--nprocs-per-node", "1"] - ) - - def test_slowmo_single_node_multigpu(self): - self._test_multigpu( - "test_slowmo_single_node", - ["--ddp-backend", "slowmo", "--nprocs-per-node", "2"], - ) - - def _test_multigpu(self, test_name, test_args): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory(test_name) as data_dir: - log = os.path.join(data_dir, "train.log") - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "fconv_iwslt_de_en", - test_args + ["--log-file", log], - world_size=min(torch.cuda.device_count(), 2), - ) - generate_main(data_dir) - assert os.path.exists(log) - - @staticmethod - def parse_logs(logfile): - logs = [] - for ln in open(logfile, "r").readlines(): - try: - logs.append(json.loads(ln)) - except json.JSONDecodeError: - continue - return logs - - def test_resume_training_fsdp(self): - self._test_resume_training(["--ddp-backend", "fully_sharded"]) - - def test_resume_training_fsdp_sharded_state(self): - self._test_resume_training( - ["--ddp-backend", "fully_sharded", "--use-sharded-state"] - ) - - def test_resume_training_noc10d(self): - self._test_resume_training([]) - - def _test_resume_training(self, extra_clargs, arch="fconv_iwslt_de_en"): - flags = [ - "--fp16", - "--log-format", - "json", - "--max-update", - "10", - "--save-interval-updates", - "2", - "--log-interval", - "1", - ] + extra_clargs - world_size = min(torch.cuda.device_count(), 2) - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fp16") as data_dir: - log = os.path.join(data_dir, "train.log") - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - arch, - flags + ["--log-file", log], - world_size=world_size, - ) - log2 = os.path.join(data_dir, "resume.log") - restore_file = os.path.join(data_dir, "checkpoint_1_2.pt") - train_translation_model( - data_dir, - arch, - flags + ["--log-file", log2, "--restore-file", restore_file], - world_size=world_size, - ) - - l1 = self.parse_logs(log) - l2 = self.parse_logs(log2) - assert int(l2[0]["num_updates"]) == 3, f"{l1}\n\n {l2}" - for k in [ - "train_loss", - "train_num_updates", - "train_ppl", - "train_gnorm", - ]: - from_scratch, resumed = l1[-1][k], l2[-1][k] - assert ( - from_scratch == resumed - ), f"difference at {k} {from_scratch} != {resumed}" - - def test_memory_efficient_fp16(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_memory_efficient_fp16") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, "fconv_iwslt_de_en", ["--memory-efficient-fp16"] - ) - generate_main(data_dir) - - def test_transformer_fp16(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "64", - "--decoder-embed-dim", - "64", - "--fp16", - ], - run_validation=True, - ) - generate_main(data_dir) - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_amp(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_amp") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model(data_dir, "fconv_iwslt_de_en", ["--amp"]) - generate_main(data_dir) - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_transformer_amp(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "64", - "--decoder-embed-dim", - "64", - "--amp", - ], - run_validation=True, - ) - generate_main(data_dir) - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_levenshtein_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_levenshtein_transformer" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ["--joined-dictionary"]) - train_translation_model( - data_dir, - "levenshtein_transformer", - [ - "--apply-bert-init", - "--early-exit", - "6,6,6", - "--criterion", - "nat_loss", - ], - task="translation_lev", - ) - gen_config = [ - "--task", - "translation_lev", - "--iter-decode-max-iter", - "9", - "--iter-decode-eos-penalty", - "0", - "--print-step", - ] - # non-ensemble generation - generate_main(data_dir, gen_config) - # ensemble generation - generate_main( - data_dir, - gen_config, - path=os.pathsep.join( - [ - os.path.join(data_dir, "checkpoint_last.pt"), - os.path.join(data_dir, "checkpoint_last.pt"), - ] - ), - ) - - def test_fsdp_checkpoint_generate(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir: - log = os.path.join(data_dir, "train.log") - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - world_size = min(torch.cuda.device_count(), 2) - train_translation_model( - data_dir, - "fconv_iwslt_de_en", - ["--log-file", log, "--ddp-backend", "fully_sharded"], - world_size=world_size, - ) - generate_main(data_dir) - assert os.path.exists(log) - - def test_fsdp_sharded_checkpoint_generate(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fsdp_sharded") as data_dir: - log = os.path.join(data_dir, "train.log") - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - world_size = min(torch.cuda.device_count(), 2) - train_translation_model( - data_dir, - "fconv_iwslt_de_en", - [ - "--log-file", - log, - "--ddp-backend", - "fully_sharded", - "--use-sharded-state", - ], - world_size=world_size, - ) - generate_main(data_dir, ["--checkpoint-shard-count", str(world_size)]) - assert os.path.exists(log) - - -def _quantize_language_model(data_dir, arch, extra_flags=None, run_validation=False): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - "language_modeling", - data_dir, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--criterion", - "adaptive_loss", - "--adaptive-softmax-cutoff", - "5,10,15", - "--max-tokens", - "500", - "--tokens-per-sample", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - "1", - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - train.main(train_args) - - # try scalar quantization - scalar_quant_train_parser = options.get_training_parser() - scalar_quant_train_args = options.parse_args_and_arch( - scalar_quant_train_parser, - [ - "--task", - "language_modeling", - data_dir, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--criterion", - "adaptive_loss", - "--adaptive-softmax-cutoff", - "5,10,15", - "--max-tokens", - "500", - "--tokens-per-sample", - "500", - "--save-dir", - data_dir, - "--max-update", - "3", - "--no-progress-bar", - "--distributed-world-size", - "1", - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - "--quant-noise-scalar", - "0.5", - ] - + (extra_flags or []), - ) - train.main(scalar_quant_train_args) - - # try iterative PQ quantization - quantize_parser = options.get_training_parser() - quantize_args = options.parse_args_and_arch( - quantize_parser, - [ - "--task", - "language_modeling", - data_dir, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--criterion", - "adaptive_loss", - "--adaptive-softmax-cutoff", - "5,10,15", - "--max-tokens", - "50", - "--tokens-per-sample", - "50", - "--max-update", - "6", - "--no-progress-bar", - "--distributed-world-size", - "1", - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - "--restore-file", - os.path.join(data_dir, "checkpoint_last.pt"), - "--reset-optimizer", - "--quantization-config-path", - os.path.join( - os.path.dirname(__file__), "transformer_quantization_config.yaml" - ), - ] - + (extra_flags or []), - ) - train.main(quantize_args) - - -@unittest.skipIf( - int(torch.__version__[2]) < 10, reason="quantized kernels are only supported on CPU" -) -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestQuantization(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_quantization(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_quantization") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - # tests both scalar and iterative PQ quantization - _quantize_language_model(data_dir, "transformer_lm") - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestOptimizersGPU(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_flat_grads(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_flat_grads") as data_dir: - # Use just a bit of data and tiny model to keep this test runtime reasonable - create_dummy_data(data_dir, num_examples=10, maxlen=5) - preprocess_translation_data(data_dir) - with self.assertRaises(RuntimeError): - # adafactor isn't compatible with flat grads, which - # are used by default with --fp16 - train_translation_model( - data_dir, - "lstm", - [ - "--required-batch-size-multiple", - "1", - "--encoder-layers", - "1", - "--encoder-hidden-size", - "32", - "--decoder-layers", - "1", - "--optimizer", - "adafactor", - "--fp16", - ], - ) - # but it should pass once we set --fp16-no-flatten-grads - train_translation_model( - data_dir, - "lstm", - [ - "--required-batch-size-multiple", - "1", - "--encoder-layers", - "1", - "--encoder-hidden-size", - "32", - "--decoder-layers", - "1", - "--optimizer", - "adafactor", - "--fp16", - "--fp16-no-flatten-grads", - ], - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/test_ema_gpu.py b/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/test_ema_gpu.py deleted file mode 100644 index a071b3dc0089b4a3ceba7ba267dd666cb47be0b6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/test_ema_gpu.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from copy import deepcopy -from dataclasses import dataclass -from typing import Optional - -import torch -from fairseq.models.ema import EMA - - -class DummyModule(torch.nn.Module): - def __init__(self) -> None: - """LightningModule for testing purposes - - Args: - epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum - validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. - """ - super().__init__() - self.layer = torch.nn.Linear(in_features=32, out_features=2) - self.another_layer = torch.nn.Linear(in_features=2, out_features=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.layer(x) - return self.another_layer(x) - - -@dataclass -class EMAConfig(object): - ema_decay: float = 0.99 - ema_start_update: int = 0 - ema_fp32: bool = False - ema_seed_model: Optional[str] = None - ema_update_freq: int = 1 - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestEMAGPU(unittest.TestCase): - def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): - diff = x.float() - y.float() - diff_norm = torch.norm(diff) - other_norm = torch.norm(y.float()) - - if msg is None: - msg = "|input - other| > {} + {} * |other|".format(atol, rtol) - - self.assertLessEqual( - diff_norm, - atol + rtol * other_norm, - msg=msg, - ) - - def test_ema(self): - model = DummyModule().cuda() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig() - ema = EMA(model, config) - - # set decay - ema._set_decay(config.ema_decay) - self.assertEqual(ema.get_decay(), config.ema_decay) - - # get model - self.assertEqual(ema.get_model(), ema.model) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - # EMA step - x = torch.randn(32).cuda() - y = model(x) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - ema_state_dict = ema.get_model().state_dict() - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema_state_dict[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - # Load EMA into model - model2 = DummyModule().cuda() - ema.reverse(model2) - - for key, param in model2.state_dict().items(): - ema_param = ema_state_dict[key] - self.assertTrue(torch.allclose(ema_param, param)) - - def test_ema_fp32(self): - model = DummyModule().cuda().half() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_fp32=True) - ema = EMA(model, config) - - x = torch.randn(32).cuda() - y = model(x.half()) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema.get_model().state_dict()[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - self.assertIn(key, ema.fp32_params) - - # EMA update is done in fp32, and hence the EMA param must be - # closer to the EMA update done in fp32 than in fp16. - self.assertLessEqual( - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param.float() - + (1 - config.ema_decay) * param.float() - ) - .half() - .float() - ), - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param + (1 - config.ema_decay) * param - ).float() - ), - ) - self.assertTorchAllClose( - ema_param, - ( - config.ema_decay * prev_param.float() - + (1 - config.ema_decay) * param.float() - ).half(), - ) - - def test_ema_fp16(self): - model = DummyModule().cuda().half() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_fp32=False) - ema = EMA(model, config) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - x = torch.randn(32).cuda() - y = model(x.half()) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema.get_model().state_dict()[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - - # EMA update is done in fp16, and hence the EMA param must be - # closer to the EMA update done in fp16 than in fp32. - self.assertLessEqual( - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param + (1 - config.ema_decay) * param - ).float() - ), - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param.float() - + (1 - config.ema_decay) * param.float() - ) - .half() - .float() - ), - ) - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/transformer_quantization_config.yaml b/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/transformer_quantization_config.yaml deleted file mode 100644 index de31d8116ced675b81eb74119642217d768e7736..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/gpu/transformer_quantization_config.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This file defines example configuration arguments for quantizing -# a transformer model with product quantization - -n_centroids: - Linear: - key: in_features - value: {"*": 8} - Embedding: - key: embedding_dim - value: {"*": 8} - -block_sizes: - Linear: - key: fuzzy_name - value: {fc: 8, attn: 4, emb: 4} - Embedding: - key: fuzzy_name - value: {emb: 8} - -layers_to_quantize: - - decoder\\.layers\\.\d+\\.fc[12] - - decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01] - - decoder\\.layers\\.\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/__init__.py deleted file mode 100644 index 75ca5e9aea72c3f7247b9fdd60cf673244648fb6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/__init__.py +++ /dev/null @@ -1,211 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import re -import unittest -import zipfile -from argparse import Namespace -from pathlib import Path -from typing import Dict, List, Optional - -import torch -from fairseq import utils -from fairseq.checkpoint_utils import load_model_ensemble_and_task -from fairseq.scoring.bleu import SacrebleuScorer -from fairseq.scoring.wer import WerScorer -from tqdm import tqdm - -S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq" - - -class TestFairseqSpeech(unittest.TestCase): - @classmethod - def download(cls, base_url: str, out_root: Path, filename: str): - url = f"{base_url}/{filename}" - path = out_root / filename - if not path.exists(): - torch.hub.download_url_to_file(url, path.as_posix(), progress=True) - return path - - def _set_up(self, dataset_id: str, s3_dir: str, data_filenames: List[str]): - self.use_cuda = torch.cuda.is_available() - self.root = Path.home() / ".cache" / "fairseq" / dataset_id - self.root.mkdir(exist_ok=True, parents=True) - os.chdir(self.root) - self.base_url = ( - s3_dir if re.search("^https:", s3_dir) else f"{S3_BASE_URL}/{s3_dir}" - ) - for filename in data_filenames: - self.download(self.base_url, self.root, filename) - - def set_up_librispeech(self): - self._set_up( - "librispeech", - "s2t/librispeech", - [ - "cfg_librispeech.yaml", - "spm_librispeech_unigram10000.model", - "spm_librispeech_unigram10000.txt", - "librispeech_test-other.tsv", - "librispeech_test-other.zip", - ], - ) - - def set_up_ljspeech(self): - self._set_up( - "ljspeech", - "s2/ljspeech", - [ - "cfg_ljspeech_g2p.yaml", - "ljspeech_g2p_gcmvn_stats.npz", - "ljspeech_g2p.txt", - "ljspeech_test.tsv", - "ljspeech_test.zip", - ], - ) - - def set_up_sotasty_es_en(self): - self._set_up( - "sotasty_es_en", - "s2t/big/es-en", - [ - "cfg_es_en.yaml", - "spm_bpe32768_es_en.model", - "spm_bpe32768_es_en.txt", - "sotasty_es_en_test_ted.tsv", - "sotasty_es_en_test_ted.zip", - ], - ) - - def set_up_mustc_de_fbank(self): - self._set_up( - "mustc_de_fbank", - "https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/must_c/en_de", - [ - "config.yaml", - "spm.model", - "dict.txt", - "src_dict.txt", - "tgt_dict.txt", - "tst-COMMON.tsv", - "tst-COMMON.zip", - ], - ) - - def download_and_load_checkpoint( - self, - checkpoint_filename: str, - arg_overrides: Optional[Dict[str, str]] = None, - strict: bool = True, - ): - path = self.download(self.base_url, self.root, checkpoint_filename) - _arg_overrides = arg_overrides or {} - _arg_overrides["data"] = self.root.as_posix() - models, cfg, task = load_model_ensemble_and_task( - [path.as_posix()], arg_overrides=_arg_overrides, strict=strict - ) - if self.use_cuda: - for model in models: - model.cuda() - - return models, cfg, task, self.build_generator(task, models, cfg) - - def build_generator( - self, - task, - models, - cfg, - ): - return task.build_generator(models, cfg) - - @classmethod - def get_batch_iterator(cls, task, test_split, max_tokens, max_positions): - task.load_dataset(test_split) - return task.get_batch_iterator( - dataset=task.dataset(test_split), - max_tokens=max_tokens, - max_positions=max_positions, - num_workers=1, - ).next_epoch_itr(shuffle=False) - - @classmethod - def get_wer_scorer( - cls, tokenizer="none", lowercase=False, remove_punct=False, char_level=False - ): - scorer_args = { - "wer_tokenizer": tokenizer, - "wer_lowercase": lowercase, - "wer_remove_punct": remove_punct, - "wer_char_level": char_level, - } - return WerScorer(Namespace(**scorer_args)) - - @classmethod - def get_bleu_scorer(cls, tokenizer="13a", lowercase=False, char_level=False): - scorer_args = { - "sacrebleu_tokenizer": tokenizer, - "sacrebleu_lowercase": lowercase, - "sacrebleu_char_level": char_level, - } - return SacrebleuScorer(Namespace(**scorer_args)) - - @torch.no_grad() - def base_test( - self, - ckpt_name, - reference_score, - score_delta=0.3, - dataset="librispeech_test-other", - max_tokens=65_536, - max_positions=(4_096, 1_024), - arg_overrides=None, - strict=True, - score_type="wer", - ): - models, _, task, generator = self.download_and_load_checkpoint( - ckpt_name, arg_overrides=arg_overrides, strict=strict - ) - if not self.use_cuda: - return - - batch_iterator = self.get_batch_iterator( - task, dataset, max_tokens, max_positions - ) - if score_type == "bleu": - scorer = self.get_bleu_scorer() - elif score_type == "wer": - scorer = self.get_wer_scorer() - else: - raise Exception(f"Unsupported score type {score_type}") - - progress = tqdm(enumerate(batch_iterator), total=len(batch_iterator)) - for batch_idx, sample in progress: - sample = utils.move_to_cuda(sample) if self.use_cuda else sample - hypo = task.inference_step(generator, models, sample) - for i, sample_id in enumerate(sample["id"].tolist()): - tgt_str, hypo_str = self.postprocess_tokens( - task, - sample["target"][i, :], - hypo[i][0]["tokens"].int().cpu(), - ) - if batch_idx == 0 and i < 3: - print(f"T-{sample_id} {tgt_str}") - print(f"H-{sample_id} {hypo_str}") - scorer.add_string(tgt_str, hypo_str) - - print(scorer.result_string() + f" (reference: {reference_score})") - self.assertAlmostEqual(scorer.score(), reference_score, delta=score_delta) - - def postprocess_tokens(self, task, target, hypo_tokens): - tgt_tokens = utils.strip_pad(target, task.tgt_dict.pad()).int().cpu() - tgt_str = task.tgt_dict.string(tgt_tokens, "sentencepiece") - hypo_str = task.tgt_dict.string(hypo_tokens, "sentencepiece") - return tgt_str, hypo_str - - def unzip_files(self, zip_file_name): - zip_file_path = self.root / zip_file_name - with zipfile.ZipFile(zip_file_path, "r") as zip_ref: - zip_ref.extractall(self.root / zip_file_name.strip(".zip")) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_convtransformer_simul_trans.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_convtransformer_simul_trans.py deleted file mode 100644 index 55a12d87ca8b94d34e7e134251b309aa6a80b739..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_convtransformer_simul_trans.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -from tests.speech import TestFairseqSpeech - -S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq/" - - -class TestConvtransformerSimulTrans(TestFairseqSpeech): - def setUp(self): - self._set_up( - "simul", - "speech_tests/simul", - ["config_gcmvn_specaug.yaml", "dict.txt", "dev.tsv"], - ) - - def test_waitk_checkpoint(self): - """Only test model loading since fairseq currently doesn't support inference of simultaneous models""" - _, _, _, _ = self.download_and_load_checkpoint( - "checkpoint_best.pt", - arg_overrides={ - "config_yaml": "config_gcmvn_specaug.yaml", - "load_pretrained_encoder_from": None, - }, - ) - return - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_dual_input_wav_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_dual_input_wav_transformer.py deleted file mode 100644 index 4d37de69081f133f451609bf8cd09a89aceb0698..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_dual_input_wav_transformer.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from collections import namedtuple -from pathlib import Path - -import fairseq -import torch -from fairseq import utils -from fairseq.checkpoint_utils import load_model_ensemble_and_task -from fairseq.scoring.bleu import SacrebleuScorer -from fairseq.tasks import import_tasks -from tqdm import tqdm - -from tests.speech import S3_BASE_URL, TestFairseqSpeech - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestLibrispeechDualInputWavTransformer(TestFairseqSpeech): - def setUp(self): - dataset_id = "librispeech_wvtrasnformer" - base_url = "https://dl.fbaipublicfiles.com/joint_speech_text_4_s2t/acl2022/librispeech/finetuned" - data_filenames = [ - "checkpoint_ave_10.pt", - "spm.model", - "src_dict.txt", - "tgt_dict.txt", - "config.yaml", - ] - self._set_up( - dataset_id, - "s2t", - [ - "librispeech_flac_test-other.tsv", - "librispeech_flac_test-other.zip", - ], - ) - for filename in data_filenames: - self.download(base_url, self.root, filename) - - def import_user_module(self): - user_dir = ( - Path(fairseq.__file__).parent.parent / "examples/speech_text_joint_to_text" - ) - Arg = namedtuple("Arg", ["user_dir"]) - arg = Arg(user_dir.__str__()) - utils.import_user_module(arg) - - @torch.no_grad() - def test_librispeech_dualinput_wav_transformer_checkpoint(self): - self.import_user_module() - checkpoint_filename = "checkpoint_ave_10.pt" - arg_overrides = { - "config_yaml": "config.yaml", - "load_pretrained_speech_text_encoder": "", - "load_pretrained_speech_text_decoder": "", - "beam": 10, - "nbest": 1, - "lenpen": 1.0, - "load_speech_only": True, - } - self.base_test( - checkpoint_filename, - 4.6, - dataset="librispeech_flac_test-other", - max_tokens=800000, - max_positions=(800000, 1024), - arg_overrides=arg_overrides, - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_dualinput_s2t_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_dualinput_s2t_transformer.py deleted file mode 100644 index 2d23429fa59ddbbe53d00a5047c56f318bb7dd9e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_dualinput_s2t_transformer.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from argparse import Namespace -from collections import namedtuple -from pathlib import Path - -import fairseq -import torch -from fairseq import utils -from fairseq.checkpoint_utils import load_model_ensemble_and_task -from fairseq.scoring.bleu import SacrebleuScorer -from fairseq.tasks import import_tasks -from tqdm import tqdm - -from tests.speech import TestFairseqSpeech - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestDualInputS2TTransformer(TestFairseqSpeech): - def setUp(self): - self.set_up_mustc_de_fbank() - - def import_user_module(self): - user_dir = ( - Path(fairseq.__file__).parent.parent / "examples/speech_text_joint_to_text" - ) - Arg = namedtuple("Arg", ["user_dir"]) - arg = Arg(user_dir.__str__()) - utils.import_user_module(arg) - - @torch.no_grad() - def test_mustc_de_fbank_dualinput_s2t_transformer_checkpoint(self): - self.import_user_module() - checkpoint_filename = "checkpoint_ave_10.pt" - path = self.download(self.base_url, self.root, checkpoint_filename) - models, cfg, task = load_model_ensemble_and_task( - [path.as_posix()], - arg_overrides={ - "data": self.root.as_posix(), - "config_yaml": "config.yaml", - "load_pretrain_speech_encoder": "", - "load_pretrain_text_encoder_last": "", - "load_pretrain_decoder": "", - "beam": 10, - "nbest": 1, - "lenpen": 1.0, - "load_speech_only": True, - }, - ) - if self.use_cuda: - for model in models: - model.cuda() - generator = task.build_generator(models, cfg) - test_split = "tst-COMMON" - task.load_dataset(test_split) - batch_iterator = task.get_batch_iterator( - dataset=task.dataset(test_split), - max_tokens=250_000, - max_positions=(10_000, 1_024), - num_workers=1, - ).next_epoch_itr(shuffle=False) - - tokenizer = task.build_tokenizer(cfg.tokenizer) - bpe = task.build_bpe(cfg.bpe) - - def decode_fn(x): - if bpe is not None: - x = bpe.decode(x) - if tokenizer is not None: - x = tokenizer.decode(x) - return x - - scorer_args = { - "sacrebleu_tokenizer": "13a", - "sacrebleu_lowercase": False, - "sacrebleu_char_level": False, - } - scorer = SacrebleuScorer(Namespace(**scorer_args)) - progress = tqdm(enumerate(batch_iterator), total=len(batch_iterator)) - for batch_idx, sample in progress: - sample = utils.move_to_cuda(sample) if self.use_cuda else sample - hypo = task.inference_step(generator, models, sample) - for i, sample_id in enumerate(sample["id"].tolist()): - tgt_tokens = ( - utils.strip_pad(sample["target"][i, :], task.tgt_dict.pad()) - .int() - .cpu() - ) - - tgt_str = task.tgt_dict.string(tgt_tokens, "sentencepiece") - hypo_str = task.tgt_dict.string( - hypo[i][0]["tokens"].int().cpu(), "sentencepiece" - ) - if batch_idx == 0 and i < 3: - print(f"T-{sample_id} {tgt_str}") - print(f"D-{sample_id} {hypo_str}") - scorer.add_string(tgt_str, hypo_str) - reference_bleu = 27.3 - result = scorer.result_string() - print(result + f" (reference: {reference_bleu})") - res_bleu = float(result.split()[2]) - self.assertAlmostEqual(res_bleu, reference_bleu, delta=0.3) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_fastspeech2.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_fastspeech2.py deleted file mode 100644 index 329fc25d4de780acb49d3291e68f593e0b2e138a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_fastspeech2.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq import utils -from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion -from tqdm import tqdm - -from tests.speech import TestFairseqSpeech - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestFastSpeech2(TestFairseqSpeech): - def setUp(self): - self.set_up_ljspeech() - - @torch.no_grad() - def test_ljspeech_fastspeech2_checkpoint(self): - models, cfg, task, generator = self.download_and_load_checkpoint( - "ljspeech_fastspeech2_g2p.pt", - arg_overrides={ - "config_yaml": "cfg_ljspeech_g2p.yaml", - "vocoder": "griffin_lim", - "fp16": False, - }, - ) - - batch_iterator = self.get_batch_iterator(task, "ljspeech_test", 65_536, 4_096) - progress = tqdm(batch_iterator, total=len(batch_iterator)) - mcd, n_samples = 0.0, 0 - for sample in progress: - sample = utils.move_to_cuda(sample) if self.use_cuda else sample - hypos = generator.generate(models[0], sample, has_targ=True) - rets = batch_mel_cepstral_distortion( - [hypo["targ_waveform"] for hypo in hypos], - [hypo["waveform"] for hypo in hypos], - sr=task.sr, - ) - mcd += sum(d.item() for d, _ in rets) - n_samples += len(sample["id"].tolist()) - - mcd = round(mcd / n_samples, 1) - reference_mcd = 3.2 - print(f"MCD: {mcd} (reference: {reference_mcd})") - self.assertAlmostEqual(mcd, reference_mcd, delta=0.1) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2s_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2s_transformer.py deleted file mode 100644 index 27cfa52caba213d92f95ad0ed964938640a03b8a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2s_transformer.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -from fairseq import utils - -from tests.speech import TestFairseqSpeech - -S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq/" - - -class TestS2STransformer(TestFairseqSpeech): - def setUp(self): - self._set_up( - "s2s", - "speech_tests/s2s", - [ - "dev_shuf200.tsv", - "src_feat.zip", - "config_specaug_lb.yaml", - "vocoder", - "vocoder_config.json", - ], - ) - - def test_s2s_transformer_checkpoint(self): - self.base_test( - ckpt_name="s2u_transformer_reduced_fisher.pt", - reference_score=38.3, - dataset="dev_shuf200", - arg_overrides={ - "config_yaml": "config_specaug_lb.yaml", - "multitask_config_yaml": None, - "target_is_code": True, - "target_code_size": 100, - "eval_inference": False, - }, - score_type="bleu", - strict=False, - ) - - def postprocess_tokens(self, task, target, hypo_tokens): - tgt_tokens = utils.strip_pad(target, task.tgt_dict.pad()).int().cpu() - tgt_str = task.tgt_dict.string(tgt_tokens) - hypo_str = task.tgt_dict.string(hypo_tokens) - return tgt_str, hypo_str - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2t_conformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2t_conformer.py deleted file mode 100644 index 2b286fa5d9eb7647a4116ecd59d8d8d2e809e1bd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2t_conformer.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -from tests.speech import TestFairseqSpeech - - -class TestS2TConformer(TestFairseqSpeech): - def setUp(self): - self.set_up_librispeech() - - def test_librispeech_s2t_conformer_s_checkpoint(self): - self.base_test( - ckpt_name="librispeech_conformer_rel_pos_s.pt", - reference_score=12, - arg_overrides={"config_yaml": "cfg_librispeech.yaml"}, - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2t_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2t_transformer.py deleted file mode 100644 index b48085229c08ef1fc027495d4a792ed1e114b75c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_s2t_transformer.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -from tests.speech import TestFairseqSpeech - - -class TestS2TTransformer(TestFairseqSpeech): - def setUp(self): - self.set_up_librispeech() - - def test_librispeech_s2t_transformer_s_checkpoint(self): - self.base_test( - ckpt_name="librispeech_transformer_s.pt", - reference_score=9, - arg_overrides={"config_yaml": "cfg_librispeech.yaml"}, - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_tts_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_tts_transformer.py deleted file mode 100644 index aea76caddd54354deb73e465037b4b79345003ae..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_tts_transformer.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq import utils -from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion -from tqdm import tqdm - -from tests.speech import TestFairseqSpeech - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestTTSTransformer(TestFairseqSpeech): - def setUp(self): - self.set_up_ljspeech() - - @torch.no_grad() - def test_ljspeech_tts_transformer_checkpoint(self): - models, cfg, task, generator = self.download_and_load_checkpoint( - "ljspeech_transformer_g2p.pt", - arg_overrides={ - "config_yaml": "cfg_ljspeech_g2p.yaml", - "vocoder": "griffin_lim", - "fp16": False, - }, - ) - - batch_iterator = self.get_batch_iterator(task, "ljspeech_test", 65_536, 1024) - progress = tqdm(batch_iterator, total=len(batch_iterator)) - mcd, n_samples = 0.0, 0 - for sample in progress: - sample = utils.move_to_cuda(sample) if self.use_cuda else sample - hypos = generator.generate(models[0], sample, has_targ=True) - rets = batch_mel_cepstral_distortion( - [hypo["targ_waveform"] for hypo in hypos], - [hypo["waveform"] for hypo in hypos], - sr=task.sr, - ) - mcd += sum(d.item() for d, _ in rets) - n_samples += len(sample["id"].tolist()) - - mcd = round(mcd / n_samples, 1) - reference_mcd = 3.3 - print(f"MCD: {mcd} (reference: {reference_mcd})") - self.assertAlmostEqual(mcd, reference_mcd, delta=0.1) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_wav2vec2.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_wav2vec2.py deleted file mode 100644 index 06d566923e0e76aa1de2dd785dba5841b60374fa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_wav2vec2.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq import utils -from fairseq.data.data_utils import post_process -from omegaconf import open_dict - -from tests.speech import TestFairseqSpeech - -S3_BASE_URL = "https://dl.fbaipublicfiles.com/fairseq" - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestWav2Vec2(TestFairseqSpeech): - def setUp(self): - self._set_up( - "librispeech_w2v2", - "conformer/wav2vec2/librispeech", - [ - "test_librispeech-other.ltr", - "test_librispeech-other.tsv", - "test_librispeech-other_small.ltr_100", - "test_librispeech-other_small.tsv", - "test-other.zip", - "dict.ltr.txt", - "dict.ltr_100.txt", - ], - ) - self.unzip_files( - "test-other.zip", - ) - - def test_transformer_w2v2(self): - self.base_test( - ckpt_name="transformer_oss_small_100h.pt", - reference_score=38, - score_delta=1, - dataset="test_librispeech-other", - max_tokens=1000000, - max_positions=(700000, 1000), - arg_overrides={ - "task": "audio_finetuning", - "labels": "ltr", - "nbest": 1, - "tpu": False, - }, - strict=False, - ) - - def test_conformer_w2v2(self): - self.base_test( - ckpt_name="conformer_LS_PT_LS_FT_rope.pt", - reference_score=4.5, - score_delta=1, - dataset="test_librispeech-other_small", - max_tokens=1000000, - max_positions=(700000, 1000), - arg_overrides={ - "task": "audio_finetuning", - "labels": "ltr_100", - "nbest": 1, - "tpu": False, - }, - strict=True, - ) - - def build_generator(self, task, models, cfg): - try: - from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder - except Exception: - raise Exception("Cannot run this test without flashlight dependency") - with open_dict(cfg): - cfg.nbest = 1 - return W2lViterbiDecoder(cfg, task.target_dictionary) - - def postprocess_tokens(self, task, target, hypo_tokens): - tgt_tokens = utils.strip_pad(target, task.target_dictionary.pad()).int().cpu() - tgt_str = task.target_dictionary.string(tgt_tokens) - tgt_str = post_process(tgt_str, "letter") - - hypo_pieces = task.target_dictionary.string(hypo_tokens) - hypo_str = post_process(hypo_pieces, "letter") - return tgt_str, hypo_str - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_xm_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_xm_transformer.py deleted file mode 100644 index 8dd695a1cf7f9b508f1a34acfc343014237def40..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech/test_xm_transformer.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -from tests.speech import TestFairseqSpeech - - -class TestXMTransformer(TestFairseqSpeech): - def setUp(self): - self.set_up_sotasty_es_en() - - def test_sotasty_es_en_600m_checkpoint(self): - self.base_test( - ckpt_name="xm_transformer_600m_es_en_md.pt", - reference_score=30.42, - score_delta=0.2, - max_tokens=3_000_000, - max_positions=(1_000_000, 1_024), - dataset="sotasty_es_en_test_ted", - arg_overrides={"config_yaml": "cfg_es_en.yaml"}, - score_type="bleu", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/asr_test_base.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/asr_test_base.py deleted file mode 100644 index 013c6c97f98a718c187a029e5db023ba564b89fa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/asr_test_base.py +++ /dev/null @@ -1,556 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import os -import unittest -from inspect import currentframe, getframeinfo - -import numpy as np -import torch -from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask -from fairseq.data import data_utils as fairseq_data_utils -from fairseq.data.dictionary import Dictionary -from fairseq.models import ( - BaseFairseqModel, - FairseqDecoder, - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqEncoderModel, - FairseqModel, -) -from fairseq.tasks.fairseq_task import LegacyFairseqTask - -DEFAULT_TEST_VOCAB_SIZE = 100 - - -# /////////////////////////////////////////////////////////////////////////// -# utility function to setup dummy dict/task/input -# /////////////////////////////////////////////////////////////////////////// - - -def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): - dummy_dict = Dictionary() - # add dummy symbol to satisfy vocab size - for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), 1000) - return dummy_dict - - -class DummyTask(LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = get_dummy_dictionary() - if getattr(self.args, "ctc", False): - self.dictionary.add_symbol("") - self.tgt_dict = self.dictionary - - @property - def target_dictionary(self): - return self.dictionary - - -def get_dummy_task_and_parser(): - """ - to build a fariseq model, we need some dummy parse and task. This function - is used to create dummy task and parser to faciliate model/criterion test - - Note: we use FbSpeechRecognitionTask as the dummy task. You may want - to use other task by providing another function - """ - parser = argparse.ArgumentParser( - description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS - ) - DummyTask.add_args(parser) - args = parser.parse_args([]) - task = DummyTask.setup_task(args) - return task, parser - - -def get_dummy_input(T=100, D=80, B=5, K=100): - forward_input = {} - # T max sequence length - # D feature vector dimension - # B batch size - # K target dimension size - feature = torch.randn(B, T, D) - # this (B, T, D) layout is just a convention, you can override it by - # write your own _prepare_forward_input function - src_lengths = torch.from_numpy( - np.random.randint(low=1, high=T, size=B, dtype=np.int64) - ) - src_lengths[0] = T # make sure the maximum length matches - prev_output_tokens = [] - for b in range(B): - token_length = np.random.randint(low=1, high=src_lengths[b].item() + 1) - tokens = np.random.randint(low=0, high=K, size=token_length, dtype=np.int64) - prev_output_tokens.append(torch.from_numpy(tokens)) - - prev_output_tokens = fairseq_data_utils.collate_tokens( - prev_output_tokens, - pad_idx=1, - eos_idx=2, - left_pad=False, - move_eos_to_beginning=False, - ) - src_lengths, sorted_order = src_lengths.sort(descending=True) - forward_input["src_tokens"] = feature.index_select(0, sorted_order) - forward_input["src_lengths"] = src_lengths - forward_input["prev_output_tokens"] = prev_output_tokens - - return forward_input - - -def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)): - """ - This only provides an example to generate dummy encoder output - """ - (T, B, D) = encoder_out_shape - encoder_out = {} - - encoder_out["encoder_out"] = torch.from_numpy( - np.random.randn(*encoder_out_shape).astype(np.float32) - ) - seq_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B)) - # some dummy mask - encoder_out["encoder_padding_mask"] = torch.arange(T).view(1, T).expand( - B, -1 - ) >= seq_lengths.view(B, 1).expand(-1, T) - encoder_out["encoder_padding_mask"].t_() - - # encoer_padding_mask is (T, B) tensor, with (t, b)-th element indicate - # whether encoder_out[t, b] is valid (=0) or not (=1) - return encoder_out - - -def _current_postion_info(): - cf = currentframe() - frameinfo = " (at {}:{})".format( - os.path.basename(getframeinfo(cf).filename), cf.f_back.f_lineno - ) - return frameinfo - - -def check_encoder_output(encoder_output, batch_size=None): - """we expect encoder_output to be a dict with the following - key/value pairs: - - encoder_out: a Torch.Tensor - - encoder_padding_mask: a binary Torch.Tensor - """ - if not isinstance(encoder_output, dict): - msg = ( - "FairseqEncoderModel.forward(...) must be a dict" + _current_postion_info() - ) - return False, msg - - if "encoder_out" not in encoder_output: - msg = ( - "FairseqEncoderModel.forward(...) must contain encoder_out" - + _current_postion_info() - ) - return False, msg - - if "encoder_padding_mask" not in encoder_output: - msg = ( - "FairseqEncoderModel.forward(...) must contain encoder_padding_mask" - + _current_postion_info() - ) - return False, msg - - if not isinstance(encoder_output["encoder_out"], torch.Tensor): - msg = "encoder_out must be a torch.Tensor" + _current_postion_info() - return False, msg - - if encoder_output["encoder_out"].dtype != torch.float32: - msg = "encoder_out must have float32 dtype" + _current_postion_info() - return False, msg - - mask = encoder_output["encoder_padding_mask"] - if mask is not None: - if not isinstance(mask, torch.Tensor): - msg = ( - "encoder_padding_mask must be a torch.Tensor" + _current_postion_info() - ) - return False, msg - if mask.dtype != torch.uint8 and ( - not hasattr(torch, "bool") or mask.dtype != torch.bool - ): - msg = ( - "encoder_padding_mask must have dtype of uint8" - + _current_postion_info() - ) - return False, msg - - if mask.dim() != 2: - msg = ( - "we expect encoder_padding_mask to be a 2-d tensor, in shape (T, B)" - + _current_postion_info() - ) - return False, msg - - if batch_size is not None and mask.size(1) != batch_size: - msg = ( - "we expect encoder_padding_mask to be a 2-d tensor, with size(1)" - + " being the batch size" - + _current_postion_info() - ) - return False, msg - return True, None - - -def check_decoder_output(decoder_output): - """we expect output from a decoder is a tuple with the following constraint: - - the first element is a torch.Tensor - - the second element can be anything (reserved for future use) - """ - if not isinstance(decoder_output, tuple): - msg = "FariseqDecoder output must be a tuple" + _current_postion_info() - return False, msg - - if len(decoder_output) != 2: - msg = "FairseqDecoder output must be 2-elem tuple" + _current_postion_info() - return False, msg - - if not isinstance(decoder_output[0], torch.Tensor): - msg = ( - "FariseqDecoder output[0] must be a torch.Tensor" + _current_postion_info() - ) - return False, msg - - return True, None - - -# /////////////////////////////////////////////////////////////////////////// -# Base Test class -# /////////////////////////////////////////////////////////////////////////// - - -class TestBaseFairseqModelBase(unittest.TestCase): - """ - This class is used to facilitate writing unittest for any class derived from - `BaseFairseqModel`. - """ - - @classmethod - def setUpClass(cls): - if cls is TestBaseFairseqModelBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpModel(self, model): - self.assertTrue(isinstance(model, BaseFairseqModel)) - self.model = model - - def setupInput(self): - pass - - def setUp(self): - self.model = None - self.forward_input = None - pass - - -class TestFairseqEncoderDecoderModelBase(TestBaseFairseqModelBase): - """ - base code to test FairseqEncoderDecoderModel (formally known as - `FairseqModel`) must be derived from this base class - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqEncoderDecoderModelBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpModel(self, model_cls, extra_args_setters=None): - self.assertTrue( - issubclass(model_cls, (FairseqEncoderDecoderModel, FairseqModel)), - msg="This class only tests for FairseqModel subclasses", - ) - - task, parser = get_dummy_task_and_parser() - model_cls.add_args(parser) - - args = parser.parse_args([]) - - if extra_args_setters is not None: - for args_setter in extra_args_setters: - args_setter(args) - model = model_cls.build_model(args, task) - self.model = model - - def setUpInput(self, input=None): - self.forward_input = get_dummy_input() if input is None else input - - def setUp(self): - super().setUp() - - def test_forward(self): - if self.model and self.forward_input: - forward_output = self.model.forward(**self.forward_input) - # for FairseqEncoderDecoderModel, forward returns a tuple of two - # elements, the first one is a Torch.Tensor - succ, msg = check_decoder_output(forward_output) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_output = forward_output - - def test_get_normalized_probs(self): - if self.model and self.forward_input: - forward_output = self.model.forward(**self.forward_input) - logprob = self.model.get_normalized_probs(forward_output, log_probs=True) - prob = self.model.get_normalized_probs(forward_output, log_probs=False) - - # in order for different models/criterion to play with each other - # we need to know whether the logprob or prob output is batch_first - # or not. We assume an additional attribute will be attached to logprob - # or prob. If you find your code failed here, simply override - # FairseqModel.get_normalized_probs, see example at - # https://fburl.com/batch_first_example - self.assertTrue(hasattr(logprob, "batch_first")) - self.assertTrue(hasattr(prob, "batch_first")) - - self.assertTrue(torch.is_tensor(logprob)) - self.assertTrue(torch.is_tensor(prob)) - - -class TestFairseqEncoderModelBase(TestBaseFairseqModelBase): - """ - base class to test FairseqEncoderModel - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqEncoderModelBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpModel(self, model_cls, extra_args_setters=None): - self.assertTrue( - issubclass(model_cls, FairseqEncoderModel), - msg="This class is only used for testing FairseqEncoderModel", - ) - task, parser = get_dummy_task_and_parser() - model_cls.add_args(parser) - args = parser.parse_args([]) - if extra_args_setters is not None: - for args_setter in extra_args_setters: - args_setter(args) - - model = model_cls.build_model(args, task) - self.model = model - - def setUpInput(self, input=None): - self.forward_input = get_dummy_input() if input is None else input - # get_dummy_input() is originally for s2s, here we delete extra dict - # items, so it can be used for EncoderModel / Encoder as well - self.forward_input.pop("prev_output_tokens", None) - - def setUp(self): - super().setUp() - - def test_forward(self): - if self.forward_input and self.model: - bsz = self.forward_input["src_tokens"].size(0) - forward_output = self.model.forward(**self.forward_input) - - # we expect forward_output to be a dict with the following - # key/value pairs: - # - encoder_out: a Torch.Tensor - # - encoder_padding_mask: a binary Torch.Tensor - succ, msg = check_encoder_output(forward_output, batch_size=bsz) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_output = forward_output - - def test_get_normalized_probs(self): - if self.model and self.forward_input: - forward_output = self.model.forward(**self.forward_input) - logprob = self.model.get_normalized_probs(forward_output, log_probs=True) - prob = self.model.get_normalized_probs(forward_output, log_probs=False) - - # in order for different models/criterion to play with each other - # we need to know whether the logprob or prob output is batch_first - # or not. We assume an additional attribute will be attached to logprob - # or prob. If you find your code failed here, simply override - # FairseqModel.get_normalized_probs, see example at - # https://fburl.com/batch_first_example - self.assertTrue(hasattr(logprob, "batch_first")) - self.assertTrue(hasattr(prob, "batch_first")) - - self.assertTrue(torch.is_tensor(logprob)) - self.assertTrue(torch.is_tensor(prob)) - - -class TestFairseqEncoderBase(unittest.TestCase): - """ - base class to test FairseqEncoder - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqEncoderBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpEncoder(self, encoder): - self.assertTrue( - isinstance(encoder, FairseqEncoder), - msg="This class is only used for test FairseqEncoder", - ) - self.encoder = encoder - - def setUpInput(self, input=None): - self.forward_input = get_dummy_input() if input is None else input - # get_dummy_input() is originally for s2s, here we delete extra dict - # items, so it can be used for EncoderModel / Encoder as well - self.forward_input.pop("prev_output_tokens", None) - - def setUp(self): - self.encoder = None - self.forward_input = None - - def test_forward(self): - if self.encoder and self.forward_input: - bsz = self.forward_input["src_tokens"].size(0) - - forward_output = self.encoder.forward(**self.forward_input) - succ, msg = check_encoder_output(forward_output, batch_size=bsz) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_output = forward_output - - -class TestFairseqDecoderBase(unittest.TestCase): - """ - base class to test FairseqDecoder - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqDecoderBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpDecoder(self, decoder): - self.assertTrue( - isinstance(decoder, FairseqDecoder), - msg="This class is only used for test FairseqDecoder", - ) - self.decoder = decoder - - def setUpInput(self, input=None): - self.forward_input = get_dummy_encoder_output() if input is None else input - - def setUpPrevOutputTokens(self, tokens=None): - if tokens is None: - self.encoder_input = get_dummy_input() - self.prev_output_tokens = self.encoder_input["prev_output_tokens"] - else: - self.prev_output_tokens = tokens - - def setUp(self): - self.decoder = None - self.forward_input = None - self.prev_output_tokens = None - - def test_forward(self): - if ( - self.decoder is not None - and self.forward_input is not None - and self.prev_output_tokens is not None - ): - forward_output = self.decoder.forward( - prev_output_tokens=self.prev_output_tokens, - encoder_out=self.forward_input, - ) - succ, msg = check_decoder_output(forward_output) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_input = forward_output - - -class DummyEncoderModel(FairseqEncoderModel): - def __init__(self, encoder): - super().__init__(encoder) - - @classmethod - def build_model(cls, args, task): - return cls(DummyEncoder()) - - def get_logits(self, net_output): - # Inverse of sigmoid to use with BinaryCrossEntropyWithLogitsCriterion as - # F.binary_cross_entropy_with_logits combines sigmoid and CE - return torch.log( - torch.div(net_output["encoder_out"], 1 - net_output["encoder_out"]) - ) - - def get_normalized_probs(self, net_output, log_probs, sample=None): - lprobs = super().get_normalized_probs(net_output, log_probs, sample=sample) - lprobs.batch_first = True - return lprobs - - -class DummyEncoder(FairseqEncoder): - def __init__(self): - super().__init__(None) - - def forward(self, src_tokens, src_lengths): - mask, max_len = lengths_to_encoder_padding_mask(src_lengths) - return {"encoder_out": src_tokens, "encoder_padding_mask": mask} - - -class CrossEntropyCriterionTestBase(unittest.TestCase): - @classmethod - def setUpClass(cls): - if cls is CrossEntropyCriterionTestBase: - raise unittest.SkipTest("Skipping base class test case") - super().setUpClass() - - def setUpArgs(self): - args = argparse.Namespace() - args.sentence_avg = False - args.threshold = 0.1 # to use with BinaryCrossEntropyWithLogitsCriterion - return args - - def setUp(self): - args = self.setUpArgs() - self.model = DummyEncoderModel(encoder=DummyEncoder()) - self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args)) - - def get_src_tokens(self, correct_prediction, aggregate): - """ - correct_prediction: True if the net_output (src_tokens) should - predict the correct target - aggregate: True if the criterion expects net_output (src_tokens) - aggregated across time axis - """ - predicted_idx = 0 if correct_prediction else 1 - if aggregate: - src_tokens = torch.zeros((2, 2), dtype=torch.float) - for b in range(2): - src_tokens[b][predicted_idx] = 1.0 - else: - src_tokens = torch.zeros((2, 10, 2), dtype=torch.float) - for b in range(2): - for t in range(10): - src_tokens[b][t][predicted_idx] = 1.0 - return src_tokens - - def get_target(self, soft_target): - if soft_target: - target = torch.zeros((2, 2), dtype=torch.float) - for b in range(2): - target[b][0] = 1.0 - else: - target = torch.zeros((2, 10), dtype=torch.long) - return target - - def get_test_sample(self, correct, soft_target, aggregate): - src_tokens = self.get_src_tokens(correct, aggregate) - target = self.get_target(soft_target) - L = src_tokens.size(1) - return { - "net_input": {"src_tokens": src_tokens, "src_lengths": torch.tensor([L])}, - "target": target, - "ntokens": src_tokens.size(0) * src_tokens.size(1), - } diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_collaters.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_collaters.py deleted file mode 100644 index 6a5029a48faea2426d7a0277655a2c7c08c1d16c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_collaters.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import numpy as np -import torch -from examples.speech_recognition.data.collaters import Seq2SeqCollater - - -class TestSeq2SeqCollator(unittest.TestCase): - def test_collate(self): - - eos_idx = 1 - pad_idx = 0 - collater = Seq2SeqCollater( - feature_index=0, label_index=1, pad_index=pad_idx, eos_index=eos_idx - ) - - # 2 frames in the first sample and 3 frames in the second one - frames1 = np.array([[7, 8], [9, 10]]) - frames2 = np.array([[1, 2], [3, 4], [5, 6]]) - target1 = np.array([4, 2, 3, eos_idx]) - target2 = np.array([3, 2, eos_idx]) - sample1 = {"id": 0, "data": [frames1, target1]} - sample2 = {"id": 1, "data": [frames2, target2]} - batch = collater.collate([sample1, sample2]) - - # collate sort inputs by frame's length before creating the batch - self.assertTensorEqual(batch["id"], torch.tensor([1, 0])) - self.assertEqual(batch["ntokens"], 7) - self.assertTensorEqual( - batch["net_input"]["src_tokens"], - torch.tensor( - [[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [pad_idx, pad_idx]]] - ), - ) - self.assertTensorEqual( - batch["net_input"]["prev_output_tokens"], - torch.tensor([[eos_idx, 3, 2, pad_idx], [eos_idx, 4, 2, 3]]), - ) - self.assertTensorEqual(batch["net_input"]["src_lengths"], torch.tensor([3, 2])) - self.assertTensorEqual( - batch["target"], - torch.tensor([[3, 2, eos_idx, pad_idx], [4, 2, 3, eos_idx]]), - ) - self.assertEqual(batch["nsentences"], 2) - - def assertTensorEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_cross_entropy.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_cross_entropy.py deleted file mode 100644 index b05400ed95e22762c3e3e5e8fd3ebfa6caf1e325..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_cross_entropy.py +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from examples.speech_recognition.criterions.cross_entropy_acc import ( - CrossEntropyWithAccCriterion, -) - -from .asr_test_base import CrossEntropyCriterionTestBase - - -class CrossEntropyWithAccCriterionTest(CrossEntropyCriterionTestBase): - def setUp(self): - self.criterion_cls = CrossEntropyWithAccCriterion - super().setUp() - - def test_cross_entropy_all_correct(self): - sample = self.get_test_sample(correct=True, soft_target=False, aggregate=False) - loss, sample_size, logging_output = self.criterion( - self.model, sample, "sum", log_probs=True - ) - assert logging_output["correct"] == 20 - assert logging_output["total"] == 20 - assert logging_output["sample_size"] == 20 - assert logging_output["ntokens"] == 20 - - def test_cross_entropy_all_wrong(self): - sample = self.get_test_sample(correct=False, soft_target=False, aggregate=False) - loss, sample_size, logging_output = self.criterion( - self.model, sample, "sum", log_probs=True - ) - assert logging_output["correct"] == 0 - assert logging_output["total"] == 20 - assert logging_output["sample_size"] == 20 - assert logging_output["ntokens"] == 20 diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_data_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_data_utils.py deleted file mode 100644 index a72e0b66948da1349d87eafdef4c4004dd535c96..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_data_utils.py +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -import unittest - -import torch -from examples.speech_recognition.data import data_utils - - -class DataUtilsTest(unittest.TestCase): - def test_normalization(self): - sample_len1 = torch.tensor( - [ - [ - -0.7661, - -1.3889, - -2.0972, - -0.9134, - -0.7071, - -0.9765, - -0.8700, - -0.8283, - 0.7512, - 1.3211, - 2.1532, - 2.1174, - 1.2800, - 1.2633, - 1.6147, - 1.6322, - 2.0723, - 3.1522, - 3.2852, - 2.2309, - 2.5569, - 2.2183, - 2.2862, - 1.5886, - 0.8773, - 0.8725, - 1.2662, - 0.9899, - 1.1069, - 1.3926, - 1.2795, - 1.1199, - 1.1477, - 1.2687, - 1.3843, - 1.1903, - 0.8355, - 1.1367, - 1.2639, - 1.4707, - ] - ] - ) - out = data_utils.apply_mv_norm(sample_len1) - assert not torch.isnan(out).any() - assert (out == sample_len1).all() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_vggtransformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_vggtransformer.py deleted file mode 100644 index 4dc73b8c7379970dc0bcc16fcb088a64a1bd7e3b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/speech_recognition/test_vggtransformer.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python3 - -# import models/encoder/decoder to be tested -from examples.speech_recognition.models.vggtransformer import ( - TransformerDecoder, - VGGTransformerEncoder, - VGGTransformerModel, - vggtransformer_1, - vggtransformer_2, - vggtransformer_base, -) - -# import base test class -from .asr_test_base import ( - DEFAULT_TEST_VOCAB_SIZE, - TestFairseqDecoderBase, - TestFairseqEncoderBase, - TestFairseqEncoderDecoderModelBase, - get_dummy_dictionary, - get_dummy_encoder_output, - get_dummy_input, -) - - -class VGGTransformerModelTest_mid(TestFairseqEncoderDecoderModelBase): - def setUp(self): - def override_config(args): - """ - vggtrasformer_1 use 14 layers of transformer, - for testing purpose, it is too expensive. For fast turn-around - test, reduce the number of layers to 3. - """ - args.transformer_enc_config = ( - "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" - ) - - super().setUp() - extra_args_setter = [vggtransformer_1, override_config] - - self.setUpModel(VGGTransformerModel, extra_args_setter) - self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) - - -class VGGTransformerModelTest_big(TestFairseqEncoderDecoderModelBase): - def setUp(self): - def override_config(args): - """ - vggtrasformer_2 use 16 layers of transformer, - for testing purpose, it is too expensive. For fast turn-around - test, reduce the number of layers to 3. - """ - args.transformer_enc_config = ( - "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" - ) - - super().setUp() - extra_args_setter = [vggtransformer_2, override_config] - - self.setUpModel(VGGTransformerModel, extra_args_setter) - self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) - - -class VGGTransformerModelTest_base(TestFairseqEncoderDecoderModelBase): - def setUp(self): - def override_config(args): - """ - vggtrasformer_base use 12 layers of transformer, - for testing purpose, it is too expensive. For fast turn-around - test, reduce the number of layers to 3. - """ - args.transformer_enc_config = ( - "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3" - ) - - super().setUp() - extra_args_setter = [vggtransformer_base, override_config] - - self.setUpModel(VGGTransformerModel, extra_args_setter) - self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) - - -class VGGTransformerEncoderTest(TestFairseqEncoderBase): - def setUp(self): - super().setUp() - - self.setUpInput(get_dummy_input(T=50, D=80, B=5)) - - def test_forward(self): - print("1. test standard vggtransformer") - self.setUpEncoder(VGGTransformerEncoder(input_feat_per_channel=80)) - super().test_forward() - print("2. test vggtransformer with limited right context") - self.setUpEncoder( - VGGTransformerEncoder( - input_feat_per_channel=80, transformer_context=(-1, 5) - ) - ) - super().test_forward() - print("3. test vggtransformer with limited left context") - self.setUpEncoder( - VGGTransformerEncoder( - input_feat_per_channel=80, transformer_context=(5, -1) - ) - ) - super().test_forward() - print("4. test vggtransformer with limited right context and sampling") - self.setUpEncoder( - VGGTransformerEncoder( - input_feat_per_channel=80, - transformer_context=(-1, 12), - transformer_sampling=(2, 2), - ) - ) - super().test_forward() - print("5. test vggtransformer with windowed context and sampling") - self.setUpEncoder( - VGGTransformerEncoder( - input_feat_per_channel=80, - transformer_context=(12, 12), - transformer_sampling=(2, 2), - ) - ) - - -class TransformerDecoderTest(TestFairseqDecoderBase): - def setUp(self): - super().setUp() - - dict = get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE) - decoder = TransformerDecoder(dict) - dummy_encoder_output = get_dummy_encoder_output(encoder_out_shape=(50, 5, 256)) - - self.setUpDecoder(decoder) - self.setUpInput(dummy_encoder_output) - self.setUpPrevOutputTokens() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/tasks/test_masked_lm.py b/audio/safeear/safeear_repo/fairseq_ours/tests/tasks/test_masked_lm.py deleted file mode 100644 index 9ddda1149cb872d83fdb51c9dac2c13a3e1617d9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/tasks/test_masked_lm.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import unittest -from tempfile import TemporaryDirectory - -from fairseq.binarizer import FileBinarizer, VocabularyDatasetBinarizer -from fairseq.tasks.masked_lm import MaskedLMConfig, MaskedLMTask - -from tests.utils import build_vocab, make_data - - -class TestMaskedLM(unittest.TestCase): - def test_masks_tokens(self): - with TemporaryDirectory() as dirname: - - # prep input file - raw_file = os.path.join(dirname, "raw") - data = make_data(out_file=raw_file) - vocab = build_vocab(data) - - # binarize - binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False) - split = "train" - bin_file = os.path.join(dirname, split) - FileBinarizer.multiprocess_dataset( - input_file=raw_file, - binarizer=binarizer, - dataset_impl="mmap", - vocab_size=len(vocab), - output_prefix=bin_file, - ) - - # setup task - cfg = MaskedLMConfig( - data=dirname, - seed=42, - mask_prob=0.5, # increasing the odds of masking - random_token_prob=0, # avoiding random tokens for exact match - leave_unmasked_prob=0, # always masking for exact match - ) - task = MaskedLMTask(cfg, binarizer.dict) - - original_dataset = task._load_dataset_split(bin_file, 1, False) - - # load datasets - task.load_dataset(split) - masked_dataset = task.dataset(split) - - mask_index = task.source_dictionary.index("") - iterator = task.get_batch_iterator( - dataset=masked_dataset, - max_tokens=65_536, - max_positions=4_096, - ).next_epoch_itr(shuffle=False) - for batch in iterator: - for sample in range(len(batch)): - net_input = batch["net_input"] - masked_src_tokens = net_input["src_tokens"][sample] - masked_src_length = net_input["src_lengths"][sample] - masked_tgt_tokens = batch["target"][sample] - - sample_id = batch["id"][sample] - original_tokens = original_dataset[sample_id] - original_tokens = original_tokens.masked_select( - masked_src_tokens[:masked_src_length] == mask_index - ) - masked_tokens = masked_tgt_tokens.masked_select( - masked_tgt_tokens != task.source_dictionary.pad() - ) - - assert masked_tokens.equal(original_tokens) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_activation_checkpointing.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_activation_checkpointing.py deleted file mode 100644 index 647a9572886f8aff09a4aadc0b21e1d5817ff38e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_activation_checkpointing.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -import torch.nn as nn -from fairseq.modules.checkpoint_activations import checkpoint_wrapper -from torch.utils.checkpoint import checkpoint - - -class Model(nn.Module): - def __init__( - self, use_pytorch_checkpoint=False, use_fairseq_checkpoint=False, **kwargs - ): - super().__init__() - torch.manual_seed(0) - self.use_pytorch_checkpoint = use_pytorch_checkpoint - self.ffn = nn.Sequential( - nn.Linear(32, 128), - # add a Dropout layer to test RNG save/restore - nn.Dropout(p=0.5), - nn.Linear(128, 32), - ) - if use_fairseq_checkpoint: - self.ffn = checkpoint_wrapper(self.ffn, **kwargs) - self.out = nn.Linear(32, 1) - - def forward(self, x): - if self.use_pytorch_checkpoint: - x = checkpoint(self.ffn, x) - else: - x = self.ffn(x) - return self.out(x) - - -class TestActivationCheckpointing(unittest.TestCase): - def _test_checkpoint_wrapper(self, device, log_memory_usage=False): - def get_loss_and_gnorm(model): - torch.manual_seed(1) - input = torch.rand(2, 16, 32).requires_grad_(True).to(device) - model.zero_grad() - loss = model(input).sum() - loss.backward() - gnorm = torch.norm( - torch.stack([torch.norm(p.grad.detach()) for p in model.parameters()]) - ) - return {"loss": loss, "gnorm": gnorm} - - model = Model().to(device) - no_cpt = get_loss_and_gnorm(model) - - model = Model(use_pytorch_checkpoint=True).to(device) - pyt_cpt = get_loss_and_gnorm(model) - torch.testing.assert_allclose(no_cpt["loss"], pyt_cpt["loss"]) - torch.testing.assert_allclose(no_cpt["gnorm"], pyt_cpt["gnorm"]) - - model = Model(use_fairseq_checkpoint=True).to(device) - fairseq_cpt = get_loss_and_gnorm(model) - torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt["loss"]) - torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt["gnorm"]) - - model = Model(use_fairseq_checkpoint=True, offload_to_cpu=True).to(device) - fairseq_cpt_offload = get_loss_and_gnorm(model) - torch.testing.assert_allclose(no_cpt["loss"], fairseq_cpt_offload["loss"]) - torch.testing.assert_allclose(no_cpt["gnorm"], fairseq_cpt_offload["gnorm"]) - - def test_checkpoint_wrapper_cpu(self): - self._test_checkpoint_wrapper(device=torch.device("cpu")) - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_checkpoint_wrapper_cuda(self): - self._test_checkpoint_wrapper(device=torch.device("cuda")) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_amp_optimizer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_amp_optimizer.py deleted file mode 100644 index 21d97b1fe888b01e223d5c3d52911260ffa86a1b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_amp_optimizer.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import copy -import unittest - -import torch -from fairseq.optim import build_optimizer -from torch.cuda.amp import GradScaler, autocast - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestGradientScalingAMP(unittest.TestCase): - def setUp(self): - self.x = torch.tensor([2.0]).cuda().half() - weight = 3.0 - bias = 5.0 - self.error = 1.0 - self.target = torch.tensor([self.x * weight + bias + self.error]).cuda() - self.loss_fn = torch.nn.L1Loss() - - self.model = torch.nn.Linear(1, 1) - self.model.weight.data = torch.tensor([[weight]]) - self.model.bias.data = torch.tensor([bias]) - self.model.cuda() - self.params = list(self.model.parameters()) - - self.namespace_dls = argparse.Namespace( - optimizer="adam", - lr=[0.1], - adam_betas="(0.9, 0.999)", - adam_eps=1e-8, - weight_decay=0.0, - threshold_loss_scale=1, - min_loss_scale=1e-4, - ) - self.scaler = GradScaler( - init_scale=1, - growth_interval=1, - ) - - def run_iter(self, model, params, optimizer): - optimizer.zero_grad() - with autocast(): - y = model(self.x) - loss = self.loss_fn(y, self.target) - self.scaler.scale(loss).backward() - self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) - - self.scaler.unscale_(optimizer) - grad_norm = optimizer.clip_grad_norm(0) - self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) - - self.scaler.step(optimizer) - self.scaler.update() - self.assertEqual( - model.weight, - torch.tensor([[3.1]], device="cuda:0", requires_grad=True), - ) - self.assertEqual( - model.bias, - torch.tensor([5.1], device="cuda:0", requires_grad=True), - ) - self.assertEqual(self.scaler.get_scale(), 2.0) - - def test_automatic_mixed_precision(self): - model = copy.deepcopy(self.model) - params = list(model.parameters()) - optimizer = build_optimizer(self.namespace_dls, params) - - self.run_iter(model, params, optimizer) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_average_checkpoints.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_average_checkpoints.py deleted file mode 100644 index aff82ab66170b13d642d0beddeb0f5a7fb99e935..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_average_checkpoints.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import collections -import os -import shutil -import tempfile -import unittest - -import numpy as np -import torch -from torch import nn - -from scripts.average_checkpoints import average_checkpoints - - -class ModelWithSharedParameter(nn.Module): - def __init__(self): - super(ModelWithSharedParameter, self).__init__() - self.embedding = nn.Embedding(1000, 200) - self.FC1 = nn.Linear(200, 200) - self.FC2 = nn.Linear(200, 200) - # tie weight in FC2 to FC1 - self.FC2.weight = nn.Parameter(self.FC1.weight) - self.FC2.bias = nn.Parameter(self.FC1.bias) - - self.relu = nn.ReLU() - - def forward(self, input): - return self.FC2(self.ReLU(self.FC1(input))) + self.FC1(input) - - -class TestAverageCheckpoints(unittest.TestCase): - def test_average_checkpoints(self): - params_0 = collections.OrderedDict( - [ - ("a", torch.DoubleTensor([100.0])), - ("b", torch.FloatTensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])), - ("c", torch.IntTensor([7, 8, 9])), - ] - ) - params_1 = collections.OrderedDict( - [ - ("a", torch.DoubleTensor([1.0])), - ("b", torch.FloatTensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])), - ("c", torch.IntTensor([2, 2, 2])), - ] - ) - params_avg = collections.OrderedDict( - [ - ("a", torch.DoubleTensor([50.5])), - ("b", torch.FloatTensor([[1.0, 1.5, 2.0], [2.5, 3.0, 3.5]])), - # We expect truncation for integer division - ("c", torch.IntTensor([4, 5, 5])), - ] - ) - - fd_0, path_0 = tempfile.mkstemp() - fd_1, path_1 = tempfile.mkstemp() - torch.save(collections.OrderedDict([("model", params_0)]), path_0) - torch.save(collections.OrderedDict([("model", params_1)]), path_1) - - output = average_checkpoints([path_0, path_1])["model"] - - os.close(fd_0) - os.remove(path_0) - os.close(fd_1) - os.remove(path_1) - - for (k_expected, v_expected), (k_out, v_out) in zip( - params_avg.items(), output.items() - ): - self.assertEqual( - k_expected, - k_out, - "Key mismatch - expected {} but found {}. " - "(Expected list of keys: {} vs actual list of keys: {})".format( - k_expected, k_out, params_avg.keys(), output.keys() - ), - ) - np.testing.assert_allclose( - v_expected.numpy(), - v_out.numpy(), - err_msg="Tensor value mismatch for key {}".format(k_expected), - ) - - def test_average_checkpoints_with_shared_parameters(self): - def _construct_model_with_shared_parameters(path, value): - m = ModelWithSharedParameter() - nn.init.constant_(m.FC1.weight, value) - torch.save({"model": m.state_dict()}, path) - return m - - tmpdir = tempfile.mkdtemp() - paths = [] - path = os.path.join(tmpdir, "m1.pt") - m1 = _construct_model_with_shared_parameters(path, 1.0) - paths.append(path) - - path = os.path.join(tmpdir, "m2.pt") - m2 = _construct_model_with_shared_parameters(path, 2.0) - paths.append(path) - - path = os.path.join(tmpdir, "m3.pt") - m3 = _construct_model_with_shared_parameters(path, 3.0) - paths.append(path) - - new_model = average_checkpoints(paths) - self.assertTrue( - torch.equal( - new_model["model"]["embedding.weight"], - (m1.embedding.weight + m2.embedding.weight + m3.embedding.weight) / 3.0, - ) - ) - - self.assertTrue( - torch.equal( - new_model["model"]["FC1.weight"], - (m1.FC1.weight + m2.FC1.weight + m3.FC1.weight) / 3.0, - ) - ) - - self.assertTrue( - torch.equal( - new_model["model"]["FC2.weight"], - (m1.FC2.weight + m2.FC2.weight + m3.FC2.weight) / 3.0, - ) - ) - shutil.rmtree(tmpdir) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_backtranslation_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_backtranslation_dataset.py deleted file mode 100644 index 4fc2e981e63731199a273dfd5cc591de77361fcd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_backtranslation_dataset.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.data import ( - BacktranslationDataset, - LanguagePairDataset, - TransformEosDataset, -) -from fairseq.sequence_generator import SequenceGenerator - -import tests.utils as test_utils - - -class TestBacktranslationDataset(unittest.TestCase): - def setUp(self): - ( - self.tgt_dict, - self.w1, - self.w2, - self.src_tokens, - self.src_lengths, - self.model, - ) = test_utils.sequence_generator_setup() - - dummy_src_samples = self.src_tokens - - self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples) - self.cuda = torch.cuda.is_available() - - def _backtranslation_dataset_helper( - self, - remove_eos_from_input_src, - remove_eos_from_output_src, - ): - tgt_dataset = LanguagePairDataset( - src=self.tgt_dataset, - src_sizes=self.tgt_dataset.sizes, - src_dict=self.tgt_dict, - tgt=None, - tgt_sizes=None, - tgt_dict=None, - ) - - generator = SequenceGenerator( - [self.model], - tgt_dict=self.tgt_dict, - max_len_a=0, - max_len_b=200, - beam_size=2, - unk_penalty=0, - ) - - backtranslation_dataset = BacktranslationDataset( - tgt_dataset=TransformEosDataset( - dataset=tgt_dataset, - eos=self.tgt_dict.eos(), - # remove eos from the input src - remove_eos_from_src=remove_eos_from_input_src, - ), - src_dict=self.tgt_dict, - backtranslation_fn=( - lambda sample: generator.generate([self.model], sample) - ), - output_collater=TransformEosDataset( - dataset=tgt_dataset, - eos=self.tgt_dict.eos(), - # if we remove eos from the input src, then we need to add it - # back to the output tgt - append_eos_to_tgt=remove_eos_from_input_src, - remove_eos_from_src=remove_eos_from_output_src, - ).collater, - cuda=self.cuda, - ) - dataloader = torch.utils.data.DataLoader( - backtranslation_dataset, - batch_size=2, - collate_fn=backtranslation_dataset.collater, - ) - backtranslation_batch_result = next(iter(dataloader)) - - eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2 - - # Note that we sort by src_lengths and add left padding, so actually - # ids will look like: [1, 0] - expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]]) - if remove_eos_from_output_src: - expected_src = expected_src[:, :-1] - expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]]) - generated_src = backtranslation_batch_result["net_input"]["src_tokens"] - tgt_tokens = backtranslation_batch_result["target"] - - self.assertTensorEqual(expected_src, generated_src) - self.assertTensorEqual(expected_tgt, tgt_tokens) - - def test_backtranslation_dataset_no_eos_in_output_src(self): - self._backtranslation_dataset_helper( - remove_eos_from_input_src=False, - remove_eos_from_output_src=True, - ) - - def test_backtranslation_dataset_with_eos_in_output_src(self): - self._backtranslation_dataset_helper( - remove_eos_from_input_src=False, - remove_eos_from_output_src=False, - ) - - def test_backtranslation_dataset_no_eos_in_input_src(self): - self._backtranslation_dataset_helper( - remove_eos_from_input_src=True, - remove_eos_from_output_src=False, - ) - - def assertTensorEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_binaries.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_binaries.py deleted file mode 100644 index 8eb1bf07e4626c234e155580a780944933d3739d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_binaries.py +++ /dev/null @@ -1,1908 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import json -import logging -import os -import random -import sys -import tempfile -import unittest -from io import StringIO -from typing import Dict, List - -import torch -from fairseq import options -from fairseq_cli import eval_lm, train - -from tests.utils import ( - create_dummy_data, - create_laser_data_and_config_json, - generate_main, - preprocess_lm_data, - preprocess_summarization_data, - preprocess_translation_data, - train_language_model, - train_translation_model, -) - -try: - import transformers # noqa - - has_hf_transformers = True -except ImportError: - has_hf_transformers = False - - -class TestTranslation(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_fconv(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fconv") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model(data_dir, "fconv_iwslt_de_en") - generate_main(data_dir) - - def test_raw(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fconv_raw") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ["--dataset-impl", "raw"]) - train_translation_model( - data_dir, "fconv_iwslt_de_en", ["--dataset-impl", "raw"] - ) - generate_main(data_dir, ["--dataset-impl", "raw"]) - - def test_update_freq(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_update_freq") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, "fconv_iwslt_de_en", ["--update-freq", "3"] - ) - generate_main(data_dir) - - def test_max_positions(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_max_positions") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - with self.assertRaises(Exception) as context: - train_translation_model( - data_dir, - "fconv_iwslt_de_en", - ["--max-target-positions", "5"], - ) - self.assertTrue( - "skip this example with --skip-invalid-size-inputs-valid-test" - in str(context.exception) - ) - train_translation_model( - data_dir, - "fconv_iwslt_de_en", - [ - "--max-target-positions", - "5", - "--skip-invalid-size-inputs-valid-test", - ], - ) - with self.assertRaises(Exception) as context: - generate_main(data_dir) - generate_main(data_dir, ["--skip-invalid-size-inputs-valid-test"]) - - def test_generation(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_sampling") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model(data_dir, "fconv_iwslt_de_en") - generate_main( - data_dir, - [ - "--sampling", - "--temperature", - "2", - "--beam", - "2", - "--nbest", - "2", - ], - ) - generate_main( - data_dir, - [ - "--sampling", - "--sampling-topk", - "3", - "--beam", - "2", - "--nbest", - "2", - ], - ) - generate_main( - data_dir, - [ - "--sampling", - "--sampling-topp", - "0.2", - "--beam", - "2", - "--nbest", - "2", - ], - ) - generate_main( - data_dir, - [ - "--diversity-rate", - "0.5", - "--beam", - "6", - ], - ) - with self.assertRaises(ValueError): - generate_main( - data_dir, - [ - "--diverse-beam-groups", - "4", - "--match-source-len", - ], - ) - generate_main(data_dir, ["--prefix-size", "2"]) - generate_main(data_dir, ["--retain-dropout"]) - - def test_eval_bleu(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_eval_bleu") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "fconv_iwslt_de_en", - [ - "--eval-bleu", - "--eval-bleu-print-samples", - "--eval-bleu-remove-bpe", - "--eval-bleu-detok", - "space", - "--eval-bleu-args", - '{"beam": 4, "min_len": 10}', - ], - ) - - def test_lstm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_lstm") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "lstm_wiseman_iwslt_de_en", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--decoder-out-embed-dim", - "8", - ], - ) - generate_main(data_dir) - - def test_lstm_bidirectional(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_lstm_bidirectional") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "lstm", - [ - "--encoder-layers", - "2", - "--encoder-bidirectional", - "--encoder-hidden-size", - "16", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--decoder-out-embed-dim", - "8", - "--decoder-layers", - "2", - ], - ) - generate_main(data_dir) - - def test_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - ], - run_validation=True, - ) - generate_main(data_dir) - - def test_multilingual_transformer(self): - # test with all combinations of encoder/decoder lang tokens - encoder_langtok_flags = [ - [], - ["--encoder-langtok", "src"], - ["--encoder-langtok", "tgt"], - ] - decoder_langtok_flags = [[], ["--decoder-langtok"]] - with contextlib.redirect_stdout(StringIO()): - for i in range(len(encoder_langtok_flags)): - for j in range(len(decoder_langtok_flags)): - enc_ltok_flag = encoder_langtok_flags[i] - dec_ltok_flag = decoder_langtok_flags[j] - with tempfile.TemporaryDirectory( - f"test_multilingual_transformer_{i}_{j}" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - arch="multilingual_transformer", - task="multilingual_translation", - extra_flags=[ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - ] - + enc_ltok_flag - + dec_ltok_flag, - lang_flags=["--lang-pairs", "in-out,out-in"], - run_validation=True, - extra_valid_flags=enc_ltok_flag + dec_ltok_flag, - ) - generate_main( - data_dir, - extra_flags=[ - "--task", - "multilingual_translation", - "--lang-pairs", - "in-out,out-in", - "--source-lang", - "in", - "--target-lang", - "out", - ] - + enc_ltok_flag - + dec_ltok_flag, - ) - - @unittest.skipIf( - sys.platform.lower() == "darwin", "skip latent depth test on MacOS" - ) - def test_multilingual_translation_latent_depth(self): - # test with latent depth in encoder, decoder, or both - encoder_latent_layer = [[], ["--encoder-latent-layer"]] - decoder_latent_layer = [[], ["--decoder-latent-layer"]] - with contextlib.redirect_stdout(StringIO()): - for i in range(len(encoder_latent_layer)): - for j in range(len(decoder_latent_layer)): - if i == 0 and j == 0: - continue - enc_ll_flag = encoder_latent_layer[i] - dec_ll_flag = decoder_latent_layer[j] - with tempfile.TemporaryDirectory( - f"test_multilingual_translation_latent_depth_{i}_{j}" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data( - data_dir, extra_flags=["--joined-dictionary"] - ) - train_translation_model( - data_dir, - arch="latent_multilingual_transformer", - task="multilingual_translation_latent_depth", - extra_flags=[ - "--user-dir", - "examples/latent_depth/latent_depth_src", - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--share-encoders", - "--share-decoders", - "--sparsity-weight", - "0.1", - ] - + enc_ll_flag - + dec_ll_flag, - lang_flags=["--lang-pairs", "in-out,out-in"], - run_validation=True, - extra_valid_flags=[ - "--user-dir", - "examples/latent_depth/latent_depth_src", - ] - + enc_ll_flag - + dec_ll_flag, - ) - generate_main( - data_dir, - extra_flags=[ - "--user-dir", - "examples/latent_depth/latent_depth_src", - "--task", - "multilingual_translation_latent_depth", - "--lang-pairs", - "in-out,out-in", - "--source-lang", - "in", - "--target-lang", - "out", - ] - + enc_ll_flag - + dec_ll_flag, - ) - - def test_translation_multi_simple_epoch(self): - # test with all combinations of encoder/decoder lang tokens - encoder_langtok_flags = [ - [], - ["--encoder-langtok", "src"], - ["--encoder-langtok", "tgt"], - ] - decoder_langtok_flags = [[], ["--decoder-langtok"]] - with contextlib.redirect_stdout(StringIO()): - for i in range(len(encoder_langtok_flags)): - for j in range(len(decoder_langtok_flags)): - enc_ltok_flag = encoder_langtok_flags[i] - dec_ltok_flag = decoder_langtok_flags[j] - with tempfile.TemporaryDirectory( - f"test_translation_multi_simple_epoch_{i}_{j}" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data( - data_dir, extra_flags=["--joined-dictionary"] - ) - train_translation_model( - data_dir, - arch="transformer", - task="translation_multi_simple_epoch", - extra_flags=[ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--sampling-method", - "temperature", - "--sampling-temperature", - "1.5", - "--virtual-epoch-size", - "1000", - ] - + enc_ltok_flag - + dec_ltok_flag, - lang_flags=["--lang-pairs", "in-out,out-in"], - run_validation=True, - extra_valid_flags=enc_ltok_flag + dec_ltok_flag, - ) - generate_main( - data_dir, - extra_flags=[ - "--task", - "translation_multi_simple_epoch", - "--lang-pairs", - "in-out,out-in", - "--source-lang", - "in", - "--target-lang", - "out", - ] - + enc_ltok_flag - + dec_ltok_flag, - ) - - def test_translation_multi_simple_epoch_no_vepoch(self): - # test with all combinations of encoder/decoder lang tokens - with contextlib.redirect_stdout(StringIO()): - enc_ltok_flag = ["--encoder-langtok", "src"] - dec_ltok_flag = ["--decoder-langtok"] - with tempfile.TemporaryDirectory( - "test_translation_multi_simple_epoch_dict" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, extra_flags=[]) - train_translation_model( - data_dir, - arch="transformer", - task="translation_multi_simple_epoch", - extra_flags=[ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--sampling-method", - "temperature", - "--sampling-temperature", - "1.5", - ] - + enc_ltok_flag - + dec_ltok_flag, - lang_flags=["--lang-pairs", "in-out"], - run_validation=True, - extra_valid_flags=enc_ltok_flag + dec_ltok_flag, - ) - generate_main( - data_dir, - extra_flags=[ - "--task", - "translation_multi_simple_epoch", - "--lang-pairs", - "in-out", - "--source-lang", - "in", - "--target-lang", - "out", - ] - + enc_ltok_flag - + dec_ltok_flag, - ) - - def test_translation_multi_simple_epoch_dicts(self): - # test with all combinations of encoder/decoder lang tokens - with contextlib.redirect_stdout(StringIO()): - enc_ltok_flag = ["--encoder-langtok", "src"] - dec_ltok_flag = ["--decoder-langtok"] - with tempfile.TemporaryDirectory( - "test_translation_multi_simple_epoch_dict" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, extra_flags=[]) - train_translation_model( - data_dir, - arch="transformer", - task="translation_multi_simple_epoch", - extra_flags=[ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--sampling-method", - "temperature", - "--sampling-temperature", - "1.5", - "--virtual-epoch-size", - "1000", - ] - + enc_ltok_flag - + dec_ltok_flag, - lang_flags=["--lang-pairs", "in-out"], - run_validation=True, - extra_valid_flags=enc_ltok_flag + dec_ltok_flag, - ) - generate_main( - data_dir, - extra_flags=[ - "--task", - "translation_multi_simple_epoch", - "--lang-pairs", - "in-out", - "--source-lang", - "in", - "--target-lang", - "out", - ] - + enc_ltok_flag - + dec_ltok_flag, - ) - - def test_translation_multi_simple_epoch_src_tgt_dict_spec(self): - # test the specification of explicit --src-dict and --tgt-dict - with contextlib.redirect_stdout(StringIO()): - enc_ltok_flag = ["--encoder-langtok", "src"] - dec_ltok_flag = ["--decoder-langtok"] - with tempfile.TemporaryDirectory( - "test_translation_multi_simple_epoch_dict" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, extra_flags=[]) - train_translation_model( - data_dir, - arch="transformer", - task="translation_multi_simple_epoch", - extra_flags=[ - "--source-dict", - f"{data_dir}/dict.in.txt", - "--target-dict", - f"{data_dir}/dict.out.txt", - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--sampling-method", - "temperature", - "--sampling-temperature", - "1.5", - "--virtual-epoch-size", - "1000", - ] - + enc_ltok_flag - + dec_ltok_flag, - lang_flags=["--lang-pairs", "in-out"], - run_validation=True, - extra_valid_flags=enc_ltok_flag + dec_ltok_flag, - ) - generate_main( - data_dir, - extra_flags=[ - "--task", - "translation_multi_simple_epoch", - "--lang-pairs", - "in-out", - "--source-lang", - "in", - "--target-lang", - "out", - ] - + enc_ltok_flag - + dec_ltok_flag, - ) - - def test_transformer_cross_self_attention(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_transformer_cross_self_attention" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--no-cross-attention", - "--cross-self-attention", - ], - run_validation=True, - ) - generate_main(data_dir, extra_flags=[]) - - def test_transformer_pointer_generator(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_transformer_pointer_generator" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_summarization_data(data_dir) - train_translation_model( - data_dir, - "transformer_pointer_generator", - extra_flags=[ - "--user-dir", - "examples/pointer_generator/pointer_generator_src", - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--alignment-layer", - "-1", - "--alignment-heads", - "1", - "--source-position-markers", - "0", - ], - run_validation=True, - extra_valid_flags=[ - "--user-dir", - "examples/pointer_generator/pointer_generator_src", - ], - ) - generate_main( - data_dir, - extra_flags=[ - "--user-dir", - "examples/pointer_generator/pointer_generator_src", - ], - ) - - def test_lightconv(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_lightconv") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "lightconv_iwslt_de_en", - [ - "--encoder-conv-type", - "lightweight", - "--decoder-conv-type", - "lightweight", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - ], - ) - generate_main(data_dir) - - def test_dynamicconv(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_dynamicconv") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "lightconv_iwslt_de_en", - [ - "--encoder-conv-type", - "dynamic", - "--decoder-conv-type", - "dynamic", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - ], - ) - generate_main(data_dir) - - def test_cmlm_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_cmlm_transformer") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ["--joined-dictionary"]) - train_translation_model( - data_dir, - "cmlm_transformer", - [ - "--apply-bert-init", - "--criterion", - "nat_loss", - "--noise", - "full_mask", - "--pred-length-offset", - "--length-loss-factor", - "0.1", - ], - task="translation_lev", - ) - generate_main( - data_dir, - [ - "--task", - "translation_lev", - "--iter-decode-max-iter", - "9", - "--iter-decode-eos-penalty", - "0", - "--print-step", - ], - ) - - def test_nonautoregressive_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_nonautoregressive_transformer" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ["--joined-dictionary"]) - train_translation_model( - data_dir, - "nonautoregressive_transformer", - [ - "--apply-bert-init", - "--src-embedding-copy", - "--criterion", - "nat_loss", - "--noise", - "full_mask", - "--pred-length-offset", - "--length-loss-factor", - "0.1", - ], - task="translation_lev", - ) - generate_main( - data_dir, - [ - "--task", - "translation_lev", - "--iter-decode-max-iter", - "0", - "--iter-decode-eos-penalty", - "0", - "--print-step", - ], - ) - - # def test_nat_crf_transformer(self): - # with contextlib.redirect_stdout(StringIO()): - # with tempfile.TemporaryDirectory('test_nat_crf_transformer') as data_dir: - # create_dummy_data(data_dir) - # preprocess_translation_data(data_dir, ['--joined-dictionary']) - # train_translation_model(data_dir, 'nacrf_transformer', [ - # '--apply-bert-init', '--criterion', - # 'nat_loss', '--noise', 'full_mask', '--pred-length-offset', - # '--length-loss-factor', '0.1', - # '--word-ins-loss-factor', '0.5', - # '--crf-lowrank-approx', '1', - # '--crf-beam-approx', '1' - # ], task='translation_lev') - # generate_main(data_dir, [ - # '--task', 'translation_lev', - # '--iter-decode-max-iter', '0', - # '--iter-decode-eos-penalty', '0', - # '--print-step', - # ]) - - def test_iterative_nonautoregressive_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_iterative_nonautoregressive_transformer" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ["--joined-dictionary"]) - train_translation_model( - data_dir, - "iterative_nonautoregressive_transformer", - [ - "--apply-bert-init", - "--src-embedding-copy", - "--criterion", - "nat_loss", - "--noise", - "full_mask", - "--stochastic-approx", - "--dae-ratio", - "0.5", - "--train-step", - "3", - ], - task="translation_lev", - ) - generate_main( - data_dir, - [ - "--task", - "translation_lev", - "--iter-decode-max-iter", - "9", - "--iter-decode-eos-penalty", - "0", - "--print-step", - ], - ) - - def test_insertion_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_insertion_transformer") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir, ["--joined-dictionary"]) - train_translation_model( - data_dir, - "insertion_transformer", - [ - "--apply-bert-init", - "--criterion", - "nat_loss", - "--noise", - "random_mask", - ], - task="translation_lev", - ) - generate_main( - data_dir, - [ - "--task", - "translation_lev", - "--iter-decode-max-iter", - "9", - "--iter-decode-eos-penalty", - "0", - "--print-step", - ], - ) - - def test_mixture_of_experts(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_moe") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--task", - "translation_moe", - "--user-dir", - "examples/translation_moe/translation_moe_src", - "--method", - "hMoElp", - "--mean-pool-gating-network", - "--num-experts", - "3", - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - ], - ) - generate_main( - data_dir, - [ - "--task", - "translation_moe", - "--user-dir", - "examples/translation_moe/translation_moe_src", - "--method", - "hMoElp", - "--mean-pool-gating-network", - "--num-experts", - "3", - "--gen-expert", - "0", - ], - ) - - def test_alignment(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_alignment") as data_dir: - create_dummy_data(data_dir, alignment=True) - preprocess_translation_data(data_dir, ["--align-suffix", "align"]) - train_translation_model( - data_dir, - "transformer_align", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--load-alignments", - "--alignment-layer", - "1", - "--criterion", - "label_smoothed_cross_entropy_with_alignment", - ], - run_validation=True, - ) - generate_main(data_dir) - - def test_laser_lstm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_laser_lstm") as data_dir: - laser_config_file = create_laser_data_and_config_json(data_dir) - train_translation_model( - laser_config_file.name, - "laser_lstm", - [ - "--user-dir", - "examples/laser/laser_src", - "--weighting-alpha", - "0.3", - "--encoder-bidirectional", - "--encoder-hidden-size", - "512", - "--encoder-layers", - "5", - "--decoder-layers", - "1", - "--encoder-embed-dim", - "320", - "--decoder-embed-dim", - "320", - "--decoder-lang-embed-dim", - "32", - "--save-dir", - data_dir, - "--disable-validation", - ], - task="laser", - lang_flags=[], - ) - - def test_laser_transformer(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_laser_transformer") as data_dir: - laser_config_file = create_laser_data_and_config_json(data_dir) - train_translation_model( - laser_config_file.name, - "laser_transformer", - [ - "--user-dir", - "examples/laser/laser_src", - "--weighting-alpha", - "0.3", - "--encoder-embed-dim", - "320", - "--decoder-embed-dim", - "320", - "--decoder-lang-embed-dim", - "32", - "--save-dir", - data_dir, - "--disable-validation", - ], - task="laser", - lang_flags=[], - ) - - def test_alignment_full_context(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_alignment") as data_dir: - create_dummy_data(data_dir, alignment=True) - preprocess_translation_data(data_dir, ["--align-suffix", "align"]) - train_translation_model( - data_dir, - "transformer_align", - [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--load-alignments", - "--alignment-layer", - "1", - "--criterion", - "label_smoothed_cross_entropy_with_alignment", - "--full-context-alignment", - ], - run_validation=True, - ) - generate_main(data_dir) - - def test_transformer_layerdrop(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer_layerdrop") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--encoder-layers", - "3", - "--decoder-layers", - "3", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--encoder-layerdrop", - "0.01", - "--decoder-layerdrop", - "0.01", - ], - ) - generate_main(data_dir) - generate_main( - data_dir, - [ - "--model-overrides", - "{'encoder_layers_to_keep':'0,2','decoder_layers_to_keep':'1'}", - ], - ) - - -class TestStories(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_fconv_self_att_wp(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fconv_self_att_wp") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - config = [ - "--encoder-layers", - "[(128, 3)] * 2", - "--decoder-layers", - "[(128, 3)] * 2", - "--decoder-attention", - "True", - "--encoder-attention", - "False", - "--gated-attention", - "True", - "--self-attention", - "True", - "--project-input", - "True", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--decoder-out-embed-dim", - "8", - "--multihead-self-attention-nheads", - "2", - ] - train_translation_model(data_dir, "fconv_self_att_wp", config) - generate_main(data_dir) - - # fusion model - os.rename( - os.path.join(data_dir, "checkpoint_last.pt"), - os.path.join(data_dir, "pretrained.pt"), - ) - config.extend( - [ - "--pretrained", - "True", - "--pretrained-checkpoint", - os.path.join(data_dir, "pretrained.pt"), - "--save-dir", - os.path.join(data_dir, "fusion_model"), - ] - ) - train_translation_model(data_dir, "fconv_self_att_wp", config) - - -class TestLanguageModeling(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_fconv_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_fconv_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "fconv_lm", - [ - "--decoder-layers", - "[(850, 3)] * 2 + [(1024,4)]", - "--decoder-embed-dim", - "280", - "--optimizer", - "nag", - "--lr", - "0.1", - ], - ) - eval_lm_main(data_dir) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - def test_transformer_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "transformer_lm", - ["--add-bos-token", "--nval", "1"], - run_validation=True, - ) - eval_lm_main(data_dir) - eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - def test_normformer_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "transformer_lm", - [ - "--add-bos-token", - "--nval", - "1", - "--scale-fc", - "--scale-heads", - "--scale-attn", - "--scale-fc", - ], - run_validation=True, - ) - eval_lm_main(data_dir) - eval_lm_main(data_dir, extra_flags=["--context-window", "25"]) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - def test_transformer_lm_with_adaptive_softmax(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_transformer_lm_with_adaptive_softmax" - ) as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "transformer_lm", - [ - "--add-bos-token", - "--criterion", - "adaptive_loss", - "--adaptive-softmax-cutoff", - "5,10,15", - ], - run_validation=True, - ) - eval_lm_main(data_dir) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - def test_lightconv_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_lightconv_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "lightconv_lm", - ["--add-bos-token"], - run_validation=True, - ) - eval_lm_main(data_dir) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - def test_lstm_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_lstm_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "lstm_lm", - ["--add-bos-token"], - run_validation=True, - ) - eval_lm_main(data_dir) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - def test_lstm_lm_residuals(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_lstm_lm_residuals") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "lstm_lm", - ["--add-bos-token", "--residuals"], - run_validation=True, - ) - eval_lm_main(data_dir) - generate_main( - data_dir, - [ - "--task", - "language_modeling", - "--sample-break-mode", - "eos", - "--tokens-per-sample", - "500", - ], - ) - - @unittest.skipIf(not has_hf_transformers, "skip test if transformers is missing") - def test_transformer_xl_bptt_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer_xl_bptt_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - task_flags = [ - "--user-dir", - "examples/truncated_bptt", - "--task", - "truncated_bptt_lm", - "--batch-size", - "2", - "--tokens-per-sample", - "50", - ] - train_language_model( - data_dir=data_dir, - arch="transformer_xl", - extra_flags=task_flags - + [ - "--n-layer", - "2", - ], - task="truncated_bptt_lm", - run_validation=True, - extra_valid_flags=task_flags, - ) - eval_lm_main(data_dir, extra_flags=task_flags) - # Train with activation offloading - train_language_model( - data_dir=data_dir, - arch="transformer_xl", - extra_flags=task_flags - + [ - "--n-layer", - "2", - "--offload-activations", - ], - task="truncated_bptt_lm", - run_validation=True, - extra_valid_flags=task_flags, - ) - - -class TestMaskedLanguageModel(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_legacy_masked_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_legacy_mlm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_legacy_masked_language_model(data_dir, "masked_lm") - - def test_roberta_masked_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_roberta_mlm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_masked_lm( - data_dir, "roberta_base", extra_flags=["--encoder-layers", "2"] - ) - - def test_roberta_sentence_prediction(self): - num_classes = 3 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_roberta_head") as data_dir: - create_dummy_roberta_head_data(data_dir, num_classes=num_classes) - preprocess_lm_data(os.path.join(data_dir, "input0")) - preprocess_lm_data(os.path.join(data_dir, "label")) - train_roberta_head(data_dir, "roberta_base", num_classes=num_classes) - - def test_roberta_regression_single(self): - num_classes = 1 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_roberta_regression_single" - ) as data_dir: - create_dummy_roberta_head_data( - data_dir, num_classes=num_classes, regression=True - ) - preprocess_lm_data(os.path.join(data_dir, "input0")) - train_roberta_head( - data_dir, - "roberta_base", - num_classes=num_classes, - extra_flags=["--regression-target"], - ) - - def test_roberta_regression_multiple(self): - num_classes = 3 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_roberta_regression_multiple" - ) as data_dir: - create_dummy_roberta_head_data( - data_dir, num_classes=num_classes, regression=True - ) - preprocess_lm_data(os.path.join(data_dir, "input0")) - train_roberta_head( - data_dir, - "roberta_base", - num_classes=num_classes, - extra_flags=["--regression-target"], - ) - - def test_linformer_roberta_masked_lm(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_linformer_roberta_mlm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_masked_lm( - data_dir, - "linformer_roberta_base", - extra_flags=[ - "--user-dir", - "examples/linformer/linformer_src", - "--encoder-layers", - "2", - ], - ) - - def test_linformer_roberta_sentence_prediction(self): - num_classes = 3 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_linformer_roberta_head") as data_dir: - create_dummy_roberta_head_data(data_dir, num_classes=num_classes) - preprocess_lm_data(os.path.join(data_dir, "input0")) - preprocess_lm_data(os.path.join(data_dir, "label")) - train_roberta_head( - data_dir, - "linformer_roberta_base", - num_classes=num_classes, - extra_flags=["--user-dir", "examples/linformer/linformer_src"], - ) - - def test_linformer_roberta_regression_single(self): - num_classes = 1 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_linformer_roberta_regression_single" - ) as data_dir: - create_dummy_roberta_head_data( - data_dir, num_classes=num_classes, regression=True - ) - preprocess_lm_data(os.path.join(data_dir, "input0")) - train_roberta_head( - data_dir, - "linformer_roberta_base", - num_classes=num_classes, - extra_flags=[ - "--regression-target", - "--user-dir", - "examples/linformer/linformer_src", - ], - ) - - def test_linformer_roberta_regression_multiple(self): - num_classes = 3 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory( - "test_linformer_roberta_regression_multiple" - ) as data_dir: - create_dummy_roberta_head_data( - data_dir, num_classes=num_classes, regression=True - ) - preprocess_lm_data(os.path.join(data_dir, "input0")) - train_roberta_head( - data_dir, - "linformer_roberta_base", - num_classes=num_classes, - extra_flags=[ - "--regression-target", - "--user-dir", - "examples/linformer/linformer_src", - ], - ) - - def _test_pretrained_masked_lm_for_translation(self, learned_pos_emb, encoder_only): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_mlm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_legacy_masked_language_model( - data_dir, - arch="masked_lm", - extra_args=("--encoder-learned-pos",) if learned_pos_emb else (), - ) - with tempfile.TemporaryDirectory( - "test_mlm_translation" - ) as translation_dir: - create_dummy_data(translation_dir) - preprocess_translation_data( - translation_dir, extra_flags=["--joined-dictionary"] - ) - # Train transformer with data_dir/checkpoint_last.pt - train_translation_model( - translation_dir, - arch="transformer_from_pretrained_xlm", - extra_flags=[ - "--decoder-layers", - "1", - "--decoder-embed-dim", - "32", - "--decoder-attention-heads", - "1", - "--decoder-ffn-embed-dim", - "32", - "--encoder-layers", - "1", - "--encoder-embed-dim", - "32", - "--encoder-attention-heads", - "1", - "--encoder-ffn-embed-dim", - "32", - "--pretrained-xlm-checkpoint", - "{}/checkpoint_last.pt".format(data_dir), - "--activation-fn", - "gelu", - "--max-source-positions", - "500", - "--max-target-positions", - "500", - ] - + ( - ["--encoder-learned-pos", "--decoder-learned-pos"] - if learned_pos_emb - else [] - ) - + (["--init-encoder-only"] if encoder_only else []), - task="translation_from_pretrained_xlm", - ) - - def test_pretrained_masked_lm_for_translation_learned_pos_emb(self): - self._test_pretrained_masked_lm_for_translation(True, False) - - def test_pretrained_masked_lm_for_translation_sinusoidal_pos_emb(self): - self._test_pretrained_masked_lm_for_translation(False, False) - - def test_pretrained_masked_lm_for_translation_encoder_only(self): - self._test_pretrained_masked_lm_for_translation(True, True) - - def test_r4f_roberta(self): - num_classes = 3 - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_r4f_roberta_head") as data_dir: - create_dummy_roberta_head_data(data_dir, num_classes=num_classes) - preprocess_lm_data(os.path.join(data_dir, "input0")) - preprocess_lm_data(os.path.join(data_dir, "label")) - train_roberta_head( - data_dir, - "roberta_base", - num_classes=num_classes, - extra_flags=[ - "--user-dir", - "examples/rxf/rxf_src", - "--criterion", - "sentence_prediction_r3f", - "--spectral-norm-classification-head", - ], - ) - - -def train_legacy_masked_language_model(data_dir, arch, extra_args=()): - train_parser = options.get_training_parser() - # TODO: langs should be in and out right? - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - "cross_lingual_lm", - data_dir, - "--arch", - arch, - # Optimizer args - "--optimizer", - "adam", - "--lr-scheduler", - "reduce_lr_on_plateau", - "--lr-shrink", - "0.5", - "--lr", - "0.0001", - "--stop-min-lr", - "1e-09", - # dropout, attention args - "--dropout", - "0.1", - "--attention-dropout", - "0.1", - # MLM args - "--criterion", - "legacy_masked_lm_loss", - "--masked-lm-only", - "--monolingual-langs", - "in,out", - "--num-segment", - "5", - # Transformer args: use a small transformer model for fast training - "--encoder-layers", - "1", - "--encoder-embed-dim", - "32", - "--encoder-attention-heads", - "1", - "--encoder-ffn-embed-dim", - "32", - # Other training args - "--max-tokens", - "500", - "--tokens-per-sample", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - "1", - "--dataset-impl", - "raw", - "--num-workers", - "0", - ] - + list(extra_args), - ) - train.main(train_args) - - -class TestOptimizers(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_optimizers(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_optimizers") as data_dir: - # Use just a bit of data and tiny model to keep this test runtime reasonable - create_dummy_data(data_dir, num_examples=10, maxlen=5) - preprocess_translation_data(data_dir) - optimizers = ["adafactor", "adam", "nag", "adagrad", "sgd", "adadelta"] - last_checkpoint = os.path.join(data_dir, "checkpoint_last.pt") - for optimizer in optimizers: - if os.path.exists(last_checkpoint): - os.remove(last_checkpoint) - train_translation_model( - data_dir, - "lstm", - [ - "--required-batch-size-multiple", - "1", - "--encoder-layers", - "1", - "--encoder-hidden-size", - "32", - "--decoder-layers", - "1", - "--optimizer", - optimizer, - ], - ) - generate_main(data_dir) - - -def read_last_log_entry( - logs: List[logging.LogRecord], logger_name: str -) -> Dict[str, float]: - for x in reversed(logs): - if x.name == logger_name: - return json.loads(x.message) - raise ValueError(f"No entries from {logger_name} found in captured logs") - - -class TestActivationCheckpointing(unittest.TestCase): - base_flags = [ - "--encoder-layers", - "2", - "--decoder-layers", - "2", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--restore-file", - "x.pt", - "--log-format", - "json", - "--log-interval", - "1", - "--max-update", - "2", - ] - - def _train(self, data_dir, extra_flags): - with self.assertLogs() as logs: - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - self.base_flags + extra_flags, - run_validation=True, - extra_valid_flags=["--log-format", "json"], - ) - return logs.records - - def test_activation_offloading_does_not_change_metrics(self): - """Neither ----checkpoint-activations nor --offload-activations should change loss""" - with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: - - with self.assertLogs(): - create_dummy_data(data_dir, num_examples=20) - preprocess_translation_data(data_dir) - offload_logs = self._train(data_dir, ["--offload-activations"]) - baseline_logs = self._train(data_dir, []) - - assert len(baseline_logs) == len(offload_logs) - - baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") - offload_valid_stats = read_last_log_entry(offload_logs, "valid") - baseline_train_stats = read_last_log_entry(baseline_logs, "train") - offload_train_stats = read_last_log_entry(offload_logs, "train") - - assert ( - baseline_train_stats["train_loss"] == offload_train_stats["train_loss"] - ) - assert ( - baseline_valid_stats["valid_loss"] == offload_valid_stats["valid_loss"] - ) - - def test_activation_checkpointing_does_not_change_metrics(self): - """--checkpoint-activations should not change loss""" - - with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir: - with self.assertLogs(): - create_dummy_data(data_dir, num_examples=20) - preprocess_translation_data(data_dir) - ckpt_logs = self._train(data_dir, ["--checkpoint-activations"]) - baseline_logs = self._train(data_dir, []) - assert len(baseline_logs) == len(ckpt_logs) - - baseline_train_stats = read_last_log_entry(baseline_logs, "train") - ckpt_train_stats = read_last_log_entry(ckpt_logs, "train") - assert baseline_train_stats["train_loss"] == ckpt_train_stats["train_loss"] - - baseline_valid_stats = read_last_log_entry(baseline_logs, "valid") - ckpt_valid_stats = read_last_log_entry(ckpt_logs, "valid") - assert baseline_valid_stats["valid_loss"] == ckpt_valid_stats["valid_loss"] - - -def create_dummy_roberta_head_data( - data_dir, num_examples=100, maxlen=10, num_classes=2, regression=False -): - input_dir = "input0" - - def _create_dummy_data(filename): - random_data = torch.rand(num_examples * maxlen) - input_data = 97 + torch.floor(26 * random_data).int() - if regression: - output_data = torch.rand((num_examples, num_classes)) - else: - output_data = 1 + torch.floor(num_classes * torch.rand(num_examples)).int() - with open(os.path.join(data_dir, input_dir, filename + ".out"), "w") as f_in: - label_filename = filename + ".label" if regression else filename + ".out" - with open(os.path.join(data_dir, "label", label_filename), "w") as f_out: - offset = 0 - for i in range(num_examples): - # write example input - ex_len = random.randint(1, maxlen) - ex_str = " ".join(map(chr, input_data[offset : offset + ex_len])) - print(ex_str, file=f_in) - # write example label - if regression: - class_str = " ".join(map(str, output_data[i].numpy())) - print(class_str, file=f_out) - else: - class_str = "class{}".format(output_data[i]) - print(class_str, file=f_out) - offset += ex_len - - os.mkdir(os.path.join(data_dir, input_dir)) - os.mkdir(os.path.join(data_dir, "label")) - _create_dummy_data("train") - _create_dummy_data("valid") - _create_dummy_data("test") - - -def train_masked_lm(data_dir, arch, extra_flags=None): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - "masked_lm", - data_dir, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--criterion", - "masked_lm", - "--batch-size", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - "1", - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - train.main(train_args) - - -def train_roberta_head(data_dir, arch, num_classes=2, extra_flags=None): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - "sentence_prediction", - data_dir, - "--arch", - arch, - "--encoder-layers", - "2", - "--num-classes", - str(num_classes), - "--optimizer", - "adam", - "--lr", - "0.0001", - "--criterion", - "sentence_prediction", - "--max-tokens", - "500", - "--max-positions", - "500", - "--batch-size", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - "1", - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - train.main(train_args) - - -def eval_lm_main(data_dir, extra_flags=None): - eval_lm_parser = options.get_eval_lm_parser() - eval_lm_args = options.parse_args_and_arch( - eval_lm_parser, - [ - data_dir, - "--path", - os.path.join(data_dir, "checkpoint_last.pt"), - "--no-progress-bar", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - eval_lm.main(eval_lm_args) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_binarizer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_binarizer.py deleted file mode 100644 index 57c17b94def17a0ff1fd610ef1b480c9aff2c414..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_binarizer.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import os -import typing as tp -import unittest -from tempfile import TemporaryDirectory - -from fairseq.binarizer import BinarizeSummary, FileBinarizer, VocabularyDatasetBinarizer -from fairseq.data import Dictionary, indexed_dataset - -from tests.utils import make_data, sizes - - -def build_vocab(data: tp.List[tp.List[str]]) -> Dictionary: - d = Dictionary() - for s in data: - for token in s: - d.add_symbol(token) - d.finalize() - return d - - -class TestBinarizer(unittest.TestCase): - def compare_ds_data(self, summary, data, prefix, impl, vocab): - self.assertEqual(summary.num_seq, len(data)) - self.assertEqual(summary.num_tok, sum([len(s) for s in data])) - - dataset = indexed_dataset.make_dataset(prefix, impl) - - self.assertEqual(len(dataset), len(data)) - decoded = [vocab.string(dataset[i]).split() for i in range(0, len(dataset))] - - self.assertEqual(decoded, data) - data_sizes = [i.item() for i in dataset.sizes] - self.assertEqual(data_sizes, sizes(data)) - - def test_can_binarize_line(self): - data = make_data(length=1) - vocab = build_vocab(data) - - binarizer = VocabularyDatasetBinarizer( - vocab, - ) - - sentence = data[0] - summary = BinarizeSummary() - - tensor = binarizer.binarize_line( - " ".join(sentence), - summary, - ) - - self.assertEqual(len(tensor), len(sentence) + 1) - - self.assertEqual(summary.num_tok, len(sentence) + 1) - self.assertEqual(summary.num_seq, 1) - - def test_can_binarize_file_chunk(self): - # test without multiprocess logic - with TemporaryDirectory() as dirname: - raw_file = os.path.join(dirname, "raw1") - prefix = os.path.join(dirname, "test1") - impl = "mmap" - - data = make_data(out_file=raw_file) - vocab = build_vocab(data) - - binarizer = VocabularyDatasetBinarizer( - vocab, - append_eos=False, - ) - - summary = FileBinarizer._binarize_chunk_and_finalize( - binarizer, - raw_file, - offset_start=0, - offset_end=-1, - output_prefix=prefix, - dataset_impl=impl, - vocab_size=len(vocab), - ) - - self.compare_ds_data(summary, data, prefix, impl, vocab) - - def test_can_multiprocess(self): - with TemporaryDirectory() as dirname: - raw_file = os.path.join(dirname, "raw1") - prefix = os.path.join(dirname, "test1") - impl = "mmap" - data = make_data(out_file=raw_file) - vocab = build_vocab(data) - binarizer = VocabularyDatasetBinarizer( - vocab, - append_eos=False, - ) - # with one worker - summary = FileBinarizer.multiprocess_dataset( - raw_file, - impl, - binarizer, - output_prefix=prefix, - vocab_size=len(vocab), - num_workers=1, - ) - - self.compare_ds_data(summary, data, prefix, impl, vocab) - - # with multiple worker - prefix_multi = os.path.join(dirname, "test2") - summary = FileBinarizer.multiprocess_dataset( - raw_file, - impl, - binarizer, - output_prefix=prefix_multi, - vocab_size=len(vocab), - num_workers=3, - ) - - self.compare_ds_data(summary, data, prefix_multi, impl, vocab) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_character_token_embedder.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_character_token_embedder.py deleted file mode 100644 index 24940ebd21a0e4465ca6052409353a3179e9cf6d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_character_token_embedder.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.data import Dictionary -from fairseq.modules import CharacterTokenEmbedder - - -class TestCharacterTokenEmbedder(unittest.TestCase): - def test_character_token_embedder(self): - vocab = Dictionary() - vocab.add_symbol("hello") - vocab.add_symbol("there") - - embedder = CharacterTokenEmbedder( - vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2 - ) - - test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]] - max_len = max(len(s) for s in test_sents) - input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad()) - for i in range(len(test_sents)): - input[i][0] = vocab.eos() - for j in range(len(test_sents[i])): - input[i][j + 1] = vocab.index(test_sents[i][j]) - input[i][j + 2] = vocab.eos() - embs = embedder(input) - - assert embs.size() == (len(test_sents), max_len + 2, 5) - self.assertAlmostEqual(embs[0][0], embs[1][0]) - self.assertAlmostEqual(embs[0][0], embs[0][-1]) - self.assertAlmostEqual(embs[0][1], embs[2][1]) - self.assertAlmostEqual(embs[0][3], embs[1][1]) - - embs.sum().backward() - assert embedder.char_embeddings.weight.grad is not None - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-6) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_checkpoint_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_checkpoint_utils.py deleted file mode 100644 index de241adc38ac9108709a57dab04b71a49cd1e0f1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_checkpoint_utils.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import logging -import os -import tempfile -import unittest -from io import StringIO -from unittest.mock import patch - -import torch -from fairseq import checkpoint_utils -from omegaconf import OmegaConf - -from tests.utils import ( - create_dummy_data, - preprocess_translation_data, - train_translation_model, -) - - -class TestCheckpointUtils(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - @contextlib.contextmanager - def _train_transformer(self, seed, extra_args=None): - if extra_args is None: - extra_args = [] - with tempfile.TemporaryDirectory(f"_train_transformer_seed{seed}") as data_dir: - create_dummy_data(data_dir) - preprocess_translation_data(data_dir) - train_translation_model( - data_dir, - "transformer_iwslt_de_en", - [ - "--encoder-layers", - "3", - "--decoder-layers", - "3", - "--encoder-embed-dim", - "8", - "--decoder-embed-dim", - "8", - "--seed", - str(seed), - ] - + extra_args, - ) - yield os.path.join(data_dir, "checkpoint_last.pt") - - def test_load_model_ensemble_and_task(self): - # with contextlib.redirect_stdout(StringIO()): - with self._train_transformer(seed=123) as model1: - with self._train_transformer(seed=456) as model2: - ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task( - filenames=[model1, model2] - ) - self.assertEqual(len(ensemble), 2) - - # after Transformer has been migrated to Hydra, this will probably - # become cfg.common.seed - self.assertEqual(ensemble[0].args.seed, 123) - self.assertEqual(ensemble[1].args.seed, 456) - - # the task from the first model should be returned - self.assertTrue("seed123" in task.cfg.data) - - # last cfg is saved - self.assertEqual(cfg.common.seed, 456) - - def test_prune_state_dict(self): - with contextlib.redirect_stdout(StringIO()): - extra_args = ["--encoder-layerdrop", "0.01", "--decoder-layerdrop", "0.01"] - with self._train_transformer(seed=1, extra_args=extra_args) as model: - ensemble, cfg, task = checkpoint_utils.load_model_ensemble_and_task( - filenames=[model], - arg_overrides={ - "encoder_layers_to_keep": "0,2", - "decoder_layers_to_keep": "1", - }, - ) - self.assertEqual(len(ensemble), 1) - self.assertEqual(len(ensemble[0].encoder.layers), 2) - self.assertEqual(len(ensemble[0].decoder.layers), 1) - - def test_torch_persistent_save_async(self): - state_dict = {} - filename = "async_checkpoint.pt" - - with patch(f"{checkpoint_utils.__name__}.PathManager.opena") as mock_opena: - with patch( - f"{checkpoint_utils.__name__}._torch_persistent_save" - ) as mock_save: - checkpoint_utils.torch_persistent_save( - state_dict, filename, async_write=True - ) - mock_opena.assert_called_with(filename, "wb") - mock_save.assert_called() - - def test_load_ema_from_checkpoint(self): - dummy_state = {"a": torch.tensor([1]), "b": torch.tensor([0.1])} - with patch(f"{checkpoint_utils.__name__}.PathManager.open") as mock_open, patch( - f"{checkpoint_utils.__name__}.torch.load" - ) as mock_load: - - mock_load.return_value = {"extra_state": {"ema": dummy_state}} - filename = "ema_checkpoint.pt" - state = checkpoint_utils.load_ema_from_checkpoint(filename) - - mock_open.assert_called_with(filename, "rb") - mock_load.assert_called() - - self.assertIn("a", state["model"]) - self.assertIn("b", state["model"]) - self.assertTrue(torch.allclose(dummy_state["a"], state["model"]["a"])) - self.assertTrue(torch.allclose(dummy_state["b"], state["model"]["b"])) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_concat_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_concat_dataset.py deleted file mode 100644 index 8bf4facb570f518aa14a0c58489139cb88bba863..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_concat_dataset.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.data import LanguagePairDataset, TokenBlockDataset -from fairseq.data.concat_dataset import ConcatDataset - -from tests.test_train import mock_dict - - -class TestConcatDataset(unittest.TestCase): - def setUp(self): - d = mock_dict() - tokens_1 = torch.LongTensor([1]).view(1, -1) - tokens_ds1 = TokenBlockDataset( - tokens_1, - sizes=[tokens_1.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_1 = LanguagePairDataset( - tokens_ds1, tokens_ds1.sizes, d, shuffle=False - ) - tokens_2 = torch.LongTensor([2]).view(1, -1) - tokens_ds2 = TokenBlockDataset( - tokens_2, - sizes=[tokens_2.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_2 = LanguagePairDataset( - tokens_ds2, tokens_ds2.sizes, d, shuffle=False - ) - - def test_concat_dataset_basics(self): - d = ConcatDataset([self.dataset_1, self.dataset_2]) - assert len(d) == 2 - assert d[0]["source"][0] == 1 - assert d[1]["source"][0] == 2 - - d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[1, 2]) - assert len(d) == 3 - assert d[0]["source"][0] == 1 - assert d[1]["source"][0] == 2 - assert d[2]["source"][0] == 2 - - d = ConcatDataset([self.dataset_1, self.dataset_2], sample_ratios=[2, 1]) - assert len(d) == 3 - assert d[0]["source"][0] == 1 - assert d[1]["source"][0] == 1 - assert d[2]["source"][0] == 2 diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_constraints.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_constraints.py deleted file mode 100644 index 4f98ef6e9af1670111ce8243ea993c9169708148..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_constraints.py +++ /dev/null @@ -1,274 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from typing import List - -import torch -from fairseq.token_generation_constraints import ( - ConstraintNode, - OrderedConstraintState, - UnorderedConstraintState, - pack_constraints, -) - - -def tensorize(constraints: List[List[int]]) -> torch.Tensor: - return [torch.tensor(x) for x in constraints] - - -class TestHelperRoutines(unittest.TestCase): - def setUp(self): - self.examples = [ - ([[]], torch.tensor([[0]])), - ([[], []], torch.tensor([[0], [0]])), - ([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])), - ( - [ - [ - torch.tensor([3, 1, 2]), - torch.tensor([3]), - torch.tensor([4, 5, 6, 7]), - ], - [], - [torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])], - ], - torch.tensor( - [ - [3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0], - ] - ), - ), - ] - - def test_packing(self): - """Ensures the list of lists of tensors gets packed correctly.""" - for batch_constraints, expected_tensor in self.examples: - packed = pack_constraints(batch_constraints) - assert torch.equal(packed, expected_tensor) - - -class TestUnorderedConstraintState(unittest.TestCase): - def setUp(self): - # Tuples of (contraint set, expected printed graph, token counts per node) - self.examples = [ - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - "([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))", # noqa - {1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1}, - ), - ([], "[None].False#0", {}), - (tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}), - ( - tensorize([[100000, 1, 2, 3, 4, 5]]), - "([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))", - {100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, - ), - ( - tensorize([[1, 2], [1, 2]]), - "([None].False#2 ([1].False#2 [2].True#2))", - {1: 2, 2: 2}, - ), - ( - tensorize([[1, 2], [3, 4]]), - "([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))", - {1: 1, 2: 1, 3: 1, 4: 1}, - ), - ] - - self.sequences = [ - ( - self.examples[0][0], - [], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - [1, 2], - {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, - ), - ( - self.examples[0][0], - [1, 2, 94], - {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - [1, 3, 999, 1, 4], - {"bank": 4, "num_completed": 2, "finished": False, "is_root": False}, - ), - ( - self.examples[0][0], - [1, 3, 999, 1, 4, 999], - {"bank": 4, "num_completed": 2, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - [4, 5, 6, 8], - {"bank": 2, "num_completed": 1, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - # Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5] - # [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]], - [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, - ), - ( - self.examples[0][0], - [1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": True}, - ), - ( - tensorize([[1], [2, 3]]), - # Should not be able to get credit for entering 1 a second time - [1, 1], - {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, - ), - ( - self.examples[4][0], - [1, 2, 1, 2], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ( - self.examples[4][0], - [1, 2, 1, 2, 1], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, - ), - ( - self.examples[5][0], - [1, 2, 3, 4, 5], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, - ), - ] - - def test_graphs(self): - """ - Test whether unordered graph systems are created correctly. - """ - for example in self.examples: - constraints, expected, gold_counts = example - c = ConstraintNode.create(constraints) - assert ( - ConstraintNode.print_graph(c) == expected - ), f"got {ConstraintNode.print_graph(c)}, expected {expected}" - assert ( - c.token_counts() == gold_counts - ), f"{c} got {c.token_counts()} wanted {gold_counts}" - - def test_next_tokens(self): - """ - Tests that the set of next tokens is correct. - """ - for example in self.examples: - constraints, expected, gold_counts = example - root = ConstraintNode.create(constraints) - - root_tokens = set(root.children.keys()) - for sequence in constraints: - state = UnorderedConstraintState(root) - for token in sequence: - all_tokens = root_tokens.union(state.node.children.keys()) - assert ( - all_tokens == state.next_tokens() - ), f"ALL {all_tokens} NEXT {state.next_tokens()}" - state = state.advance(token) - - def test_sequences(self): - for constraints, tokens, expected in self.sequences: - state = UnorderedConstraintState.create(pack_constraints([constraints])[0]) - for token in tokens: - state = state.advance(token) - result = {} - for attr in expected.keys(): - result[attr] = getattr(state, attr) - - assert ( - result == expected - ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" - - -class TestOrderedConstraintState(unittest.TestCase): - def setUp(self): - self.sequences = [ - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2], - {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 94], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 3, 999, 1, 4], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 3, 999, 999], - {"bank": 3, "num_completed": 1, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 3, 77, 1, 3, 1], - {"bank": 6, "num_completed": 2, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, - ), - ( - tensorize([[1], [2, 3]]), - [1, 1], - {"bank": 1, "num_completed": 1, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2], [1, 2]]), - [1, 2, 1, 2], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ( - tensorize([[1, 2], [1, 2]]), - [1, 2, 1, 2, 1], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ( - tensorize([[1, 2], [3, 4]]), - [1, 2, 3, 4, 5], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ] - - def test_sequences(self): - for i, (constraints, tokens, expected) in enumerate(self.sequences): - state = OrderedConstraintState.create(pack_constraints([constraints])[0]) - for token in tokens: - state = state.advance(token) - result = {} - for attr in expected.keys(): - result[attr] = getattr(state, attr) - assert ( - result == expected - ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_convtbc.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_convtbc.py deleted file mode 100644 index 3a3c9b91e70f597ab77b9b01459cc429db5d7956..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_convtbc.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -import torch.nn as nn -from fairseq.modules import ConvTBC - - -class TestConvTBC(unittest.TestCase): - def test_convtbc(self): - # ksz, in_channels, out_channels - conv_tbc = ConvTBC(4, 5, kernel_size=3, padding=1) - # out_channels, in_channels, ksz - conv1d = nn.Conv1d(4, 5, kernel_size=3, padding=1) - - conv_tbc.weight.data.copy_(conv1d.weight.data.transpose(0, 2)) - conv_tbc.bias.data.copy_(conv1d.bias.data) - - input_tbc = torch.randn(7, 2, 4, requires_grad=True) - input1d = input_tbc.data.transpose(0, 1).transpose(1, 2) - input1d.requires_grad = True - - output_tbc = conv_tbc(input_tbc) - output1d = conv1d(input1d) - - self.assertAlmostEqual( - output_tbc.data.transpose(0, 1).transpose(1, 2), output1d.data - ) - - grad_tbc = torch.randn(output_tbc.size()) - grad1d = grad_tbc.transpose(0, 1).transpose(1, 2).contiguous() - - output_tbc.backward(grad_tbc) - output1d.backward(grad1d) - - self.assertAlmostEqual( - conv_tbc.weight.grad.data.transpose(0, 2), conv1d.weight.grad.data - ) - self.assertAlmostEqual(conv_tbc.bias.grad.data, conv1d.bias.grad.data) - self.assertAlmostEqual( - input_tbc.grad.data.transpose(0, 1).transpose(1, 2), input1d.grad.data - ) - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-4) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_data_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_data_utils.py deleted file mode 100644 index 925e6db4fe7aec0140daa4d948be314cc808663e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_data_utils.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import numpy as np -from fairseq.data.data_utils_fast import batch_by_size_fn, batch_by_size_vec - - -class TestBatchBySize(unittest.TestCase): - @classmethod - def batch_by_size_baseline( - cls, - indices, - num_tokens_vec, - max_tokens, - max_sentences, - bsz_mult, - ): - """Simple, reliable and slow implementation of batch by size""" - batches = [] - start = 0 - while start < len(indices): - for end in range(start + 1, len(indices) + 1): - max_val = max(num_tokens_vec[pos] for pos in range(start, end)) - sent_count = end - start - num_tokens = max_val * sent_count - overflow = num_tokens > max_tokens > 0 or sent_count > max_sentences > 0 - terminate = overflow or end == len(indices) - if overflow: - sent_count -= 1 - if terminate: - if sent_count > bsz_mult: - sent_count = sent_count - sent_count % bsz_mult - batches.append(indices[start : start + sent_count]) - start = start + sent_count - break - return batches - - @classmethod - def _get_error_message( - cls, max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results - ): - return f"""Reference batch_by_size implementation should produce - same output as the baseline method. - Params: - max_sentences={max_sentences}, - max_tokens={max_tokens}, - bsz_mult={bsz_mult}, - num_tokens_vec={num_tokens_vec}, - expected_batches={validation}, - returned_batches={results}""" - - def _compare_results( - self, - indices_len, - batch_by_size_impl, - max_sentences, - max_tokens, - bsz_mult, - num_tokens_vec, - ): - indices = np.array(list(range(indices_len))) - validation = self.batch_by_size_baseline( - indices, - num_tokens_vec, - max_tokens=max_tokens, - max_sentences=max_sentences, - bsz_mult=bsz_mult, - ) - results = batch_by_size_impl( - indices, - num_tokens_vec, - max_tokens=max_tokens, - max_sentences=max_sentences, - bsz_mult=bsz_mult, - ) - error_msg = self._get_error_message( - max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results - ) - self.assertEqual(len(validation), len(results), error_msg) - for first, second in zip(validation, results): - self.assertTrue(np.array_equal(first, second), error_msg) - - def _run_compare_with_baseline_sweep(self, batch_by_size_impl): - """Compare reference batch_by_size implementation with batch_by_size_baseline - across a dense grid of hyperparam values""" - MAX_MAX_TOKENS = 10 - NUM_TOKENS_VECS_COUNT = 5 - for indices_len in [10, 11]: # try odd and even len of indices - for max_sentences in range(0, indices_len + 2): - for max_tokens in range(0, MAX_MAX_TOKENS): - for bsz_mult in range(1, max(MAX_MAX_TOKENS, indices_len) + 2): - for _ in range(NUM_TOKENS_VECS_COUNT): - num_tokens_vec = np.random.randint( - 0, max_tokens + 1, size=indices_len - ) - self._compare_results( - indices_len, - batch_by_size_impl, - max_sentences, - max_tokens, - bsz_mult, - num_tokens_vec, - ) - - -class TestBatchBySizeVec(TestBatchBySize): - def test_compare_with_baseline(self): - self._run_compare_with_baseline_sweep(batch_by_size_vec) - - -class TestBatchBySizeFn(TestBatchBySize): - def test_compare_with_baseline(self): - def batch_by_size_fn_wrapper( - indices, - num_tokens_vec, - max_tokens, - max_sentences, - bsz_mult, - ): - def num_tokens_fn(idx): - return num_tokens_vec[idx] - - return batch_by_size_fn( - indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult - ) - - self._run_compare_with_baseline_sweep(batch_by_size_fn_wrapper) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_dataclass_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_dataclass_utils.py deleted file mode 100644 index 231f86b6ee6f12fb70aff6e13cde6890eea76917..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_dataclass_utils.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from argparse import ArgumentParser -from dataclasses import dataclass, field - -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import gen_parser_from_dataclass - - -@dataclass -class A(FairseqDataclass): - data: str = field(default="test", metadata={"help": "the data input"}) - num_layers: int = field(default=200, metadata={"help": "more layers is better?"}) - - -@dataclass -class B(FairseqDataclass): - bar: A = field(default=A()) - foo: int = field(default=0, metadata={"help": "not a bar"}) - - -@dataclass -class D(FairseqDataclass): - arch: A = field(default=A()) - foo: int = field(default=0, metadata={"help": "not a bar"}) - - -@dataclass -class C(FairseqDataclass): - data: str = field(default="test", metadata={"help": "root level data input"}) - encoder: D = field(default=D()) - decoder: A = field(default=A()) - lr: int = field(default=0, metadata={"help": "learning rate"}) - - -class TestDataclassUtils(unittest.TestCase): - def test_argparse_convert_basic(self): - parser = ArgumentParser() - gen_parser_from_dataclass(parser, A(), True) - args = parser.parse_args(["--num-layers", "10", "the/data/path"]) - self.assertEqual(args.num_layers, 10) - self.assertEqual(args.data, "the/data/path") - - def test_argparse_recursive(self): - parser = ArgumentParser() - gen_parser_from_dataclass(parser, B(), True) - args = parser.parse_args(["--num-layers", "10", "--foo", "10", "the/data/path"]) - self.assertEqual(args.num_layers, 10) - self.assertEqual(args.foo, 10) - self.assertEqual(args.data, "the/data/path") - - def test_argparse_recursive_prefixing(self): - self.maxDiff = None - parser = ArgumentParser() - gen_parser_from_dataclass(parser, C(), True, "") - args = parser.parse_args( - [ - "--encoder-arch-data", - "ENCODER_ARCH_DATA", - "--encoder-arch-num-layers", - "10", - "--encoder-foo", - "10", - "--decoder-data", - "DECODER_DATA", - "--decoder-num-layers", - "10", - "--lr", - "10", - "the/data/path", - ] - ) - self.assertEqual(args.encoder_arch_data, "ENCODER_ARCH_DATA") - self.assertEqual(args.encoder_arch_num_layers, 10) - self.assertEqual(args.encoder_foo, 10) - self.assertEqual(args.decoder_data, "DECODER_DATA") - self.assertEqual(args.decoder_num_layers, 10) - self.assertEqual(args.lr, 10) - self.assertEqual(args.data, "the/data/path") - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_dataset.py deleted file mode 100644 index aa2c7bd982fb038901e96def12926774e3f40d2c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_dataset.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import unittest -from typing import Sequence - -from fairseq.data import LanguagePairDataset, ListDataset, RoundRobinZipDatasets - -from tests.test_train import mock_dict - - -def lang_pair_dataset(lengths: Sequence[int]) -> LanguagePairDataset: - tokens = [[i] * l for i, l in enumerate(lengths)] - return LanguagePairDataset(ListDataset(tokens), lengths, mock_dict()) - - -def sample(id: int, length: int): - return {"id": id, "source": [id] * length, "target": None} - - -class TestDataset(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_round_robin_zip_datasets(self): - long_dataset = lang_pair_dataset([10, 9, 8, 11]) - short_dataset = lang_pair_dataset([11, 9]) - - dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) - # Dataset is now sorted by sentence length - dataset.ordered_indices() - assert dataset.longest_dataset is long_dataset - self.assertEqual(dict(dataset[0]), {"a": sample(2, 8), "b": sample(1, 9)}) - # The item 2 of dataset 'a' is with item (2 % 2 = 0) of dataset 'b' - self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 9)}) - - def test_round_robin_zip_datasets_filtered(self): - long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12]) - short_dataset = lang_pair_dataset([11, 20, 9, 1000]) - - dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) - # Dataset is now sorted by sentence length - idx = dataset.ordered_indices() - idx, _ = dataset.filter_indices_by_size(idx, {"a": 19, "b": 900}) - self.assertEqual(list(idx), [0, 1, 2, 3, 4]) - self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)}) - self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 20)}) - self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(0, 11)}) - - def test_round_robin_zip_datasets_filtered_with_tuple(self): - long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12]) - short_dataset = lang_pair_dataset([11, 20, 9, 1000]) - - dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset}) - # Dataset is now sorted by sentence length - idx = dataset.ordered_indices() - idx, _ = dataset.filter_indices_by_size(idx, 19) - self.assertEqual(list(idx), [0, 1, 2, 3, 4]) - self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)}) - self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(2, 9)}) - self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(2, 9)}) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_dictionary.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_dictionary.py deleted file mode 100644 index dc9d71b3c722ce3066e182d4b237b2a72999d4d0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_dictionary.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import io -import os -import string -import tempfile -import unittest - -import torch -from fairseq import tokenizer -from fairseq.data import Dictionary - - -class TestDictionary(unittest.TestCase): - def test_finalize(self): - txt = [ - "A B C D", - "B C D", - "C D", - "D", - ] - ref_ids1 = list( - map( - torch.IntTensor, - [ - [4, 5, 6, 7, 2], - [5, 6, 7, 2], - [6, 7, 2], - [7, 2], - ], - ) - ) - ref_ids2 = list( - map( - torch.IntTensor, - [ - [7, 6, 5, 4, 2], - [6, 5, 4, 2], - [5, 4, 2], - [4, 2], - ], - ) - ) - - # build dictionary - d = Dictionary() - for line in txt: - d.encode_line(line, add_if_not_exist=True) - - def get_ids(dictionary): - ids = [] - for line in txt: - ids.append(dictionary.encode_line(line, add_if_not_exist=False)) - return ids - - def assertMatch(ids, ref_ids): - for toks, ref_toks in zip(ids, ref_ids): - self.assertEqual(toks.size(), ref_toks.size()) - self.assertEqual(0, (toks != ref_toks).sum().item()) - - ids = get_ids(d) - assertMatch(ids, ref_ids1) - - # check finalized dictionary - d.finalize() - finalized_ids = get_ids(d) - assertMatch(finalized_ids, ref_ids2) - - # write to disk and reload - with tempfile.NamedTemporaryFile(mode="w") as tmp_dict: - d.save(tmp_dict.name) - d = Dictionary.load(tmp_dict.name) - reload_ids = get_ids(d) - assertMatch(reload_ids, ref_ids2) - assertMatch(finalized_ids, reload_ids) - - def test_overwrite(self): - # for example, Camembert overwrites , and - dict_file = io.StringIO( - " 999 #fairseq:overwrite\n" - " 999 #fairseq:overwrite\n" - " 999 #fairseq:overwrite\n" - ", 999\n" - "▁de 999\n" - ) - d = Dictionary() - d.add_from_file(dict_file) - self.assertEqual(d.index(""), 1) - self.assertEqual(d.index("foo"), 3) - self.assertEqual(d.index(""), 4) - self.assertEqual(d.index(""), 5) - self.assertEqual(d.index(""), 6) - self.assertEqual(d.index(","), 7) - self.assertEqual(d.index("▁de"), 8) - - def test_no_overwrite(self): - # for example, Camembert overwrites , and - dict_file = io.StringIO( - " 999\n" " 999\n" " 999\n" ", 999\n" "▁de 999\n" - ) - d = Dictionary() - with self.assertRaisesRegex(RuntimeError, "Duplicate"): - d.add_from_file(dict_file) - - def test_space(self): - # for example, character models treat space as a symbol - dict_file = io.StringIO(" 999\n" "a 999\n" "b 999\n") - d = Dictionary() - d.add_from_file(dict_file) - self.assertEqual(d.index(" "), 4) - self.assertEqual(d.index("a"), 5) - self.assertEqual(d.index("b"), 6) - - def test_add_file_to_dict(self): - counts = {} - num_lines = 100 - per_line = 10 - with tempfile.TemporaryDirectory("test_sampling") as data_dir: - filename = os.path.join(data_dir, "dummy.txt") - with open(filename, "w", encoding="utf-8") as data: - for c in string.ascii_letters: - line = f"{c} " * per_line - for _ in range(num_lines): - data.write(f"{line}\n") - counts[c] = per_line * num_lines - per_line += 5 - - dict = Dictionary() - Dictionary.add_file_to_dictionary( - filename, dict, tokenizer.tokenize_line, 10 - ) - dict.finalize(threshold=0, nwords=-1, padding_factor=8) - - for c in string.ascii_letters: - count = dict.get_count(dict.index(c)) - self.assertEqual( - counts[c], count, f"{c} count is {count} but should be {counts[c]}" - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_ema.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_ema.py deleted file mode 100644 index 1f43b87965f8d34e7ef467f41e5281fba74dc4c2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_ema.py +++ /dev/null @@ -1,274 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from copy import deepcopy -from dataclasses import dataclass -from typing import Optional -from unittest.mock import patch - -import torch -from fairseq.models.ema import EMA - - -class DummyModule(torch.nn.Module): - def __init__(self) -> None: - """LightningModule for testing purposes - - Args: - epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum - validation loss for testing purposes (zero based). If None this is ignored. Defaults to None. - """ - super().__init__() - self.layer = torch.nn.Linear(in_features=32, out_features=2) - self.another_layer = torch.nn.Linear(in_features=2, out_features=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.layer(x) - return self.another_layer(x) - - -@dataclass -class EMAConfig(object): - ema_decay: float = 0.99 - ema_start_update: int = 0 - ema_fp32: bool = False - ema_seed_model: Optional[str] = None - ema_update_freq: int = 1 - - -class TestEMA(unittest.TestCase): - def assertTorchAllClose(self, x, y, atol=1e-8, rtol=1e-5, msg=None): - diff = x.float() - y.float() - diff_norm = torch.norm(diff) - other_norm = torch.norm(y.float()) - - if msg is None: - msg = "|input - other| > {} + {} * |other|".format(atol, rtol) - - self.assertLessEqual( - diff_norm, - atol + rtol * other_norm, - msg=msg, - ) - - def test_ema(self): - model = DummyModule() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig() - ema = EMA(model, config) - - # set decay - ema._set_decay(config.ema_decay) - self.assertEqual(ema.get_decay(), config.ema_decay) - - # get model - self.assertEqual(ema.get_model(), ema.model) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - # EMA step - x = torch.randn(32) - y = model(x) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - ema_state_dict = ema.get_model().state_dict() - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema_state_dict[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - # Load EMA into model - model2 = DummyModule() - ema.reverse(model2) - - for key, param in model2.state_dict().items(): - ema_param = ema_state_dict[key] - self.assertTrue(torch.allclose(ema_param, param)) - - # Check that step_internal is called once - with patch.object(ema, "_step_internal", return_value=None) as mock_method: - ema.step(model) - mock_method.assert_called_once_with(model, None) - - def _test_ema_start_update(self, updates): - model = DummyModule() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_start_update=1) - ema = EMA(model, config) - - # EMA step - x = torch.randn(32) - y = model(x) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model, updates=updates) - ema_state_dict = ema.get_model().state_dict() - - self.assertEqual(ema.get_decay(), 0 if updates == 0 else config.ema_decay) - - for key, param in model.state_dict().items(): - ema_param = ema_state_dict[key] - prev_param = state[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - if updates == 0: - self.assertTorchAllClose( - ema_param, - param, - ) - else: - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Check that step_internal is called once - with patch.object(ema, "_step_internal", return_value=None) as mock_method: - ema.step(model, updates=updates) - mock_method.assert_called_once_with(model, updates) - - def test_ema_before_start_update(self): - self._test_ema_start_update(updates=0) - - def test_ema_after_start_update(self): - self._test_ema_start_update(updates=1) - - def test_ema_fp32(self): - # CPU no longer supports Linear in half precision - dtype = torch.half if torch.cuda.is_available() else torch.float - - model = DummyModule().to(dtype) - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_fp32=True) - ema = EMA(model, config) - - x = torch.randn(32) - y = model(x.to(dtype)) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema.get_model().state_dict()[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - self.assertIn(key, ema.fp32_params) - - # EMA update is done in fp32, and hence the EMA param must be - # closer to the EMA update done in fp32 than in fp16. - self.assertLessEqual( - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param.float() - + (1 - config.ema_decay) * param.float() - ) - .to(dtype) - .float() - ), - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param + (1 - config.ema_decay) * param - ).float() - ), - ) - self.assertTorchAllClose( - ema_param, - ( - config.ema_decay * prev_param.float() - + (1 - config.ema_decay) * param.float() - ).to(dtype), - ) - - def test_ema_fp16(self): - # CPU no longer supports Linear in half precision - if not torch.cuda.is_available(): - return - - model = DummyModule().half() - optimizer = torch.optim.SGD(model.parameters(), lr=0.01) - state = deepcopy(model.state_dict()) - config = EMAConfig(ema_fp32=False) - ema = EMA(model, config) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - x = torch.randn(32) - y = model(x.half()) - loss = y.sum() - loss.backward() - optimizer.step() - - ema.step(model) - - for key, param in model.state_dict().items(): - prev_param = state[key] - ema_param = ema.get_model().state_dict()[key] - - if "version" in key: - # Do not decay a model.version pytorch param - continue - - # EMA update is done in fp16, and hence the EMA param must be - # closer to the EMA update done in fp16 than in fp32. - self.assertLessEqual( - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param + (1 - config.ema_decay) * param - ).float() - ), - torch.norm( - ema_param.float() - - ( - config.ema_decay * prev_param.float() - + (1 - config.ema_decay) * param.float() - ) - .half() - .float() - ), - ) - self.assertTorchAllClose( - ema_param, - config.ema_decay * prev_param + (1 - config.ema_decay) * param, - ) - - # Since fp32 params is not used, it should be of size 0 - self.assertEqual(len(ema.fp32_params), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_espnet_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_espnet_multihead_attention.py deleted file mode 100644 index 693c1ca562cc6a512cf97f2c615eeb8cad2b0b97..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_espnet_multihead_attention.py +++ /dev/null @@ -1,177 +0,0 @@ -import unittest - -import numpy as np -import torch -from fairseq.modules import ( - ESPNETMultiHeadedAttention, - RelPositionMultiHeadedAttention, - RotaryPositionMultiHeadedAttention, -) - -torch.use_deterministic_algorithms(True) - - -class TestESPNETMultiHeadedAttention(unittest.TestCase): - def setUp(self) -> None: - self.T = 3 - self.B = 1 - self.C = 2 - torch.manual_seed(0) - self.sample = torch.randn(self.T, self.B, self.C) # TBC - self.sample_scores = torch.randn(self.B, 1, self.T, self.T) - self.MHA = ESPNETMultiHeadedAttention(self.C, 1, dropout=0) - - def test_forward(self): - expected_scores = torch.tensor( - [[[0.1713, -0.3776]], [[0.2263, -0.4486]], [[0.2243, -0.4538]]] - ) - scores, _ = self.MHA(self.sample, self.sample, self.sample) - self.assertTrue( - np.allclose( - expected_scores.cpu().detach().numpy(), - scores.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - def test_forward_qkv(self): - expected_query = torch.tensor( - [[[[-1.0235, 0.0409], [0.4008, 1.3077], [0.5396, 2.0698]]]] - ) - expected_key = torch.tensor( - [[[[0.5053, -0.4965], [-0.3730, -0.9473], [-0.7019, -0.1935]]]] - ) - expected_val = torch.tensor( - [[[[-0.9940, 0.5403], [0.5924, -0.7619], [0.7504, -1.0892]]]] - ) - sample_t = self.sample.transpose(0, 1) - query, key, val = self.MHA.forward_qkv(sample_t, sample_t, sample_t) - self.assertTrue( - np.allclose( - expected_query.cpu().detach().numpy(), - query.cpu().detach().numpy(), - atol=1e-4, - ) - ) - self.assertTrue( - np.allclose( - expected_key.cpu().detach().numpy(), - key.cpu().detach().numpy(), - atol=1e-4, - ) - ) - self.assertTrue( - np.allclose( - expected_val.cpu().detach().numpy(), - val.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - def test_forward_attention(self): - expected_scores = torch.tensor( - [[[0.1627, -0.6249], [-0.2547, -0.6487], [-0.0711, -0.8545]]] - ) - scores = self.MHA.forward_attention( - self.sample.transpose(0, 1).view(self.B, 1, self.T, self.C), - self.sample_scores, - mask=None, - ) - self.assertTrue( - np.allclose( - expected_scores.cpu().detach().numpy(), - scores.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - -class TestRelPositionMultiHeadedAttention(unittest.TestCase): - def setUp(self) -> None: - self.T = 3 - self.B = 1 - self.C = 2 - torch.manual_seed(0) - self.sample = torch.randn(self.T, self.B, self.C) # TBC - self.sample_x = torch.randn(self.B, 1, self.T, self.T * 2 - 1) - self.sample_pos = torch.randn(self.B, self.T * 2 - 1, self.C) - self.MHA = RelPositionMultiHeadedAttention(self.C, 1, dropout=0) - - def test_rel_shift(self): - expected_x = torch.tensor( - [ - [ - [ - [-0.7193, -0.4033, -0.5966], - [-0.8567, 1.1006, -1.0712], - [-0.5663, 0.3731, -0.8920], - ] - ] - ] - ) - x = self.MHA.rel_shift(self.sample_x) - self.assertTrue( - np.allclose( - expected_x.cpu().detach().numpy(), - x.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - def test_forward(self): - expected_scores = torch.tensor( - [ - [[-0.9609, -0.5020]], - [[-0.9308, -0.4890]], - [[-0.9473, -0.4948]], - [[-0.9609, -0.5020]], - [[-0.9308, -0.4890]], - [[-0.9473, -0.4948]], - [[-0.9609, -0.5020]], - [[-0.9308, -0.4890]], - [[-0.9473, -0.4948]], - [[-0.9609, -0.5020]], - [[-0.9308, -0.4890]], - [[-0.9473, -0.4948]], - [[-0.9609, -0.5020]], - [[-0.9308, -0.4890]], - [[-0.9473, -0.4948]], - ] - ) - scores, _ = self.MHA(self.sample, self.sample, self.sample, self.sample_pos) - self.assertTrue( - np.allclose( - expected_scores.cpu().detach().numpy(), - scores.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - -class TestRotaryPositionMultiHeadedAttention(unittest.TestCase): - def setUp(self) -> None: - self.T = 3 - self.B = 1 - self.C = 2 - torch.manual_seed(0) - self.sample = torch.randn(self.T, self.B, self.C) # TBC - self.MHA = RotaryPositionMultiHeadedAttention( - self.C, 1, dropout=0, precision=None - ) - - def test_forward(self): - expected_scores = torch.tensor( - [[[-0.3220, -0.4726]], [[-1.2813, -0.0979]], [[-0.3138, -0.4758]]] - ) - scores, _ = self.MHA(self.sample, self.sample, self.sample) - self.assertTrue( - np.allclose( - expected_scores.cpu().detach().numpy(), - scores.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_export.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_export.py deleted file mode 100644 index 6e22b098f0be908833744ba1c38d3cbfb2d1a8f1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_export.py +++ /dev/null @@ -1,119 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import tempfile -import unittest - -import torch -from fairseq.data.dictionary import Dictionary -from fairseq.models.transformer import TransformerModel -from fairseq.modules import multihead_attention, sinusoidal_positional_embedding -from fairseq.tasks.fairseq_task import LegacyFairseqTask - -DEFAULT_TEST_VOCAB_SIZE = 100 - - -class DummyTask(LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = get_dummy_dictionary() - if getattr(self.args, "ctc", False): - self.dictionary.add_symbol("") - self.src_dict = self.dictionary - self.tgt_dict = self.dictionary - - @property - def source_dictionary(self): - return self.src_dict - - @property - def target_dictionary(self): - return self.dictionary - - -def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): - dummy_dict = Dictionary() - # add dummy symbol to satisfy vocab size - for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), 1000) - return dummy_dict - - -def get_dummy_task_and_parser(): - """ - Return a dummy task and argument parser, which can be used to - create a model/criterion. - """ - parser = argparse.ArgumentParser( - description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS - ) - DummyTask.add_args(parser) - args = parser.parse_args([]) - task = DummyTask.setup_task(args) - return task, parser - - -def _test_save_and_load(scripted_module): - with tempfile.NamedTemporaryFile() as f: - scripted_module.save(f.name) - torch.jit.load(f.name) - - -class TestExportModels(unittest.TestCase): - def test_export_multihead_attention(self): - module = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) - scripted = torch.jit.script(module) - _test_save_and_load(scripted) - - def test_incremental_state_multihead_attention(self): - module1 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) - module1 = torch.jit.script(module1) - module2 = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2) - module2 = torch.jit.script(module2) - - state = {} - state = module1.set_incremental_state(state, "key", {"a": torch.tensor([1])}) - state = module2.set_incremental_state(state, "key", {"a": torch.tensor([2])}) - v1 = module1.get_incremental_state(state, "key")["a"] - v2 = module2.get_incremental_state(state, "key")["a"] - - self.assertEqual(v1, 1) - self.assertEqual(v2, 2) - - def test_positional_embedding(self): - module = sinusoidal_positional_embedding.SinusoidalPositionalEmbedding( - embedding_dim=8, padding_idx=1 - ) - scripted = torch.jit.script(module) - _test_save_and_load(scripted) - - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) - def test_export_transformer(self): - task, parser = get_dummy_task_and_parser() - TransformerModel.add_args(parser) - args = parser.parse_args([]) - model = TransformerModel.build_model(args, task) - scripted = torch.jit.script(model) - _test_save_and_load(scripted) - - @unittest.skipIf( - torch.__version__ < "1.6.0", "Targeting OSS scriptability for the 1.6 release" - ) - def test_export_transformer_no_token_pos_emb(self): - task, parser = get_dummy_task_and_parser() - TransformerModel.add_args(parser) - args = parser.parse_args([]) - args.no_token_positional_embeddings = True - model = TransformerModel.build_model(args, task) - scripted = torch.jit.script(model) - _test_save_and_load(scripted) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_file_chunker_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_file_chunker_utils.py deleted file mode 100644 index 5cded04572f0ab68c81db9ad14de1c18951a1a10..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_file_chunker_utils.py +++ /dev/null @@ -1,63 +0,0 @@ -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import shutil -import tempfile -import unittest -from typing import Optional - - -class TestFileChunker(unittest.TestCase): - _tmpdir: Optional[str] = None - _tmpfile: Optional[str] = None - _line_content = "Hello, World\n" - _num_bytes = None - _num_lines = 200 - _num_splits = 20 - - @classmethod - def setUpClass(cls) -> None: - cls._num_bytes = len(cls._line_content.encode("utf-8")) - cls._tmpdir = tempfile.mkdtemp() - with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f: - cls._tmpfile = f.name - for _i in range(cls._num_lines): - f.write(cls._line_content) - f.flush() - - @classmethod - def tearDownClass(cls) -> None: - # Cleanup temp working dir. - if cls._tmpdir is not None: - shutil.rmtree(cls._tmpdir) # type: ignore - - def test_find_offsets(self): - from fairseq.file_chunker_utils import find_offsets - - offsets = find_offsets(self._tmpfile, self._num_splits) - self.assertEqual(len(offsets), self._num_splits + 1) - (zero, *real_offsets, last) = offsets - self.assertEqual(zero, 0) - for i, o in enumerate(real_offsets): - self.assertEqual( - o, - self._num_bytes - + ((i + 1) * self._num_bytes * self._num_lines / self._num_splits), - ) - self.assertEqual(last, self._num_bytes * self._num_lines) - - def test_readchunks(self): - from fairseq.file_chunker_utils import Chunker, find_offsets - - offsets = find_offsets(self._tmpfile, self._num_splits) - for start, end in zip(offsets, offsets[1:]): - with Chunker(self._tmpfile, start, end) as lines: - all_lines = list(lines) - num_lines = self._num_lines / self._num_splits - self.assertAlmostEqual( - len(all_lines), num_lines, delta=1 - ) # because we split on the bites, we might end up with one more/less line in a chunk - self.assertListEqual( - all_lines, [self._line_content for _ in range(len(all_lines))] - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_file_io.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_file_io.py deleted file mode 100644 index af7c4cedb8b7f24b69905d768c9ba5cd75d48cfd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_file_io.py +++ /dev/null @@ -1,59 +0,0 @@ -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import shutil -import sys -import tempfile -import unittest -from typing import Optional -from unittest.mock import MagicMock - - -class TestFileIO(unittest.TestCase): - - _tmpdir: Optional[str] = None - _tmpfile: Optional[str] = None - _tmpfile_contents = "Hello, World" - - @classmethod - def setUpClass(cls) -> None: - cls._tmpdir = tempfile.mkdtemp() - with open(os.path.join(cls._tmpdir, "test.txt"), "w") as f: - cls._tmpfile = f.name - f.write(cls._tmpfile_contents) - f.flush() - - @classmethod - def tearDownClass(cls) -> None: - # Cleanup temp working dir. - if cls._tmpdir is not None: - shutil.rmtree(cls._tmpdir) # type: ignore - - def test_file_io(self): - from fairseq.file_io import PathManager - - with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: - s = f.read() - self.assertEqual(s, self._tmpfile_contents) - - def test_file_io_oss(self): - # Mock iopath to simulate oss environment. - sys.modules["iopath"] = MagicMock() - from fairseq.file_io import PathManager - - with PathManager.open(os.path.join(self._tmpdir, "test.txt"), "r") as f: - s = f.read() - self.assertEqual(s, self._tmpfile_contents) - - def test_file_io_async(self): - # ioPath `PathManager` is initialized after the first `opena` call. - try: - from fairseq.file_io import PathManager - - _asyncfile = os.path.join(self._tmpdir, "async.txt") - f = PathManager.opena(_asyncfile, "wb") - f.close() - - finally: - self.assertTrue(PathManager.async_close()) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_fp16_optimizer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_fp16_optimizer.py deleted file mode 100644 index 27085a12da615e5fa287b45987562410ca4d80b2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_fp16_optimizer.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import copy -import logging -import unittest - -import torch -from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer -from omegaconf import OmegaConf - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestGradientScaling(unittest.TestCase): - def setUp(self): - self.x = torch.tensor([2.0]).cuda().half() - weight = 3.0 - bias = 5.0 - self.error = 1.0 - self.target = torch.tensor([self.x * weight + bias + self.error]).cuda().half() - self.loss_fn = torch.nn.L1Loss() - - self.model = torch.nn.Linear(1, 1) - self.model.weight.data = torch.tensor([[weight]]) - self.model.bias.data = torch.tensor([bias]) - self.model.cuda().half() - self.params = list(self.model.parameters()) - - self.cfg_dls = OmegaConf.create( - { - "optimization": { - "lr": [0.1], - }, - "optimizer": { - "_name": "adam", - "lr": [0.1], - "adam_betas": "(0.9, 0.999)", - "adam_eps": 1e-8, - "weight_decay": 0.0, - }, - "common": { - "fp16_init_scale": 1, - "fp16_scale_window": 1, - "fp16_scale_tolerance": 1, - "threshold_loss_scale": 1, - "min_loss_scale": 1e-4, - "tpu": False, - }, - } - ) - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def run_iter(self, model, params, optimizer): - optimizer.zero_grad() - y = model(self.x) - loss = self.loss_fn(y, self.target) - optimizer.backward(loss) - self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) - - grad_norm = optimizer.clip_grad_norm(0) - self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) - - optimizer.step() - self.assertEqual( - model.weight, - torch.tensor( - [[3.0996]], device="cuda:0", dtype=torch.float16, requires_grad=True - ), - ) - self.assertEqual( - model.bias, - torch.tensor( - [5.1016], device="cuda:0", dtype=torch.float16, requires_grad=True - ), - ) - self.assertEqual(optimizer.scaler.loss_scale, 2.0) - - def test_mixed_precision(self): - model = copy.deepcopy(self.model) - params = list(model.parameters()) - optimizer = FP16Optimizer.build_optimizer(self.cfg_dls, params) - - self.run_iter(model, params, optimizer) - self.assertTrue( - all( - torch.all( - fp32_params.eq( - torch.tensor( - [3.1000, 5.1000], device="cuda:0", requires_grad=True - ) - ) - ) - for fp32_params in optimizer.fp32_params.values() - ) - ) - - def test_memory_efficient(self): - model = copy.deepcopy(self.model) - params = list(model.parameters()) - optimizer = MemoryEfficientFP16Optimizer.build_optimizer(self.cfg_dls, params) - - self.run_iter(model, params, optimizer) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_hf_hub.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_hf_hub.py deleted file mode 100644 index 5cfef70d061049602cc47292ede2a0d7aa105608..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_hf_hub.py +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch - -try: - import huggingface_hub -except ImportError: - huggingface_hub = None - -from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub - - -@unittest.skipIf(not huggingface_hub, "Requires huggingface_hub install") -class TestHuggingFaceHub(unittest.TestCase): - @torch.no_grad() - def test_hf_fastspeech2(self): - hf_model_id = "facebook/fastspeech2-en-ljspeech" - models, cfg, task = load_model_ensemble_and_task_from_hf_hub(hf_model_id) - self.assertTrue(len(models) > 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_huffman.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_huffman.py deleted file mode 100644 index 58f01b8d331aed79aef67666d638493cb225dde5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_huffman.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import typing as tp -import unittest -from collections import Counter -from tempfile import NamedTemporaryFile, TemporaryDirectory - -from fairseq.data import Dictionary, indexed_dataset -from fairseq.data.huffman import ( - HuffmanCodeBuilder, - HuffmanCoder, - HuffmanMMapIndexedDataset, - HuffmanMMapIndexedDatasetBuilder, -) - -from tests.utils import POPULATION, make_data, sizes - - -def make_counts(data: tp.List[tp.List[str]]) -> Counter: - return Counter([symbol for sentence in data for symbol in sentence]) - - -def make_code_builder(data: tp.List[tp.List[str]]) -> HuffmanCodeBuilder: - builder = HuffmanCodeBuilder() - for sentence in data: - builder.add_symbols(*sentence) - return builder - - -class TestCodeBuilder(unittest.TestCase): - def test_code_builder_can_count(self): - data = make_data() - counts = make_counts(data) - builder = make_code_builder(data) - - self.assertEqual(builder.symbols, counts) - - def test_code_builder_can_add(self): - data = make_data() - counts = make_counts(data) - builder = make_code_builder(data) - - new_builder = builder + builder - - self.assertEqual(new_builder.symbols, counts + counts) - - def test_code_builder_can_io(self): - data = make_data() - builder = make_code_builder(data) - - with NamedTemporaryFile() as tmp_fp: - builder.to_file(tmp_fp.name) - other_builder = HuffmanCodeBuilder.from_file(tmp_fp.name) - - self.assertEqual(builder.symbols, other_builder.symbols) - - -class TestCoder(unittest.TestCase): - def test_coder_can_io(self): - data = make_data() - builder = make_code_builder(data) - coder = builder.build_code() - - with NamedTemporaryFile() as tmp_fp: - coder.to_file(tmp_fp.name) - other_coder = HuffmanCoder.from_file(tmp_fp.name) - - self.assertEqual(coder, other_coder) - - def test_coder_can_encode_decode(self): - data = make_data() - builder = make_code_builder(data) - coder = builder.build_code() - - encoded = [coder.encode(sentence) for sentence in data] - decoded = [[n.symbol for n in coder.decode(enc)] for enc in encoded] - - self.assertEqual(decoded, data) - - unseen_data = make_data() - unseen_encoded = [coder.encode(sentence) for sentence in unseen_data] - unseen_decoded = [ - [n.symbol for n in coder.decode(enc)] for enc in unseen_encoded - ] - self.assertEqual(unseen_decoded, unseen_data) - - -def build_dataset(prefix, data, coder): - with HuffmanMMapIndexedDatasetBuilder(prefix, coder) as builder: - for sentence in data: - builder.add_item(sentence) - - -class TestHuffmanDataset(unittest.TestCase): - def test_huffman_can_encode_decode(self): - data = make_data() - builder = make_code_builder(data) - coder = builder.build_code() - - with TemporaryDirectory() as dirname: - prefix = os.path.join(dirname, "test1") - build_dataset(prefix, data, coder) - dataset = HuffmanMMapIndexedDataset(prefix) - - self.assertEqual(len(dataset), len(data)) - decoded = [list(dataset.get_symbols(i)) for i in range(0, len(dataset))] - - self.assertEqual(decoded, data) - data_sizes = [i.item() for i in dataset.sizes] - self.assertEqual(data_sizes, sizes(data)) - - def test_huffman_compresses(self): - data = make_data() - builder = make_code_builder(data) - coder = builder.build_code() - - with TemporaryDirectory() as dirname: - prefix = os.path.join(dirname, "huffman") - build_dataset(prefix, data, coder) - - prefix_mmap = os.path.join(dirname, "mmap") - mmap_builder = indexed_dataset.make_builder( - indexed_dataset.data_file_path(prefix_mmap), - "mmap", - vocab_size=len(POPULATION), - ) - dictionary = Dictionary() - for c in POPULATION: - dictionary.add_symbol(c) - dictionary.finalize() - for sentence in data: - mmap_builder.add_item(dictionary.encode_line(" ".join(sentence))) - mmap_builder.finalize(indexed_dataset.index_file_path(prefix_mmap)) - - huff_size = os.stat(indexed_dataset.data_file_path(prefix)).st_size - mmap_size = os.stat(indexed_dataset.data_file_path(prefix_mmap)).st_size - self.assertLess(huff_size, mmap_size) - - def test_huffman_can_append(self): - data1 = make_data() - builder = make_code_builder(data1) - coder = builder.build_code() - - with TemporaryDirectory() as dirname: - prefix1 = os.path.join(dirname, "test1") - build_dataset(prefix1, data1, coder) - - data2 = make_data() - prefix2 = os.path.join(dirname, "test2") - build_dataset(prefix2, data2, coder) - - prefix3 = os.path.join(dirname, "test3") - - with HuffmanMMapIndexedDatasetBuilder(prefix3, coder) as builder: - builder.append(prefix1) - builder.append(prefix2) - - dataset = HuffmanMMapIndexedDataset(prefix3) - - self.assertEqual(len(dataset), len(data1) + len(data2)) - - decoded1 = [list(dataset.get_symbols(i)) for i in range(0, len(data1))] - self.assertEqual(decoded1, data1) - - decoded2 = [ - list(dataset.get_symbols(i)) for i in range(len(data1), len(dataset)) - ] - self.assertEqual(decoded2, data2) - - data_sizes = [i.item() for i in dataset.sizes] - self.assertEqual(data_sizes[: len(data1)], sizes(data1)) - self.assertEqual(data_sizes[len(data1) : len(dataset)], sizes(data2)) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_inference_dropout.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_inference_dropout.py deleted file mode 100644 index fffc89c8a4909816b7d510ce9426a8f7778b5d22..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_inference_dropout.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import unittest - -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.models.transformer import TransformerModel - -from tests.test_sequence_generator import get_dummy_task_and_parser - - -class TestInferenceDropout(unittest.TestCase): - def setUp(self): - self.task, self.parser = get_dummy_task_and_parser() - TransformerModel.add_args(self.parser) - self.args = self.parser.parse_args([]) - self.args.encoder_layers = 2 - self.args.decoder_layers = 1 - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_sets_inference_dropout_to_true(self): - self.args.retain_dropout = True - self.transformer_model = TransformerModel.build_model(self.args, self.task) - cfg = convert_namespace_to_omegaconf(self.args) - self.transformer_model.prepare_for_inference_(cfg) - assert self.transformer_model.encoder.dropout_module.apply_during_inference - assert self.transformer_model.decoder.dropout_module.apply_during_inference - for layer in self.transformer_model.encoder.layers: - assert layer.dropout_module.apply_during_inference - - def test_inference_dropout_false_by_default(self): - self.transformer_model = TransformerModel.build_model(self.args, self.task) - cfg = convert_namespace_to_omegaconf(self.args) - self.transformer_model.prepare_for_inference_(cfg) - assert not self.transformer_model.encoder.dropout_module.apply_during_inference - assert not self.transformer_model.decoder.dropout_module.apply_during_inference - for layer in self.transformer_model.encoder.layers: - assert not layer.dropout_module.apply_during_inference - for layer in self.transformer_model.decoder.layers: - assert not layer.dropout_module.apply_during_inference - - def test_applies_training_mode(self): - self.transformer_model = TransformerModel.build_model(self.args, self.task) - assert self.transformer_model.encoder.dropout_module.training - for layer in self.transformer_model.encoder.layers: - assert layer.dropout_module.training - - self.transformer_model.eval() - assert not self.transformer_model.decoder.dropout_module.training - for layer in self.transformer_model.encoder.layers: - assert not layer.dropout_module.training - - def test_retain_modules(self): - self.args.retain_dropout = True - self.args.retain_dropout_modules = [ - "TransformerEncoder", - "TransformerEncoderLayer", - ] - self.transformer_model = TransformerModel.build_model(self.args, self.task) - cfg = convert_namespace_to_omegaconf(self.args) - self.transformer_model.prepare_for_inference_(cfg) - assert self.transformer_model.encoder.dropout_module.apply_during_inference - assert not self.transformer_model.decoder.dropout_module.apply_during_inference - for layer in self.transformer_model.decoder.layers: - assert not layer.dropout_module.apply_during_inference diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_iopath.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_iopath.py deleted file mode 100644 index 48230a6379a2253b4f8dd6210762c4cb8221b91d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_iopath.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from unittest import mock - - -class TestIOPath(unittest.TestCase): - def test_no_iopath(self): - from .test_reproducibility import TestReproducibility - - with mock.patch.dict("sys.modules", {"iopath": None}): - # reuse reproducibility tests, which are e2e tests that should cover - # most checkpoint related functionality - TestReproducibility._test_reproducibility(self, "test_reproducibility") - - def test_no_supports_rename(self): - from .test_reproducibility import TestReproducibility - - with mock.patch("fairseq.file_io.PathManager.supports_rename") as mock_fn: - mock_fn.return_value = False - TestReproducibility._test_reproducibility(self, "test_reproducibility") - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_iterators.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_iterators.py deleted file mode 100644 index 6d3d6f05a54fb033b60f56c189180aebc1acb58e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_iterators.py +++ /dev/null @@ -1,194 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -from fairseq.data import ListDataset, iterators - - -class TestIterators(unittest.TestCase): - def test_counting_iterator_index(self, ref=None, itr=None): - # Test the indexing functionality of CountingIterator - if ref is None: - assert itr is None - ref = list(range(10)) - itr = iterators.CountingIterator(ref) - else: - assert len(ref) == 10 - assert itr is not None - - self.assertTrue(itr.has_next()) - self.assertEqual(itr.n, 0) - self.assertEqual(next(itr), ref[0]) - self.assertEqual(itr.n, 1) - self.assertEqual(next(itr), ref[1]) - self.assertEqual(itr.n, 2) - itr.skip(3) - self.assertEqual(itr.n, 5) - self.assertEqual(next(itr), ref[5]) - itr.skip(2) - self.assertEqual(itr.n, 8) - self.assertEqual(list(itr), [ref[8], ref[9]]) - self.assertFalse(itr.has_next()) - - def test_counting_iterator_length_mismatch(self): - ref = list(range(10)) - # When the underlying iterable is longer than the CountingIterator, - # the remaining items in the iterable should be ignored - itr = iterators.CountingIterator(ref, total=8) - self.assertEqual(list(itr), ref[:8]) - # When the underlying iterable is shorter than the CountingIterator, - # raise an IndexError when the underlying iterable is exhausted - itr = iterators.CountingIterator(ref, total=12) - self.assertRaises(IndexError, list, itr) - - def test_counting_iterator_take(self): - # Test the "take" method of CountingIterator - ref = list(range(10)) - itr = iterators.CountingIterator(ref) - itr.take(5) - self.assertEqual(len(itr), len(list(iter(itr)))) - self.assertEqual(len(itr), 5) - - itr = iterators.CountingIterator(ref) - itr.take(5) - self.assertEqual(next(itr), ref[0]) - self.assertEqual(next(itr), ref[1]) - itr.skip(2) - self.assertEqual(next(itr), ref[4]) - self.assertFalse(itr.has_next()) - - def test_grouped_iterator(self): - # test correctness - x = list(range(10)) - itr = iterators.GroupedIterator(x, 1) - self.assertEqual(list(itr), [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]) - itr = iterators.GroupedIterator(x, 4) - self.assertEqual(list(itr), [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9]]) - itr = iterators.GroupedIterator(x, 5) - self.assertEqual(list(itr), [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) - - # test the GroupIterator also works correctly as a CountingIterator - x = list(range(30)) - ref = list(iterators.GroupedIterator(x, 3)) - itr = iterators.GroupedIterator(x, 3) - self.test_counting_iterator_index(ref, itr) - - def test_sharded_iterator(self): - # test correctness - x = list(range(10)) - itr = iterators.ShardedIterator(x, num_shards=1, shard_id=0) - self.assertEqual(list(itr), x) - itr = iterators.ShardedIterator(x, num_shards=2, shard_id=0) - self.assertEqual(list(itr), [0, 2, 4, 6, 8]) - itr = iterators.ShardedIterator(x, num_shards=2, shard_id=1) - self.assertEqual(list(itr), [1, 3, 5, 7, 9]) - itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0) - self.assertEqual(list(itr), [0, 3, 6, 9]) - itr = iterators.ShardedIterator(x, num_shards=3, shard_id=1) - self.assertEqual(list(itr), [1, 4, 7, None]) - itr = iterators.ShardedIterator(x, num_shards=3, shard_id=2) - self.assertEqual(list(itr), [2, 5, 8, None]) - - # test CountingIterator functionality - x = list(range(30)) - ref = list(iterators.ShardedIterator(x, num_shards=3, shard_id=0)) - itr = iterators.ShardedIterator(x, num_shards=3, shard_id=0) - self.test_counting_iterator_index(ref, itr) - - def test_counting_iterator_buffered_iterator_take(self): - ref = list(range(10)) - buffered_itr = iterators.BufferedIterator(2, ref) - itr = iterators.CountingIterator(buffered_itr) - itr.take(5) - self.assertEqual(len(itr), len(list(iter(itr)))) - self.assertEqual(len(itr), 5) - - buffered_itr = iterators.BufferedIterator(2, ref) - itr = iterators.CountingIterator(buffered_itr) - itr.take(5) - self.assertEqual(len(buffered_itr), 5) - self.assertEqual(len(list(iter(buffered_itr))), 5) - - buffered_itr = iterators.BufferedIterator(2, ref) - itr = iterators.CountingIterator(buffered_itr) - itr.take(5) - self.assertEqual(next(itr), ref[0]) - self.assertEqual(next(itr), ref[1]) - itr.skip(2) - self.assertEqual(next(itr), ref[4]) - self.assertFalse(itr.has_next()) - self.assertRaises(StopIteration, next, buffered_itr) - - ref = list(range(4, 10)) - buffered_itr = iterators.BufferedIterator(2, ref) - itr = iterators.CountingIterator(buffered_itr, start=4) - itr.take(5) - self.assertEqual(len(itr), 5) - self.assertEqual(len(buffered_itr), 1) - self.assertEqual(next(itr), ref[0]) - self.assertFalse(itr.has_next()) - self.assertRaises(StopIteration, next, buffered_itr) - - def test_epoch_batch_iterator_skip_remainder_batch(self): - reference = [1, 2, 3] - itr1 = _get_epoch_batch_itr(reference, 2, True) - self.assertEqual(len(itr1), 1) - itr2 = _get_epoch_batch_itr(reference, 2, False) - self.assertEqual(len(itr2), 2) - itr3 = _get_epoch_batch_itr(reference, 1, True) - self.assertEqual(len(itr3), 2) - itr4 = _get_epoch_batch_itr(reference, 1, False) - self.assertEqual(len(itr4), 3) - itr5 = _get_epoch_batch_itr(reference, 4, True) - self.assertEqual(len(itr5), 0) - self.assertFalse(itr5.has_next()) - itr6 = _get_epoch_batch_itr(reference, 4, False) - self.assertEqual(len(itr6), 1) - - def test_grouped_iterator_skip_remainder_batch(self): - reference = [1, 2, 3, 4, 5, 6, 7, 8, 9] - itr1 = _get_epoch_batch_itr(reference, 3, False) - grouped_itr1 = iterators.GroupedIterator(itr1, 2, True) - self.assertEqual(len(grouped_itr1), 1) - - itr2 = _get_epoch_batch_itr(reference, 3, False) - grouped_itr2 = iterators.GroupedIterator(itr2, 2, False) - self.assertEqual(len(grouped_itr2), 2) - - itr3 = _get_epoch_batch_itr(reference, 3, True) - grouped_itr3 = iterators.GroupedIterator(itr3, 2, True) - self.assertEqual(len(grouped_itr3), 1) - - itr4 = _get_epoch_batch_itr(reference, 3, True) - grouped_itr4 = iterators.GroupedIterator(itr4, 2, False) - self.assertEqual(len(grouped_itr4), 1) - - itr5 = _get_epoch_batch_itr(reference, 5, True) - grouped_itr5 = iterators.GroupedIterator(itr5, 2, True) - self.assertEqual(len(grouped_itr5), 0) - - itr6 = _get_epoch_batch_itr(reference, 5, True) - grouped_itr6 = iterators.GroupedIterator(itr6, 2, False) - self.assertEqual(len(grouped_itr6), 1) - - -def _get_epoch_batch_itr(ref, bsz, skip_remainder_batch): - dsz = len(ref) - indices = range(dsz) - starts = indices[::bsz] - batch_sampler = [indices[s : s + bsz] for s in starts] - dataset = ListDataset(ref) - itr = iterators.EpochBatchIterator( - dataset=dataset, - collate_fn=dataset.collater, - batch_sampler=batch_sampler, - skip_remainder_batch=skip_remainder_batch, - ) - return itr.next_epoch_itr() - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_label_smoothing.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_label_smoothing.py deleted file mode 100644 index 39bcb5912dd3b5fd4795eb5a07c32ce3e6bb8dc9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_label_smoothing.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import copy -import unittest - -import torch -from fairseq.criterions.cross_entropy import CrossEntropyCriterion -from fairseq.criterions.label_smoothed_cross_entropy import ( - LabelSmoothedCrossEntropyCriterion, -) - -import tests.utils as test_utils - - -class TestLabelSmoothing(unittest.TestCase): - def setUp(self): - # build dictionary - self.d = test_utils.dummy_dictionary(3) - vocab = len(self.d) - self.assertEqual(vocab, 4 + 3) # 4 special + 3 tokens - self.assertEqual(self.d.pad(), 1) - self.assertEqual(self.d.eos(), 2) - self.assertEqual(self.d.unk(), 3) - pad, eos, unk, w1, w2, w3 = 1, 2, 3, 4, 5, 6 # noqa: F841 - - # build dataset - self.data = [ - # the first batch item has padding - { - "source": torch.LongTensor([w1, eos]), - "target": torch.LongTensor([w1, eos]), - }, - { - "source": torch.LongTensor([w1, eos]), - "target": torch.LongTensor([w1, w1, eos]), - }, - ] - self.sample = next(test_utils.dummy_dataloader(self.data)) - - # build model - self.args = argparse.Namespace() - self.args.sentence_avg = False - self.args.report_accuracy = False - self.args.probs = ( - torch.FloatTensor( - [ - # pad eos unk w1 w2 w3 - [0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05], - [0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10], - [0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15], - ] - ) - .unsqueeze(0) - .expand(2, 3, 7) - ) # add batch dimension - self.task = test_utils.TestTranslationTask.setup_task(self.args, self.d, self.d) - self.model = self.task.build_model(self.args) - - def test_nll_loss(self): - self.args.label_smoothing = 0.1 - nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) - smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion( - self.args, self.task - ) - nll_loss, nll_sample_size, nll_logging_output = nll_crit( - self.model, self.sample - ) - smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit( - self.model, self.sample - ) - self.assertLess(abs(nll_loss - nll_logging_output["loss"]), 1e-6) - self.assertLess(abs(nll_loss - smooth_logging_output["nll_loss"]), 1e-6) - - def test_padding(self): - self.args.label_smoothing = 0.1 - crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) - loss, _, logging_output = crit(self.model, self.sample) - - def get_one_no_padding(idx): - # create a new sample with just a single batch item so that there's - # no padding - sample1 = next(test_utils.dummy_dataloader([self.data[idx]])) - args1 = copy.copy(self.args) - args1.probs = args1.probs[idx, :, :].unsqueeze(0) - model1 = self.task.build_model(args1) - loss1, _, _ = crit(model1, sample1) - return loss1 - - loss1 = get_one_no_padding(0) - loss2 = get_one_no_padding(1) - self.assertAlmostEqual(loss, loss1 + loss2) - - def test_reduction(self): - self.args.label_smoothing = 0.1 - crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task) - loss, _, logging_output = crit(self.model, self.sample, reduce=True) - unreduced_loss, _, _ = crit(self.model, self.sample, reduce=False) - self.assertAlmostEqual(loss, unreduced_loss.sum()) - - def test_zero_eps(self): - self.args.label_smoothing = 0.0 - nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task) - smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion( - self.args, self.task - ) - nll_loss, nll_sample_size, nll_logging_output = nll_crit( - self.model, self.sample - ) - smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit( - self.model, self.sample - ) - self.assertAlmostEqual(nll_loss, smooth_loss) - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-6) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_lm_context_window.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_lm_context_window.py deleted file mode 100644 index 1c77f6b94779f9d095d65b91721bd93751b14f23..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_lm_context_window.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.data import MonolingualDataset -from fairseq.tasks.language_modeling import LanguageModelingConfig, LanguageModelingTask - -from tests import utils as test_utils - - -class TestLMContextWindow(unittest.TestCase): - def test_eval_dataloader(self): - dictionary = test_utils.dummy_dictionary(10) - assert len(dictionary) == 14 # 4 extra special symbols - assert dictionary.pad() == 1 - - dataset = test_utils.TestDataset( - [ - torch.tensor([4, 5, 6, 7], dtype=torch.long), - torch.tensor([8, 9, 10, 11], dtype=torch.long), - torch.tensor([12, 13], dtype=torch.long), - ] - ) - dataset = MonolingualDataset(dataset, sizes=[4, 4, 2], src_vocab=dictionary) - - config = LanguageModelingConfig(tokens_per_sample=4) - task = LanguageModelingTask(config, dictionary) - - eval_dataloader = task.eval_lm_dataloader( - dataset=dataset, - batch_size=1, - context_window=2, - num_workers=0, - ) - - batch = next(eval_dataloader) - assert batch["net_input"]["src_tokens"][0].tolist() == [4, 5, 6, 7, 1, 1] - assert batch["target"][0].tolist() == [4, 5, 6, 7, 1, 1] - - batch = next(eval_dataloader) - assert batch["net_input"]["src_tokens"][0].tolist() == [6, 7, 8, 9, 10, 11] - assert batch["target"][0].tolist() == [1, 1, 8, 9, 10, 11] - - batch = next(eval_dataloader) - assert batch["net_input"]["src_tokens"][0].tolist() == [10, 11, 12, 13] - assert batch["target"][0].tolist() == [1, 1, 12, 13] - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_lstm_jitable.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_lstm_jitable.py deleted file mode 100644 index 65c0bb4a919cb782c4282028a05669e694ec2baf..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_lstm_jitable.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import tempfile -import unittest - -import torch -from fairseq.data.dictionary import Dictionary -from fairseq.models.lstm import LSTMModel -from fairseq.tasks.fairseq_task import LegacyFairseqTask - -DEFAULT_TEST_VOCAB_SIZE = 100 - - -class DummyTask(LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = get_dummy_dictionary() - if getattr(self.args, "ctc", False): - self.dictionary.add_symbol("") - self.src_dict = self.dictionary - self.tgt_dict = self.dictionary - - @property - def source_dictionary(self): - return self.src_dict - - @property - def target_dictionary(self): - return self.dictionary - - -def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): - dummy_dict = Dictionary() - # add dummy symbol to satisfy vocab size - for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), 1000) - return dummy_dict - - -def get_dummy_task_and_parser(): - """ - to build a fariseq model, we need some dummy parse and task. This function - is used to create dummy task and parser to faciliate model/criterion test - - Note: we use FbSpeechRecognitionTask as the dummy task. You may want - to use other task by providing another function - """ - parser = argparse.ArgumentParser( - description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS - ) - DummyTask.add_args(parser) - args = parser.parse_args([]) - task = DummyTask.setup_task(args) - return task, parser - - -class TestJitLSTMModel(unittest.TestCase): - def _test_save_and_load(self, scripted_module): - with tempfile.NamedTemporaryFile() as f: - scripted_module.save(f.name) - torch.jit.load(f.name) - - def assertTensorEqual(self, t1, t2): - t1 = t1[~torch.isnan(t1)] # can cause size mismatch errors if there are NaNs - t2 = t2[~torch.isnan(t2)] - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - def test_jit_and_export_lstm(self): - task, parser = get_dummy_task_and_parser() - LSTMModel.add_args(parser) - args = parser.parse_args([]) - args.criterion = "" - model = LSTMModel.build_model(args, task) - scripted_model = torch.jit.script(model) - self._test_save_and_load(scripted_model) - - def test_assert_jit_vs_nonjit_(self): - task, parser = get_dummy_task_and_parser() - LSTMModel.add_args(parser) - args = parser.parse_args([]) - args.criterion = "" - model = LSTMModel.build_model(args, task) - model.eval() - scripted_model = torch.jit.script(model) - scripted_model.eval() - idx = len(task.source_dictionary) - iter = 100 - # Inject random input and check output - seq_len_tensor = torch.randint(1, 10, (iter,)) - num_samples_tensor = torch.randint(1, 10, (iter,)) - for i in range(iter): - seq_len = seq_len_tensor[i] - num_samples = num_samples_tensor[i] - src_token = (torch.randint(0, idx, (num_samples, seq_len)),) - src_lengths = torch.randint(1, seq_len + 1, (num_samples,)) - src_lengths, _ = torch.sort(src_lengths, descending=True) - # Force the first sample to have seq_len - src_lengths[0] = seq_len - prev_output_token = (torch.randint(0, idx, (num_samples, 1)),) - result = model(src_token[0], src_lengths, prev_output_token[0], None) - scripted_result = scripted_model( - src_token[0], src_lengths, prev_output_token[0], None - ) - self.assertTensorEqual(result[0], scripted_result[0]) - self.assertTensorEqual(result[1], scripted_result[1]) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_memory_efficient_fp16.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_memory_efficient_fp16.py deleted file mode 100644 index 2bf2f29888d6027896128930626b1aafe7f18475..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_memory_efficient_fp16.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import logging -import unittest - -import torch -from fairseq.optim.adam import FairseqAdam -from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer -from omegaconf import OmegaConf - - -@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") -class TestMemoryEfficientFP16(unittest.TestCase): - def setUp(self): - logging.disable(logging.CRITICAL) - - def tearDown(self): - logging.disable(logging.NOTSET) - - def test_load_state_dict(self): - # define simple FP16 model - model = torch.nn.Linear(5, 5).cuda().half() - params = list(model.parameters()) - - # initialize memory efficient FP16 optimizer - # with pseudo DictConfigs - optimizer = FairseqAdam( - cfg=OmegaConf.create( - vars( - argparse.Namespace( - adam_betas="(0.9, 0.999)", - adam_eps=1e-8, - weight_decay=0.0, - lr=[0.00001], - ) - ) - ), - params=params, - ) - me_optimizer = MemoryEfficientFP16Optimizer( - cfg=OmegaConf.create( - { - "common": vars( - argparse.Namespace( - fp16_init_scale=1, - fp16_scale_window=1, - fp16_scale_tolerance=1, - threshold_loss_scale=1, - min_loss_scale=1e-4, - ) - ) - } - ), - params=params, - optimizer=optimizer, - ) - - # optimizer state is created in the first step - loss = model(torch.rand(5).cuda().half()).sum() - me_optimizer.backward(loss) - me_optimizer.step() - - # reload state - state = me_optimizer.state_dict() - me_optimizer.load_state_dict(state) - for k, v in me_optimizer.optimizer.state.items(): - self.assertTrue(k.dtype == torch.float16) - for v_i in v.values(): - if torch.is_tensor(v_i): - self.assertTrue(v_i.dtype == torch.float32) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_metrics.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_metrics.py deleted file mode 100644 index 2de6969cf4445bc6cda44dacf6de765ea30d5f5b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_metrics.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -import uuid - -from fairseq import metrics - - -class TestMetrics(unittest.TestCase): - def test_nesting(self): - with metrics.aggregate() as a: - metrics.log_scalar("loss", 1) - with metrics.aggregate() as b: - metrics.log_scalar("loss", 2) - - self.assertEqual(a.get_smoothed_values()["loss"], 1.5) - self.assertEqual(b.get_smoothed_values()["loss"], 2) - - def test_new_root(self): - with metrics.aggregate() as a: - metrics.log_scalar("loss", 1) - with metrics.aggregate(new_root=True) as b: - metrics.log_scalar("loss", 2) - - self.assertEqual(a.get_smoothed_values()["loss"], 1) - self.assertEqual(b.get_smoothed_values()["loss"], 2) - - def test_nested_new_root(self): - with metrics.aggregate() as layer1: - metrics.log_scalar("loss", 1) - with metrics.aggregate(new_root=True) as layer2: - metrics.log_scalar("loss", 2) - with metrics.aggregate() as layer3: - metrics.log_scalar("loss", 3) - with metrics.aggregate(new_root=True) as layer4: - metrics.log_scalar("loss", 4) - metrics.log_scalar("loss", 1.5) - - self.assertEqual(layer4.get_smoothed_values()["loss"], 4) - self.assertEqual(layer3.get_smoothed_values()["loss"], 3) - self.assertEqual(layer2.get_smoothed_values()["loss"], 2.5) - self.assertEqual(layer1.get_smoothed_values()["loss"], 1.25) - - def test_named(self): - name = str(uuid.uuid4()) - metrics.reset_meters(name) - - with metrics.aggregate(name): - metrics.log_scalar("loss", 1) - - metrics.log_scalar("loss", 3) - - with metrics.aggregate(name): - metrics.log_scalar("loss", 2) - - self.assertEqual(metrics.get_smoothed_values(name)["loss"], 1.5) - - def test_nested_duplicate_names(self): - name = str(uuid.uuid4()) - metrics.reset_meters(name) - - with metrics.aggregate(name): - metrics.log_scalar("loss", 1) - with metrics.aggregate() as other: - with metrics.aggregate(name): - metrics.log_scalar("loss", 2) - metrics.log_scalar("loss", 6) - - self.assertEqual(metrics.get_smoothed_values(name)["loss"], 3) - self.assertEqual(other.get_smoothed_values()["loss"], 2) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_multi_corpus_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_multi_corpus_dataset.py deleted file mode 100644 index 03af8690993903571f10e8daaee766657502aea0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_multi_corpus_dataset.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from collections import OrderedDict - -import torch -from fairseq.data import LanguagePairDataset, TokenBlockDataset -from fairseq.data.multi_corpus_dataset import MultiCorpusDataset - -from tests.test_train import mock_dict - - -class TestMultiCorpusDataset(unittest.TestCase): - def setUp(self): - d = mock_dict() - tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, -1) - tokens_ds1 = TokenBlockDataset( - tokens_1, - sizes=[tokens_1.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_1 = LanguagePairDataset( - tokens_ds1, tokens_ds1.sizes, d, shuffle=False - ) - tokens_2 = torch.LongTensor([i for i in range(0, 5000, 2)]).view(1, -1) - tokens_ds2 = TokenBlockDataset( - tokens_2, - sizes=[tokens_2.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_2 = LanguagePairDataset( - tokens_ds2, tokens_ds2.sizes, d, shuffle=False - ) - - def _test_sample_helper( - self, - distribution, - ): - m = MultiCorpusDataset( - OrderedDict({0: self.dataset_1, 1: self.dataset_2}), - distribution=distribution, - seed=0, - sort_indices=True, - ) - m.set_epoch(1) - indices = m.ordered_indices() - count_sample_from_first_dataset = 0 - items = set() - for i in indices: - item = m[i]["source"].item() - if item % 2 == 1: - count_sample_from_first_dataset += 1 - - items.add(item) - sample_from_first_ds_percentage = ( - 1.0 * count_sample_from_first_dataset / len(indices) - ) - self.assertLess( - abs(sample_from_first_ds_percentage - distribution[0]), - 0.01, - ) - self.assertEqual( - len(items), - int( - min(len(self.dataset_1), len(indices) * distribution[0]) - + min(len(self.dataset_1), len(indices) * distribution[1]) - ), - ) - print(distribution) - - def test_multi_corpus_dataset(self): - for distribution in [[0.5, 0.5], [0.1, 0.9], [0.9, 0.1], [0.0, 1.0]]: - self._test_sample_helper(distribution=distribution) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_multi_corpus_sampled_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_multi_corpus_sampled_dataset.py deleted file mode 100644 index 987f8c614cf82931c51f627c459743115172481e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_multi_corpus_sampled_dataset.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from collections import OrderedDict - -import numpy as np -import torch -from fairseq.data import LanguagePairDataset, TokenBlockDataset -from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset - -from tests.test_train import mock_dict - - -class TestMultiCorpusSampledDataset(unittest.TestCase): - def setUp(self): - d = mock_dict() - tokens_1 = torch.LongTensor([1]).view(1, -1) - tokens_ds1 = TokenBlockDataset( - tokens_1, - sizes=[tokens_1.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_1 = LanguagePairDataset( - tokens_ds1, tokens_ds1.sizes, d, shuffle=False - ) - tokens_2 = torch.LongTensor([2]).view(1, -1) - tokens_ds2 = TokenBlockDataset( - tokens_2, - sizes=[tokens_2.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - self.dataset_2 = LanguagePairDataset( - tokens_ds2, tokens_ds2.sizes, d, shuffle=False - ) - - def _test_sample_helper( - self, - expected_sample_from_first_ds_percentage, - num_samples=1000, - sampling_func=None, - ): - # To make sure test is not flaky - np.random.seed(0) - if sampling_func is None: - m = MultiCorpusSampledDataset( - OrderedDict({0: self.dataset_1, 1: self.dataset_2}), - ) - else: - m = MultiCorpusSampledDataset( - OrderedDict({0: self.dataset_1, 1: self.dataset_2}), - sampling_func=sampling_func, - ) - m.ordered_indices() - count_sample_from_first_dataset = 0 - for _ in range(num_samples): - if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1: - count_sample_from_first_dataset += 1 - sample_from_first_ds_percentage = ( - 1.0 * count_sample_from_first_dataset / num_samples - ) - self.assertLess( - abs( - sample_from_first_ds_percentage - - expected_sample_from_first_ds_percentage - ), - 0.01, - ) - - def test_multi_corpus_sampled_dataset_uniform_sample(self): - self._test_sample_helper(expected_sample_from_first_ds_percentage=0.5) - - def test_multi_corpus_sampled_dataset_weighted_sample(self): - def naive_weighted_sample(weights): - def f(input): - v = np.random.random() - agg = 0 - for i, weight in enumerate(weights): - agg += weight - if agg > v: - return i - - return f - - self._test_sample_helper( - expected_sample_from_first_ds_percentage=0.9, - sampling_func=naive_weighted_sample(weights=[0.9, 0.1]), - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_multihead_attention.py deleted file mode 100644 index 1de640905960697f27a4b04e8b0352969f87e2e1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_multihead_attention.py +++ /dev/null @@ -1,486 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import random -import unittest - -import pytest -import torch -from fairseq.modules.multihead_attention import MultiheadAttention, _mask_for_xformers - -BATCH = [20, 41, 97] -SEQ = [64] -EMB = [48] -HEADS = [4] -DROP = 0.1 -DEVICE = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"] -ATTN_MASK_DTYPE = [None, torch.uint8, torch.bool, torch.float] -KEY_PADDING_MASK_DTYPE = [None, torch.uint8, torch.bool] - - -# FIXME: some tests fail when decimal=2, fix this and set decimal to 2 -def assert_almost_equal(x, y, decimal=1, err_msg=""): - import numpy.testing as npt - - if isinstance(x, torch.Tensor): - x = x.cpu().detach().numpy() - if isinstance(y, torch.Tensor): - y = y.cpu().detach().numpy() - npt.assert_array_almost_equal(x, y, err_msg=err_msg, decimal=decimal) - - -def _reset_seeds(): - torch.manual_seed(0) - torch.random.manual_seed(0) - random.seed(0) - torch.cuda.manual_seed_all(0) - - -def _get_mask(to_dtype: torch.dtype, dim0: int, dim1: int): - if to_dtype == torch.float: - mask = torch.randint(0, 2, (dim0, dim1)).to(dtype=torch.bool) - return mask.to(dtype=to_dtype).masked_fill(mask, -float("inf")) - return torch.randint(0, 2, (dim0, dim1)).to(dtype=to_dtype) - - -def test_mask_for_xformers(): - # Additive Mask - m_float_add = torch.tensor([float("-inf"), 0]).to(torch.float) - m_float_add_flipped = torch.tensor([0, float("-inf")]).to(torch.float) - m_float16_add = torch.tensor([float("-inf"), 0]).to(torch.float16) - m_float16_add_flipped = torch.tensor([0, float("-inf")]).to(torch.float16) - m_uint = torch.tensor([1, 0]).to(torch.uint8) - m_uint_flipped = torch.tensor([0, 1]).to(torch.uint8) - m_bool = torch.tensor([False, True]) - - assert torch.equal(_mask_for_xformers(m_float_add), m_float_add) - assert torch.equal(_mask_for_xformers(m_float16_add), m_float16_add) - assert torch.equal(_mask_for_xformers(m_uint), m_uint_flipped) - assert torch.equal(_mask_for_xformers(m_bool), ~m_bool) - - assert torch.equal( - _mask_for_xformers(m_float_add, to_dtype=torch.float16), m_float16_add - ) - assert torch.equal( - _mask_for_xformers(m_float_add, to_dtype=torch.float), m_float_add - ) - assert torch.equal(_mask_for_xformers(m_float_add, to_dtype=torch.bool), m_bool) - assert torch.equal( - _mask_for_xformers(m_float_add, to_dtype=torch.uint8), m_uint_flipped - ) - - assert torch.equal( - _mask_for_xformers(m_float16_add, to_dtype=torch.float16), m_float16_add - ) - assert torch.equal( - _mask_for_xformers(m_float16_add, to_dtype=torch.float), m_float_add - ) - assert torch.equal(_mask_for_xformers(m_float16_add, to_dtype=torch.bool), m_bool) - assert torch.equal( - _mask_for_xformers(m_float16_add, to_dtype=torch.uint8), m_uint_flipped - ) - - assert torch.equal( - _mask_for_xformers(m_bool, to_dtype=torch.float16), m_float16_add_flipped - ) - assert torch.equal( - _mask_for_xformers(m_bool, to_dtype=torch.float), m_float_add_flipped - ) - assert torch.equal(_mask_for_xformers(m_bool, to_dtype=torch.bool), ~m_bool) - assert torch.equal(_mask_for_xformers(m_bool, to_dtype=torch.uint8), m_uint) - - assert torch.equal( - _mask_for_xformers(m_uint, to_dtype=torch.float16), m_float16_add - ) - assert torch.equal(_mask_for_xformers(m_uint, to_dtype=torch.float), m_float_add) - assert torch.equal(_mask_for_xformers(m_uint, to_dtype=torch.bool), m_bool) - assert torch.equal(_mask_for_xformers(m_uint, to_dtype=torch.uint8), m_uint_flipped) - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason="blocksparse requires gpu") -@pytest.mark.parametrize("device", ["cuda"]) -@pytest.mark.parametrize("add_zero_attn", [False]) -@pytest.mark.parametrize("batch_size", [20]) -@pytest.mark.parametrize("embedding", [64]) -@pytest.mark.parametrize("seq_len", [64]) -@pytest.mark.parametrize("num_heads", [4]) -def test_xformers_blocksparse_parity( - device, - add_zero_attn, - batch_size, - embedding, - seq_len, - num_heads, -): - - xformers_att_config = '{"name": "scaled_dot_product"}' - xformers_blocksparse_blocksize = 16 - xformers_blocksparse_layout = torch.ones( - seq_len // xformers_blocksparse_blocksize, - seq_len // xformers_blocksparse_blocksize, - dtype=torch.int32, - ) - - q = torch.rand(seq_len, batch_size, embedding).to(device).half() - q.requires_grad = True - k = torch.rand(seq_len, batch_size, embedding).to(device).half() - k.requires_grad = True - v = torch.rand(seq_len, batch_size, embedding).to(device).half() - v.requires_grad = True - - q_ = q.detach().clone().half() - q_.requires_grad = True - k_ = k.detach().clone().half() - k_.requires_grad = True - v_ = v.detach().clone().half() - v_.requires_grad = True - - _reset_seeds() - xf_blocksparse_mha = ( - MultiheadAttention( - embedding, - num_heads, - dropout=0.0, - add_zero_attn=add_zero_attn, - xformers_att_config=xformers_att_config, - xformers_blocksparse_layout=xformers_blocksparse_layout, - xformers_blocksparse_blocksize=xformers_blocksparse_blocksize, - ) - .to(device) - .half() - ) - - xf_blocksparse_output, _ = xf_blocksparse_mha( - q, - k, - v, - ) - - _reset_seeds() - xformers_mha = ( - MultiheadAttention( - embedding, - num_heads, - dropout=0.0, - add_zero_attn=add_zero_attn, - xformers_att_config=xformers_att_config, - xformers_blocksparse_layout=None, - ) - .to(device) - .half() - ) - - xformers_output, _ = xformers_mha( - q_, - k_, - v_, - ) - - # # account for when nan != nan - rand = random.uniform(0, 1) - xformers_output = xformers_output.masked_fill(xformers_output.isnan(), rand) - xf_blocksparse_output = xf_blocksparse_output.masked_fill( - xf_blocksparse_output.isnan(), rand - ) - - assert_almost_equal(xformers_output, xf_blocksparse_output) - - loss_blocksparse = torch.norm(xformers_output) - loss_original = torch.norm(xf_blocksparse_output) - loss_blocksparse.backward() - loss_original.backward() - - q.masked_fill(q.isnan(), rand) - q_.masked_fill(q_.isnan(), rand) - k.masked_fill(k.isnan(), rand) - k_.masked_fill(k_.isnan(), rand) - v.masked_fill(v.isnan(), rand) - v_.masked_fill(v_.isnan(), rand) - - assert_almost_equal(q.grad, q_.grad) - assert_almost_equal(k.grad, k_.grad) - assert_almost_equal(v.grad, v_.grad) - - -@pytest.mark.parametrize("device", DEVICE) -@pytest.mark.parametrize("attn_dtype", ATTN_MASK_DTYPE) -@pytest.mark.parametrize("key_padding_dtype", KEY_PADDING_MASK_DTYPE) -@pytest.mark.parametrize("add_bias_kv", [True, False]) -@pytest.mark.parametrize("add_zero_attn", [True, False]) -# TODO: test with static_kv True -@pytest.mark.parametrize("static_kv", [False]) -@pytest.mark.parametrize("batch_size", BATCH) -@pytest.mark.parametrize("embedding", EMB) -@pytest.mark.parametrize("seq_len", SEQ) -@pytest.mark.parametrize("num_heads", HEADS) -def test_xformers_single_forward_parity( - device, - attn_dtype, - key_padding_dtype, - add_bias_kv, - add_zero_attn, - static_kv, - batch_size, - embedding, - seq_len, - num_heads, -): - - xformers_att_config = '{"name": "scaled_dot_product"}' - - attn_mask = ( - None - if attn_dtype is None - else _get_mask(to_dtype=attn_dtype, dim0=seq_len, dim1=seq_len).to(device) - ) - key_padding_mask = ( - None - if key_padding_dtype is None - else _get_mask(to_dtype=key_padding_dtype, dim0=batch_size, dim1=seq_len).to( - device - ) - ) - - q = torch.rand(seq_len, batch_size, embedding).to(device) - q.requires_grad = True - k = torch.rand(seq_len, batch_size, embedding).to(device) - k.requires_grad = True - v = torch.rand(seq_len, batch_size, embedding).to(device) - v.requires_grad = True - - q_ = q.detach().clone() - q_.requires_grad = True - k_ = k.detach().clone() - k_.requires_grad = True - v_ = v.detach().clone() - v_.requires_grad = True - - # TODO: dropouts in the two implementations lead to different entries dropped. - _reset_seeds() - xformers_mha = MultiheadAttention( - embedding, - num_heads, - dropout=0.0, - xformers_att_config=xformers_att_config, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - ).to(device) - xformers_output, _ = xformers_mha( - q, - k, - v, - key_padding_mask=key_padding_mask, - attn_mask=attn_mask, - static_kv=static_kv, - ) - - _reset_seeds() - original_mha = MultiheadAttention( - embedding, - num_heads, - dropout=0.0, - xformers_att_config=None, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - ).to(device) - original_output, _ = original_mha( - q_, - k_, - v_, - key_padding_mask=key_padding_mask, - attn_mask=attn_mask, - static_kv=static_kv, - ) - - # account for when nan != nan - if xformers_output.isnan().any() or original_output.isnan().any(): - rand = random.uniform(0, 1) - xformers_output = xformers_output.masked_fill(xformers_output.isnan(), rand) - original_output = original_output.masked_fill(original_output.isnan(), rand) - - # torch.equal works for cpu, on cuda allclose is needed. - assert torch.allclose( - xformers_output, original_output, atol=1e-06 - ), f"max diff is {torch.max(torch.abs(xformers_output - original_output))}" - - loss_xformers = torch.norm(xformers_output) - loss_original = torch.norm(original_output) - loss_xformers.backward() - loss_original.backward() - - # torch.equal works for cpu, on cuda allclose is needed. - assert torch.allclose( - q.grad, q_.grad - ), f"max diff is {torch.max(torch.abs(q.grad - q_.grad))}" - assert torch.allclose( - k.grad, k_.grad - ), f"max diff is {torch.max(torch.abs(k.grad - k_.grad))}" - assert torch.allclose( - v.grad, v_.grad - ), f"max diff is {torch.max(torch.abs(v.grad - v_.grad))}" - - -def test_mask_padding_parity(): - def old_padding_code(key_padding_mask, attn_mask): - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [ - key_padding_mask, - torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask), - ], - dim=1, - ) - return key_padding_mask, attn_mask - - # values don't matter for this test. - mha = MultiheadAttention( - embed_dim=8, - num_heads=2, - dropout=0.0, - add_bias_kv=True, - add_zero_attn=True, - ) - - key_padding_mask = torch.rand((8, 64)) - attn_mask = torch.rand((64, 64)) - - kp_mask_orig, a_mask_orig = old_padding_code(key_padding_mask, attn_mask) - kp_mask_new, a_mask_new = mha._pad_masks(key_padding_mask, attn_mask) - - assert kp_mask_orig.size() == kp_mask_new.size() - assert a_mask_orig.size() == a_mask_new.size() - assert torch.equal(kp_mask_orig, kp_mask_new) - assert torch.equal(a_mask_orig, a_mask_new) - - -def test_add_bias_parity(): - # values don't matter for this test. - mha = MultiheadAttention( - embed_dim=8, - num_heads=2, - dropout=0.0, - add_bias_kv=True, - add_zero_attn=True, - ) - - def old_bias_code(k, v, key_padding_mask, attn_mask, bsz): - k = torch.cat([k, mha.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, mha.bias_v.repeat(1, bsz, 1)]) - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [ - key_padding_mask, - key_padding_mask.new_zeros(key_padding_mask.size(0), 1), - ], - dim=1, - ) - return k, v, key_padding_mask, attn_mask - - seq_len = 64 - bsz = 8 - embedding = 8 - key_padding_mask = torch.rand((bsz, seq_len)) - attn_mask = torch.rand((seq_len, seq_len)) - k = torch.rand((seq_len, bsz, embedding)) - v = torch.rand((seq_len, bsz, embedding)) - - k_orig, v_orig, kp_mask_orig, a_mask_orig = old_bias_code( - k, v, key_padding_mask, attn_mask, bsz - ) - k_new, v_new, kp_mask_new, a_mask_new = mha._add_bias( - k, v, key_padding_mask, attn_mask, bsz - ) - - assert torch.equal(k_orig, k_new) - assert torch.equal(v_orig, v_new) - assert torch.equal(kp_mask_orig, kp_mask_new) - assert torch.equal(a_mask_orig, a_mask_new) - - -class TestMultiheadAttention(unittest.TestCase): - def test_append_prev_key_padding_mask(self): - bsz = 1 - src_len = 4 - - cases = [ - # no padding mask - (None, None, None), - # current padding mask only - ( - torch.tensor([[1]]).bool(), - None, - torch.tensor([[0, 0, 0, 1]]).bool(), - ), - # previous padding mask only - ( - None, - torch.tensor([[0, 1, 0]]).bool(), - torch.tensor([[0, 1, 0, 0]]).bool(), - ), - # both padding masks - ( - torch.tensor([[1]]).bool(), - torch.tensor([[0, 1, 0]]).bool(), - torch.tensor([[0, 1, 0, 1]]).bool(), - ), - # prev_key_padding_mask already full - ( - torch.tensor([[0, 1, 0, 1]]).bool(), - None, - torch.tensor([[0, 1, 0, 1]]).bool(), - ), - # key_padding_mask already full - ( - None, - torch.tensor([[0, 1, 0, 1]]).bool(), - torch.tensor([[0, 1, 0, 1]]).bool(), - ), - ] - for c in cases: - key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( - c[0], - c[1], - batch_size=bsz, - src_len=src_len, - static_kv=False, - ) - - if key_padding_mask is not None: - self.assertTrue( - torch.all(torch.eq(key_padding_mask, c[2])), - f"Unexpected resultant key padding mask: {key_padding_mask}" - f" given current: {c[0]} and previous: {c[1]}", - ) - self.assertEqual(key_padding_mask.size(0), bsz) - self.assertEqual(key_padding_mask.size(1), src_len) - else: - self.assertIsNone(c[2]) - - def test_pruning_heads(self): - embed_dim = 768 - num_heads = 12 - num_heads_to_keep = 8 - dummy_input = torch.randn(32, 2, embed_dim) - mha = MultiheadAttention(embed_dim=embed_dim, num_heads=num_heads) - reserve_head_index = mha._get_reserve_head_index( - num_heads_to_keep=num_heads_to_keep - ) - mha._adaptive_prune_heads(reserve_head_index=reserve_head_index) - mha._set_skip_embed_dim_check() - mha(query=dummy_input, key=dummy_input, value=dummy_input) - self.assertEqual(mha.head_dim, embed_dim / num_heads) - self.assertEqual(mha.num_heads, num_heads_to_keep) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_noising.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_noising.py deleted file mode 100644 index 3a346b29ba82e9ea94d4749aedd16986a59a5bf6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_noising.py +++ /dev/null @@ -1,531 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest -from typing import Dict, List - -import torch -from fairseq import utils -from fairseq.data import ( - Dictionary, - LanguagePairDataset, - TransformEosDataset, - data_utils, - noising, -) - -import tests.utils as test_utils - - -class TestDataNoising(unittest.TestCase): - def _get_test_data_with_bpe_cont_marker(self, append_eos=True): - """ - Args: - append_eos: if True, each input sentence in the source tokens tensor - will have an EOS appended to the end. - - Returns: - vocabs: BPE vocab with continuation markers as suffixes to denote - non-end of word tokens. This is the standard BPE format used in - fairseq's preprocessing. - x: input tensor containing numberized source tokens, with EOS at the - end if append_eos is true - src_lengths: and source lengths. - """ - vocab = Dictionary() - vocab.add_symbol("he@@") - vocab.add_symbol("llo") - vocab.add_symbol("how") - vocab.add_symbol("are") - vocab.add_symbol("y@@") - vocab.add_symbol("ou") - vocab.add_symbol("n@@") - vocab.add_symbol("ew") - vocab.add_symbol("or@@") - vocab.add_symbol("k") - - src_tokens = [ - ["he@@", "llo", "n@@", "ew", "y@@", "or@@", "k"], - ["how", "are", "y@@", "ou"], - ] - x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor( - vocab=vocab, src_tokens=src_tokens, append_eos=append_eos - ) - return vocab, x, src_lengths - - def _get_test_data_with_bpe_end_marker(self, append_eos=True): - """ - Args: - append_eos: if True, each input sentence in the source tokens tensor - will have an EOS appended to the end. - - Returns: - vocabs: BPE vocab with end-of-word markers as suffixes to denote - tokens at the end of a word. This is an alternative to fairseq's - standard preprocessing framework and is not generally supported - within fairseq. - x: input tensor containing numberized source tokens, with EOS at the - end if append_eos is true - src_lengths: and source lengths. - """ - vocab = Dictionary() - vocab.add_symbol("he") - vocab.add_symbol("llo_EOW") - vocab.add_symbol("how_EOW") - vocab.add_symbol("are_EOW") - vocab.add_symbol("y") - vocab.add_symbol("ou_EOW") - vocab.add_symbol("n") - vocab.add_symbol("ew_EOW") - vocab.add_symbol("or") - vocab.add_symbol("k_EOW") - - src_tokens = [ - ["he", "llo_EOW", "n", "ew_EOW", "y", "or", "k_EOW"], - ["how_EOW", "are_EOW", "y", "ou_EOW"], - ] - x, src_lengths = x, src_lengths = self._convert_src_tokens_to_tensor( - vocab=vocab, src_tokens=src_tokens, append_eos=append_eos - ) - return vocab, x, src_lengths - - def _get_test_data_with_word_vocab(self, append_eos=True): - """ - Args: - append_eos: if True, each input sentence in the source tokens tensor - will have an EOS appended to the end. - - Returns: - vocabs: word vocab - x: input tensor containing numberized source tokens, with EOS at the - end if append_eos is true - src_lengths: and source lengths. - """ - vocab = Dictionary() - - vocab.add_symbol("hello") - vocab.add_symbol("how") - vocab.add_symbol("are") - vocab.add_symbol("you") - vocab.add_symbol("new") - vocab.add_symbol("york") - src_tokens = [ - ["hello", "new", "york", "you"], - ["how", "are", "you", "new", "york"], - ] - x, src_lengths = self._convert_src_tokens_to_tensor( - vocab=vocab, src_tokens=src_tokens, append_eos=append_eos - ) - return vocab, x, src_lengths - - def _convert_src_tokens_to_tensor( - self, vocab: Dictionary, src_tokens: List[List[str]], append_eos: bool - ): - src_len = [len(x) for x in src_tokens] - # If we have to append EOS, we include EOS in counting src length - if append_eos: - src_len = [length + 1 for length in src_len] - - x = torch.LongTensor(len(src_tokens), max(src_len)).fill_(vocab.pad()) - for i in range(len(src_tokens)): - for j in range(len(src_tokens[i])): - x[i][j] = vocab.index(src_tokens[i][j]) - if append_eos: - x[i][j + 1] = vocab.eos() - - x = x.transpose(1, 0) - return x, torch.LongTensor(src_len) - - def assert_eos_at_end(self, x, x_len, eos): - """Asserts last token of every sentence in x is EOS""" - for i in range(len(x_len)): - self.assertEqual( - x[x_len[i] - 1][i], - eos, - ( - "Expected eos (token id {eos}) at the end of sentence {i} " - "but got {other} instead" - ).format(i=i, eos=eos, other=x[i][-1]), - ) - - def assert_word_dropout_correct(self, x, x_noised, x_len, l_noised): - # Expect only the first word (2 bpe tokens) of the first example - # was dropped out - self.assertEqual(x_len[0] - 2, l_noised[0]) - for i in range(l_noised[0]): - self.assertEqual(x_noised[i][0], x[i + 2][0]) - - def test_word_dropout_with_eos(self): - vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True) - - with data_utils.numpy_seed(1234): - noising_gen = noising.WordDropout(vocab) - x_noised, l_noised = noising_gen.noising(x, x_len, 0.2) - self.assert_word_dropout_correct( - x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised - ) - self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) - - def assert_word_blanking_correct(self, x, x_noised, x_len, l_noised, unk): - # Expect only the first word (2 bpe tokens) of the first example - # was blanked out - self.assertEqual(x_len[0], l_noised[0]) - for i in range(l_noised[0]): - if i < 2: - self.assertEqual(x_noised[i][0], unk) - else: - self.assertEqual(x_noised[i][0], x[i][0]) - - def test_word_blank_with_eos(self): - vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True) - - with data_utils.numpy_seed(1234): - noising_gen = noising.WordDropout(vocab) - x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk()) - self.assert_word_blanking_correct( - x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk() - ) - self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) - - def generate_unchanged_shuffle_map(self, length): - return {i: i for i in range(length)} - - def assert_word_shuffle_matches_expected( - self, - x, - x_len, - max_shuffle_distance: int, - vocab: Dictionary, - expected_shufle_maps: List[Dict[int, int]], - expect_eos_at_end: bool, - bpe_end_marker=None, - ): - """ - This verifies that with a given x, x_len, max_shuffle_distance, and - vocab, we get the expected shuffle result. - - Args: - x: Tensor of shape (T x B) = (sequence_length, batch_size) - x_len: Tensor of length B = batch_size - max_shuffle_distance: arg to pass to noising - expected_shuffle_maps: List[mapping] where mapping is a - Dict[old_index, new_index], mapping x's elements from their - old positions in x to their new positions in x. - expect_eos_at_end: if True, check the output to make sure there is - an EOS at the end. - bpe_end_marker: str denoting the BPE end token. If this is not None, we - set the BPE cont token to None in the noising classes. - """ - bpe_cont_marker = None - if bpe_end_marker is None: - bpe_cont_marker = "@@" - - with data_utils.numpy_seed(1234): - word_shuffle = noising.WordShuffle( - vocab, bpe_cont_marker=bpe_cont_marker, bpe_end_marker=bpe_end_marker - ) - x_noised, l_noised = word_shuffle.noising( - x, x_len, max_shuffle_distance=max_shuffle_distance - ) - - # For every example, we have a different expected shuffle map. We check - # that each example is shuffled as expected according to each - # corresponding shuffle map. - for i in range(len(expected_shufle_maps)): - shuffle_map = expected_shufle_maps[i] - for k, v in shuffle_map.items(): - self.assertEqual(x[k][i], x_noised[v][i]) - - # Shuffling should not affect the length of each example - for pre_shuffle_length, post_shuffle_length in zip(x_len, l_noised): - self.assertEqual(pre_shuffle_length, post_shuffle_length) - if expect_eos_at_end: - self.assert_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) - - def test_word_shuffle_with_eos(self): - vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=True) - - # Assert word shuffle with max shuffle distance 0 causes input to be - # unchanged - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - max_shuffle_distance=0, - vocab=vocab, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(example_len) - for example_len in x_len - ], - expect_eos_at_end=True, - ) - - # Assert word shuffle with max shuffle distance 3 matches our expected - # shuffle order - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - vocab=vocab, - max_shuffle_distance=3, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(x_len[0]), - {0: 0, 1: 3, 2: 1, 3: 2}, - ], - expect_eos_at_end=True, - ) - - def test_word_shuffle_with_eos_nonbpe(self): - """The purpose of this is to test shuffling logic with word vocabs""" - vocab, x, x_len = self._get_test_data_with_word_vocab(append_eos=True) - - # Assert word shuffle with max shuffle distance 0 causes input to be - # unchanged - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - max_shuffle_distance=0, - vocab=vocab, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(example_len) - for example_len in x_len - ], - expect_eos_at_end=True, - ) - - # Assert word shuffle with max shuffle distance 3 matches our expected - # shuffle order - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - vocab=vocab, - max_shuffle_distance=3, - expected_shufle_maps=[ - {0: 0, 1: 1, 2: 3, 3: 2}, - {0: 0, 1: 2, 2: 1, 3: 3, 4: 4}, - ], - expect_eos_at_end=True, - ) - - def test_word_shuffle_without_eos(self): - """Same result as word shuffle with eos except no EOS at end""" - vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False) - - # Assert word shuffle with max shuffle distance 0 causes input to be - # unchanged - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - max_shuffle_distance=0, - vocab=vocab, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(example_len) - for example_len in x_len - ], - expect_eos_at_end=False, - ) - - # Assert word shuffle with max shuffle distance 3 matches our expected - # shuffle order - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - vocab=vocab, - max_shuffle_distance=3, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(x_len[0]), - {0: 0, 1: 3, 2: 1, 3: 2}, - ], - expect_eos_at_end=False, - ) - - def test_word_shuffle_without_eos_with_bpe_end_marker(self): - """Same result as word shuffle without eos except using BPE end token""" - vocab, x, x_len = self._get_test_data_with_bpe_end_marker(append_eos=False) - - # Assert word shuffle with max shuffle distance 0 causes input to be - # unchanged - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - max_shuffle_distance=0, - vocab=vocab, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(example_len) - for example_len in x_len - ], - expect_eos_at_end=False, - bpe_end_marker="_EOW", - ) - - # Assert word shuffle with max shuffle distance 3 matches our expected - # shuffle order - self.assert_word_shuffle_matches_expected( - x=x, - x_len=x_len, - vocab=vocab, - max_shuffle_distance=3, - expected_shufle_maps=[ - self.generate_unchanged_shuffle_map(x_len[0]), - {0: 0, 1: 3, 2: 1, 3: 2}, - ], - expect_eos_at_end=False, - bpe_end_marker="_EOW", - ) - - def assert_no_eos_at_end(self, x, x_len, eos): - """Asserts that the last token of each sentence in x is not EOS""" - for i in range(len(x_len)): - self.assertNotEqual( - x[x_len[i] - 1][i], - eos, - "Expected no eos (token id {eos}) at the end of sentence {i}.".format( - eos=eos, i=i - ), - ) - - def test_word_dropout_without_eos(self): - """Same result as word dropout with eos except no EOS at end""" - vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False) - - with data_utils.numpy_seed(1234): - noising_gen = noising.WordDropout(vocab) - x_noised, l_noised = noising_gen.noising(x, x_len, 0.2) - self.assert_word_dropout_correct( - x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised - ) - self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) - - def test_word_blank_without_eos(self): - """Same result as word blank with eos except no EOS at end""" - vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False) - - with data_utils.numpy_seed(1234): - noising_gen = noising.WordDropout(vocab) - x_noised, l_noised = noising_gen.noising(x, x_len, 0.2, vocab.unk()) - self.assert_word_blanking_correct( - x=x, x_noised=x_noised, x_len=x_len, l_noised=l_noised, unk=vocab.unk() - ) - self.assert_no_eos_at_end(x=x_noised, x_len=l_noised, eos=vocab.eos()) - - def _get_noising_dataset_batch( - self, - src_tokens_no_pad, - src_dict, - append_eos_to_tgt=False, - ): - """ - Constructs a NoisingDataset and the corresponding - ``LanguagePairDataset(NoisingDataset(src), src)``. If - *append_eos_to_tgt* is True, wrap the source dataset in - :class:`TransformEosDataset` to append EOS to the clean source when - using it as the target. - """ - src_dataset = test_utils.TestDataset(data=src_tokens_no_pad) - - noising_dataset = noising.NoisingDataset( - src_dataset=src_dataset, - src_dict=src_dict, - seed=1234, - max_word_shuffle_distance=3, - word_dropout_prob=0.2, - word_blanking_prob=0.2, - noising_class=noising.UnsupervisedMTNoising, - ) - tgt = src_dataset - language_pair_dataset = LanguagePairDataset( - src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict - ) - language_pair_dataset = TransformEosDataset( - language_pair_dataset, - src_dict.eos(), - append_eos_to_tgt=append_eos_to_tgt, - ) - - dataloader = torch.utils.data.DataLoader( - dataset=language_pair_dataset, - batch_size=2, - collate_fn=language_pair_dataset.collater, - ) - denoising_batch_result = next(iter(dataloader)) - return denoising_batch_result - - def test_noising_dataset_with_eos(self): - src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker( - append_eos=True - ) - - # Format data for src_dataset - src_tokens = torch.t(src_tokens) - src_tokens_no_pad = [] - for src_sentence in src_tokens: - src_tokens_no_pad.append( - utils.strip_pad(tensor=src_sentence, pad=src_dict.pad()) - ) - denoising_batch_result = self._get_noising_dataset_batch( - src_tokens_no_pad=src_tokens_no_pad, src_dict=src_dict - ) - - eos, pad = src_dict.eos(), src_dict.pad() - - # Generated noisy source as source - expected_src = torch.LongTensor( - [[4, 5, 10, 11, 8, 12, 13, eos], [pad, pad, pad, 6, 8, 9, 7, eos]] - ) - # Original clean source as target (right-padded) - expected_tgt = torch.LongTensor( - [[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]] - ) - generated_src = denoising_batch_result["net_input"]["src_tokens"] - tgt_tokens = denoising_batch_result["target"] - - self.assertTensorEqual(expected_src, generated_src) - self.assertTensorEqual(expected_tgt, tgt_tokens) - - def test_noising_dataset_without_eos(self): - """ - Similar to test noising dataset with eos except that we have to set - *append_eos_to_tgt* to ``True``. - """ - - src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker( - append_eos=False - ) - - # Format data for src_dataset - src_tokens = torch.t(src_tokens) - src_tokens_no_pad = [] - for src_sentence in src_tokens: - src_tokens_no_pad.append( - utils.strip_pad(tensor=src_sentence, pad=src_dict.pad()) - ) - denoising_batch_result = self._get_noising_dataset_batch( - src_tokens_no_pad=src_tokens_no_pad, - src_dict=src_dict, - append_eos_to_tgt=True, - ) - - eos, pad = src_dict.eos(), src_dict.pad() - - # Generated noisy source as source - expected_src = torch.LongTensor( - [[4, 5, 10, 11, 8, 12, 13], [pad, pad, pad, 6, 8, 9, 7]] - ) - # Original clean source as target (right-padded) - expected_tgt = torch.LongTensor( - [[4, 5, 10, 11, 8, 12, 13, eos], [6, 7, 8, 9, eos, pad, pad, pad]] - ) - - generated_src = denoising_batch_result["net_input"]["src_tokens"] - tgt_tokens = denoising_batch_result["target"] - - self.assertTensorEqual(expected_src, generated_src) - self.assertTensorEqual(expected_tgt, tgt_tokens) - - def assertTensorEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_online_backtranslation.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_online_backtranslation.py deleted file mode 100644 index 56c95121bdfffef4a6f29d71df8a8ee32bbea9cc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_online_backtranslation.py +++ /dev/null @@ -1,207 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import tempfile -import unittest -from pathlib import Path -from typing import Any, Dict, Sequence - -import fairseq.data.indexed_dataset as indexed_dataset -import fairseq.options -import fairseq.tasks.online_backtranslation as obt -import torch - -from tests import utils - - -def mk_sample(tokens: Sequence[int], batch_size: int = 2) -> Dict[str, Any]: - batch = torch.stack([torch.tensor(tokens, dtype=torch.long)] * batch_size) - sample = { - "net_input": { - "src_tokens": batch, - "prev_output_tokens": batch, - "src_lengths": torch.tensor([len(tokens)] * batch_size, dtype=torch.long), - }, - "target": batch[:, 1:], - } - return sample - - -def mk_dataset(num_samples: int, max_len: int, output: Path): - output.parent.mkdir(exist_ok=True) - idx = indexed_dataset.IndexedDatasetBuilder(str(output)) - data = torch.randint(5, 100, (num_samples, max_len)) - lengths = torch.randint(3, max_len, (num_samples,)) - for d, l in zip(data, lengths): - d[0] = 0 - idx.add_item(d[:l]) - idx.finalize(output.with_suffix(".idx")) - assert output.exists() - assert output.with_suffix(".idx").exists() - - -class OnlineBacktranslationTest(unittest.TestCase): - - tmp_dir = Path(tempfile.mkdtemp(suffix="OnlineBacktranslationTest")) - - @classmethod - def obt_task( - cls, languages: Sequence[str], data: Path = None, language_mapping: str = None - ): - dict_path = cls.tmp_dir / "dict.txt" - if not dict_path.exists(): - dictionary = utils.dummy_dictionary(100) - dictionary.save(str(dict_path)) - - if data is not None: - (data / "dict.txt").write_text(dict_path.read_text()) - else: - data = cls.tmp_dir - assert len(languages) >= 2 - - kwargs = { - "arch": "transformer", - # --max-sentences=1 for better predictability of batches - "max_sentences": 1, - # Use characteristics dimensions - "encoder_layers": 3, - "encoder_embed_dim": 12, - "encoder_ffn_embed_dim": 14, - "encoder_attention_heads": 4, - "decoder_layers": 3, - "decoder_embed_dim": 12, - "decoder_output_dim": 12, - "decoder_ffn_embed_dim": 14, - "decoder_attention_heads": 4, - # Disable dropout so we have comparable tests. - "dropout": 0, - "attention_dropout": 0, - "activation_dropout": 0, - "encoder_layerdrop": 0, - } - - args = fairseq.options.get_args( - data, - task="online_backtranslation", - mono_langs=",".join(languages), - valid_lang_pairs=f"{languages[0]}-{languages[1]}", - tokens_per_sample=256, - language_mapping=language_mapping, - **kwargs, - ) - task = obt.OnlineBackTranslationTask.setup_task(args) - # we need to build the model to have the correct dictionary - model = task.build_model(task.args) - return task, model - - def tmp_path(self, test_case: str) -> Path: - return Path(tempfile.mkdtemp(test_case, dir=self.tmp_dir)) - - def test_lang_tokens(self): - task, model = self.obt_task(["en", "ro", "zh"]) - assert obt._lang_token("en") in task.dictionary - assert obt._lang_token("ro") in task.dictionary - assert obt._lang_token("zh") in task.dictionary - - en_bos = obt._lang_token_index(task.common_dict, "en") - assert "en" == task.common_dict[en_bos].strip("_") - zh_bos = obt._lang_token_index(task.common_dict, "zh") - assert "zh" == task.common_dict[zh_bos].strip("_") - zh_sample = mk_sample([zh_bos, 16, 14, 12, 10]) - - # we expect to receive the bos token for translation - assert task.get_bos_token_from_sample(zh_sample) == en_bos - - def test_backtranslate_sample(self): - task, model = self.obt_task(["en", "ro", "zh"]) - - en_bos = obt._lang_token_index(task.common_dict, "en") - zh_bos = obt._lang_token_index(task.common_dict, "zh") - sample = mk_sample([zh_bos, 16, 14, 12, 10]) - - task.backtranslate_sample(sample, "zh", "en") - target_zh = list(sample["target"][0]) - assert target_zh == [16, 14, 12, 10] # original zh sentence - generated_en = sample["net_input"]["src_tokens"][0] - assert generated_en[0] == en_bos - - def test_train_dataset(self): - data = self.tmp_path("test_train_dataset") - mk_dataset(20, 10, data / "en" / "train.bin") - mk_dataset(10, 10, data / "zh" / "train.bin") - task, model = self.obt_task(["en", "zh"], data) - task.load_dataset("train") - - en_bos = obt._lang_token_index(task.common_dict, "en") - zh_bos = obt._lang_token_index(task.common_dict, "zh") - - train = task.datasets["train"] - train.ordered_indices() - train.prefetch([0, 19]) - sample_0 = train[0] - sample_19 = train[19] - self.assertEqual( - set(sample_0.keys()), {"en-BT", "en-DENOISE", "zh-BT", "zh-DENOISE"} - ) - for sample in (sample_0, sample_19): - self.assertEqual(sample["en-BT"]["source"][0], en_bos) - # bt target isn't ready to look at. - self.assertEqual(sample["en-DENOISE"]["source"][0], en_bos) - # TODO What could we check on the target side ? - - for i in range(10): - # Zh dataset is shorter, and is wrapped around En dataset. - train.prefetch([i, i + 10]) - self.assertEqual( - list(train[i]["zh-DENOISE"]["source"]), - list(train[i + 10]["zh-DENOISE"]["source"]), - ) - self.assertEqual(train[i]["zh-DENOISE"]["source"][0].item(), zh_bos) - - # Sorted by increasing len - self.assertLess( - len(sample_0["en-BT"]["source"]), len(sample_19["en-BT"]["source"]) - ) - - def test_valid_dataset(self): - data = self.tmp_path("test_valid_dataset") - mk_dataset(10, 21, data / "valid.en-zh.en.bin") - mk_dataset(10, 21, data / "valid.en-zh.zh.bin") - - task, model = self.obt_task(["en", "zh"], data) - valid = task.load_dataset("valid") - en_bos = obt._lang_token_index(task.common_dict, "en") - - assert valid is not None - valid.prefetch(range(10)) - sample_0 = valid[0] - sample_9 = valid[9] - self.assertEqual(sample_0["id"], 0) - self.assertEqual(sample_9["id"], 9) - self.assertEqual(sample_0["source"][0], en_bos) - self.assertEqual(sample_9["source"][0], en_bos) - # TODO: could we test the target side ? - - def assertFnMatch(self, fn, values): - for x, y in values.items(): - fn_x = fn(x) - self.assertEqual(fn_x, y, f"Fn has wrong value: fn({x}) = {fn_x} != {y}") - - def test_piecewise_linear_fn(self): - self.assertFnMatch( - obt.PiecewiseLinearFn.from_string("1.0"), {0: 1, 100: 1, 500: 1, 1000: 1} - ) - self.assertFnMatch( - obt.PiecewiseLinearFn.from_string("0:1,1000:0"), - {0: 1, 500: 0.5, 1000: 0, 2000: 0}, - ) - self.assertFnMatch( - obt.PiecewiseLinearFn.from_string("0:0,1000:1"), - {0: 0, 500: 0.5, 1000: 1, 2000: 1}, - ) - self.assertFnMatch( - obt.PiecewiseLinearFn.from_string("0:0,1000:1,2000:0"), - {0: 0, 500: 0.5, 1000: 1, 1500: 0.5, 2000: 0, 3000: 0}, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_plasma_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_plasma_utils.py deleted file mode 100644 index 50665974f1741cc35b609755a7315e32b428a34a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_plasma_utils.py +++ /dev/null @@ -1,126 +0,0 @@ -import contextlib -import tempfile -import unittest -from io import StringIO - -import numpy as np - -from tests.utils import create_dummy_data, preprocess_lm_data, train_language_model - -try: - from fairseq.data.plasma_utils import PlasmaStore, PlasmaView - from pyarrow import plasma - - PYARROW_AVAILABLE = True -except ImportError: - PYARROW_AVAILABLE = False - -dummy_path = "dummy" - - -@unittest.skipUnless(PYARROW_AVAILABLE, "") -class TestPlasmaView(unittest.TestCase): - def setUp(self) -> None: - self.tmp_file = tempfile.NamedTemporaryFile() # noqa: P201 - self.path = self.tmp_file.name - self.server = PlasmaStore.start(path=self.path, nbytes=10000) - self.client = plasma.connect(self.path, num_retries=10) - - def tearDown(self) -> None: - self.client.disconnect() - self.tmp_file.close() - self.server.kill() - - def test_two_servers_do_not_share_object_id_space(self): - data_server_1 = np.array([0, 1]) - data_server_2 = np.array([2, 3]) - server_2_path = self.path - with tempfile.NamedTemporaryFile() as server_1_path: - server = PlasmaStore.start(path=server_1_path.name, nbytes=10000) - arr1 = PlasmaView( - data_server_1, dummy_path, 1, plasma_path=server_1_path.name - ) - assert len(arr1.client.list()) == 1 - assert (arr1.array == data_server_1).all() - arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=server_2_path) - assert (arr2.array == data_server_2).all() - assert (arr1.array == data_server_1).all() - server.kill() - - def test_hash_collision(self): - data_server_1 = np.array([0, 1]) - data_server_2 = np.array([2, 3]) - arr1 = PlasmaView(data_server_1, dummy_path, 1, plasma_path=self.path) - assert len(arr1.client.list()) == 1 - arr2 = PlasmaView(data_server_2, dummy_path, 1, plasma_path=self.path) - assert len(arr1.client.list()) == 1 - assert len(arr2.client.list()) == 1 - assert (arr2.array == data_server_1).all() - # New hash key based on tuples - arr3 = PlasmaView( - data_server_2, dummy_path, (1, 12312312312, None), plasma_path=self.path - ) - assert ( - len(arr2.client.list()) == 2 - ), "No new object was created by using a novel hash key" - assert ( - arr3.object_id in arr2.client.list() - ), "No new object was created by using a novel hash key" - assert ( - arr3.object_id in arr3.client.list() - ), "No new object was created by using a novel hash key" - del arr3, arr2, arr1 - - @staticmethod - def _assert_view_equal(pv1, pv2): - np.testing.assert_array_equal(pv1.array, pv2.array) - - def test_putting_same_array_twice(self): - data = np.array([4, 4, 4]) - arr1 = PlasmaView(data, dummy_path, 1, plasma_path=self.path) - assert len(self.client.list()) == 1 - arr1b = PlasmaView( - data, dummy_path, 1, plasma_path=self.path - ) # should not change contents of store - arr1c = PlasmaView( - None, dummy_path, 1, plasma_path=self.path - ) # should not change contents of store - - assert len(self.client.list()) == 1 - self._assert_view_equal(arr1, arr1b) - self._assert_view_equal(arr1, arr1c) - PlasmaView( - data, dummy_path, 2, plasma_path=self.path - ) # new object id, adds new entry - assert len(self.client.list()) == 2 - - new_client = plasma.connect(self.path) - assert len(new_client.list()) == 2 # new client can access same objects - assert isinstance(arr1.object_id, plasma.ObjectID) - del arr1b - del arr1c - - def test_plasma_store_full_raises(self): - with tempfile.NamedTemporaryFile() as new_path: - server = PlasmaStore.start(path=new_path.name, nbytes=10000) - with self.assertRaises(plasma.PlasmaStoreFull): - # 2000 floats is more than 2000 bytes - PlasmaView( - np.random.rand(10000, 1), dummy_path, 1, plasma_path=new_path.name - ) - server.kill() - - def test_object_id_overflow(self): - PlasmaView.get_object_id("", 2**21) - - def test_training_lm_plasma(self): - with contextlib.redirect_stdout(StringIO()): - with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: - create_dummy_data(data_dir) - preprocess_lm_data(data_dir) - train_language_model( - data_dir, - "transformer_lm", - ["--use-plasma-view", "--plasma-path", self.path], - run_validation=True, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_positional_encoding.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_positional_encoding.py deleted file mode 100644 index 9e7e6e8d158954307f8ea56c18fb29ff4ed4e5f7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_positional_encoding.py +++ /dev/null @@ -1,63 +0,0 @@ -import unittest - -import numpy as np -import torch -from fairseq.modules import RelPositionalEncoding - - -class TestRelPositionalEncoding(unittest.TestCase): - def setUp(self) -> None: - self.T = 3 - self.B = 1 - self.C = 2 - torch.manual_seed(0) - self.sample = torch.randn(self.T, self.B, self.C) # TBC - self.rel_pos_enc = RelPositionalEncoding(max_len=4, d_model=self.C) - - def test_extend_pe(self): - inp = self.sample.transpose(0, 1) - self.rel_pos_enc.extend_pe(inp) - expected_pe = torch.tensor( - [ - [ - [0.1411, -0.9900], - [0.9093, -0.4161], - [0.8415, 0.5403], - [0.0000, 1.0000], - [-0.8415, 0.5403], - [-0.9093, -0.4161], - [-0.1411, -0.9900], - ] - ] - ) - - self.assertTrue( - np.allclose( - expected_pe.cpu().detach().numpy(), - self.rel_pos_enc.pe.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - def test_forward(self): - pos_enc = self.rel_pos_enc(self.sample) - expected_pos_enc = torch.tensor( - [ - [[0.9093, -0.4161]], - [[0.8415, 0.5403]], - [[0.0000, 1.0000]], - [[-0.8415, 0.5403]], - [[-0.9093, -0.4161]], - ] - ) - self.assertTrue( - np.allclose( - pos_enc.cpu().detach().numpy(), - expected_pos_enc.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_reproducibility.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_reproducibility.py deleted file mode 100644 index b285593272151ed95d99313d371e2f12628face6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_reproducibility.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os -import tempfile -import unittest - -import torch - -from . import test_binaries - - -class TestReproducibility(unittest.TestCase): - def _test_reproducibility( - self, - name, - extra_flags=None, - delta=0.0001, - resume_checkpoint="checkpoint1.pt", - max_epoch=3, - ): - def get_last_log_stats_containing_string(log_records, search_string): - for log_record in logs.records[::-1]: - if isinstance(log_record.msg, str) and search_string in log_record.msg: - return json.loads(log_record.msg) - - if extra_flags is None: - extra_flags = [] - - with tempfile.TemporaryDirectory(name) as data_dir: - with self.assertLogs() as logs: - test_binaries.create_dummy_data(data_dir) - test_binaries.preprocess_translation_data(data_dir) - - # train epochs 1 and 2 together - with self.assertLogs() as logs: - test_binaries.train_translation_model( - data_dir, - "fconv_iwslt_de_en", - [ - "--dropout", - "0.0", - "--log-format", - "json", - "--log-interval", - "1", - "--max-epoch", - str(max_epoch), - ] - + extra_flags, - ) - train_log = get_last_log_stats_containing_string(logs.records, "train_loss") - valid_log = get_last_log_stats_containing_string(logs.records, "valid_loss") - - # train epoch 2, resuming from previous checkpoint 1 - os.rename( - os.path.join(data_dir, resume_checkpoint), - os.path.join(data_dir, "checkpoint_last.pt"), - ) - with self.assertLogs() as logs: - test_binaries.train_translation_model( - data_dir, - "fconv_iwslt_de_en", - [ - "--dropout", - "0.0", - "--log-format", - "json", - "--log-interval", - "1", - "--max-epoch", - str(max_epoch), - ] - + extra_flags, - ) - train_res_log = get_last_log_stats_containing_string( - logs.records, "train_loss" - ) - valid_res_log = get_last_log_stats_containing_string( - logs.records, "valid_loss" - ) - - for k in ["train_loss", "train_ppl", "train_num_updates", "train_gnorm"]: - self.assertAlmostEqual( - float(train_log[k]), float(train_res_log[k]), delta=delta - ) - for k in [ - "valid_loss", - "valid_ppl", - "valid_num_updates", - "valid_best_loss", - ]: - self.assertAlmostEqual( - float(valid_log[k]), float(valid_res_log[k]), delta=delta - ) - - def test_reproducibility(self): - self._test_reproducibility("test_reproducibility") - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_reproducibility_fp16(self): - self._test_reproducibility( - "test_reproducibility_fp16", - [ - "--fp16", - "--fp16-init-scale", - "4096", - ], - delta=0.011, - ) - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_reproducibility_memory_efficient_fp16(self): - self._test_reproducibility( - "test_reproducibility_memory_efficient_fp16", - [ - "--memory-efficient-fp16", - "--fp16-init-scale", - "4096", - ], - ) - - @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") - def test_reproducibility_amp(self): - self._test_reproducibility( - "test_reproducibility_amp", - [ - "--amp", - "--fp16-init-scale", - "4096", - ], - delta=0.011, - ) - - def test_mid_epoch_reproducibility(self): - self._test_reproducibility( - "test_mid_epoch_reproducibility", - ["--save-interval-updates", "3"], - resume_checkpoint="checkpoint_1_3.pt", - max_epoch=1, - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_resampling_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_resampling_dataset.py deleted file mode 100644 index ccb53a253ce6ca0d8e972adfa708144b4299b3cb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_resampling_dataset.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import collections -import unittest - -import numpy as np -from fairseq.data import ListDataset, ResamplingDataset - - -class TestResamplingDataset(unittest.TestCase): - def setUp(self): - self.strings = ["ab", "c", "def", "ghij"] - self.weights = [4.0, 2.0, 7.0, 1.5] - self.size_ratio = 2 - self.dataset = ListDataset( - self.strings, np.array([len(s) for s in self.strings]) - ) - - def _test_common(self, resampling_dataset, iters): - assert len(self.dataset) == len(self.strings) == len(self.weights) - assert len(resampling_dataset) == self.size_ratio * len(self.strings) - - results = {"ordered_by_size": True, "max_distribution_diff": 0.0} - - totalfreqs = 0 - freqs = collections.defaultdict(int) - - for epoch_num in range(iters): - resampling_dataset.set_epoch(epoch_num) - - indices = resampling_dataset.ordered_indices() - assert len(indices) == len(resampling_dataset) - - prev_size = -1 - - for i in indices: - cur_size = resampling_dataset.size(i) - # Make sure indices map to same sequences within an epoch - assert resampling_dataset[i] == resampling_dataset[i] - - # Make sure length of sequence is correct - assert cur_size == len(resampling_dataset[i]) - - freqs[resampling_dataset[i]] += 1 - totalfreqs += 1 - - if prev_size > cur_size: - results["ordered_by_size"] = False - - prev_size = cur_size - - assert set(freqs.keys()) == set(self.strings) - for s, weight in zip(self.strings, self.weights): - freq = freqs[s] / totalfreqs - expected_freq = weight / sum(self.weights) - results["max_distribution_diff"] = max( - results["max_distribution_diff"], abs(expected_freq - freq) - ) - - return results - - def test_resampling_dataset_batch_by_size_false(self): - resampling_dataset = ResamplingDataset( - self.dataset, - self.weights, - size_ratio=self.size_ratio, - batch_by_size=False, - seed=0, - ) - - results = self._test_common(resampling_dataset, iters=1000) - - # For batch_by_size = False, the batches should be returned in - # arbitrary order of size. - assert not results["ordered_by_size"] - - # Allow tolerance in distribution error of 2%. - assert results["max_distribution_diff"] < 0.02 - - def test_resampling_dataset_batch_by_size_true(self): - resampling_dataset = ResamplingDataset( - self.dataset, - self.weights, - size_ratio=self.size_ratio, - batch_by_size=True, - seed=0, - ) - - results = self._test_common(resampling_dataset, iters=1000) - - # For batch_by_size = True, the batches should be returned in - # increasing order of size. - assert results["ordered_by_size"] - - # Allow tolerance in distribution error of 2%. - assert results["max_distribution_diff"] < 0.02 - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_roberta.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_roberta.py deleted file mode 100644 index d9112cec921cc044b24856c814b93b85cbe2f85a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_roberta.py +++ /dev/null @@ -1,345 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import functools -import unittest -from typing import Any, Dict, Sequence - -import fairseq -import fairseq.options -import fairseq.tasks -import torch - -from tests.utils import dummy_dictionary - -VOCAB_SIZE = 100 - - -@fairseq.tasks.register_task("fake_task") -class FakeTask(fairseq.tasks.LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = dummy_dictionary(VOCAB_SIZE - 4) - assert len(self.dictionary) == VOCAB_SIZE - - @property - def source_dictionary(self): - return self.dictionary - - @property - def target_dictionary(self): - return self.dictionary - - -@functools.lru_cache() -def get_toy_model( - device: str, - architecture: str = "roberta_enc_dec", - **extra_args: Any, -): - assert device in ("gpu", "cpu") - kwargs = { - "arch": architecture, - # Use characteristics dimensions - "encoder_layers": 3, - "encoder_embed_dim": 12, - "encoder_ffn_embed_dim": 14, - "encoder_attention_heads": 4, - "decoder_layers": 3, - "decoder_embed_dim": 12, - "decoder_ffn_embed_dim": 14, - "decoder_attention_heads": 4, - # Disable dropout so we have comparable tests. - "dropout": 0, - "attention_dropout": 0, - "activation_dropout": 0, - "encoder_layerdrop": 0, - # required args - "tokens_per_sample": 256, - "data": "/tmp/test_roberta", - } - kwargs.update(extra_args) - fake_task = FakeTask(kwargs) - args = fairseq.options.get_args( - task="online_backtranslation", - mono_langs="en,ro", - valid_lang_pairs="en-ro", - **kwargs, - ) - torch.manual_seed(0) - model = fake_task.build_model(args) - if device == "gpu": - model.cuda() - return fake_task, model - - -def mk_sample( - lang: str, device: str, tok: Sequence[int] = None, batch_size: int = 2 -) -> Dict[str, Any]: - assert device in ("gpu", "cpu") - if not tok: - if lang == "en": - tok = [10, 11, 12, 13, 14, 15, 2] - else: - tok = [20, 21, 22, 23, 24, 25, 26, 27, 2] - - batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) - if device == "gpu": - batch = batch.cuda() - sample = { - "net_input": { - "src_tokens": batch, - "prev_output_tokens": batch, - "src_lengths": torch.tensor( - [len(tok)] * batch_size, dtype=torch.long, device=batch.device - ), - }, - "target": batch[:, 1:], - } - return sample - - -def cpu_gpu(fn): - def helper(self): - fn(self, "cpu") - if torch.cuda.is_available(): - fn(self, "gpu") - - return helper - - -def architectures(fn): - def helper(self): - for arch in ["roberta_enc_dec", "transformer"]: - fn(self, arch) - - return helper - - -class RobertaTest(unittest.TestCase): - def assertTensorEqual(self, t1, t2, delta: float = 1e-6): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - if delta == 0.0: - self.assertEqual(t1.ne(t2).long().sum(), 0) - else: - self.assertEqual(((t2 - t1).abs() > delta).long().sum(), 0) - - def assertSharing(self, model, link_groups: Sequence[Sequence[str]]): - ids = {} - for group in link_groups: - group_ids = {name: id(params(model, name)) for name in group} - shared_id = group_ids[group[0]] - self.assertEqual(group_ids, {name: shared_id for name in group}) - self.assertNotIn(shared_id, ids) - ids[shared_id] = group - - def test_roberta_shared_params(self): - _, roberta = get_toy_model("cpu", architecture="roberta") - self.assertSharing( - roberta, - [ - [ - "encoder.sentence_encoder.embed_tokens.weight", - "encoder.lm_head.weight", - ] - ], - ) - - _, roberta = get_toy_model( - "cpu", architecture="roberta", untie_weights_roberta=True - ) - self.assertSharing( - roberta, - [ - ["encoder.sentence_encoder.embed_tokens.weight"], - ["encoder.lm_head.weight"], - ], - ) - - def test_roberta_enc_dec_shared_params(self): - # 3 distinct embeddings - _, enc_dec = get_toy_model("cpu", architecture="roberta_enc_dec") - self.assertSharing( - enc_dec, - [ - ["encoder.embed_tokens.weight"], - ["decoder.embed_tokens.weight"], - ["decoder.output_projection.weight"], - ], - ) - - # 2 distinct embeddings, one for encoder, one for decoder - _, enc_dec = get_toy_model( - "cpu", architecture="roberta_enc_dec", share_decoder_input_output_embed=True - ) - self.assertSharing( - enc_dec, - [ - ["encoder.embed_tokens.weight"], - [ - "decoder.embed_tokens.weight", - "decoder.output_projection.weight", - ], - ], - ) - - # shared embeddings - _, enc_dec = get_toy_model( - "cpu", architecture="roberta_enc_dec", share_all_embeddings=True - ) - self.assertSharing( - enc_dec, - [ - [ - "encoder.embed_tokens.weight", - "decoder.embed_tokens.weight", - "decoder.output_projection.weight", - ] - ], - ) - - def test_roberta_max_positions_is_correctly_set(self): - device = "cpu" - task, model = get_toy_model(device) - max_pos = model.max_decoder_positions() - self.assertEqual(max_pos, 256) - self.assertEqual(max_pos, model.decoder.max_positions()) - self.assertEqual(max_pos, model.encoder.max_positions()) - self.assertEqual(max_pos, model.encoder.embed_positions.max_positions) - - sentence = [31 for _ in range(max_pos)] - sample = mk_sample("en", device, sentence, batch_size=1) - self.assertEqual(list(sample["net_input"]["src_lengths"]), [max_pos]) - self.assertEqual(len(sample["net_input"]["src_tokens"][0]), max_pos) - x, _ = model.forward(**sample["net_input"]) - self.assertEqual(x.shape, (1, max_pos, VOCAB_SIZE)) - - @cpu_gpu - def test_roberta_forward_backward(self, device: str): - _, model = get_toy_model(device) - sample = mk_sample("en", device) - en_tokens = sample["net_input"]["src_tokens"] - (bs, l) = en_tokens.shape - # Forward - logits, _ = model(**sample["net_input"]) - self.assertEqual(logits.shape, (bs, l, VOCAB_SIZE)) - - # Backward - loss = logits.sum() - loss.backward() - - @cpu_gpu - def test_roberta_forward_backward_bs1(self, device: str): - _, model = get_toy_model(device) - sample = mk_sample("en", device, batch_size=1) - o, _ = model.forward(**sample["net_input"]) - loss = o.sum() - sample2 = mk_sample("ro", device, batch_size=1) - o, _ = model.forward(**sample2["net_input"]) - loss += o.sum() - loss.backward() - - @cpu_gpu - def test_roberta_batching(self, device: str): - """ - Checks that the batch of size 2 give twice the same results than the batch of size 1. - """ - _, model = get_toy_model(device) - sample = mk_sample("en", device, batch_size=1) - slen = sample["net_input"]["src_lengths"][0] - sample2 = mk_sample("en", device, batch_size=2) - with torch.no_grad(): - z = model.encoder.forward( - sample["net_input"]["src_tokens"], sample["net_input"]["src_lengths"] - ) - z = z["encoder_out"][-1] - logits, _ = model.forward(**sample["net_input"]) - - z2 = model.encoder.forward( - sample2["net_input"]["src_tokens"], sample["net_input"]["src_lengths"] - ) - z2 = z2["encoder_out"][-1] - logits2, _ = model.forward(**sample2["net_input"]) - - self.assertEqual(z.shape, (slen, 1, 12)) - self.assertEqual(z2.shape, (slen, 2, 12)) - self.assertTensorEqual(logits2[0], logits2[1]) - self.assertTensorEqual(logits[0], logits2[0]) - - @cpu_gpu - def test_roberta_incremental_decoder(self, device: str): - """ - Checks that incremental decoding yields the same result than non incremental one. - """ - task, model = get_toy_model(device) - - en_sample = mk_sample("en", device) - en_tokens = en_sample["net_input"]["src_tokens"] - ro_sample = mk_sample("ro", device) - ro_tokens = ro_sample["net_input"]["src_tokens"] - - en_enc = model.encoder.forward( - en_tokens, src_lengths=en_sample["net_input"]["src_lengths"] - ) - (bs, tgt_len) = ro_tokens.shape - - # Decode without incremental state - ro_dec, _ = model.decoder.forward(ro_tokens, encoder_out=en_enc) - self.assertEqual(ro_dec.shape, (bs, tgt_len, VOCAB_SIZE)) - self.assertTensorEqual(ro_dec[0], ro_dec[1]) - - # Decode with incremental state - inc_state = {} - ro_dec_inc = [] - for i in range(tgt_len): - ro, _ = model.decoder.forward( - ro_tokens[:, : i + 1], encoder_out=en_enc, incremental_state=inc_state - ) - self.assertEqual(ro.shape, (bs, 1, VOCAB_SIZE)) - ro_dec_inc.append(ro) - - for i in range(tgt_len): - # Intra-batch - self.assertTensorEqual(ro_dec_inc[i][0], ro_dec_inc[i][1]) - # Incremental vs non-incremental - self.assertTensorEqual(ro_dec_inc[i][:, 0], ro_dec[:, i]) - - @cpu_gpu - def test_regularize_for_adaprune_in_roberta(self, device: str): - _, model = get_toy_model( - device=device, - architecture="roberta_base", - mha_reg_scale_factor=0.000375, - ffn_reg_scale_factor=0.000375, - ) - sample = mk_sample("en", device, batch_size=1) - task_loss, _ = model.forward(**sample["net_input"]) - head_loss = model._get_adaptive_head_loss() - ffn_loss = model._get_adaptive_ffn_loss() - loss = task_loss.sum() + head_loss + ffn_loss - loss.backward() - - @cpu_gpu - def test_ffn_prune_for_adaprune_in_roberta(self, device: str): - _, model = get_toy_model( - device=device, - architecture="roberta_base", - ) - sample = mk_sample("en", device, batch_size=1) - for layer in model.encoder.sentence_encoder.layers: - fc1_original_size = layer.fc1.out_features - remove_index = layer._get_fc_rank(remove_num=2) - layer._prune_fc_layer(remove_index=remove_index) - self.assertEqual(layer.fc1.out_features, fc1_original_size - 2) - - task_loss, _ = model.forward(**sample["net_input"]) - - -def params(model, name): - if "." not in name: - return getattr(model, name) - - prefix, name = name.split(".", 1) - return params(getattr(model, prefix), name) diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_rotary_positional_embedding.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_rotary_positional_embedding.py deleted file mode 100644 index 1bc16e16d001397f990374e98131355876d909f1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_rotary_positional_embedding.py +++ /dev/null @@ -1,65 +0,0 @@ -import unittest - -import numpy as np -import torch -from fairseq.modules import RotaryPositionalEmbedding -from fairseq.modules.rotary_positional_embedding import apply_rotary_pos_emb - - -class TestRotaryPositionalEmbedding(unittest.TestCase): - def setUp(self) -> None: - self.T = 3 - self.B = 1 - self.C = 2 - torch.manual_seed(0) - self.sample = torch.randn(self.T, self.B, self.C) # TBC - self.rope_pos_emd = RotaryPositionalEmbedding(dim=self.C) - - def test_forward(self): - expected_cos = torch.tensor( - [[[[1.0000, 1.0000]]], [[[0.5403, 0.5403]]], [[[-0.4161, -0.4161]]]] - ) - expected_sin = torch.tensor( - [[[[0.0000, 0.0000]]], [[[0.8415, 0.8415]]], [[[0.9093, 0.9093]]]] - ) - cos, sin = self.rope_pos_emd(self.sample, self.T) - self.assertTrue( - np.allclose( - expected_cos.cpu().detach().numpy(), - cos.cpu().detach().numpy(), - atol=1e-4, - ) - ) - self.assertTrue( - np.allclose( - expected_sin.cpu().detach().numpy(), - sin.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - def test_apply_rotary_pos_emb(self): - cos, sin = self.rope_pos_emd(self.sample, self.T) - query = self.sample.view(self.T, self.B, 1, self.C) - expected_query = torch.tensor( - [[[[1.5410, -0.2934]]], [[[-1.6555, -1.5263]]], [[[1.7231, -0.4041]]]] - ) - new_query, new_key = apply_rotary_pos_emb(query, query, cos, sin) - self.assertTrue( - np.allclose( - expected_query.cpu().detach().numpy(), - new_query.cpu().detach().numpy(), - atol=1e-4, - ) - ) - self.assertTrue( - np.allclose( - expected_query.cpu().detach().numpy(), - new_key.cpu().detach().numpy(), - atol=1e-4, - ) - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_sequence_generator.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_sequence_generator.py deleted file mode 100644 index 97212614a1f9bf0f9f462cc60ba25ce3dd27dea3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_sequence_generator.py +++ /dev/null @@ -1,744 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import math -import tempfile -import unittest - -import numpy as np -import torch -from fairseq import search -from fairseq.data.dictionary import Dictionary -from fairseq.models.transformer import TransformerModel -from fairseq.ngram_repeat_block import NGramRepeatBlock -from fairseq.sequence_generator import EnsembleModel, SequenceGenerator -from fairseq.tasks.fairseq_task import LegacyFairseqTask - -import tests.utils as test_utils - -DEFAULT_TEST_VOCAB_SIZE = 100 - - -class DummyTask(LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = get_dummy_dictionary() - if getattr(self.args, "ctc", False): - self.dictionary.add_symbol("") - self.src_dict = self.dictionary - self.tgt_dict = self.dictionary - - @property - def source_dictionary(self): - return self.src_dict - - @property - def target_dictionary(self): - return self.dictionary - - -def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): - dummy_dict = Dictionary() - # add dummy symbol to satisfy vocab size - for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), n=1000) - return dummy_dict - - -def get_dummy_task_and_parser(): - """ - to build a fariseq model, we need some dummy parse and task. This function - is used to create dummy task and parser to faciliate model/criterion test - - Note: we use FbSpeechRecognitionTask as the dummy task. You may want - to use other task by providing another function - """ - parser = argparse.ArgumentParser( - description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS - ) - DummyTask.add_args(parser) - args = parser.parse_args([]) - task = DummyTask.setup_task(args) - return task, parser - - -class TestJitSequenceGeneratorBase(unittest.TestCase): - def setUp(self): - self.task, self.parser = get_dummy_task_and_parser() - eos = self.task.tgt_dict.eos() - src_tokens = torch.randint(3, 50, (2, 10)).long() - src_tokens = torch.cat((src_tokens, torch.LongTensor([[eos], [eos]])), -1) - src_lengths = torch.LongTensor([2, 10]) - self.sample = { - "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths} - } - TransformerModel.add_args(self.parser) - args = self.parser.parse_args([]) - args.encoder_layers = 2 - args.decoder_layers = 1 - self.transformer_model = TransformerModel.build_model(args, self.task) - - def assertOutputEqual(self, hypo, pos_probs): - pos_scores = torch.FloatTensor(pos_probs).log() - self.assertTensorSizeEqual(hypo["positional_scores"], pos_scores) - self.assertTensorSizeEqual(pos_scores.numel(), hypo["tokens"].numel()) - - def assertTensorSizeEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-4) - - def assertTensorEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - def assertHypoEqual(self, h1, h2): - "Check two hypos are equal" - self.assertTensorEqual(h1["tokens"], h2["tokens"]) - self.assertAlmostEqual(h1["positional_scores"], h2["positional_scores"]) - self.assertLess(abs(h1["score"] - h2["score"]), 1e-6) - self.assertAlmostEqual(h1["attention"], h2["attention"]) - - def _test_save_and_load(self, scripted_module): - with tempfile.NamedTemporaryFile() as f: - scripted_module.save(f.name) - torch.jit.load(f.name) - - -JIT_MSG = "Targeting OSS scriptability for the 1.6 release" - - -@unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG) -class TestJitSequenceGenerator(TestJitSequenceGeneratorBase): - def test_export_transformer(self): - model = self.transformer_model - torch.jit.script(model) - - def test_ensemble_sequence_generator(self): - model = self.transformer_model - generator = SequenceGenerator( - [model], - self.task.tgt_dict, - beam_size=2, - no_repeat_ngram_size=2, - max_len_b=10, - ) - scripted_model = torch.jit.script(generator) - self._test_save_and_load(scripted_model) - - def test_export_ensemble_model(self): - model = self.transformer_model - ensemble_models = EnsembleModel([model]) - torch.jit.script(ensemble_models) - - -class TestExportSearch(unittest.TestCase): - def setUp(self): - task, _ = get_dummy_task_and_parser() - self.tgt_dict = task.tgt_dict - self.min_top1_prob = 0.4 - - def test_export_diverse_bs(self): - search_strategy = search.DiverseBeamSearch( - self.tgt_dict, num_groups=2, diversity_strength=0.0 - ) - torch.jit.script(search_strategy) - - def test_export_sampling(self): - low_sampling_topp = self.min_top1_prob / 2.0 - search_strategy = search.Sampling( - self.tgt_dict, sampling_topp=low_sampling_topp - ) - torch.jit.script(search_strategy) - - def test_export_diverse_siblings_search(self): - search_strategy = search.DiverseSiblingsSearch( - self.tgt_dict, diversity_rate=0.5 - ) - torch.jit.script(search_strategy) - - -class TestSequenceGeneratorBase(unittest.TestCase): - def assertHypoTokens(self, hypo, tokens): - self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens)) - - def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): - pos_scores = torch.FloatTensor(pos_probs).log() - self.assertAlmostEqual(hypo["positional_scores"], pos_scores) - self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) - score = pos_scores.sum() - if normalized: - score /= pos_scores.numel() ** lenpen - self.assertLess(abs(score - hypo["score"]), 1e-6) - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-4) - - def assertTensorEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - -class TestSequenceGenerator(TestSequenceGeneratorBase): - def setUp(self): - ( - self.tgt_dict, - self.w1, - self.w2, - src_tokens, - src_lengths, - self.model, - ) = test_utils.sequence_generator_setup() - self.sample = { - "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths} - } - - def test_with_normalization(self): - generator = SequenceGenerator([self.model], self.tgt_dict, beam_size=2) - hypos = generator.forward(self.sample) - eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 1.0]) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos]) - self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0]) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0]) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w2, eos]) - self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6]) - - def test_without_normalization(self): - # Sentence 1: unchanged from the normalized case - # Sentence 2: beams swap order - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, normalize_scores=False - ) - hypos = generator.forward(self.sample) - eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 1.0], normalized=False) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos]) - self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], normalized=False) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], normalized=False) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos]) - self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], normalized=False) - - def test_with_lenpen_favoring_short_hypos(self): - lenpen = 0.6 - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen - ) - hypos = generator.forward(self.sample) - eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 1.0], lenpen=lenpen) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w2, w1, w2, eos]) - self.assertHypoScore(hypos[0][1], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6], lenpen=lenpen) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w2, w1, eos]) - self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen) - - def test_with_lenpen_favoring_long_hypos(self): - lenpen = 5.0 - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, len_penalty=lenpen - ) - hypos = generator.forward(self.sample) - eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w2, w1, w2, eos]) - self.assertHypoScore(hypos[0][0], [0.1, 0.9, 0.9, 1.0], lenpen=lenpen) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w1, eos]) - self.assertHypoScore(hypos[0][1], [0.9, 1.0], lenpen=lenpen) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, w1, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.4, 1.0], lenpen=lenpen) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w2, eos]) - self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.6], lenpen=lenpen) - - def test_maxlen(self): - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, max_len_b=2 - ) - hypos = generator.forward(self.sample) - eos, w1, w2 = self.tgt_dict.eos(), self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 1.0]) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w2, w2, eos]) - self.assertHypoScore(hypos[0][1], [0.1, 0.1, 0.6]) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.6]) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w2, w2, eos]) - self.assertHypoScore(hypos[1][1], [0.3, 0.9, 0.01]) - - def test_encoder_with_different_output_len(self): - args = self.model.encoder.args - task = test_utils.TestTranslationTask.setup_task( - args, self.tgt_dict, self.tgt_dict - ) - reshaping_model = test_utils.TestReshapingModel.build_model(args, task) - generator = SequenceGenerator( - [reshaping_model], self.tgt_dict, beam_size=2, max_len_b=2 - ) - hypos = generator.forward(self.sample) - for sent in [0, 1]: - for beam in [0, 1]: - assert hypos[sent][beam]["attention"] is not None - - def test_generation_with_additional_input(self): - args = self.model.encoder.args - task = test_utils.TestTranslationTask.setup_task( - args, self.tgt_dict, self.tgt_dict - ) - add_input_model = test_utils.TestAdditionalInputModel.build_model(args, task) - generator = SequenceGenerator([add_input_model], self.tgt_dict, beam_size=2) - sample = self.sample.copy() - sample["net_input"]["fancy_other_input"] = sample["net_input"]["src_tokens"] - hypos = generator.forward(self.sample) - eos, w1 = self.tgt_dict.eos(), self.w1 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 1.0]) - - -@unittest.skipUnless(torch.cuda.is_available(), "") -class TestRepeatNgramBlocking(TestSequenceGeneratorBase): - @classmethod - def setUpClass(cls): - ( - cls.tgt_dict, - cls.w1, - cls.w2, - src_tokens, - src_lengths, - cls.model, - ) = test_utils.sequence_generator_setup() - return cls - - def test_finds_repetitive_tokens(self): - bsz, vocab_size, beam_size, step = 2, 4, 1, 3 - generated_tok = torch.tensor( - [[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda" - ) - lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda") - desired_result = lprobs.new_tensor( - [[0.0, 0.0, -math.inf, 0.0], [0.0, 0.0, 0.0, -math.inf]] - ) - - cuda_ext_result, baseline_result = self._compare_cuda_ext_to_default_implem( - bsz, beam_size, generated_tok, lprobs, step, 2 - ) - self.assertTensorEqual(cuda_ext_result, desired_result) - self.assertTensorEqual(baseline_result, desired_result) - - @unittest.skipIf(torch.__version__ < "1.6.0", JIT_MSG) - def test_jit_no_extension(self): - bsz, vocab_size, beam_size, step = 2, 4, 1, 3 - generated_tok = torch.tensor( - [[2, 2, 2, 2], [3, 3, 3, 3]], dtype=torch.long, device="cuda" - ) - lprobs = torch.zeros((beam_size * bsz, vocab_size), device="cuda") - blocker = NGramRepeatBlock(2, use_extension=False) - base_result = blocker(generated_tok, lprobs.clone(), bsz, beam_size, step) - scripted_blocker = torch.jit.script(blocker) - jit_result = scripted_blocker( - generated_tok, lprobs.clone(), bsz, beam_size, step - ) - self.assertTensorEqual(base_result, jit_result) - - def test_ngram_blocking_same_as_default_implem(self): - """Test that cuda extension returns same things as default impl in many settings.""" - vocab_size = 4 - step = 6 - for _ in range(2): - block_param = np.random.choice([1, 2, 3, 4]) - batch_size = np.random.randint(1, 8) - beam_size = np.random.choice([1, 2, 4, 8]) - lprobs = torch.zeros((beam_size * batch_size, vocab_size), device="cuda") - - generated_tok = torch.tensor( - np.random.randint( - 0, vocab_size, size=(batch_size * beam_size, step + 1) - ), - device="cuda", - dtype=torch.long, - ) - self._compare_cuda_ext_to_default_implem( - batch_size, - beam_size, - generated_tok, - lprobs, - step, - block_param, - ) - - def _compare_cuda_ext_to_default_implem( - self, bsz, beam_size, generated_tok, lprobs, step, block_param - ): - """Assert that cuda extension and default implem return the same thing.""" - blocker = NGramRepeatBlock(block_param) - assert blocker.use_extension, "Extension not compiled" - cuda_ext_result = blocker( - generated_tok, - lprobs.clone(), - bsz, - beam_size, - step, - ) - blocker.use_extension = False - baseline_result = blocker( - generated_tok, - lprobs.clone(), - bsz, - beam_size, - step, - ) - self.assertTensorEqual(cuda_ext_result, baseline_result) - blocker.use_extension = True - return cuda_ext_result, baseline_result - - -class TestDiverseBeamSearch(TestSequenceGeneratorBase): - def setUp(self): - # construct dummy dictionary - d = test_utils.dummy_dictionary(vocab_size=2) - self.assertEqual(d.pad(), 1) - self.assertEqual(d.eos(), 2) - self.assertEqual(d.unk(), 3) - self.eos = d.eos() - self.w1 = 4 - self.w2 = 5 - - # construct source data - self.src_tokens = torch.LongTensor( - [ - [self.w1, self.w2, self.eos], - [self.w1, self.w2, self.eos], - ] - ) - self.src_lengths = torch.LongTensor([2, 2]) - - args = argparse.Namespace() - unk = 0.0 - args.beam_probs = [ - # step 0: - torch.FloatTensor( - [ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.9, 0.1], # beam 1 - [0.0, unk, 0.9, 0.1], # beam 2 - # sentence 2: - [0.0, unk, 0.7, 0.3], - [0.0, unk, 0.7, 0.3], - ] - ), - # step 1: - torch.FloatTensor( - [ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.6, 0.4], - [0.0, unk, 0.6, 0.4], - # sentence 2: - [0.25, unk, 0.35, 0.4], - [0.25, unk, 0.35, 0.4], - ] - ), - # step 2: - torch.FloatTensor( - [ - # eos w1 w2 - # sentence 1: - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - # sentence 2: - [0.9, unk, 0.1, 0.0], - [0.9, unk, 0.1, 0.0], - ] - ), - ] - - task = test_utils.TestTranslationTask.setup_task(args, d, d) - self.model = task.build_model(args) - self.tgt_dict = task.target_dictionary - - def test_diverse_beam_search(self): - search_strategy = search.DiverseBeamSearch( - self.tgt_dict, num_groups=2, diversity_strength=0.0 - ) - generator = SequenceGenerator( - [self.model], - self.tgt_dict, - beam_size=2, - search_strategy=search_strategy, - ) - sample = { - "net_input": { - "src_tokens": self.src_tokens, - "src_lengths": self.src_lengths, - } - } - hypos = generator.forward(sample) - eos, w1, w2 = self.eos, self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0]) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w1, w1, eos]) - self.assertHypoScore(hypos[0][1], [0.9, 0.6, 1.0]) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9]) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w2, eos]) - self.assertHypoScore(hypos[1][1], [0.7, 0.4, 0.9]) - - -class TestDiverseSiblingsSearch(TestDiverseBeamSearch): - def assertHypoScore( - self, hypo, pos_probs, sibling_rank, diversity_rate, normalized=True, lenpen=1.0 - ): - pos_scores = torch.FloatTensor(pos_probs).log() - pos_scores.sub_(torch.Tensor(sibling_rank) * diversity_rate) - self.assertAlmostEqual(hypo["positional_scores"], pos_scores) - self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) - score = pos_scores.sum() - if normalized: - score /= pos_scores.numel() ** lenpen - self.assertLess(abs(score - hypo["score"]), 1e-6) - - def test_diverse_beam_search(self): - search_strategy = search.DiverseSiblingsSearch( - self.tgt_dict, diversity_rate=0.5 - ) - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy - ) - sample = { - "net_input": { - "src_tokens": self.src_tokens, - "src_lengths": self.src_lengths, - } - } - hypos = generator.forward(sample) - eos, w1, w2 = self.eos, self.w1, self.w2 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, w1, eos]) - self.assertHypoScore(hypos[0][0], [0.9, 0.6, 1.0], [0, 1, 1], 0.5) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w1, w2, eos]) - self.assertHypoScore(hypos[0][1], [0.9, 0.4, 1.0], [0, 2, 1], 0.5) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w2, eos]) - self.assertHypoScore(hypos[1][0], [0.7, 0.4, 0.9], [0, 1, 1], 0.5) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w1, eos]) - self.assertHypoScore(hypos[1][1], [0.7, 0.35, 0.9], [0, 2, 1], 0.5) - - -class TestTopPSamplingSearch(TestSequenceGeneratorBase): - def setUp(self): - # construct dummy dictionary - d = test_utils.dummy_dictionary(vocab_size=2) - self.assertEqual(d.pad(), 1) - self.assertEqual(d.eos(), 2) - self.assertEqual(d.unk(), 3) - self.eos = d.eos() - self.w1 = 4 - self.w2 = 5 - - # construct source data - self.src_tokens = torch.LongTensor( - [ - [self.w1, self.w2, self.eos], - [self.w1, self.w2, self.eos], - ] - ) - self.src_lengths = torch.LongTensor([2, 2]) - - args = argparse.Namespace() - unk = 0.0 - # The minimal probability of top 2 tokens. - self.min_top2_prob = 0.75 - # The minimal probability of the top 1 token. - self.min_top1_prob = 0.4 - - w1_prob = self.min_top1_prob - w2_prob = self.min_top2_prob - self.min_top1_prob - eos_prob = 1 - self.min_top2_prob - - args.beam_probs = [ - # step 0: - torch.FloatTensor( - [ - # eos w1 w2 - [0.0, unk, 1.0, 0.0], - [0.0, unk, 1.0, 0.0], - [0.0, unk, 1.0, 0.0], - [0.0, unk, 1.0, 0.0], - ] - ), - # step 1: - torch.FloatTensor( - [ - # eos w1 w2 - [eos_prob, unk, w1_prob, w2_prob], - [eos_prob, unk, w1_prob, w2_prob], - [eos_prob, unk, w1_prob, w2_prob], - [eos_prob, unk, w1_prob, w2_prob], - ] - ), - # step 2: - torch.FloatTensor( - [ - # eos w1 w2 - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - [1.0, unk, 0.0, 0.0], - ] - ), - ] - - task = test_utils.TestTranslationTask.setup_task(args, d, d) - self.model = task.build_model(args) - self.tgt_dict = task.target_dictionary - - def test_topp_sampling_search_low_prob(self): - # Given a prob low enough to top-P sampling, we expect only the top - # 1 token to be sampled, which always results in the same output. - low_sampling_topp = self.min_top1_prob / 2.0 - search_strategy = search.Sampling( - self.tgt_dict, sampling_topp=low_sampling_topp - ) - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy - ) - sample = { - "net_input": { - "src_tokens": self.src_tokens, - "src_lengths": self.src_lengths, - } - } - hypos = generator.forward(sample) - eos, w1 = self.eos, self.w1 - # sentence 1, beam 1 - self.assertHypoTokens(hypos[0][0], [w1, w1, eos]) - self.assertHypoScore(hypos[0][0], [1.0, 0.4, 1.0]) - # sentence 1, beam 2 - self.assertHypoTokens(hypos[0][1], [w1, w1, eos]) - self.assertHypoScore(hypos[0][1], [1.0, 0.4, 1.0]) - # sentence 2, beam 1 - self.assertHypoTokens(hypos[1][0], [w1, w1, eos]) - self.assertHypoScore(hypos[1][0], [1.0, 0.4, 1.0]) - # sentence 2, beam 2 - self.assertHypoTokens(hypos[1][1], [w1, w1, eos]) - self.assertHypoScore(hypos[1][1], [1.0, 0.4, 1.0]) - - def test_topp_sampling_search_high_prob(self): - # Given a prob high enough to top-P sampling, any of the top 2 - # tokens could be sampled. This can cause different outputs. - high_sampling_topp = (self.min_top1_prob + self.min_top2_prob) / 2.0 - search_strategy = search.Sampling( - self.tgt_dict, sampling_topp=high_sampling_topp - ) - generator = SequenceGenerator( - [self.model], self.tgt_dict, beam_size=2, search_strategy=search_strategy - ) - sample = { - "net_input": { - "src_tokens": self.src_tokens, - "src_lengths": self.src_lengths, - } - } - hypos = generator.forward(sample) - eos, w1, w2 = self.eos, self.w1, self.w2 - # sentence 1, beam 1 - self.assertTrue( - self.hypoTokens(hypos[0][0], [w1, w1, eos]) - or self.hypoTokens(hypos[0][0], [w1, w2, eos]) - ) - self.assertTrue( - self.hypoScore(hypos[0][0], [1.0, 0.4, 1.0]) - or self.hypoScore(hypos[0][0], [1.0, 0.35, 1.0]) - ) - - # sentence 1, beam 2 - self.assertTrue( - self.hypoTokens(hypos[0][1], [w1, w1, eos]) - or self.hypoTokens(hypos[0][1], [w1, w2, eos]) - ) - self.assertTrue( - self.hypoScore(hypos[0][1], [1.0, 0.4, 1.0]) - or self.hypoScore(hypos[0][1], [1.0, 0.35, 1.0]) - ) - - # sentence 2, beam 1 - self.assertTrue( - self.hypoTokens(hypos[1][0], [w1, w1, eos]) - or self.hypoTokens(hypos[1][0], [w1, w2, eos]) - ) - self.assertTrue( - self.hypoScore(hypos[1][0], [1.0, 0.4, 1.0]) - or self.hypoScore(hypos[1][0], [1.0, 0.35, 1.0]) - ) - - # sentence 2, beam 2 - self.assertTrue( - self.hypoTokens(hypos[1][1], [w1, w1, eos]) - or self.hypoTokens(hypos[1][1], [w1, w2, eos]) - ) - self.assertTrue( - self.hypoScore(hypos[1][1], [1.0, 0.4, 1.0]) - or self.hypoScore(hypos[1][1], [1.0, 0.35, 1.0]) - ) - - def hypoTokens(self, hypo, tokens): - return self.tensorEqual(hypo["tokens"], torch.LongTensor(tokens)) - - def hypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): - pos_scores = torch.FloatTensor(pos_probs).log() - if not self.almostEqual(hypo["positional_scores"], pos_scores): - return False - if pos_scores.numel() != hypo["tokens"].numel(): - return False - score = pos_scores.sum() - if normalized: - score /= pos_scores.numel() ** lenpen - return abs(score - hypo["score"]) < 1e-6 - - def almostEqual(self, t1, t2): - return t1.size() == t2.size() and (t1 - t2).abs().max() < 1e-4 - - def tensorEqual(self, t1, t2): - return t1.size() == t2.size() and t1.ne(t2).long().sum() == 0 - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_sequence_scorer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_sequence_scorer.py deleted file mode 100644 index dc73e6cc1deabfc2ea26317c46f0dcfc2c79d2a0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_sequence_scorer.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import unittest - -import torch -from fairseq.sequence_scorer import SequenceScorer - -import tests.utils as test_utils - - -class TestSequenceScorer(unittest.TestCase): - def test_sequence_scorer(self): - # construct dummy dictionary - d = test_utils.dummy_dictionary(vocab_size=2) - self.assertEqual(d.pad(), 1) - self.assertEqual(d.eos(), 2) - self.assertEqual(d.unk(), 3) - eos = d.eos() - w1 = 4 - w2 = 5 - - # construct dataloader - data = [ - { - "source": torch.LongTensor([w1, w2, eos]), - "target": torch.LongTensor([w1, w2, w1, eos]), - }, - { - "source": torch.LongTensor([w2, eos]), - "target": torch.LongTensor([w2, w1, eos]), - }, - { - "source": torch.LongTensor([w2, eos]), - "target": torch.LongTensor([w2, eos]), - }, - ] - data_itr = test_utils.dummy_dataloader(data) - - # specify expected output probabilities - args = argparse.Namespace() - unk = 0.0 - args.beam_probs = [ - # step 0: - torch.FloatTensor( - [ - # eos w1 w2 - [0.0, unk, 0.6, 0.4], # sentence 1 - [0.0, unk, 0.4, 0.6], # sentence 2 - [0.0, unk, 0.7, 0.3], # sentence 3 - ] - ), - # step 1: - torch.FloatTensor( - [ - # eos w1 w2 - [0.0, unk, 0.2, 0.7], # sentence 1 - [0.0, unk, 0.8, 0.2], # sentence 2 - [0.7, unk, 0.1, 0.2], # sentence 3 - ] - ), - # step 2: - torch.FloatTensor( - [ - # eos w1 w2 - [0.10, unk, 0.50, 0.4], # sentence 1 - [0.15, unk, 0.15, 0.7], # sentence 2 - [0.00, unk, 0.00, 0.0], # sentence 3 - ] - ), - # step 3: - torch.FloatTensor( - [ - # eos w1 w2 - [0.9, unk, 0.05, 0.05], # sentence 1 - [0.0, unk, 0.00, 0.0], # sentence 2 - [0.0, unk, 0.00, 0.0], # sentence 3 - ] - ), - ] - expected_scores = [ - [0.6, 0.7, 0.5, 0.9], # sentence 1 - [0.6, 0.8, 0.15], # sentence 2 - [0.3, 0.7], # sentence 3 - ] - - task = test_utils.TestTranslationTask.setup_task(args, d, d) - model = task.build_model(args) - scorer = SequenceScorer(task.target_dictionary) - for sample in data_itr: - hypos = task.inference_step(scorer, [model], sample) - for id, hypos_id in zip(sample["id"].tolist(), hypos): - self.assertHypoTokens(hypos_id[0], data[id]["target"]) - self.assertHypoScore(hypos_id[0], expected_scores[id]) - - def assertHypoTokens(self, hypo, tokens): - self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens)) - - def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0): - pos_scores = torch.FloatTensor(pos_probs).log() - self.assertAlmostEqual(hypo["positional_scores"], pos_scores) - self.assertEqual(pos_scores.numel(), hypo["tokens"].numel()) - score = pos_scores.sum() - if normalized: - score /= pos_scores.numel() ** lenpen - self.assertLess(abs(score - hypo["score"]), 1e-6) - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess((t1 - t2).abs().max(), 1e-4) - - def assertTensorEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertEqual(t1.ne(t2).long().sum(), 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_sparse_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_sparse_multihead_attention.py deleted file mode 100644 index 3e32b25a7fb1e12295b84d0c65064f8e42b7bdd3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_sparse_multihead_attention.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention - - -class TestSparseMultiheadAttention(unittest.TestCase): - def test_sparse_multihead_attention(self): - attn_weights = torch.randn(1, 8, 8) - bidirectional_sparse_mask = torch.tensor( - [ - [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], - [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], - [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], - [0, 0, 0, 0, 0, float("-inf"), float("-inf"), 0], - [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], - [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], - [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], - [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], - ] - ) - - bidirectional_attention = SparseMultiheadAttention( - 16, 1, stride=4, expressivity=1, is_bidirectional=True - ) - bidirectional_attention_sparse_mask = ( - bidirectional_attention.buffered_sparse_mask(attn_weights, 8, 8) - ) - torch.all( - torch.eq(bidirectional_attention_sparse_mask, bidirectional_sparse_mask) - ) - - sparse_mask = torch.tensor( - [ - [ - 0, - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - ], - [ - 0, - 0, - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - ], - [ - 0, - 0, - 0, - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - ], - [ - 0, - 0, - 0, - 0, - float("-inf"), - float("-inf"), - float("-inf"), - float("-inf"), - ], - [0, 0, 0, 0, 0, float("-inf"), float("-inf"), float("-inf")], - [ - float("-inf"), - float("-inf"), - float("-inf"), - 0, - 0, - 0, - float("-inf"), - float("-inf"), - ], - [ - float("-inf"), - float("-inf"), - float("-inf"), - 0, - 0, - 0, - 0, - float("-inf"), - ], - [float("-inf"), float("-inf"), float("-inf"), 0, 0, 0, 0, 0], - ] - ) - - attention = SparseMultiheadAttention( - 16, 1, stride=4, expressivity=1, is_bidirectional=False - ) - attention_sparse_mask = attention.buffered_sparse_mask(attn_weights, 8, 8) - - torch.all(torch.eq(attention_sparse_mask, sparse_mask)) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_token_block_dataset.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_token_block_dataset.py deleted file mode 100644 index c92947076f64dd398952ba06df120458693cb140..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_token_block_dataset.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq.data import TokenBlockDataset - -import tests.utils as test_utils - - -class TestTokenBlockDataset(unittest.TestCase): - def _build_dataset(self, data, **kwargs): - sizes = [len(x) for x in data] - underlying_ds = test_utils.TestDataset(data) - return TokenBlockDataset(underlying_ds, sizes, **kwargs) - - def test_eos_break_mode(self): - data = [ - torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), - torch.tensor([1], dtype=torch.long), - torch.tensor([8, 7, 6, 1], dtype=torch.long), - ] - ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos") - self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) - self.assertEqual(ds[1].tolist(), [1]) - self.assertEqual(ds[2].tolist(), [8, 7, 6, 1]) - - data = [ - torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), - torch.tensor([8, 7, 6, 1], dtype=torch.long), - torch.tensor([1], dtype=torch.long), - ] - ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos") - self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) - self.assertEqual(ds[1].tolist(), [8, 7, 6, 1]) - self.assertEqual(ds[2].tolist(), [1]) - - def test_block_break_mode(self): - data = [ - torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), - torch.tensor([8, 7, 6, 1], dtype=torch.long), - torch.tensor([9, 1], dtype=torch.long), - ] - ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none") - self.assertEqual(ds[0].tolist(), [5, 4, 3]) - self.assertEqual(ds[1].tolist(), [2, 1, 8]) - self.assertEqual(ds[2].tolist(), [7, 6, 1]) - self.assertEqual(ds[3].tolist(), [9, 1]) - - def test_complete_break_mode(self): - data = [ - torch.tensor([5, 4, 3, 2, 1], dtype=torch.long), - torch.tensor([8, 7, 6, 1], dtype=torch.long), - torch.tensor([9, 1], dtype=torch.long), - ] - ds = self._build_dataset( - data, block_size=6, pad=0, eos=1, break_mode="complete" - ) - self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1]) - self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1]) - - data = [ - torch.tensor([4, 3, 2, 1], dtype=torch.long), - torch.tensor([5, 1], dtype=torch.long), - torch.tensor([1], dtype=torch.long), - torch.tensor([6, 1], dtype=torch.long), - ] - ds = self._build_dataset( - data, block_size=3, pad=0, eos=1, break_mode="complete" - ) - self.assertEqual(ds[0].tolist(), [4, 3, 2, 1]) - self.assertEqual(ds[1].tolist(), [5, 1, 1]) - self.assertEqual(ds[2].tolist(), [6, 1]) - - def test_4billion_tokens(self): - """Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745""" - data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000 - ds = self._build_dataset( - data, block_size=6, pad=0, eos=1, break_mode="complete" - ) - ds[-1] # __getitem__ works - start, end = ds.slice_indices[-1] - assert end > 4294967295 # data must be sufficiently large to overflow uint32 - assert not isinstance( - end + 1, float - ) # this would also raise, since np.uint64(1) + 1 => 2.0 - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_train.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_train.py deleted file mode 100644 index 02ef94cc5b80c05485144db67501b2acedbaf291..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_train.py +++ /dev/null @@ -1,247 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import contextlib -import logging -import unittest -from io import StringIO -from unittest.mock import MagicMock, patch - -import torch -from fairseq import checkpoint_utils, data -from omegaconf import OmegaConf - - -def mock_trainer(epoch, num_updates, iterations_in_epoch): - trainer = MagicMock() - trainer.load_checkpoint.return_value = { - "train_iterator": { - "epoch": epoch, - "iterations_in_epoch": iterations_in_epoch, - "shuffle": False, - }, - } - trainer.get_num_updates.return_value = num_updates - return trainer - - -def mock_dict(): - d = MagicMock() - d.pad.return_value = 1 - d.eos.return_value = 2 - d.unk.return_value = 3 - return d - - -def get_trainer_and_epoch_itr(epoch, epoch_size, num_updates, iterations_in_epoch): - tokens = torch.LongTensor(list(range(epoch_size))).view(1, -1) - tokens_ds = data.TokenBlockDataset( - tokens, - sizes=[tokens.size(-1)], - block_size=1, - pad=0, - eos=1, - include_targets=False, - ) - trainer = mock_trainer(epoch, num_updates, iterations_in_epoch) - dataset = data.LanguagePairDataset( - tokens_ds, tokens_ds.sizes, mock_dict(), shuffle=False - ) - epoch_itr = data.EpochBatchIterator( - dataset=dataset, - collate_fn=dataset.collater, - batch_sampler=[[i] for i in range(epoch_size)], - ) - return trainer, epoch_itr - - -def get_mock_cfg(finetune_from_model): - cfg_mock = OmegaConf.create( - { - "checkpoint": { - "save_dir": None, - "optimizer_overrides": "{}", - "reset_dataloader": False, - "reset_meters": False, - "reset_optimizer": False, - "reset_lr_scheduler": False, - "finetune_from_model": finetune_from_model, - "model_parallel_size": 1, - "restore_file": "checkpoint_last.pt", - }, - "common": { - "model_parallel_size": 1, - }, - } - ) - return cfg_mock - - -class TestLoadCheckpoint(unittest.TestCase): - def setUp(self): - self.cfg_mock = get_mock_cfg(None) - self.patches = { - "os.makedirs": MagicMock(), - "os.path.join": MagicMock(), - "os.path.isfile": MagicMock(return_value=True), - "os.path.isabs": MagicMock(return_value=False), - "fairseq.file_io.PathManager.exists": MagicMock(return_value=False), - } - self.applied_patches = [patch(p, d) for p, d in self.patches.items()] - [p.start() for p in self.applied_patches] - logging.disable(logging.CRITICAL) - - def tearDown(self): - patch.stopall() - logging.disable(logging.NOTSET) - - def test_load_partial_checkpoint(self): - with contextlib.redirect_stdout(StringIO()): - trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 200, 50) - trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - - _, epoch_itr = checkpoint_utils.load_checkpoint( - self.cfg_mock.checkpoint, trainer - ) - - self.assertEqual(epoch_itr.epoch, 2) - self.assertEqual(epoch_itr.iterations_in_epoch, 50) - - itr = epoch_itr.next_epoch_itr(shuffle=False) - self.assertEqual(epoch_itr.epoch, 2) - self.assertEqual(epoch_itr.iterations_in_epoch, 50) - - self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 50) - self.assertEqual(epoch_itr.iterations_in_epoch, 51) - - for _ in range(150 - 52): - next(itr) - self.assertEqual(epoch_itr.iterations_in_epoch, 149) - self.assertTrue(itr.has_next()) - next(itr) - self.assertFalse(itr.has_next()) - - itr = epoch_itr.next_epoch_itr(shuffle=False) - self.assertTrue(itr.has_next()) - self.assertEqual(epoch_itr.epoch, 3) - self.assertEqual(epoch_itr.iterations_in_epoch, 0) - - def test_load_full_checkpoint(self): - with contextlib.redirect_stdout(StringIO()): - trainer, epoch_itr = get_trainer_and_epoch_itr(2, 150, 300, 150) - trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - - _, epoch_itr = checkpoint_utils.load_checkpoint( - self.cfg_mock.checkpoint, trainer - ) - itr = epoch_itr.next_epoch_itr(shuffle=False) - - self.assertEqual(epoch_itr.epoch, 3) - self.assertEqual(epoch_itr.iterations_in_epoch, 0) - self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0) - - def test_load_no_checkpoint(self): - with contextlib.redirect_stdout(StringIO()): - trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) - trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - self.patches["os.path.isfile"].return_value = False - - _, epoch_itr = checkpoint_utils.load_checkpoint( - self.cfg_mock.checkpoint, trainer - ) - itr = epoch_itr.next_epoch_itr(shuffle=False) - - self.assertEqual(epoch_itr.epoch, 1) - self.assertEqual(epoch_itr.iterations_in_epoch, 0) - self.assertEqual(next(itr)["net_input"]["src_tokens"][0].item(), 0) - - def test_finetune_from_model_args_conflict(self): - with contextlib.redirect_stdout(StringIO()): - trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) - trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - - for arg in [ - "reset_optimizer", - "reset_lr_scheduler", - "reset_meters", - "reset_dataloader", - ]: - with self.subTest(arg=arg): - cfg_mock = get_mock_cfg("/temp/checkpoint_pretrained.pt") - cfg_mock["checkpoint"][arg] = True - with self.assertRaises(Exception) as context: - _, _ = checkpoint_utils.load_checkpoint( - cfg_mock.checkpoint, trainer - ) - - self.assertTrue( - "--finetune-from-model can not be set together with either --reset-optimizer" - " or reset_lr_scheduler or reset_meters or reset_dataloader" - in str(context.exception) - ) - - def test_finetune_from_model(self): - with contextlib.redirect_stdout(StringIO()): - trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) - trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - from_model_path = "/temp/checkpoint_pretrained.pt" - - def mock_finetune_exist(path): - if path == from_model_path: - return True - else: - return False - - self.patches[ - "fairseq.file_io.PathManager.exists" - ].side_effect = mock_finetune_exist - cfg_mock = get_mock_cfg(from_model_path) - cfg_mock.checkpoint.restore_file = "checkpoint_last.pt" - _, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer) - ( - checkpoint_path, - reset_optimizer, - reset_lr_scheduler, - optimizer_overrides, - ) = trainer.load_checkpoint.call_args[0] - reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"] - self.assertTrue(reset_optimizer) - self.assertTrue(reset_lr_scheduler) - self.assertTrue(reset_meters) - - def test_finetune_from_model_resume(self): - with contextlib.redirect_stdout(StringIO()): - trainer, epoch_itr = get_trainer_and_epoch_itr(1, 150, 0, 0) - trainer.get_train_iterator = MagicMock(return_value=epoch_itr) - from_model_path = "/temp/checkpoint_pretrained.pt" - - # launch second time - # both restore_file=checkpoint_last.pt and finetune_from_model are set - def mock_finetune_exist(path): - if path == from_model_path or path.endsWith("checkpoint_last.pt"): - return True - else: - return False - - self.patches[ - "fairseq.file_io.PathManager.exists" - ].side_effect = mock_finetune_exist - cfg_mock = get_mock_cfg(from_model_path) - cfg_mock.checkpoint.restore_file = "checkpoint_last.pt" - _, _ = checkpoint_utils.load_checkpoint(cfg_mock.checkpoint, trainer) - ( - checkpoint_path, - reset_optimizer, - reset_lr_scheduler, - optimizer_overrides, - ) = trainer.load_checkpoint.call_args[0] - reset_meters = trainer.load_checkpoint.call_args[1]["reset_meters"] - self.assertFalse(reset_optimizer) - self.assertFalse(reset_lr_scheduler) - self.assertFalse(reset_meters) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_transformer.py deleted file mode 100644 index de5c5bdbd49692e63fb1cb50108a791304425dc1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_transformer.py +++ /dev/null @@ -1,65 +0,0 @@ -import argparse -import unittest -from typing import Any, Dict, Sequence - -import torch -from fairseq.models import transformer - -from tests.test_roberta import FakeTask - - -def mk_sample(tok: Sequence[int] = None, batch_size: int = 2) -> Dict[str, Any]: - if not tok: - tok = [10, 11, 12, 13, 14, 15, 2] - - batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) - sample = { - "net_input": { - "src_tokens": batch, - "prev_output_tokens": batch, - "src_lengths": torch.tensor( - [len(tok)] * batch_size, dtype=torch.long, device=batch.device - ), - }, - "target": batch[:, 1:], - } - return sample - - -def mk_transformer(**extra_args: Any): - overrides = { - # Use characteristics dimensions - "encoder_embed_dim": 12, - "encoder_ffn_embed_dim": 14, - "decoder_embed_dim": 12, - "decoder_ffn_embed_dim": 14, - # Disable dropout so we have comparable tests. - "dropout": 0, - "attention_dropout": 0, - "activation_dropout": 0, - "encoder_layerdrop": 0, - } - overrides.update(extra_args) - # Overrides the defaults from the parser - args = argparse.Namespace(**overrides) - transformer.tiny_architecture(args) - - torch.manual_seed(0) - task = FakeTask(args) - return transformer.TransformerModel.build_model(args, task) - - -class TransformerTestCase(unittest.TestCase): - def test_forward_backward(self): - model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=12) - sample = mk_sample() - o, _ = model.forward(**sample["net_input"]) - loss = o.sum() - loss.backward() - - def test_different_encoder_decoder_embed_dim(self): - model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=16) - sample = mk_sample() - o, _ = model.forward(**sample["net_input"]) - loss = o.sum() - loss.backward() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_utils.py deleted file mode 100644 index 79195903e0f34372a24fa50312a6e00170c14471..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_utils.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import unittest - -import torch -from fairseq import utils - - -class TestUtils(unittest.TestCase): - def test_convert_padding_direction(self): - pad = 1 - left_pad = torch.LongTensor( - [ - [2, 3, 4, 5, 6], - [1, 7, 8, 9, 10], - [1, 1, 1, 11, 12], - ] - ) - right_pad = torch.LongTensor( - [ - [2, 3, 4, 5, 6], - [7, 8, 9, 10, 1], - [11, 12, 1, 1, 1], - ] - ) - - self.assertAlmostEqual( - right_pad, - utils.convert_padding_direction( - left_pad, - pad, - left_to_right=True, - ), - ) - self.assertAlmostEqual( - left_pad, - utils.convert_padding_direction( - right_pad, - pad, - right_to_left=True, - ), - ) - - def test_make_positions(self): - pad = 1 - left_pad_input = torch.LongTensor( - [ - [9, 9, 9, 9, 9], - [1, 9, 9, 9, 9], - [1, 1, 1, 9, 9], - ] - ) - left_pad_output = torch.LongTensor( - [ - [2, 3, 4, 5, 6], - [1, 2, 3, 4, 5], - [1, 1, 1, 2, 3], - ] - ) - right_pad_input = torch.LongTensor( - [ - [9, 9, 9, 9, 9], - [9, 9, 9, 9, 1], - [9, 9, 1, 1, 1], - ] - ) - right_pad_output = torch.LongTensor( - [ - [2, 3, 4, 5, 6], - [2, 3, 4, 5, 1], - [2, 3, 1, 1, 1], - ] - ) - - self.assertAlmostEqual( - left_pad_output, - utils.make_positions(left_pad_input, pad), - ) - self.assertAlmostEqual( - right_pad_output, - utils.make_positions(right_pad_input, pad), - ) - - def test_clip_grad_norm_(self): - params = torch.nn.Parameter(torch.zeros(5)).requires_grad_(False) - grad_norm = utils.clip_grad_norm_(params, 1.0) - self.assertTrue(torch.is_tensor(grad_norm)) - self.assertEqual(grad_norm, 0.0) - - params = [torch.nn.Parameter(torch.zeros(5)) for i in range(3)] - for p in params: - p.grad = torch.full((5,), fill_value=2.0) - grad_norm = utils.clip_grad_norm_(params, 1.0) - exp_grad_norm = torch.full((15,), fill_value=2.0).norm() - self.assertTrue(torch.is_tensor(grad_norm)) - self.assertEqual(grad_norm, exp_grad_norm) - - grad_norm = utils.clip_grad_norm_(params, 1.0) - self.assertAlmostEqual(grad_norm, torch.tensor(1.0)) - - def test_resolve_max_positions_with_tuple(self): - resolved = utils.resolve_max_positions(None, (2000, 100, 2000), 12000) - self.assertEqual(resolved, (2000, 100, 2000)) - - def assertAlmostEqual(self, t1, t2): - self.assertEqual(t1.size(), t2.size(), "size mismatch") - self.assertLess(utils.item((t1 - t2).abs().max()), 1e-4) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/test_valid_subset_checks.py b/audio/safeear/safeear_repo/fairseq_ours/tests/test_valid_subset_checks.py deleted file mode 100644 index 788b87f0ae0d6466ad5de4da82038b30d1583e5a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/test_valid_subset_checks.py +++ /dev/null @@ -1,139 +0,0 @@ -import os -import shutil -import tempfile -import unittest - -from fairseq import options -from fairseq.data.data_utils import raise_if_valid_subsets_unintentionally_ignored -from fairseq.dataclass.utils import convert_namespace_to_omegaconf - -from .utils import create_dummy_data, preprocess_lm_data, train_language_model - - -def make_lm_config( - data_dir=None, - extra_flags=None, - task="language_modeling", - arch="transformer_lm_gpt2_tiny", -): - task_args = [task] - if data_dir is not None: - task_args += [data_dir] - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - *task_args, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--max-tokens", - "500", - "--tokens-per-sample", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - ] - + (extra_flags or []), - ) - cfg = convert_namespace_to_omegaconf(train_args) - return cfg - - -def write_empty_file(path): - with open(path, "w"): - pass - assert os.path.exists(path) - - -class TestValidSubsetsErrors(unittest.TestCase): - """Test various filesystem, clarg combinations and ensure that error raising happens as expected""" - - def _test_case(self, paths, extra_flags): - with tempfile.TemporaryDirectory() as data_dir: - [ - write_empty_file(os.path.join(data_dir, f"{p}.bin")) - for p in paths + ["train"] - ] - cfg = make_lm_config(data_dir, extra_flags=extra_flags) - raise_if_valid_subsets_unintentionally_ignored(cfg) - - def test_default_raises(self): - with self.assertRaises(ValueError): - self._test_case(["valid", "valid1"], []) - with self.assertRaises(ValueError): - self._test_case( - ["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"] - ) - - def partially_specified_valid_subsets(self): - with self.assertRaises(ValueError): - self._test_case( - ["valid", "valid1", "valid2"], ["--valid-subset", "valid,valid1"] - ) - # Fix with ignore unused - self._test_case( - ["valid", "valid1", "valid2"], - ["--valid-subset", "valid,valid1", "--ignore-unused-valid-subsets"], - ) - - def test_legal_configs(self): - self._test_case(["valid"], []) - self._test_case(["valid", "valid1"], ["--ignore-unused-valid-subsets"]) - self._test_case(["valid", "valid1"], ["--combine-val"]) - self._test_case(["valid", "valid1"], ["--valid-subset", "valid,valid1"]) - self._test_case(["valid", "valid1"], ["--valid-subset", "valid1"]) - self._test_case( - ["valid", "valid1"], ["--combine-val", "--ignore-unused-valid-subsets"] - ) - self._test_case( - ["valid1"], ["--valid-subset", "valid1"] - ) # valid.bin doesn't need to be ignored. - - def test_disable_validation(self): - self._test_case([], ["--disable-validation"]) - self._test_case(["valid", "valid1"], ["--disable-validation"]) - - def test_dummy_task(self): - cfg = make_lm_config(task="dummy_lm") - raise_if_valid_subsets_unintentionally_ignored(cfg) - - def test_masked_dummy_task(self): - cfg = make_lm_config(task="dummy_masked_lm") - raise_if_valid_subsets_unintentionally_ignored(cfg) - - -class TestCombineValidSubsets(unittest.TestCase): - def _train(self, extra_flags): - with self.assertLogs() as logs: - with tempfile.TemporaryDirectory("test_transformer_lm") as data_dir: - create_dummy_data(data_dir, num_examples=20) - preprocess_lm_data(data_dir) - - shutil.copyfile(f"{data_dir}/valid.bin", f"{data_dir}/valid1.bin") - shutil.copyfile(f"{data_dir}/valid.idx", f"{data_dir}/valid1.idx") - train_language_model( - data_dir, - "transformer_lm", - ["--max-update", "0", "--log-format", "json"] + extra_flags, - run_validation=False, - ) - return [x.message for x in logs.records] - - def test_combined(self): - flags = ["--combine-valid-subsets"] - logs = self._train(flags) - assert any(["valid1" in x for x in logs]) # loaded 100 examples from valid1 - assert not any(["valid1_ppl" in x for x in logs]) # metrics are combined - - def test_subsets(self): - flags = ["--valid-subset", "valid,valid1"] - logs = self._train(flags) - assert any(["valid_ppl" in x for x in logs]) # loaded 100 examples from valid1 - assert any(["valid1_ppl" in x for x in logs]) # metrics are combined diff --git a/audio/safeear/safeear_repo/fairseq_ours/tests/utils.py b/audio/safeear/safeear_repo/fairseq_ours/tests/utils.py deleted file mode 100644 index 9187e952dabb7fde250025c6ad9a5e82b488af80..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/tests/utils.py +++ /dev/null @@ -1,796 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import json -import os -import random -import shutil -import string -import sys -import typing as tp -from io import StringIO - -import fairseq.distributed.utils as distributed_utils -import torch -import torch.nn.functional as F -from fairseq import options, utils -from fairseq.data import Dictionary -from fairseq.data.language_pair_dataset import collate -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, -) -from fairseq.models.fairseq_encoder import EncoderOut -from fairseq.tasks import LegacyFairseqTask -from fairseq_cli import generate, interactive, preprocess, train, validate - - -def dummy_dictionary(vocab_size, prefix="token_"): - d = Dictionary() - for i in range(vocab_size): - token = prefix + str(i) - d.add_symbol(token) - d.finalize(padding_factor=1) # don't add extra padding symbols - return d - - -def dummy_dataloader( - samples, - padding_idx=1, - eos_idx=2, - batch_size=None, -): - if batch_size is None: - batch_size = len(samples) - - # add any missing data to samples - for i, sample in enumerate(samples): - if "id" not in sample: - sample["id"] = i - - # create dataloader - dataset = TestDataset(samples) - dataloader = torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)), - ) - return iter(dataloader) - - -def sequence_generator_setup(): - # construct dummy dictionary - d = dummy_dictionary(vocab_size=2) - - eos = d.eos() - w1 = 4 - w2 = 5 - - # construct source data - src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]]) - src_lengths = torch.LongTensor([2, 2]) - - args = argparse.Namespace() - unk = 0.0 - args.beam_probs = [ - # step 0: - torch.FloatTensor( - [ - # eos w1 w2 - # sentence 1: - [0.0, unk, 0.9, 0.1], # beam 1 - [0.0, unk, 0.9, 0.1], # beam 2 - # sentence 2: - [0.0, unk, 0.7, 0.3], - [0.0, unk, 0.7, 0.3], - ] - ), - # step 1: - torch.FloatTensor( - [ - # eos w1 w2 prefix - # sentence 1: - [1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 : 0.9*1.0) - [0.0, unk, 0.9, 0.1], # w2: 0.1 - # sentence 2: - [0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 : 0.7*0.25) - [0.00, unk, 0.10, 0.9], # w2: 0.3 - ] - ), - # step 2: - torch.FloatTensor( - [ - # eos w1 w2 prefix - # sentence 1: - [0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9 - [ - 0.6, - unk, - 0.2, - 0.2, - ], # w2 w2: 0.1*0.1 (emit: w2 w2 : 0.1*0.1*0.6) - # sentence 2: - [ - 0.60, - unk, - 0.4, - 0.00, - ], # w1 w2: 0.7*0.4 (emit: w1 w2 : 0.7*0.4*0.6) - [0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9 - ] - ), - # step 3: - torch.FloatTensor( - [ - # eos w1 w2 prefix - # sentence 1: - [ - 1.0, - unk, - 0.0, - 0.0, - ], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 : 0.1*0.9*0.9*1.0) - [ - 1.0, - unk, - 0.0, - 0.0, - ], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 : 0.1*0.9*0.1*1.0) - # sentence 2: - [ - 0.1, - unk, - 0.5, - 0.4, - ], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 : 0.3*0.9*0.99*0.1) - [ - 1.0, - unk, - 0.0, - 0.0, - ], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 : 0.7*0.4*0.4*1.0) - ] - ), - ] - - task = TestTranslationTask.setup_task(args, d, d) - model = task.build_model(args) - tgt_dict = task.target_dictionary - - return tgt_dict, w1, w2, src_tokens, src_lengths, model - - -def create_dummy_data( - data_dir, num_examples=100, maxlen=20, alignment=False, languages=None -): - def _create_dummy_data(dir, filename): - data = torch.rand(num_examples * maxlen) - data = 97 + torch.floor(26 * data).int() - with open(os.path.join(dir, filename), "w") as h: - offset = 0 - for _ in range(num_examples): - ex_len = random.randint(1, maxlen) - ex_str = " ".join(map(chr, data[offset : offset + ex_len])) - print(ex_str, file=h) - offset += ex_len - - def _create_dummy_alignment_data(filename_src, filename_tgt, filename): - with open(os.path.join(data_dir, filename_src), "r") as src_f, open( - os.path.join(data_dir, filename_tgt), "r" - ) as tgt_f, open(os.path.join(data_dir, filename), "w") as h: - for src, tgt in zip(src_f, tgt_f): - src_len = len(src.split()) - tgt_len = len(tgt.split()) - avg_len = (src_len + tgt_len) // 2 - num_alignments = random.randint(avg_len // 2, 2 * avg_len) - src_indices = torch.floor(torch.rand(num_alignments) * src_len).int() - tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int() - ex_str = " ".join( - [ - "{}-{}".format(src, tgt) - for src, tgt in zip(src_indices, tgt_indices) - ] - ) - print(ex_str, file=h) - - files_to_write = [ - "train.in", - "train.out", - "valid.in", - "valid.out", - "test.in", - "test.out", - ] - if languages is None: # En only dummy dataset - for f in files_to_write: - _create_dummy_data(data_dir, f) - else: - for lang in languages: - lang_dir = os.path.join(data_dir, lang) - os.makedirs(lang_dir, exist_ok=True) - for f in files_to_write: - _create_dummy_data(lang_dir, f) - - if alignment: - _create_dummy_alignment_data("train.in", "train.out", "train.align") - _create_dummy_alignment_data("valid.in", "valid.out", "valid.align") - _create_dummy_alignment_data("test.in", "test.out", "test.align") - - -def preprocess_lm_data(data_dir, languages=None): - preprocess_parser = options.get_preprocessing_parser() - if languages is None: - preprocess_args = preprocess_parser.parse_args( - [ - "--only-source", - "--trainpref", - os.path.join(data_dir, "train.out"), - "--validpref", - os.path.join(data_dir, "valid.out"), - "--testpref", - os.path.join(data_dir, "test.out"), - "--destdir", - data_dir, - ] - ) - preprocess.main(preprocess_args) - else: - for lang in languages: - lang_dir = os.path.join(data_dir, lang) - assert os.path.exists(lang_dir) - preprocess_args = preprocess_parser.parse_args( - [ - "--only-source", - "--trainpref", - os.path.join(lang_dir, "train.out"), - "--validpref", - os.path.join(lang_dir, "valid.out"), - "--testpref", - os.path.join(lang_dir, "test.out"), - "--destdir", - lang_dir, - ] - ) - preprocess.main(preprocess_args) - shutil.copyfile( - os.path.join(data_dir, languages[0], "dict.txt"), - os.path.join(data_dir, "dict.txt"), - ) - - -def preprocess_translation_data(data_dir, extra_flags=None): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args( - [ - "--source-lang", - "in", - "--target-lang", - "out", - "--trainpref", - os.path.join(data_dir, "train"), - "--validpref", - os.path.join(data_dir, "valid"), - "--testpref", - os.path.join(data_dir, "test"), - "--thresholdtgt", - "0", - "--thresholdsrc", - "0", - "--destdir", - data_dir, - ] - + (extra_flags or []), - ) - preprocess.main(preprocess_args) - - -def preprocess_summarization_data(data_dir, extra_flags=None): - preprocess_parser = options.get_preprocessing_parser() - preprocess_args = preprocess_parser.parse_args( - [ - "--source-lang", - "in", - "--target-lang", - "out", - "--trainpref", - os.path.join(data_dir, "train"), - "--validpref", - os.path.join(data_dir, "valid"), - "--testpref", - os.path.join(data_dir, "test"), - "--thresholdtgt", - "0", - "--thresholdsrc", - "0", - "--joined-dictionary", - "--destdir", - data_dir, - ] - + (extra_flags or []), - ) - preprocess.main(preprocess_args) - - -def create_laser_data_and_config_json(data_dir): - src_langs = ["de", "fr", "ru", "tr", "zh"] - tgt_langs = ["en", "es"] - config_json = {} - config_train_json = [] - src_vocab = None - tgt_vocab = None - - for src_lang in src_langs: - for tgt_lang in tgt_langs: - langpair_folder = f"{src_lang}-{tgt_lang}" - - langpair_path = os.path.join(data_dir, langpair_folder) - os.mkdir(langpair_path) - create_dummy_data(langpair_path) - preprocess_translation_data(langpair_path, ["--dataset-impl", "cached"]) - - src_vocab = os.path.join(langpair_path, "dict.in.txt") - tgt_vocab = os.path.join(langpair_path, "dict.out.txt") - config_train_json.append( - { - "id": 0 if tgt_lang == "en" else 1, - "src": os.path.join(langpair_path, "train.in-out.in"), - "tgt": os.path.join(langpair_path, "train.in-out.out"), - } - ) - - config_json["src_vocab"] = src_vocab - config_json["tgt_vocab"] = tgt_vocab - config_json["train"] = config_train_json - - with open(os.path.join(data_dir, "laserconfig.json"), "w") as config_file: - json.dump(config_json, config_file) - - return config_file - - -def train_translation_model( - data_dir, - arch, - extra_flags=None, - task="translation", - run_validation=False, - lang_flags=None, - extra_valid_flags=None, - world_size=1, -): - if lang_flags is None: - lang_flags = [ - "--source-lang", - "in", - "--target-lang", - "out", - ] - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - task, - data_dir, - "--save-dir", - data_dir, - "--arch", - arch, - "--optimizer", - "nag", - "--lr", - "0.05", - "--max-tokens", - "500", - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - str(world_size), - "--num-workers", - "0", - ] - + lang_flags - + (extra_flags or []), - ) - - cfg = convert_namespace_to_omegaconf(train_args) - distributed_utils.call_main(cfg, train.main) - - if run_validation: - # test validation - validate_parser = options.get_validation_parser() - validate_args = options.parse_args_and_arch( - validate_parser, - [ - "--task", - task, - data_dir, - "--path", - os.path.join(data_dir, "checkpoint_last.pt"), - "--valid-subset", - "valid", - "--max-tokens", - "500", - "--no-progress-bar", - "--num-workers", - "0", - ] - + lang_flags - + (extra_valid_flags or []), - ) - validate.main(validate_args) - - -def generate_main(data_dir, extra_flags=None, path=None): - if extra_flags is None: - extra_flags = [ - "--print-alignment", - ] - if path is None: - path = os.path.join(data_dir, "checkpoint_last.pt") - generate_parser = options.get_generation_parser() - generate_args = options.parse_args_and_arch( - generate_parser, - [ - data_dir, - "--path", - path, - "--beam", - "3", - "--batch-size", - "64", - "--max-len-b", - "5", - "--gen-subset", - "valid", - "--no-progress-bar", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - - # evaluate model in batch mode - generate.main(generate_args) - - # evaluate model interactively - generate_args.buffer_size = 0 - generate_args.input = "-" - generate_args.batch_size = None - orig_stdin = sys.stdin - sys.stdin = StringIO("h e l l o\n") - interactive.main(generate_args) - sys.stdin = orig_stdin - - -class TestDataset(torch.utils.data.Dataset): - def __init__(self, data): - super().__init__() - self.data = data - self.sizes = None - - def __getitem__(self, index): - return self.data[index] - - def __len__(self): - return len(self.data) - - -class TestTranslationTask(LegacyFairseqTask): - def __init__(self, args, src_dict, tgt_dict, model): - super().__init__(args) - self.src_dict = src_dict - self.tgt_dict = tgt_dict - self.model = model - - @classmethod - def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None): - return cls(args, src_dict, tgt_dict, model) - - def build_model(self, args, from_checkpoint=False): - return TestModel.build_model(args, self) - - @property - def source_dictionary(self): - return self.src_dict - - @property - def target_dictionary(self): - return self.tgt_dict - - -class TestModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, args, task): - encoder = TestEncoder(args, task.source_dictionary) - decoder = TestIncrementalDecoder(args, task.target_dictionary) - return cls(encoder, decoder) - - -class TestEncoder(FairseqEncoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - self.args = args - - def forward(self, src_tokens, src_lengths=None, **kwargs): - return EncoderOut( - encoder_out=src_tokens, - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - def reorder_encoder_out(self, encoder_out, new_order): - return EncoderOut( - encoder_out=encoder_out.encoder_out.index_select(0, new_order), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - -class TestIncrementalDecoder(FairseqIncrementalDecoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - assert hasattr(args, "beam_probs") or hasattr(args, "probs") - args.max_decoder_positions = getattr(args, "max_decoder_positions", 100) - self.args = args - - def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None): - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - bbsz = prev_output_tokens.size(0) - vocab = len(self.dictionary) - src_len = encoder_out.encoder_out.size(1) - tgt_len = prev_output_tokens.size(1) - - # determine number of steps - if incremental_state is not None: - # cache step number - step = utils.get_incremental_state(self, incremental_state, "step") - if step is None: - step = 0 - utils.set_incremental_state(self, incremental_state, "step", step + 1) - steps = [step] - else: - steps = list(range(tgt_len)) - - # define output in terms of raw probs - if hasattr(self.args, "probs"): - assert ( - self.args.probs.dim() == 3 - ), "expected probs to have size bsz*steps*vocab" - probs = self.args.probs.index_select(1, torch.LongTensor(steps)) - else: - probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_() - for i, step in enumerate(steps): - # args.beam_probs gives the probability for every vocab element, - # starting with eos, then unknown, and then the rest of the vocab - if step < len(self.args.beam_probs): - probs[:, i, self.dictionary.eos() :] = self.args.beam_probs[step] - else: - probs[:, i, self.dictionary.eos()] = 1.0 - - # random attention - attn = torch.rand(bbsz, tgt_len, src_len) - - dev = prev_output_tokens.device - return probs.to(dev), {"attn": [attn.to(dev)]} - - def get_normalized_probs(self, net_output, log_probs, _): - # the decoder returns probabilities directly - probs = net_output[0] - if log_probs: - return probs.log() - else: - return probs - - def max_positions(self): - return self.args.max_decoder_positions - - -class TestReshapingEncoder(FairseqEncoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - self.args = args - - def forward(self, src_tokens, src_lengths=None, **kwargs): - b_sz, t_sz = src_tokens.shape - padding_needed = t_sz % 2 - x = src_tokens - if padding_needed > 0: - padding_needed = 2 - padding_needed - x = F.pad(x, (0, padding_needed)) - - return EncoderOut( - encoder_out=x.view(b_sz, -1, 2), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - def reorder_encoder_out(self, encoder_out, new_order): - return EncoderOut( - encoder_out=encoder_out.encoder_out.index_select(0, new_order), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - -class TestReshapingModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, args, task): - encoder = TestReshapingEncoder(args, task.source_dictionary) - decoder = TestIncrementalDecoder(args, task.target_dictionary) - return cls(encoder, decoder) - - -class TestAdditionalInputEncoder(FairseqEncoder): - def __init__(self, args, dictionary): - super().__init__(dictionary) - self.args = args - - def forward(self, src_tokens, src_lengths=None, **kwargs): - assert "fancy_other_input" in kwargs - assert kwargs["fancy_other_input"] is not None - return EncoderOut( - encoder_out=src_tokens, - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - def reorder_encoder_out(self, encoder_out, new_order): - return EncoderOut( - encoder_out=encoder_out.encoder_out.index_select(0, new_order), - encoder_padding_mask=None, - encoder_embedding=None, - encoder_states=None, - src_tokens=None, - src_lengths=None, - ) - - -class TestAdditionalInputModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @classmethod - def build_model(cls, args, task): - encoder = TestAdditionalInputEncoder(args, task.source_dictionary) - decoder = TestIncrementalDecoder(args, task.target_dictionary) - return cls(encoder, decoder) - - def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): - encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) - decoder_out = self.decoder( - prev_output_tokens, encoder_out=encoder_out, **kwargs - ) - return decoder_out - - -def train_language_model( - data_dir, - arch, - extra_flags=None, - run_validation=False, - extra_valid_flags=None, - task="language_modeling", - world_size=1, -): - train_parser = options.get_training_parser() - train_args = options.parse_args_and_arch( - train_parser, - [ - "--task", - task, - data_dir, - "--arch", - arch, - "--optimizer", - "adam", - "--lr", - "0.0001", - "--max-tokens", - "500", - "--tokens-per-sample", - "500", - "--save-dir", - data_dir, - "--max-epoch", - "1", - "--no-progress-bar", - "--distributed-world-size", - str(world_size), - "--ddp-backend", - "no_c10d", - "--num-workers", - "0", - ] - + (extra_flags or []), - ) - cfg = convert_namespace_to_omegaconf(train_args) - distributed_utils.call_main(cfg, train.main) - - if run_validation: - # test validation - validate_parser = options.get_validation_parser() - validate_args = options.parse_args_and_arch( - validate_parser, - [ - "--task", - task, - data_dir, - "--path", - os.path.join(data_dir, "checkpoint_last.pt"), - "--valid-subset", - "valid", - "--max-tokens", - "500", - "--no-progress-bar", - "--num-workers", - "0", - ] - + (extra_valid_flags or []), - ) - validate.main(validate_args) - - -def sizes(data): - return [len(sentence) for sentence in data] - - -POPULATION = string.ascii_letters + string.digits - - -def make_sentence() -> tp.List[str]: - length = random.randint(10, 50) - return random.choices( - population=POPULATION, k=length, weights=range(1, len(POPULATION) + 1) - ) - - -def make_data(length=1000, out_file=None) -> tp.List[tp.List[str]]: - data = ( - [make_sentence() for _ in range(0, length)] - # add all the symbols at least once - + [list(string.ascii_letters), list(string.digits)] - ) - if out_file is not None: - with open(out_file, "w", encoding="utf-8") as out: - for s in data: - print(" ".join(s), file=out) - - return data - - -def build_vocab(data: tp.List[tp.List[str]]) -> Dictionary: - d = Dictionary() - for s in data: - for token in s: - d.add_symbol(token) - d.finalize() - return d diff --git a/audio/safeear/safeear_repo/fairseq_ours/train.py b/audio/safeear/safeear_repo/fairseq_ours/train.py deleted file mode 100644 index fc82901103cba692ccca533e273d57d21611b11e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/train.py +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Legacy entry point. Use fairseq_cli/train.py or fairseq-train instead. -""" - -from fairseq_cli.train import cli_main - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/requirements.txt b/audio/safeear/safeear_repo/requirements.txt deleted file mode 100644 index 581bfa7667488cc011bb2a83670b4447468a3937..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/requirements.txt +++ /dev/null @@ -1,123 +0,0 @@ -absl-py==2.1.0 -aiohttp==3.9.0 -aiosignal==1.3.1 -antlr4-python3-runtime==4.8 -appdirs==1.4.4 -asttokens==2.4.1 -async-timeout==4.0.3 -attrs==23.1.0 -audioread==3.0.1 -bitarray==2.8.3 -blessed==1.20.0 -certifi==2022.12.7 -cffi==1.16.0 -charset-normalizer==2.1.1 -click==8.1.7 -cmake==3.25.0 -colorama==0.4.6 -contourpy==1.2.0 -cycler==0.12.1 -Cython==3.0.5 -decorator==5.1.1 -docker-pycreds==0.4.0 -einops==0.7.0 -exceptiongroup==1.2.0 -executing==2.0.1 -# Editable install with no version control (fairseq==1.0.0a0) --e fairseq_ours -fast-bss-eval==0.1.4 -filelock==3.9.0 -fonttools==4.45.0 -frozenlist==1.4.0 -fsspec==2023.10.0 -gitdb==4.0.11 -GitPython==3.1.40 -gpustat==1.1.1 -grpcio==1.63.0 -huggingface-hub==0.19.4 -hydra-core==1.0.7 -idna==3.4 -importlib-resources==6.1.1 -importlib_metadata==7.1.0 -ipdb==0.13.13 -ipython==8.18.1 -jedi==0.19.1 -Jinja2==3.1.2 -joblib==1.4.2 -kiwisolver==1.4.5 -lazy_loader==0.4 -librosa==0.10.2 -lightning-utilities==0.10.0 -lit==15.0.7 -llvmlite==0.42.0 -lxml==4.9.3 -Markdown==3.6 -markdown-it-py==3.0.0 -MarkupSafe==2.1.3 -matplotlib==3.8.2 -matplotlib-inline==0.1.6 -mdurl==0.1.2 -mpmath==1.3.0 -msgpack==1.0.8 -multidict==6.0.4 -networkx==3.0 -numba==0.59.1 -numpy==1.23.5 -nvidia-ml-py==12.535.133 -opencv-python==4.9.0.80 -packaging==23.2 -parso==0.8.3 -pexpect==4.9.0 -Pillow==9.3.0 -platformdirs==4.2.1 -pooch==1.8.1 -portalocker==2.8.2 -prompt-toolkit==3.0.43 -protobuf==4.25.1 -psutil==5.9.6 -ptyprocess==0.7.0 -pure-eval==0.2.2 -pycparser==2.21 -pyDeprecate==0.3.2 -Pygments==2.17.2 -pyparsing==3.1.1 -python-dateutil==2.8.2 -pytorch-lightning==1.6.3 -pytorch-ranger==0.1.1 -PyYAML==6.0.1 -regex==2023.10.3 -requests==2.28.1 -rich==13.7.0 -sacrebleu==2.3.2 -safetensors==0.4.0 -scikit-learn==1.4.2 -scipy==1.11.4 -sentry-sdk==1.36.0 -setproctitle==1.3.3 -six==1.16.0 -smmap==5.0.1 -soundfile>=0.11.0 -soxr==0.3.7 -stack-data==0.6.3 -sympy==1.12 -tabulate==0.9.0 -tensorboard==2.16.2 -tensorboard-data-server==0.7.2 -thop==0.1.1.post2209072238 -threadpoolctl==3.5.0 -timm==0.9.11 -tomli==2.0.1 -torch-mir-eval==0.4 -torch-optimizer==0.3.0 -torchmetrics==1.2.0 -tqdm==4.66.1 -traitlets==5.14.1 -triton==2.0.0 -typing_extensions==4.4.0 -urllib3==1.26.13 -wandb==0.16.0 -wcwidth==0.2.12 -Werkzeug==3.0.2 -yarl==1.9.3 -zipp==3.17.0 -npy_append_array==0.9.16 \ No newline at end of file diff --git a/audio/safeear/safeear_repo/safeear/datas/__init__.py b/audio/safeear/safeear_repo/safeear/datas/__init__.py deleted file mode 100644 index afcdba91d00a3af1427f1fbec06727cc087cdc78..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/datas/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .asvspoof19 import ASVSppof2019 -from .asvspoof21 import ASVSppof2021 - -__all__ = [ - "ASVSppof2019", - "ASVSppof2021", -] \ No newline at end of file diff --git a/audio/safeear/safeear_repo/safeear/datas/asvspoof19.py b/audio/safeear/safeear_repo/safeear/datas/asvspoof19.py deleted file mode 100644 index e90f218b6b2b4d047d698e82af6c46dffc3771b2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/datas/asvspoof19.py +++ /dev/null @@ -1,284 +0,0 @@ -import glob -import os -import random -from pathlib import Path - -import numpy as np -import torch -import torchaudio -import torchaudio.functional -from pytorch_lightning import LightningDataModule -from torch.utils.data import DataLoader, Dataset - - -def get_path_iterator(tsv): - """ - Get the root path and list of file lines from the TSV file. - - Args: - tsv (str): Path to the TSV file. - - Returns: - tuple: Root path and list of file lines. - """ - with open(tsv, "r") as f: - root = f.readline().rstrip() - lines = [line.rstrip() for line in f] - return root, lines - -def load_feature(feat_path): - """ - Load feature from the specified path. - - Args: - feat_path (str): Path to the feature file. - - Returns: - np.ndarray: Loaded feature. - """ - feat = np.load(feat_path, mmap_mode="r") - return feat - -class ASVSppof2019(Dataset): - def __init__(self, tsv_path, protocol_path, feat_dir, max_len=64600, is_train=True): - """ - Initialize the dataset with paths and parameters. - - Args: - tsv_path (str): Path to the TSV file containing data paths. - protocol_path (str): Path to the protocol file containing metadata. - feat_dir (str): Directory where features are stored. - max_len (int): Maximum length of audio data in samples. - is_train (bool): Flag indicating if the dataset is for training. - """ - super().__init__() - root, self.lines = get_path_iterator(tsv_path) - self.feat_dir = Path(feat_dir) - _, self.sr = torchaudio.load(root + "/" + self.lines[0].split('\t')[0]) - self.max_len = max_len - self.is_train = is_train - self.root = Path(root) - - with open(protocol_path) as file: - meta_infos = file.readlines() - self.meta_infos = meta_infos - self.mapping = { - meta_info.replace('\n', '').split(' ')[1]: meta_info.replace('\n', '').split(' ')[-1] - for meta_info in meta_infos - } - - def __len__(self): - """ - Return the total number of samples in the dataset. - """ - return len(self.lines) - - def __getitem__(self, index): - """ - Retrieve an item from the dataset at the specified index. - - Args: - index (int): Index of the item to retrieve. - - Returns: - tuple: A tuple containing audio data, Hubert features, and the target label. - """ - feat_duration = self.max_len // 320 - - relative_path = Path(self.lines[index].split('\t')[0]) - feat_path = self.feat_dir / relative_path - audio_path = self.root / relative_path - audio = torchaudio.load(str(audio_path))[0] - avg_hubert_feat = torch.tensor(load_feature(feat_path.with_suffix(".npy"))) - waveform_info = self.mapping[self.lines[index].split('.')[0]] - target = 1 if waveform_info == 'spoof' else 0 - - if avg_hubert_feat.ndim == 3: - avg_hubert_feat = avg_hubert_feat.permute(2, 1, 0).squeeze(1) # [T,1,768] -> [1,T,768] - else: - avg_hubert_feat = avg_hubert_feat.permute(1, 0) # [T,768] -> [768, T] - - if self.is_train and audio.shape[1] > self.max_len: - st = random.randint(0, audio.shape[1] - self.max_len - 1) - feat_st = st // 320 - ed = st + self.max_len - if avg_hubert_feat[:, feat_st:feat_st + feat_duration].shape[1] < feat_duration: - avg_hubert_feat = avg_hubert_feat[:, feat_st:feat_st + feat_duration] - avg_hubert_feat = torch.nn.functional.pad(avg_hubert_feat, (0, feat_duration - avg_hubert_feat.shape[1]), "constant", 0) - return audio[:, st:ed], avg_hubert_feat, target - else: - return audio[:, st:ed], avg_hubert_feat[:, feat_st:feat_st + feat_duration], target - - if self.is_train == False and audio.shape[1] > self.max_len: - st = 0 - feat_st = 0 - ed = st + self.max_len - if avg_hubert_feat[:, feat_st:feat_st + feat_duration].shape[1] < feat_duration: - avg_hubert_feat = avg_hubert_feat[:, feat_st:feat_st + feat_duration] - avg_hubert_feat = torch.nn.functional.pad(avg_hubert_feat, (0, feat_duration - avg_hubert_feat.shape[1]), "constant", 0) - return audio[:, st:ed], avg_hubert_feat, target - else: - return audio[:, st:ed], avg_hubert_feat[:, feat_st:feat_st + feat_duration], target, str(audio_path) - - if audio.shape[1] < self.max_len: - audio_pad_length = self.max_len - audio.shape[1] - audio = torch.nn.functional.pad(audio, (0, audio_pad_length), "constant", 0) - - if avg_hubert_feat.shape[1] < feat_duration: - avg_hubert_feat = torch.nn.functional.pad(avg_hubert_feat, (0, feat_duration - avg_hubert_feat.shape[1]), "constant", 0) - - if self.is_train == False: - return audio, avg_hubert_feat, target, str(audio_path) - - return audio, avg_hubert_feat, target - -def pad_sequence(batch): - """ - Pad a sequence of tensors to have the same length. - - Args: - batch (list of Tensors): List of tensors to pad. - - Returns: - Tensor: Padded tensor with shape (batch_size, max_length, feature_dim). - """ - batch = [item.permute(1, 0) for item in batch] # Change shape from (feature_dim, length) to (length, feature_dim) - batch = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=0.0) # Pad sequences - return batch.permute(0, 2, 1) # Change shape back to (batch_size, feature_dim, max_length) - -def collate_fn(batch): - """ - Collate function to combine multiple samples into a batch. - - Args: - batch (list of tuples): Each tuple contains (wav, feat, target). - - Returns: - tuple: Batch of wavs, feats, and targets. - """ - wavs = [] - feats = [] - targets = [] - for wav, feat, target in batch: - wavs.append(wav) - feats.append(feat) - targets.append(target) - - wavs = pad_sequence(wavs) # Pad wavs to the same length - feats = pad_sequence(feats).permute(0, 2, 1) # Pad feats and permute to (batch_size, max_length, feature_dim) - return wavs, feats, torch.tensor(targets).long() # Convert targets to tensor - -class DataClass: - def __init__( - self, - train_path, - val_path, - test_path, - max_len=64600, - ) -> None: - - super().__init__() - - self.train_path = train_path - self.val_path = val_path - self.test_path = test_path - self.max_len = max_len - - # Get different datasets - self.train = ASVSppof2019( - self.train_path[0], - self.train_path[1], - self.train_path[2], - self.max_len, - is_train=True - ) - self.val = ASVSppof2019( - self.val_path[0], - self.val_path[1], - self.val_path[2], - self.max_len, - is_train=True - ) - self.test = ASVSppof2019( - self.test_path[0], - self.test_path[1], - self.test_path[2], - self.max_len, - is_train=False - ) - def __call__(self, mode: str) -> ASVSppof2019: - """Get dataset for a given mode. - - Args: - ---- - mode (str): Mode of the dataset. - - Returns: - ------- - ASVSppof2019: Dataset for the given mode. - - """ - if mode == "train": - return self.train - elif mode == "val": - return self.val - elif mode == "test": - return self.test - else: - raise ValueError(f"Unknown mode: {mode}.") - -class DataModule(LightningDataModule): - def __init__(self, DataClass_dict, batch_size, num_workers, pin_memory): - super().__init__() - self.save_hyperparameters(logger=False) - DataClass_dict.pop("_target_") - self.dataset_select = DataClass(**DataClass_dict) - - self.data_train: Dataset = None - self.data_val: Dataset = None - self.data_test: Dataset = None - - def setup(self, stage = None) -> None: - """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. - - This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and - `trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after - `self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to - `self.setup()` once the data is prepared and available for use. - - :param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``. - """ - # load and split datasets only if not loaded already - if not self.data_train and not self.data_val and not self.data_test: - self.data_train = self.dataset_select("train") - self.data_val = self.dataset_select("val") - self.data_test = self.dataset_select("test") - - def train_dataloader(self) -> DataLoader: - return DataLoader( - self.data_train, - batch_size=self.hparams.batch_size, - num_workers=self.hparams.num_workers, - pin_memory=self.hparams.pin_memory, - shuffle=True, - collate_fn=collate_fn - ) - - def val_dataloader(self) -> DataLoader: - return DataLoader( - self.data_val, - batch_size=self.hparams.batch_size, - num_workers=self.hparams.num_workers, - pin_memory=self.hparams.pin_memory, - shuffle=False, - collate_fn=collate_fn - ) - - def test_dataloader(self) -> DataLoader: - return DataLoader( - self.data_test, - batch_size=self.hparams.batch_size, - num_workers=self.hparams.num_workers, - pin_memory=self.hparams.pin_memory, - shuffle=False, - ) diff --git a/audio/safeear/safeear_repo/safeear/datas/asvspoof21.py b/audio/safeear/safeear_repo/safeear/datas/asvspoof21.py deleted file mode 100644 index e82ec716191bcfce4995996a263d3d421fd80eb9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/datas/asvspoof21.py +++ /dev/null @@ -1,299 +0,0 @@ -import glob -import os -import random -from pathlib import Path - -import numpy as np -import torch -import torchaudio -import torchaudio.functional -from pytorch_lightning import LightningDataModule -from torch.utils.data import DataLoader, Dataset - - -def get_path_iterator(tsv): - """ - Get the root path and list of file lines from the TSV file. - - Args: - tsv (str): Path to the TSV file. - - Returns: - tuple: Root path and list of file lines. - """ - with open(tsv, "r") as f: - root = f.readline().rstrip() - lines = [line.rstrip() for line in f] - return root, lines - -def load_feature(feat_path): - """ - Load feature from the specified path. - - Args: - feat_path (str): Path to the feature file. - - Returns: - np.ndarray: Loaded feature. - """ - feat = np.load(feat_path, mmap_mode="r") - return feat - - -class ASVSppof2021(Dataset): - def __init__(self, tsv_path, protocol_path, feat_dir, max_len=64600, is_train=True, codec=True): - """ - Initialize the dataset with paths and parameters. - - Args: - tsv_path (str): Path to the TSV file containing data paths. - protocol_path (str): Path to the protocol file containing metadata. - feat_dir (str): Directory where features are stored. - max_len (int): Maximum length of audio data in samples. - is_train (bool): Flag indicating if the dataset is for training. - codec (bool): Flag indicating if audio codec transformations should be applied. - """ - super().__init__() - root, self.lines = get_path_iterator(tsv_path) - self.feat_dir = Path(feat_dir) - _, self.sr = torchaudio.load(root + "/" + self.lines[0].split('\t')[0]) - self.max_len = max_len - self.is_train = is_train - self.codec = codec - self.root = Path(root) - - with open(protocol_path) as file: - meta_infos = file.readlines() - self.meta_infos = meta_infos - self.mapping = { - meta_info.replace('\n', '').split(' ')[1]: meta_info.replace('\n', '').split(' ')[-1] - for meta_info in meta_infos - } - - self.formated_lines = ["wav", "mp3", "ogg", "vorbis", "amr-nb", "amb", "flac", "sph", "gsm", "htk"] - - def __len__(self): - """ - Return the total number of samples in the dataset. - """ - return len(self.lines) - - def __getitem__(self, index): - """ - Retrieve an item from the dataset at the specified index. - - Args: - index (int): Index of the item to retrieve. - - Returns: - tuple: A tuple containing audio data, Hubert features, and the target label. - """ - feat_duration = self.max_len // 320 - relative_path = Path(self.lines[index].split('\t')[0]) - feat_path = self.feat_dir / relative_path - audio_path = self.root / relative_path - audio, sr = torchaudio.load(str(audio_path)) - - if random.random() < 0.5 and self.codec: - # Apply codec transformation - format_select = random.choice(self.formated_lines) - if format_select == "gsm": - audio = torchaudio.functional.resample(audio, sr, 8000) - audio = torchaudio.functional.apply_codec(audio, 8000, format_select) - audio = torchaudio.functional.resample(audio, 8000, sr) - else: - audio = torchaudio.functional.apply_codec(audio, sr, format_select) - - avg_hubert_feat = torch.tensor(load_feature(feat_path.with_suffix(".npy"))) - waveform_info = self.mapping[self.lines[index].split('.')[0]] - target = 1 if waveform_info == 'spoof' else 0 - - if avg_hubert_feat.ndim == 3: - avg_hubert_feat = avg_hubert_feat.permute(2, 1, 0).squeeze(1) # [T,1,768] -> [1,T,768] - else: - avg_hubert_feat = avg_hubert_feat.permute(1, 0) # [T,768] -> [768, T] - - if self.is_train and audio.shape[1] > self.max_len: - st = random.randint(0, audio.shape[1] - self.max_len - 1) - feat_st = st // 320 - ed = st + self.max_len - if avg_hubert_feat[:, feat_st:feat_st + feat_duration].shape[1] < feat_duration: - avg_hubert_feat = avg_hubert_feat[:, feat_st:feat_st + feat_duration] - avg_hubert_feat = torch.nn.functional.pad(avg_hubert_feat, (0, feat_duration - avg_hubert_feat.shape[1]), "constant", 0) - return audio[:, st:ed], avg_hubert_feat, target - else: - return audio[:, st:ed], avg_hubert_feat[:, feat_st:feat_st + feat_duration], target - - if self.is_train == False and audio.shape[1] > self.max_len: - st = 0 - feat_st = 0 - ed = st + self.max_len - if avg_hubert_feat[:, feat_st:feat_st + feat_duration].shape[1] < feat_duration: - avg_hubert_feat = avg_hubert_feat[:, feat_st:feat_st + feat_duration] - avg_hubert_feat = torch.nn.functional.pad(avg_hubert_feat, (0, feat_duration - avg_hubert_feat.shape[1]), "constant", 0) - return audio[:, st:ed], avg_hubert_feat, target, str(audio_path) - else: - return audio[:, st:ed], avg_hubert_feat[:, feat_st:feat_st + feat_duration], target, str(audio_path) - - if self.is_train and audio.shape[1] < self.max_len: - audio_pad_length = self.max_len - audio.shape[1] - audio = torch.nn.functional.pad(audio, (0, audio_pad_length), "constant", 0) - - if self.is_train and avg_hubert_feat.shape[1] < feat_duration: - avg_hubert_feat = torch.nn.functional.pad(avg_hubert_feat, (0, feat_duration - avg_hubert_feat.shape[1]), "constant", 0) - - if self.is_train == False: - return audio, avg_hubert_feat, target, str(audio_path) - - return audio, avg_hubert_feat, target - -def pad_sequence(batch): - """ - Pad a sequence of tensors to have the same length. - - Args: - batch (list of Tensors): List of tensors to pad. - - Returns: - Tensor: Padded tensor with shape (batch_size, max_length, feature_dim). - """ - batch = [item.permute(1, 0) for item in batch] # Change shape from (feature_dim, length) to (length, feature_dim) - batch = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=0.0) # Pad sequences - return batch.permute(0, 2, 1) # Change shape back to (batch_size, feature_dim, max_length) - -def collate_fn(batch): - """ - Collate function to combine multiple samples into a batch. - - Args: - batch (list of tuples): Each tuple contains (wav, feat, target). - - Returns: - tuple: Batch of wavs, feats, and targets. - """ - wavs = [] - feats = [] - targets = [] - for wav, feat, target in batch: - wavs.append(wav) - feats.append(feat) - targets.append(target) - - wavs = pad_sequence(wavs) # Pad wavs to the same length - feats = pad_sequence(feats).permute(0, 2, 1) # Pad feats and permute to (batch_size, max_length, feature_dim) - return wavs, feats, torch.tensor(targets).long() # Convert targets to tensor - -class DataClass: - def __init__( - self, - train_path, - val_path, - test_path, - max_len=64600, - ) -> None: - - super().__init__() - - self.train_path = train_path - self.val_path = val_path - self.test_path = test_path - self.max_len = max_len - - # Get different datasets - self.train = ASVSppof2021( - self.train_path[0], - self.train_path[1], - self.train_path[2], - self.max_len, - is_train=True - ) - self.val = ASVSppof2021( - self.val_path[0], - self.val_path[1], - self.val_path[2], - self.max_len, - is_train=True - ) - self.test = ASVSppof2021( - self.test_path[0], - self.test_path[1], - self.test_path[2], - is_train=False, - codec=False - ) - def __call__(self, mode: str) -> ASVSppof2021: - """Get dataset for a given mode. - - Args: - ---- - mode (str): Mode of the dataset. - - Returns: - ------- - ASVSppof2019: Dataset for the given mode. - - """ - if mode == "train": - return self.train - elif mode == "val": - return self.val - elif mode == "test": - return self.test - else: - raise ValueError(f"Unknown mode: {mode}.") - -class DataModule(LightningDataModule): - def __init__(self, DataClass_dict, batch_size, num_workers, pin_memory): - super().__init__() - self.save_hyperparameters(logger=False) - DataClass_dict.pop("_target_") - self.dataset_select = DataClass(**DataClass_dict) - - self.data_train: Dataset = None - self.data_val: Dataset = None - self.data_test: Dataset = None - - def setup(self, stage = None) -> None: - """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`. - - This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and - `trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after - `self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to - `self.setup()` once the data is prepared and available for use. - - :param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``. - """ - # load and split datasets only if not loaded already - if not self.data_train and not self.data_val and not self.data_test: - self.data_train = self.dataset_select("train") - self.data_val = self.dataset_select("val") - self.data_test = self.dataset_select("test") - - def train_dataloader(self) -> DataLoader: - return DataLoader( - self.data_train, - batch_size=self.hparams.batch_size, - num_workers=self.hparams.num_workers, - pin_memory=self.hparams.pin_memory, - shuffle=True, - collate_fn=collate_fn - ) - - def val_dataloader(self) -> DataLoader: - return DataLoader( - self.data_val, - batch_size=self.hparams.batch_size, - num_workers=self.hparams.num_workers, - pin_memory=self.hparams.pin_memory, - shuffle=False, - collate_fn=collate_fn - ) - - def test_dataloader(self) -> DataLoader: - return DataLoader( - self.data_test, - batch_size=self.hparams.batch_size, - num_workers=self.hparams.num_workers, - pin_memory=self.hparams.pin_memory, - shuffle=False, - ) diff --git a/audio/safeear/safeear_repo/safeear/losses/loss.py b/audio/safeear/safeear_repo/safeear/losses/loss.py deleted file mode 100644 index 2a0d6ffaafdfd5a8764a3cc773bb90cfa52cf737..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/losses/loss.py +++ /dev/null @@ -1,216 +0,0 @@ -import numpy as np -import torch -import torch.nn.functional as F -from torchaudio.transforms import MelSpectrogram - - -def adversarial_g_loss(y_disc_gen): - """Hinge loss""" - loss = 0.0 - for i in range(len(y_disc_gen)): - stft_loss = F.relu(1 - y_disc_gen[i]).mean().squeeze() - loss += stft_loss - return loss / len(y_disc_gen) - - -def feature_loss(fmap_r, fmap_gen): - loss = 0.0 - for i in range(len(fmap_r)): - for j in range(len(fmap_r[i])): - stft_loss = ((fmap_r[i][j] - fmap_gen[i][j]).abs() / - (fmap_r[i][j].abs().mean())).mean() - loss += stft_loss - return loss / (len(fmap_r) * len(fmap_r[0])) - - -def sim_loss(y_disc_r, y_disc_gen): - loss = 0.0 - for i in range(len(y_disc_r)): - loss += F.mse_loss(y_disc_r[i], y_disc_gen[i]) - return loss / len(y_disc_r) - -def reconstruction_loss(x, G_x, lamdba_wav=100, sr=16000, eps=1e-7): - # NOTE (lsx): hard-coded now - L = lamdba_wav * F.mse_loss(x, G_x) # wav L1 loss - # loss_sisnr = sisnr_loss(G_x, x) # - # L += 0.01*loss_sisnr - # 2^6=64 -> 2^10=1024 - # NOTE (lsx): add 2^11 - for i in range(6, 12): - # for i in range(5, 12): # Encodec setting - s = 2**i - melspec = MelSpectrogram( - sample_rate=sr, - n_fft=s, - hop_length=s // 4, - n_mels=64, - wkwargs={"device": x.device}).to(x.device) - S_x = melspec(x) - S_G_x = melspec(G_x) - loss = ((S_x - S_G_x).abs().mean() + ( - ((torch.log(S_x.abs() + eps) - torch.log(S_G_x.abs() + eps))**2 - ).mean(dim=-2)**0.5).mean()) / i - L += loss - return L - - -def criterion_d(y_disc_r, y_disc_gen, fmap_r_det, fmap_gen_det, y_df_hat_r, - y_df_hat_g, fmap_f_r, fmap_f_g, y_ds_hat_r, y_ds_hat_g, - fmap_s_r, fmap_s_g): - """Hinge Loss""" - loss = 0.0 - loss1 = 0.0 - loss2 = 0.0 - loss3 = 0.0 - for i in range(len(y_disc_r)): - loss1 += F.relu(1 - y_disc_r[i]).mean() + F.relu(1 + y_disc_gen[ - i]).mean() - for i in range(len(y_df_hat_r)): - loss2 += F.relu(1 - y_df_hat_r[i]).mean() + F.relu(1 + y_df_hat_g[ - i]).mean() - for i in range(len(y_ds_hat_r)): - loss3 += F.relu(1 - y_ds_hat_r[i]).mean() + F.relu(1 + y_ds_hat_g[ - i]).mean() - - loss = (loss1 / len(y_disc_gen) + loss2 / len(y_df_hat_r) + loss3 / - len(y_ds_hat_r)) / 3.0 - - return loss - - -def criterion_g(commit_loss, x, G_x, fmap_r, fmap_gen, y_disc_r, y_disc_gen, - y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g, y_ds_hat_r, - y_ds_hat_g, fmap_s_r, fmap_s_g, lamdba_wav=100, lamdba_com=1000, lamdba_adv=1, lamdba_feat=1, lamdba_rec=1, sr=16000): - adv_g_loss = adversarial_g_loss(y_disc_gen) - feat_loss = (feature_loss(fmap_r, fmap_gen) + sim_loss( - y_disc_r, y_disc_gen) + feature_loss(fmap_f_r, fmap_f_g) + sim_loss( - y_df_hat_r, y_df_hat_g) + feature_loss(fmap_s_r, fmap_s_g) + - sim_loss(y_ds_hat_r, y_ds_hat_g)) / 3.0 - rec_loss = reconstruction_loss(x.contiguous(), G_x.contiguous(), lamdba_wav, sr) - total_loss = lamdba_com * commit_loss + lamdba_adv * adv_g_loss + lamdba_feat * feat_loss + lamdba_rec * rec_loss - return total_loss, adv_g_loss, feat_loss, rec_loss - - -def adopt_weight(weight, global_step, threshold=0, value=0.): - if global_step < threshold: - weight = value - return weight - - -def adopt_dis_weight(weight, global_step, threshold=0, value=0.): - # 0,3,6,9,13....这些时间步，不更新dis - if global_step % 3 == 0: - weight = value - return weight - - -def calculate_adaptive_weight(nll_loss, g_loss, last_layer, lamdba_adv=1): - if last_layer is not None: - nll_grads = torch.autograd.grad( - nll_loss, last_layer, retain_graph=True)[0] - g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] - else: - print('last_layer cannot be none') - assert 1 == 2 - d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) - d_weight = torch.clamp(d_weight, 1.0, 1.0).detach() - d_weight = d_weight * lamdba_adv - return d_weight - -def loss_g(codebook_loss, - inputs, - reconstructions, - fmap_r, - fmap_gen, - y_disc_r, - y_disc_gen, - global_step, - y_df_hat_r, - y_df_hat_g, - y_ds_hat_r, - y_ds_hat_g, - fmap_f_r, - fmap_f_g, - fmap_s_r, - fmap_s_g, - lamdba_wav=100, - lamdba_com=1000, - lamdba_adv=1, - lamdba_feat=1, - sr=16000, - discriminator_iter_start=500 - ): - """ - args: - codebook_loss: commit loss. - inputs: ground-truth wav. - reconstructions: reconstructed wav. - fmap_r: real stft-D feature map. - fmap_gen: fake stft-D feature map. - y_disc_r: real stft-D logits. - y_disc_gen: fake stft-D logits. - global_step: global training step. - y_df_hat_r: real MPD logits. - y_df_hat_g: fake MPD logits. - y_ds_hat_r: real MSD logits. - y_ds_hat_g: fake MSD logits. - fmap_f_r: real MPD feature map. - fmap_f_g: fake MPD feature map. - fmap_s_r: real MSD feature map. - fmap_s_g: fake MSD feature map. - """ - rec_loss = reconstruction_loss(inputs.contiguous(), - reconstructions.contiguous(), lamdba_wav, sr) - adv_g_loss = adversarial_g_loss(y_disc_gen) - adv_mpd_loss = adversarial_g_loss(y_df_hat_g) - adv_msd_loss = adversarial_g_loss(y_ds_hat_g) - adv_loss = (adv_g_loss + adv_mpd_loss + adv_msd_loss - ) / 3.0 # NOTE(lsx): need to divide by 3? - feat_loss = feature_loss( - fmap_r, - fmap_gen) #+ sim_loss(y_disc_r, y_disc_gen) # NOTE(lsx): need logits? - feat_loss_mpd = feature_loss(fmap_f_r, - fmap_f_g) #+ sim_loss(y_df_hat_r, y_df_hat_g) - feat_loss_msd = feature_loss(fmap_s_r, - fmap_s_g) #+ sim_loss(y_ds_hat_r, y_ds_hat_g) - feat_loss_tot = (feat_loss + feat_loss_mpd + feat_loss_msd) / 3.0 - d_weight = torch.tensor(1.0) - disc_factor = adopt_weight( - lamdba_adv, global_step, threshold=discriminator_iter_start) - if disc_factor == 0.: - fm_loss_wt = 0 - else: - fm_loss_wt = lamdba_feat - loss = rec_loss + d_weight * disc_factor * adv_loss + \ - fm_loss_wt * feat_loss_tot + lamdba_com * codebook_loss - return loss, rec_loss, adv_loss, feat_loss_tot, d_weight - -def compute_det_curve(target_scores, nontarget_scores): - - n_scores = target_scores.size + nontarget_scores.size - all_scores = np.concatenate((target_scores, nontarget_scores)) - labels = np.concatenate((np.ones(target_scores.size), np.zeros(nontarget_scores.size))) - - # Sort labels based on scores - indices = np.argsort(all_scores, kind='mergesort') - labels = labels[indices] - - # Compute false rejection and false acceptance rates - tar_trial_sums = np.cumsum(labels) - nontarget_trial_sums = nontarget_scores.size - (np.arange(1, n_scores + 1) - tar_trial_sums) - - frr = np.concatenate((np.atleast_1d(0), tar_trial_sums / target_scores.size)) # false rejection rates - far = np.concatenate((np.atleast_1d(1), nontarget_trial_sums / nontarget_scores.size)) # false acceptance rates - thresholds = np.concatenate((np.atleast_1d(all_scores[indices[0]] - 0.001), all_scores[indices])) # Thresholds are the sorted scores - - return frr, far, thresholds - - -def compute_eer(target_scores, nontarget_scores): - """ Returns equal error rate (EER) and the corresponding threshold. """ - frr, far, thresholds = compute_det_curve(target_scores, nontarget_scores) - abs_diffs = np.abs(frr - far) - min_index = np.argmin(abs_diffs) - eer = np.mean((frr[min_index], far[min_index])) - print(thresholds[min_index]) - return eer, thresholds[min_index] \ No newline at end of file diff --git a/audio/safeear/safeear_repo/safeear/models/decouple.py b/audio/safeear/safeear_repo/safeear/models/decouple.py deleted file mode 100644 index b9b9fbb1a211811028dc88d2ca524d9a421edb82..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/decouple.py +++ /dev/null @@ -1,207 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Aug 30 15:47:55 2023 -@author: zhangxin -""" -import torch -import torch.nn as nn -from einops import rearrange - -from .modules.quantization import ResidualVectorQuantizer -from .modules.seanet import SEANetDecoder, SEANetEncoder - - -class SpeechTokenizer(nn.Module): - def __init__(self, n_filters, dimension, strides, lstm_layers, bidirectional, dilation_base, residual_kernel_size, n_residual_layers, activation, sample_rate, n_q, semantic_dimension, codebook_size): - ''' - - Parameters - ---------- - n_filters : int - Number of filters in the SEANet encoder/decoder. - dimension : int - Dimensionality of the encoder/decoder. - strides : list - List of stride values for the SEANet encoder/decoder. - lstm_layers : int - Number of LSTM layers in the encoder/decoder. - bidirectional : bool - Whether to use bidirectional LSTM in the encoder. - dilation_base : int - Base dilation rate for the residual blocks in the encoder/decoder. - residual_kernel_size : int - Kernel size for the residual blocks in the encoder/decoder. - n_residual_layers : int - Number of residual layers in the encoder/decoder. - activation : str - Activation function to use in the encoder/decoder. - sample_rate : int - Sample rate of the audio. - n_q : int - Number of quantization levels. - semantic_dimension : int - Dimensionality of the semantic representation. - codebook_size : int - Size of the codebook for vector quantization. - - ''' - super().__init__() - self.encoder = SEANetEncoder(n_filters=n_filters, - dimension=dimension, - ratios=strides, - lstm=lstm_layers, - bidirectional=bidirectional, - dilation_base=dilation_base, - residual_kernel_size=residual_kernel_size, - n_residual_layers=n_residual_layers, - activation=activation) - self.sample_rate = sample_rate - self.n_q = n_q - if dimension != semantic_dimension: - self.transform = nn.Linear(dimension, semantic_dimension) - else: - self.transform = nn.Identity() - self.quantizer = ResidualVectorQuantizer(dimension=dimension, n_q=n_q, bins=codebook_size) - self.decoder = SEANetDecoder(n_filters=n_filters, - dimension=dimension, - ratios=strides, - lstm=lstm_layers, - bidirectional=False, - dilation_base=dilation_base, - residual_kernel_size=residual_kernel_size, - n_residual_layers=n_residual_layers, - activation=activation) - - @classmethod - def load_from_checkpoint(cls, - config_path: str, - ckpt_path: str): - ''' - - Parameters - ---------- - config_path : str - Path of model configuration file. - ckpt_path : str - Path of model checkpoint. - - Returns - ------- - model : SpeechTokenizer - SpeechTokenizer model. - - ''' - import json - with open(config_path) as f: - cfg = json.load(f) - model = cls(cfg) - params = torch.load(ckpt_path, map_location='cpu') - model.load_state_dict(params) - return model - - - def forward(self, - x: torch.tensor, - n_q: int=None, - layers: list=[0]): - ''' - - Parameters - ---------- - x : torch.tensor - Input wavs. Shape: (batch, channels, timesteps). - n_q : int, optional - Number of quantizers in RVQ used to encode. The default is all layers. - layers : list[int], optional - Layers of RVQ should return quantized result. The default is the first layer. - - Returns - ------- - o : torch.tensor - Output wavs. Shape: (batch, channels, timesteps). - commit_loss : torch.tensor - Commitment loss from residual vector quantizers. - feature : torch.tensor - Output of RVQ's first layer. Shape: (batch, timesteps, dimension) - - ''' - n_q = n_q if n_q else self.n_q - e = self.encoder(x) - quantized, codes, commit_loss, quantized_list = self.quantizer(e, n_q=n_q, layers=layers) - feature = rearrange(quantized_list[0], 'b d t -> b t d') # b,t,1024 - feature = self.transform(feature) #b,t,768 - o = self.decoder(quantized) - return o, commit_loss, feature, quantized_list[1:] - - def forward_feature(self, - x: torch.tensor, - layers: list=None): - ''' - - Parameters - ---------- - x : torch.tensor - Input wavs. Shape should be (batch, channels, timesteps). - layers : list[int], optional - Layers of RVQ should return quantized result. The default is all layers. - - Returns - ------- - quantized_list : list[torch.tensor] - Quantized of required layers. - - ''' - e = self.encoder(x) - layers = layers if layers else list(range(self.n_q)) - quantized, codes, commit_loss, quantized_list = self.quantizer(e, layers=layers) - return quantized_list - - def encode(self, - x: torch.tensor, - n_q: int=None, - st: int=None): - ''' - - Parameters - ---------- - x : torch.tensor - Input wavs. Shape: (batch, channels, timesteps). - n_q : int, optional - Number of quantizers in RVQ used to encode. The default is all layers. - st : int, optional - Start quantizer index in RVQ. The default is 0. - - Returns - ------- - codes : torch.tensor - Output indices for each quantizer. Shape: (n_q, batch, timesteps) - - ''' - e = self.encoder(x) - if st is None: - st = 0 - n_q = n_q if n_q else self.n_q - codes = self.quantizer.encode(e, n_q=n_q, st=st) - return codes - - def decode(self, - codes: torch.tensor, - st: int=0): - ''' - - Parameters - ---------- - codes : torch.tensor - Indices for each quantizer. Shape: (n_q, batch, timesteps). - st : int, optional - Start quantizer index in RVQ. The default is 0. - - Returns - ------- - o : torch.tensor - Reconstruct wavs from codes. Shape: (batch, channels, timesteps) - - ''' - quantized = self.quantizer.decode(codes, st=st) - o = self.decoder(quantized) - return o \ No newline at end of file diff --git a/audio/safeear/safeear_repo/safeear/models/discriminator.py b/audio/safeear/safeear_repo/safeear/models/discriminator.py deleted file mode 100644 index 48a0c3f6b98bf48d1498ec92c6282c22681b911b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/discriminator.py +++ /dev/null @@ -1,421 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -"""MS-STFT discriminator, provided here for reference.""" -import typing as tp - -import einops -import torch -import torchaudio -from einops import rearrange -from torch import nn -from torch.nn import AvgPool1d -from torch.nn import functional as F -from torch.nn.utils import spectral_norm, weight_norm - -FeatureMapType = tp.List[torch.Tensor] -LogitsType = torch.Tensor -DiscriminatorOutput = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]] - -CONV_NORMALIZATIONS = frozenset([ - 'none', 'weight_norm', 'spectral_norm', 'time_layer_norm', 'layer_norm', - 'time_group_norm' -]) - -class ConvLayerNorm(nn.LayerNorm): - """ - Convolution-friendly LayerNorm that moves channels to last dimensions - before running the normalization and moves them back to original position right after. - """ - - def __init__(self, - normalized_shape: tp.Union[int, tp.List[int], torch.Size], - **kwargs): - super().__init__(normalized_shape, **kwargs) - - def forward(self, x): - x = einops.rearrange(x, 'b ... t -> b t ...') - x = super().forward(x) - x = einops.rearrange(x, 'b t ... -> b ... t') - return - - -def apply_parametrization_norm(module: nn.Module, - norm: str='none') -> nn.Module: - assert norm in CONV_NORMALIZATIONS - if norm == 'weight_norm': - return weight_norm(module) - elif norm == 'spectral_norm': - return spectral_norm(module) - else: - # We already check was in CONV_NORMALIZATION, so any other choice - # doesn't need reparametrization. - return module - - -def get_norm_module(module: nn.Module, - causal: bool=False, - norm: str='none', - **norm_kwargs) -> nn.Module: - """Return the proper normalization module. If causal is True, this will ensure the returned - module is causal, or return an error if the normalization doesn't support causal evaluation. - """ - assert norm in CONV_NORMALIZATIONS - if norm == 'layer_norm': - assert isinstance(module, nn.modules.conv._ConvNd) - return ConvLayerNorm(module.out_channels, **norm_kwargs) - elif norm == 'time_group_norm': - if causal: - raise ValueError("GroupNorm doesn't support causal evaluation.") - assert isinstance(module, nn.modules.conv._ConvNd) - return nn.GroupNorm(1, module.out_channels, **norm_kwargs) - else: - return nn.Identity() - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - -class NormConv1d(nn.Module): - """Wrapper around Conv1d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - - def __init__(self, - *args, - causal: bool=False, - norm: str='none', - norm_kwargs: tp.Dict[str, tp.Any]={}, - **kwargs): - super().__init__() - self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm) - self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs) - self.norm_type = norm - - def forward(self, x): - x = self.conv(x) - x = self.norm(x) - return x - - -class NormConv2d(nn.Module): - """Wrapper around Conv2d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - - def __init__(self, - *args, - norm: str='none', - norm_kwargs: tp.Dict[str, tp.Any]={}, - **kwargs): - super().__init__() - self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm) - self.norm = get_norm_module( - self.conv, causal=False, norm=norm, **norm_kwargs) - self.norm_type = norm - - def forward(self, x): - x = self.conv(x) - x = self.norm(x) - return x - - -def get_2d_padding(kernel_size: tp.Tuple[int, int], - dilation: tp.Tuple[int, int]=(1, 1)): - return (((kernel_size[0] - 1) * dilation[0]) // 2, ( - (kernel_size[1] - 1) * dilation[1]) // 2) - - -class DiscriminatorSTFT(nn.Module): - """STFT sub-discriminator. - Args: - filters (int): Number of filters in convolutions - in_channels (int): Number of input channels. Default: 1 - out_channels (int): Number of output channels. Default: 1 - n_fft (int): Size of FFT for each scale. Default: 1024 - hop_length (int): Length of hop between STFT windows for each scale. Default: 256 - kernel_size (tuple of int): Inner Conv2d kernel sizes. Default: ``(3, 9)`` - stride (tuple of int): Inner Conv2d strides. Default: ``(1, 2)`` - dilations (list of int): Inner Conv2d dilation on the time dimension. Default: ``[1, 2, 4]`` - win_length (int): Window size for each scale. Default: 1024 - normalized (bool): Whether to normalize by magnitude after stft. Default: True - norm (str): Normalization method. Default: `'weight_norm'` - activation (str): Activation function. Default: `'LeakyReLU'` - activation_params (dict): Parameters to provide to the activation function. - growth (int): Growth factor for the filters. Default: 1 - """ - - def __init__(self, - filters: int, - in_channels: int=1, - out_channels: int=1, - n_fft: int=1024, - hop_length: int=256, - win_length: int=1024, - max_filters: int=1024, - filters_scale: int=1, - kernel_size: tp.Tuple[int, int]=(3, 9), - dilations: tp.List=[1, 2, 4], - stride: tp.Tuple[int, int]=(1, 2), - normalized: bool=True, - norm: str='weight_norm', - activation: str='LeakyReLU', - activation_params: dict={'negative_slope': 0.2}): - super().__init__() - assert len(kernel_size) == 2 - assert len(stride) == 2 - self.filters = filters - self.in_channels = in_channels - self.out_channels = out_channels - self.n_fft = n_fft - self.hop_length = hop_length - self.win_length = win_length - self.normalized = normalized - self.activation = getattr(torch.nn, activation)(**activation_params) - self.spec_transform = torchaudio.transforms.Spectrogram( - n_fft=self.n_fft, - hop_length=self.hop_length, - win_length=self.win_length, - window_fn=torch.hann_window, - normalized=self.normalized, - center=False, - pad_mode=None, - power=None) - spec_channels = 2 * self.in_channels - self.convs = nn.ModuleList() - self.convs.append( - NormConv2d( - spec_channels, - self.filters, - kernel_size=kernel_size, - padding=get_2d_padding(kernel_size))) - in_chs = min(filters_scale * self.filters, max_filters) - for i, dilation in enumerate(dilations): - out_chs = min((filters_scale**(i + 1)) * self.filters, max_filters) - self.convs.append( - NormConv2d( - in_chs, - out_chs, - kernel_size=kernel_size, - stride=stride, - dilation=(dilation, 1), - padding=get_2d_padding(kernel_size, (dilation, 1)), - norm=norm)) - in_chs = out_chs - out_chs = min((filters_scale**(len(dilations) + 1)) * self.filters, - max_filters) - self.convs.append( - NormConv2d( - in_chs, - out_chs, - kernel_size=(kernel_size[0], kernel_size[0]), - padding=get_2d_padding((kernel_size[0], kernel_size[0])), - norm=norm)) - self.conv_post = NormConv2d( - out_chs, - self.out_channels, - kernel_size=(kernel_size[0], kernel_size[0]), - padding=get_2d_padding((kernel_size[0], kernel_size[0])), - norm=norm) - - def forward(self, x: torch.Tensor): - fmap = [] - # print('x ', x.shape) - z = self.spec_transform(x) # [B, 2, Freq, Frames, 2] - # print('z ', z.shape) - z = torch.cat([z.real, z.imag], dim=1) - # print('cat_z ', z.shape) - z = rearrange(z, 'b c w t -> b c t w') - for i, layer in enumerate(self.convs): - z = layer(z) - z = self.activation(z) - # print('z i', i, z.shape) - fmap.append(z) - z = self.conv_post(z) - # print('logit ', z.shape) - return z, fmap - - -class MultiScaleSTFTDiscriminator(nn.Module): - """Multi-Scale STFT (MS-STFT) discriminator. - Args: - filters (int): Number of filters in convolutions - in_channels (int): Number of input channels. Default: 1 - out_channels (int): Number of output channels. Default: 1 - n_ffts (Sequence[int]): Size of FFT for each scale - hop_lengths (Sequence[int]): Length of hop between STFT windows for each scale - win_lengths (Sequence[int]): Window size for each scale - **kwargs: additional args for STFTDiscriminator - """ - - def __init__(self, - filters: int, - in_channels: int=1, - out_channels: int=1, - n_ffts: tp.List[int]=[1024, 2048, 512, 256, 128], - hop_lengths: tp.List[int]=[256, 512, 128, 64, 32], - win_lengths: tp.List[int]=[1024, 2048, 512, 256, 128], - **kwargs): - super().__init__() - assert len(n_ffts) == len(hop_lengths) == len(win_lengths) - self.discriminators = nn.ModuleList([ - DiscriminatorSTFT( - filters, - in_channels=in_channels, - out_channels=out_channels, - n_fft=n_ffts[i], - win_length=win_lengths[i], - hop_length=hop_lengths[i], - **kwargs) for i in range(len(n_ffts)) - ]) - self.num_discriminators = len(self.discriminators) - - def forward(self, x: torch.Tensor) -> DiscriminatorOutput: - logits = [] - fmaps = [] - for disc in self.discriminators: - logit, fmap = disc(x) - logits.append(logit) - fmaps.append(fmap) - return logits, fmaps - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, - period, - kernel_size=5, - stride=3, - use_spectral_norm=False, - activation: str='LeakyReLU', - activation_params: dict={'negative_slope': 0.2}): - super(DiscriminatorP, self).__init__() - self.period = period - norm_f = weight_norm if use_spectral_norm is False else spectral_norm - self.activation = getattr(torch.nn, activation)(**activation_params) - self.convs = nn.ModuleList([ - NormConv2d( - 1, - 32, (kernel_size, 1), (stride, 1), - padding=(get_padding(5, 1), 0)), - NormConv2d( - 32, - 32, (kernel_size, 1), (stride, 1), - padding=(get_padding(5, 1), 0)), - NormConv2d( - 32, - 32, (kernel_size, 1), (stride, 1), - padding=(get_padding(5, 1), 0)), - NormConv2d( - 32, - 32, (kernel_size, 1), (stride, 1), - padding=(get_padding(5, 1), 0)), - NormConv2d(32, 32, (kernel_size, 1), 1, padding=(2, 0)), - ]) - self.conv_post = NormConv2d(32, 1, (3, 1), 1, padding=(1, 0)) - - def forward(self, x): - fmap = [] - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = self.activation(x) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiPeriodDiscriminator, self).__init__() - self.discriminators = nn.ModuleList([ - DiscriminatorP(2), - DiscriminatorP(3), - DiscriminatorP(5), - DiscriminatorP(7), - DiscriminatorP(11), - ]) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, - use_spectral_norm=False, - activation: str='LeakyReLU', - activation_params: dict={'negative_slope': 0.2}): - super(DiscriminatorS, self).__init__() - self.activation = getattr(torch.nn, activation)(**activation_params) - self.convs = nn.ModuleList([ - NormConv1d(1, 32, 15, 1, padding=7), - NormConv1d(32, 32, 41, 2, groups=4, padding=20), - NormConv1d(32, 32, 41, 2, groups=16, padding=20), - NormConv1d(32, 32, 41, 4, groups=16, padding=20), - NormConv1d(32, 32, 41, 4, groups=16, padding=20), - NormConv1d(32, 32, 41, 1, groups=16, padding=20), - NormConv1d(32, 32, 5, 1, padding=2), - ]) - self.conv_post = NormConv1d(32, 1, 3, 1, padding=1) - - def forward(self, x): - fmap = [] - for l in self.convs: - x = l(x) - x = self.activation(x) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - return x, fmap - - -class MultiScaleDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiScaleDiscriminator, self).__init__() - self.discriminators = nn.ModuleList([ - DiscriminatorS(), - DiscriminatorS(), - DiscriminatorS(), - ]) - self.meanpools = nn.ModuleList( - [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/audio/safeear/safeear_repo/safeear/models/modules/__init__.py b/audio/safeear/safeear_repo/safeear/models/modules/__init__.py deleted file mode 100644 index 833872ee632eba8e564ccbd3367ec548752872bb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Torch modules.""" - -# flake8: noqa -from .conv import ( - NormConv1d, - NormConv2d, - NormConvTranspose1d, - NormConvTranspose2d, - SConv1d, - SConvTranspose1d, - pad1d, - unpad1d, -) -from .lstm import SLSTM -from .seanet import SEANetDecoder, SEANetEncoder diff --git a/audio/safeear/safeear_repo/safeear/models/modules/conv.py b/audio/safeear/safeear_repo/safeear/models/modules/conv.py deleted file mode 100644 index ba8ddf83506ed5ad59103931a9850d8c1b5bbc6d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/conv.py +++ /dev/null @@ -1,251 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Convolutional layers wrappers and utilities.""" - -import math -import typing as tp -import warnings - -import torch -from torch import nn -from torch.nn import functional as F -from torch.nn.utils import spectral_norm, weight_norm - -from .norm import ConvLayerNorm - -CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm', - 'time_layer_norm', 'layer_norm', 'time_group_norm']) - - -def apply_parametrization_norm(module: nn.Module, norm: str = 'none') -> nn.Module: - assert norm in CONV_NORMALIZATIONS - if norm == 'weight_norm': - return weight_norm(module) - elif norm == 'spectral_norm': - return spectral_norm(module) - else: - # We already check was in CONV_NORMALIZATION, so any other choice - # doesn't need reparametrization. - return module - - -def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs) -> nn.Module: - """Return the proper normalization module. If causal is True, this will ensure the returned - module is causal, or return an error if the normalization doesn't support causal evaluation. - """ - assert norm in CONV_NORMALIZATIONS - if norm == 'layer_norm': - assert isinstance(module, nn.modules.conv._ConvNd) - return ConvLayerNorm(module.out_channels, **norm_kwargs) - elif norm == 'time_group_norm': - if causal: - raise ValueError("GroupNorm doesn't support causal evaluation.") - assert isinstance(module, nn.modules.conv._ConvNd) - return nn.GroupNorm(1, module.out_channels, **norm_kwargs) - else: - return nn.Identity() - - -def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, - padding_total: int = 0) -> int: - """See `pad_for_conv1d`. - """ - length = x.shape[-1] - n_frames = (length - kernel_size + padding_total) / stride + 1 - ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total) - return ideal_length - length - - -def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0): - """Pad for a convolution to make sure that the last window is full. - Extra padding is added at the end. This is required to ensure that we can rebuild - an output of the same length, as otherwise, even with padding, some time steps - might get removed. - For instance, with total padding = 4, kernel size = 4, stride = 2: - 0 0 1 2 3 4 5 0 0 # (0s are padding) - 1 2 3 # (output frames of a convolution, last 0 is never used) - 0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding) - 1 2 3 4 # once you removed padding, we are missing one time step ! - """ - extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total) - return F.pad(x, (0, extra_padding)) - - -def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', value: float = 0.): - """Tiny wrapper around F.pad, just to allow for reflect padding on small input. - If this is the case, we insert extra 0 padding to the right before the reflection happen. - """ - length = x.shape[-1] - padding_left, padding_right = paddings - assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) - if mode == 'reflect': - max_pad = max(padding_left, padding_right) - extra_pad = 0 - if length <= max_pad: - extra_pad = max_pad - length + 1 - x = F.pad(x, (0, extra_pad)) - padded = F.pad(x, paddings, mode, value) - end = padded.shape[-1] - extra_pad - return padded[..., :end] - else: - return F.pad(x, paddings, mode, value) - - -def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]): - """Remove padding from x, handling properly zero padding. Only for 1d!""" - padding_left, padding_right = paddings - assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right) - assert (padding_left + padding_right) <= x.shape[-1] - end = x.shape[-1] - padding_right - return x[..., padding_left: end] - - -class NormConv1d(nn.Module): - """Wrapper around Conv1d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - def __init__(self, *args, causal: bool = False, norm: str = 'none', - norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): - super().__init__() - self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm) - self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs) - self.norm_type = norm - - def forward(self, x): - x = self.conv(x) - x = self.norm(x) - return x - - -class NormConv2d(nn.Module): - """Wrapper around Conv2d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - def __init__(self, *args, norm: str = 'none', - norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): - super().__init__() - self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm) - self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs) - self.norm_type = norm - - def forward(self, x): - x = self.conv(x) - x = self.norm(x) - return x - - -class NormConvTranspose1d(nn.Module): - """Wrapper around ConvTranspose1d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - def __init__(self, *args, causal: bool = False, norm: str = 'none', - norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): - super().__init__() - self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm) - self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs) - self.norm_type = norm - - def forward(self, x): - x = self.convtr(x) - x = self.norm(x) - return x - - -class NormConvTranspose2d(nn.Module): - """Wrapper around ConvTranspose2d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - def __init__(self, *args, norm: str = 'none', - norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs): - super().__init__() - self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm) - self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs) - - def forward(self, x): - x = self.convtr(x) - x = self.norm(x) - return x - - -class SConv1d(nn.Module): - """Conv1d with some builtin handling of asymmetric or causal padding - and normalization. - """ - def __init__(self, in_channels: int, out_channels: int, - kernel_size: int, stride: int = 1, dilation: int = 1, - groups: int = 1, bias: bool = True, causal: bool = False, - norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, - pad_mode: str = 'reflect'): - super().__init__() - # warn user on unusual setup between dilation and stride - if stride > 1 and dilation > 1: - warnings.warn('SConv1d has been initialized with stride > 1 and dilation > 1' - f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).') - self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride, - dilation=dilation, groups=groups, bias=bias, causal=causal, - norm=norm, norm_kwargs=norm_kwargs) - self.causal = causal - self.pad_mode = pad_mode - - def forward(self, x): - B, C, T = x.shape - kernel_size = self.conv.conv.kernel_size[0] - stride = self.conv.conv.stride[0] - dilation = self.conv.conv.dilation[0] - padding_total = (kernel_size - 1) * dilation - (stride - 1) - extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total) - if self.causal: - # Left padding for causal - x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode) - else: - # Asymmetric padding required for odd strides - padding_right = padding_total // 2 - padding_left = padding_total - padding_right - x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode) - return self.conv(x) - - -class SConvTranspose1d(nn.Module): - """ConvTranspose1d with some builtin handling of asymmetric or causal padding - and normalization. - """ - def __init__(self, in_channels: int, out_channels: int, - kernel_size: int, stride: int = 1, causal: bool = False, - norm: str = 'none', trim_right_ratio: float = 1., - norm_kwargs: tp.Dict[str, tp.Any] = {}): - super().__init__() - self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride, - causal=causal, norm=norm, norm_kwargs=norm_kwargs) - self.causal = causal - self.trim_right_ratio = trim_right_ratio - assert self.causal or self.trim_right_ratio == 1., \ - "`trim_right_ratio` != 1.0 only makes sense for causal convolutions" - assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1. - - def forward(self, x): - kernel_size = self.convtr.convtr.kernel_size[0] - stride = self.convtr.convtr.stride[0] - padding_total = kernel_size - stride - - y = self.convtr(x) - - # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be - # removed at the very end, when keeping only the right length for the output, - # as removing it here would require also passing the length at the matching layer - # in the encoder. - if self.causal: - # Trim the padding on the right according to the specified ratio - # if trim_right_ratio = 1.0, trim everything from right - padding_right = math.ceil(padding_total * self.trim_right_ratio) - padding_left = padding_total - padding_right - y = unpad1d(y, (padding_left, padding_right)) - else: - # Asymmetric padding required for odd strides - padding_right = padding_total // 2 - padding_left = padding_total - padding_right - y = unpad1d(y, (padding_left, padding_right)) - return y diff --git a/audio/safeear/safeear_repo/safeear/models/modules/lstm.py b/audio/safeear/safeear_repo/safeear/models/modules/lstm.py deleted file mode 100644 index f68010d2c46f5b3f0b56c6fc55b8c62f32e08b89..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/lstm.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""LSTM layers module.""" - -from torch import nn - - -class SLSTM(nn.Module): - """ - LSTM without worrying about the hidden state, nor the layout of the data. - Expects input as convolutional layout. - """ - def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True, bidirectional: bool=False): - super().__init__() - self.bidirectional = bidirectional - self.skip = skip - self.lstm = nn.LSTM(dimension, dimension, num_layers, bidirectional=bidirectional) - - def forward(self, x): - x = x.permute(2, 0, 1) - y, _ = self.lstm(x) - if self.bidirectional: - x = x.repeat(1, 1, 2) - if self.skip: - y = y + x - y = y.permute(1, 2, 0) - return y - diff --git a/audio/safeear/safeear_repo/safeear/models/modules/norm.py b/audio/safeear/safeear_repo/safeear/models/modules/norm.py deleted file mode 100644 index 19970e0a21ea1c10461cb56d776619dd5f64ff36..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/norm.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Normalization modules.""" - -import typing as tp - -import einops -import torch -from torch import nn - - -class ConvLayerNorm(nn.LayerNorm): - """ - Convolution-friendly LayerNorm that moves channels to last dimensions - before running the normalization and moves them back to original position right after. - """ - def __init__(self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs): - super().__init__(normalized_shape, **kwargs) - - def forward(self, x): - x = einops.rearrange(x, 'b ... t -> b t ...') - x = super().forward(x) - x = einops.rearrange(x, 'b t ... -> b ... t') - return diff --git a/audio/safeear/safeear_repo/safeear/models/modules/quantization/__init__.py b/audio/safeear/safeear_repo/safeear/models/modules/quantization/__init__.py deleted file mode 100644 index bfabe52b8cb6f260cdda6137b34df2f4736bd02f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/quantization/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -# flake8: noqa -from .vq import QuantizedResult, ResidualVectorQuantizer diff --git a/audio/safeear/safeear_repo/safeear/models/modules/quantization/ac.py b/audio/safeear/safeear_repo/safeear/models/modules/quantization/ac.py deleted file mode 100644 index dfb980e263106d63b31322cbc851206addc858a2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/quantization/ac.py +++ /dev/null @@ -1,293 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Arithmetic coder.""" - -import io -import math -import random -import typing as tp - -import torch - -from ..binary import BitPacker, BitUnpacker - - -def build_stable_quantized_cdf(pdf: torch.Tensor, total_range_bits: int, - roundoff: float = 1e-8, min_range: int = 2, - check: bool = True) -> torch.Tensor: - """Turn the given PDF into a quantized CDF that splits - [0, 2 ** self.total_range_bits - 1] into chunks of size roughly proportional - to the PDF. - - Args: - pdf (torch.Tensor): probability distribution, shape should be `[N]`. - total_range_bits (int): see `ArithmeticCoder`, the typical range we expect - during the coding process is `[0, 2 ** total_range_bits - 1]`. - roundoff (float): will round the pdf up to that level to remove difference coming - from e.g. evaluating the Language Model on different architectures. - min_range (int): minimum range width. Should always be at least 2 for numerical - stability. Use this to avoid pathological behavior is a value - that is expected to be rare actually happens in real life. - check (bool): if True, checks that nothing bad happened, can be deactivated for speed. - """ - pdf = pdf.detach() - if roundoff: - pdf = (pdf / roundoff).floor() * roundoff - # interpolate with uniform distribution to achieve desired minimum probability. - total_range = 2 ** total_range_bits - cardinality = len(pdf) - alpha = min_range * cardinality / total_range - assert alpha <= 1, "you must reduce min_range" - ranges = (((1 - alpha) * total_range) * pdf).floor().long() - ranges += min_range - quantized_cdf = torch.cumsum(ranges, dim=-1) - if min_range < 2: - raise ValueError("min_range must be at least 2.") - if check: - assert quantized_cdf[-1] <= 2 ** total_range_bits, quantized_cdf[-1] - if ((quantized_cdf[1:] - quantized_cdf[:-1]) < min_range).any() or quantized_cdf[0] < min_range: - raise ValueError("You must increase your total_range_bits.") - return quantized_cdf - - -class ArithmeticCoder: - """ArithmeticCoder, - Let us take a distribution `p` over `N` symbols, and assume we have a stream - of random variables `s_t` sampled from `p`. Let us assume that we have a budget - of `B` bits that we can afford to write on device. There are `2**B` possible numbers, - corresponding to the range `[0, 2 ** B - 1]`. We can map each of those number to a single - sequence `(s_t)` by doing the following: - - 1) Initialize the current range to` [0 ** 2 B - 1]`. - 2) For each time step t, split the current range into contiguous chunks, - one for each possible outcome, with size roughly proportional to `p`. - For instance, if `p = [0.75, 0.25]`, and the range is `[0, 3]`, the chunks - would be `{[0, 2], [3, 3]}`. - 3) Select the chunk corresponding to `s_t`, and replace the current range with this. - 4) When done encoding all the values, just select any value remaining in the range. - - You will notice that this procedure can fail: for instance if at any point in time - the range is smaller than `N`, then we can no longer assign a non-empty chunk to each - possible outcome. Intuitively, the more likely a value is, the less the range width - will reduce, and the longer we can go on encoding values. This makes sense: for any efficient - coding scheme, likely outcomes would take less bits, and more of them can be coded - with a fixed budget. - - In practice, we do not know `B` ahead of time, but we have a way to inject new bits - when the current range decreases below a given limit (given by `total_range_bits`), without - having to redo all the computations. If we encode mostly likely values, we will seldom - need to inject new bits, but a single rare value can deplete our stock of entropy! - - In this explanation, we assumed that the distribution `p` was constant. In fact, the present - code works for any sequence `(p_t)` possibly different for each timestep. - We also assume that `s_t ~ p_t`, but that doesn't need to be true, although the smaller - the KL between the true distribution and `p_t`, the most efficient the coding will be. - - Args: - fo (IO[bytes]): file-like object to which the bytes will be written to. - total_range_bits (int): the range `M` described above is `2 ** total_range_bits. - Any time the current range width fall under this limit, new bits will - be injected to rescale the initial range. - """ - - def __init__(self, fo: tp.IO[bytes], total_range_bits: int = 24): - assert total_range_bits <= 30 - self.total_range_bits = total_range_bits - self.packer = BitPacker(bits=1, fo=fo) # we push single bits at a time. - self.low: int = 0 - self.high: int = 0 - self.max_bit: int = -1 - self._dbg: tp.List[tp.Any] = [] - self._dbg2: tp.List[tp.Any] = [] - - @property - def delta(self) -> int: - """Return the current range width.""" - return self.high - self.low + 1 - - def _flush_common_prefix(self): - # If self.low and self.high start with the sames bits, - # those won't change anymore as we always just increase the range - # by powers of 2, and we can flush them out to the bit stream. - assert self.high >= self.low, (self.low, self.high) - assert self.high < 2 ** (self.max_bit + 1) - while self.max_bit >= 0: - b1 = self.low >> self.max_bit - b2 = self.high >> self.max_bit - if b1 == b2: - self.low -= (b1 << self.max_bit) - self.high -= (b1 << self.max_bit) - assert self.high >= self.low, (self.high, self.low, self.max_bit) - assert self.low >= 0 - self.max_bit -= 1 - self.packer.push(b1) - else: - break - - def push(self, symbol: int, quantized_cdf: torch.Tensor): - """Push the given symbol on the stream, flushing out bits - if possible. - - Args: - symbol (int): symbol to encode with the AC. - quantized_cdf (torch.Tensor): use `build_stable_quantized_cdf` - to build this from your pdf estimate. - """ - while self.delta < 2 ** self.total_range_bits: - self.low *= 2 - self.high = self.high * 2 + 1 - self.max_bit += 1 - - range_low = 0 if symbol == 0 else quantized_cdf[symbol - 1].item() - range_high = quantized_cdf[symbol].item() - 1 - effective_low = int(math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))) - effective_high = int(math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))) - assert self.low <= self.high - self.high = self.low + effective_high - self.low = self.low + effective_low - assert self.low <= self.high, (effective_low, effective_high, range_low, range_high) - self._dbg.append((self.low, self.high)) - self._dbg2.append((self.low, self.high)) - outs = self._flush_common_prefix() - assert self.low <= self.high - assert self.max_bit >= -1 - assert self.max_bit <= 61, self.max_bit - return outs - - def flush(self): - """Flush the remaining information to the stream. - """ - while self.max_bit >= 0: - b1 = (self.low >> self.max_bit) & 1 - self.packer.push(b1) - self.max_bit -= 1 - self.packer.flush() - - -class ArithmeticDecoder: - """ArithmeticDecoder, see `ArithmeticCoder` for a detailed explanation. - - Note that this must be called with **exactly** the same parameters and sequence - of quantized cdf as the arithmetic encoder or the wrong values will be decoded. - - If the AC encoder current range is [L, H], with `L` and `H` having the some common - prefix (i.e. the same most significant bits), then this prefix will be flushed to the stream. - For instances, having read 3 bits `b1 b2 b3`, we know that `[L, H]` is contained inside - `[b1 b2 b3 0 ... 0 b1 b3 b3 1 ... 1]`. Now this specific sub-range can only be obtained - for a specific sequence of symbols and a binary-search allows us to decode those symbols. - At some point, the prefix `b1 b2 b3` will no longer be sufficient to decode new symbols, - and we will need to read new bits from the stream and repeat the process. - - """ - def __init__(self, fo: tp.IO[bytes], total_range_bits: int = 24): - self.total_range_bits = total_range_bits - self.low: int = 0 - self.high: int = 0 - self.current: int = 0 - self.max_bit: int = -1 - self.unpacker = BitUnpacker(bits=1, fo=fo) # we pull single bits at a time. - # Following is for debugging - self._dbg: tp.List[tp.Any] = [] - self._dbg2: tp.List[tp.Any] = [] - self._last: tp.Any = None - - @property - def delta(self) -> int: - return self.high - self.low + 1 - - def _flush_common_prefix(self): - # Given the current range [L, H], if both have a common prefix, - # we know we can remove it from our representation to avoid handling large numbers. - while self.max_bit >= 0: - b1 = self.low >> self.max_bit - b2 = self.high >> self.max_bit - if b1 == b2: - self.low -= (b1 << self.max_bit) - self.high -= (b1 << self.max_bit) - self.current -= (b1 << self.max_bit) - assert self.high >= self.low - assert self.low >= 0 - self.max_bit -= 1 - else: - break - - def pull(self, quantized_cdf: torch.Tensor) -> tp.Optional[int]: - """Pull a symbol, reading as many bits from the stream as required. - This returns `None` when the stream has been exhausted. - - Args: - quantized_cdf (torch.Tensor): use `build_stable_quantized_cdf` - to build this from your pdf estimate. This must be **exatly** - the same cdf as the one used at encoding time. - """ - while self.delta < 2 ** self.total_range_bits: - bit = self.unpacker.pull() - if bit is None: - return None - self.low *= 2 - self.high = self.high * 2 + 1 - self.current = self.current * 2 + bit - self.max_bit += 1 - - def bin_search(low_idx: int, high_idx: int): - # Binary search is not just for coding interviews :) - if high_idx < low_idx: - raise RuntimeError("Binary search failed") - mid = (low_idx + high_idx) // 2 - range_low = quantized_cdf[mid - 1].item() if mid > 0 else 0 - range_high = quantized_cdf[mid].item() - 1 - effective_low = int(math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))) - effective_high = int(math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))) - low = effective_low + self.low - high = effective_high + self.low - if self.current >= low: - if self.current <= high: - return (mid, low, high, self.current) - else: - return bin_search(mid + 1, high_idx) - else: - return bin_search(low_idx, mid - 1) - - self._last = (self.low, self.high, self.current, self.max_bit) - sym, self.low, self.high, self.current = bin_search(0, len(quantized_cdf) - 1) - self._dbg.append((self.low, self.high, self.current)) - self._flush_common_prefix() - self._dbg2.append((self.low, self.high, self.current)) - - return sym - - -def test(): - torch.manual_seed(1234) - random.seed(1234) - for _ in range(4): - pdfs = [] - cardinality = random.randrange(4000) - steps = random.randrange(100, 500) - fo = io.BytesIO() - encoder = ArithmeticCoder(fo) - symbols = [] - for step in range(steps): - pdf = torch.softmax(torch.randn(cardinality), dim=0) - pdfs.append(pdf) - q_cdf = build_stable_quantized_cdf(pdf, encoder.total_range_bits) - symbol = torch.multinomial(pdf, 1).item() - symbols.append(symbol) - encoder.push(symbol, q_cdf) - encoder.flush() - - fo.seek(0) - decoder = ArithmeticDecoder(fo) - for idx, (pdf, symbol) in enumerate(zip(pdfs, symbols)): - q_cdf = build_stable_quantized_cdf(pdf, encoder.total_range_bits) - decoded_symbol = decoder.pull(q_cdf) - assert decoded_symbol == symbol, idx - assert decoder.pull(torch.zeros(1)) is None - - -if __name__ == "__main__": - test() diff --git a/audio/safeear/safeear_repo/safeear/models/modules/quantization/core_vq.py b/audio/safeear/safeear_repo/safeear/models/modules/quantization/core_vq.py deleted file mode 100644 index ae60fdbb4c88bfe6f6a30699eb593f6bbdb5624b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/quantization/core_vq.py +++ /dev/null @@ -1,366 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# This implementation is inspired from -# https://github.com/lucidrains/vector-quantize-pytorch -# which is released under MIT License. Hereafter, the original license: -# MIT License -# -# Copyright (c) 2020 Phil Wang -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -"""Core vector quantization implementation.""" -import typing as tp - -import torch -import torch.nn.functional as F -from einops import rearrange, repeat -from torch import nn - -from .distrib import broadcast_tensors, rank - - -def default(val: tp.Any, d: tp.Any) -> tp.Any: - return val if val is not None else d - - -def ema_inplace(moving_avg, new, decay: float): - moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) - - -def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5): - return (x + epsilon) / (x.sum() + n_categories * epsilon) - - -def uniform_init(*shape: int): - t = torch.empty(shape) - nn.init.kaiming_uniform_(t) - return t - - -def sample_vectors(samples, num: int): - num_samples, device = samples.shape[0], samples.device - - if num_samples >= num: - indices = torch.randperm(num_samples, device=device)[:num] - else: - indices = torch.randint(0, num_samples, (num,), device=device) - - return samples[indices] - - -def kmeans(samples, num_clusters: int, num_iters: int = 10): - dim, dtype = samples.shape[-1], samples.dtype - - means = sample_vectors(samples, num_clusters) - - for _ in range(num_iters): - diffs = rearrange(samples, "n d -> n () d") - rearrange( - means, "c d -> () c d" - ) - dists = -(diffs ** 2).sum(dim=-1) - - buckets = dists.max(dim=-1).indices - bins = torch.bincount(buckets, minlength=num_clusters) - zero_mask = bins == 0 - bins_min_clamped = bins.masked_fill(zero_mask, 1) - - new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype) - new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples) - new_means = new_means / bins_min_clamped[..., None] - - means = torch.where(zero_mask[..., None], means, new_means) - - return means, bins - - -class EuclideanCodebook(nn.Module): - """Codebook with Euclidean distance. - Args: - dim (int): Dimension. - codebook_size (int): Codebook size. - kmeans_init (bool): Whether to use k-means to initialize the codebooks. - If set to true, run the k-means algorithm on the first training batch and use - the learned centroids as initialization. - kmeans_iters (int): Number of iterations used for k-means algorithm at initialization. - decay (float): Decay for exponential moving average over the codebooks. - epsilon (float): Epsilon value for numerical stability. - threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes - that have an exponential moving average cluster size less than the specified threshold with - randomly selected vector from the current batch. - """ - def __init__( - self, - dim: int, - codebook_size: int, - kmeans_init: int = False, - kmeans_iters: int = 10, - decay: float = 0.99, - epsilon: float = 1e-5, - threshold_ema_dead_code: int = 2, - ): - super().__init__() - self.decay = decay - init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros - embed = init_fn(codebook_size, dim) - - self.codebook_size = codebook_size - - self.kmeans_iters = kmeans_iters - self.epsilon = epsilon - self.threshold_ema_dead_code = threshold_ema_dead_code - - self.register_buffer("inited", torch.Tensor([not kmeans_init])) - self.register_buffer("cluster_size", torch.zeros(codebook_size)) - self.register_buffer("embed", embed) - self.register_buffer("embed_avg", embed.clone()) - - @torch.jit.ignore - def init_embed_(self, data): - if self.inited: - return - - embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters) - self.embed.data.copy_(embed) - self.embed_avg.data.copy_(embed.clone()) - self.cluster_size.data.copy_(cluster_size) - self.inited.data.copy_(torch.Tensor([True])) - # Make sure all buffers across workers are in sync after initialization - #broadcast_tensors(self.buffers()) - - def replace_(self, samples, mask): - modified_codebook = torch.where( - mask[..., None], sample_vectors(samples, self.codebook_size), self.embed - ) - self.embed.data.copy_(modified_codebook) - - def expire_codes_(self, batch_samples): - if self.threshold_ema_dead_code == 0: - return - - expired_codes = self.cluster_size < self.threshold_ema_dead_code - if not torch.any(expired_codes): - return - - batch_samples = rearrange(batch_samples, "... d -> (...) d") - self.replace_(batch_samples, mask=expired_codes) - #broadcast_tensors(self.buffers()) - - def preprocess(self, x): - x = rearrange(x, "... d -> (...) d") - return x - - def quantize(self, x): - embed = self.embed.t() - dist = -( - x.pow(2).sum(1, keepdim=True) - - 2 * x @ embed - + embed.pow(2).sum(0, keepdim=True) - ) - embed_ind = dist.max(dim=-1).indices - return embed_ind - - def postprocess_emb(self, embed_ind, shape): - return embed_ind.view(*shape[:-1]) - - def dequantize(self, embed_ind): - quantize = F.embedding(embed_ind, self.embed) - return quantize - - def encode(self, x): - shape = x.shape - # pre-process - x = self.preprocess(x) - # quantize - embed_ind = self.quantize(x) - # post-process - embed_ind = self.postprocess_emb(embed_ind, shape) - return embed_ind - - def decode(self, embed_ind): - quantize = self.dequantize(embed_ind) - return quantize - - def forward(self, x): - shape, dtype = x.shape, x.dtype - x = self.preprocess(x) - - self.init_embed_(x) - - embed_ind = self.quantize(x) - embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype) - embed_ind = self.postprocess_emb(embed_ind, shape) - quantize = self.dequantize(embed_ind) - - if self.training: - # We do the expiry of code at that point as buffers are in sync - # and all the workers will take the same decision. - self.expire_codes_(x) - ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay) - embed_sum = x.t() @ embed_onehot - ema_inplace(self.embed_avg, embed_sum.t(), self.decay) - cluster_size = ( - laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon) - * self.cluster_size.sum() - ) - embed_normalized = self.embed_avg / cluster_size.unsqueeze(1) - self.embed.data.copy_(embed_normalized) - - return quantize, embed_ind - - -class VectorQuantization(nn.Module): - """Vector quantization implementation. - Currently supports only euclidean distance. - Args: - dim (int): Dimension - codebook_size (int): Codebook size - codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim. - decay (float): Decay for exponential moving average over the codebooks. - epsilon (float): Epsilon value for numerical stability. - kmeans_init (bool): Whether to use kmeans to initialize the codebooks. - kmeans_iters (int): Number of iterations used for kmeans initialization. - threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes - that have an exponential moving average cluster size less than the specified threshold with - randomly selected vector from the current batch. - commitment_weight (float): Weight for commitment loss. - """ - def __init__( - self, - dim: int, - codebook_size: int, - codebook_dim: tp.Optional[int] = None, - decay: float = 0.99, - epsilon: float = 1e-5, - kmeans_init: bool = True, - kmeans_iters: int = 50, - threshold_ema_dead_code: int = 2, - commitment_weight: float = 1., - ): - super().__init__() - _codebook_dim: int = default(codebook_dim, dim) - - requires_projection = _codebook_dim != dim - self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()) - self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()) - - self.epsilon = epsilon - self.commitment_weight = commitment_weight - - self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size, - kmeans_init=kmeans_init, kmeans_iters=kmeans_iters, - decay=decay, epsilon=epsilon, - threshold_ema_dead_code=threshold_ema_dead_code) - self.codebook_size = codebook_size - - @property - def codebook(self): - return self._codebook.embed - - def encode(self, x): - x = rearrange(x, "b d n -> b n d") - x = self.project_in(x) - embed_in = self._codebook.encode(x) - return embed_in - - def decode(self, embed_ind): - quantize = self._codebook.decode(embed_ind) - quantize = self.project_out(quantize) - quantize = rearrange(quantize, "b n d -> b d n") - return quantize - - def forward(self, x): - device = x.device - x = rearrange(x, "b d n -> b n d") - x = self.project_in(x) - - quantize, embed_ind = self._codebook(x) - - if self.training: - quantize = x + (quantize - x).detach() - - loss = torch.tensor([0.0], device=device, requires_grad=self.training) - - if self.training: - if self.commitment_weight > 0: - commit_loss = F.mse_loss(quantize.detach(), x) - loss = loss + commit_loss * self.commitment_weight - - quantize = self.project_out(quantize) - quantize = rearrange(quantize, "b n d -> b d n") - return quantize, embed_ind, loss - - -class ResidualVectorQuantization(nn.Module): - """Residual vector quantization implementation. - Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf - """ - def __init__(self, *, num_quantizers, **kwargs): - super().__init__() - self.layers = nn.ModuleList( - [VectorQuantization(**kwargs) for _ in range(num_quantizers)] - ) - - def forward(self, x, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None): - quantized_out = 0.0 - residual = x - - all_losses = [] - all_indices = [] - out_quantized = [] - - n_q = n_q or len(self.layers) - - for i, layer in enumerate(self.layers[:n_q]): - quantized, indices, loss = layer(residual) - residual = residual - quantized - quantized_out = quantized_out + quantized - - all_indices.append(indices) - all_losses.append(loss) - if layers and i in layers: - out_quantized.append(quantized) - - out_losses, out_indices = map(torch.stack, (all_losses, all_indices)) - return quantized_out, out_indices, out_losses, out_quantized - - def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int]= None) -> torch.Tensor: - residual = x - all_indices = [] - n_q = n_q or len(self.layers) - st = st or 0 - for layer in self.layers[st:n_q]: - indices = layer.encode(residual) - quantized = layer.decode(indices) - residual = residual - quantized - all_indices.append(indices) - out_indices = torch.stack(all_indices) - return out_indices - - def decode(self, q_indices: torch.Tensor, st: int=0) -> torch.Tensor: - quantized_out = torch.tensor(0.0, device=q_indices.device) - for i, indices in enumerate(q_indices): - layer = self.layers[st + i] - quantized = layer.decode(indices) - quantized_out = quantized_out + quantized - return quantized_out diff --git a/audio/safeear/safeear_repo/safeear/models/modules/quantization/distrib.py b/audio/safeear/safeear_repo/safeear/models/modules/quantization/distrib.py deleted file mode 100644 index 4edc88a532c9cf842bd2ee9a56f2c0b5dcb7c83d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/quantization/distrib.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Torch distributed utilities.""" - -import typing as tp - -import torch - - -def rank(): - if torch.distributed.is_initialized(): - return torch.distributed.get_rank() - else: - return 0 - - -def world_size(): - if torch.distributed.is_initialized(): - return torch.distributed.get_world_size() - else: - return 1 - - -def is_distributed(): - return world_size() > 1 - - -def all_reduce(tensor: torch.Tensor, op=torch.distributed.ReduceOp.SUM): - if is_distributed(): - return torch.distributed.all_reduce(tensor, op) - - -def _is_complex_or_float(tensor): - return torch.is_floating_point(tensor) or torch.is_complex(tensor) - - -def _check_number_of_params(params: tp.List[torch.Tensor]): - # utility function to check that the number of params in all workers is the same, - # and thus avoid a deadlock with distributed all reduce. - if not is_distributed() or not params: - return - #print('params[0].device ', params[0].device) - tensor = torch.tensor([len(params)], device=params[0].device, dtype=torch.long) - all_reduce(tensor) - if tensor.item() != len(params) * world_size(): - # If not all the workers have the same number, for at least one of them, - # this inequality will be verified. - raise RuntimeError(f"Mismatch in number of params: ours is {len(params)}, " - "at least one worker has a different one.") - - -def broadcast_tensors(tensors: tp.Iterable[torch.Tensor], src: int = 0): - """Broadcast the tensors from the given parameters to all workers. - This can be used to ensure that all workers have the same model to start with. - """ - if not is_distributed(): - return - tensors = [tensor for tensor in tensors if _is_complex_or_float(tensor)] - _check_number_of_params(tensors) - handles = [] - for tensor in tensors: - # src = int(rank()) # added code - handle = torch.distributed.broadcast(tensor.data, src=src, async_op=True) - handles.append(handle) - for handle in handles: - handle.wait() - - -def sync_buffer(buffers, average=True): - """ - Sync grad for buffers. If average is False, broadcast instead of averaging. - """ - if not is_distributed(): - return - handles = [] - for buffer in buffers: - if torch.is_floating_point(buffer.data): - if average: - handle = torch.distributed.all_reduce( - buffer.data, op=torch.distributed.ReduceOp.SUM, async_op=True) - else: - handle = torch.distributed.broadcast( - buffer.data, src=0, async_op=True) - handles.append((buffer, handle)) - for buffer, handle in handles: - handle.wait() - if average: - buffer.data /= world_size - - -def sync_grad(params): - """ - Simpler alternative to DistributedDataParallel, that doesn't rely - on any black magic. For simple models it can also be as fast. - Just call this on your model parameters after the call to backward! - """ - if not is_distributed(): - return - handles = [] - for p in params: - if p.grad is not None: - handle = torch.distributed.all_reduce( - p.grad.data, op=torch.distributed.ReduceOp.SUM, async_op=True) - handles.append((p, handle)) - for p, handle in handles: - handle.wait() - p.grad.data /= world_size() - - -def average_metrics(metrics: tp.Dict[str, float], count=1.): - """Average a dictionary of metrics across all workers, using the optional - `count` as unormalized weight. - """ - if not is_distributed(): - return metrics - keys, values = zip(*metrics.items()) - device = 'cuda' if torch.cuda.is_available() else 'cpu' - tensor = torch.tensor(list(values) + [1], device=device, dtype=torch.float32) - tensor *= count - all_reduce(tensor) - averaged = (tensor[:-1] / tensor[-1]).cpu().tolist() - return dict(zip(keys, averaged)) diff --git a/audio/safeear/safeear_repo/safeear/models/modules/quantization/vq.py b/audio/safeear/safeear_repo/safeear/models/modules/quantization/vq.py deleted file mode 100644 index b91d178057cab24530df2c3c4d11a1a9af78af03..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/quantization/vq.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Residual vector quantizer implementation.""" - -import math -import typing as tp -from dataclasses import dataclass, field - -import torch -from torch import nn - -from .core_vq import ResidualVectorQuantization - - -@dataclass -class QuantizedResult: - quantized: torch.Tensor - codes: torch.Tensor - bandwidth: torch.Tensor # bandwidth in kb/s used, per batch item. - penalty: tp.Optional[torch.Tensor] = None - metrics: dict = field(default_factory=dict) - - -class ResidualVectorQuantizer(nn.Module): - """Residual Vector Quantizer. - Args: - dimension (int): Dimension of the codebooks. - n_q (int): Number of residual vector quantizers used. - bins (int): Codebook size. - decay (float): Decay for exponential moving average over the codebooks. - kmeans_init (bool): Whether to use kmeans to initialize the codebooks. - kmeans_iters (int): Number of iterations used for kmeans initialization. - threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes - that have an exponential moving average cluster size less than the specified threshold with - randomly selected vector from the current batch. - """ - def __init__( - self, - dimension: int = 256, - n_q: int = 8, - bins: int = 1024, - decay: float = 0.99, - kmeans_init: bool = True, - kmeans_iters: int = 50, - threshold_ema_dead_code: int = 2, - ): - super().__init__() - self.n_q = n_q - self.dimension = dimension - self.bins = bins - self.decay = decay - self.kmeans_init = kmeans_init - self.kmeans_iters = kmeans_iters - self.threshold_ema_dead_code = threshold_ema_dead_code - self.vq = ResidualVectorQuantization( - dim=self.dimension, - codebook_size=self.bins, - num_quantizers=self.n_q, - decay=self.decay, - kmeans_init=self.kmeans_init, - kmeans_iters=self.kmeans_iters, - threshold_ema_dead_code=self.threshold_ema_dead_code, - ) - - def forward(self, x: torch.Tensor, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None) -> QuantizedResult: - """Residual vector quantization on the given input tensor. - Args: - x (torch.Tensor): Input tensor. - n_q (int): Number of quantizer used to quantize. Default: All quantizers. - layers (list): Layer that need to return quantized. Defalt: None. - Returns: - QuantizedResult: - The quantized (or approximately quantized) representation with - the associated numbert quantizers and layer quantized required to return. - """ - n_q = n_q if n_q else self.n_q - if layers and max(layers) >= n_q: - raise ValueError(f'Last layer index in layers: A {max(layers)}. Number of quantizers in RVQ: B {self.n_q}. A must less than B.') - quantized, codes, commit_loss, quantized_list = self.vq(x, n_q=n_q, layers=layers) - return quantized, codes, torch.mean(commit_loss), quantized_list - - - def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None) -> torch.Tensor: - """Encode a given input tensor with the specified sample rate at the given bandwidth. - The RVQ encode method sets the appropriate number of quantizer to use - and returns indices for each quantizer. - Args: - x (torch.Tensor): Input tensor. - n_q (int): Number of quantizer used to quantize. Default: All quantizers. - st (int): Start to encode input from which layers. Default: 0. - """ - n_q = n_q if n_q else self.n_q - st = st or 0 - codes = self.vq.encode(x, n_q=n_q, st=st) - return codes - - def decode(self, codes: torch.Tensor, st: int = 0) -> torch.Tensor: - """Decode the given codes to the quantized representation. - Args: - codes (torch.Tensor): Input indices for each quantizer. - st (int): Start to decode input codes from which layers. Default: 0. - """ - quantized = self.vq.decode(codes, st=st) - return quantized diff --git a/audio/safeear/safeear_repo/safeear/models/modules/seanet.py b/audio/safeear/safeear_repo/safeear/models/modules/seanet.py deleted file mode 100644 index d694a29a7dcae4cddcecc0656899887fca9380c0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/modules/seanet.py +++ /dev/null @@ -1,271 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -"""Encodec SEANet-based encoder and decoder implementation.""" - -import typing as tp - -import numpy as np -import torch -import torch.nn as nn - -from . import SLSTM, SConv1d, SConvTranspose1d - - -@torch.jit.script -def snake(x, alpha): - shape = x.shape - x = x.reshape(shape[0], shape[1], -1) - x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2) - x = x.reshape(shape) - return x - - -class Snake1d(nn.Module): - def __init__(self, channels): - super().__init__() - self.alpha = nn.Parameter(torch.ones(1, channels, 1)) - - def forward(self, x): - return snake(x, self.alpha) - -class SEANetResnetBlock(nn.Module): - """Residual block from SEANet model. - Args: - dim (int): Dimension of the input/output - kernel_sizes (list): List of kernel sizes for the convolutions. - dilations (list): List of dilations for the convolutions. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - compress (int): Reduced dimensionality in residual branches (from Demucs v3) - true_skip (bool): Whether to use true skip connection or a simple convolution as the skip connection. - """ - def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1], - activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False, - pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True): - super().__init__() - assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations' - act = getattr(nn, activation) if activation != 'Snake' else Snake1d - hidden = dim // compress - block = [] - for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)): - in_chs = dim if i == 0 else hidden - out_chs = dim if i == len(kernel_sizes) - 1 else hidden - block += [ - act(**activation_params) if activation != 'Snake' else act(in_chs), - SConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation, - norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode), - ] - self.block = nn.Sequential(*block) - self.shortcut: nn.Module - if true_skip: - self.shortcut = nn.Identity() - else: - self.shortcut = SConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - - def forward(self, x): - return self.shortcut(x) + self.block(x) - - - -class SEANetEncoder(nn.Module): - """SEANet encoder. - Args: - channels (int): Audio channels. - dimension (int): Intermediate representation dimension. - n_filters (int): Base width for the model. - n_residual_layers (int): nb of residual layers. - ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of - upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here - that must match the decoder order - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - kernel_size (int): Kernel size for the initial convolution. - last_kernel_size (int): Kernel size for the initial convolution. - residual_kernel_size (int): Kernel size for the residual layers. - dilation_base (int): How much to increase the dilation with each layer. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - true_skip (bool): Whether to use true skip connection or a simple - (streamable) convolution as the skip connection in the residual network blocks. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - lstm (int): Number of LSTM layers at the end of the encoder. - """ - def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, - ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, - last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, - pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2, bidirectional:bool = False): - super().__init__() - self.channels = channels - self.dimension = dimension - self.n_filters = n_filters - self.ratios = list(reversed(ratios)) - del ratios - self.n_residual_layers = n_residual_layers - self.hop_length = np.prod(self.ratios) # 计算乘积 - - act = getattr(nn, activation) if activation != 'Snake' else Snake1d - mult = 1 - model: tp.List[nn.Module] = [ - SConv1d(channels, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - ] - # Downsample to raw audio scale - for i, ratio in enumerate(self.ratios): - # Add residual layers - for j in range(n_residual_layers): - model += [ - SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1], - dilations=[dilation_base ** j, 1], - norm=norm, norm_params=norm_params, - activation=activation, activation_params=activation_params, - causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] - - # Add downsampling layers - model += [ - act(**activation_params) if activation != 'Snake' else act(mult * n_filters), - SConv1d(mult * n_filters, mult * n_filters * 2, - kernel_size=ratio * 2, stride=ratio, - norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode), - ] - mult *= 2 - - if lstm: - model += [SLSTM(mult * n_filters, num_layers=lstm, bidirectional=bidirectional)] - - mult = mult * 2 if bidirectional else mult - model += [ - act(**activation_params) if activation != 'Snake' else act(mult * n_filters), - SConv1d(mult * n_filters, dimension, last_kernel_size, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - ] - - self.model = nn.Sequential(*model) - - def forward(self, x): - return self.model(x) - - -class SEANetDecoder(nn.Module): - """SEANet decoder. - Args: - channels (int): Audio channels. - dimension (int): Intermediate representation dimension. - n_filters (int): Base width for the model. - n_residual_layers (int): nb of residual layers. - ratios (Sequence[int]): kernel size and stride ratios - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function - final_activation (str): Final activation function after all convolutions. - final_activation_params (dict): Parameters to provide to the activation function - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - kernel_size (int): Kernel size for the initial convolution. - last_kernel_size (int): Kernel size for the initial convolution. - residual_kernel_size (int): Kernel size for the residual layers. - dilation_base (int): How much to increase the dilation with each layer. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - true_skip (bool): Whether to use true skip connection or a simple - (streamable) convolution as the skip connection in the residual network blocks. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - lstm (int): Number of LSTM layers at the end of the encoder. - trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup. - If equal to 1.0, it means that all the trimming is done at the right. - """ - def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, - ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None, - norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, - last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, - pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2, - trim_right_ratio: float = 1.0, bidirectional:bool = False): - super().__init__() - self.dimension = dimension - self.channels = channels - self.n_filters = n_filters - self.ratios = ratios - del ratios - self.n_residual_layers = n_residual_layers - self.hop_length = np.prod(self.ratios) - - act = getattr(nn, activation) if activation != 'Snake' else Snake1d - mult = int(2 ** len(self.ratios)) - model: tp.List[nn.Module] = [ - SConv1d(dimension, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - ] - - if lstm: - model += [SLSTM(mult * n_filters, num_layers=lstm, bidirectional=bidirectional)] - - # Upsample to raw audio scale - for i, ratio in enumerate(self.ratios): - # Add upsampling layers - model += [ - act(**activation_params) if activation != 'Snake' else act(mult * n_filters), - SConvTranspose1d(mult * n_filters, mult * n_filters // 2, - kernel_size=ratio * 2, stride=ratio, - norm=norm, norm_kwargs=norm_params, - causal=causal, trim_right_ratio=trim_right_ratio), - ] - # Add residual layers - for j in range(n_residual_layers): - model += [ - SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1], - dilations=[dilation_base ** j, 1], - activation=activation, activation_params=activation_params, - norm=norm, norm_params=norm_params, causal=causal, - pad_mode=pad_mode, compress=compress, true_skip=true_skip)] - - mult //= 2 - - # Add final layers - model += [ - act(**activation_params) if activation != 'Snake' else act(n_filters), - SConv1d(n_filters, channels, last_kernel_size, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - ] - # Add optional final activation to decoder (eg. tanh) - if final_activation is not None: - final_act = getattr(nn, final_activation) - final_activation_params = final_activation_params or {} - model += [ - final_act(**final_activation_params) - ] - self.model = nn.Sequential(*model) - - def forward(self, z): - y = self.model(z) - return y - - -def test(): - import torch - encoder = SEANetEncoder() - decoder = SEANetDecoder() - x = torch.randn(1, 1, 24000) - z = encoder(x) - print('z ', z.shape) - assert 1==2 - assert list(z.shape) == [1, 128, 75], z.shape - y = decoder(z) - assert y.shape == x.shape, (x.shape, y.shape) - - -if __name__ == '__main__': - test() diff --git a/audio/safeear/safeear_repo/safeear/models/safeear.py b/audio/safeear/safeear_repo/safeear/models/safeear.py deleted file mode 100644 index ec90d12d13fab1ee9c23789af9144dd1c7cf339c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/models/safeear.py +++ /dev/null @@ -1,969 +0,0 @@ -import math -import random -from typing import Union - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from timm.models.layers import DropPath, trunc_normal_ -from torch import Tensor -from torch.nn import ( - Dropout, - Identity, - LayerNorm, - Linear, - Module, - ModuleList, - Parameter, - init, -) - - -def conv3x3(in_planes, out_planes, stride=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) - -class SELayer(nn.Module): - def __init__(self, channel, reduction=16): - super(SELayer, self).__init__() - # print('se reduction: ', reduction) - # print(channel // reduction) - self.avg_pool = nn.AdaptiveAvgPool2d(1) # F_squeeze - self.fc = nn.Sequential( - nn.Linear(channel, channel // reduction, bias=False), - nn.ReLU(inplace=True), - nn.Linear(channel // reduction, channel, bias=False), - nn.Sigmoid() - ) - - def forward(self, x): # x: B*C*D*T - b, c, _, _ = x.size() - y = self.avg_pool(x).view(b, c) - y = self.fc(y).view(b, c, 1, 1) - return x * y.expand_as(x) - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - -class SEBasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16): - super(SEBasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes, 1) - self.bn2 = nn.BatchNorm2d(planes) - self.se = SELayer(planes, reduction) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.se(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - -class Bottleneck(nn.Module): - expansion = 2 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - -class SEBottleneck(nn.Module): - expansion = 2 - - def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16): - super(SEBottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.se = SELayer(planes * self.expansion, reduction) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - out = self.se(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class Bottle2neck(nn.Module): - expansion = 2 - - def __init__(self, - inplanes, - planes, - stride=1, - downsample=None, - baseWidth=26, - scale=4, - stype='normal'): - """ Constructor - Args: - inplanes: input channel dimensionality - planes: output channel dimensionality - stride: conv stride. Replaces pooling layer. - downsample: None when stride = 1 - baseWidth: basic width of conv3x3 - scale: number of scale. - type: 'normal': normal set. 'stage': first block of a new stage. - """ - super(Bottle2neck, self).__init__() - - width = int(math.floor(planes * (baseWidth / 64.0))) - self.conv1 = nn.Conv2d(inplanes, - width * scale, - kernel_size=1, - bias=False) - self.bn1 = nn.BatchNorm2d(width * scale) - - if scale == 1: - self.nums = 1 - else: - self.nums = scale - 1 - if stype == 'stage': - self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1) - convs = [] - bns = [] - for i in range(self.nums): - convs.append( - nn.Conv2d(width, - width, - kernel_size=3, - stride=stride, - padding=1, - bias=False)) - bns.append(nn.BatchNorm2d(width)) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - - self.conv3 = nn.Conv2d(width * scale, - planes * self.expansion, - kernel_size=1, - bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - - self.relu = nn.ReLU(inplace=True) - if stride != 1 or inplanes != planes * self.expansion: - downsample = nn.Sequential( - nn.AvgPool2d(kernel_size=stride, - stride=stride, - ceil_mode=True, - count_include_pad=False), - nn.Conv2d(inplanes, - planes * self.expansion, - kernel_size=1, - stride=1, - bias=False), - nn.BatchNorm2d(planes * self.expansion), - ) - self.downsample = downsample - self.stype = stype - self.scale = scale - self.width = width - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - spx = torch.split(out, self.width, 1) - for i in range(self.nums): - if i == 0 or self.stype == 'stage': - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.convs[i](sp) - sp = self.relu(self.bns[i](sp)) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - if self.scale != 1 and self.stype == 'normal': - out = torch.cat((out, spx[self.nums]), 1) - elif self.scale != 1 and self.stype == 'stage': - out = torch.cat((out, self.pool(spx[self.nums])), 1) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - -class SEBottle2neck(nn.Module): - expansion = 1 - - def __init__(self, - inplanes, - planes, - stride=1, - kernel_size = 1, - padding=1, - downsample=None, - baseWidth=26, - scale=4, - stype='normal'): - """ Constructor - Args: - inplanes: input channel dimensionality - planes: output channel dimensionality - stride: conv stride. Replaces pooling layer. - downsample: None when stride = 1 - baseWidth: basic width of conv3x3 - scale: number of scale. - type: 'normal': normal set. 'stage': first block of a new stage. - """ - super(SEBottle2neck, self).__init__() - - width = int(math.floor(planes * (baseWidth / 64.0))) - self.conv1 = nn.Conv2d(inplanes, - width * scale, - kernel_size=kernel_size, - padding=padding, - bias=False) - self.bn1 = nn.BatchNorm2d(width * scale) - - if scale == 1: - self.nums = 1 - else: - self.nums = scale - 1 - if stype == 'stage': - self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1) - convs = [] - bns = [] - for i in range(self.nums): - convs.append( - nn.Conv2d(width, - width, - kernel_size=3, - stride=stride, - padding=1, - bias=False)) - bns.append(nn.BatchNorm2d(width)) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - - self.conv3 = nn.Conv2d(width * scale, - planes * self.expansion, - kernel_size=kernel_size, - padding=(0,1), - bias=False) - self.bn3 = nn.BatchNorm2d(planes * self.expansion) - self.se = SELayer(planes * self.expansion, reduction=16) - self.relu = nn.ReLU(inplace=True) - if inplanes != planes: - self.downsample = True - self.conv_downsample = nn.Conv2d(in_channels=inplanes, - out_channels=planes, - padding=(0, 0), - kernel_size=(1, 1), - stride=1) - - else: - self.downsample = False - self.stype = stype - self.scale = scale - self.width = width - - def forward(self, x): - residual = x - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - spx = torch.split(out, self.width, 1) - for i in range(self.nums): - if i == 0 or self.stype == 'stage': - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.convs[i](sp) - sp = self.relu(self.bns[i](sp)) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - if self.scale != 1 and self.stype == 'normal': - out = torch.cat((out, spx[self.nums]), 1) - elif self.scale != 1 and self.stype == 'stage': - out = torch.cat((out, self.pool(spx[self.nums])), 1) - - out = self.conv3(out) - out = self.bn3(out) - out = self.se(out) - - if self.downsample: - residual = self.conv_downsample(residual) - - out += residual - out = self.relu(out) - - return out - -class Res2Net(nn.Module): - def __init__(self, block, layers, baseWidth=26, scale=4, m=0.35, num_classes=1000, loss='softmax', **kwargs): - self.inplanes = 16 - super(Res2Net, self).__init__() - self.loss = loss - self.baseWidth = baseWidth - self.scale = scale - self.conv1 = nn.Sequential(nn.Conv2d(1, 16, 3, 1, 1, bias=False), - nn.BatchNorm2d(16), nn.ReLU(inplace=True), - nn.Conv2d(16, 16, 3, 1, 1, bias=False), - nn.BatchNorm2d(16), nn.ReLU(inplace=True), - nn.Conv2d(16, 16, 3, 1, 1, bias=False)) - self.bn1 = nn.BatchNorm2d(16) - self.relu = nn.ReLU() - # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(block, 16, layers[0])#64 - self.layer2 = self._make_layer(block, 32, layers[1], stride=2)#128 - self.layer3 = self._make_layer(block, 64, layers[2], stride=2)#256 - self.layer4 = self._make_layer(block, 128, layers[3], stride=2)#512 - self.avgpool = nn.AdaptiveAvgPool2d(1) - # self.stats_pooling = StatsPooling() - - if self.loss == 'softmax': - # self.cls_layer = nn.Linear(2*8*128*block.expansion, num_classes) - self.cls_layer = nn.Linear(128*block.expansion, num_classes) - else: - raise NotImplementedError - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, - mode='fan_out', - nonlinearity='relu') - elif isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.AvgPool2d(kernel_size=stride, - stride=stride, - ceil_mode=True, - count_include_pad=False), - nn.Conv2d(self.inplanes, - planes * block.expansion, - kernel_size=1, - stride=1, - bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append( - block(self.inplanes, - planes, - stride, - downsample=downsample, - stype='stage', - baseWidth=self.baseWidth, - scale=self.scale)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append( - block(self.inplanes, - planes, - baseWidth=self.baseWidth, - scale=self.scale)) - - return nn.Sequential(*layers) - - def _forward(self, x): - x = x.unsqueeze(dim=1) - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.avgpool(x) - x = torch.flatten(x, 1) - x = self.cls_layer(x) - - return F.log_softmax(x, dim=-1) - - def extract(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.avgpool(x) - x = torch.flatten(x, 1) - return x - # Allow for accessing forward method in a inherited class - forward = _forward - -def se_res2net50_v1b_14w_8s(**kwargs): - """Constructs a Res2Net-50_v1b model. - Res2Net-50 refers to the Res2Net-50_v1b_26w_4s. - """ - model = Res2Net(SEBottle2neck, [3, 4, 6, 3], baseWidth=14, scale=8, **kwargs) - return model - - -class CONV(nn.Module): - @staticmethod - def to_mel(hz): - return 2595 * np.log10(1 + hz / 700) - - @staticmethod - def to_hz(mel): - return 700 * (10**(mel / 2595) - 1) - - def __init__(self, - out_channels, - kernel_size, - sample_rate=16000, - in_channels=1, - stride=1, - padding=0, - dilation=1, - bias=False, - groups=1, - mask=False): - super().__init__() - if in_channels != 1: - - msg = "SincConv only support one input channel (here, in_channels = {%i})" % ( - in_channels) - raise ValueError(msg) - self.out_channels = out_channels - self.kernel_size = kernel_size - self.sample_rate = sample_rate - - # Forcing the filters to be odd (i.e, perfectly symmetrics) - if kernel_size % 2 == 0: - self.kernel_size = self.kernel_size + 1 - self.stride = stride - self.padding = padding - self.dilation = dilation - self.mask = mask - if bias: - raise ValueError('SincConv does not support bias.') - if groups > 1: - raise ValueError('SincConv does not support groups.') - - NFFT = 512 - f = int(self.sample_rate / 2) * np.linspace(0, 1, int(NFFT / 2) + 1) - fmel = self.to_mel(f) - fmelmax = np.max(fmel) - fmelmin = np.min(fmel) - filbandwidthsmel = np.linspace(fmelmin, fmelmax, self.out_channels + 1) - filbandwidthsf = self.to_hz(filbandwidthsmel) - - self.mel = filbandwidthsf - self.hsupp = torch.arange(-(self.kernel_size - 1) / 2, - (self.kernel_size - 1) / 2 + 1) - self.band_pass = torch.zeros(self.out_channels, self.kernel_size) - for i in range(len(self.mel) - 1): - fmin = self.mel[i] - fmax = self.mel[i + 1] - hHigh = (2*fmax/self.sample_rate) * \ - np.sinc(2*fmax*self.hsupp/self.sample_rate) - hLow = (2*fmin/self.sample_rate) * \ - np.sinc(2*fmin*self.hsupp/self.sample_rate) - hideal = hHigh - hLow - - self.band_pass[i, :] = Tensor(np.hamming( - self.kernel_size)) * Tensor(hideal) - - def forward(self, x, mask=False): - band_pass_filter = self.band_pass.clone().to(x.device) - if mask: - A = np.random.uniform(0, 20) - A = int(A) - A0 = random.randint(0, band_pass_filter.shape[0] - A) - band_pass_filter[A0:A0 + A, :] = 0 - else: - band_pass_filter = band_pass_filter - - self.filters = (band_pass_filter).view(self.out_channels, 1, - self.kernel_size) - - return F.conv1d(x, - self.filters, - stride=self.stride, - padding=self.padding, - dilation=self.dilation, - bias=None, - groups=1) - - -class My_Residual_block(nn.Module): - def __init__(self, nb_filts, first=False, conv1=[2, 3, 1, 1, 1, 1], conv2=[2, 3, 0, 1, 1, 3], conv3=[1, 3, 0, 1, 1, 3], pool=(1, 3)): - super().__init__() - self.first = first - - if not self.first: - self.bn1 = nn.BatchNorm2d(num_features=nb_filts[0]) - self.conv1 = nn.Conv2d(in_channels=nb_filts[0], - out_channels=nb_filts[1], - kernel_size=(conv1[0], conv1[1]), - padding=(conv1[2], conv1[3]), - stride=(conv1[4], conv1[5])) - self.selu = nn.SELU(inplace=True) - - self.bn2 = nn.BatchNorm2d(num_features=nb_filts[1]) - self.conv2 = nn.Conv2d(in_channels=nb_filts[1], - out_channels=nb_filts[1], - kernel_size=(conv2[0], conv2[1]), - padding=(conv2[2], conv2[3]), - stride=(conv2[4], conv2[5])) - - self.downsample = True - self.conv_downsample = nn.Conv2d(in_channels=nb_filts[0], - out_channels=nb_filts[1], - kernel_size=(conv3[0], conv3[1]), - padding=(conv3[2], conv3[3]), - stride=(conv3[4], conv3[5])) - - # self.mp = nn.MaxPool2d((1,4)) - self.mp = nn.MaxPool2d((pool[0], pool[1])) - - def forward(self, x): - identity = x - if not self.first: - out = self.bn1(x) - out = self.selu(out) - else: - out = x - out = self.conv1(x) - - out = self.bn2(out) - out = self.selu(out) - out = self.conv2(out) - - if self.downsample: - identity = self.conv_downsample(identity) - - out += identity - out = self.mp(out) - return out - - -class My_SERes2Net_block(nn.Module): - def __init__(self, nb_filts, first=False, conv1=[2, 3, 1, 1, 1, 1], conv2=[3, 3, 1, 1, 1, 3], conv3=[1, 3, 0, 1, 1, 3], pool=(1, 3), radix=2, groups=2): - super().__init__() - self.first = first - - if not self.first: - self.bn1 = nn.BatchNorm2d(num_features=nb_filts[0]) - - self.conv1 = SEBottle2neck(inplanes=nb_filts[0], - planes=nb_filts[1], kernel_size=(conv1[0], conv1[1])) - self.selu = nn.SELU(inplace=True) - - self.bn2 = nn.BatchNorm2d(num_features=nb_filts[1]) - self.conv2 = nn.Conv2d(in_channels=nb_filts[1], - out_channels=nb_filts[1], - kernel_size=(conv2[0], conv2[1]), - padding=(conv2[2], conv2[3]), - stride=(conv2[4], conv2[5])) - - self.downsample = True - self.conv_downsample = nn.Conv2d(in_channels=nb_filts[0], - out_channels=nb_filts[1], - kernel_size=(conv3[0], conv3[1]), - padding=(conv3[2], conv3[3]), - stride=(conv3[4], conv3[5])) - - self.mp = nn.MaxPool2d((pool[0], pool[1])) - - def forward(self, x): - identity = x - if not self.first: - out = self.bn1(x) - out = self.selu(out) - else: - out = x - - out = self.conv1(x) - out = self.bn2(out) - out = self.selu(out) - out = self.conv2(out) - if self.downsample: - identity = self.conv_downsample(identity) - - out += identity - out = self.mp(out) - return out - - -class Attention(Module): - """ - Obtained from timm: github.com:rwightman/pytorch-image-models - """ - - def __init__(self, dim, num_heads=8, attention_dropout=0.1, projection_dropout=0.1): - super().__init__() - self.num_heads = num_heads - head_dim = dim // self.num_heads - self.scale = head_dim ** -0.5 - - self.qkv = Linear(dim, dim * 3, bias=False) - self.attn_drop = Dropout(attention_dropout) - self.proj = Linear(dim, dim) - self.proj_drop = Dropout(projection_dropout) - - def forward(self, x): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // - self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class SimpleRelativeAttention(nn.Module): - # we implement this relative position embedding here., this is not used in our experiments - def __init__(self, dim, seq_length, num_heads=8, qkv_bias=True, qk_scale=None, attn_drop=0.1, proj_drop=0.1): - super().__init__() - self.dim = dim - self.length = seq_length - self.num_heads = num_heads - head_dim = dim//num_heads - self.scale = qk_scale or head_dim**-0.5 - self.relative_position_table = nn.Parameter( - torch.zeros(size=(seq_length*2-1, num_heads))) - coords = torch.arange(seq_length) - relative_coords = coords[:, None]-coords[None, :] - relative_coords = relative_coords+seq_length-1 - self.register_buffer('relative_index', relative_coords) - self.qkv = nn.Linear(dim, dim*3) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_table, std=0.02) - - def forward(self, x): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // - self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] - q = q*self.scale - attn = torch.einsum('bhqe,bhke->bhqk', q, k) - relative_position_bias = self.relative_position_table[self.relative_index.reshape(-1)].reshape( - self.length, self.length, self.num_heads - ) - relative_position_bias = relative_position_bias.permute( - 2, 0, 1).contiguous() - attn = attn+relative_position_bias.unsqueeze(0) - attn = attn.softmax(-1) - attn = self.attn_drop(attn) - x = torch.einsum('bnqk,bnqe->bnqe', attn, - v).transpose(1, 2).reshape(B, N, C) - x = self.proj_drop(self.proj(x)) - return x - - -class TransformerEncoderLayer(Module): - """ - Inspired by torch.nn.TransformerEncoderLayer and timm. - """ - - def __init__(self, d_model, nhead, atten=Attention, dim_feedforward=2048, dropout=0.1, - attention_dropout=0.1, drop_path_rate=0.1): - super(TransformerEncoderLayer, self).__init__() - self.pre_norm = LayerNorm(d_model) - self.self_attn = atten(dim=d_model, num_heads=nhead, - attention_dropout=attention_dropout, projection_dropout=dropout) - - self.linear1 = Linear(d_model, dim_feedforward) - self.dropout1 = Dropout(dropout) - self.norm1 = LayerNorm(d_model) - self.linear2 = Linear(dim_feedforward, d_model) - self.dropout2 = Dropout(dropout) - - self.drop_path = DropPath( - drop_path_rate) if drop_path_rate > 0 else Identity() - - self.activation = F.gelu - - def forward(self, src: torch.Tensor, *args, **kwargs) -> torch.Tensor: - src = src + self.drop_path(self.self_attn(self.pre_norm(src))) - src = self.norm1(src) - src2 = self.linear2(self.dropout1(self.activation(self.linear1(src)))) - src = src + self.drop_path(self.dropout2(src2)) - return src - - -class TransformerClassifier(Module): - """ - Adopted from https://github.com/SHI-Labs/Compact-Transformers.git - """ - - def __init__(self, - embedding_dim=768, - num_classes=1000, - num_layers=12, - num_heads=12, - mlp_ratio=4.0, - dropout_rate=0.1, - attention_dropout=0.1, - stochastic_depth_rate=0.1, - positional_embedding='sine', - sequence_length=10000, - *args, **kwargs): - super().__init__() - positional_embedding = positional_embedding if \ - positional_embedding in ['sine', 'learnable', 'none'] else 'sine' - dim_feedforward = int(embedding_dim * mlp_ratio) - self.embedding_dim = embedding_dim - self.sequence_length = sequence_length - - assert sequence_length is not None or positional_embedding == 'none' - - if positional_embedding != 'none': - if positional_embedding == 'learnable': - self.positional_emb = Parameter(torch.zeros(1, sequence_length, embedding_dim), - requires_grad=True) - init.trunc_normal_(self.positional_emb, std=0.2) - else: - print('here!!! sinusoidal_embedding') - self.positional_emb = Parameter(self.sinusoidal_embedding(sequence_length, embedding_dim), - requires_grad=False) - else: - self.positional_emb = None - - self.dropout = Dropout(p=dropout_rate) - dpr = [x.item() for x in torch.linspace( - 0, stochastic_depth_rate, num_layers)] - self.blocks = ModuleList([ - TransformerEncoderLayer(d_model=embedding_dim, nhead=num_heads, - dim_feedforward=dim_feedforward, dropout=dropout_rate, - attention_dropout=attention_dropout, drop_path_rate=dpr[i]) - for i in range(num_layers)]) - self.norm = LayerNorm(embedding_dim) - self.flattener = nn.Flatten(2, 3) - self.attention_pool = Linear(self.embedding_dim, 1) - self.fc = Linear(embedding_dim, num_classes) - self.apply(self.init_weight) - - def forward(self, x): - x = torch.transpose(x,-1,-2) - seq_len = x.size(1) - x += self.positional_emb[:, :seq_len, :] - - x = self.dropout(x) - for blk in self.blocks: - x = blk(x) - x = self.norm(x) - - feature = torch.matmul(F.softmax(self.attention_pool( - x), dim=1).transpose(-1, -2), x).squeeze(-2) - logits = self.fc(feature) - - return logits, feature - - - @staticmethod - def init_weight(m): - if isinstance(m, Linear): - init.trunc_normal_(m.weight, std=.02) - if isinstance(m, Linear) and m.bias is not None: - init.constant_(m.bias, 0) - elif isinstance(m, LayerNorm): - init.constant_(m.bias, 0) - init.constant_(m.weight, 1.0) - - @staticmethod - def sinusoidal_embedding(n_channels, dim): - pe = torch.FloatTensor([[p / (10000 ** (2 * (i // 2) / dim)) for i in range(dim)] - for p in range(n_channels)]) - pe[:, 0::2] = torch.sin(pe[:, 0::2]) - pe[:, 1::2] = torch.cos(pe[:, 1::2]) - return pe.unsqueeze(0) - -class SE_Rawformer_front(nn.Module): - def __init__(self, conv1 = [2,3,1,1,1,1],conv2 = [3,3,1,1,1,2],conv3 = [1,3,0,1,1,2]): - super().__init__() - filts = [70, [1, 32], [32, 32], [32, 64], [64, 64]] - self.conv_time = CONV(out_channels=filts[0], - kernel_size=128, - in_channels=1) # 70 129 - self.first_bn = nn.BatchNorm2d(num_features=1) - self.drop = nn.Dropout(0.5, inplace=True) - self.drop_way = nn.Dropout(0.2, inplace=True) - self.selu = nn.SELU(inplace=True) - - self.encoder = nn.Sequential( - nn.Sequential(My_Residual_block(nb_filts=filts[1], conv1 = conv1,conv2 = [2,3,0,1,1,2],conv3 = conv3,first=True)), - nn.Sequential(My_SERes2Net_block(nb_filts=filts[2], conv1 = conv1,conv2 = conv2,conv3 = conv3)), - nn.Sequential(My_SERes2Net_block(nb_filts=filts[3], conv1 = conv1,conv2 = conv2,conv3 = conv3)), - nn.Sequential(My_SERes2Net_block(nb_filts=filts[4], conv1 = conv1,conv2 = conv2,conv3 = conv3))) - - def forward(self, x, Freq_aug=False): - x = self.conv_time(x, mask=Freq_aug) - x = x.unsqueeze(dim=1) - x = F.max_pool2d(torch.abs(x), (3, 3)) - x = self.first_bn(x) - x = self.selu(x) - - encoder = self.encoder(x) - return encoder - - -class SafeEar(nn.Module): - def __init__(self,front, *args, **kwargs): - super().__init__() - # self.front = front - self.bottleneck = nn.Sequential( - nn.Conv1d(kwargs["embedding_dim"]*7, kwargs["embedding_dim"], kernel_size=1), - nn.BatchNorm1d(kwargs["embedding_dim"]) - ) - self.classifier = TransformerClassifier(*args, **kwargs) - - def forward(self, encoder): - encoder = self.bottleneck(torch.cat(encoder, dim=1)) - batch_size, feature_dim, frame_num = encoder.size() - - for i in range(0, frame_num, 50): - encoder[:, :, i:i+50] = torch.flip(encoder[:, :, i:i+50], dims=[2]) - - logits, feature = self.classifier(encoder) - - return logits, feature - - -class SafeEar1s(nn.Module): - def __init__(self,front, *args, **kwargs): - super().__init__() - # self.front = front - self.bottleneck = nn.Sequential( - nn.Conv1d(kwargs["embedding_dim"]*7, kwargs["embedding_dim"], kernel_size=1), - nn.BatchNorm1d(kwargs["embedding_dim"]) - ) - self.classifier = TransformerClassifier(*args, **kwargs) - - def forward(self, encoder): - encoder = self.bottleneck(torch.cat(encoder, dim=1)) - - batch_size, feature_dim, frame_num = encoder.size() - for i in range(0, frame_num, 50): - end = i+50 if i+50 <= frame_num else frame_num - indices = torch.randperm(end - i) - encoder[:, :, i:end] = encoder[:, :, i+indices] - - logits, feature = self.classifier(encoder) - - return logits, feature diff --git a/audio/safeear/safeear_repo/safeear/trainer/safeear_trainer.py b/audio/safeear/safeear_repo/safeear/trainer/safeear_trainer.py deleted file mode 100644 index 5529e928f509a1c8871f5700433798b8b48c771d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/trainer/safeear_trainer.py +++ /dev/null @@ -1,192 +0,0 @@ -import math -import warnings - -import numpy as np -import pytorch_lightning as pl -import torch - -from ..losses.loss import compute_eer - -warnings.filterwarnings("ignore") - -def get_input(x): - x = x.to(memory_format=torch.contiguous_format) - return x.float() - -class SafeEarTrainer(pl.LightningModule): - def __init__( - self, - decouple_model, - detect_model, - lr_raw_former, - save_score_path - ) -> None: - super().__init__() - - self.decouple_model = decouple_model - self.detect_model = detect_model - self.lr_raw_former = lr_raw_former - self.save_score_path = save_score_path - - self.detect_loss = torch.nn.BCELoss() - - self.automatic_optimization = False - - self.val_index_loader = [] - self.val_score_loader = [] - self.eval_index_loader = [] - self.eval_score_loader = [] - self.eval_filename_loader = [] - self.default_monitor = "val_eer" - - def forward(self, batch, is_train=True): - if is_train: - x, feat, target = batch - else: - if len(batch) == 4: - x, feat, target, audio_path = batch - else: - x, feat, target = batch - audio_path = None - x_wav = get_input(x) - with torch.no_grad(): - self.decouple_model.eval() - G_x, commit_loss, last_layer, acoustic_tokens = self.decouple_model(x_wav, layers=[0,1,2,3,4,5,6,7]) - raw_logits, raw_feature = self.detect_model(acoustic_tokens) - - if is_train: - onehot_target = torch.eye(2).to(self.device)[target, :] - raw_logits = torch.softmax(raw_logits, dim=-1) - raw_former_loss_ = self.detect_loss(raw_logits,onehot_target) - return raw_former_loss_, raw_logits, target - else: - raw_logits = torch.softmax(raw_logits, dim=-1)[:, 0] - raw_former_loss_ = 0 - return audio_path, raw_former_loss_, raw_logits, target - - def training_step(self, batch, batch_idx): - raw_opt = self.optimizers() - - raw_former_loss_, raw_logits, target = self(batch, is_train=True) - raw_opt.zero_grad() - self.manual_backward(raw_former_loss_) - raw_opt.step() - - self.log_dict( - { - 'train_loss': raw_former_loss_ - }, - on_step=True, - on_epoch=True, - prog_bar=True, - sync_dist=True, - logger=True) - - def validation_step(self, batch, batch_idx): - _, raw_former_loss_, raw_logits, target = self(batch, is_train=False) - - self.val_index_loader.append(target) - self.val_score_loader.append(raw_logits) - - self.log_dict( - { - 'val_loss': raw_former_loss_, - }, - on_epoch=True, - prog_bar=True, - sync_dist=True, - logger=True) - - def on_validation_epoch_end(self): - all_index = self.all_gather(torch.cat(self.val_index_loader, dim=0)).view(-1).cpu().numpy() - all_score = self.all_gather(torch.cat(self.val_score_loader, dim=0)).view(-1).cpu().numpy() - val_eer = compute_eer(all_score[all_index == 0], all_score[all_index == 1])[0] - other_val_eer = compute_eer(-all_score[all_index == 0], -all_score[all_index == 1])[0] - val_eer = min(val_eer, other_val_eer) - self.log_dict( - { - "val_eer": val_eer, - }, - sync_dist=True, - on_epoch=True, - prog_bar=True, - logger=True) - - self.val_index_loader.clear() # free memory - self.val_score_loader.clear() # free memory - - self.log_dict( - { - "lr": self.optimizers().param_groups[0]['lr'], - }, - sync_dist=True, - on_epoch=True, - prog_bar=False, - logger=True - ) - - adjust_learning_rate(self.optimizers(), self.current_epoch, self.lr_raw_former, self.trainer.max_epochs*0.1, self.trainer.max_epochs) - - def test_step(self, batch, batch_idx): - - audio_path, raw_former_loss_, raw_logits, target = self(batch, is_train=False) - - self.eval_index_loader.append(target) - self.eval_score_loader.append(raw_logits) - self.eval_filename_loader.append(audio_path) - self.log_dict( - { - 'val_loss_rawformer': raw_former_loss_, - }, - on_epoch=True, - prog_bar=True, - sync_dist=True, - logger=True) - - def on_test_epoch_end(self): - - string_list = [list(item) for item in self.eval_filename_loader] - - all_filename = np.array(string_list) - all_filename = all_filename.reshape(-1, 1) - - - all_index = self.all_gather(torch.cat(self.eval_index_loader, dim=0)).view(-1).cpu().numpy() - all_score = self.all_gather(torch.cat(self.eval_score_loader, dim=0)).view(-1).cpu().numpy() - - # gpu_id = torch.cuda.current_device() - - data_to_write = zip(all_filename, all_score,all_index) - csv_filename = self.save_score_path + '/score.csv' - eval_eer = compute_eer(all_score[all_index == 0], all_score[all_index == 1])[0] - other_eval_eer = compute_eer(-all_score[all_index == 0], -all_score[all_index == 1])[0] - eval_eer = min(eval_eer, other_eval_eer) - - self.log_dict( - { - "test_eer": eval_eer, - }, - sync_dist=True, - on_epoch=True, - prog_bar=True, - logger=True) - - self.eval_index_loader.clear() # free memory - self.eval_score_loader.clear() # free memory - self.eval_filename_loader.clear() # free memory - - def configure_optimizers(self): - optimizer_rawformer = torch.optim.AdamW(self.detect_model.parameters(), lr=self.lr_raw_former, weight_decay=1e-4) - - return [optimizer_rawformer] - -def adjust_learning_rate(optimizer, epoch, lr, warmup, epochs=100): - lr = lr - if epoch < warmup: - lr = lr / (warmup - epoch) - else: - lr *= 0.5 * (1. + math.cos(math.pi * - (epoch - warmup) / (epochs - warmup))) - - for param_group in optimizer.param_groups: - param_group['lr'] = lr \ No newline at end of file diff --git a/audio/safeear/safeear_repo/safeear/utils/dump_hubert_feature.py b/audio/safeear/safeear_repo/safeear/utils/dump_hubert_feature.py deleted file mode 100644 index d349bc4b94708d57e2eb36a012a6b52075ed5a1a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/safeear/utils/dump_hubert_feature.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import sys - -import tqdm - -sys.path.append('../../fairseq_ours/') # we recommend an abosulte path here. -from pathlib import Path - -import fairseq -import soundfile as sf -import torch -import torch.nn.functional as F -from npy_append_array import NpyAppendArray - -# from feature_utils import get_path_iterator, dump_feature - - -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("dump_hubert_feature") - - -class HubertFeatureReader(object): - def __init__(self, ckpt_path, layer, max_chunk=1600000): - ( - model, - cfg, - task, - ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) - self.model = model[0].eval().cuda() - self.task = task - self.layer = layer - self.max_chunk = max_chunk - logger.info(f"TASK CONFIG:\n{self.task.cfg}") - logger.info(f" max_chunk = {self.max_chunk}") - - def read_audio(self, path, ref_len=None): - wav, sr = sf.read(path) - assert sr == self.task.cfg.sample_rate, sr - if wav.ndim == 2: - wav = wav.mean(-1) - assert wav.ndim == 1, wav.ndim - if ref_len is not None and abs(ref_len - len(wav)) > 160: - logging.warning(f"ref {ref_len} != read {len(wav)} ({path})") - return wav - - def get_feats(self, path, ref_len=None): - x = self.read_audio(path, ref_len) - with torch.no_grad(): - x = torch.from_numpy(x).float().cuda() - if self.task.cfg.normalize: - x = F.layer_norm(x, x.shape) - x = x.view(1, -1) - - feat = [] - for start in range(0, x.size(1), self.max_chunk): - x_chunk = x[:, start: start + self.max_chunk] - feat_chunk, _, _ = self.model.extract_features( - source=x_chunk, - padding_mask=None, - mask=False, - output_layer=self.layer, - ) - feat.append(feat_chunk) - return torch.cat(feat, 1).squeeze(0) - -def dump_feature(reader,audio_dir,save_dir): - save_dir = Path(save_dir) - audio_dir = Path(audio_dir) - - audio_files = list(audio_dir.glob("**/*.flac")) - - for audio_file in tqdm.tqdm(audio_files): - releative_path = audio_file.relative_to(audio_dir).with_suffix(".npy") - save_path = save_dir / releative_path - # import pdb; pdb.set_trace() - if not save_path.parent.exists(): - save_path.parent.mkdir(parents=True) - - feat_f = NpyAppendArray(save_path) - feat = reader.get_feats(audio_file) - feat_f.append(feat.cpu().numpy()) - logger.info("finished successfully") - -def main(audio_dir, save_dir, ckpt_path, layer, max_chunk): - reader = HubertFeatureReader(ckpt_path, layer, max_chunk) - dump_feature(reader, audio_dir, save_dir) - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("--audio_dir", default="./datasets/ASVSpoof2021/ASVspoof2021_LA_eval/flac") - parser.add_argument("--save_dir", default="./datasets/ASVSpoof2021/ASVspoof2021_LA_eval/Hubert_L9") - - parser.add_argument("--ckpt_path", default="./model_zoo/hubert/hubert_base_ls960.pt") - parser.add_argument("--layer", type=int, default=9) - parser.add_argument("--max_chunk", type=int, default=1600000) - args = parser.parse_args() - logger.info(args) - - main(**vars(args)) diff --git a/audio/safeear/safeear_repo/test.py b/audio/safeear/safeear_repo/test.py deleted file mode 100644 index c03b0f308e7a4b941bf8d44b9f3cfb512a2256cd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/test.py +++ /dev/null @@ -1,79 +0,0 @@ -import argparse -import importlib -import json -import os -from typing import Any, Dict, List, Optional, Tuple - -import hydra -import pytorch_lightning as pl -import torch - -torch.set_float32_matmul_precision("high") - -from omegaconf import DictConfig, OmegaConf -from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer -from pytorch_lightning.strategies.ddp import DDPStrategy -from pytorch_lightning.utilities import rank_zero_only - - -@rank_zero_only -def print_only(message: str): - """Prints a message only on rank 0.""" - print(message) - -def train(cfg: DictConfig, args) -> Tuple[Dict[str, Any], Dict[str, Any]]: - - # instantiate datamodule - print_only(f"Instantiating datamodule <{cfg.datamodule._target_}>") - datamodule: LightningDataModule = hydra.utils.instantiate(cfg.datamodule) - - # instantiate decouple model - print_only(f"Instantiating decouple model <{cfg.decouple_model._target_}>") - decouple_model: torch.nn.Module = hydra.utils.instantiate(cfg.decouple_model) - decouple_model.load_state_dict(torch.load(cfg.speechtokenizer_path)) - # import pdb; pdb.set_trace() - - # instantiate detect model - print(f"Instantiating detect model <{cfg.detect_model._target_}>") - detect_model: torch.nn.Module = hydra.utils.instantiate(cfg.detect_model) - # import pdb; pdb.set_trace() - - # instantiate system - print_only(f"Instantiating system <{cfg.system._target_}>") - system: LightningModule = hydra.utils.instantiate( - cfg.system, - decouple_model=decouple_model, - detect_model=detect_model, - ) - - # instantiate trainer - print_only(f"Instantiating trainer <{cfg.trainer._target_}>") - trainer: Trainer = hydra.utils.instantiate( - cfg.trainer, - strategy=DDPStrategy(find_unused_parameters=True), - ) - - trainer.test(system, datamodule=datamodule, ckpt_path=args.ckpt_path) - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument( - "--conf_dir", - default="local/conf.yml", - help="Full path to save best validation model", - ) - parser.add_argument( - "--ckpt_path", - help="Full path to save best validation model", - ) - - args = parser.parse_args() - cfg = OmegaConf.load(args.conf_dir) - - os.makedirs(os.path.join(cfg.exp.dir, cfg.exp.name), exist_ok=True) - # 保存配置到新的文件 - OmegaConf.save(cfg, os.path.join(cfg.exp.dir, cfg.exp.name, "config.yaml")) - - train(cfg, args) - \ No newline at end of file diff --git a/audio/safeear/safeear_repo/train.py b/audio/safeear/safeear_repo/train.py deleted file mode 100644 index dad0df0d9e93d8d375b6b451fddce2d394c68c7c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/train.py +++ /dev/null @@ -1,104 +0,0 @@ -import importlib -import json -import os -import warnings - -warnings.filterwarnings("ignore") -import argparse -from typing import Any, Dict, List, Optional, Tuple - -import hydra -import pytorch_lightning as pl -import torch - -torch.set_float32_matmul_precision("high") - -from omegaconf import DictConfig, OmegaConf -from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer -from pytorch_lightning.strategies.ddp import DDPStrategy -from pytorch_lightning.utilities import rank_zero_only - - -@rank_zero_only -def print_only(message: str): - """Prints a message only on rank 0.""" - print(message) - -def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: - - # instantiate datamodule - print_only(f"Instantiating datamodule <{cfg.datamodule._target_}>") - datamodule: LightningDataModule = hydra.utils.instantiate(cfg.datamodule) - datamodule.setup() - - # instantiate decouple model - print_only(f"Instantiating decouple model <{cfg.decouple_model._target_}>") - decouple_model: torch.nn.Module = hydra.utils.instantiate(cfg.decouple_model) - decouple_model.load_state_dict(torch.load(cfg.speechtokenizer_path)) - # import pdb; pdb.set_trace() - - # instantiate detect model - print(f"Instantiating detect model <{cfg.detect_model._target_}>") - detect_model: torch.nn.Module = hydra.utils.instantiate(cfg.detect_model) - # import pdb; pdb.set_trace() - - # instantiate system - print_only(f"Instantiating system <{cfg.system._target_}>") - system: LightningModule = hydra.utils.instantiate( - cfg.system, - decouple_model=decouple_model, - detect_model=detect_model, - ) - # instantiate callbacks - callbacks: List[Callback] = [] - if cfg.get("early_stopping"): - print_only(f"Instantiating early_stopping <{cfg.early_stopping._target_}>") - callbacks.append(hydra.utils.instantiate(cfg.early_stopping)) - if cfg.get("checkpoint"): - print_only(f"Instantiating checkpoint <{cfg.checkpoint._target_}>") - checkpoint: pl.callbacks.ModelCheckpoint = hydra.utils.instantiate(cfg.checkpoint) - callbacks.append(checkpoint) - - # instantiate logger - print_only(f"Instantiating logger <{cfg.logger._target_}>") - os.makedirs(os.path.join(cfg.exp.dir, cfg.exp.name, "logs"), exist_ok=True) - logger = hydra.utils.instantiate(cfg.logger) - - # instantiate trainer - print_only(f"Instantiating trainer <{cfg.trainer._target_}>") - trainer: Trainer = hydra.utils.instantiate( - cfg.trainer, - callbacks=callbacks, - logger=logger, - strategy=DDPStrategy(find_unused_parameters=True), - ) - - trainer.fit(system, datamodule=datamodule) - print_only("Training finished!") - best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()} - with open(os.path.join(cfg.exp.dir, cfg.exp.name, "best_k_models.json"), "w") as f: - json.dump(best_k, f, indent=0) - - import wandb - if wandb.run: - print_only("Closing wandb!") - wandb.finish() - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument( - "--conf_dir", - default="local/conf.yml", - help="Full path to save best validation model", - ) - - args = parser.parse_args() - cfg = OmegaConf.load(args.conf_dir) - - os.makedirs(os.path.join(cfg.exp.dir, cfg.exp.name), exist_ok=True) - # 保存配置到新的文件 - OmegaConf.save(cfg, os.path.join(cfg.exp.dir, cfg.exp.name, "config.yaml")) - - train(cfg) - diff --git a/audio/shiftyspeech/Dockerfile b/audio/shiftyspeech/Dockerfile deleted file mode 100644 index ce862b0ac4f1d9ab15157a56061777ec2f968c7e..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/Dockerfile +++ /dev/null @@ -1,43 +0,0 @@ -FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04 - -ENV DEBIAN_FRONTEND=noninteractive -ENV PYTHONUNBUFFERED=1 - -RUN apt-get update && apt-get install -y --no-install-recommends \ - python3 python3-pip python3-dev \ - git ffmpeg libsndfile1 \ - build-essential g++ \ - && rm -rf /var/lib/apt/lists/* - -RUN ln -sf /usr/bin/python3 /usr/bin/python - -WORKDIR /app - -# Install PyTorch with CUDA 12.1 -RUN pip install --no-cache-dir \ - torch==2.5.1 torchaudio==2.5.1 \ - --index-url https://download.pytorch.org/whl/cu121 - -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -# Clone fairseq with patched C extensions -RUN git clone https://github.com/facebookresearch/fairseq.git /app/fairseq_repo && \ - cd /app/fairseq_repo && \ - git checkout a54021305d6b3c4c5959ac9395135f63202db8f1 && \ - sed -i 's/ext_modules=extensions/ext_modules=[]/' setup.py && \ - pip install --no-cache-dir --no-deps -e . - -COPY synthetic_speech_detection /app/synthetic_speech_detection -RUN mkdir -p /app/models -COPY api.py . -RUN mkdir -p /app/weights -COPY weights/ /app/weights/ -RUN ln -sf /app/weights/xlsr_53_56k.pt /app/models/xlsr2_300m.pt - -EXPOSE 8001 - -RUN adduser --disabled-password --gecos '' appuser -USER appuser - -CMD ["python", "api.py"] diff --git a/audio/shiftyspeech/Images/logo-png.png b/audio/shiftyspeech/Images/logo-png.png deleted file mode 100644 index 9ef644ad657e346101151c7844dfa02ff7eac41c..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/Images/logo-png.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f340b95d8b8275201ef3f5fb83e8f3133ceba14ba73d1abd6a41ceccc29ac11c -size 450772 diff --git a/audio/shiftyspeech/Images/logo-transparent-png.png b/audio/shiftyspeech/Images/logo-transparent-png.png deleted file mode 100644 index 9fb2d313cebd40be1df1da3034bf5de59249a4be..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/Images/logo-transparent-png.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:847251e1115b2f4d769ed73944fc0966bec4085309c893b3e88cd1c79f5f168e -size 415432 diff --git a/audio/shiftyspeech/Images/shiftyspeech_logo.png b/audio/shiftyspeech/Images/shiftyspeech_logo.png deleted file mode 100644 index 26338cdbe0147f6af6c742cade04d96462855b28..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/Images/shiftyspeech_logo.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fa0cd7186bae0d4a0d38f87cf4bfc4d7ff1182fa1fc96cdfc6eb22fae0e08709 -size 1285263 diff --git a/audio/shiftyspeech/Readme.md b/audio/shiftyspeech/Readme.md deleted file mode 100644 index 67768a0658f7b3e7fa8362e8c6392564693a8156..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/Readme.md +++ /dev/null @@ -1,325 +0,0 @@ -

- - ShiftySpeech Logo - -

- -# 🌀 *ShiftySpeech*: A Large-Scale Synthetic Speech Dataset with Distribution Shifts -This is the official repository of *ShiftySpeech* – a diverse and extensive dataset containing **3000+ hours** of synthetic speech generated using various **TTS systems** and **vocoders**, while covering multiple **distribution shifts**. - -## 🔥 Key Features -- 3000+ hours of synthetic speech -- **Diverse Distribution Shifts**: The dataset spans **7 key distribution shifts**, including: - - 📖 **Reading Style** - - 🎙️ **Podcast** - - 🎥 **YouTube** - - 🗣️ **Languages (Three different languages)** - - 🌎 **Demographics (including variations in age, accent, and gender)** -- **Multiple Speech Generation Systems**: Includes data synthesized from various **TTS models** and **vocoders**. - -## 💡 Why We Built This Dataset -> Driven by advances in self-supervised learning for speech, state-of-the-art synthetic speech detectors have achieved low error rates on popular benchmarks such as ASVspoof. However, prior benchmarks do not address the wide range of real-world variability in speech. Are reported error rates realistic in real-world conditions? To assess detector failure modes and robustness under controlled distribution shifts, we introduce **ShiftySpeech**, a benchmark with more than 3000 hours of synthetic speech from 7 domains, 6 TTS systems, 12 vocoders, and 3 languages. - -## Downloading the dataset - -### ShiftySpeech -Dataset can be downloaded from [HuggingFace](https://huggingface.co/datasets/ash56/ShiftySpeech/tree/main) - -##### Dataset Structure -The dataset is structured as follows: -```plaintext -/ShiftySpeech - ├── Vocoders/ - │ ├── vocoder-1/ - │ │ ├── vocoder-1_aishell_flac.tar.gz - │ │ ├── vocoder-1_jsut_flac.tar.gz - │ │ ├── vocoder-1_youtube_flac.tar.gz - │ │ ├──vocoder-1_audiobook_flac.tar.gz - │ │ ├── vocoder-1_podcast_flac.tar.gz - │ │ ├── vocoder-1_voxceleb_test_flac.tar.gz - │ │ ├── vocoder-1_commonvoice_flac.tar.gz - │ │ ├── vocoder-1_ljspeech_flac.tar.gz - │ │ ├── vocoder-1_train-clean-360_flac.tar.gz - │ │ - │ ├── vocoder-2/ - │ ├── ... - │ - │ - │ - ├── TTS/ # Contains multiple TTS-based generated speech - │ ├── TTS_Grad-TTS.tar.gz - │ ├── TTS_Glow-TTS.tar.gz - │ ├── TTS_FastPitch.tar.gz - │ ├── TTS_VITS.tar.gz - │ ├── TTS_XTTS.tar.gz - │ ├── TTS_YourTTS.tar.gz - │ ├── TTS_hfg_vocoded.tar.gz #Note: ``TTS_hfg_vocoded.tar.gz`` contains synthetic speech generated using HiFiGAN vocoder trained on LJSpeech - │ - -``` -The source datasets covered by different TTS and Vocoder systems are listed in [tts.yaml](https://github.com/Ashigarg123/ShiftySpeech/tree/main/dataset/tts.yaml) and [vocoders.yaml](https://github.com/Ashigarg123/ShiftySpeech/tree/main/dataset/vocoders.yaml) - -Source dataset for above synthetic speech can be download using the following links: -- [JSUT Basic5000](https://sites.google.com/site/shinnosuketakamichi/publication/jsut) -- [AISHELL-1](https://www.openslr.org/33/) -- [VoxCeleb2](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html) -- [Audiobook](https://www.openslr.org/12) -- [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) -- [CommonVoice 19.0](https://commonvoice.mozilla.org/tw/datasets) - - -Wav list corresponding to the source datasets can be found here : -```sh - dataset/wav_lists -``` - -We utilize WaveFake dataset for training. It can be downloaded from [here](https://zenodo.org/records/5642694) -Train, dev and test split used for WaveFake can be found in ``dataset/train_wav_list`` -## Downloading the pre-trained models - -Pre-trained models can be downloaded from [here](https://huggingface.co/ash56/ShiftySpeech/tree/main/detection-systems) - -Individual models based on the below folder structure -Example, to download ``hifigan.pt``: -```bash -wget https://huggingface.co/ash56/ShiftySpeech/resolve/main/detection-systems/single_vocoder/hifigan.pth -``` -Folder structure: -```plaintext -detection-system/ -│ -├──single_vocoder/ # Selecting synthetic speech to train on -│ ├──hifigan.pth # Detectors trained on synthetic speech from a single vocoder -│ ├──melgan.pth # Synthetic speech derived from MelGAN vocoder -│ ├── ... -│ -├──leave_one_out/ # Detectors trained on synthetic speech from multiple vocoders -│ ├──leave_hifigan.pth # Synthetic speech derived from vocoders other than HiFiGAN -│ ├──leave_melgan.pth -│ ├── ... -│ -├──num_spks/ # Detectors trained on synthetic speech from ``n`` number of speakers -│ ├──exp-1/ # Round one of selecting different set of speakers to train on -| | ├──spk1.pt # Synthetic speech derived from single speaker -│ │ ├──spks4.pt # Synthetic speech derived from four speakers -│ │ -│ ├──... -│ │ -│ │ -│ ├──exp-5/ -| | ├──spk1.pt/ -│ │ ├──spks4.pt/ -| -├──augmentations/ # Detectors trained on synthetic speech from HiFiGAN vocoder -| ├──hfg_aug_1_2.pt # Augmentations applied during training: -│ (1) Linear and non-linear convolutive noise -| (2) Impulsive signal-dependent additive noise -│ -``` - -## Reproducing Experiments -To reproduce the experiments, please follow the following instructions: - ---- -**Environment Setup** -```sh -conda create -n SSL python=3.10.14 -``` -```sh -conda activate SSL -``` - -```sh -conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.1 -c pytorch -c nvidia -``` - -Download and install fairseq from [here](https://github.com/facebookresearch/fairseq/tree/a54021305d6b3c4c5959ac9395135f63202db8f1) - -```sh -pip install -r requirements.txt -``` -Download xlsr model from [here](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec/xlsr) and add in /synthetic_speech_detection/SSL_Anti-spoofing/model.py - - -**Prepare the data** - -- Create evaluation file of the format : -```sh - bonafide - spoof -``` -- `bonafide` represents genuine audio. -- `spoof` represents synthetic or fake audio. - -The helper script for creating the evaluation file is ``prepare_test.py``. See below for an example usage: -```sh -dataset/prepare_test.py --bona_dir dataset/real-data/jsut --spoof_dir dataset/Shiftyspeech/Vocoders/bigvgan/jsut_flac --save_path dataset/test_files/bigvgan_jsut_test.txt -``` -🚀**Evaluating In-the-Wild** -- Download In-the-Wild dataset from [here](https://owncloud.fraunhofer.de/index.php/s/JZgXh0JEAF0elxa) and create the evaluation file in the above format -- Evaluate the model trained on HiFiGAN generated utterances with and without augmentations -Download pre-trained models: -```bash -wget https://huggingface.co/ash56/ShiftySpeech/resolve/main/detection-systems/single_vocoder/hifigan.pth -``` - -```bash -wget https://huggingface.co/ash56/ShiftySpeech/resolve/main/detection-systems/augmentations/hfg_aug_1_2.pt -``` - -Evaluate using SSL-AASIST model: -```sh -python train.py --eval \ - --test_score_dir \ - --model_name SSL-AASIST \ - --test_list_path \ - --model_path -``` -You can also train the models on WaveFake dataset using the following commands: -```sh -python train.py --trn_list_path \ - --dev_list_path \ - --save_path -``` -For the following experiments, create the evaluation files for all distribution shifts as mentioned in ``Prepare the data`` section - -**Impact of Synthetic Speech Selection** - -Here, we study the impact of training on single vocoder vs multiple-vocoder systems. In addition, we analyze the effect of training on one vocoder vs the other. -- Load the pre-trained model saved in ``models/pre-trained`` - -Example evaluation: -```bash -python train.py --eval \ - --test_score_dir dataset/test_scores \ - --model_name SSL-AASIST \ - --test_list_path dataset/test_files/bigvgan_jsut.txt \ - --model_path models/pre-trained/hifigan.pt> -``` -🗣️**Training on more speakers** - -We analyze the impact of training a detector on single speaker vs training on multiple speakers. Number of speakers in training vary from 1 to 10. -We release pre-trained models for models trained on single-speaker and four speakers. Training data used is LibriTTS. Five different models are trained for both single and multi-speaker experiment. Speakers are randomly selected. - -You can download the pre-trained models as follows: - -```sh -wget https://huggingface.co/ash56/ShiftySpeech/resolve/main/detection-systems/num_spks/exp-1/spk1.pt -``` -Similarly pre-trained model for experiment 2 and single speaker model can be downloaded from -- -```sh -wget https://huggingface.co/ash56/ShiftySpeech/resolve/main/detection-systems/num_spks/exp-2/spk1.pt -``` -Example evaluation: -```bash -python train.py --eval \ - --test_score_dir dataset/test_scores \ - --model_name SSL-AASIST \ - --test_list_path dataset/test_files/bigvgan_jsut.txt \ - --model_path models/pre-trained/spk1.pt> -``` - -**Newly released vocoder** - -Now, we include new vocoders in training in chronological order of release. -For vocoder systems not included in WaveFake dataset, we release the generated samples for training and can be downloaded from folder -- -```bash - -wget https://huggingface.co/datasets/ash56/ShiftySpeech/tree/main/Vocoders//ljspeech_flac.tar.gz - -``` -Trained models can then be evaluated on distribution shifts similar to above experiments - -**Training on Vocoded speech vs training on TTS speech** - -In previous experiments we train detection systems by using vocoders as the only source of synthetic speech generation. Here, we train the detection system with synthetic speech generated using end-to-end TTS systems. For this experiment, we train detectors using following systems as the source of generation: -- Grad-TTS -- VITS -- Glow-TTS -- HFG Vocoded -- Combination of above - -Note: Vocoder utilized during synthesis for TTS systems is HiFiGAN (HFG). All the systems utilized are trained on LJSpeech. -Above TTS systems can be downloaded as part of coqui-ai repository. Please follow coqui-ai [Readme](https://github.com/Ashigarg123/ShiftySpeech/tree/main/speech_synthesis/tts/TTS#readme) for more instructions. Below we list the models used for synthesis: - -```sh -tts = TTS("tts_models/en/ljspeech/glow-tts", gpu=True, vocoder_path="vocoder_models/en/ljspeech/hifigan_v2") -``` -```sh -tts = TTS("tts_models/en/ljspeech/vits", gpu=True, vocoder_path="vocoder_models/en/ljspeech/hifigan_v2") -``` -Grad-TTS can be used to synthesize speech using the following command: -```sh -python inference.py -f -c speech_synthesis/tts/Grad-TTS/checkpts/grad-tts.pt -``` -Example format for transcripts: -```sh -Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|LJ001-0001.wav -``` -HiFiGAN model utilized is derived from [Hugging Face](https://huggingface.co/speechbrain/tts-hifigan-ljspeech) - -Train, test and dev splits for LJSpeech are same as defined above for previous experiments. Trained models can then be evaluated on synthesized test set of above TTS and vocoder systems - -**Language distribution shift** - -Here, we train detection systems on English corpus and evaluate on Chinese corpus and vice-versa. We utilize VCTK as the source corpus for English and AISHELL-1 corpus for Chinese. Multi-lingual XTTS system is used for generation. It can be loaded from coqui-ai as: -```sh -tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True) -``` - -Wav list for training can be found in : ``dataset/train_wav_list`` -Trained detectors can then be evaluated on test set of AISHELL-1 corpus and JSUT basic5000 corpus generated using XTTS - -Please note that for a given sample audio, source speaker from original audio is used as reference, example: -```sh - tts.tts_to_file(text=txt, - file_path=save_pth, - speaker= source_speaker, - language="ja", - split_sentences=False - ) -``` - -## **Synthetic speech detection system** -This repository utilizes [SSL-AASIST](https://arxiv.org/abs/2202.12233) model for spoof speech detection. Implementation is derived from [official](https://github.com/TakHemlata/SSL_Anti-spoofing) GitHub repository. - -## **Speech synthesis systems** -This section lists TTS and vocoder models used in this repository -#### **Text-to-Speech (TTS) Systems** -- [Grad-TTS](https://github.com/huawei-noah/Speech-Backbones/blob/main/Grad-TTS/README.md) -- [Coqui-ai](https://github.com/coqui-ai/TTS/tree/dev) - -#### **Vocoder Systems** -- [ParalleWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN/tree/master) -- [BigVGAN](https://github.com/NVIDIA/BigVGAN) -- [BigVSAN](https://github.com/sony/bigvsan) -- [UnivNet](https://github.com/maum-ai/univnet/tree/master) -- [Vocos](https://github.com/gemelo-ai/vocos) -- [WaveGrad](https://github.com/lmnt-com/wavegrad) -- [iSTFTNET](https://github.com/rishikksh20/iSTFTNet-pytorch/tree/master) -- [APNet2](https://github.com/BakerBunker/FreeV/tree/main) - -### **FAQ ❓** - -Please feel free to reach out if you have any questions or comments about the resource using GitHub issues or contacting us via email at [agarg22@jhu.edu](mailto:agarg22@jhu.edu) or [noa@jhu.edu](mailto:noa@jhu.edu). - -### **Citation** - -If you find this dataset or repository useful, please cite our work: -```bibtex -@misc{garg2025shiftyspeechlargescalesyntheticspeech, - title={ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts}, - author={Ashi Garg and Zexin Cai and Lin Zhang and Henry Li Xinyuan and Leibny Paola García-Perera and Kevin Duh and Sanjeev Khudanpur and Matthew Wiesner and Nicholas Andrews}, - year={2025}, - eprint={2502.05674}, - archivePrefix={arXiv}, - primaryClass={eess.AS}, - url={https://arxiv.org/abs/2502.05674}, -} -``` -## Acknowledgment -This work was supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the ARTS Program under contract D2023-2308110001. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. - -We thank Rafael Rivera-Soto, Rachel Wicks and Andrew Wang for their valuable discussions and feedback, which contributed to this work. - - diff --git a/audio/shiftyspeech/api.py b/audio/shiftyspeech/api.py deleted file mode 100644 index 729e5449a82830f0f8be9b51c7c4ae8dd3bea8a9..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/api.py +++ /dev/null @@ -1,315 +0,0 @@ -"""ShiftySpeech (SSL-AASIST) Audio Deepfake Detection API. - -Detects synthetic speech using the SSL-AASIST model architecture: -- Frontend: XLSR wav2vec 2.0 (Self-Supervised Learning) -- Backend: AASIST (Audio Anti-Spoofing using Integrated - Spectro-Temporal Graph Attention Networks) - -Reference: https://github.com/Ashigarg123/ShiftySpeech -""" - -import base64 -import io -import logging -import os -import platform -import sys -import time -import warnings -from typing import Optional - -import librosa -import numpy as np -import torch -import uvicorn -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel, Field - -# Suppress deprecation warnings from fairseq/omegaconf compatibility -warnings.filterwarnings("ignore", category=DeprecationWarning) - -# Monkey-patch omegaconf for fairseq compatibility (older fairseq -# expects is_primitive_type which was removed in newer omegaconf). -import omegaconf._utils as _omegaconf_utils - -if not hasattr(_omegaconf_utils, "is_primitive_type"): - _omegaconf_utils.is_primitive_type = lambda t: t in (int, float, bool, str, bytes) - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", -) -logger = logging.getLogger("shiftyspeech_api") - -# Add the SSL_Anti-spoofing model code to the path -MODEL_CODE_PATH = "/app/synthetic_speech_detection/SSL_Anti-spoofing" -if MODEL_CODE_PATH not in sys.path: - sys.path.insert(0, MODEL_CODE_PATH) - -# Import model class (deferred to allow path setup) -try: - from model import Model as SSLAASISTModel -except ImportError as e: - logger.error(f"Failed to import SSL-AASIST model: {e}") - SSLAASISTModel = None - -# Constants -MODEL_NAME = "shiftyspeech" -MODEL_ID = "ssl_aasist_augmented" -WEIGHTS_PATH = "/app/weights/hfg_aug_1_2.pt" -XLSR_DIR = "/app/models" - - -def _get_device(): - """Select optimal device: MPS (Apple) > CUDA (NVIDIA) > CPU.""" - override = os.environ.get("DEEPSAFE_DEVICE", "").strip().lower() - if override == "cpu": - return torch.device("cpu") - if override == "cuda" and torch.cuda.is_available(): - return torch.device("cuda") - if ( - override == "mps" - and hasattr(torch.backends, "mps") - and torch.backends.mps.is_available() - ): - return torch.device("mps") - if override: - pass # Invalid override, fall through to auto-detect - if ( - platform.system() == "Darwin" - and hasattr(torch.backends, "mps") - and torch.backends.mps.is_available() - ): - return torch.device("mps") - if torch.cuda.is_available(): - return torch.device("cuda") - return torch.device("cpu") - - -DEVICE = _get_device() - -if DEVICE.type == "cuda": - torch.backends.cudnn.benchmark = True - torch.set_float32_matmul_precision("high") - -if DEVICE.type == "cuda": - logger.info( - "Device: cuda (%s, %.1f GB VRAM)", - torch.cuda.get_device_name(0), - torch.cuda.get_device_properties(0).total_memory / 1024**3, - ) -else: - logger.warning( - "Device: %s (no CUDA available -- check nvidia-container-toolkit)", - DEVICE, - ) - -SAMPLE_RATE = 16000 -TARGET_SAMPLES = 64600 # ~4.04 seconds at 16kHz - -# Global model instance -model = None - - -class AudioInput(BaseModel): - """Request schema for audio deepfake detection.""" - - audio_data: str = Field( - ..., description="Base64 encoded audio string (WAV/MP3/etc)" - ) - threshold: Optional[float] = Field( - 0.5, ge=0.0, le=1.0, description="Classification threshold" - ) - - -app = FastAPI( - title="ShiftySpeech Audio Deepfake Detection API", - description=( - "Service for detecting synthetic speech using the " - "SSL-AASIST model (XLSR wav2vec 2.0 + AASIST backend)." - ), - version="1.0.0", -) - - -def load_model(): - """Load the SSL-AASIST model with augmented weights. - - Returns: - The loaded model, or None if loading fails. - """ - global model - if model is not None: - return model - - logger.info(f"Loading SSL-AASIST model onto {DEVICE}...") - - if SSLAASISTModel is None: - logger.error("SSL-AASIST model class not available.") - return None - - if not os.path.exists(WEIGHTS_PATH): - logger.error(f"Model weights not found at {WEIGHTS_PATH}") - return None - - try: - # Ensure XLSR model directory exists for architecture init - os.makedirs(XLSR_DIR, exist_ok=True) - - import argparse - - args = argparse.Namespace() - model = SSLAASISTModel(args, str(DEVICE)) - - # Load fine-tuned weights (includes XLSR weights) - try: - state_dict = torch.load( - WEIGHTS_PATH, - map_location=DEVICE, - weights_only=False, - ) - except TypeError: - state_dict = torch.load(WEIGHTS_PATH, map_location=DEVICE) - - model.load_state_dict(state_dict) - model.to(DEVICE) - model.eval() - - logger.info("SSL-AASIST model loaded successfully.") - return model - except Exception as e: - logger.exception(f"Failed to load SSL-AASIST model: {e}") - model = None - return None - - -@app.on_event("startup") -async def startup_event(): - """Load model on service startup.""" - load_model() - - -def _gpu_health_info() -> dict: - """Return GPU metrics for the health endpoint.""" - if torch.cuda.is_available() and DEVICE.type == "cuda": - return { - "gpu_name": torch.cuda.get_device_name(0), - "vram_used_mb": round(torch.cuda.memory_allocated(0) / 1024**2), - "vram_total_mb": round( - torch.cuda.get_device_properties(0).total_memory / 1024**2 - ), - } - return {} - - -@app.get("/health") -async def health(): - """Health check endpoint.""" - return { - "status": "healthy" if model is not None else "degraded", - "model": MODEL_NAME, - "model_id": MODEL_ID, - "device": str(DEVICE), - "weights_found": os.path.exists(WEIGHTS_PATH), - **_gpu_health_info(), - } - - -def preprocess_audio(audio_bytes: bytes) -> torch.Tensor: - """Preprocess audio for SSL-AASIST inference. - - Loads audio, resamples to 16kHz mono, and pads/trims - to TARGET_SAMPLES using tiling (matching original training - preprocessing from data_utils.py). - - Args: - audio_bytes: Raw audio file bytes. - - Returns: - Audio tensor of shape (1, TARGET_SAMPLES). - - Raises: - ValueError: If audio preprocessing fails. - """ - try: - logger.info("Starting audio preprocessing...") - audio, sr = librosa.load(io.BytesIO(audio_bytes), sr=SAMPLE_RATE, mono=True) - logger.info(f"Audio loaded. Length: {len(audio)} samples at {sr}Hz") - - # Pad/trim to TARGET_SAMPLES using tiling - # (matches original data_utils.pad function) - if len(audio) >= TARGET_SAMPLES: - audio = audio[:TARGET_SAMPLES] - else: - num_repeats = TARGET_SAMPLES // len(audio) + 1 - audio = np.tile(audio, num_repeats)[:TARGET_SAMPLES] - - logger.info(f"Audio padded/trimmed to {TARGET_SAMPLES} samples") - - audio_tensor = torch.FloatTensor(audio).unsqueeze(0).to(DEVICE) - return audio_tensor - except Exception as e: - logger.error(f"Error preprocessing audio: {e}") - raise ValueError(f"Audio preprocessing failed: {str(e)}") - - -@app.post("/predict") -async def predict(input_data: AudioInput): - """Run deepfake detection on base64-encoded audio. - - The model outputs 2 logits: [spoof_score, bonafide_score]. - Class 0 = spoof (fake), Class 1 = bonafide (real). - The returned probability is the spoof/fake probability. - """ - if model is None: - if load_model() is None: - raise HTTPException(status_code=503, detail="Model not loaded") - - try: - start_time = time.time() - logger.info( - f"Prediction request. Data size: " f"{len(input_data.audio_data)} chars" - ) - - # Decode base64 audio - audio_bytes = base64.b64decode(input_data.audio_data) - - # Preprocess - audio_tensor = preprocess_audio(audio_bytes) - - # Inference - logger.info("Starting model inference...") - with torch.no_grad(): - output = model(audio_tensor) - - # output shape: [batch, 2] - # Index 0 = spoof logit, Index 1 = bonafide logit - probs = torch.softmax(output, dim=1) - prob_fake = probs[0, 0].item() - - prediction = 1 if prob_fake >= input_data.threshold else 0 - verdict = "fake" if prediction == 1 else "real" - inference_time = time.time() - start_time - - logger.info( - f"Prediction: {verdict} (prob_fake={prob_fake:.4f}, " - f"time={inference_time:.3f}s)" - ) - - return { - "model": MODEL_NAME, - "probability": float(prob_fake), - "prediction": int(prediction), - "class": verdict, - "inference_time": float(inference_time), - } - - except Exception as e: - logger.exception(f"Error during prediction: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -if __name__ == "__main__": - port = int(os.environ.get("MODEL_PORT", 8001)) - uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/audio/shiftyspeech/dataset/prepare_test.py b/audio/shiftyspeech/dataset/prepare_test.py deleted file mode 100644 index 7276201ec1d4ba92310074c09f8b687eadf90246..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/prepare_test.py +++ /dev/null @@ -1,31 +0,0 @@ -import argparse -import os -import sys - - -def create_test_file(bona_dir, spoof_dir, save_name): - with open(save_name, 'w') as out: - for root, _, files in os.walk(bona_dir): - for file in files: - if file.endswith(".wav") or file.endswith(".flac"): - utt_pth = os.path.join(root, file) - out.write(f"{utt_pth} bonafide\n") - - for root, _, files in os.walk(spoof_dir): - for file in files: - if file.endswith(".wav") or file.endswith(".flac"): - utt_pth = os.path.join(root, file) - out.write(f"{utt_pth} spoof\n") - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Prepare test file') - parser.add_argument('--save_path', type=str, default="test.txt", help="Path to save the test file") - parser.add_argument('--bona_dir', type=str, help="Path to directory with original/real audio samples") - parser.add_argument('--spoof_dir', type=str, help="Path to directory with fake/spoof audio samples") - - args = parser.parse_args() - create_test_file(args.bona_dir, args.spoof_dir, args.save_path) - - - diff --git a/audio/shiftyspeech/dataset/train_wav_list/aishell_test.txt b/audio/shiftyspeech/dataset/train_wav_list/aishell_test.txt deleted file mode 100644 index 11d6e16c0fffb234ec0a35f94acec6729af4c751..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/train_wav_list/aishell_test.txt +++ /dev/null @@ -1,7176 +0,0 @@ -BAC009S0905W0302.wav -BAC009S0905W0226.wav -BAC009S0905W0131.wav -BAC009S0905W0408.wav -BAC009S0905W0221.wav -BAC009S0905W0136.wav 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-p376_255.wav -p376_256.wav -p376_257.wav -p376_258.wav -p376_259.wav -p376_260.wav -p376_261.wav -p376_262.wav -p376_263.wav -p376_264.wav -p376_265.wav -p376_266.wav -p376_267.wav -p376_268.wav -p376_269.wav -p376_270.wav -p376_271.wav -p376_272.wav -p376_273.wav -p376_274.wav -p376_275.wav -p376_276.wav -p376_277.wav -p376_278.wav -p376_279.wav -p376_280.wav -p376_282.wav -p376_283.wav -p376_284.wav -p376_285.wav -p376_286.wav -p376_287.wav -p376_288.wav -p376_289.wav -p376_290.wav -p376_291.wav -p376_292.wav -p376_293.wav -p376_294.wav -p376_295.wav diff --git a/audio/shiftyspeech/dataset/tts.yaml b/audio/shiftyspeech/dataset/tts.yaml deleted file mode 100644 index 87a4cf7d53d8849b955903d43f3db9d07ce86ee1..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/tts.yaml +++ /dev/null @@ -1,17 +0,0 @@ -FastPitch: - - VCTK -Glow-TTS: - - AISHELL-dev -Grad-TTS: - - LJSpeech -VITS: - - VCTK - - LJSpeech -XTTS: - - VCTK - - AISHELL-dev - - AISHELL-test - - AISHELL-train - - JSUT -YourTTS: - - VCTK diff --git a/audio/shiftyspeech/dataset/vocoders.yaml b/audio/shiftyspeech/dataset/vocoders.yaml deleted file mode 100644 index ebef0e7f02d108c5a2228300c0cd40643a740002..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/vocoders.yaml +++ /dev/null @@ -1,104 +0,0 @@ -apnet2: - - aishell - - audiobook - - jsut - - podcast - - voxceleb_test - - youtube -bivgan: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - voxceleb_test - - youtube -bigvsan: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - voxceleb_test -hifigan: - - aishell - - audiobook - - commonvoice - - jsut - - podcast - - train-clean-360 - - voxceleb_test - - youtube -mb-melgan: - - aishell - - audiobook - - commonvoice - - jsut - - podcast - - train-clean-360 - - voxceleb_test - - youtube -pwg: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - train-clean-360 - - voxceleb_test - - youtube -style-melgan: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - train-clean-360 - - voxceleb_test - - youtube -univnet-c16: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - voxceleb_test -univnet-c32: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - voxceleb_test - - youtube -vocos: - - aishell - - audiobook - - commonvoice - - jsut - - ljspeech - - podcast - - voxceleb_test - - youtube -wavegrad: - - aishell - - audiobook - - jsut - - ljspeech - - podcast - - voxceleb_test - - youtube -istftnet: - - aishell - - audiobook - - jsut - - ljspeech - - podcast - - voxceleb_test - - youtube diff --git a/audio/shiftyspeech/dataset/wav_lists/aishell.txt b/audio/shiftyspeech/dataset/wav_lists/aishell.txt deleted file mode 100644 index 4f1eb28ee7fea18e59860e8d27a2282831f85fc6..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/aishell.txt +++ 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d1e1a4ebbc79ad8ff484121521e4ca4f566a8906..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/female/seventies/style-mel_seventies.wavlist +++ /dev/null @@ -1,334 +0,0 @@ -common_voice_en_125399.mp3 -common_voice_en_588051.mp3 -common_voice_en_588052.mp3 -common_voice_en_19729156.mp3 -common_voice_en_19729158.mp3 -common_voice_en_19729159.mp3 -common_voice_en_19729160.mp3 -common_voice_en_694200.mp3 -common_voice_en_694205.mp3 -common_voice_en_32669997.mp3 -common_voice_en_32669999.mp3 -common_voice_en_32670003.mp3 -common_voice_en_32670084.mp3 -common_voice_en_32670087.mp3 -common_voice_en_32670088.mp3 -common_voice_en_92748.mp3 -common_voice_en_92749.mp3 -common_voice_en_92750.mp3 -common_voice_en_92751.mp3 -common_voice_en_92752.mp3 -common_voice_en_92754.mp3 -common_voice_en_92755.mp3 -common_voice_en_92757.mp3 -common_voice_en_92758.mp3 -common_voice_en_92759.mp3 -common_voice_en_92761.mp3 -common_voice_en_92762.mp3 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a/audio/shiftyspeech/dataset/wav_lists/british/female/sixties/hfg_sixties.wavlist b/audio/shiftyspeech/dataset/wav_lists/british/female/sixties/hfg_sixties.wavlist deleted file mode 100644 index e6d5f924b36b942d52b43b37deb37adaf6737f81..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/female/sixties/hfg_sixties.wavlist +++ /dev/null @@ -1,359 +0,0 @@ -common_voice_en_17768667.mp3 -common_voice_en_157257.mp3 -common_voice_en_157258.mp3 -common_voice_en_20321970.mp3 -common_voice_en_20321973.mp3 -common_voice_en_20250737.mp3 -common_voice_en_20250738.mp3 -common_voice_en_20250739.mp3 -common_voice_en_20250740.mp3 -common_voice_en_20250741.mp3 -common_voice_en_19705534.mp3 -common_voice_en_19705537.mp3 -common_voice_en_19705539.mp3 -common_voice_en_19705541.mp3 -common_voice_en_19705542.mp3 -common_voice_en_19703726.mp3 -common_voice_en_22939419.mp3 -common_voice_en_22939420.mp3 -common_voice_en_22939421.mp3 -common_voice_en_22939423.mp3 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a/audio/shiftyspeech/dataset/wav_lists/british/female/thirties/bigvsan_thirties.wavlist b/audio/shiftyspeech/dataset/wav_lists/british/female/thirties/bigvsan_thirties.wavlist deleted file mode 100644 index 08a97d68180f7792c1f72f1cf0ee9bd3c34721bd..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/female/thirties/bigvsan_thirties.wavlist +++ /dev/null @@ -1,1289 +0,0 @@ -common_voice_en_39882276.mp3 -common_voice_en_40599044.mp3 -common_voice_en_30699809.mp3 -common_voice_en_30699821.mp3 -common_voice_en_30699823.mp3 -common_voice_en_39817358.mp3 -common_voice_en_104860.mp3 -common_voice_en_104861.mp3 -common_voice_en_32117456.mp3 -common_voice_en_29984391.mp3 -common_voice_en_29984396.mp3 -common_voice_en_30750967.mp3 -common_voice_en_31767559.mp3 -common_voice_en_31767562.mp3 -common_voice_en_20215308.mp3 -common_voice_en_20215311.mp3 -common_voice_en_19705571.mp3 -common_voice_en_19705572.mp3 -common_voice_en_19732441.mp3 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a/audio/shiftyspeech/dataset/wav_lists/british/female/thirties/univnet_1_thirties.wavlist b/audio/shiftyspeech/dataset/wav_lists/british/female/thirties/univnet_1_thirties.wavlist deleted file mode 100644 index 08a97d68180f7792c1f72f1cf0ee9bd3c34721bd..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/female/thirties/univnet_1_thirties.wavlist +++ /dev/null @@ -1,1289 +0,0 @@ -common_voice_en_39882276.mp3 -common_voice_en_40599044.mp3 -common_voice_en_30699809.mp3 -common_voice_en_30699821.mp3 -common_voice_en_30699823.mp3 -common_voice_en_39817358.mp3 -common_voice_en_104860.mp3 -common_voice_en_104861.mp3 -common_voice_en_32117456.mp3 -common_voice_en_29984391.mp3 -common_voice_en_29984396.mp3 -common_voice_en_30750967.mp3 -common_voice_en_31767559.mp3 -common_voice_en_31767562.mp3 -common_voice_en_20215308.mp3 -common_voice_en_20215311.mp3 -common_voice_en_19705571.mp3 -common_voice_en_19705572.mp3 -common_voice_en_19732441.mp3 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a/audio/shiftyspeech/dataset/wav_lists/british/female/twenties/mb-melgan_twenties.wavlist b/audio/shiftyspeech/dataset/wav_lists/british/female/twenties/mb-melgan_twenties.wavlist deleted file mode 100644 index 467a385cd52d821243872fd9ac10a83475f29dc8..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/female/twenties/mb-melgan_twenties.wavlist +++ /dev/null @@ -1,1326 +0,0 @@ -common_voice_en_18238482.mp3 -common_voice_en_18733382.mp3 -common_voice_en_20263456.mp3 -common_voice_en_528776.mp3 -common_voice_en_528777.mp3 -common_voice_en_40969969.mp3 -common_voice_en_40969995.mp3 -common_voice_en_39744110.mp3 -common_voice_en_20491387.mp3 -common_voice_en_20491390.mp3 -common_voice_en_17627140.mp3 -common_voice_en_21112318.mp3 -common_voice_en_21112321.mp3 -common_voice_en_23703270.mp3 -common_voice_en_23703271.mp3 -common_voice_en_23703272.mp3 -common_voice_en_23703273.mp3 -common_voice_en_19262159.mp3 -common_voice_en_19262161.mp3 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a/audio/shiftyspeech/dataset/wav_lists/british/female/twenties/style-mel_twenties.wavlist b/audio/shiftyspeech/dataset/wav_lists/british/female/twenties/style-mel_twenties.wavlist deleted file mode 100644 index 467a385cd52d821243872fd9ac10a83475f29dc8..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/female/twenties/style-mel_twenties.wavlist +++ /dev/null @@ -1,1326 +0,0 @@ -common_voice_en_18238482.mp3 -common_voice_en_18733382.mp3 -common_voice_en_20263456.mp3 -common_voice_en_528776.mp3 -common_voice_en_528777.mp3 -common_voice_en_40969969.mp3 -common_voice_en_40969995.mp3 -common_voice_en_39744110.mp3 -common_voice_en_20491387.mp3 -common_voice_en_20491390.mp3 -common_voice_en_17627140.mp3 -common_voice_en_21112318.mp3 -common_voice_en_21112321.mp3 -common_voice_en_23703270.mp3 -common_voice_en_23703271.mp3 -common_voice_en_23703272.mp3 -common_voice_en_23703273.mp3 -common_voice_en_19262159.mp3 -common_voice_en_19262161.mp3 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a/audio/shiftyspeech/dataset/wav_lists/british/male/fifties/bigvgan_fifties.wavlist b/audio/shiftyspeech/dataset/wav_lists/british/male/fifties/bigvgan_fifties.wavlist deleted file mode 100644 index 2e98c02dad7fb2da0d5d43adb58fcb36b7de3923..0000000000000000000000000000000000000000 --- a/audio/shiftyspeech/dataset/wav_lists/british/male/fifties/bigvgan_fifties.wavlist +++ /dev/null @@ -1,2573 +0,0 @@ -common_voice_en_633986.mp3 -common_voice_en_17399383.mp3 -common_voice_en_17405020.mp3 -common_voice_en_20542865.mp3 -common_voice_en_20542866.mp3 -common_voice_en_20542869.mp3 -common_voice_en_523242.mp3 -common_voice_en_20270942.mp3 -common_voice_en_20270944.mp3 -common_voice_en_20270946.mp3 -common_voice_en_17850547.mp3 -common_voice_en_17850548.mp3 -common_voice_en_17249772.mp3 -common_voice_en_32269931.mp3 -common_voice_en_19450975.mp3 -common_voice_en_19450976.mp3 -common_voice_en_19450978.mp3 -common_voice_en_19644867.mp3 -common_voice_en_19644868.mp3 -common_voice_en_18546203.mp3 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