diff --git a/audio/aasist3/.gitignore b/audio/aasist3/.gitignore deleted file mode 100644 index d285b0dd1d6c257ec0dea379665e40a74f8e8759..0000000000000000000000000000000000000000 --- a/audio/aasist3/.gitignore +++ /dev/null @@ -1 +0,0 @@ -weights/ diff --git a/audio/aasist3/Dockerfile b/audio/aasist3/Dockerfile deleted file mode 100644 index 2539f7771b2cc49301a55c41599384eb68e391f0..0000000000000000000000000000000000000000 --- a/audio/aasist3/Dockerfile +++ /dev/null @@ -1,35 +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 \ - ffmpeg libsndfile1 \ - && 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 - -RUN python -c "from transformers import Wav2Vec2Config; Wav2Vec2Config.from_pretrained('facebook/wav2vec2-large-xlsr-53', cache_dir='/app/w2v_cache')" - -COPY model/ /app/model/ -COPY api.py . -RUN mkdir -p /app/weights -COPY weights/ /app/weights/ - -EXPOSE 8005 - -RUN adduser --disabled-password --gecos '' appuser -USER appuser - -CMD ["python", "api.py"] diff --git a/audio/aasist3/api.py b/audio/aasist3/api.py deleted file mode 100644 index b1fc8a868dd8686a3eb1725eb76aa67e543c5e8e..0000000000000000000000000000000000000000 --- a/audio/aasist3/api.py +++ /dev/null @@ -1,345 +0,0 @@ -"""AASIST3 Audio Deepfake Detection API. - -Detects synthetic speech using the AASIST3 model architecture: -- Frontend: XLSR wav2vec 2.0 (HuggingFace Transformers) -- Backend: AASIST with KAN (Kolmogorov-Arnold Network) linear - layers and Graph Attention Networks - -Reference: https://github.com/AI4Bharat/AASIST3 -""" - -import base64 -import io -import logging -import os -import platform -import time -from typing import Optional - -import numpy as np -import soundfile as sf -import torch -import torch.nn.functional as F -import uvicorn -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel, Field - -# Point transformers cache to pre-cached wav2vec2 config -# (must be set before importing model code) -os.environ["TRANSFORMERS_CACHE"] = "/app/w2v_cache" - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", -) -logger = logging.getLogger("aasist3_api") - -# Import model class -try: - from model import aasist3 as AASIST3Model -except ImportError as e: - logger.error(f"Failed to import AASIST3 model: {e}") - AASIST3Model = None - -# Constants -MODEL_NAME = "aasist3" -MODEL_ID = "aasist3_kan_mlaad" -WEIGHTS_DIR = "/app/weights" - - -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="AASIST3 Audio Deepfake Detection API", - description=( - "Service for detecting synthetic speech using the " - "AASIST3 model (HuggingFace wav2vec 2.0 + AASIST " - "with KAN layers)." - ), - version="1.0.0", -) - - -def load_model(): - """Load the AASIST3 model from pretrained weights. - - Returns: - The loaded model, or None if loading fails. - """ - global model - if model is not None: - return model - - logger.info(f"Loading AASIST3 model onto {DEVICE}...") - - if AASIST3Model is None: - logger.error("AASIST3 model class not available.") - return None - - weights_safetensors = os.path.join(WEIGHTS_DIR, "model.safetensors") - weights_config = os.path.join(WEIGHTS_DIR, "config.json") - - if not os.path.exists(weights_safetensors): - logger.error(f"Model weights not found at {weights_safetensors}") - return None - - if not os.path.exists(weights_config): - logger.error(f"Model config not found at {weights_config}") - return None - - try: - model = AASIST3Model.from_pretrained(WEIGHTS_DIR) - model.to(DEVICE) - # Set model to inference mode (disables dropout, batchnorm) - model.train(False) - - logger.info("AASIST3 model loaded successfully.") - return model - except Exception as e: - logger.exception(f"Failed to load AASIST3 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(os.path.join(WEIGHTS_DIR, "model.safetensors")), - **_gpu_health_info(), - } - - -def _load_audio_bytes(audio_bytes: bytes) -> tuple: - """Load audio from raw bytes using soundfile with torchaudio fallback. - - Args: - audio_bytes: Raw audio file bytes. - - Returns: - Tuple of (audio_numpy_array, sample_rate). - - Raises: - ValueError: If audio cannot be loaded by any backend. - """ - # Try soundfile first (handles WAV, FLAC natively) - sf_error = None - try: - audio, sr = sf.read(io.BytesIO(audio_bytes), dtype="float32") - if audio.ndim > 1: - audio = audio.mean(axis=1) # Convert to mono - return audio, sr - except Exception as sf_err: - sf_error = sf_err - logger.debug(f"soundfile failed, trying torchaudio: {sf_err}") - - # Fallback to torchaudio (handles MP3, compressed formats) - try: - import torchaudio - - buf = io.BytesIO(audio_bytes) - waveform, sr = torchaudio.load(buf) - if waveform.shape[0] > 1: - waveform = waveform.mean(dim=0, keepdim=True) - return waveform.squeeze(0).numpy(), sr - except Exception as ta_err: - raise ValueError( - f"Failed to load audio with soundfile and torchaudio: " - f"sf={sf_error}, ta={ta_err}" - ) - - -def preprocess_audio(audio_bytes: bytes) -> torch.Tensor: - """Preprocess audio for AASIST3 inference. - - Loads audio, resamples to 16kHz mono, and zero-pads or - truncates to TARGET_SAMPLES. - - 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 = _load_audio_bytes(audio_bytes) - logger.info(f"Audio loaded. Length: {len(audio)} samples at {sr}Hz") - - # Resample to 16kHz if needed - if sr != SAMPLE_RATE: - import torchaudio - - resampler = torchaudio.transforms.Resample( - orig_freq=sr, new_freq=SAMPLE_RATE - ) - audio_tensor = torch.FloatTensor(audio).unsqueeze(0) - audio_tensor = resampler(audio_tensor).squeeze(0) - audio = audio_tensor.numpy() - logger.info( - f"Resampled from {sr}Hz to {SAMPLE_RATE}Hz. " - f"New length: {len(audio)} samples" - ) - - # Zero-pad or truncate to TARGET_SAMPLES - if len(audio) >= TARGET_SAMPLES: - audio = audio[:TARGET_SAMPLES] - else: - pad_length = TARGET_SAMPLES - len(audio) - audio = np.pad(audio, (0, pad_length), mode="constant") - - 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: [bonafide_score, spoof_score]. - Class 0 = bonafide (real), Class 1 = spoof (fake). - 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 = bonafide logit, Index 1 = spoof logit - probs = torch.softmax(output, dim=1) - prob_fake = probs[0, 1].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", 8005)) - uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/audio/aasist3/model/__init__.py b/audio/aasist3/model/__init__.py deleted file mode 100644 index 71783b17169a638280c0985f13af7df6b89a4c05..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .full_model import aasist3 \ No newline at end of file diff --git a/audio/aasist3/model/branch.py b/audio/aasist3/model/branch.py deleted file mode 100644 index e35e26f0367cd3cbca62102e6f8ab52532268701..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/branch.py +++ /dev/null @@ -1,34 +0,0 @@ -import torch.nn as nn - -from .hs_gal import HtrgGraphAttentionLayer -from .pool import GraphPool - -class InferenceBranch(nn.Module): - def __init__(self, gat_dims, temperature, pool_ratio, size): - super().__init__() - self.htrg_gat1 = HtrgGraphAttentionLayer( - gat_dims[0], gat_dims[1], temperature=temperature, size=size - ) - self.htrg_gat2 = HtrgGraphAttentionLayer( - gat_dims[1], gat_dims[1], temperature=temperature, size=size - ) - - self.pool_hS = GraphPool(pool_ratio, gat_dims[1], 0.3, size=size) - self.pool_hT = GraphPool(pool_ratio, gat_dims[1], 0.3, size=size) - - def forward(self, out_T, out_S, master): - # Первая стадия - out_T_res, out_S_res, master_res = self.htrg_gat1(out_T, out_S, master=master) - - # Пулинг - out_S_res = self.pool_hS(out_S_res) - out_T_res = self.pool_hT(out_T_res) - - # Вторая стадия с residual connection - out_T_aug, out_S_aug, master_aug = self.htrg_gat2(out_T_res, out_S_res, master=master_res) - - out_T_final = out_T_res + out_T_aug - out_S_final = out_S_res + out_S_aug - master_final = master_res + master_aug - - return out_T_final, out_S_final, master_final \ No newline at end of file diff --git a/audio/aasist3/model/full_model.py b/audio/aasist3/model/full_model.py deleted file mode 100644 index dddccd136c9e5576157067bd9f2666af63149684..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/full_model.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python - -import torch -import torch.nn.functional as F -import torch.nn as nn -from huggingface_hub import PyTorchModelHubMixin - -from .kan import KANLinear -from .gat import GraphAttentionLayer -from .pool import GraphPool -from .branch import InferenceBranch -from .residual import Residual_block -from .wav2vec import Wav2Vec2Encoder - - -class aasist3(nn.Module, PyTorchModelHubMixin): - def __init__(self, d_args={ - "architecture": "AASIST", - "nb_samp": 64600, - "first_conv": 128, - "filts": [70, [1, 32], [32, 32], [32, 64], [64, 64]], - "gat_dims": [64, 32], - "pool_ratios": [0.5, 0.7, 0.5, 0.5], - "temperatures": [2.0, 2.0, 100.0, 100.0], - }, size=200, w2v_cache_dir="weights/", load_pretrained=True): - super().__init__() - - self.w2v_encoder = Wav2Vec2Encoder(cache_dir=w2v_cache_dir, load_pretrained=load_pretrained) - self.bridge = KANLinear(1024, 128) - - self.d_args = d_args - filts = d_args["filts"] - gat_dims = d_args["gat_dims"] - pool_ratios = d_args["pool_ratios"] - temperatures = d_args["temperatures"] - - self.first_bn = nn.BatchNorm2d(num_features=1) - self.selu = nn.SELU(inplace=True) - self.drop = nn.Dropout(0.5, inplace=True) - self.drop_way = nn.Dropout(0.2, inplace=True) - - self.encoder = nn.Sequential( - nn.Sequential(Residual_block(nb_filts=filts[1], first=True)), - nn.Sequential(Residual_block(nb_filts=filts[2])), - nn.Sequential(Residual_block(nb_filts=filts[3])), - nn.Sequential(Residual_block(nb_filts=filts[4])), - nn.Sequential(Residual_block(nb_filts=filts[4])), - nn.Sequential(Residual_block(nb_filts=filts[4]))) - - self.pos_S = nn.Parameter(torch.randn(1, 42, filts[-1][-1])) - self.pos_T = nn.Parameter(torch.randn(1, 67, filts[-1][-1])) - - self.GAT_layer_S = GraphAttentionLayer(filts[-1][-1], gat_dims[0], temperature=temperatures[0], size=size) - self.GAT_layer_T = GraphAttentionLayer(filts[-1][-1], gat_dims[0], temperature=temperatures[1], size=size) - - self.pool_S = GraphPool(pool_ratios[0], gat_dims[0], 0.3, size=size) - self.pool_T = GraphPool(pool_ratios[1], gat_dims[0], 0.3, size=size) - - self.master1 = nn.Parameter(torch.randn(1, 1, gat_dims[0])) - self.master2 = nn.Parameter(torch.randn(1, 1, gat_dims[0])) - self.master3 = nn.Parameter(torch.randn(1, 1, gat_dims[0])) - self.master4 = nn.Parameter(torch.randn(1, 1, gat_dims[0])) - - self.inference_branch1 = InferenceBranch( - gat_dims=gat_dims, - temperature=temperatures[2], - pool_ratio=pool_ratios[2], - size=size - ) - self.inference_branch2 = InferenceBranch( - gat_dims=gat_dims, - temperature=temperatures[2], - pool_ratio=pool_ratios[2], - size=size - ) - self.inference_branch3 = InferenceBranch( - gat_dims=gat_dims, - temperature=temperatures[2], - pool_ratio=pool_ratios[2], - size=size - ) - self.inference_branch4 = InferenceBranch( - gat_dims=gat_dims, - temperature=temperatures[2], - pool_ratio=pool_ratios[2], - size=size - ) - - self.out_layer = KANLinear(5 * gat_dims[1], 2) - - def forward(self, x, Freq_aug=False): - x = self.w2v_encoder(x) - x = self.bridge(x) - x = x.transpose(1, 2) - x = x.unsqueeze(dim=1) - x = F.max_pool2d(torch.abs(x), (3, 3)) - x = self.first_bn(x) - x = self.selu(x) - - e = self.encoder(x) - - # GAT-S - e_S, _ = torch.max(torch.abs(e), dim=3) - e_S = e_S.transpose(1, 2) + self.pos_S - gat_S = self.GAT_layer_S(e_S) - out_S = self.pool_S(gat_S) - - # GAT-T - e_T, _ = torch.max(torch.abs(e), dim=2) - e_T = e_T.transpose(1, 2) + self.pos_T - gat_T = self.GAT_layer_T(e_T) - out_T = self.pool_T(gat_T) - - out_T1, out_S1, master1 = self.inference_branch1(out_T, out_S, self.master1) - out_T2, out_S2, master2 = self.inference_branch2(out_T, out_S, self.master2) - out_T3, out_S3, master3 = self.inference_branch3(out_T, out_S, self.master3) - out_T4, out_S4, master4 = self.inference_branch4(out_T, out_S, self.master4) - - out_T1, out_T2 = self.drop_way(out_T1), self.drop_way(out_T2) - out_T3, out_T4 = self.drop_way(out_T3), self.drop_way(out_T4) - out_S1, out_S2 = self.drop_way(out_S1), self.drop_way(out_S2) - out_S3, out_S4 = self.drop_way(out_S3), self.drop_way(out_S4) - master1, master2 = self.drop_way(master1), self.drop_way(master2) - master3, master4 = self.drop_way(master3), self.drop_way(master4) - - out_T = torch.stack([out_T1, out_T2, out_T3, out_T4]).max(dim=0)[0] - out_S = torch.stack([out_S1, out_S2, out_S3, out_S4]).max(dim=0)[0] - master = torch.stack([master1, master2, master3, master4]).max(dim=0)[0] - - T_max, _ = torch.max(torch.abs(out_T), dim=1) - T_avg = torch.mean(out_T, dim=1) - S_max, _ = torch.max(torch.abs(out_S), dim=1) - S_avg = torch.mean(out_S, dim=1) - - last_hidden = torch.cat([T_max, T_avg, S_max, S_avg, master.squeeze(1)], dim=1) - last_hidden = self.drop(last_hidden) - output = self.out_layer(last_hidden) - - return output diff --git a/audio/aasist3/model/gat.py b/audio/aasist3/model/gat.py deleted file mode 100644 index 9acbceffffaeebbaaee5d68607b7dd3e12fe46ff..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/gat.py +++ /dev/null @@ -1,99 +0,0 @@ -import torch, torch.nn as nn, torch.nn.functional as F - -from .kan import KANLinear - - -class GraphAttentionLayer(nn.Module): - def __init__(self, in_dim, out_dim, size, **kwargs): - super().__init__() - - # attention map - self.att_proj = KANLinear(in_dim, out_dim) - self.att_weight = self._init_new_params(out_dim, 1) - - # project - self.proj_with_att = KANLinear(in_dim, out_dim) - self.proj_without_att = KANLinear(in_dim, out_dim) - - # batch norm - self.bn = nn.BatchNorm1d(out_dim) - - # dropout for inputs - self.input_drop = nn.Dropout(p=0.2) - - # activate - self.act = nn.SELU(inplace=True) - - # temperature - self.temp = 1. - if "temperature" in kwargs: - self.temp = kwargs["temperature"] - - def forward(self, x): - ''' - x :(#bs, #node, #dim) - ''' - # apply input dropout - x = self.input_drop(x) - - # derive attention map - att_map = self._derive_att_map(x) - - # projection - x = self._project(x, att_map) - - # apply batch norm - x = self._apply_BN(x) - x = self.act(x) - return x - - def _pairwise_mul_nodes(self, x): - ''' - Calculates pairwise multiplication of nodes. - - for attention map - x :(#bs, #node, #dim) - out_shape :(#bs, #node, #node, #dim) - ''' - - nb_nodes = x.size(1) - x = x.unsqueeze(2).expand(-1, -1, nb_nodes, -1) - x_mirror = x.transpose(1, 2) - - return x * x_mirror - - def _derive_att_map(self, x): - ''' - x :(#bs, #node, #dim) - out_shape :(#bs, #node, #node, 1) - ''' - att_map = self._pairwise_mul_nodes(x) - # size: (#bs, #node, #node, #dim_out) - att_map = torch.tanh(self.att_proj(att_map)) - # size: (#bs, #node, #node, 1) - att_map = torch.matmul(att_map, self.att_weight) - - # apply temperature - att_map = att_map / self.temp - - att_map = F.softmax(att_map, dim=-2) - - return att_map - - def _project(self, x, att_map): - x1 = self.proj_with_att(torch.matmul(att_map.squeeze(-1), x)) - x2 = self.proj_without_att(x) - - return x1 + x2 - - def _apply_BN(self, x): - org_size = x.size() - x = x.view(-1, org_size[-1]) - x = self.bn(x) - x = x.view(org_size) - - return x - - def _init_new_params(self, *size): - out = nn.Parameter(torch.FloatTensor(*size)) - nn.init.xavier_normal_(out) - return out diff --git a/audio/aasist3/model/hs_gal.py b/audio/aasist3/model/hs_gal.py deleted file mode 100644 index 99edcae0769edf34ca835b0128355002b8ba1ad5..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/hs_gal.py +++ /dev/null @@ -1,176 +0,0 @@ -import torch, torch.nn as nn, torch.nn.functional as F - -from .kan import KANLinear - - -class HtrgGraphAttentionLayer(nn.Module): - def __init__(self, in_dim, out_dim, size, **kwargs): - super().__init__() - - self.proj_type1 = KANLinear(in_dim, in_dim) - self.proj_type2 = KANLinear(in_dim, in_dim) - - # attention map - self.att_proj = KANLinear(in_dim, out_dim) - self.att_projM = KANLinear(in_dim, out_dim) - - self.att_weight11 = self._init_new_params(out_dim, 1) - self.att_weight22 = self._init_new_params(out_dim, 1) - self.att_weight12 = self._init_new_params(out_dim, 1) - self.att_weightM = self._init_new_params(out_dim, 1) - - # project - self.proj_with_att = KANLinear(in_dim, out_dim) - self.proj_without_att = KANLinear(in_dim, out_dim) - - self.proj_with_attM = KANLinear(in_dim, out_dim) - self.proj_without_attM = KANLinear(in_dim, out_dim) - - # batch norm - self.bn = nn.BatchNorm1d(out_dim) - - # dropout for inputs - self.input_drop = nn.Dropout(p=0.2) - - # activate - self.act = nn.SELU(inplace=True) - - # temperature - self.temp = 1. - if "temperature" in kwargs: - self.temp = kwargs["temperature"] - - def forward(self, x1, x2, master=None): - ''' - x1 :(#bs, #node, #dim) - x2 :(#bs, #node, #dim) - ''' - num_type1 = x1.size(1) - num_type2 = x2.size(1) - - x1 = self.proj_type1(x1) - x2 = self.proj_type2(x2) - - x = torch.cat([x1, x2], dim=1) - - if master is None: - master = torch.mean(x, dim=1, keepdim=True) - - # apply input dropout - x = self.input_drop(x) - - # derive attention map - att_map = self._derive_att_map(x, num_type1, num_type2) - - # directional edge for master node - master = self._update_master(x, master) - - # projection - x = self._project(x, att_map) - - # apply batch norm - x = self._apply_BN(x) - # x = self.act(x) - - x1 = x.narrow(1, 0, num_type1) - x2 = x.narrow(1, num_type1, num_type2) - - return x1, x2, master - - def _update_master(self, x, master): - - att_map = self._derive_att_map_master(x, master) - master = self._project_master(x, master, att_map) - - return master - - def _pairwise_mul_nodes(self, x): - ''' - Calculates pairwise multiplication of nodes. - - for attention map - x :(#bs, #node, #dim) - out_shape :(#bs, #node, #node, #dim) - ''' - - nb_nodes = x.size(1) - x = x.unsqueeze(2).expand(-1, -1, nb_nodes, -1) - x_mirror = x.transpose(1, 2) - - return x * x_mirror - - def _derive_att_map_master(self, x, master): - ''' - x :(#bs, #node, #dim) - out_shape :(#bs, #node, #node, 1) - ''' - att_map = x * master - att_map = torch.tanh(self.att_projM(att_map)) - - att_map = torch.matmul(att_map, self.att_weightM) - - # apply temperature - att_map = att_map / self.temp - - att_map = F.softmax(att_map, dim=-2) - - return att_map - - def _derive_att_map(self, x, num_type1, num_type2): - ''' - x :(#bs, #node, #dim) - out_shape :(#bs, #node, #node, 1) - ''' - att_map = self._pairwise_mul_nodes(x) - # size: (#bs, #node, #node, #dim_out) - att_map = torch.tanh(self.att_proj(att_map)) - # size: (#bs, #node, #node, 1) - - att_board = torch.zeros_like(att_map[:, :, :, 0]).unsqueeze(-1) - - att_board[:, :num_type1, :num_type1, :] = torch.matmul( - att_map[:, :num_type1, :num_type1, :], self.att_weight11) - att_board[:, num_type1:, num_type1:, :] = torch.matmul( - att_map[:, num_type1:, num_type1:, :], self.att_weight22) - att_board[:, :num_type1, num_type1:, :] = torch.matmul( - att_map[:, :num_type1, num_type1:, :], self.att_weight12) - att_board[:, num_type1:, :num_type1, :] = torch.matmul( - att_map[:, num_type1:, :num_type1, :], self.att_weight12) - - att_map = att_board - - # att_map = torch.matmul(att_map, self.att_weight12) - - # apply temperature - att_map = att_map / self.temp - - att_map = F.softmax(att_map, dim=-2) - - return att_map - - def _project(self, x, att_map): - x1 = self.proj_with_att(torch.matmul(att_map.squeeze(-1), x)) - x2 = self.proj_without_att(x) - - return x1 + x2 - - def _project_master(self, x, master, att_map): - - x1 = self.proj_with_attM(torch.matmul( - att_map.squeeze(-1).unsqueeze(1), x)) - x2 = self.proj_without_attM(master) - - return x1 + x2 - - def _apply_BN(self, x): - org_size = x.size() - x = x.view(-1, org_size[-1]) - x = self.bn(x) - x = x.view(org_size) - - return x - - def _init_new_params(self, *size): - out = nn.Parameter(torch.FloatTensor(*size)) - nn.init.xavier_normal_(out) - return out - diff --git a/audio/aasist3/model/kan.py b/audio/aasist3/model/kan.py deleted file mode 100644 index 9ceb844778c85c7abc5b3b0eb71ed5c346457917..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/kan.py +++ /dev/null @@ -1,213 +0,0 @@ -import torch, math, torch.nn.functional as F - - -class KANLinear(torch.nn.Module): - def __init__( - self, - in_features, - out_features, - grid_size=16, - spline_order=4, - scale_noise=0.1, - scale_base=1.0, - scale_spline=1.0, - enable_standalone_scale_spline=True, - base_activation=torch.nn.PReLU, - grid_eps=0.02, - grid_range=[-1, 1], - ): - super(KANLinear, self).__init__() - self.in_features = in_features - self.out_features = out_features - self.grid_size = grid_size - self.spline_order = spline_order - - h = (grid_range[1] - grid_range[0]) / grid_size - grid = ( - ( - torch.arange(-spline_order, grid_size + spline_order + 1) * h - + grid_range[0] - ) - .expand(in_features, -1) - .contiguous() - ) - self.register_buffer("grid", grid) - - self.base_weight = torch.nn.Parameter(torch.Tensor(out_features, in_features)) - self.spline_weight = torch.nn.Parameter( - torch.Tensor(out_features, in_features, grid_size + spline_order) - ) - if enable_standalone_scale_spline: - self.spline_scaler = torch.nn.Parameter( - torch.Tensor(out_features, in_features) - ) - - self.scale_noise = scale_noise - self.scale_base = scale_base - self.scale_spline = scale_spline - self.enable_standalone_scale_spline = enable_standalone_scale_spline - self.base_activation = base_activation() - self.grid_eps = grid_eps - - self.reset_parameters() - - def reset_parameters(self): - torch.nn.init.kaiming_uniform_(self.base_weight, a=math.sqrt(5) * self.scale_base) - with torch.no_grad(): - noise = ( - ( - torch.rand(self.grid_size + 1, self.in_features, self.out_features) - - 1 / 2 - ) - * self.scale_noise - / self.grid_size - ) - self.spline_weight.data.copy_( - (self.scale_spline if not self.enable_standalone_scale_spline else 1.0) - * self.curve2coeff( - self.grid.T[self.spline_order : -self.spline_order], - noise, - ) - ) - if self.enable_standalone_scale_spline: - # torch.nn.init.constant_(self.spline_scaler, self.scale_spline) - torch.nn.init.kaiming_uniform_(self.spline_scaler, a=math.sqrt(5) * self.scale_spline) - - def b_splines(self, x: torch.Tensor): - """ - Compute the B-spline bases for the given input tensor. - - Args: - x (torch.Tensor): Input tensor of shape (batch_size, in_features). - - Returns: - torch.Tensor: B-spline bases tensor of shape (batch_size, in_features, grid_size + spline_order). - """ - assert x.dim() == 2 and x.size(1) == self.in_features - - grid: torch.Tensor = ( - self.grid - ) # (in_features, grid_size + 2 * spline_order + 1) - x = x.unsqueeze(-1) - bases = ((x >= grid[:, :-1]) & (x < grid[:, 1:])).to(x.dtype) - for k in range(1, self.spline_order + 1): - bases = ( - (x - grid[:, : -(k + 1)]) - / (grid[:, k:-1] - grid[:, : -(k + 1)]) - * bases[:, :, :-1] - ) + ( - (grid[:, k + 1 :] - x) - / (grid[:, k + 1 :] - grid[:, 1:(-k)]) - * bases[:, :, 1:] - ) - - assert bases.size() == ( - x.size(0), - self.in_features, - self.grid_size + self.spline_order, - ) - return bases.contiguous() - - def curve2coeff(self, x: torch.Tensor, y: torch.Tensor): - """ - Compute the coefficients of the curve that interpolates the given points. - - Args: - x (torch.Tensor): Input tensor of shape (batch_size, in_features). - y (torch.Tensor): Output tensor of shape (batch_size, in_features, out_features). - - Returns: - torch.Tensor: Coefficients tensor of shape (out_features, in_features, grid_size + spline_order). - """ - assert x.dim() == 2 and x.size(1) == self.in_features - assert y.size() == (x.size(0), self.in_features, self.out_features) - - A = self.b_splines(x).transpose( - 0, 1 - ) # (in_features, batch_size, grid_size + spline_order) - B = y.transpose(0, 1) # (in_features, batch_size, out_features) - solution = torch.linalg.lstsq( - A, B - ).solution # (in_features, grid_size + spline_order, out_features) - result = solution.permute( - 2, 0, 1 - ) # (out_features, in_features, grid_size + spline_order) - - assert result.size() == ( - self.out_features, - self.in_features, - self.grid_size + self.spline_order, - ) - return result.contiguous() - - @property - def scaled_spline_weight(self): - return self.spline_weight * ( - self.spline_scaler.unsqueeze(-1) - if self.enable_standalone_scale_spline - else 1.0 - ) - - def forward(self, x: torch.Tensor): - assert x.size(-1) == self.in_features - original_shape = x.shape - x = x.reshape(-1, self.in_features) - - base_output = F.linear(self.base_activation(x), self.base_weight) - spline_output = F.linear( - self.b_splines(x).view(x.size(0), -1), - self.scaled_spline_weight.reshape(self.out_features, -1), - ) - output = base_output + spline_output - # print(*original_shape[:-1], output.shape) - output = output.view(*original_shape[:-1], self.out_features) - return output - - @torch.no_grad() - def update_grid(self, x: torch.Tensor, margin=0.01): - assert x.dim() == 2 and x.size(1) == self.in_features - batch = x.size(0) - - splines = self.b_splines(x) # (batch, in, coeff) - splines = splines.permute(1, 0, 2) # (in, batch, coeff) - orig_coeff = self.scaled_spline_weight # (out, in, coeff) - orig_coeff = orig_coeff.permute(1, 2, 0) # (in, coeff, out) - unreduced_spline_output = torch.bmm(splines, orig_coeff) # (in, batch, out) - unreduced_spline_output = unreduced_spline_output.permute( - 1, 0, 2 - ) # (batch, in, out) - - # sort each channel individually to collect data distribution - x_sorted = torch.sort(x, dim=0)[0] - grid_adaptive = x_sorted[ - torch.linspace( - 0, batch - 1, self.grid_size + 1, dtype=torch.int64, device=x.device - ) - ] - - uniform_step = (x_sorted[-1] - x_sorted[0] + 2 * margin) / self.grid_size - grid_uniform = ( - torch.arange( - self.grid_size + 1, dtype=torch.float32, device=x.device - ).unsqueeze(1) - * uniform_step - + x_sorted[0] - - margin - ) - - grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive - grid = torch.concatenate( - [ - grid[:1] - - uniform_step - * torch.arange(self.spline_order, 0, -1, device=x.device).unsqueeze(1), - grid, - grid[-1:] - + uniform_step - * torch.arange(1, self.spline_order + 1, device=x.device).unsqueeze(1), - ], - dim=0, - ) - - self.grid.copy_(grid.T) - self.spline_weight.data.copy_(self.curve2coeff(x, unreduced_spline_output)) diff --git a/audio/aasist3/model/pool.py b/audio/aasist3/model/pool.py deleted file mode 100644 index b9d5c3b79e3c4b67d106bb62c4e7bb6e51c1fa3f..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/pool.py +++ /dev/null @@ -1,45 +0,0 @@ -import torch, torch.nn as nn -from typing import Union - -from .kan import KANLinear - - -class GraphPool(nn.Module): - def __init__(self, k: float, in_dim: int, p: Union[float, int], size): - super().__init__() - self.k = k - self.sigmoid = nn.Sigmoid() - self.proj = KANLinear(in_dim, 1) - self.drop = nn.Dropout(p=p) if p > 0 else nn.Identity() - self.in_dim = in_dim - - def forward(self, h): - Z = self.drop(h) - weights = self.proj(Z) - scores = self.sigmoid(weights) - new_h = self.top_k_graph(scores, h, self.k) - - return new_h - - def top_k_graph(self, scores, h, k): - """ - args - ===== - scores: attention-based weights (#bs, #node, 1) - h: graph data (#bs, #node, #dim) - k: ratio of remaining nodes, (float) - - returns - ===== - h: graph pool applied data (#bs, #node', #dim) - """ - _, n_nodes, n_feat = h.size() - n_nodes = max(int(n_nodes * k), 1) - _, idx = torch.topk(scores, n_nodes, dim=1) - idx = idx.expand(-1, -1, n_feat) - - h = h * scores - h = torch.gather(h, 1, idx) - - return h - diff --git a/audio/aasist3/model/residual.py b/audio/aasist3/model/residual.py deleted file mode 100644 index 6fd300a30e8eaca4d5c59135b3c1f2990c7cfcd7..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/residual.py +++ /dev/null @@ -1,56 +0,0 @@ -import torch.nn as nn - -class Residual_block(nn.Module): - def __init__(self, nb_filts, first=False): - 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=(2, 3), - padding=(1, 1), - stride=1) - self.selu = nn.SELU() - - 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=(2, 3), - padding=(0, 1), - stride=1) - - if nb_filts[0] != nb_filts[1]: - self.downsample = True - self.conv_downsample = nn.Conv2d(in_channels=nb_filts[0], - out_channels=nb_filts[1], - padding=(0, 1), - kernel_size=(1, 3), - stride=1) - - else: - self.downsample = False - # self.mp = nn.MaxPool2d((1, 3)) # self.mp = nn.MaxPool2d((1,4)) - - 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) - - # print('out',out.shape) - out = self.bn2(out) - out = self.selu(out) - # print('out',out.shape) - out = self.conv2(out) - #print('conv2 out',out.shape) - if self.downsample: - identity = self.conv_downsample(identity) - - out += identity - # out = self.mp(out) - return out \ No newline at end of file diff --git a/audio/aasist3/model/wav2vec.py b/audio/aasist3/model/wav2vec.py deleted file mode 100644 index 72a787e3a0a235709f1cbf599dae5954df4b6621..0000000000000000000000000000000000000000 --- a/audio/aasist3/model/wav2vec.py +++ /dev/null @@ -1,82 +0,0 @@ -import torch, torch.nn as nn -from transformers import Wav2Vec2Model, Wav2Vec2Config - - -class Wav2Vec2Encoder(nn.Module): - """SSL encoder based on Hugging Face's Wav2Vec2 model.""" - - def __init__(self, - model_name_or_path: str = "facebook/wav2vec2-large-xlsr-53", - ssl_out_dim: int = 768, - use_ssl_n_layers: int = None, - freeze_ssl_n_layers: int = 0, - output_attentions: bool = False, - output_hidden_states: bool = False, - normalize_waveform: bool = True, - cache_dir: str = "weights", - load_pretrained: bool = True): - """Initialize the Wav2Vec2 encoder. - - Args: - model_name_or_path: HuggingFace model name or path to local model. - ssl_out_dim: Output dimension of the Wav2Vec2 encoder. - use_ssl_n_layers: Number of Wav2Vec2 layers to use. If None, use all layers. - freeze_ssl_n_layers: Number of Wav2Vec2 layers to freeze during training. - output_attentions: Whether to output attentions. - output_hidden_states: Whether to output hidden states. - normalize_waveform: Whether to normalize the waveform input. - cache_dir: Directory to cache pretrained models. - load_pretrained: Whether to load pretrained weights. If False, initializes with random weights. - """ - super().__init__() - - self.model_name_or_path = model_name_or_path - self.ssl_out_dim = ssl_out_dim - self.use_ssl_n_layers = use_ssl_n_layers - self.freeze_ssl_n_layers = freeze_ssl_n_layers - self.output_attentions = output_attentions - self.output_hidden_states = output_hidden_states - self.normalize_waveform = normalize_waveform - - if load_pretrained: - self.model = Wav2Vec2Model.from_pretrained(model_name_or_path, cache_dir=cache_dir) - else: - config = Wav2Vec2Config.from_pretrained( - model_name_or_path, - cache_dir=cache_dir, - local_files_only=False - ) - self.model = Wav2Vec2Model(config) - self.model.init_weights() - - def forward(self, x): - """Forward pass through the Wav2Vec2 encoder. - - Args: - x: Input tensor of shape (batch_size, sequence_length, channels) - - Returns: - Extracted features of shape (batch_size, sequence_length, ssl_out_dim) - """ - # Handle shape: convert (batch_size, sequence_length, channels) to (batch_size, sequence_length) - if x.ndim == 3: - x = x.squeeze(-1) # Remove channel dimension if present - - if self.normalize_waveform: - x = x / (torch.max(torch.abs(x), dim=1, keepdim=True)[0] + 1e-8) - - outputs = self.model( - x, - output_attentions=self.output_attentions, - output_hidden_states=self.output_hidden_states, - return_dict=True - ) - - last_hidden_state = outputs.last_hidden_state - - if self.use_ssl_n_layers is not None and self.output_hidden_states and outputs.hidden_states is not None: - selected = outputs.hidden_states[-self.use_ssl_n_layers:] - last_hidden_state = torch.mean(torch.stack(selected, dim=0), dim=0) - del outputs - - return last_hidden_state \ No newline at end of file diff --git a/audio/aasist3/requirements.txt b/audio/aasist3/requirements.txt deleted file mode 100644 index e77b3643d0b1ad3e86b7d3b0f79b85c8425e4af4..0000000000000000000000000000000000000000 --- a/audio/aasist3/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -torch==2.5.1 -torchaudio==2.5.1 -transformers>=4.40.0 -huggingface-hub>=0.20.0 -safetensors>=0.4.0 -numpy<2.0 -soundfile -fastapi -uvicorn[standard] -pydantic -python-multipart diff --git a/audio/nes2net/.gitignore b/audio/nes2net/.gitignore deleted file mode 100644 index d285b0dd1d6c257ec0dea379665e40a74f8e8759..0000000000000000000000000000000000000000 --- a/audio/nes2net/.gitignore +++ /dev/null @@ -1 +0,0 @@ -weights/ diff --git a/audio/nes2net/Dockerfile b/audio/nes2net/Dockerfile deleted file mode 100644 index d2a777d1882caa3eb548a65dd14f343de8190b92..0000000000000000000000000000000000000000 --- a/audio/nes2net/Dockerfile +++ /dev/null @@ -1,41 +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 wget \ - 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 (same as ShiftySpeech) -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 model_scripts /app/model_scripts -COPY api.py . -RUN mkdir -p /app/weights -COPY weights/ /app/weights/ - -EXPOSE 8004 - -RUN adduser --disabled-password --gecos '' appuser -USER appuser - -CMD ["python", "api.py"] diff --git a/audio/nes2net/api.py b/audio/nes2net/api.py deleted file mode 100644 index d085da9d55e6041cb882565c2077fbf39868e0ba..0000000000000000000000000000000000000000 --- a/audio/nes2net/api.py +++ /dev/null @@ -1,315 +0,0 @@ -"""Nes2Net (XLSR + Nested Res2Net TDNN) Audio Deepfake Detection API. - -Detects synthetic speech using the Nes2Net model architecture: -- Frontend: XLSR wav2vec 2.0 (Self-Supervised Learning) -- Backend: Nested Res2Net TDNN with SE modules - -Reference: https://github.com/TianchiLiu/Nes2Net -""" - -import argparse -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("nes2net_api") - -# Add the model code to the path -if "/app" not in sys.path: - sys.path.insert(0, "/app") - -# Import model class (deferred to allow path setup) -try: - from model_scripts.wav2vec2_Nes2Net_X import ( - wav2vec2_Nes2Net_no_Res_w_allT as Nes2NetModel, - ) -except ImportError as e: - logger.error(f"Failed to import Nes2Net model: {e}") - Nes2NetModel = None - -# Constants -MODEL_NAME = "nes2net" -MODEL_ID = "nes2net_xlsr_itw_valaug" -WEIGHTS_PATH = "/app/weights/nes2net_itw_valaug.pt" - - -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="Nes2Net Audio Deepfake Detection API", - description=( - "Service for detecting synthetic speech using the " - "Nes2Net model (XLSR wav2vec 2.0 + Nested Res2Net TDNN)." - ), - version="1.0.0", -) - - -def load_model(): - """Load the Nes2Net model with fine-tuned weights. - - Returns: - The loaded model, or None if loading fails. - """ - global model - if model is not None: - return model - - logger.info(f"Loading Nes2Net model onto {DEVICE}...") - - if Nes2NetModel is None: - logger.error("Nes2Net 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: - args = argparse.Namespace( - n_output_logits=2, - dilation=2, - pool_func="mean", - SE_ratio=[1], - Nes_ratio=[8, 8], - ) - model = Nes2NetModel(args, str(DEVICE)) - - # Load fine-tuned 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("Nes2Net model loaded successfully.") - return model - except Exception as e: - logger.exception(f"Failed to load Nes2Net 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 Nes2Net inference. - - Loads audio, resamples to 16kHz mono, and pads/trims - to TARGET_SAMPLES using tiling (matching original training - preprocessing). - - 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 - 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", 8004)) - uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/audio/nes2net/model_scripts/__init__.py b/audio/nes2net/model_scripts/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/nes2net/model_scripts/wav2vec2_Nes2Net_X.py b/audio/nes2net/model_scripts/wav2vec2_Nes2Net_X.py deleted file mode 100644 index 4d8b5917d96c3217fe457828e619f1c1abbc69a3..0000000000000000000000000000000000000000 --- a/audio/nes2net/model_scripts/wav2vec2_Nes2Net_X.py +++ /dev/null @@ -1,317 +0,0 @@ -import math - -import fairseq -import torch -import torch.nn as nn - -___author__ = "Tianchi Liu" -__email__ = "tianchi_liu@u.nus.edu" -# modified from the model script from Hemlata Tak - - -class SSLModel(nn.Module): - def __init__(self, device): - super(SSLModel, self).__init__() - cp_path = ( - "/app/weights/xlsr2_300m.pt" # Change the pre-trained XLSR model path. - ) - model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( - [cp_path] - ) - self.model = model[0] - self.device = device - self.out_dim = 1024 - return - - def extract_feat(self, input_data): - # put the model to GPU if it not there - if ( - next(self.model.parameters()).device != input_data.device - or next(self.model.parameters()).dtype != input_data.dtype - ): - self.model.to(input_data.device, dtype=input_data.dtype) - self.model.train() - if True: - # input should be in shape (batch, length) - if input_data.ndim == 3: - input_tmp = input_data[:, :, 0] - else: - input_tmp = input_data - # [batch, length, dim] - emb = self.model(input_tmp, mask=False, features_only=True)["x"] - return emb - - -class SEModule(nn.Module): - def __init__(self, channels, SE_ratio=8): - super(SEModule, self).__init__() - self.se = nn.Sequential( - nn.AdaptiveAvgPool1d(1), - nn.Conv1d(channels, channels // SE_ratio, kernel_size=1, padding=0), - nn.ReLU(), - nn.Conv1d(channels // SE_ratio, channels, kernel_size=1, padding=0), - nn.Sigmoid(), - ) - - def forward(self, input): - x = self.se(input) - return input * x - - -class Bottle2neck(nn.Module): - - def __init__( - self, inplanes, planes, kernel_size=None, dilation=None, scale=8, SE_ratio=8 - ): - super(Bottle2neck, self).__init__() - width = int(math.floor(planes / scale)) - self.conv1 = nn.Conv1d(inplanes, width * scale, kernel_size=1) - self.bn1 = nn.BatchNorm1d(width * scale) - self.nums = scale - 1 - convs = [] - bns = [] - weighted_sum = [] - num_pad = math.floor(kernel_size / 2) * dilation - for i in range(self.nums): - convs.append( - nn.Conv2d( - width, - width, - kernel_size=(kernel_size, 1), - dilation=(dilation, 1), - padding=(num_pad, 0), - ) - ) - bns.append(nn.BatchNorm2d(width)) - initial_value = torch.ones(1, 1, 1, i + 2) * (1 / (i + 2)) - weighted_sum.append(nn.Parameter(initial_value, requires_grad=True)) - self.weighted_sum = nn.ParameterList(weighted_sum) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - self.conv3 = nn.Conv1d(width * scale, planes, kernel_size=1) - self.bn3 = nn.BatchNorm1d(planes) - self.relu = nn.ReLU() - self.width = width - self.se = SEModule(planes, SE_ratio) - - def forward(self, x): - residual = x - out = self.conv1(x) - out = self.relu(out) - out = self.bn1(out).unsqueeze(-1) # bz c T 1 - - spx = torch.split(out, self.width, 1) - sp = spx[self.nums] - for i in range(self.nums): - sp = torch.cat((sp, spx[i]), -1) - - sp = self.bns[i](self.relu(self.convs[i](sp))) - sp_s = sp * self.weighted_sum[i] - sp_s = torch.sum(sp_s, dim=-1, keepdim=False) - - if i == 0: - out = sp_s - else: - out = torch.cat((out, sp_s), 1) - out = torch.cat((out, spx[self.nums].squeeze(-1)), 1) - out = self.conv3(out) - out = self.relu(out) - out = self.bn3(out) - out = self.se(out) - out += residual - return out - - -class ASTP(nn.Module): - """Attentive statistics pooling: Channel- and context-dependent - statistics pooling, first used in ECAPA_TDNN. - """ - - def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False): - super(ASTP, self).__init__() - self.global_context_att = global_context_att - - # Use Conv1d with stride == 1 rather than Linear, then we don't - # need to transpose inputs. - if global_context_att: - self.linear1 = nn.Conv1d( - in_dim * 3, bottleneck_dim, kernel_size=1 - ) # equals W and b in the paper - else: - self.linear1 = nn.Conv1d( - in_dim, bottleneck_dim, kernel_size=1 - ) # equals W and b in the paper - self.linear2 = nn.Conv1d( - bottleneck_dim, in_dim, kernel_size=1 - ) # equals V and k in the paper - - def forward(self, x): - """ - x: a 3-dimensional tensor in tdnn-based architecture (B,F,T) - or a 4-dimensional tensor in resnet architecture (B,C,F,T) - 0-dim: batch-dimension, last-dim: time-dimension (frame-dimension) - """ - if len(x.shape) == 4: - x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3]) - assert len(x.shape) == 3 - - if self.global_context_att: - context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x) - context_std = torch.sqrt( - torch.var(x, dim=-1, keepdim=True) + 1e-10 - ).expand_as(x) - x_in = torch.cat((x, context_mean, context_std), dim=1) - else: - x_in = x - - # DON'T use ReLU here! ReLU may be hard to converge. - alpha = torch.tanh(self.linear1(x_in)) # alpha = F.relu(self.linear1(x_in)) - alpha = torch.softmax(self.linear2(alpha), dim=2) - mean = torch.sum(alpha * x, dim=2) - var = torch.sum(alpha * (x**2), dim=2) - mean**2 - std = torch.sqrt(var.clamp(min=1e-10)) - return torch.cat([mean, std], dim=1) - - -class Nested_Res2Net_TDNN(nn.Module): - - def __init__( - self, - Nes_ratio=[8, 8], - input_channel=1024, - n_output_logits=2, - dilation=2, - pool_func="mean", - SE_ratio=[8], - ): - - super(Nested_Res2Net_TDNN, self).__init__() - self.Nes_ratio = Nes_ratio[0] - assert input_channel % Nes_ratio[0] == 0 - C = input_channel // Nes_ratio[0] - self.C = C - Build_in_Res2Nets = [] - bns = [] - for i in range(Nes_ratio[0] - 1): - Build_in_Res2Nets.append( - Bottle2neck( - C, - C, - kernel_size=3, - dilation=dilation, - scale=Nes_ratio[1], - SE_ratio=SE_ratio[0], - ) - ) - bns.append(nn.BatchNorm1d(C)) - self.Build_in_Res2Nets = nn.ModuleList(Build_in_Res2Nets) - self.bns = nn.ModuleList(bns) - self.bn = nn.BatchNorm1d(1024) - self.relu = nn.ReLU() - self.pool_func = pool_func - if pool_func == "mean": - self.fc = nn.Linear(1024, n_output_logits) - elif pool_func == "ASTP": - self.pooling = ASTP( - in_dim=input_channel, bottleneck_dim=128, global_context_att=False - ) - self.fc = nn.Linear(2048, n_output_logits) - - def forward(self, x): - spx = torch.split(x, self.C, 1) - for i in range(self.Nes_ratio - 1): - if i == 0: - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.Build_in_Res2Nets[i](sp) - sp = self.relu(sp) - sp = self.bns[i](sp) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - out = torch.cat((out, spx[-1]), 1) - out = self.bn(out) - out = self.relu(out) - if self.pool_func == "mean": - out = torch.mean(out, dim=-1) - elif self.pool_func == "ASTP": - out = self.pooling(out) - out = self.fc(out) - return out - - -class wav2vec2_Nes2Net_no_Res_w_allT(nn.Module): - def __init__(self, args, device): - super().__init__() - self.device = device - - self.n_output_logits = args.n_output_logits - - #### - # create network wav2vec 2.0 - #### - self.ssl_model = SSLModel(self.device) - self.Nested_Res2Net_TDNN = Nested_Res2Net_TDNN( - Nes_ratio=args.Nes_ratio, - input_channel=1024, - n_output_logits=self.n_output_logits, - dilation=args.dilation, - pool_func=args.pool_func, - SE_ratio=args.SE_ratio, - ) - - def forward(self, x): - # -------pre-trained Wav2vec model fine tunning ------------------------## - x_ssl_feat = self.ssl_model.extract_feat(x.squeeze(-1)) - x_ssl_feat = x_ssl_feat.permute(0, 2, 1) - output = self.Nested_Res2Net_TDNN(x_ssl_feat) - - return output - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("--n_output_logits", type=int, default=2) - parser.add_argument("--dilation", type=int, default=2) # not important - parser.add_argument( - "--pool_func", - type=str, - default="mean", - choices=["mean", "ASTP"], - help="pooling function, choose from mean and ASTP", - ) - parser.add_argument( - "--Nes_ratio", - type=int, - nargs="+", - default=[8, 8], - help="Nes_ratio, from outer to inner", - ) - parser.add_argument( - "--SE_ratio", - type=int, - nargs="+", - default=[1], - help="SE downsampling ratio in the bottleneck", - ) - args = parser.parse_args() - - model = wav2vec2_Nes2Net_no_Res_w_allT(args=args, device="cpu") - x = torch.rand((4, 32000)).to("cpu") - model = model.to("cpu") - y = model(x) - print(y) - trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("all:", trainable_params) - trainable_params = sum( - p.numel() for p in model.ssl_model.parameters() if p.requires_grad - ) - print("SSL:", trainable_params) - trainable_params = sum( - p.numel() for p in model.Nested_Res2Net_TDNN.parameters() if p.requires_grad - ) - print("Backend:", trainable_params) diff --git a/audio/nes2net/requirements.txt b/audio/nes2net/requirements.txt deleted file mode 100644 index 9ba4009e0d0d24b612425b63f22112303c5e49cc..0000000000000000000000000000000000000000 --- a/audio/nes2net/requirements.txt +++ /dev/null @@ -1,13 +0,0 @@ -torch==2.5.1 -torchaudio==2.5.1 -numpy==1.23.5 -librosa==0.9.1 -soundfile -scipy -omegaconf -hydra-core -bitarray -fastapi -uvicorn[standard] -pydantic -python-multipart diff --git a/audio/safeear/Dockerfile b/audio/safeear/Dockerfile deleted file mode 100644 index 414d643ddcd0a1e946dd376377e7727ca01af359..0000000000000000000000000000000000000000 --- a/audio/safeear/Dockerfile +++ /dev/null @@ -1,49 +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 wget \ - build-essential \ - && 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 SafeEar repository (for model code imports) -RUN git clone --depth 1 https://github.com/LetterLiGo/SafeEar.git /app/safeear_repo - -# Install the fairseq fork with C extensions PATCHED OUT -# (same proven patch used by ShiftySpeech and Nes2Net -- -# C extensions are not needed for checkpoint loading) -WORKDIR /app/safeear_repo/fairseq_ours -RUN sed -i 's/ext_modules=extensions/ext_modules=[]/' setup.py && \ - pip install --no-cache-dir --no-deps -e . -WORKDIR /app - -# Download model weights from HuggingFace -RUN mkdir -p /app/weights && \ - wget -q -O /app/weights/SpeechTokenizer.pt \ - "https://huggingface.co/TEC2004/SafeEar-ASV19-spoof-detection/resolve/main/SpeechTokenizer.pt" && \ - wget -q -O /app/weights/model.ckpt \ - "https://huggingface.co/TEC2004/SafeEar-ASV19-spoof-detection/resolve/main/model.ckpt" - -COPY api.py . - -EXPOSE 8002 - -RUN adduser --disabled-password --gecos '' appuser -USER appuser - -CMD ["python", "api.py"] diff --git a/audio/safeear/api.py b/audio/safeear/api.py deleted file mode 100644 index 0ea75779e64d3e101dd30afcbcd81f982ea4e697..0000000000000000000000000000000000000000 --- a/audio/safeear/api.py +++ /dev/null @@ -1,321 +0,0 @@ -"""SafeEar audio deepfake detection API service. - -Uses the SafeEar content privacy-preserving model (CCS 2024) to detect -synthetic speech. Two-stage pipeline: - 1. SpeechTokenizer (neural audio codec) decouples acoustic features - 2. SafeEar1s (transformer classifier) detects spoofing from acoustic tokens - -Weights: HuggingFace TEC2004/SafeEar-ASV19-spoof-detection -""" - -import base64 -import logging -import os -import sys -import tempfile -import time -from typing import Optional - -import uvicorn -from fastapi import FastAPI, HTTPException -from pydantic import BaseModel, Field - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", -) -logger = logging.getLogger("safeear_api") - -import platform - -import librosa -import numpy as np -import torch - -# Add SafeEar repo to path for model imports -SAFEEAR_REPO_PATH = os.environ.get( - "SAFEEAR_REPO_PATH", - os.path.join(os.path.dirname(__file__), "safeear_repo"), -) -if SAFEEAR_REPO_PATH not in sys.path: - sys.path.insert(0, SAFEEAR_REPO_PATH) - - -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 ( - 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") - - -# Constants -MODEL_NAME = "safeear" -WEIGHTS_DIR = os.environ.get( - "WEIGHTS_DIR", - os.path.join(os.path.dirname(__file__), "weights"), -) -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 -MAX_AUDIO_LENGTH = 64600 # ~4 seconds at 16kHz (ASVspoof standard) -SOFTMAX_TEMPERATURE = 5.0 # Calibration temperature for out-of-distribution data -NUM_INFERENCE_PASSES = 5 # Monte Carlo passes for stable predictions - -# Global model instances -decouple_model = None -detect_model = None - - -class AudioInput(BaseModel): - """Schema for audio prediction requests.""" - - 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="SafeEar Audio Deepfake Detection API", - description="Content privacy-preserving deepfake detection using SafeEar.", - version="1.0.0", -) - - -def load_models(): - """Load both the decouple model (SpeechTokenizer) and detect model.""" - global decouple_model, detect_model - - if decouple_model is not None and detect_model is not None: - return True - - speech_tokenizer_path = os.path.join(WEIGHTS_DIR, "SpeechTokenizer.pt") - checkpoint_path = os.path.join(WEIGHTS_DIR, "model.ckpt") - - if not os.path.exists(speech_tokenizer_path): - logger.error(f"SpeechTokenizer weights not found: {speech_tokenizer_path}") - return False - if not os.path.exists(checkpoint_path): - logger.error(f"Model checkpoint not found: {checkpoint_path}") - return False - - try: - # --- Load SpeechTokenizer (decouple model) --- - from safeear.models.decouple import SpeechTokenizer - - logger.info("Loading SpeechTokenizer...") - decouple_model = SpeechTokenizer( - n_filters=64, - strides=[8, 5, 4, 2], - dimension=1024, - semantic_dimension=768, - bidirectional=True, - dilation_base=2, - residual_kernel_size=3, - n_residual_layers=1, - lstm_layers=2, - activation="ELU", - codebook_size=1024, - n_q=8, - sample_rate=16000, - ) - st_state = torch.load(speech_tokenizer_path, map_location="cpu") - decouple_model.load_state_dict(st_state) - decouple_model.to(DEVICE) - decouple_model.eval() - logger.info("SpeechTokenizer loaded.") - - # --- Load SafeEar1s (detect model) from Lightning checkpoint --- - from safeear.models.safeear import SafeEar1s, SE_Rawformer_front - - logger.info("Loading SafeEar1s detect model...") - detect_model = SafeEar1s( - front=SE_Rawformer_front(), - embedding_dim=1024, - dropout_rate=0.1, - attention_dropout=0.1, - stochastic_depth=0.1, - num_layers=2, - num_heads=8, - num_classes=2, - positional_embedding="sine", - mlp_ratio=1.0, - ) - - # The .ckpt is a PyTorch Lightning checkpoint - ckpt = torch.load(checkpoint_path, map_location="cpu") - state_dict = ckpt.get("state_dict", ckpt) - - # Lightning prefixes keys with "detect_model." - detect_state = {} - for k, v in state_dict.items(): - if k.startswith("detect_model."): - detect_state[k.replace("detect_model.", "", 1)] = v - - detect_model.load_state_dict(detect_state) - detect_model.to(DEVICE) - detect_model.eval() - logger.info("SafeEar1s detect model loaded.") - return True - - except Exception as e: - logger.exception(f"Failed to load SafeEar models: {e}") - decouple_model = None - detect_model = None - return False - - -def preprocess_audio(audio_bytes: bytes) -> torch.Tensor: - """Load audio bytes, resample to 16kHz mono, pad/trim.""" - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp: - tmp.write(audio_bytes) - tmp_path = tmp.name - - try: - waveform, _ = librosa.load(tmp_path, sr=SAMPLE_RATE, mono=True) - finally: - os.unlink(tmp_path) - - if len(waveform) < MAX_AUDIO_LENGTH: - waveform = np.pad(waveform, (0, MAX_AUDIO_LENGTH - len(waveform))) - else: - waveform = waveform[:MAX_AUDIO_LENGTH] - - # Shape: (1, 1, samples) -- batch=1, channels=1, time - tensor = torch.FloatTensor(waveform).unsqueeze(0).unsqueeze(0).to(DEVICE) - return tensor - - -@app.on_event("startup") -async def startup_event(): - """Attempt to load models at startup.""" - load_models() - - -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(): - """Return service health status and model availability.""" - models_loaded = decouple_model is not None and detect_model is not None - return { - "status": "healthy" if models_loaded else "degraded", - "model": MODEL_NAME, - "device": str(DEVICE), - "weights_found": ( - os.path.exists(os.path.join(WEIGHTS_DIR, "SpeechTokenizer.pt")) - and os.path.exists(os.path.join(WEIGHTS_DIR, "model.ckpt")) - ), - **_gpu_health_info(), - } - - -@app.post("/predict") -async def predict(input_data: AudioInput): - """Run SafeEar inference on base64-encoded audio data.""" - if decouple_model is None or detect_model is None: - if not load_models(): - raise HTTPException(status_code=503, detail="Models not loaded") - - try: - start_time = time.time() - logger.info( - "Received prediction request. " - f"Data size: {len(input_data.audio_data)} chars" - ) - - audio_bytes = base64.b64decode(input_data.audio_data) - x_wav = preprocess_audio(audio_bytes) - - with torch.no_grad(): - # Step 1: Extract acoustic tokens via SpeechTokenizer - # forward() returns: - # (reconstructed, commit_loss, semantic_feature, acoustic_tokens) - # layers=[0,1,2,3,4,5,6,7] means layer 0 goes to - # semantic_feature; layers 1-7 go to acoustic_tokens list - _, _, _, acoustic_tokens = decouple_model( - x_wav, layers=[0, 1, 2, 3, 4, 5, 6, 7] - ) - - # Step 2: Run detection model with Monte Carlo averaging - # SafeEar1s uses torch.randperm() in forward, so we average - # multiple passes for stable predictions - logit_sum = torch.zeros(1, 2, device=DEVICE) - for _ in range(NUM_INFERENCE_PASSES): - raw_logits, _ = detect_model(acoustic_tokens) - logit_sum += raw_logits - avg_logits = logit_sum / NUM_INFERENCE_PASSES - - # Step 3: Get fake probability with temperature-scaled softmax - # The model produces extreme logits that saturate standard - # softmax. Temperature scaling preserves discrimination while - # giving more interpretable probabilities. - probs = torch.softmax(avg_logits / SOFTMAX_TEMPERATURE, dim=-1) - prob_fake = probs[0, 1].item() - - prediction = 1 if prob_fake >= input_data.threshold else 0 - verdict = "fake" if prediction == 1 else "real" - inference_time = time.time() - start_time - - 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", 8002)) - uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/audio/safeear/download_weights.sh b/audio/safeear/download_weights.sh deleted file mode 100644 index beb0406d96ce1f1b333965c429952c57325e2222..0000000000000000000000000000000000000000 --- a/audio/safeear/download_weights.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -WEIGHTS_DIR="${WEIGHTS_DIR:-/app/weights}" -REPO_DIR="${REPO_DIR:-/app/safeear_repo}" - -mkdir -p "$WEIGHTS_DIR" - -echo "==> Cloning SafeEar source repository..." -if [ ! -d "$REPO_DIR/.git" ]; then - git clone --depth 1 https://github.com/LetterLiGo/SafeEar.git "$REPO_DIR" -fi - -echo "==> Downloading SpeechTokenizer.pt from HuggingFace..." -wget -q --show-progress -O "$WEIGHTS_DIR/SpeechTokenizer.pt" \ - "https://huggingface.co/TEC2004/SafeEar-ASV19-spoof-detection/resolve/main/SpeechTokenizer.pt" - -echo "==> Downloading model.ckpt from HuggingFace..." -wget -q --show-progress -O "$WEIGHTS_DIR/model.ckpt" \ - "https://huggingface.co/TEC2004/SafeEar-ASV19-spoof-detection/resolve/main/model.ckpt" - -echo "==> Weights downloaded to $WEIGHTS_DIR" -ls -lh "$WEIGHTS_DIR" diff --git a/audio/safeear/requirements.txt b/audio/safeear/requirements.txt deleted file mode 100644 index 2c3414f7989d54b3981bddd947a17d53b3b5e394..0000000000000000000000000000000000000000 --- a/audio/safeear/requirements.txt +++ /dev/null @@ -1,15 +0,0 @@ -torch==2.5.1 -torchaudio==2.5.1 -librosa>=0.10.0 -soundfile>=0.11.0 -numpy>=1.23.0 -einops>=0.7.0 -timm>=0.9.0 -hydra-core>=1.0.7 -omegaconf>=2.1.0 -pytorch-lightning>=1.6.0 -scipy>=1.11.0 -fastapi>=0.100.0 -uvicorn[standard]>=0.20.0 -python-multipart>=0.0.5 -pydantic>=2.0.0 diff --git a/audio/safeear/safeear_repo/.gitignore b/audio/safeear/safeear_repo/.gitignore deleted file mode 100644 index 290e216340cefdeac36872c41c79f4217a6dce42..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/.gitignore +++ /dev/null @@ -1,167 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ -model_zoos/* -Exps/* -datas/datasets -datas/ASVSpoof2019/LA -datas/ASVSpoof2021/ASVspoof2021_LA_eval -datas/ASVSpoof2021/keys -create_tsv.py \ No newline at end of file diff --git a/audio/safeear/safeear_repo/LICENSE b/audio/safeear/safeear_repo/LICENSE deleted file mode 100644 index 7afa6327c9be0d2fc4ddba8c5a6556e340e38b62..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/LICENSE +++ /dev/null @@ -1,23 +0,0 @@ -# Creative Commons Attribution 4.0 International License - -## License - -You are free to: - -- Share — copy and redistribute the material in any medium or format -- Adapt — remix, transform, and build upon the material for any purpose, even commercially. - -Under the following terms: - -1. **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. - -2. **No additional restrictions** — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. - -## Other Terms - -- This license applies to all types of works, including but not limited to text, images, audio, video, etc. -- This license does not apply to any third-party materials included in the work, for which you must obtain permission separately. - -## Disclaimer - -This work is provided on an "as is" basis, without any warranties or conditions of any kind, either express or implied, including but not limited to implied warranties of merchantability, fitness for a particular purpose, or non-infringement. diff --git a/audio/safeear/safeear_repo/README.md b/audio/safeear/safeear_repo/README.md deleted file mode 100644 index eac42179818705bb45aee6b45e8a40efcc0b48b8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/README.md +++ /dev/null @@ -1,134 +0,0 @@ -# SafeEaricon: Content Privacy-Preserving Audio Deepfake Detection - -[![arXiv](https://img.shields.io/badge/arXiv-2409.09272-b31b1b.svg)](https://arxiv.org/abs/2409.09272) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](https://makeapullrequest.com) -[![CC BY 4.0](https://img.shields.io/badge/license-CC%20BY%204.0-blue.svg)](https://creativecommons.org/licenses/by/4.0/) -![GitHub stars](https://img.shields.io/github/stars/LetterLiGo/SafeEar) -![GitHub forks](https://img.shields.io/github/forks/LetterLiGo/SafeEar) -![Website](https://img.shields.io/website?url=https://safeearweb.github.io/Project/) - - -By [1] Zhejiang University, [2] Tsinghua University. -* [Xinfeng Li](https://letterligo.github.io)* [1], [Kai Li](https://cslikai.cn)* [2], Yifan Zheng [1], Chen Yan† [1], Xiaoyu Ji [1], Wenyuan Xu [1]. - -This repository is an official implementation of the SafeEar accepted to **ACM CCS 2024** (Core-A*, CCF-A, Big4) . - -Please also visit our (1) Project Website, (2) Full CVoiceFake Dataset, and (3) Sampled CVoiceFake Dataset. - -## 🔥News - -[2025-03-18]: Supported the batch testing for ASVspoof 2019 and 2021, fixed some bugs for datasets and trainer. - -[2024-12-10]: Fixed all the bugs for training and test, and uploaded the files for data generation `datas/`. - -[2024-12-01]: Uploaded the checkpoint for data generation `datas/`. - -## ✨Key Highlights: - -In this paper, we propose SafeEar, a novel framework that aims to detect deepfake audios without relying on accessing the speech content within. Our key idea is to devise a neural audio codec into a novel decoupling model that well separates the semantic and acoustic information from audio samples, and only use the acoustic information (e.g., prosody and timbre) for deepfake detection. In this way, no semantic content will be exposed to the detector. To overcome the challenge of identifying diverse deepfake audio without semantic clues, we enhance our deepfake detector with multi-head self-attention and codec augmentation. Extensive experiments conducted on four benchmark datasets demonstrate SafeEar’s effectiveness in detecting various deepfake techniques with an equal error rate (EER) down to 2.02%. Simultaneously, it shields five-language speech content from being deciphered by both machine and human auditory analysis, demonstrated by word error rates (WERs) all above 93.93% and our user study. Furthermore, our benchmark constructed for anti-deepfake and anti-content recovery evaluation helps provide a basis for future research in the realms of audio privacy preservation and deepfake detection. - -## 🚀Overall Pipeline - -![pipeline](assert/overall.gif) - -## 🔧Installation - -1. Clone the repository: - -```shell -git clone git@github.com:LetterLiGo/SafeEar.git -cd SafeEar/ -``` - -2. Create and activate the conda environment: - -```shell -conda create -n safeear python=3.9 -conda activate safeear -``` - -3. Install PyTorch and torchvision following the [official instructions](https://pytorch.org). The code requires `python=3.9`, `pytorch=1.13`, `torchvision=0.14`. - - -```shell -pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 - -``` -4. Install other dependencies: - -```shell -pip install pip==24.0 -pip install -r requirements.txt -``` - -## 📊Model Performance -### ASVspoof 2019 & 2021 -![](assert/ASVSpoof-results.png) -### Speech Recognition Performance -![](assert/exp1.png) - -## Data preparation - -### AVSpoof 2019 & 2021 - -Please download the [ASVspoof 2019](https://datashare.is.ed.ac.uk/handle/10283/3336) and [ASVspoof 2021](https://www.asvspoof.org/index2021.html) datasets and extract them to the `datas/datasets` directory. - -```shell -datas/datasets/ASVspoof2019 -datas/datasets/ASVspoof2021 -``` - -#### Generate the Hubert L9 feature files - -```shell -mkdir model_zoos -cd model_zoos -wget https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt -wget https://cloud.tsinghua.edu.cn/f/413a0cd2e6f749eea956/?dl=1 -O SpeechTokenizer.pt -cd ../datas -# Generate the Hubert L9 feature files for ASVspoof 2019 -python dump_hubert_avg_feature.py datasets/ASVSpoof2019 datasets/ASVSpoof2019_Hubert_L9 -# Generate the Hubert L9 feature files for ASVspoof 2021 -python dump_hubert_avg_feature.py datasets/ASVSpoof2021 datasets/ASVSpoof2021_Hubert_L9 -``` - -## 📚Training - -Before starting training, please modify the parameter configurations in [`configs`](configs). - -Use the following commands to start training: - -```shell -python train.py --conf_dir config/train19.yaml -python train.py --conf_dir config/train21.yaml -``` - -## 📈Testing/Inference - -To evaluate a model on one or more GPUs, specify the `CUDA_VISIBLE_DEVICES`, `dataset`, `model` and `checkpoint`: - -```shell -python test.py --conf_dir Exps/ASVspoof19/config.yaml -python test.py --conf_dir Exps/ASVspoof21/config.yaml -``` - -## Bugs and Issues - -If you meet `RuntimeError: Failed to load audio from <_io.BytesIO object at 0x7f45cb978f90>`, please use the following command to fix it: - -```shell -conda install -c anaconda 'ffmpeg<4.4' -``` - -## 📜Citation - -If you find our work/code/dataset helpful, please consider citing: - -``` -@inproceedings{li2024safeear, - author = {Li, Xinfeng and Li, Kai and Zheng, Yifan and Yan, Chen and Ji, Xiaoyu, and Xu, Wenyuan}, - title = {{SafeEar: Content Privacy-Preserving Audio Deepfake Detection}}, - booktitle = {Proceedings of the 2024 {ACM} {SIGSAC} Conference on Computer and Communications Security (CCS)} - year = {2024}, -} -``` diff --git a/audio/safeear/safeear_repo/assert/ASVSpoof-results.png b/audio/safeear/safeear_repo/assert/ASVSpoof-results.png deleted file mode 100644 index 6f3d1a76d5c6200898cfb599ca5608df5392537b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/assert/ASVSpoof-results.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0697de878b12c7b96ad4609569ddc20388027764ca441575b9c66dc6696d53d2 -size 684419 diff --git a/audio/safeear/safeear_repo/assert/Fig1.jpg b/audio/safeear/safeear_repo/assert/Fig1.jpg deleted file mode 100644 index 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5d1c58f347f6ce8d3a6b5d3553f4f7eca803c60f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/config/train19.yaml +++ /dev/null @@ -1,87 +0,0 @@ -datamodule: - _target_: safeear.datas.asvspoof19.DataModule - batch_size: 2 - num_workers: 8 - pin_memory: true - DataClass_dict: - _target_: safeear.datas.asvspoof19.DataClass - train_path: ["datas/ASVSpoof2019/train.tsv", "datas/ASVSpoof2019/ASVspoof2019.LA.cm.train.trn.txt", "datas/datasets/ASVSpoof2019_Hubert_L9/ASVspoof2019_LA_train/flac"] - val_path: ["datas/ASVSpoof2019/dev.tsv", "datas/ASVSpoof2019/ASVspoof2019.LA.cm.dev.trl.txt", "datas/datasets/ASVSpoof2019_Hubert_L9/ASVspoof2019_LA_dev/flac"] - test_path: ["datas/ASVSpoof2019/eval.tsv", "datas/ASVSpoof2019/ASVspoof2019.LA.cm.eval.trl.txt", "datas/datasets/ASVSpoof2019_Hubert_L9/ASVspoof2019_LA_eval/flac"] - max_len: 64600 - -decouple_model: - _target_: safeear.models.decouple.SpeechTokenizer - n_filters: 64 - strides: [8,5,4,2] - dimension: 1024 - semantic_dimension: 768 - bidirectional: true - dilation_base: 2 - residual_kernel_size: 3 - n_residual_layers: 1 - lstm_layers: 2 - activation: ELU - codebook_size: 1024 - n_q: 8 - sample_rate: 16000 - -speechtokenizer_path: model_zoos/SpeechTokenizer.pt - -detect_model: - _target_: safeear.models.safeear.SafeEar1s - front: - _target_: safeear.models.safeear.SE_Rawformer_front - embedding_dim: 1024 - dropout_rate: 0.1 - attention_dropout: 0.1 - stochastic_depth: 0.1 - num_layers: 2 - num_heads: 8 - num_classes: 2 - positional_embedding: 'sine' - mlp_ratio: 1.0 - -system: - _target_: safeear.trainer.safeear_trainer.SafeEarTrainer - lr_raw_former: 3.0e-4 - save_score_path: ${exp.dir}/${exp.name} - -exp: - dir: Exps/ # 修改 - name: ASVspoof19 # 修改 - -early_stopping: - _target_: pytorch_lightning.callbacks.EarlyStopping - monitor: val_eer # 修改 - mode: min - patience: 40 - verbose: true - -checkpoint: - _target_: pytorch_lightning.callbacks.ModelCheckpoint - dirpath: ${exp.dir}/${exp.name}/checkpoints - monitor: val_eer # 修改 - mode: min - verbose: true - save_top_k: 1 - save_last: true - filename: '{epoch}-{val_eer:.4f}' # 修改 - -logger: - _target_: pytorch_lightning.loggers.WandbLogger - name: ${exp.name} - save_dir: ${exp.dir}/${exp.name}/logs - offline: true - project: SafeEar - -trainer: - _target_: pytorch_lightning.Trainer - devices: [0] - max_epochs: 500 - sync_batchnorm: true - default_root_dir: ${exp.dir}/${exp.name}/ - accelerator: gpu - limit_train_batches: 1.0 - limit_val_batches: 1.0 - fast_dev_run: false \ No newline at end of file diff --git a/audio/safeear/safeear_repo/config/train21.yaml b/audio/safeear/safeear_repo/config/train21.yaml deleted file mode 100644 index afe4eb6de5daa34b9a92500a927f4d8976361228..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/config/train21.yaml +++ /dev/null @@ -1,87 +0,0 @@ -datamodule: - _target_: safeear.datas.asvspoof21.DataModule - batch_size: 2 - num_workers: 8 - pin_memory: true - DataClass_dict: - _target_: safeear.datas.asvspoof21.DataClass - train_path: ["datas/ASVSpoof2019/train.tsv", "datas/ASVSpoof2019/ASVspoof2019.LA.cm.train.trn.txt", "datas/datasets/ASVSpoof2019_Hubert_L9/ASVspoof2019_LA_train/flac"] - val_path: ["datas/ASVSpoof2019/dev.tsv", "datas/ASVSpoof2019/ASVspoof2019.LA.cm.dev.trl.txt", "datas/datasets/ASVSpoof2019_Hubert_L9/ASVspoof2019_LA_dev/flac"] - test_path: ["datas/ASVSpoof2021/eval.tsv", "datas/ASVSpoof2021/ASVspoof2021.LA.cm.eval.trl.txt", "datas/datasets/ASVSpoof2021_Hubert_L9"] - max_len: 64600 - -decouple_model: - _target_: safeear.models.decouple.SpeechTokenizer - n_filters: 64 - strides: [8,5,4,2] - dimension: 1024 - semantic_dimension: 768 - bidirectional: true - dilation_base: 2 - residual_kernel_size: 3 - n_residual_layers: 1 - lstm_layers: 2 - activation: ELU - codebook_size: 1024 - n_q: 8 - sample_rate: 16000 - -speechtokenizer_path: model_zoos/SpeechTokenizer.pt - -detect_model: - _target_: safeear.models.safeear.SafeEar1s - front: - _target_: safeear.models.safeear.SE_Rawformer_front - embedding_dim: 1024 - dropout_rate: 0.1 - attention_dropout: 0.1 - stochastic_depth: 0.1 - num_layers: 2 - num_heads: 8 - num_classes: 2 - positional_embedding: 'sine' - mlp_ratio: 1.0 - -system: - _target_: safeear.trainer.safeear_trainer.SafeEarTrainer - lr_raw_former: 3.0e-4 - save_score_path: ${exp.dir}/${exp.name} - -exp: - dir: Exps/ # 修改 - name: ASVspoof21 # 修改 - -early_stopping: - _target_: pytorch_lightning.callbacks.EarlyStopping - monitor: val_eer # 修改 - mode: min - patience: 40 - verbose: true - -checkpoint: - _target_: pytorch_lightning.callbacks.ModelCheckpoint - dirpath: ${exp.dir}/${exp.name}/checkpoints - monitor: val_eer # 修改 - mode: min - verbose: true - save_top_k: 1 - save_last: true - filename: '{epoch}-{val_eer:.4f}' # 修改 - -logger: - _target_: pytorch_lightning.loggers.WandbLogger - name: ${exp.name} - save_dir: ${exp.dir}/${exp.name}/logs - offline: true - project: 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LA_D_2775660 - A03 spoof -LA_0070 LA_D_2789615 - A03 spoof -LA_0070 LA_D_2792636 - A03 spoof -LA_0070 LA_D_2847063 - A03 spoof -LA_0070 LA_D_2872375 - A03 spoof -LA_0070 LA_D_2912164 - A03 spoof -LA_0070 LA_D_3018508 - A03 spoof -LA_0070 LA_D_3053810 - A03 spoof -LA_0070 LA_D_3072931 - A03 spoof -LA_0070 LA_D_3105367 - A03 spoof -LA_0070 LA_D_3111530 - A03 spoof -LA_0070 LA_D_3150766 - A03 spoof -LA_0070 LA_D_3152935 - A03 spoof -LA_0070 LA_D_3165811 - A03 spoof -LA_0070 LA_D_3179444 - A03 spoof -LA_0070 LA_D_3190273 - A03 spoof -LA_0070 LA_D_3200479 - A03 spoof -LA_0070 LA_D_3252875 - A03 spoof -LA_0070 LA_D_3324013 - A03 spoof -LA_0070 LA_D_3361173 - A03 spoof -LA_0070 LA_D_3408645 - A03 spoof -LA_0070 LA_D_3427528 - A03 spoof -LA_0070 LA_D_3488221 - A03 spoof -LA_0070 LA_D_3504387 - A03 spoof -LA_0070 LA_D_3574339 - A03 spoof -LA_0070 LA_D_3593296 - A03 spoof -LA_0070 LA_D_3595774 - A03 spoof -LA_0070 LA_D_3597570 - A03 spoof -LA_0070 LA_D_3706722 - A03 spoof -LA_0070 LA_D_3756928 - A03 spoof -LA_0070 LA_D_3810637 - A03 spoof -LA_0070 LA_D_3889704 - A03 spoof -LA_0070 LA_D_3923572 - A03 spoof -LA_0070 LA_D_3979451 - A03 spoof -LA_0070 LA_D_4049151 - A03 spoof -LA_0070 LA_D_4070821 - A03 spoof -LA_0070 LA_D_4175580 - A03 spoof -LA_0070 LA_D_4192869 - A03 spoof -LA_0070 LA_D_4232324 - A03 spoof -LA_0070 LA_D_4248561 - A03 spoof -LA_0070 LA_D_4269380 - A03 spoof -LA_0070 LA_D_4273910 - A03 spoof -LA_0070 LA_D_4294810 - A03 spoof -LA_0070 LA_D_4308789 - A03 spoof -LA_0070 LA_D_4371236 - A03 spoof -LA_0070 LA_D_4422044 - A03 spoof -LA_0070 LA_D_4470112 - A03 spoof -LA_0070 LA_D_4545230 - A03 spoof -LA_0070 LA_D_4574130 - A03 spoof -LA_0070 LA_D_4611216 - A03 spoof -LA_0070 LA_D_4648091 - A03 spoof -LA_0070 LA_D_4663964 - A03 spoof -LA_0070 LA_D_4675984 - A03 spoof -LA_0070 LA_D_4710338 - A03 spoof -LA_0070 LA_D_4844408 - A03 spoof -LA_0070 LA_D_4914857 - A03 spoof -LA_0070 LA_D_4947867 - A03 spoof -LA_0070 LA_D_4976077 - A03 spoof -LA_0070 LA_D_5064217 - A03 spoof 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A03 spoof -LA_0071 LA_D_5597072 - A03 spoof -LA_0071 LA_D_5605656 - A03 spoof -LA_0071 LA_D_5613224 - A03 spoof -LA_0071 LA_D_5614406 - A03 spoof -LA_0071 LA_D_5631357 - A03 spoof -LA_0071 LA_D_5637928 - A03 spoof -LA_0071 LA_D_5656124 - A03 spoof -LA_0071 LA_D_5695559 - A03 spoof -LA_0071 LA_D_5721193 - A03 spoof -LA_0071 LA_D_5800550 - A03 spoof -LA_0071 LA_D_5840187 - A03 spoof -LA_0071 LA_D_5866255 - A03 spoof -LA_0071 LA_D_5891155 - A03 spoof -LA_0071 LA_D_5913608 - A03 spoof -LA_0071 LA_D_5926059 - A03 spoof -LA_0071 LA_D_5997151 - A03 spoof -LA_0071 LA_D_6049791 - A03 spoof -LA_0071 LA_D_6060780 - A03 spoof -LA_0071 LA_D_6097465 - A03 spoof -LA_0071 LA_D_6146856 - A03 spoof -LA_0071 LA_D_6176750 - A03 spoof -LA_0071 LA_D_6188881 - A03 spoof -LA_0071 LA_D_6196078 - A03 spoof -LA_0071 LA_D_6293245 - A03 spoof -LA_0071 LA_D_6320996 - A03 spoof -LA_0071 LA_D_6328554 - A03 spoof -LA_0071 LA_D_6368871 - A03 spoof -LA_0071 LA_D_6434711 - A03 spoof -LA_0071 LA_D_6445884 - A03 spoof 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A03 spoof -LA_0072 LA_D_4947818 - A03 spoof -LA_0072 LA_D_4985773 - A03 spoof -LA_0072 LA_D_4986753 - A03 spoof -LA_0072 LA_D_4999216 - A03 spoof -LA_0072 LA_D_5028641 - A03 spoof -LA_0072 LA_D_5040114 - A03 spoof -LA_0072 LA_D_5051597 - A03 spoof -LA_0072 LA_D_5051913 - A03 spoof -LA_0072 LA_D_5067087 - A03 spoof -LA_0072 LA_D_5077584 - A03 spoof -LA_0072 LA_D_5133384 - A03 spoof -LA_0072 LA_D_5167234 - A03 spoof -LA_0072 LA_D_5189187 - A03 spoof -LA_0072 LA_D_5322570 - A03 spoof -LA_0072 LA_D_5341323 - A03 spoof -LA_0072 LA_D_5345453 - A03 spoof -LA_0072 LA_D_5419620 - A03 spoof -LA_0072 LA_D_5442437 - A03 spoof -LA_0072 LA_D_5458288 - A03 spoof -LA_0072 LA_D_5476324 - A03 spoof -LA_0072 LA_D_5493977 - A03 spoof -LA_0072 LA_D_5510010 - A03 spoof -LA_0072 LA_D_5512449 - A03 spoof -LA_0072 LA_D_5519425 - A03 spoof -LA_0072 LA_D_5592740 - A03 spoof -LA_0072 LA_D_5609165 - A03 spoof -LA_0072 LA_D_5646610 - A03 spoof -LA_0072 LA_D_5742661 - A03 spoof -LA_0072 LA_D_5800896 - A03 spoof 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A03 spoof -LA_0072 LA_D_6898133 - A03 spoof -LA_0072 LA_D_6898383 - A03 spoof -LA_0072 LA_D_6935294 - A03 spoof -LA_0072 LA_D_6945696 - A03 spoof -LA_0072 LA_D_6951006 - A03 spoof -LA_0072 LA_D_7035543 - A03 spoof -LA_0072 LA_D_7049162 - A03 spoof -LA_0072 LA_D_7053354 - A03 spoof -LA_0072 LA_D_7091196 - A03 spoof -LA_0072 LA_D_7094041 - A03 spoof -LA_0072 LA_D_7205018 - A03 spoof -LA_0072 LA_D_7211753 - A03 spoof -LA_0072 LA_D_7212590 - A03 spoof -LA_0072 LA_D_7248069 - A03 spoof -LA_0072 LA_D_7254050 - A03 spoof -LA_0072 LA_D_7281159 - A03 spoof -LA_0072 LA_D_7283308 - A03 spoof -LA_0072 LA_D_7298541 - A03 spoof -LA_0072 LA_D_7300764 - A03 spoof -LA_0072 LA_D_7318854 - A03 spoof -LA_0072 LA_D_7326976 - A03 spoof -LA_0072 LA_D_7331566 - A03 spoof -LA_0072 LA_D_7339440 - A03 spoof -LA_0072 LA_D_7373710 - A03 spoof -LA_0072 LA_D_7399269 - A03 spoof -LA_0072 LA_D_7421536 - A03 spoof -LA_0072 LA_D_7428865 - A03 spoof -LA_0072 LA_D_7451358 - A03 spoof -LA_0072 LA_D_7516190 - A03 spoof 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A06 spoof -LA_0070 LA_D_2245820 - A06 spoof -LA_0070 LA_D_2431217 - A06 spoof -LA_0070 LA_D_2452864 - A06 spoof -LA_0070 LA_D_2474451 - A06 spoof -LA_0070 LA_D_2496402 - A06 spoof -LA_0070 LA_D_2503752 - A06 spoof -LA_0070 LA_D_2519640 - A06 spoof -LA_0070 LA_D_2559709 - A06 spoof -LA_0070 LA_D_2587582 - A06 spoof -LA_0070 LA_D_2648488 - A06 spoof -LA_0070 LA_D_2692844 - A06 spoof -LA_0070 LA_D_2697285 - A06 spoof -LA_0070 LA_D_2706144 - A06 spoof -LA_0070 LA_D_2740410 - A06 spoof -LA_0070 LA_D_2749594 - A06 spoof -LA_0070 LA_D_2770309 - A06 spoof -LA_0070 LA_D_2818232 - A06 spoof -LA_0070 LA_D_2850788 - A06 spoof -LA_0070 LA_D_2858116 - A06 spoof -LA_0070 LA_D_2937524 - A06 spoof -LA_0070 LA_D_3016130 - A06 spoof -LA_0070 LA_D_3063803 - A06 spoof -LA_0070 LA_D_3110494 - A06 spoof -LA_0070 LA_D_3120664 - A06 spoof -LA_0070 LA_D_3139923 - A06 spoof -LA_0070 LA_D_3173801 - A06 spoof -LA_0070 LA_D_3184034 - A06 spoof -LA_0070 LA_D_3219891 - A06 spoof -LA_0070 LA_D_3267906 - A06 spoof 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A06 spoof -LA_0070 LA_D_4777058 - A06 spoof -LA_0070 LA_D_4778408 - A06 spoof -LA_0070 LA_D_4797826 - A06 spoof -LA_0070 LA_D_4808965 - A06 spoof -LA_0070 LA_D_4827296 - A06 spoof -LA_0070 LA_D_4840118 - A06 spoof -LA_0070 LA_D_4841813 - A06 spoof -LA_0070 LA_D_4880667 - A06 spoof -LA_0070 LA_D_5040821 - A06 spoof -LA_0070 LA_D_5058835 - A06 spoof -LA_0070 LA_D_5066305 - A06 spoof -LA_0070 LA_D_5085644 - A06 spoof -LA_0070 LA_D_5154611 - A06 spoof -LA_0070 LA_D_5178700 - A06 spoof -LA_0070 LA_D_5184992 - A06 spoof -LA_0070 LA_D_5220940 - A06 spoof -LA_0070 LA_D_5241773 - A06 spoof -LA_0070 LA_D_5259263 - A06 spoof -LA_0070 LA_D_5269116 - A06 spoof -LA_0070 LA_D_5277230 - A06 spoof -LA_0070 LA_D_5303690 - A06 spoof -LA_0070 LA_D_5353056 - A06 spoof -LA_0070 LA_D_5376558 - A06 spoof -LA_0070 LA_D_5387772 - A06 spoof -LA_0070 LA_D_5403183 - A06 spoof -LA_0070 LA_D_5406612 - A06 spoof -LA_0070 LA_D_5407124 - A06 spoof -LA_0070 LA_D_5437611 - A06 spoof -LA_0070 LA_D_5444377 - A06 spoof 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LA_D_4627319 - A06 spoof -LA_0071 LA_D_4685861 - A06 spoof -LA_0071 LA_D_4708179 - A06 spoof -LA_0071 LA_D_4735638 - A06 spoof -LA_0071 LA_D_4744246 - A06 spoof -LA_0071 LA_D_4767892 - A06 spoof -LA_0071 LA_D_4793229 - A06 spoof -LA_0071 LA_D_4796348 - A06 spoof -LA_0071 LA_D_4969991 - A06 spoof -LA_0071 LA_D_4991294 - A06 spoof -LA_0071 LA_D_5012127 - A06 spoof -LA_0071 LA_D_5055165 - A06 spoof -LA_0071 LA_D_5109561 - A06 spoof -LA_0071 LA_D_5140508 - A06 spoof -LA_0071 LA_D_5163761 - A06 spoof -LA_0071 LA_D_5193544 - A06 spoof -LA_0071 LA_D_5220521 - A06 spoof -LA_0071 LA_D_5253806 - A06 spoof -LA_0071 LA_D_5269911 - A06 spoof -LA_0071 LA_D_5312245 - A06 spoof -LA_0071 LA_D_5404458 - A06 spoof -LA_0071 LA_D_5409509 - A06 spoof -LA_0071 LA_D_5459095 - A06 spoof -LA_0071 LA_D_5476884 - A06 spoof -LA_0071 LA_D_5486513 - A06 spoof -LA_0071 LA_D_5536552 - A06 spoof -LA_0071 LA_D_5549592 - A06 spoof -LA_0071 LA_D_5609423 - A06 spoof -LA_0071 LA_D_5613500 - A06 spoof -LA_0071 LA_D_5622630 - A06 spoof -LA_0071 LA_D_5673312 - A06 spoof -LA_0071 LA_D_5679192 - A06 spoof -LA_0071 LA_D_5685998 - A06 spoof -LA_0071 LA_D_5769309 - A06 spoof -LA_0071 LA_D_5801020 - A06 spoof -LA_0071 LA_D_5830923 - A06 spoof -LA_0071 LA_D_5861399 - A06 spoof -LA_0071 LA_D_5887076 - A06 spoof -LA_0071 LA_D_5908876 - A06 spoof -LA_0071 LA_D_6028756 - A06 spoof -LA_0071 LA_D_6062114 - A06 spoof -LA_0071 LA_D_6079609 - A06 spoof -LA_0071 LA_D_6113648 - A06 spoof -LA_0071 LA_D_6141449 - A06 spoof -LA_0071 LA_D_6161090 - A06 spoof -LA_0071 LA_D_6161483 - A06 spoof -LA_0071 LA_D_6165815 - A06 spoof -LA_0071 LA_D_6184039 - A06 spoof -LA_0071 LA_D_6206158 - A06 spoof -LA_0071 LA_D_6291610 - A06 spoof -LA_0071 LA_D_6432124 - A06 spoof -LA_0071 LA_D_6449816 - A06 spoof -LA_0071 LA_D_6462722 - A06 spoof -LA_0071 LA_D_6525457 - A06 spoof -LA_0071 LA_D_6603638 - A06 spoof -LA_0071 LA_D_6634413 - A06 spoof -LA_0071 LA_D_6656541 - A06 spoof -LA_0071 LA_D_6698865 - A06 spoof -LA_0071 LA_D_6709571 - A06 spoof 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LA_D_7683536 - A06 spoof -LA_0071 LA_D_7773540 - A06 spoof -LA_0071 LA_D_7803058 - A06 spoof -LA_0071 LA_D_7804582 - A06 spoof -LA_0071 LA_D_7824709 - A06 spoof -LA_0071 LA_D_7828554 - A06 spoof -LA_0071 LA_D_7853660 - A06 spoof -LA_0071 LA_D_7895951 - A06 spoof -LA_0071 LA_D_7905993 - A06 spoof -LA_0071 LA_D_7944594 - A06 spoof -LA_0071 LA_D_8006888 - A06 spoof -LA_0071 LA_D_8030557 - A06 spoof -LA_0071 LA_D_8103199 - A06 spoof -LA_0071 LA_D_8125961 - A06 spoof -LA_0071 LA_D_8167492 - A06 spoof -LA_0071 LA_D_8172970 - A06 spoof -LA_0071 LA_D_8257425 - A06 spoof -LA_0071 LA_D_8261981 - A06 spoof -LA_0071 LA_D_8263001 - A06 spoof -LA_0071 LA_D_8282334 - A06 spoof -LA_0071 LA_D_8352186 - A06 spoof -LA_0071 LA_D_8368027 - A06 spoof -LA_0071 LA_D_8377834 - A06 spoof -LA_0071 LA_D_8445966 - A06 spoof -LA_0071 LA_D_8450378 - A06 spoof -LA_0071 LA_D_8478346 - A06 spoof -LA_0071 LA_D_8481884 - A06 spoof -LA_0071 LA_D_8511990 - A06 spoof -LA_0071 LA_D_8521403 - A06 spoof -LA_0071 LA_D_8582209 - 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A06 spoof -LA_0078 LA_D_6679225 - A06 spoof -LA_0078 LA_D_6684696 - A06 spoof -LA_0078 LA_D_6687114 - A06 spoof -LA_0078 LA_D_6713163 - A06 spoof -LA_0078 LA_D_6713386 - A06 spoof -LA_0078 LA_D_6739854 - A06 spoof -LA_0078 LA_D_6741158 - A06 spoof -LA_0078 LA_D_6758446 - A06 spoof -LA_0078 LA_D_6779535 - A06 spoof -LA_0078 LA_D_6800766 - A06 spoof -LA_0078 LA_D_6821694 - A06 spoof -LA_0078 LA_D_6823282 - A06 spoof -LA_0078 LA_D_6845165 - A06 spoof -LA_0078 LA_D_6861442 - A06 spoof -LA_0078 LA_D_6863890 - A06 spoof -LA_0078 LA_D_6885418 - A06 spoof -LA_0078 LA_D_6902681 - A06 spoof -LA_0078 LA_D_6904078 - A06 spoof -LA_0078 LA_D_6906302 - A06 spoof -LA_0078 LA_D_6908542 - A06 spoof -LA_0078 LA_D_6987015 - A06 spoof -LA_0078 LA_D_6993130 - A06 spoof -LA_0078 LA_D_6995410 - A06 spoof -LA_0078 LA_D_7091071 - A06 spoof -LA_0078 LA_D_7094965 - A06 spoof -LA_0078 LA_D_7110756 - A06 spoof -LA_0078 LA_D_7133043 - A06 spoof -LA_0078 LA_D_7139747 - A06 spoof -LA_0078 LA_D_7151634 - A06 spoof -LA_0078 LA_D_7159338 - A06 spoof -LA_0078 LA_D_7206772 - A06 spoof -LA_0078 LA_D_7207336 - A06 spoof -LA_0078 LA_D_7230301 - A06 spoof -LA_0078 LA_D_7241582 - A06 spoof -LA_0078 LA_D_7243431 - A06 spoof -LA_0078 LA_D_7270172 - A06 spoof -LA_0078 LA_D_7291272 - A06 spoof -LA_0078 LA_D_7291592 - A06 spoof -LA_0078 LA_D_7306537 - A06 spoof -LA_0078 LA_D_7312913 - A06 spoof -LA_0078 LA_D_7316873 - A06 spoof -LA_0078 LA_D_7317184 - A06 spoof -LA_0078 LA_D_7396958 - A06 spoof -LA_0078 LA_D_7403679 - A06 spoof -LA_0078 LA_D_7412514 - A06 spoof -LA_0078 LA_D_7424653 - A06 spoof -LA_0078 LA_D_7428181 - A06 spoof -LA_0078 LA_D_7458005 - A06 spoof -LA_0078 LA_D_7470485 - A06 spoof -LA_0078 LA_D_7483902 - A06 spoof -LA_0078 LA_D_7545322 - A06 spoof -LA_0078 LA_D_7551004 - A06 spoof -LA_0078 LA_D_7554141 - A06 spoof -LA_0078 LA_D_7555425 - A06 spoof -LA_0078 LA_D_7599767 - A06 spoof -LA_0078 LA_D_7601022 - A06 spoof -LA_0078 LA_D_7622749 - A06 spoof -LA_0078 LA_D_7635024 - A06 spoof -LA_0078 LA_D_7659228 - A06 spoof -LA_0078 LA_D_7674503 - A06 spoof -LA_0078 LA_D_7692442 - A06 spoof -LA_0078 LA_D_7727110 - A06 spoof -LA_0078 LA_D_7765093 - A06 spoof -LA_0078 LA_D_7796473 - A06 spoof -LA_0078 LA_D_7820794 - A06 spoof -LA_0078 LA_D_7830688 - A06 spoof -LA_0078 LA_D_7838652 - A06 spoof -LA_0078 LA_D_7886473 - A06 spoof -LA_0078 LA_D_7888867 - A06 spoof -LA_0078 LA_D_7961283 - A06 spoof -LA_0078 LA_D_7986122 - A06 spoof -LA_0078 LA_D_8014280 - A06 spoof -LA_0078 LA_D_8029967 - A06 spoof -LA_0078 LA_D_8049091 - A06 spoof -LA_0078 LA_D_8078840 - A06 spoof -LA_0078 LA_D_8078878 - A06 spoof -LA_0078 LA_D_8083150 - A06 spoof -LA_0078 LA_D_8095777 - A06 spoof -LA_0078 LA_D_8127459 - A06 spoof -LA_0078 LA_D_8130762 - A06 spoof -LA_0078 LA_D_8158331 - A06 spoof -LA_0078 LA_D_8158959 - A06 spoof -LA_0078 LA_D_8169547 - A06 spoof -LA_0078 LA_D_8170099 - A06 spoof -LA_0078 LA_D_8172351 - A06 spoof -LA_0078 LA_D_8193450 - A06 spoof -LA_0078 LA_D_8199067 - A06 spoof -LA_0078 LA_D_8209765 - A06 spoof -LA_0078 LA_D_8217946 - A06 spoof -LA_0078 LA_D_8223036 - A06 spoof -LA_0078 LA_D_8237136 - A06 spoof -LA_0078 LA_D_8250644 - A06 spoof -LA_0078 LA_D_8324721 - A06 spoof -LA_0078 LA_D_8346311 - A06 spoof -LA_0078 LA_D_8364154 - A06 spoof -LA_0078 LA_D_8447186 - A06 spoof -LA_0078 LA_D_8506451 - A06 spoof -LA_0078 LA_D_8510080 - A06 spoof -LA_0078 LA_D_8564217 - A06 spoof -LA_0078 LA_D_8592715 - A06 spoof -LA_0078 LA_D_8596130 - A06 spoof -LA_0078 LA_D_8597270 - A06 spoof -LA_0078 LA_D_8599650 - A06 spoof -LA_0078 LA_D_8635399 - A06 spoof -LA_0078 LA_D_8637190 - A06 spoof -LA_0078 LA_D_8651444 - A06 spoof -LA_0078 LA_D_8676257 - A06 spoof -LA_0078 LA_D_8683862 - A06 spoof -LA_0078 LA_D_8711965 - A06 spoof -LA_0078 LA_D_8717286 - A06 spoof -LA_0078 LA_D_8763483 - A06 spoof -LA_0078 LA_D_8815066 - A06 spoof -LA_0078 LA_D_8817485 - A06 spoof -LA_0078 LA_D_8818567 - A06 spoof -LA_0078 LA_D_8870983 - A06 spoof -LA_0078 LA_D_8873162 - A06 spoof -LA_0078 LA_D_8882131 - A06 spoof -LA_0078 LA_D_8899360 - A06 spoof -LA_0078 LA_D_8949695 - A06 spoof -LA_0078 LA_D_8956054 - A06 spoof -LA_0078 LA_D_8971637 - A06 spoof -LA_0078 LA_D_9007888 - A06 spoof -LA_0078 LA_D_9032929 - A06 spoof -LA_0078 LA_D_9101014 - A06 spoof -LA_0078 LA_D_9116569 - A06 spoof -LA_0078 LA_D_9140736 - A06 spoof -LA_0078 LA_D_9165073 - A06 spoof -LA_0078 LA_D_9196640 - A06 spoof -LA_0078 LA_D_9258337 - A06 spoof -LA_0078 LA_D_9288317 - A06 spoof -LA_0078 LA_D_9314827 - A06 spoof -LA_0078 LA_D_9319189 - A06 spoof -LA_0078 LA_D_9333593 - A06 spoof -LA_0078 LA_D_9341670 - A06 spoof -LA_0078 LA_D_9362528 - A06 spoof -LA_0078 LA_D_9377948 - A06 spoof -LA_0078 LA_D_9393969 - A06 spoof -LA_0078 LA_D_9396779 - A06 spoof -LA_0078 LA_D_9457819 - A06 spoof -LA_0078 LA_D_9475659 - A06 spoof -LA_0078 LA_D_9476497 - A06 spoof -LA_0078 LA_D_9481106 - A06 spoof -LA_0078 LA_D_9483379 - A06 spoof -LA_0078 LA_D_9487713 - A06 spoof -LA_0078 LA_D_9497869 - A06 spoof -LA_0078 LA_D_9500981 - A06 spoof -LA_0078 LA_D_9515921 - A06 spoof -LA_0078 LA_D_9517790 - A06 spoof -LA_0078 LA_D_9518120 - A06 spoof -LA_0078 LA_D_9523992 - A06 spoof -LA_0078 LA_D_9542497 - A06 spoof -LA_0078 LA_D_9557083 - A06 spoof -LA_0078 LA_D_9566187 - A06 spoof -LA_0078 LA_D_9593338 - A06 spoof -LA_0078 LA_D_9594827 - A06 spoof -LA_0078 LA_D_9596577 - A06 spoof -LA_0078 LA_D_9601777 - A06 spoof -LA_0078 LA_D_9629887 - A06 spoof -LA_0078 LA_D_9633079 - A06 spoof -LA_0078 LA_D_9648310 - A06 spoof -LA_0078 LA_D_9730838 - A06 spoof -LA_0078 LA_D_9749096 - A06 spoof -LA_0078 LA_D_9779729 - A06 spoof -LA_0078 LA_D_9780174 - A06 spoof -LA_0078 LA_D_9789524 - A06 spoof -LA_0078 LA_D_9792280 - A06 spoof -LA_0078 LA_D_9812014 - A06 spoof -LA_0078 LA_D_9832311 - A06 spoof -LA_0078 LA_D_9835323 - A06 spoof -LA_0078 LA_D_9839655 - A06 spoof -LA_0078 LA_D_9847163 - A06 spoof -LA_0078 LA_D_9863698 - A06 spoof -LA_0078 LA_D_9876246 - A06 spoof -LA_0078 LA_D_9908787 - A06 spoof -LA_0078 LA_D_9922697 - A06 spoof -LA_0078 LA_D_9924204 - A06 spoof -LA_0078 LA_D_9931163 - A06 spoof -LA_0078 LA_D_9935163 - A06 spoof -LA_0078 LA_D_9944718 - A06 spoof -LA_0078 LA_D_9967770 - A06 spoof diff --git a/audio/safeear/safeear_repo/datas/ASVSpoof2019/ASVspoof2019.LA.cm.eval.trl.txt b/audio/safeear/safeear_repo/datas/ASVSpoof2019/ASVspoof2019.LA.cm.eval.trl.txt deleted file mode 100644 index e1dacde67ebaf048d070e0f88556fb475c0d0ee7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/datas/ASVSpoof2019/ASVspoof2019.LA.cm.eval.trl.txt +++ /dev/null @@ -1,71237 +0,0 @@ -LA_0039 LA_E_2834763 - A11 spoof -LA_0014 LA_E_8877452 - A14 spoof -LA_0040 LA_E_6828287 - A16 spoof -LA_0022 LA_E_6977360 - A09 spoof -LA_0031 LA_E_5932896 - A13 spoof -LA_0030 LA_E_5849185 - - bonafide -LA_0001 LA_E_6163791 - A09 spoof -LA_0033 LA_E_4581379 - - bonafide -LA_0002 LA_E_8814547 - A12 spoof -LA_0048 LA_E_9157999 - A18 spoof -LA_0005 LA_E_1611480 - A13 spoof -LA_0018 LA_E_6841754 - A16 spoof -LA_0023 LA_E_1781840 - A15 spoof -LA_0002 LA_E_8872199 - A08 spoof -LA_0042 LA_E_1837629 - A17 spoof -LA_0039 LA_E_6314733 - - bonafide -LA_0042 LA_E_8469141 - A08 spoof -LA_0037 LA_E_3379393 - - bonafide -LA_0038 LA_E_7783830 - A16 spoof -LA_0005 LA_E_8339197 - A10 spoof -LA_0043 LA_E_9472752 - A13 spoof -LA_0005 LA_E_1425990 - A17 spoof -LA_0022 LA_E_9088738 - A18 spoof -LA_0047 LA_E_2520601 - A14 spoof -LA_0031 LA_E_2355000 - A13 spoof -LA_0005 LA_E_7535126 - A15 spoof -LA_0018 LA_E_2394352 - A17 spoof -LA_0002 LA_E_5884357 - A13 spoof -LA_0009 LA_E_8787897 - A16 spoof -LA_0014 LA_E_3125426 - A17 spoof -LA_0025 LA_E_6320499 - A13 spoof -LA_0030 LA_E_8617121 - A18 spoof -LA_0035 LA_E_2608310 - A11 spoof -LA_0015 LA_E_7203940 - A13 spoof -LA_0024 LA_E_8868279 - A10 spoof -LA_0017 LA_E_7462445 - A14 spoof -LA_0017 LA_E_8844552 - A07 spoof -LA_0029 LA_E_9120891 - A10 spoof -LA_0024 LA_E_2634822 - A17 spoof -LA_0028 LA_E_3757378 - - bonafide -LA_0044 LA_E_4550461 - A09 spoof -LA_0024 LA_E_4920751 - A17 spoof -LA_0029 LA_E_9817776 - A10 spoof -LA_0011 LA_E_4557471 - A10 spoof -LA_0020 LA_E_1070406 - A16 spoof -LA_0037 LA_E_3003752 - - bonafide -LA_0026 LA_E_8806575 - A12 spoof -LA_0044 LA_E_2417530 - A12 spoof -LA_0031 LA_E_5323454 - - bonafide -LA_0024 LA_E_2947508 - A09 spoof -LA_0016 LA_E_8469160 - A13 spoof -LA_0057 LA_E_1027220 - - bonafide -LA_0039 LA_E_9328266 - A17 spoof -LA_0005 LA_E_3820322 - A07 spoof -LA_0031 LA_E_4751686 - A16 spoof -LA_0012 LA_E_7655544 - A09 spoof -LA_0004 LA_E_8925219 - A10 spoof -LA_0031 LA_E_8110643 - A08 spoof -LA_0006 LA_E_2775552 - A14 spoof -LA_0037 LA_E_9276097 - A09 spoof -LA_0039 LA_E_5246322 - A07 spoof -LA_0021 LA_E_6092883 - A15 spoof -LA_0007 LA_E_7355163 - A12 spoof -LA_0033 LA_E_9804952 - A13 spoof -LA_0016 LA_E_2985346 - A13 spoof -LA_0031 LA_E_8285179 - A14 spoof -LA_0011 LA_E_5118048 - A19 spoof -LA_0037 LA_E_4430413 - A08 spoof -LA_0006 LA_E_3558965 - A12 spoof -LA_0006 LA_E_4732931 - A09 spoof -LA_0008 LA_E_4757272 - - bonafide -LA_0001 LA_E_8992946 - A16 spoof -LA_0010 LA_E_8155315 - A15 spoof -LA_0038 LA_E_2143322 - A07 spoof -LA_0037 LA_E_9382115 - A18 spoof -LA_0012 LA_E_4641783 - A18 spoof -LA_0024 LA_E_5210371 - A11 spoof -LA_0047 LA_E_1746654 - A08 spoof -LA_0033 LA_E_7824929 - - bonafide -LA_0001 LA_E_8816717 - A11 spoof -LA_0024 LA_E_3746504 - A19 spoof -LA_0030 LA_E_8463157 - A07 spoof -LA_0025 LA_E_7642353 - A10 spoof -LA_0005 LA_E_5157926 - A14 spoof -LA_0024 LA_E_8979583 - A16 spoof -LA_0011 LA_E_2665242 - A10 spoof -LA_0041 LA_E_6154503 - - bonafide -LA_0061 LA_E_1395552 - - bonafide -LA_0006 LA_E_9500557 - A08 spoof -LA_0021 LA_E_5194826 - A18 spoof -LA_0033 LA_E_1424685 - A08 spoof -LA_0017 LA_E_6624193 - A18 spoof -LA_0010 LA_E_5871315 - - bonafide -LA_0046 LA_E_3378367 - A10 spoof -LA_0014 LA_E_9853957 - A12 spoof -LA_0037 LA_E_4988348 - A07 spoof -LA_0046 LA_E_2161075 - - bonafide -LA_0020 LA_E_3750625 - A15 spoof -LA_0025 LA_E_4850719 - A12 spoof -LA_0018 LA_E_8562955 - A08 spoof -LA_0015 LA_E_9989698 - A19 spoof -LA_0009 LA_E_5291521 - A09 spoof -LA_0032 LA_E_6816952 - A15 spoof -LA_0013 LA_E_4282641 - A14 spoof -LA_0002 LA_E_9944556 - A16 spoof -LA_0022 LA_E_8767294 - A17 spoof -LA_0035 LA_E_8534064 - A13 spoof -LA_0014 LA_E_1144382 - A12 spoof -LA_0042 LA_E_8697552 - A09 spoof -LA_0037 LA_E_4770782 - A09 spoof -LA_0006 LA_E_7779557 - A12 spoof -LA_0007 LA_E_2268693 - A12 spoof -LA_0030 LA_E_9140267 - A15 spoof -LA_0027 LA_E_9528968 - - bonafide -LA_0022 LA_E_2323050 - A08 spoof -LA_0014 LA_E_4735828 - A12 spoof -LA_0010 LA_E_3424730 - A16 spoof -LA_0032 LA_E_8536145 - A15 spoof -LA_0031 LA_E_2278532 - A11 spoof -LA_0045 LA_E_1223847 - A18 spoof -LA_0031 LA_E_5783993 - - bonafide -LA_0002 LA_E_8450317 - A11 spoof -LA_0044 LA_E_2407835 - A12 spoof -LA_0028 LA_E_1249312 - A17 spoof -LA_0028 LA_E_4692719 - A15 spoof -LA_0013 LA_E_2402721 - A17 spoof -LA_0068 LA_E_1235824 - - bonafide -LA_0007 LA_E_8579433 - A07 spoof -LA_0012 LA_E_7411665 - A17 spoof -LA_0032 LA_E_1890704 - A09 spoof -LA_0009 LA_E_1148952 - A07 spoof -LA_0004 LA_E_9577092 - A10 spoof -LA_0008 LA_E_1255269 - A11 spoof -LA_0021 LA_E_9129430 - A12 spoof -LA_0008 LA_E_5015729 - A19 spoof -LA_0042 LA_E_9210968 - A14 spoof -LA_0012 LA_E_1528017 - A14 spoof -LA_0008 LA_E_9237057 - A17 spoof -LA_0036 LA_E_9979372 - A07 spoof -LA_0012 LA_E_2440860 - A13 spoof -LA_0064 LA_E_8739004 - - bonafide -LA_0022 LA_E_3476113 - A15 spoof -LA_0039 LA_E_1970710 - A11 spoof -LA_0017 LA_E_2120378 - A11 spoof -LA_0019 LA_E_7546380 - A10 spoof -LA_0009 LA_E_2851478 - A17 spoof -LA_0031 LA_E_6932495 - A19 spoof -LA_0012 LA_E_2492808 - A12 spoof -LA_0003 LA_E_6427721 - A13 spoof -LA_0041 LA_E_3231034 - A10 spoof -LA_0018 LA_E_4716734 - - bonafide -LA_0043 LA_E_6237111 - A13 spoof -LA_0019 LA_E_3379472 - - bonafide -LA_0034 LA_E_7125272 - A13 spoof -LA_0033 LA_E_2309675 - A12 spoof -LA_0008 LA_E_9588641 - A11 spoof -LA_0020 LA_E_8080518 - A19 spoof -LA_0006 LA_E_1350979 - A18 spoof -LA_0014 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-LA_0014 LA_E_3947047 - A14 spoof -LA_0026 LA_E_2533620 - A15 spoof -LA_0061 LA_E_7055986 - - bonafide -LA_0016 LA_E_6606104 - A13 spoof -LA_0013 LA_E_4467014 - A18 spoof -LA_0031 LA_E_1922228 - A17 spoof -LA_0057 LA_E_6708601 - - bonafide -LA_0001 LA_E_5145719 - A19 spoof -LA_0022 LA_E_7388592 - A17 spoof -LA_0012 LA_E_4710255 - A19 spoof -LA_0008 LA_E_2777737 - A12 spoof -LA_0032 LA_E_9547535 - A13 spoof -LA_0010 LA_E_8488489 - A10 spoof -LA_0006 LA_E_5908243 - A19 spoof -LA_0022 LA_E_1440587 - A18 spoof -LA_0046 LA_E_6071432 - A09 spoof -LA_0004 LA_E_6936624 - A09 spoof -LA_0034 LA_E_4194702 - A12 spoof -LA_0042 LA_E_2602047 - A16 spoof -LA_0037 LA_E_2461188 - A16 spoof -LA_0012 LA_E_5722427 - A17 spoof -LA_0026 LA_E_1507526 - A18 spoof -LA_0019 LA_E_3674965 - A09 spoof -LA_0042 LA_E_8468045 - A12 spoof -LA_0001 LA_E_1774414 - A19 spoof -LA_0019 LA_E_6774439 - A08 spoof -LA_0028 LA_E_2104346 - A10 spoof -LA_0009 LA_E_3635712 - A10 spoof -LA_0010 LA_E_4078898 - A10 spoof -LA_0004 LA_E_1665632 - - bonafide -LA_0038 LA_E_5085671 - A09 spoof -LA_0012 LA_E_4926022 - A16 spoof -LA_0052 LA_E_2894498 - - bonafide -LA_0009 LA_E_4689563 - A19 spoof diff --git a/audio/safeear/safeear_repo/datas/ASVSpoof2019/ASVspoof2019.LA.cm.train.trn.txt b/audio/safeear/safeear_repo/datas/ASVSpoof2019/ASVspoof2019.LA.cm.train.trn.txt deleted file mode 100644 index 90fbbeb3f881e5a5ae7f9c4bbef62aeec6ac9afa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/datas/ASVSpoof2019/ASVspoof2019.LA.cm.train.trn.txt +++ /dev/null @@ -1,25380 +0,0 @@ -LA_0079 LA_T_1138215 - - bonafide -LA_0079 LA_T_1271820 - - bonafide -LA_0079 LA_T_1272637 - - bonafide -LA_0079 LA_T_1276960 - - bonafide -LA_0079 LA_T_1341447 - - bonafide -LA_0079 LA_T_1363611 - - bonafide -LA_0079 LA_T_1596451 - - bonafide -LA_0079 LA_T_1608170 - - bonafide -LA_0079 LA_T_1684951 - - bonafide -LA_0079 LA_T_1699801 - - bonafide -LA_0079 LA_T_1703395 - - bonafide -LA_0079 LA_T_1736342 - - bonafide -LA_0079 LA_T_1779188 - - bonafide -LA_0079 LA_T_1786825 - - bonafide -LA_0079 LA_T_1787246 - - bonafide -LA_0079 LA_T_1905558 - - bonafide -LA_0079 LA_T_2205687 - - bonafide -LA_0079 LA_T_2220901 - - bonafide -LA_0079 LA_T_2361751 - - bonafide -LA_0079 LA_T_2373806 - - bonafide -LA_0079 LA_T_2417641 - - bonafide -LA_0079 LA_T_2520083 - - bonafide -LA_0079 LA_T_2562689 - - bonafide -LA_0079 LA_T_2564579 - - bonafide -LA_0079 LA_T_2732709 - - bonafide -LA_0079 LA_T_2759900 - - bonafide -LA_0079 LA_T_2838981 - - bonafide -LA_0079 LA_T_2873890 - - bonafide -LA_0079 LA_T_2938316 - - bonafide -LA_0079 LA_T_3131990 - - bonafide -LA_0079 LA_T_3141223 - - bonafide -LA_0079 LA_T_3167734 - - bonafide -LA_0079 LA_T_3187715 - - bonafide -LA_0079 LA_T_3215578 - - bonafide -LA_0079 LA_T_3247389 - - bonafide -LA_0079 LA_T_3346004 - - bonafide -LA_0079 LA_T_3441957 - - bonafide -LA_0079 LA_T_3561136 - - bonafide -LA_0079 LA_T_3576775 - - bonafide -LA_0079 LA_T_3580508 - - bonafide -LA_0079 LA_T_3635733 - - bonafide -LA_0079 LA_T_3636245 - - bonafide -LA_0079 LA_T_3810183 - - bonafide -LA_0079 LA_T_3849267 - - bonafide -LA_0079 LA_T_3995089 - - bonafide -LA_0079 LA_T_3999087 - - bonafide -LA_0079 LA_T_3999267 - - bonafide -LA_0079 LA_T_4053835 - - bonafide -LA_0079 LA_T_4118798 - - bonafide -LA_0079 LA_T_4155454 - - bonafide -LA_0079 LA_T_4179989 - - bonafide -LA_0079 LA_T_4197307 - - bonafide -LA_0079 LA_T_4237787 - - bonafide -LA_0079 LA_T_4239283 - - bonafide -LA_0079 LA_T_4244329 - - bonafide -LA_0079 LA_T_4339311 - - bonafide -LA_0079 LA_T_4362109 - - bonafide -LA_0079 LA_T_4428691 - - bonafide -LA_0079 LA_T_4752788 - - bonafide -LA_0079 LA_T_4756464 - - bonafide -LA_0079 LA_T_4822766 - - bonafide -LA_0079 LA_T_5096578 - - bonafide -LA_0079 LA_T_5161420 - - bonafide -LA_0079 LA_T_5191549 - - bonafide -LA_0079 LA_T_5337549 - - bonafide -LA_0079 LA_T_5387852 - - bonafide -LA_0079 LA_T_5521272 - - bonafide -LA_0079 LA_T_5761909 - - bonafide -LA_0079 LA_T_5777819 - - bonafide -LA_0079 LA_T_5785306 - - bonafide -LA_0079 LA_T_5954195 - - bonafide -LA_0079 LA_T_6058143 - - bonafide -LA_0079 LA_T_6074694 - - bonafide -LA_0079 LA_T_6105178 - - bonafide -LA_0079 LA_T_6200206 - - bonafide -LA_0079 LA_T_6214695 - - bonafide -LA_0079 LA_T_6324553 - - bonafide -LA_0079 LA_T_6335714 - - bonafide -LA_0079 LA_T_6370865 - - bonafide -LA_0079 LA_T_6405980 - - bonafide -LA_0079 LA_T_6426017 - - bonafide -LA_0079 LA_T_6492747 - - bonafide -LA_0079 LA_T_6618587 - - bonafide -LA_0079 LA_T_6630946 - - bonafide -LA_0079 LA_T_6721654 - - bonafide -LA_0079 LA_T_6736628 - - bonafide -LA_0079 LA_T_6768659 - - bonafide -LA_0079 LA_T_6960204 - - bonafide -LA_0079 LA_T_7122854 - - bonafide -LA_0079 LA_T_7123800 - - bonafide -LA_0079 LA_T_7248140 - - bonafide -LA_0079 LA_T_7288400 - - bonafide -LA_0079 LA_T_7397041 - - bonafide -LA_0079 LA_T_7677120 - - bonafide -LA_0079 LA_T_7737943 - - bonafide -LA_0079 LA_T_7744923 - - bonafide -LA_0079 LA_T_8115679 - - bonafide -LA_0079 LA_T_8145267 - - bonafide -LA_0079 LA_T_8324598 - - bonafide -LA_0079 LA_T_8449978 - - bonafide -LA_0079 LA_T_8458654 - - bonafide -LA_0079 LA_T_8507928 - - bonafide -LA_0079 LA_T_8638376 - - bonafide -LA_0079 LA_T_8682959 - - bonafide -LA_0079 LA_T_9107429 - - bonafide -LA_0079 LA_T_9152126 - - bonafide -LA_0079 LA_T_9188299 - - bonafide -LA_0079 LA_T_9258514 - - bonafide -LA_0079 LA_T_9341221 - - bonafide -LA_0079 LA_T_9431341 - - bonafide -LA_0079 LA_T_9437384 - - bonafide -LA_0079 LA_T_9456128 - - bonafide -LA_0079 LA_T_9488159 - - bonafide -LA_0079 LA_T_9515789 - - bonafide -LA_0079 LA_T_9526930 - - bonafide -LA_0079 LA_T_9534983 - - bonafide -LA_0079 LA_T_9543922 - - bonafide -LA_0079 LA_T_9596913 - - bonafide -LA_0079 LA_T_9669914 - - bonafide -LA_0079 LA_T_9701712 - - bonafide -LA_0079 LA_T_9789409 - - bonafide -LA_0079 LA_T_9803720 - - bonafide -LA_0079 LA_T_9874922 - - bonafide -LA_0079 LA_T_9897034 - - bonafide -LA_0079 LA_T_9905136 - - bonafide -LA_0079 LA_T_9907603 - - bonafide -LA_0079 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bonafide -LA_0080 LA_T_2869730 - - bonafide -LA_0080 LA_T_2992916 - - bonafide -LA_0080 LA_T_3108269 - - bonafide -LA_0080 LA_T_3121388 - - bonafide -LA_0080 LA_T_3127213 - - bonafide -LA_0080 LA_T_3315434 - - bonafide -LA_0080 LA_T_3317496 - - bonafide -LA_0080 LA_T_3387982 - - bonafide -LA_0080 LA_T_3465801 - - bonafide -LA_0080 LA_T_3505297 - - bonafide -LA_0080 LA_T_3518487 - - bonafide -LA_0080 LA_T_3555171 - - bonafide -LA_0080 LA_T_3595333 - - bonafide -LA_0080 LA_T_3626619 - - bonafide -LA_0080 LA_T_3725354 - - bonafide -LA_0080 LA_T_3772667 - - bonafide -LA_0080 LA_T_3840528 - - bonafide -LA_0080 LA_T_3877261 - - bonafide -LA_0080 LA_T_4077869 - - bonafide -LA_0080 LA_T_4104820 - - bonafide -LA_0080 LA_T_4105004 - - bonafide -LA_0080 LA_T_4111857 - - bonafide -LA_0080 LA_T_4140258 - - bonafide -LA_0080 LA_T_4176612 - - bonafide -LA_0080 LA_T_4356490 - - bonafide -LA_0080 LA_T_4369654 - - bonafide -LA_0080 LA_T_4397800 - - bonafide -LA_0080 LA_T_4584120 - - bonafide -LA_0080 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A02 spoof -LA_0098 LA_T_7044899 - A02 spoof -LA_0098 LA_T_7080490 - A02 spoof -LA_0098 LA_T_7091716 - A02 spoof -LA_0098 LA_T_7098352 - A02 spoof -LA_0098 LA_T_7218005 - A02 spoof -LA_0098 LA_T_7220873 - A02 spoof -LA_0098 LA_T_7377587 - A02 spoof -LA_0098 LA_T_7436986 - A02 spoof -LA_0098 LA_T_7456810 - A02 spoof -LA_0098 LA_T_7457094 - A02 spoof -LA_0098 LA_T_7535571 - A02 spoof -LA_0098 LA_T_7541433 - A02 spoof -LA_0098 LA_T_7557802 - A02 spoof -LA_0098 LA_T_7633971 - A02 spoof -LA_0098 LA_T_7634678 - A02 spoof -LA_0098 LA_T_7720691 - A02 spoof -LA_0098 LA_T_7859651 - A02 spoof -LA_0098 LA_T_7860733 - A02 spoof -LA_0098 LA_T_7872753 - A02 spoof -LA_0098 LA_T_7925629 - A02 spoof -LA_0098 LA_T_7942412 - A02 spoof -LA_0098 LA_T_8017929 - A02 spoof -LA_0098 LA_T_8090127 - A02 spoof -LA_0098 LA_T_8179611 - A02 spoof -LA_0098 LA_T_8207372 - A02 spoof -LA_0098 LA_T_8263656 - A02 spoof -LA_0098 LA_T_8409467 - A02 spoof -LA_0098 LA_T_8490324 - A02 spoof -LA_0098 LA_T_8500839 - A02 spoof 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A03 spoof -LA_0096 LA_T_7407725 - A03 spoof -LA_0096 LA_T_7411791 - A03 spoof -LA_0096 LA_T_7438232 - A03 spoof -LA_0096 LA_T_7465398 - A03 spoof -LA_0096 LA_T_7476969 - A03 spoof -LA_0096 LA_T_7508719 - A03 spoof -LA_0096 LA_T_7644260 - A03 spoof -LA_0096 LA_T_7703397 - A03 spoof -LA_0096 LA_T_7729505 - A03 spoof -LA_0096 LA_T_7730667 - A03 spoof -LA_0096 LA_T_7754464 - A03 spoof -LA_0096 LA_T_7820777 - A03 spoof -LA_0096 LA_T_7847379 - A03 spoof -LA_0096 LA_T_7872955 - A03 spoof -LA_0096 LA_T_7975557 - A03 spoof -LA_0096 LA_T_8049551 - A03 spoof -LA_0096 LA_T_8092444 - A03 spoof -LA_0096 LA_T_8163796 - A03 spoof -LA_0096 LA_T_8201478 - A03 spoof -LA_0096 LA_T_8240223 - A03 spoof -LA_0096 LA_T_8281444 - A03 spoof -LA_0096 LA_T_8392986 - A03 spoof -LA_0096 LA_T_8434028 - A03 spoof -LA_0096 LA_T_8465309 - A03 spoof -LA_0096 LA_T_8465620 - A03 spoof -LA_0096 LA_T_8508886 - A03 spoof -LA_0096 LA_T_8602428 - A03 spoof -LA_0096 LA_T_8641408 - A03 spoof -LA_0096 LA_T_8674154 - A03 spoof 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A03 spoof -LA_0097 LA_T_2256253 - A03 spoof -LA_0097 LA_T_2274258 - A03 spoof -LA_0097 LA_T_2340035 - A03 spoof -LA_0097 LA_T_2363231 - A03 spoof -LA_0097 LA_T_2416931 - A03 spoof -LA_0097 LA_T_2434224 - A03 spoof -LA_0097 LA_T_2449532 - A03 spoof -LA_0097 LA_T_2496003 - A03 spoof -LA_0097 LA_T_2565556 - A03 spoof -LA_0097 LA_T_2691036 - A03 spoof -LA_0097 LA_T_2723342 - A03 spoof -LA_0097 LA_T_2744877 - A03 spoof -LA_0097 LA_T_2787985 - A03 spoof -LA_0097 LA_T_2909480 - A03 spoof -LA_0097 LA_T_2989549 - A03 spoof -LA_0097 LA_T_3052418 - A03 spoof -LA_0097 LA_T_3056041 - A03 spoof -LA_0097 LA_T_3092337 - A03 spoof -LA_0097 LA_T_3210694 - A03 spoof -LA_0097 LA_T_3213089 - A03 spoof -LA_0097 LA_T_3230586 - A03 spoof -LA_0097 LA_T_3297990 - A03 spoof -LA_0097 LA_T_3317962 - A03 spoof -LA_0097 LA_T_3382204 - A03 spoof -LA_0097 LA_T_3451633 - A03 spoof -LA_0097 LA_T_3539626 - A03 spoof -LA_0097 LA_T_3573561 - A03 spoof -LA_0097 LA_T_3673102 - A03 spoof -LA_0097 LA_T_3913904 - A03 spoof 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A03 spoof -LA_0097 LA_T_7080836 - A03 spoof -LA_0097 LA_T_7120109 - A03 spoof -LA_0097 LA_T_7124444 - A03 spoof -LA_0097 LA_T_7178310 - A03 spoof -LA_0097 LA_T_7202453 - A03 spoof -LA_0097 LA_T_7241171 - A03 spoof -LA_0097 LA_T_7278744 - A03 spoof -LA_0097 LA_T_7361307 - A03 spoof -LA_0097 LA_T_7366328 - A03 spoof -LA_0097 LA_T_7374658 - A03 spoof -LA_0097 LA_T_7407506 - A03 spoof -LA_0097 LA_T_7419344 - A03 spoof -LA_0097 LA_T_7446601 - A03 spoof -LA_0097 LA_T_7547482 - A03 spoof -LA_0097 LA_T_7672005 - A03 spoof -LA_0097 LA_T_7732788 - A03 spoof -LA_0097 LA_T_7744399 - A03 spoof -LA_0097 LA_T_7840559 - A03 spoof -LA_0097 LA_T_7930538 - A03 spoof -LA_0097 LA_T_8011329 - A03 spoof -LA_0097 LA_T_8055223 - A03 spoof -LA_0097 LA_T_8063443 - A03 spoof -LA_0097 LA_T_8157405 - A03 spoof -LA_0097 LA_T_8243158 - A03 spoof -LA_0097 LA_T_8255934 - A03 spoof -LA_0097 LA_T_8267149 - A03 spoof -LA_0097 LA_T_8274550 - A03 spoof -LA_0097 LA_T_8294221 - A03 spoof -LA_0097 LA_T_8515606 - A03 spoof 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LA_T_9689078 - A03 spoof -LA_0097 LA_T_9747186 - A03 spoof -LA_0097 LA_T_9755181 - A03 spoof -LA_0097 LA_T_9757585 - A03 spoof -LA_0097 LA_T_9763366 - A03 spoof -LA_0097 LA_T_9785075 - A03 spoof -LA_0097 LA_T_9786597 - A03 spoof -LA_0097 LA_T_9791346 - A03 spoof -LA_0097 LA_T_9904931 - A03 spoof -LA_0097 LA_T_9911337 - A03 spoof -LA_0097 LA_T_9911661 - A03 spoof -LA_0097 LA_T_9929883 - A03 spoof -LA_0098 LA_T_1007914 - A03 spoof -LA_0098 LA_T_1017164 - A03 spoof -LA_0098 LA_T_1130054 - A03 spoof -LA_0098 LA_T_1186082 - A03 spoof -LA_0098 LA_T_1284234 - A03 spoof -LA_0098 LA_T_1436220 - A03 spoof -LA_0098 LA_T_1568794 - A03 spoof -LA_0098 LA_T_1576099 - A03 spoof -LA_0098 LA_T_1637941 - A03 spoof -LA_0098 LA_T_1679251 - A03 spoof -LA_0098 LA_T_1760536 - A03 spoof -LA_0098 LA_T_1797409 - A03 spoof -LA_0098 LA_T_1862614 - A03 spoof -LA_0098 LA_T_1873972 - A03 spoof -LA_0098 LA_T_1905094 - A03 spoof -LA_0098 LA_T_1981942 - A03 spoof -LA_0098 LA_T_1982197 - A03 spoof -LA_0098 LA_T_1990953 - A03 spoof -LA_0098 LA_T_2085594 - A03 spoof -LA_0098 LA_T_2126037 - A03 spoof -LA_0098 LA_T_2193078 - A03 spoof -LA_0098 LA_T_2232102 - A03 spoof -LA_0098 LA_T_2257205 - A03 spoof -LA_0098 LA_T_2315297 - A03 spoof -LA_0098 LA_T_2390339 - A03 spoof -LA_0098 LA_T_2398977 - A03 spoof -LA_0098 LA_T_2405515 - A03 spoof -LA_0098 LA_T_2424376 - A03 spoof -LA_0098 LA_T_2451410 - A03 spoof -LA_0098 LA_T_2494075 - A03 spoof -LA_0098 LA_T_2604943 - A03 spoof -LA_0098 LA_T_2612300 - A03 spoof -LA_0098 LA_T_2624001 - A03 spoof -LA_0098 LA_T_2689385 - A03 spoof -LA_0098 LA_T_2705905 - A03 spoof -LA_0098 LA_T_2839528 - A03 spoof -LA_0098 LA_T_2879044 - A03 spoof -LA_0098 LA_T_3052951 - A03 spoof -LA_0098 LA_T_3084002 - A03 spoof -LA_0098 LA_T_3137970 - A03 spoof -LA_0098 LA_T_3267673 - A03 spoof -LA_0098 LA_T_3293273 - A03 spoof -LA_0098 LA_T_3318254 - A03 spoof -LA_0098 LA_T_3385414 - A03 spoof -LA_0098 LA_T_3417867 - A03 spoof -LA_0098 LA_T_3447747 - A03 spoof -LA_0098 LA_T_3466405 - A03 spoof 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-LA_E_2611624.flac -LA_E_2735815.flac -LA_E_1679809.flac -LA_E_4287352.flac -LA_E_6053229.flac -LA_E_4590327.flac -LA_E_2003562.flac -LA_E_8644491.flac -LA_E_2877558.flac -LA_E_2376323.flac -LA_E_3563655.flac -LA_E_7040200.flac -LA_E_6673993.flac -LA_E_6372137.flac -LA_E_2476213.flac -LA_E_7813745.flac -LA_E_5789339.flac -LA_E_8256205.flac -LA_E_2369469.flac -LA_E_8617705.flac -LA_E_7310647.flac diff --git a/audio/safeear/safeear_repo/datas/dump_hubert_avg_feature.py b/audio/safeear/safeear_repo/datas/dump_hubert_avg_feature.py deleted file mode 100644 index 159729655b422b8ea4ee81ada83a575394e84d68..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/datas/dump_hubert_avg_feature.py +++ /dev/null @@ -1,109 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 warnings - -warnings.filterwarnings('ignore') -from pathlib import Path - -import fairseq -import librosa -import soundfile as sf -import torch -import torch.nn.functional as F -import tqdm -from feature_utils import dump_feature, get_path_iterator -from npy_append_array import NpyAppendArray - -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 = librosa.load(path, sr=None) - 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) - - avg_feat = [] - for start in range(0, x.size(1), self.max_chunk): - x_chunk = x[:, start: start + self.max_chunk] - feat_chunk, _, avg_feat_chunk = self.model.extract_features( - source=x_chunk, - padding_mask=None, - mask=False, - output_layer=self.layer, - ) - avg_feat.append(avg_feat_chunk) - return torch.cat(avg_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 - 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", nargs="?", default="datasets/ASVSpoof2019", help="Directory containing audio files") - parser.add_argument("save_dir", nargs="?", default="datasets/ASVSpoof2019_Hubert_L9", help="Directory to save extracted features") - parser.add_argument("ckpt_path", nargs="?", default="../model_zoos/hubert_base_ls960.pt", help="Path to the checkpoint file") - parser.add_argument("layer", nargs="?", type=int, default=9, help="Layer number to extract features from") - parser.add_argument("--max_chunk", type=int, default=1600000, help="Maximum chunk size for processing") - args = parser.parse_args() - logger.info(args) - - main(**vars(args)) diff --git a/audio/safeear/safeear_repo/datas/feature_utils.py b/audio/safeear/safeear_repo/datas/feature_utils.py deleted file mode 100644 index 1573d3e08192d138164593014366eae4fe9acc6d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/datas/feature_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 logging -import os -import sys - -import tqdm -from npy_append_array import NpyAppendArray - -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("feature_utils") - - -def get_shard_range(tot, nshard, rank): - assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}" - start = round(tot / nshard * rank) - end = round(tot / nshard * (rank + 1)) - assert start < end, f"start={start}, end={end}" - logger.info( - f"rank {rank} of {nshard}, process {end-start} " - f"({start}-{end}) out of {tot}" - ) - return start, end - - -def get_path_iterator(tsv, nshard, rank): - with open(tsv, "r") as f: - root = f.readline().rstrip() - lines = [line.rstrip() for line in f] - start, end = get_shard_range(len(lines), nshard, rank) - lines = lines[start:end] - def iterate(): - for line in lines: - subpath, nsample = line.split("\t") - yield f"{root}/{subpath}", int(nsample) - return iterate, len(lines) - - -def dump_feature(reader, generator, num, split, nshard, rank, feat_dir): - iterator = generator() - - feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" - leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" - - os.makedirs(feat_dir, exist_ok=True) - if os.path.exists(feat_path): - os.remove(feat_path) - - feat_f = NpyAppendArray(feat_path) - with open(leng_path, "w") as leng_f: - for path, nsample in tqdm.tqdm(iterator, total=num): - feat = reader.get_feats(path, nsample) - feat_f.append(feat.cpu().numpy()) - leng_f.write(f"{len(feat)}\n") - logger.info("finished successfully") - - diff --git a/audio/safeear/safeear_repo/fairseq_ours/.circleci/config.yml b/audio/safeear/safeear_repo/fairseq_ours/.circleci/config.yml deleted file mode 100644 index 59a244f34686daee3f57ddcc104e599351b7cd2a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.circleci/config.yml +++ /dev/null @@ -1,171 +0,0 @@ -# Use 2.1 for orbs -version: 2.1 - -# ------------------------------------------------------------------------------------- -# Environments to run the jobs in -# ------------------------------------------------------------------------------------- -gpu: &gpu - environment: - CUDA_VERSION: "11.2" - machine: - image: ubuntu-2004-cuda-11.2:202103-01 - resource_class: gpu.nvidia.medium.multi - - -# ------------------------------------------------------------------------------------- -# Re-usable commands -# ------------------------------------------------------------------------------------- -cache_key: &cache_key cache-key-{{ .Environment.CIRCLE_JOB }}-{{ checksum ".circleci/config.yml" }}-{{ checksum "setup.py"}} - -install_dep_common: &install_dep_common - - run: - name: Install Common Dependencies - command: | - source activate fairseq - pip install --upgrade setuptools - pip install bitarray boto3 deepspeed editdistance fastBPE iopath ipdb ipython pyarrow pytest sacremoses sentencepiece subword-nmt hydra-core==1.0.7 omegaconf==2.0.6 - pip install --progress-bar off pytest - pip install --progress-bar off fairscale - pip install -i https://test.pypi.org/simple/ bitsandbytes-cuda111 -U - python -c 'import torch; print("Torch version:", torch.__version__)' - python -m torch.utils.collect_env - -install_dep_fused_ops: &install_dep_fused_ops - - run: - name: Install Megatron/Apex Dependencies - working_directory: ~/ - command: | - source activate fairseq - git clone https://github.com/NVIDIA/apex - cd apex - git checkout e2083df5eb96643c61613b9df48dd4eea6b07690 - sed -i '101,107 s/^/#/' setup.py - pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" --global-option="--xentropy" --global-option="--fast_multihead_attn" ./ - cd ~/ - git clone --depth=1 --branch v2.4 https://github.com/NVIDIA/Megatron-LM.git - cd Megatron-LM - pip install -e . - -install_dep_xformers: &install_dep_xformers - - run: - name: Install xFormers Dependencies - working_directory: ~/ - command: | - source activate fairseq - git clone https://github.com/facebookresearch/xformers.git - cd xformers - pip install -r requirements.txt - pip install -e . - -install_dep_pt19: &install_dep_pt19 - - run: - name: Install Pytorch Dependencies - command: | - source activate fairseq - pip install --upgrade setuptools - pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html - python -c 'import torch; print("Torch version:", torch.__version__)' - -install_dep_pt18: &install_dep_pt18 - - run: - name: Install Pytorch Dependencies - command: | - source activate fairseq - pip install --upgrade setuptools - pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html - python -c 'import torch; print("Torch version:", torch.__version__)' - -install_repo: &install_repo - - run: - name: Install Repository - command: | - source activate fairseq - pip install . - python setup.py build_ext --inplace - -run_unittests: &run_unittests - - run: - name: Run Unit Tests - command: | - source activate fairseq - pytest tests/gpu/test_binaries_gpu.py - -check_nvidia_driver: &check_nvidia_driver - - run: - name: Check NVIDIA Driver - working_directory: ~/ - command: | - pyenv versions - nvidia-smi - -create_conda_env: &create_conda_env - - run: - name: Install and Create Conda Environment - command: | - curl -o ~/miniconda.sh -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh - chmod +x ~/miniconda.sh - ~/miniconda.sh -b -p $HOME/miniconda - rm ~/miniconda.sh - echo 'export PATH=$HOME/miniconda/bin:$PATH' >> $BASH_ENV - source $BASH_ENV - if [ ! -d ~/miniconda/envs/fairseq ] - then - conda create -y -n fairseq python=3.8 - fi - source activate fairseq - python --version - pip install --upgrade pip -# ------------------------------------------------------------------------------------- -# Jobs to run -# ------------------------------------------------------------------------------------- - -jobs: - - gpu_tests_pt19: - <<: *gpu - - working_directory: ~/fairseq-py - - steps: - - checkout - - <<: *check_nvidia_driver - - <<: *create_conda_env - - restore_cache: - key: *cache_key - - <<: *install_dep_pt19 - - <<: *install_dep_common - - <<: *install_dep_fused_ops - - save_cache: - paths: - - ~/miniconda/ - key: *cache_key - - <<: *install_repo - - <<: *run_unittests - - gpu_tests_pt18: - <<: *gpu - - working_directory: ~/fairseq-py - - steps: - - checkout - - <<: *check_nvidia_driver - - <<: *create_conda_env - - restore_cache: - key: *cache_key - - <<: *install_dep_pt18 - - <<: *install_dep_common - - <<: *install_dep_fused_ops - - save_cache: - paths: - - ~/miniconda/ - key: *cache_key - - <<: *install_repo - - <<: *run_unittests - -workflows: - version: 2 - build: - jobs: - - gpu_tests_pt18 - - gpu_tests_pt19 diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/CODEOWNERS b/audio/safeear/safeear_repo/fairseq_ours/.github/CODEOWNERS deleted file mode 100644 index 4e818c6f991aa5231aa1edc9aeb699ea3ac4e1cd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/CODEOWNERS +++ /dev/null @@ -1,18 +0,0 @@ -# Setting up CODEOWNERS for UST related codebase -# Documentation for open sourced models relevant to UST -examples/speech_to_text @kahne @sravyapopuri388 @jmp84 -examples/speech_to_speech @an918tw @sravyapopuri388 @jmp84 -examples/speech_synthesis @kahne @jmp84 -examples/simultaneous_translation @kahne @jmp84 -examples/speech_text_joint_to_text @yuntang @jmp84 - -# Speech related models relevant to UST -fairseq/models/speech_to_speech @sravyapopuri388 @jmp84 -fairseq/models/speech_to_text @kahne @sravyapopuri388 @jmp84 -fairseq/models/text_to_speech @kahne @jmp84 - -# CONFORMER IMPLEMENTATION -fairseq/modules/conformer_layer.py @sravyapopuri388 @jmp84 -fairseq/modules/espnet_multihead_attention.py @sravyapopuri388 @jmp84 -fairseq/modules/rotary_positional_embedding.py @sravyapopuri388 @jmp84 -fairseq/modules/positional_encoding.py @sravyapopuri388 @jmp84 diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE.md b/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE.md deleted file mode 100644 index 5c4c4493e4a8e5386b927e4f4554df925955d129..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE.md +++ /dev/null @@ -1,3 +0,0 @@ -## 👉 [Please follow one of these issue templates](https://github.com/pytorch/fairseq/issues/new/choose) 👈 - -Note: to keep the backlog clean and actionable, issues may be immediately closed if they do not follow one of the above issue templates. diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/bug_report.md b/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index aa15123d8ef25c2de745572563505cf0ddc4e351..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -name: 🐛 Bug Report -about: Submit a bug report to help us improve -labels: 'bug, needs triage' ---- - -## 🐛 Bug - - - -### To Reproduce - -Steps to reproduce the behavior (**always include the command you ran**): - -1. Run cmd '....' -2. See error - - - - -#### Code sample - - -### Expected behavior - - - -### Environment - - - fairseq Version (e.g., 1.0 or main): - - PyTorch Version (e.g., 1.0) - - OS (e.g., Linux): - - How you installed fairseq (`pip`, source): - - Build command you used (if compiling from source): - - Python version: - - CUDA/cuDNN version: - - GPU models and configuration: - - Any other relevant information: - -### Additional context - - diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/documentation.md b/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/documentation.md deleted file mode 100644 index 3a6e2e9ea4bb71102122c17ff53051eb3770cb5e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/documentation.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -name: 📚 Documentation/Typos -about: Report an issue related to documentation or a typo -labels: 'documentation, needs triage' ---- - -## 📚 Documentation - -For typos and doc fixes, please go ahead and: - -1. Create an issue. -2. Fix the typo. -3. Submit a PR. - -Thanks! diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/feature_request.md b/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index 93c8668041f8a7af29e4c11e905d8b56b946dd51..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -name: 🚀 Feature Request -about: Submit a proposal/request for a new feature -labels: 'enhancement, help wanted, needs triage' ---- - -## 🚀 Feature Request - - -### Motivation - - - -### Pitch - - - -### Alternatives - - - -### Additional context - - diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/how-to-question.md b/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/how-to-question.md deleted file mode 100644 index 04f3f15d3ed391e26ca87f726ae88f30d1d414ab..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/ISSUE_TEMPLATE/how-to-question.md +++ /dev/null @@ -1,33 +0,0 @@ ---- -name: ❓ Questions/Help -about: If you have questions, please first search existing issues and docs -labels: 'question, needs triage' ---- - -## ❓ Questions and Help - -### Before asking: -1. search the issues. -2. search the docs. - - - -#### What is your question? - -#### Code - - - -#### What have you tried? - -#### What's your environment? - - - fairseq Version (e.g., 1.0 or main): - - PyTorch Version (e.g., 1.0) - - OS (e.g., Linux): - - How you installed fairseq (`pip`, source): - - Build command you used (if compiling from source): - - Python version: - - CUDA/cuDNN version: - - GPU models and configuration: - - Any other relevant information: diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/PULL_REQUEST_TEMPLATE.md b/audio/safeear/safeear_repo/fairseq_ours/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index d005e2df4f717ea4844a8320981d77d96e425a52..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,16 +0,0 @@ -# Before submitting - -- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) -- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)? -- [ ] Did you make sure to update the docs? -- [ ] Did you write any new necessary tests? - -## What does this PR do? -Fixes # (issue). - -## PR review -Anyone in the community is free to review the PR once the tests have passed. -If we didn't discuss your PR in Github issues there's a high chance it will not be merged. - -## Did you have fun? -Make sure you had fun coding 🙃 diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/stale.yml b/audio/safeear/safeear_repo/fairseq_ours/.github/stale.yml deleted file mode 100644 index b12867dab005e7a7608d4c7138a67d409c76f7ae..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/stale.yml +++ /dev/null @@ -1,30 +0,0 @@ -# Configuration for probot-stale - https://github.com/probot/stale -# Mostly copied from github.com/facebook/react/blob/master/.github/stale.yml -# Number of days of inactivity before an issue becomes stale -daysUntilStale: 90 -# Number of days of inactivity before a stale issue is closed -daysUntilClose: 7 -# Issues with these labels will never be considered stale -exemptLabels: - - bug -# Label to use when marking an issue as stale -staleLabel: stale -issues: - # Comment to post when marking an issue as stale. - markComment: > - This issue has been automatically marked as stale. - **If this issue is still affecting you, please leave any comment** (for example, "bump"), and we'll keep it open. - We are sorry that we haven't been able to prioritize it yet. If you have any new additional information, please include it with your comment! - # Comment to post when closing a stale issue. - closeComment: > - Closing this issue after a prolonged period of inactivity. If this issue is still present in the latest release, please create a new issue with up-to-date information. Thank you! -pulls: - # Comment to post when marking a pull request as stale. - markComment: > - This pull request has been automatically marked as stale. - **If this pull request is still relevant, please leave any comment** (for example, "bump"), and we'll keep it open. - We are sorry that we haven't been able to prioritize reviewing it yet. Your contribution is very much appreciated. - # Comment to post when closing a stale pull request. - closeComment: > - Closing this pull request after a prolonged period of inactivity. If this issue is still present in the latest release, please ask for this pull request to be reopened. Thank you! - diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/workflows/build.yml b/audio/safeear/safeear_repo/fairseq_ours/.github/workflows/build.yml deleted file mode 100644 index a14365cd23ebf9740cc7001e29c10b3f16337ead..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/workflows/build.yml +++ /dev/null @@ -1,62 +0,0 @@ -name: build - -on: - # Trigger the workflow on push to main or any pull request - push: - branches: - - main - pull_request: - -jobs: - build: - - strategy: - max-parallel: 4 - matrix: - platform: [ubuntu-latest, macos-latest] - python-version: [3.8, 3.9] - - runs-on: ${{ matrix.platform }} - - steps: - - uses: actions/checkout@v2 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - - name: Conditionally install pytorch - if: matrix.platform == 'windows-latest' - run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html - - - name: Install locally - run: | - python -m pip install --upgrade pip - git submodule update --init --recursive - python setup.py build_ext --inplace - python -m pip install --editable . - - - name: Install optional test requirements - run: | - python -m pip install iopath transformers pyarrow - python -m pip install git+https://github.com/facebookresearch/fairscale.git@main - python -m pip install --progress-bar off git+https://github.com/facebookresearch/xformers.git@main - python -m pip install pytest - - - name: Lint with flake8 - run: | - pip install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --extend-exclude fairseq/model_parallel/megatron - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --extend-exclude fairseq/model_parallel/megatron - - - name: Run tests - run: | - python setup.py test - - - name: Lint with black - run: | - pip install black==22.3.0 - black --check . --extend-exclude 'examples|fairseq\/model_parallel\/megatron' diff --git a/audio/safeear/safeear_repo/fairseq_ours/.github/workflows/release.yml b/audio/safeear/safeear_repo/fairseq_ours/.github/workflows/release.yml deleted file mode 100644 index 60934fcee73f15726bc541afa10c8b3f7f7b161f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.github/workflows/release.yml +++ /dev/null @@ -1,161 +0,0 @@ -name: Fairseq Release - -on: - workflow_dispatch: - inputs: - name: - description: 'Release Type' - default: 'patch' - required: true - -jobs: - - get_next_version: - runs-on: ubuntu-latest - steps: - - name: checkout-repo-content - uses: actions/checkout@v2 - - - name: setup-python - uses: actions/setup-python@v2 - with: - python-version: 3.8 - - - name: get next version and tag - id: get-next-version-and-tag - run: | - output=$(python3 release_utils.py --release-type ${{ github.event.inputs.name }}) - echo $output - new_version=$(echo $output | awk '{print $1}') - new_tag=$(echo $output | awk '{print $2}') - echo "new version is $new_version" - echo "new tag is $new_tag" - echo ::set-output name=version::$new_version - echo ::set-output name=tag::$new_tag - echo ::set-output name=branch_name::$new_version-release - echo "NEW_TAG=$new_tag" >> $GITHUB_ENV - echo "NEW_BRANCH=$new_version-release" >> $GITHUB_ENV - - - # update the version number in version.txt - - name: update version - id: update-version - run : | - echo "current folder = $PWD" - echo "current branch = $(git branch --show-current)" - output=$(python3 release_utils.py --release-type ${{ github.event.inputs.name }} --update-version) - - - name: add and commit - uses: EndBug/add-and-commit@v9 - with: - author_name: ${{ secrets.AUTHOR_NAME }} - author_email: ${{ secrets.AUTHOR_EMAIL }} - - # TODO: change this to main once shipit is disabled. - new_branch: '${{ env.NEW_BRANCH }}' - default_author: github_actor - message: '${{ env.NEW_TAG }} release' - pathspec_error_handling: exitAtEnd - - # Arguments for the git pull command. Use NO-PULL to avoid the action pulling at all. - # pull: 'NO-PULL' - tag: '${{ env.NEW_TAG }}' - - outputs: - new_version: ${{ steps.get-next-version-and-tag.outputs.version }} - new_tag: ${{ steps.get-next-version-and-tag.outputs.tag }} - branch_name: ${{ steps.get-next-version-and-tag.outputs.branch_name }} - - create_sdist: - runs-on: ubuntu-latest - name: Create Source Distribution - needs: get_next_version - steps: - - uses: actions/checkout@v3 - with: - ref: ${{ needs.get_next_version.outputs.branch_name }} - - - name: Install Python - uses: actions/setup-python@v2 - with: - python-version: '3.8' - - - name: Upgrade pip - run: | - python3 -m pip install --upgrade pip - - - name: Create Source Distribution - run: | - pip install setuptools wheel twine - python3 setup.py sdist - - - uses: actions/upload-artifact@v2 - with: - path: dist/*.tar.gz - - build_wheels: - name: Build wheels on ${{ matrix.os }} - runs-on: ${{ matrix.os }} - needs: get_next_version - strategy: - matrix: - os: [ubuntu-latest, macos-latest] - - steps: - - uses: actions/checkout@v3 - with: - ref: ${{ needs.get_next_version.outputs.branch_name }} - - - name: Install Python - uses: actions/setup-python@v2 - with: - python-version: '3.8' - - - name: Upgrade pip - run: | - python3 -m pip install --upgrade pip - - - name: Install cibuildwheel - run: | - python3 -m pip install cibuildwheel - - - name: Build wheels for CPython - run: | - python3 -m cibuildwheel --output-dir dist - env: - CIBW_BUILD: "cp36-*64 cp37-*64 cp38-*64" - CIBW_MANYLINUX_X86_64_IMAGE: manylinux1 - CIBW_BEFORE_BUILD: git submodule update --init --recursive && pip install . - # Install system library - CIBW_BEFORE_BUILD_LINUX: yum install -y libffi-devel || apt-get install -y libffi-devel || apk add --update --no-cache libffi-devel || true - CIBW_ENVIRONMENT: "PIP_ONLY_BINARY=numpy" - CIBW_SKIP: "*musllinux*" - - - uses: actions/upload-artifact@v2 - with: - path: dist - - upload: - name: Upload to PyPi and create release - runs-on: ubuntu-latest - needs: [build_wheels, create_sdist, get_next_version] - steps: - - uses: actions/download-artifact@v2 - with: - name: artifact - path: dist - - # build the PyPI package and upload it - - name: upload - env: - TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} - run: | - pip install setuptools wheel twine - python3 -m twine upload --repository pypi dist/* - - # create the release on github - - name: create release on github - uses: ncipollo/release-action@v1 - with: - tag: '${{ needs.get_next_version.outputs.new_tag }}' diff --git a/audio/safeear/safeear_repo/fairseq_ours/.gitignore b/audio/safeear/safeear_repo/fairseq_ours/.gitignore deleted file mode 100644 index 4be13638de637fe994e601f3de8e215109b30322..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.gitignore +++ /dev/null @@ -1,141 +0,0 @@ -# JetBrains PyCharm IDE -.idea/ - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# macOS dir files -.DS_Store - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# Checkpoints -checkpoints - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -# Generated files -/fairseq/temporal_convolution_tbc -/fairseq/modules/*_layer/*_forward.cu -/fairseq/modules/*_layer/*_backward.cu -/fairseq/version.py - -# data -data-bin/ - -# reranking -/examples/reranking/rerank_data - -# Cython-generated C++ source files -/fairseq/data/data_utils_fast.cpp -/fairseq/data/token_block_utils_fast.cpp - -# VSCODE -.vscode/ftp-sync.json -.vscode/settings.json - -# Experimental Folder -experimental/* - -# Weights and Biases logs -wandb/ - -# Hydra artifacts -nohup.out -multirun -outputs diff --git a/audio/safeear/safeear_repo/fairseq_ours/.gitmodules b/audio/safeear/safeear_repo/fairseq_ours/.gitmodules deleted file mode 100644 index 07a55d45d4f0bed755dbfc1f440f214ed43d206a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.gitmodules +++ /dev/null @@ -1,4 +0,0 @@ -[submodule "fairseq/model_parallel/megatron"] - path = fairseq/model_parallel/megatron - url = https://github.com/ngoyal2707/Megatron-LM - branch = fairseq diff --git a/audio/safeear/safeear_repo/fairseq_ours/.isort.cfg b/audio/safeear/safeear_repo/fairseq_ours/.isort.cfg deleted file mode 100644 index aed482f47e5b14e541ad6c5c9cadf60cc6ee93fa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.isort.cfg +++ /dev/null @@ -1,2 +0,0 @@ -[settings] -known_third_party = _cffi_backend,agg_results,aml,bitarray,boto3,botocore,dump_hubert_feature,dynamicconv_cuda,editdistance,faiss,fasttext,feature_utils,ffmpeg,g2p_en,h5py,hydra,hypothesis,indicnlp,inflect,iopath,joblib,kaldi_io,kenlm,libfb,librosa,lightconv_cuda,matplotlib,misc,mmpt,mmpt_cli,model,nltk,npy_append_array,numpy,omegaconf,pandas,pathbuilder,preprocessing,progressbar,pythainlp,random_sequence_shuffler,regex,sacrebleu,sacremoses,scipy,sentencepiece,setuptools,six,sklearn,soundfile,sweep,sweep_wmt_en2de_transformer_big_common,tabulate,torch,torchaudio,tqdm,unidecode,utils,videoreader,wav2vec_cluster_faiss,wget,yaml diff --git a/audio/safeear/safeear_repo/fairseq_ours/.pre-commit-config.yaml b/audio/safeear/safeear_repo/fairseq_ours/.pre-commit-config.yaml deleted file mode 100644 index 6b1d6aed8c7a4eb2d0c9b4eba7104ed9dc41b591..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/.pre-commit-config.yaml +++ /dev/null @@ -1,40 +0,0 @@ -exclude: 'build|stubs' - -default_language_version: - python: python3 - -repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.1.0 - hooks: - - id: trailing-whitespace - - id: check-ast - - id: check-merge-conflict - - id: no-commit-to-branch - args: ['--branch=master'] - - id: check-added-large-files - args: ['--maxkb=500'] - - id: end-of-file-fixer - -- repo: https://github.com/ambv/black - rev: 22.3.0 - hooks: - - id: black - language_version: python3.8 - -- repo: https://gitlab.com/pycqa/flake8 - rev: 3.9.2 - hooks: - - id: flake8 - args: [ - # only error for syntax errors and undefined names - "--select=E9,F63,F7,F82", - ] - -- repo: https://github.com/pycqa/isort - rev: 5.10.1 - hooks: - - id: isort - exclude: README.md - additional_dependencies: [toml] - args: ["--profile", "black"] diff --git a/audio/safeear/safeear_repo/fairseq_ours/CODE_OF_CONDUCT.md b/audio/safeear/safeear_repo/fairseq_ours/CODE_OF_CONDUCT.md deleted file mode 100644 index a0cbeaab7650bf08267fbdbc9bb54e845c88f392..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,77 +0,0 @@ -# Code of Conduct - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to make participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. - -## Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies within all project spaces, and it also applies when -an individual is representing the project or its community in public spaces. -Examples of representing a project or community include using an official -project e-mail address, posting via an official social media account, or acting -as an appointed representative at an online or offline event. Representation of -a project may be further defined and clarified by project maintainers. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at . All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq - diff --git a/audio/safeear/safeear_repo/fairseq_ours/CONTRIBUTING.md b/audio/safeear/safeear_repo/fairseq_ours/CONTRIBUTING.md deleted file mode 100644 index 60e90258877423bb458fafbc9d35781484dbe9c6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/CONTRIBUTING.md +++ /dev/null @@ -1,82 +0,0 @@ -# Contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq) -We want to make contributing to this project as easy and transparent as -possible. - -## Pull Requests -We actively welcome your pull requests. - -1. Fork the repo and create your branch from `main`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Facebook's open source projects. - -Complete your CLA here: - -## Issues -We use GitHub issues to track public bugs. Please ensure your description is -clear and has sufficient instructions to be able to reproduce the issue. - -## License -By contributing to Facebook AI Research Sequence-to-Sequence Toolkit (fairseq), -you agree that your contributions will be licensed under the LICENSE file in -the root directory of this source tree. - -## Pre-commit hooks -In order to ensure your code lints, there are pre-commit hooks configured in the repository which you can install. -After installation, they will automatically run each time you commit. -An abbreviated guide is given below; for more information, refer to [the offical pre-commit documentation](https://pre-commit.com/). - -### Installation -``` -pip install pre-commit -pre-commit install -``` - -### Usage -Just commit your changes: -``` -git commit -m "My informative commit message" -``` - -If there was a failure, you will get feedback -``` -[INFO] Initializing environment for https://github.com/PyCQA/flake8. -[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks. -[INFO] Once installed this environment will be reused. -[INFO] This may take a few minutes... -[INFO] Installing environment for https://github.com/PyCQA/flake8. -[INFO] Once installed this environment will be reused. -[INFO] This may take a few minutes... -Trim Trailing Whitespace.................................................Failed -- hook id: trailing-whitespace -- exit code: 1 -- files were modified by this hook -Fixing examples/nllb/modeling/wmt15_benchmark/eval_langs2.sh -Fix End of Files.........................................................Failed -- hook id: end-of-file-fixer -- exit code: 1 -- files were modified by this hook -Fixing examples/few_shot/scripts/schedule_jobs_few_shot.py -flake8...................................................................Passed -``` - -Certain hooks modify your files to comply. -To include these modifications, you will need to add them (i.e. `git add ...`) and commit again. - -If all is well, you should see something like: -``` -Trim Trailing Whitespace.................................................Passed -Fix End of Files.........................................................Passed -flake8...................................................................Passed -[gshard-fix-ci 8698644e1] Fix lint, add pre-commit hooks - 10 files changed, 148 insertions(+), 110 deletions(-) - create mode 100644 .flake8 - create mode 100644 .pre-commit-config.yaml - rename examples/nllb/modeling/wmt15_benchmark/{eval_langs2.py => eval_langs2.sh} (99%) - ``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/LICENSE b/audio/safeear/safeear_repo/fairseq_ours/LICENSE deleted file mode 100644 index b96dcb0480a0b0be0727976e5202a1e7b23edc3f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) Facebook, Inc. and its affiliates. - -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. diff --git a/audio/safeear/safeear_repo/fairseq_ours/README.md b/audio/safeear/safeear_repo/fairseq_ours/README.md deleted file mode 100644 index f7acbd15c3ea4708efafde7b52ee41ada61abcdb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/README.md +++ /dev/null @@ -1,240 +0,0 @@ -

- -
-
- Support Ukraine - MIT License - Latest Release - Build Status - Documentation Status - CicleCI Status -

- --------------------------------------------------------------------------------- - -Fairseq(-py) is a sequence modeling toolkit that allows researchers and -developers to train custom models for translation, summarization, language -modeling and other text generation tasks. - -We provide reference implementations of various sequence modeling papers: - -
List of implemented papers

- -* **Convolutional Neural Networks (CNN)** - + [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md) - + [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) - + [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) - + [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) - + [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) -* **LightConv and DynamicConv models** - + [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) -* **Long Short-Term Memory (LSTM) networks** - + Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015) -* **Transformer (self-attention) networks** - + Attention Is All You Need (Vaswani et al., 2017) - + [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) - + [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) - + [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/README.adaptive_inputs.md) - + [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md) - + [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)](examples/truncated_bptt/README.md) - + [Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)](examples/adaptive_span/README.md) - + [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) - + [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) - + [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) - + [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md ) - + [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) - + [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) - + [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) - + [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) - + [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md) - + [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md) - + [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) - + [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/README.md) - + [Unsupervised Cross-lingual Representation Learning for Speech Recognition (Conneau et al., 2020)](https://arxiv.org/abs/2006.13979) - + [Self-training and Pre-training are Complementary for Speech Recognition (Xu et al., 2020)](https://arxiv.org/abs/2010.11430) - + [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (Hsu, et al., 2021)](https://arxiv.org/abs/2104.01027) - + [Unsupervised Speech Recognition (Baevski, et al., 2021)](https://arxiv.org/abs/2105.11084) - + [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al., 2021)](https://arxiv.org/abs/2109.11680) - + [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (Xu et. al., 2021)](https://arxiv.org/pdf/2109.14084.pdf) - + [VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding (Xu et. al., 2021)](https://aclanthology.org/2021.findings-acl.370.pdf) - + [NormFormer: Improved Transformer Pretraining with Extra Normalization (Shleifer et. al, 2021)](examples/normformer/README.md) -* **Non-autoregressive Transformers** - + Non-Autoregressive Neural Machine Translation (Gu et al., 2017) - + Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018) - + Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019) - + Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019) - + [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) -* **Finetuning** - + [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md) - -

- -### What's New: -* May 2022 [Integration with xFormers](https://github.com/facebookresearch/xformers) -* December 2021 [Released Direct speech-to-speech translation code](examples/speech_to_speech/README.md) -* October 2021 [Released VideoCLIP and VLM models](examples/MMPT/README.md) -* October 2021 [Released multilingual finetuned XLSR-53 model](examples/wav2vec/README.md) -* September 2021 [`master` branch renamed to `main`](https://github.com/github/renaming). -* July 2021 [Released DrNMT code](examples/discriminative_reranking_nmt/README.md) -* July 2021 [Released Robust wav2vec 2.0 model](examples/wav2vec/README.md) -* June 2021 [Released XLMR-XL and XLMR-XXL models](examples/xlmr/README.md) -* May 2021 [Released Unsupervised Speech Recognition code](examples/wav2vec/unsupervised/README.md) -* March 2021 [Added full parameter and optimizer state sharding + CPU offloading](examples/fully_sharded_data_parallel/README.md) -* February 2021 [Added LASER training code](examples/laser/README.md) -* December 2020: [Added Adaptive Attention Span code](examples/adaptive_span/README.md) -* December 2020: [GottBERT model and code released](examples/gottbert/README.md) -* November 2020: Adopted the [Hydra](https://github.com/facebookresearch/hydra) configuration framework - * [see documentation explaining how to use it for new and existing projects](docs/hydra_integration.md) -* November 2020: [fairseq 0.10.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.10.0) -* October 2020: [Added R3F/R4F (Better Fine-Tuning) code](examples/rxf/README.md) -* October 2020: [Deep Transformer with Latent Depth code released](examples/latent_depth/README.md) -* October 2020: [Added CRISS models and code](examples/criss/README.md) - -
Previous updates

- -* September 2020: [Added Linformer code](examples/linformer/README.md) -* September 2020: [Added pointer-generator networks](examples/pointer_generator/README.md) -* August 2020: [Added lexically constrained decoding](examples/constrained_decoding/README.md) -* August 2020: [wav2vec2 models and code released](examples/wav2vec/README.md) -* July 2020: [Unsupervised Quality Estimation code released](examples/unsupervised_quality_estimation/README.md) -* May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq) -* April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md) -* April 2020: [Quant-Noise code released](examples/quant_noise/README.md) -* April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md) -* March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md) -* February 2020: [mBART model and code released](examples/mbart/README.md) -* February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/main/examples/backtranslation#training-your-own-model-wmt18-english-german) -* December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0) -* November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example) -* November 2019: [CamemBERT model and code released](examples/camembert/README.md) -* November 2019: [BART model and code released](examples/bart/README.md) -* November 2019: [XLM-R models and code released](examples/xlmr/README.md) -* September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md) -* August 2019: [WMT'19 models released](examples/wmt19/README.md) -* July 2019: fairseq relicensed under MIT license -* July 2019: [RoBERTa models and code released](examples/roberta/README.md) -* June 2019: [wav2vec models and code released](examples/wav2vec/README.md) - -

- -### Features: - -* multi-GPU training on one machine or across multiple machines (data and model parallel) -* fast generation on both CPU and GPU with multiple search algorithms implemented: - + beam search - + Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424)) - + sampling (unconstrained, top-k and top-p/nucleus) - + [lexically constrained decoding](examples/constrained_decoding/README.md) (Post & Vilar, 2018) -* [gradient accumulation](https://fairseq.readthedocs.io/en/latest/getting_started.html#large-mini-batch-training-with-delayed-updates) enables training with large mini-batches even on a single GPU -* [mixed precision training](https://fairseq.readthedocs.io/en/latest/getting_started.html#training-with-half-precision-floating-point-fp16) (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores)) -* [extensible](https://fairseq.readthedocs.io/en/latest/overview.html): easily register new models, criterions, tasks, optimizers and learning rate schedulers -* [flexible configuration](docs/hydra_integration.md) based on [Hydra](https://github.com/facebookresearch/hydra) allowing a combination of code, command-line and file based configuration -* [full parameter and optimizer state sharding](examples/fully_sharded_data_parallel/README.md) -* [offloading parameters to CPU](examples/fully_sharded_data_parallel/README.md) - -We also provide [pre-trained models for translation and language modeling](#pre-trained-models-and-examples) -with a convenient `torch.hub` interface: - -``` python -en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model') -en2de.translate('Hello world', beam=5) -# 'Hallo Welt' -``` - -See the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/) -and [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples. - -# Requirements and Installation - -* [PyTorch](http://pytorch.org/) version >= 1.5.0 -* Python version >= 3.6 -* For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl) -* **To install fairseq** and develop locally: - -``` bash -git clone https://github.com/pytorch/fairseq -cd fairseq -pip install --editable ./ - -# on MacOS: -# CFLAGS="-stdlib=libc++" pip install --editable ./ - -# to install the latest stable release (0.10.x) -# pip install fairseq -``` - -* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library: - -``` bash -git clone https://github.com/NVIDIA/apex -cd apex -pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \ - --global-option="--deprecated_fused_adam" --global-option="--xentropy" \ - --global-option="--fast_multihead_attn" ./ -``` - -* **For large datasets** install [PyArrow](https://arrow.apache.org/docs/python/install.html#using-pip): `pip install pyarrow` -* If you use Docker make sure to increase the shared memory size either with `--ipc=host` or `--shm-size` - as command line options to `nvidia-docker run` . - -# Getting Started - -The [full documentation](https://fairseq.readthedocs.io/) contains instructions -for getting started, training new models and extending fairseq with new model -types and tasks. - -# Pre-trained models and examples - -We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, -as well as example training and evaluation commands. - -* [Translation](examples/translation/README.md): convolutional and transformer models are available -* [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available - -We also have more detailed READMEs to reproduce results from specific papers: - -* [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale (Babu et al., 2021)](examples/wav2vec/xlsr/README.md) -* [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) -* [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) -* [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) -* [Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)](examples/quant_noise/README.md) -* [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md) -* [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md) -* [Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)](examples/layerdrop/README.md) -* [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md) -* [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md) -* [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md) -* [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md) -* [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md) -* [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md) -* [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md) -* [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md) -* [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel) -* [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md) -* [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md) -* [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md) -* [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/README.conv.md) - -# Join the fairseq community - -* Twitter: https://twitter.com/fairseq -* Facebook page: https://www.facebook.com/groups/fairseq.users -* Google group: https://groups.google.com/forum/#!forum/fairseq-users - -# License - -fairseq(-py) is MIT-licensed. -The license applies to the pre-trained models as well. - -# Citation - -Please cite as: - -``` bibtex -@inproceedings{ott2019fairseq, - title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling}, - author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli}, - booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations}, - year = {2019}, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/RELEASE.md b/audio/safeear/safeear_repo/fairseq_ours/RELEASE.md deleted file mode 100644 index 79480a11c58a91178e9fa189a8a8e0ceea13efc6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/RELEASE.md +++ /dev/null @@ -1,13 +0,0 @@ -# Creating a New Release - -In order to create a new release: - -1. Navigate to the [Fairseq Workflows](https://github.com/facebookresearch/fairseq/actions) and find the one named _Fairseq Release_. - -2. Under _Run Workflow_ choose the branch `main` and for _Release Type_ enter either `major`, `minor`, or `patch`. - -3. A branch named `$new_version-release` will be created where the `version.txt` file is updated. Merge those changes into `main`. - -4. Make sure that a [new PYPI package](https://pypi.org/project/fairseq/) has been uploaded. - -5. Make sure that a [new github release](https://github.com/facebookresearch/fairseq/releases) has been created. diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/Makefile b/audio/safeear/safeear_repo/fairseq_ours/docs/Makefile deleted file mode 100644 index c2f5b1a89cfc9e02d1bb09027d9e1e520ba53d53..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = python -msphinx -SPHINXPROJ = fairseq -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/_static/theme_overrides.css b/audio/safeear/safeear_repo/fairseq_ours/docs/_static/theme_overrides.css deleted file mode 100644 index 2a0764193625e1a6fd66ff8af2ccdd0ad6369188..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/_static/theme_overrides.css +++ /dev/null @@ -1,9 +0,0 @@ -.wy-table-responsive table td kbd { - white-space: nowrap; -} -.wy-table-responsive table td { - white-space: normal !important; -} -.wy-table-responsive { - overflow: visible !important; -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/command_line_tools.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/command_line_tools.rst deleted file mode 100644 index c16300ff5cd42d9a6c0070c2d9bec3a802eacfad..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/command_line_tools.rst +++ /dev/null @@ -1,85 +0,0 @@ -.. _Command-line Tools: - -Command-line Tools -================== - -Fairseq provides several command-line tools for training and evaluating models: - -- :ref:`fairseq-preprocess`: Data pre-processing: build vocabularies and binarize training data -- :ref:`fairseq-train`: Train a new model on one or multiple GPUs -- :ref:`fairseq-generate`: Translate pre-processed data with a trained model -- :ref:`fairseq-interactive`: Translate raw text with a trained model -- :ref:`fairseq-score`: BLEU scoring of generated translations against reference translations -- :ref:`fairseq-eval-lm`: Language model evaluation - - -.. _fairseq-preprocess: - -fairseq-preprocess -~~~~~~~~~~~~~~~~~~ -.. automodule:: fairseq_cli.preprocess - - .. argparse:: - :module: fairseq.options - :func: get_preprocessing_parser - :prog: fairseq-preprocess - - -.. _fairseq-train: - -fairseq-train -~~~~~~~~~~~~~ -.. automodule:: fairseq_cli.train - - .. argparse:: - :module: fairseq.options - :func: get_training_parser - :prog: fairseq-train - - -.. _fairseq-generate: - -fairseq-generate -~~~~~~~~~~~~~~~~ -.. automodule:: fairseq_cli.generate - - .. argparse:: - :module: fairseq.options - :func: get_generation_parser - :prog: fairseq-generate - - -.. _fairseq-interactive: - -fairseq-interactive -~~~~~~~~~~~~~~~~~~~ -.. automodule:: fairseq_cli.interactive - - .. argparse:: - :module: fairseq.options - :func: get_interactive_generation_parser - :prog: fairseq-interactive - - -.. _fairseq-score: - -fairseq-score -~~~~~~~~~~~~~ -.. automodule:: fairseq_cli.score - - .. argparse:: - :module: fairseq_cli.score - :func: get_parser - :prog: fairseq-score - - -.. _fairseq-eval-lm: - -fairseq-eval-lm -~~~~~~~~~~~~~~~ -.. automodule:: fairseq_cli.eval_lm - - .. argparse:: - :module: fairseq.options - :func: get_eval_lm_parser - :prog: fairseq-eval-lm diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/conf.py b/audio/safeear/safeear_repo/fairseq_ours/docs/conf.py deleted file mode 100644 index 8e66657fc30f1eb28ac8723f5b0f5b03b932c43d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/conf.py +++ /dev/null @@ -1,134 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# -# fairseq documentation build configuration file, created by -# sphinx-quickstart on Fri Aug 17 21:45:30 2018. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. - -import os -import sys - -from fairseq import __version__ - -# source code directory, relative to this file, for sphinx-autobuild -sys.path.insert(0, os.path.abspath("..")) - -source_suffix = [".rst"] - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.intersphinx", - "sphinx.ext.viewcode", - "sphinx.ext.napoleon", - "sphinxarg.ext", -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# The master toctree document. -master_doc = "index" - -# General information about the project. -project = "fairseq" -copyright = "Facebook AI Research (FAIR)" -author = "Facebook AI Research (FAIR)" - -github_doc_root = "https://github.com/pytorch/fairseq/tree/main/docs/" - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = __version__ -# The full version, including alpha/beta/rc tags. -release = __version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This patterns also effect to html_static_path and html_extra_path -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" -highlight_language = "python" - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "sphinx_rtd_theme" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -html_context = { - "css_files": [ - "_static/theme_overrides.css", # override wide tables in RTD theme - ], -} - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# This is required for the alabaster theme -# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars -# html_sidebars = { -# '**': [ -# 'about.html', -# 'navigation.html', -# 'relations.html', # needs 'show_related': True theme option to display -# 'searchbox.html', -# 'donate.html', -# ] -# } - - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = { - "numpy": ("http://docs.scipy.org/doc/numpy/", None), - "python": ("https://docs.python.org/", None), - "torch": ("https://pytorch.org/docs/master/", None), -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/criterions.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/criterions.rst deleted file mode 100644 index d6b8ca6b671a32d0da4aca7b18626e0df58a7258..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/criterions.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. role:: hidden - :class: hidden-section - -.. _Criterions: - -Criterions -========== - -Criterions compute the loss function given the model and batch, roughly:: - - loss = criterion(model, batch) - -.. automodule:: fairseq.criterions - :members: - -.. autoclass:: fairseq.criterions.FairseqCriterion - :members: - :undoc-members: - -.. autoclass:: fairseq.criterions.adaptive_loss.AdaptiveLoss - :members: - :undoc-members: -.. autoclass:: fairseq.criterions.composite_loss.CompositeLoss - :members: - :undoc-members: -.. autoclass:: fairseq.criterions.cross_entropy.CrossEntropyCriterion - :members: - :undoc-members: -.. autoclass:: fairseq.criterions.label_smoothed_cross_entropy.LabelSmoothedCrossEntropyCriterion - :members: - :undoc-members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/data.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/data.rst deleted file mode 100644 index 6a390cb336ab3c5fb28edec7448abc35a8e22bbb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/data.rst +++ /dev/null @@ -1,58 +0,0 @@ -.. role:: hidden - :class: hidden-section - -.. module:: fairseq.data - -Data Loading and Utilities -========================== - -.. _datasets: - -Datasets --------- - -**Datasets** define the data format and provide helpers for creating -mini-batches. - -.. autoclass:: fairseq.data.FairseqDataset - :members: -.. autoclass:: fairseq.data.LanguagePairDataset - :members: -.. autoclass:: fairseq.data.MonolingualDataset - :members: - -**Helper Datasets** - -These datasets wrap other :class:`fairseq.data.FairseqDataset` instances and -provide additional functionality: - -.. autoclass:: fairseq.data.BacktranslationDataset - :members: -.. autoclass:: fairseq.data.ConcatDataset - :members: -.. autoclass:: fairseq.data.ResamplingDataset - :members: -.. autoclass:: fairseq.data.RoundRobinZipDatasets - :members: -.. autoclass:: fairseq.data.TransformEosDataset - :members: - - -Dictionary ----------- - -.. autoclass:: fairseq.data.Dictionary - :members: - - -Iterators ---------- - -.. autoclass:: fairseq.data.CountingIterator - :members: -.. autoclass:: fairseq.data.EpochBatchIterator - :members: -.. autoclass:: fairseq.data.GroupedIterator - :members: -.. autoclass:: fairseq.data.ShardedIterator - :members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/docutils.conf b/audio/safeear/safeear_repo/fairseq_ours/docs/docutils.conf deleted file mode 100644 index 526acffd32d16217160aee917db2b120354f20f0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/docutils.conf +++ /dev/null @@ -1,2 +0,0 @@ -[writers] -option-limit=0 diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/fairseq.gif b/audio/safeear/safeear_repo/fairseq_ours/docs/fairseq.gif deleted file mode 100644 index 8701763fa799e18f99ecf255f9cadb058724ab0a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/fairseq.gif +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b7551e9682c816fca1fa00458f3b657177c8d90d2e87db31e42197cb3ae80fca -size 2664833 diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/fairseq_logo.png b/audio/safeear/safeear_repo/fairseq_ours/docs/fairseq_logo.png deleted file mode 100644 index 75472cbb5ff78acc8716ad9121ed421f17f96c9a..0000000000000000000000000000000000000000 Binary files a/audio/safeear/safeear_repo/fairseq_ours/docs/fairseq_logo.png and /dev/null differ diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/getting_started.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/getting_started.rst deleted file mode 100644 index 745ad7763cee67a8dec25bdd7ba7b79cbe0b7754..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/getting_started.rst +++ /dev/null @@ -1,216 +0,0 @@ -Evaluating Pre-trained Models -============================= - -First, download a pre-trained model along with its vocabularies: - -.. code-block:: console - - > curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf - - -This model uses a `Byte Pair Encoding (BPE) -vocabulary `__, so we'll have to apply -the encoding to the source text before it can be translated. This can be -done with the -`apply\_bpe.py `__ -script using the ``wmt14.en-fr.fconv-cuda/bpecodes`` file. ``@@`` is -used as a continuation marker and the original text can be easily -recovered with e.g. ``sed s/@@ //g`` or by passing the ``--remove-bpe`` -flag to :ref:`fairseq-generate`. Prior to BPE, input text needs to be tokenized -using ``tokenizer.perl`` from -`mosesdecoder `__. - -Let's use :ref:`fairseq-interactive` to generate translations interactively. -Here, we use a beam size of 5 and preprocess the input with the Moses -tokenizer and the given Byte-Pair Encoding vocabulary. It will automatically -remove the BPE continuation markers and detokenize the output. - -.. code-block:: console - - > MODEL_DIR=wmt14.en-fr.fconv-py - > fairseq-interactive \ - --path $MODEL_DIR/model.pt $MODEL_DIR \ - --beam 5 --source-lang en --target-lang fr \ - --tokenizer moses \ - --bpe subword_nmt --bpe-codes $MODEL_DIR/bpecodes - | loading model(s) from wmt14.en-fr.fconv-py/model.pt - | [en] dictionary: 44206 types - | [fr] dictionary: 44463 types - | Type the input sentence and press return: - Why is it rare to discover new marine mammal species? - S-0 Why is it rare to discover new marine mam@@ mal species ? - H-0 -0.0643349438905716 Pourquoi est-il rare de découvrir de nouvelles espèces de mammifères marins? - P-0 -0.0763 -0.1849 -0.0956 -0.0946 -0.0735 -0.1150 -0.1301 -0.0042 -0.0321 -0.0171 -0.0052 -0.0062 -0.0015 - -This generation script produces three types of outputs: a line prefixed -with *O* is a copy of the original source sentence; *H* is the -hypothesis along with an average log-likelihood; and *P* is the -positional score per token position, including the -end-of-sentence marker which is omitted from the text. - -Other types of output lines you might see are *D*, the detokenized hypothesis, -*T*, the reference target, *A*, alignment info, *E* the history of generation steps. - -See the `README `__ for a -full list of pre-trained models available. - -Training a New Model -==================== - -The following tutorial is for machine translation. For an example of how -to use Fairseq for other tasks, such as :ref:`language modeling`, please see the -``examples/`` directory. - -Data Pre-processing -------------------- - -Fairseq contains example pre-processing scripts for several translation -datasets: IWSLT 2014 (German-English), WMT 2014 (English-French) and WMT -2014 (English-German). To pre-process and binarize the IWSLT dataset: - -.. code-block:: console - - > cd examples/translation/ - > bash prepare-iwslt14.sh - > cd ../.. - > TEXT=examples/translation/iwslt14.tokenized.de-en - > fairseq-preprocess --source-lang de --target-lang en \ - --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ - --destdir data-bin/iwslt14.tokenized.de-en - -This will write binarized data that can be used for model training to -``data-bin/iwslt14.tokenized.de-en``. - -Training --------- - -Use :ref:`fairseq-train` to train a new model. Here a few example settings that work -well for the IWSLT 2014 dataset: - -.. code-block:: console - - > mkdir -p checkpoints/fconv - > CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \ - --optimizer nag --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \ - --arch fconv_iwslt_de_en --save-dir checkpoints/fconv - -By default, :ref:`fairseq-train` will use all available GPUs on your machine. Use the -``CUDA_VISIBLE_DEVICES`` environment variable to select specific GPUs and/or to -change the number of GPU devices that will be used. - -Also note that the batch size is specified in terms of the maximum -number of tokens per batch (``--max-tokens``). You may need to use a -smaller value depending on the available GPU memory on your system. - -Generation ----------- - -Once your model is trained, you can generate translations using -:ref:`fairseq-generate` **(for binarized data)** or -:ref:`fairseq-interactive` **(for raw text)**: - -.. code-block:: console - - > fairseq-generate data-bin/iwslt14.tokenized.de-en \ - --path checkpoints/fconv/checkpoint_best.pt \ - --batch-size 128 --beam 5 - | [de] dictionary: 35475 types - | [en] dictionary: 24739 types - | data-bin/iwslt14.tokenized.de-en test 6750 examples - | model fconv - | loaded checkpoint trainings/fconv/checkpoint_best.pt - S-721 danke . - T-721 thank you . - ... - -To generate translations with only a CPU, use the ``--cpu`` flag. BPE -continuation markers can be removed with the ``--remove-bpe`` flag. - -Advanced Training Options -========================= - -Large mini-batch training with delayed updates ----------------------------------------------- - -The ``--update-freq`` option can be used to accumulate gradients from -multiple mini-batches and delay updating, creating a larger effective -batch size. Delayed updates can also improve training speed by reducing -inter-GPU communication costs and by saving idle time caused by variance -in workload across GPUs. See `Ott et al. -(2018) `__ for more details. - -To train on a single GPU with an effective batch size that is equivalent -to training on 8 GPUs: - -.. code-block:: console - - > CUDA_VISIBLE_DEVICES=0 fairseq-train --update-freq 8 (...) - -Training with half precision floating point (FP16) --------------------------------------------------- - -.. note:: - - FP16 training requires a Volta GPU and CUDA 9.1 or greater - -Recent GPUs enable efficient half precision floating point computation, -e.g., using `Nvidia Tensor Cores -`__. -Fairseq supports FP16 training with the ``--fp16`` flag: - -.. code-block:: console - - > fairseq-train --fp16 (...) - -Distributed training --------------------- - -Distributed training in fairseq is implemented on top of ``torch.distributed``. -The easiest way to launch jobs is with the `torch.distributed.launch -`__ tool. - -For example, to train a large English-German Transformer model on 2 nodes each -with 8 GPUs (in total 16 GPUs), run the following command on each node, -replacing ``node_rank=0`` with ``node_rank=1`` on the second node and making -sure to update ``--master_addr`` to the IP address of the first node: - -.. code-block:: console - - > python -m torch.distributed.launch --nproc_per_node=8 \ - --nnodes=2 --node_rank=0 --master_addr="192.168.1.1" \ - --master_port=12345 \ - $(which fairseq-train) data-bin/wmt16_en_de_bpe32k \ - --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \ - --lr 0.0005 \ - --dropout 0.3 --weight-decay 0.0 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --max-tokens 3584 \ - --max-epoch 70 \ - --fp16 - -On SLURM clusters, fairseq will automatically detect the number of nodes and -GPUs, but a port number must be provided: - -.. code-block:: console - - > salloc --gpus=16 --nodes 2 (...) - > srun fairseq-train --distributed-port 12345 (...). - -Sharding very large datasets ----------------------------- - -It can be challenging to train over very large datasets, particularly if your -machine does not have much system RAM. Most tasks in fairseq support training -over "sharded" datasets, in which the original dataset has been preprocessed -into non-overlapping chunks (or "shards"). - -For example, instead of preprocessing all your data into a single "data-bin" -directory, you can split the data and create "data-bin1", "data-bin2", etc. -Then you can adapt your training command like so: - -.. code-block:: console - - > fairseq-train data-bin1:data-bin2:data-bin3 (...) - -Training will now iterate over each shard, one by one, with each shard -corresponding to an "epoch", thus reducing system memory usage. diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/hydra_integration.md b/audio/safeear/safeear_repo/fairseq_ours/docs/hydra_integration.md deleted file mode 100644 index 6a15298382a6a16dfc4c5a4a812ea1cd0477ed52..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/hydra_integration.md +++ /dev/null @@ -1,284 +0,0 @@ -## Hydra - -[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python -framework that simplifies the development of research and other complex -applications. The key feature is the ability to dynamically create a -hierarchical configuration by composition and override it through config files -and the command line. The name Hydra comes from its ability to run multiple -similar jobs - much like a Hydra with multiple heads. - -## Motivation - -Until recently, all components in fairseq were configured through a shared -`args` namespace that was created at application startup. Components declared -their own `add_args` method to update the argparse parser, hoping that the names -would not clash with arguments from other components. While this model works for -smaller applications, as fairseq grew and became integrated into other -applications, this became problematic. In order to determine how to configure -each component, one needed to a) examine what args were added by this component, -and b) read the code to figure out what shared arguments it is using that were -added in other places. Reproducing models involved sharing commands that often -contained dozens of command line switches. - -The model described above is still supported by fairseq for backward -compatibility, but will be deprecated some time in the future. - -New components in fairseq should now create a dataclass that encapsulates all -parameters required to configure this component. The dataclass is registered -along with the component, and fairseq takes care of constructing and providing -this configuration object to the component's constructor. Note that sharing -parameters can optionally still work, but one has to explicitly point to the -"source of truth" (see inheritance example below). These changes make components -in fairseq more independent and re-usable by other applications: all that is -needed to create a component is to initialize its dataclass and overwrite some -of the defaults. - -While configuring fairseq through command line (using either the legacy argparse -based or the new Hydra based entry points) is still fully supported, you can now -take advantage of configuring fairseq completely or piece-by-piece through -hierarchical YAML configuration files. These files can also be shipped as -examples that others can use to run an identically configured job. - -Additionally, Hydra has a rich and growing [library of -plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that -provide functionality such as hyperparameter sweeping (including using bayesian -optimization through the [Ax](https://github.com/facebook/Ax) library), job -launching across various platforms, and more. - -## Creating or migrating components - -In general, each new (or updated) component should provide a companion -[dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are -typically located in the same file as the component and are passed as arguments -to the `register_*()` functions. Top-level configs that should be present in -every fairseq application are placed in the -[global](fairseq/dataclass/configs.py) config file and added to the -`FairseqConfig` object. - -Each dataclass is a plain-old-data object, similar to a `NamedTuple`. These -classes are decorated with a `@dataclass` decorator, and typically inherit from -`FairseqDataclass` (which adds some functionality for backward compatibility). -Each field must have a type, and generally has metadata (such as a help string) -and a default value. Only primitive types or other config objects are allowed as -data types for each field. - -#### Example: - -```python -from dataclasses import dataclass, field -from fairseq.dataclass import FairseqDataclass - -@dataclass -class InteractiveConfig(FairseqDataclass): - buffer_size: int = field( - default=0, - metadata={ - "help": "read this many sentences into a buffer before processing them" - }, - ) - input: str = field( - default="-", - metadata={"help": "file to read from; use - for stdin"}, - ) -``` - -### Inherting values - -Some components require sharing a value. For example, a learning rate scheduler -and an optimizer may both need to know the initial learning rate value. One can -declare a field that, by default, will inherit its value from another config -node in the same hierarchy: - -```python -@dataclass -FairseqAdamConfig(FairseqDataclass): - ... - lr: List[float] = II("optimization.lr") - ... -``` - -`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"`, which is -the value one can use in a YAML config file or through command line to achieve -the same effect. Note that this assumes that there is an "optimization" config -object in the root config and it has a field called "lr". - -### Tasks and Models - -Creating Tasks and Models works same as before, except that legacy -implementations now inherit from `LegacyFairseq*` base classes, while new -components inherit from `FairseqTask` and `FairseqModel` and provide a dataclass -to the `register_*()` functions. - -#### Task example: - -```python -@dataclass -class LanguageModelingConfig(FairseqDataclass): - data: Optional[str] = field( - default=None, metadata={"help": "path to data directory"} - ) - ... - -@register_task("language_modeling", dataclass=LanguageModelingConfig) -class LanguageModelingTask(FairseqTask): - ... - @classmethod - def setup_task(cls, cfg: LanguageModelingConfig): - ... -``` - -#### Model example: - -```python -@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"}) - ... - -@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig) -class TransformerLanguageModel(FairseqLanguageModel): - ... - @classmethod - def build_model(cls, cfg: TransformerLanguageModelConfig, task: FairseqTask): - ... -``` - -### Other components - -Other components work as before, but they now take their configuration dataclass -as the only constructor argument: - -```python -@dataclass -class MosesTokenizerConfig(FairseqDataclass): - source_lang: str = field(default="en", metadata={"help": "source language"}) - ... - -@register_tokenizer("moses", dataclass=MosesTokenizerConfig) -class MosesTokenizer(object): - def __init__(self, cfg: MosesTokenizerConfig): - ... -``` - -Note that if you are adding a new registry for a new set of components, you need -to add it to the `FairseqConfig` object in `fairseq/dataclass/configs.py`: - -```python -@dataclass -class FairseqConfig(object): - ... - my_new_registry: Any = None -``` - -## Training with `fairseq-hydra-train` - -To fully take advantage of configuration flexibility offered by Hydra, you may -want to train new models using the `fairseq-hydra-train` entry point. Legacy CLI -tools such as `fairseq-train` will remain supported for the foreseeable future -but will be deprecated eventually. - -On startup, Hydra will create a configuration object that contains a hierarchy -of all the necessary dataclasses populated with their default values in the -code. The default values are overwritten by values found in YAML files in -`fairseq/config` directory (which currently sets minimal defaults) and then -further overwritten by values provided through command line arguments. - -Some of the most common use cases are shown below: - -### 1. Override default values through command line: - -```shell script -$ fairseq-hydra-train \ - distributed_training.distributed_world_size=1 \ - dataset.batch_size=2 \ - task.data=data-bin \ - model=transformer_lm/transformer_lm_gpt \ - task=language_modeling \ - optimization.max_update=5000 -``` - -Note that along with explicitly providing values for parameters such as -`dataset.batch_size`, this also tells Hydra to overlay configuration found in -`fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml` over the default -values in the dataclass. If you want to train a model without specifying a -particular architecture you can simply specify `model=transformer_lm`. This only -works for migrated tasks and models. - -### 2. Replace bundled configs with an external config: - -```shell script -$ fairseq-hydra-train \ - --config-dir /path/to/external/configs \ - --config-name wiki103 -``` - -where `/path/to/external/configs/wiki103.yaml` contains: - -```yaml -# @package _group_ - -model: - _name: transformer_lm -distributed_training: - distributed_world_size: 1 -dataset: - batch_size: 2 -task: - _name: language_modeling - data: /path/to/data - add_bos_token: false - max_target_positions: 1024 -optimization: - max_update: 50000 - lr: [ 0.25 ] -criterion: cross_entropy -optimizer: adam -lr_scheduler: - _name: cosine -``` - -Note that here bundled configs from `fairseq/config` directory are not used, -however the defaults from each dataclass will still be used (unless overwritten -by your external config). - -Additionally you can choose to break up your configs by creating a directory -structure in the same location as your main config file, with the names of the -top-level fields (such as "model", "dataset", etc), and placing config files -with meaningful names that would populate that specific section of your -top-level config file (for example, you might have -`model/small_transformer_lm.yaml`, `model/big_transformer_lm.yaml`, etc). You -can then specify the correct configuration via command line, defaults in the -main config, or even launch all of them as a sweep (see Hydra documentation on -how to do this). - -### 3. Add an external config directory to Hydra search path: - -This allows combining default configuration (including using any bundled config -files), while specifying your own config files for some parts of the -configuration. - -```shell script -$ fairseq-hydra-train \ - distributed_training.distributed_world_size=1 \ - dataset.batch_size=2 \ - task.data=/path/to/data/ \ - model=transformer_lm/2_layers \ - task=language_modeling \ - optimization.max_update=5000 \ - --config-dir /path/to/external/configs -``` - -where `/path/to/external/configs` has the following structure: -``` -. -+-- model -| +-- transformer_lm -| | +-- 2_layers.yaml -``` - -and `2_layers.yaml` contains a copy of `transformer_lm_gpt.yaml` but with -`decoder_layers` set to 2. You can add other configs to configure other -components as well. diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/index.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/index.rst deleted file mode 100644 index 591db86cdf49e6f0a7a6686df2150f11418e90d0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/index.rst +++ /dev/null @@ -1,49 +0,0 @@ -.. fairseq documentation master file, created by - sphinx-quickstart on Fri Aug 17 21:45:30 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -:github_url: https://github.com/pytorch/fairseq - - -fairseq documentation -===================== - -Fairseq is a sequence modeling toolkit written in `PyTorch -`_ that allows researchers and developers to -train custom models for translation, summarization, language modeling and other -text generation tasks. - -.. toctree:: - :maxdepth: 1 - :caption: Getting Started - - getting_started - command_line_tools - -.. toctree:: - :maxdepth: 1 - :caption: Extending Fairseq - - overview - tutorial_simple_lstm - tutorial_classifying_names - -.. toctree:: - :maxdepth: 2 - :caption: Library Reference - - tasks - models - criterions - optim - lr_scheduler - data - modules - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`search` diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/lr_scheduler.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/lr_scheduler.rst deleted file mode 100644 index bbc09dc22e6a7ac05137954e0b9c80ca030f62f4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/lr_scheduler.rst +++ /dev/null @@ -1,34 +0,0 @@ -.. role:: hidden - :class: hidden-section - -.. _Learning Rate Schedulers: - -Learning Rate Schedulers -======================== - -Learning Rate Schedulers update the learning rate over the course of training. -Learning rates can be updated after each update via :func:`step_update` or at -epoch boundaries via :func:`step`. - -.. automodule:: fairseq.optim.lr_scheduler - :members: - -.. autoclass:: fairseq.optim.lr_scheduler.FairseqLRScheduler - :members: - :undoc-members: - -.. autoclass:: fairseq.optim.lr_scheduler.cosine_lr_scheduler.CosineSchedule - :members: - :undoc-members: -.. autoclass:: fairseq.optim.lr_scheduler.fixed_schedule.FixedSchedule - :members: - :undoc-members: -.. autoclass:: fairseq.optim.lr_scheduler.inverse_square_root_schedule.InverseSquareRootSchedule - :members: - :undoc-members: -.. autoclass:: fairseq.optim.lr_scheduler.reduce_lr_on_plateau.ReduceLROnPlateau - :members: - :undoc-members: -.. autoclass:: fairseq.optim.lr_scheduler.triangular_lr_scheduler.TriangularSchedule - :members: - :undoc-members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/make.bat b/audio/safeear/safeear_repo/fairseq_ours/docs/make.bat deleted file mode 100644 index 35c5085de318190514ee3b48d10060aa57a4fa50..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/make.bat +++ /dev/null @@ -1,36 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=python -msphinx -) -set SOURCEDIR=. -set BUILDDIR=_build -set SPHINXPROJ=fairseq - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The Sphinx module was not found. Make sure you have Sphinx installed, - echo.then set the SPHINXBUILD environment variable to point to the full - echo.path of the 'sphinx-build' executable. Alternatively you may add the - echo.Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/models.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/models.rst deleted file mode 100644 index 054622d587c3b7f01f17f442919140755acd8f9e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/models.rst +++ /dev/null @@ -1,104 +0,0 @@ -.. role:: hidden - :class: hidden-section - -.. module:: fairseq.models - -.. _Models: - -Models -====== - -A Model defines the neural network's ``forward()`` method and encapsulates all -of the learnable parameters in the network. Each model also provides a set of -named *architectures* that define the precise network configuration (e.g., -embedding dimension, number of layers, etc.). - -Both the model type and architecture are selected via the ``--arch`` -command-line argument. Once selected, a model may expose additional command-line -arguments for further configuration. - -.. note:: - - All fairseq Models extend :class:`BaseFairseqModel`, which in turn extends - :class:`torch.nn.Module`. Thus any fairseq Model can be used as a - stand-alone Module in other PyTorch code. - - -Convolutional Neural Networks (CNN) ------------------------------------ - -.. module:: fairseq.models.fconv -.. autoclass:: fairseq.models.fconv.FConvModel - :members: -.. autoclass:: fairseq.models.fconv.FConvEncoder - :members: - :undoc-members: -.. autoclass:: fairseq.models.fconv.FConvDecoder - :members: - - -Long Short-Term Memory (LSTM) networks --------------------------------------- - -.. module:: fairseq.models.lstm -.. autoclass:: fairseq.models.lstm.LSTMModel - :members: -.. autoclass:: fairseq.models.lstm.LSTMEncoder - :members: -.. autoclass:: fairseq.models.lstm.LSTMDecoder - :members: - - -Transformer (self-attention) networks -------------------------------------- - -.. module:: fairseq.models.transformer -.. autoclass:: fairseq.models.transformer.TransformerModel - :members: -.. autoclass:: fairseq.models.transformer.TransformerEncoder - :members: -.. autoclass:: fairseq.models.transformer.TransformerEncoderLayer - :members: -.. autoclass:: fairseq.models.transformer.TransformerDecoder - :members: -.. autoclass:: fairseq.models.transformer.TransformerDecoderLayer - :members: - - -Adding new models ------------------ - -.. currentmodule:: fairseq.models -.. autofunction:: fairseq.models.register_model -.. autofunction:: fairseq.models.register_model_architecture -.. autoclass:: fairseq.models.BaseFairseqModel - :members: - :undoc-members: -.. autoclass:: fairseq.models.FairseqEncoderDecoderModel - :members: - :undoc-members: -.. autoclass:: fairseq.models.FairseqEncoderModel - :members: - :undoc-members: -.. autoclass:: fairseq.models.FairseqLanguageModel - :members: - :undoc-members: -.. autoclass:: fairseq.models.FairseqMultiModel - :members: - :undoc-members: -.. autoclass:: fairseq.models.FairseqEncoder - :members: -.. autoclass:: fairseq.models.CompositeEncoder - :members: -.. autoclass:: fairseq.models.FairseqDecoder - :members: - - -.. _Incremental decoding: - -Incremental decoding --------------------- - -.. autoclass:: fairseq.models.FairseqIncrementalDecoder - :members: - :undoc-members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/modules.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/modules.rst deleted file mode 100644 index 9631c93d4682286e1cea1ddd961d3f6ab06f2589..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/modules.rst +++ /dev/null @@ -1,9 +0,0 @@ -Modules -======= - -Fairseq provides several stand-alone :class:`torch.nn.Module` classes that may -be helpful when implementing a new :class:`~fairseq.models.BaseFairseqModel`. - -.. automodule:: fairseq.modules - :members: - :undoc-members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/optim.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/optim.rst deleted file mode 100644 index c3326456bd9291a1d05bd3316bef5c9fb25c6c49..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/optim.rst +++ /dev/null @@ -1,38 +0,0 @@ -.. role:: hidden - :class: hidden-section - -.. _optimizers: - -Optimizers -========== - -Optimizers update the Model parameters based on the gradients. - -.. automodule:: fairseq.optim - :members: - -.. autoclass:: fairseq.optim.FairseqOptimizer - :members: - :undoc-members: - -.. autoclass:: fairseq.optim.adadelta.Adadelta - :members: - :undoc-members: -.. autoclass:: fairseq.optim.adagrad.Adagrad - :members: - :undoc-members: -.. autoclass:: fairseq.optim.adafactor.FairseqAdafactor - :members: - :undoc-members: -.. autoclass:: fairseq.optim.adam.FairseqAdam - :members: - :undoc-members: -.. autoclass:: fairseq.optim.fp16_optimizer.FP16Optimizer - :members: - :undoc-members: -.. autoclass:: fairseq.optim.nag.FairseqNAG - :members: - :undoc-members: -.. autoclass:: fairseq.optim.sgd.SGD - :members: - :undoc-members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/overview.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/overview.rst deleted file mode 100644 index 026b3b5c7b21d071d8b8a3405898977c760d05b8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/overview.rst +++ /dev/null @@ -1,74 +0,0 @@ -Overview -======== - -Fairseq can be extended through user-supplied `plug-ins -`_. We support five kinds of -plug-ins: - -- :ref:`Models` define the neural network architecture and encapsulate all of the - learnable parameters. -- :ref:`Criterions` compute the loss function given the model outputs and targets. -- :ref:`Tasks` store dictionaries and provide helpers for loading/iterating over - Datasets, initializing the Model/Criterion and calculating the loss. -- :ref:`Optimizers` update the Model parameters based on the gradients. -- :ref:`Learning Rate Schedulers` update the learning rate over the course of - training. - -**Training Flow** - -Given a ``model``, ``criterion``, ``task``, ``optimizer`` and ``lr_scheduler``, -fairseq implements the following high-level training flow:: - - for epoch in range(num_epochs): - itr = task.get_batch_iterator(task.dataset('train')) - for num_updates, batch in enumerate(itr): - task.train_step(batch, model, criterion, optimizer) - average_and_clip_gradients() - optimizer.step() - lr_scheduler.step_update(num_updates) - lr_scheduler.step(epoch) - -where the default implementation for ``task.train_step`` is roughly:: - - def train_step(self, batch, model, criterion, optimizer, **unused): - loss = criterion(model, batch) - optimizer.backward(loss) - return loss - -**Registering new plug-ins** - -New plug-ins are *registered* through a set of ``@register`` function -decorators, for example:: - - @register_model('my_lstm') - class MyLSTM(FairseqEncoderDecoderModel): - (...) - -Once registered, new plug-ins can be used with the existing :ref:`Command-line -Tools`. See the Tutorial sections for more detailed walkthroughs of how to add -new plug-ins. - -**Loading plug-ins from another directory** - -New plug-ins can be defined in a custom module stored in the user system. In -order to import the module, and make the plugin available to *fairseq*, the -command line supports the ``--user-dir`` flag that can be used to specify a -custom location for additional modules to load into *fairseq*. - -For example, assuming this directory tree:: - - /home/user/my-module/ - └── __init__.py - -with ``__init__.py``:: - - from fairseq.models import register_model_architecture - from fairseq.models.transformer import transformer_vaswani_wmt_en_de_big - - @register_model_architecture('transformer', 'my_transformer') - def transformer_mmt_big(args): - transformer_vaswani_wmt_en_de_big(args) - -it is possible to invoke the :ref:`fairseq-train` script with the new architecture with:: - - fairseq-train ... --user-dir /home/user/my-module -a my_transformer --task translation diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/requirements.txt b/audio/safeear/safeear_repo/fairseq_ours/docs/requirements.txt deleted file mode 100644 index c734a1f04f1c108d84d3a07643ac93adf6485f13..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -sphinx<2.0 -sphinx-argparse diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/tasks.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/tasks.rst deleted file mode 100644 index 5f65c3c866865e50332d8e6ca012a4a81e7bea74..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/tasks.rst +++ /dev/null @@ -1,61 +0,0 @@ -.. role:: hidden - :class: hidden-section - -.. module:: fairseq.tasks - -.. _Tasks: - -Tasks -===== - -Tasks store dictionaries and provide helpers for loading/iterating over -Datasets, initializing the Model/Criterion and calculating the loss. - -Tasks can be selected via the ``--task`` command-line argument. Once selected, a -task may expose additional command-line arguments for further configuration. - -Example usage:: - - # setup the task (e.g., load dictionaries) - task = fairseq.tasks.setup_task(args) - - # build model and criterion - model = task.build_model(args) - criterion = task.build_criterion(args) - - # load datasets - task.load_dataset('train') - task.load_dataset('valid') - - # iterate over mini-batches of data - batch_itr = task.get_batch_iterator( - task.dataset('train'), max_tokens=4096, - ) - for batch in batch_itr: - # compute the loss - loss, sample_size, logging_output = task.get_loss( - model, criterion, batch, - ) - loss.backward() - - -Translation ------------ - -.. autoclass:: fairseq.tasks.translation.TranslationTask - -.. _language modeling: - -Language Modeling ------------------ - -.. autoclass:: fairseq.tasks.language_modeling.LanguageModelingTask - - -Adding new tasks ----------------- - -.. autofunction:: fairseq.tasks.register_task -.. autoclass:: fairseq.tasks.FairseqTask - :members: - :undoc-members: diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/tutorial_classifying_names.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/tutorial_classifying_names.rst deleted file mode 100644 index b02fec0489a86e7b1ccec481342fa4fbd93a80ae..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/tutorial_classifying_names.rst +++ /dev/null @@ -1,415 +0,0 @@ -Tutorial: Classifying Names with a Character-Level RNN -====================================================== - -In this tutorial we will extend fairseq to support *classification* tasks. In -particular we will re-implement the PyTorch tutorial for `Classifying Names with -a Character-Level RNN `_ -in fairseq. It is recommended to quickly skim that tutorial before beginning -this one. - -This tutorial covers: - -1. **Preprocessing the data** to create dictionaries. -2. **Registering a new Model** that encodes an input sentence with a simple RNN - and predicts the output label. -3. **Registering a new Task** that loads our dictionaries and dataset. -4. **Training the Model** using the existing command-line tools. -5. **Writing an evaluation script** that imports fairseq and allows us to - interactively evaluate our model on new inputs. - - -1. Preprocessing the data -------------------------- - -The original tutorial provides raw data, but we'll work with a modified version -of the data that is already tokenized into characters and split into separate -train, valid and test sets. - -Download and extract the data from here: -`tutorial_names.tar.gz `_ - -Once extracted, let's preprocess the data using the :ref:`fairseq-preprocess` -command-line tool to create the dictionaries. While this tool is primarily -intended for sequence-to-sequence problems, we're able to reuse it here by -treating the label as a "target" sequence of length 1. We'll also output the -preprocessed files in "raw" format using the ``--dataset-impl`` option to -enhance readability: - -.. code-block:: console - - > fairseq-preprocess \ - --trainpref names/train --validpref names/valid --testpref names/test \ - --source-lang input --target-lang label \ - --destdir names-bin --dataset-impl raw - -After running the above command you should see a new directory, -:file:`names-bin/`, containing the dictionaries for *inputs* and *labels*. - - -2. Registering a new Model --------------------------- - -Next we'll register a new model in fairseq that will encode an input sentence -with a simple RNN and predict the output label. Compared to the original PyTorch -tutorial, our version will also work with batches of data and GPU Tensors. - -First let's copy the simple RNN module implemented in the `PyTorch tutorial -`_. -Create a new file named :file:`fairseq/models/rnn_classifier.py` with the -following contents:: - - import torch - import torch.nn as nn - - class RNN(nn.Module): - - def __init__(self, input_size, hidden_size, output_size): - super(RNN, self).__init__() - - self.hidden_size = hidden_size - - self.i2h = nn.Linear(input_size + hidden_size, hidden_size) - self.i2o = nn.Linear(input_size + hidden_size, output_size) - self.softmax = nn.LogSoftmax(dim=1) - - def forward(self, input, hidden): - combined = torch.cat((input, hidden), 1) - hidden = self.i2h(combined) - output = self.i2o(combined) - output = self.softmax(output) - return output, hidden - - def initHidden(self): - return torch.zeros(1, self.hidden_size) - -We must also *register* this model with fairseq using the -:func:`~fairseq.models.register_model` function decorator. Once the model is -registered we'll be able to use it with the existing :ref:`Command-line Tools`. - -All registered models must implement the :class:`~fairseq.models.BaseFairseqModel` -interface, so we'll create a small wrapper class in the same file and register -it in fairseq with the name ``'rnn_classifier'``:: - - from fairseq.models import BaseFairseqModel, register_model - - # Note: the register_model "decorator" should immediately precede the - # definition of the Model class. - - @register_model('rnn_classifier') - class FairseqRNNClassifier(BaseFairseqModel): - - @staticmethod - def add_args(parser): - # Models can override this method to add new command-line arguments. - # Here we'll add a new command-line argument to configure the - # dimensionality of the hidden state. - parser.add_argument( - '--hidden-dim', type=int, metavar='N', - help='dimensionality of the hidden state', - ) - - @classmethod - def build_model(cls, args, task): - # Fairseq initializes models by calling the ``build_model()`` - # function. This provides more flexibility, since the returned model - # instance can be of a different type than the one that was called. - # In this case we'll just return a FairseqRNNClassifier instance. - - # Initialize our RNN module - rnn = RNN( - # We'll define the Task in the next section, but for now just - # notice that the task holds the dictionaries for the "source" - # (i.e., the input sentence) and "target" (i.e., the label). - input_size=len(task.source_dictionary), - hidden_size=args.hidden_dim, - output_size=len(task.target_dictionary), - ) - - # Return the wrapped version of the module - return FairseqRNNClassifier( - rnn=rnn, - input_vocab=task.source_dictionary, - ) - - def __init__(self, rnn, input_vocab): - super(FairseqRNNClassifier, self).__init__() - - self.rnn = rnn - self.input_vocab = input_vocab - - # The RNN module in the tutorial expects one-hot inputs, so we can - # precompute the identity matrix to help convert from indices to - # one-hot vectors. We register it as a buffer so that it is moved to - # the GPU when ``cuda()`` is called. - self.register_buffer('one_hot_inputs', torch.eye(len(input_vocab))) - - def forward(self, src_tokens, src_lengths): - # The inputs to the ``forward()`` function are determined by the - # Task, and in particular the ``'net_input'`` key in each - # mini-batch. We'll define the Task in the next section, but for - # now just know that *src_tokens* has shape `(batch, src_len)` and - # *src_lengths* has shape `(batch)`. - bsz, max_src_len = src_tokens.size() - - # Initialize the RNN hidden state. Compared to the original PyTorch - # tutorial we'll also handle batched inputs and work on the GPU. - hidden = self.rnn.initHidden() - hidden = hidden.repeat(bsz, 1) # expand for batched inputs - hidden = hidden.to(src_tokens.device) # move to GPU - - for i in range(max_src_len): - # WARNING: The inputs have padding, so we should mask those - # elements here so that padding doesn't affect the results. - # This is left as an exercise for the reader. The padding symbol - # is given by ``self.input_vocab.pad()`` and the unpadded length - # of each input is given by *src_lengths*. - - # One-hot encode a batch of input characters. - input = self.one_hot_inputs[src_tokens[:, i].long()] - - # Feed the input to our RNN. - output, hidden = self.rnn(input, hidden) - - # Return the final output state for making a prediction - return output - -Finally let's define a *named architecture* with the configuration for our -model. This is done with the :func:`~fairseq.models.register_model_architecture` -function decorator. Thereafter this named architecture can be used with the -``--arch`` command-line argument, e.g., ``--arch pytorch_tutorial_rnn``:: - - from fairseq.models import register_model_architecture - - # The first argument to ``register_model_architecture()`` should be the name - # of the model we registered above (i.e., 'rnn_classifier'). The function we - # register here should take a single argument *args* and modify it in-place - # to match the desired architecture. - - @register_model_architecture('rnn_classifier', 'pytorch_tutorial_rnn') - def pytorch_tutorial_rnn(args): - # We use ``getattr()`` to prioritize arguments that are explicitly given - # on the command-line, so that the defaults defined below are only used - # when no other value has been specified. - args.hidden_dim = getattr(args, 'hidden_dim', 128) - - -3. Registering a new Task -------------------------- - -Now we'll register a new :class:`~fairseq.tasks.FairseqTask` that will load our -dictionaries and dataset. Tasks can also control how the data is batched into -mini-batches, but in this tutorial we'll reuse the batching provided by -:class:`fairseq.data.LanguagePairDataset`. - -Create a new file named :file:`fairseq/tasks/simple_classification.py` with the -following contents:: - - import os - import torch - - from fairseq.data import Dictionary, LanguagePairDataset - from fairseq.tasks import FairseqTask, register_task - - - @register_task('simple_classification') - class SimpleClassificationTask(LegacyFairseqTask): - - @staticmethod - def add_args(parser): - # Add some command-line arguments for specifying where the data is - # located and the maximum supported input length. - parser.add_argument('data', metavar='FILE', - help='file prefix for data') - parser.add_argument('--max-positions', default=1024, type=int, - help='max input length') - - @classmethod - def setup_task(cls, args, **kwargs): - # Here we can perform any setup required for the task. This may include - # loading Dictionaries, initializing shared Embedding layers, etc. - # In this case we'll just load the Dictionaries. - input_vocab = Dictionary.load(os.path.join(args.data, 'dict.input.txt')) - label_vocab = Dictionary.load(os.path.join(args.data, 'dict.label.txt')) - print('| [input] dictionary: {} types'.format(len(input_vocab))) - print('| [label] dictionary: {} types'.format(len(label_vocab))) - - return SimpleClassificationTask(args, input_vocab, label_vocab) - - def __init__(self, args, input_vocab, label_vocab): - super().__init__(args) - self.input_vocab = input_vocab - self.label_vocab = label_vocab - - def load_dataset(self, split, **kwargs): - """Load a given dataset split (e.g., train, valid, test).""" - - prefix = os.path.join(self.args.data, '{}.input-label'.format(split)) - - # Read input sentences. - sentences, lengths = [], [] - with open(prefix + '.input', encoding='utf-8') as file: - for line in file: - sentence = line.strip() - - # Tokenize the sentence, splitting on spaces - tokens = self.input_vocab.encode_line( - sentence, add_if_not_exist=False, - ) - - sentences.append(tokens) - lengths.append(tokens.numel()) - - # Read labels. - labels = [] - with open(prefix + '.label', encoding='utf-8') as file: - for line in file: - label = line.strip() - labels.append( - # Convert label to a numeric ID. - torch.LongTensor([self.label_vocab.add_symbol(label)]) - ) - - assert len(sentences) == len(labels) - print('| {} {} {} examples'.format(self.args.data, split, len(sentences))) - - # We reuse LanguagePairDataset since classification can be modeled as a - # sequence-to-sequence task where the target sequence has length 1. - self.datasets[split] = LanguagePairDataset( - src=sentences, - src_sizes=lengths, - src_dict=self.input_vocab, - tgt=labels, - tgt_sizes=torch.ones(len(labels)), # targets have length 1 - tgt_dict=self.label_vocab, - left_pad_source=False, - # Since our target is a single class label, there's no need for - # teacher forcing. If we set this to ``True`` then our Model's - # ``forward()`` method would receive an additional argument called - # *prev_output_tokens* that would contain a shifted version of the - # target sequence. - input_feeding=False, - ) - - def max_positions(self): - """Return the max input length allowed by the task.""" - # The source should be less than *args.max_positions* and the "target" - # has max length 1. - return (self.args.max_positions, 1) - - @property - def source_dictionary(self): - """Return the source :class:`~fairseq.data.Dictionary`.""" - return self.input_vocab - - @property - def target_dictionary(self): - """Return the target :class:`~fairseq.data.Dictionary`.""" - return self.label_vocab - - # We could override this method if we wanted more control over how batches - # are constructed, but it's not necessary for this tutorial since we can - # reuse the batching provided by LanguagePairDataset. - # - # 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, - # ): - # (...) - - -4. Training the Model ---------------------- - -Now we're ready to train the model. We can use the existing :ref:`fairseq-train` -command-line tool for this, making sure to specify our new Task (``--task -simple_classification``) and Model architecture (``--arch -pytorch_tutorial_rnn``): - -.. note:: - - You can also configure the dimensionality of the hidden state by passing the - ``--hidden-dim`` argument to :ref:`fairseq-train`. - -.. code-block:: console - - > fairseq-train names-bin \ - --task simple_classification \ - --arch pytorch_tutorial_rnn \ - --optimizer adam --lr 0.001 --lr-shrink 0.5 \ - --max-tokens 1000 - (...) - | epoch 027 | loss 1.200 | ppl 2.30 | wps 15728 | ups 119.4 | wpb 116 | bsz 116 | num_updates 3726 | lr 1.5625e-05 | gnorm 1.290 | clip 0% | oom 0 | wall 32 | train_wall 21 - | epoch 027 | valid on 'valid' subset | valid_loss 1.41304 | valid_ppl 2.66 | num_updates 3726 | best 1.41208 - | done training in 31.6 seconds - -The model files should appear in the :file:`checkpoints/` directory. - - -5. Writing an evaluation script -------------------------------- - -Finally we can write a short script to evaluate our model on new inputs. Create -a new file named :file:`eval_classifier.py` with the following contents:: - - from fairseq import checkpoint_utils, data, options, tasks - - # Parse command-line arguments for generation - parser = options.get_generation_parser(default_task='simple_classification') - args = options.parse_args_and_arch(parser) - - # Setup task - task = tasks.setup_task(args) - - # Load model - print('| loading model from {}'.format(args.path)) - models, _model_args = checkpoint_utils.load_model_ensemble([args.path], task=task) - model = models[0] - - while True: - sentence = input('\nInput: ') - - # Tokenize into characters - chars = ' '.join(list(sentence.strip())) - tokens = task.source_dictionary.encode_line( - chars, add_if_not_exist=False, - ) - - # Build mini-batch to feed to the model - batch = data.language_pair_dataset.collate( - samples=[{'id': -1, 'source': tokens}], # bsz = 1 - pad_idx=task.source_dictionary.pad(), - eos_idx=task.source_dictionary.eos(), - left_pad_source=False, - input_feeding=False, - ) - - # Feed batch to the model and get predictions - preds = model(**batch['net_input']) - - # Print top 3 predictions and their log-probabilities - top_scores, top_labels = preds[0].topk(k=3) - for score, label_idx in zip(top_scores, top_labels): - label_name = task.target_dictionary.string([label_idx]) - print('({:.2f})\t{}'.format(score, label_name)) - -Now we can evaluate our model interactively. Note that we have included the -original data path (:file:`names-bin/`) so that the dictionaries can be loaded: - -.. code-block:: console - - > python eval_classifier.py names-bin --path checkpoints/checkpoint_best.pt - | [input] dictionary: 64 types - | [label] dictionary: 24 types - | loading model from checkpoints/checkpoint_best.pt - - Input: Satoshi - (-0.61) Japanese - (-1.20) Arabic - (-2.86) Italian - - Input: Sinbad - (-0.30) Arabic - (-1.76) English - (-4.08) Russian diff --git a/audio/safeear/safeear_repo/fairseq_ours/docs/tutorial_simple_lstm.rst b/audio/safeear/safeear_repo/fairseq_ours/docs/tutorial_simple_lstm.rst deleted file mode 100644 index f52988507c5da5125668e143bd2bfe4df117b41c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/docs/tutorial_simple_lstm.rst +++ /dev/null @@ -1,518 +0,0 @@ -Tutorial: Simple LSTM -===================== - -In this tutorial we will extend fairseq by adding a new -:class:`~fairseq.models.FairseqEncoderDecoderModel` that encodes a source -sentence with an LSTM and then passes the final hidden state to a second LSTM -that decodes the target sentence (without attention). - -This tutorial covers: - -1. **Writing an Encoder and Decoder** to encode/decode the source/target - sentence, respectively. -2. **Registering a new Model** so that it can be used with the existing - :ref:`Command-line tools`. -3. **Training the Model** using the existing command-line tools. -4. **Making generation faster** by modifying the Decoder to use - :ref:`Incremental decoding`. - - -1. Building an Encoder and Decoder ----------------------------------- - -In this section we'll define a simple LSTM Encoder and Decoder. All Encoders -should implement the :class:`~fairseq.models.FairseqEncoder` interface and -Decoders should implement the :class:`~fairseq.models.FairseqDecoder` interface. -These interfaces themselves extend :class:`torch.nn.Module`, so FairseqEncoders -and FairseqDecoders can be written and used in the same ways as ordinary PyTorch -Modules. - - -Encoder -~~~~~~~ - -Our Encoder will embed the tokens in the source sentence, feed them to a -:class:`torch.nn.LSTM` and return the final hidden state. To create our encoder -save the following in a new file named :file:`fairseq/models/simple_lstm.py`:: - - import torch.nn as nn - from fairseq import utils - from fairseq.models import FairseqEncoder - - class SimpleLSTMEncoder(FairseqEncoder): - - def __init__( - self, args, dictionary, embed_dim=128, hidden_dim=128, dropout=0.1, - ): - super().__init__(dictionary) - self.args = args - - # Our encoder will embed the inputs before feeding them to the LSTM. - self.embed_tokens = nn.Embedding( - num_embeddings=len(dictionary), - embedding_dim=embed_dim, - padding_idx=dictionary.pad(), - ) - self.dropout = nn.Dropout(p=dropout) - - # We'll use a single-layer, unidirectional LSTM for simplicity. - self.lstm = nn.LSTM( - input_size=embed_dim, - hidden_size=hidden_dim, - num_layers=1, - bidirectional=False, - batch_first=True, - ) - - def forward(self, src_tokens, src_lengths): - # The inputs to the ``forward()`` function are determined by the - # Task, and in particular the ``'net_input'`` key in each - # mini-batch. We discuss Tasks in the next tutorial, but for now just - # know that *src_tokens* has shape `(batch, src_len)` and *src_lengths* - # has shape `(batch)`. - - # Note that the source is typically padded on the left. This can be - # configured by adding the `--left-pad-source "False"` command-line - # argument, but here we'll make the Encoder handle either kind of - # padding by converting everything to be right-padded. - if self.args.left_pad_source: - # Convert left-padding to right-padding. - src_tokens = utils.convert_padding_direction( - src_tokens, - padding_idx=self.dictionary.pad(), - left_to_right=True - ) - - # Embed the source. - x = self.embed_tokens(src_tokens) - - # Apply dropout. - x = self.dropout(x) - - # Pack the sequence into a PackedSequence object to feed to the LSTM. - x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True) - - # Get the output from the LSTM. - _outputs, (final_hidden, _final_cell) = self.lstm(x) - - # Return the Encoder's output. This can be any object and will be - # passed directly to the Decoder. - return { - # this will have shape `(bsz, hidden_dim)` - 'final_hidden': final_hidden.squeeze(0), - } - - # Encoders are required to implement this method so that we can rearrange - # the order of the batch elements during inference (e.g., beam search). - 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` - """ - final_hidden = encoder_out['final_hidden'] - return { - 'final_hidden': final_hidden.index_select(0, new_order), - } - - -Decoder -~~~~~~~ - -Our Decoder will predict the next word, conditioned on the Encoder's final -hidden state and an embedded representation of the previous target word -- which -is sometimes called *teacher forcing*. More specifically, we'll use a -:class:`torch.nn.LSTM` to produce a sequence of hidden states that we'll project -to the size of the output vocabulary to predict each target word. - -:: - - import torch - from fairseq.models import FairseqDecoder - - class SimpleLSTMDecoder(FairseqDecoder): - - def __init__( - self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128, - dropout=0.1, - ): - super().__init__(dictionary) - - # Our decoder will embed the inputs before feeding them to the LSTM. - self.embed_tokens = nn.Embedding( - num_embeddings=len(dictionary), - embedding_dim=embed_dim, - padding_idx=dictionary.pad(), - ) - self.dropout = nn.Dropout(p=dropout) - - # We'll use a single-layer, unidirectional LSTM for simplicity. - self.lstm = nn.LSTM( - # For the first layer we'll concatenate the Encoder's final hidden - # state with the embedded target tokens. - input_size=encoder_hidden_dim + embed_dim, - hidden_size=hidden_dim, - num_layers=1, - bidirectional=False, - ) - - # Define the output projection. - self.output_projection = nn.Linear(hidden_dim, len(dictionary)) - - # During training Decoders are expected to take the entire target sequence - # (shifted right by one position) and produce logits over the vocabulary. - # The *prev_output_tokens* tensor begins with the end-of-sentence symbol, - # ``dictionary.eos()``, followed by the target sequence. - def forward(self, prev_output_tokens, encoder_out): - """ - 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 - - 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)` - """ - bsz, tgt_len = prev_output_tokens.size() - - # Extract the final hidden state from the Encoder. - final_encoder_hidden = encoder_out['final_hidden'] - - # Embed the target sequence, which has been shifted right by one - # position and now starts with the end-of-sentence symbol. - x = self.embed_tokens(prev_output_tokens) - - # Apply dropout. - x = self.dropout(x) - - # Concatenate the Encoder's final hidden state to *every* embedded - # target token. - x = torch.cat( - [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)], - dim=2, - ) - - # Using PackedSequence objects in the Decoder is harder than in the - # Encoder, since the targets are not sorted in descending length order, - # which is a requirement of ``pack_padded_sequence()``. Instead we'll - # feed nn.LSTM directly. - initial_state = ( - final_encoder_hidden.unsqueeze(0), # hidden - torch.zeros_like(final_encoder_hidden).unsqueeze(0), # cell - ) - output, _ = self.lstm( - x.transpose(0, 1), # convert to shape `(tgt_len, bsz, dim)` - initial_state, - ) - x = output.transpose(0, 1) # convert to shape `(bsz, tgt_len, hidden)` - - # Project the outputs to the size of the vocabulary. - x = self.output_projection(x) - - # Return the logits and ``None`` for the attention weights - return x, None - - -2. Registering the Model ------------------------- - -Now that we've defined our Encoder and Decoder we must *register* our model with -fairseq using the :func:`~fairseq.models.register_model` function decorator. -Once the model is registered we'll be able to use it with the existing -:ref:`Command-line Tools`. - -All registered models must implement the -:class:`~fairseq.models.BaseFairseqModel` interface. For sequence-to-sequence -models (i.e., any model with a single Encoder and Decoder), we can instead -implement the :class:`~fairseq.models.FairseqEncoderDecoderModel` interface. - -Create a small wrapper class in the same file and register it in fairseq with -the name ``'simple_lstm'``:: - - from fairseq.models import FairseqEncoderDecoderModel, register_model - - # Note: the register_model "decorator" should immediately precede the - # definition of the Model class. - - @register_model('simple_lstm') - class SimpleLSTMModel(FairseqEncoderDecoderModel): - - @staticmethod - def add_args(parser): - # Models can override this method to add new command-line arguments. - # Here we'll add some new command-line arguments to configure dropout - # and the dimensionality of the embeddings and hidden states. - parser.add_argument( - '--encoder-embed-dim', type=int, metavar='N', - help='dimensionality of the encoder embeddings', - ) - parser.add_argument( - '--encoder-hidden-dim', type=int, metavar='N', - help='dimensionality of the encoder hidden state', - ) - parser.add_argument( - '--encoder-dropout', type=float, default=0.1, - help='encoder dropout probability', - ) - parser.add_argument( - '--decoder-embed-dim', type=int, metavar='N', - help='dimensionality of the decoder embeddings', - ) - parser.add_argument( - '--decoder-hidden-dim', type=int, metavar='N', - help='dimensionality of the decoder hidden state', - ) - parser.add_argument( - '--decoder-dropout', type=float, default=0.1, - help='decoder dropout probability', - ) - - @classmethod - def build_model(cls, args, task): - # Fairseq initializes models by calling the ``build_model()`` - # function. This provides more flexibility, since the returned model - # instance can be of a different type than the one that was called. - # In this case we'll just return a SimpleLSTMModel instance. - - # Initialize our Encoder and Decoder. - encoder = SimpleLSTMEncoder( - args=args, - dictionary=task.source_dictionary, - embed_dim=args.encoder_embed_dim, - hidden_dim=args.encoder_hidden_dim, - dropout=args.encoder_dropout, - ) - decoder = SimpleLSTMDecoder( - dictionary=task.target_dictionary, - encoder_hidden_dim=args.encoder_hidden_dim, - embed_dim=args.decoder_embed_dim, - hidden_dim=args.decoder_hidden_dim, - dropout=args.decoder_dropout, - ) - model = SimpleLSTMModel(encoder, decoder) - - # Print the model architecture. - print(model) - - return model - - # We could override the ``forward()`` if we wanted more control over how - # the encoder and decoder interact, but it's not necessary for this - # tutorial since we can inherit the default implementation provided by - # the FairseqEncoderDecoderModel base class, which looks like: - # - # def forward(self, src_tokens, src_lengths, prev_output_tokens): - # encoder_out = self.encoder(src_tokens, src_lengths) - # decoder_out = self.decoder(prev_output_tokens, encoder_out) - # return decoder_out - -Finally let's define a *named architecture* with the configuration for our -model. This is done with the :func:`~fairseq.models.register_model_architecture` -function decorator. Thereafter this named architecture can be used with the -``--arch`` command-line argument, e.g., ``--arch tutorial_simple_lstm``:: - - from fairseq.models import register_model_architecture - - # The first argument to ``register_model_architecture()`` should be the name - # of the model we registered above (i.e., 'simple_lstm'). The function we - # register here should take a single argument *args* and modify it in-place - # to match the desired architecture. - - @register_model_architecture('simple_lstm', 'tutorial_simple_lstm') - def tutorial_simple_lstm(args): - # We use ``getattr()`` to prioritize arguments that are explicitly given - # on the command-line, so that the defaults defined below are only used - # when no other value has been specified. - args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256) - args.encoder_hidden_dim = getattr(args, 'encoder_hidden_dim', 256) - args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256) - args.decoder_hidden_dim = getattr(args, 'decoder_hidden_dim', 256) - - -3. Training the Model ---------------------- - -Now we're ready to train the model. We can use the existing :ref:`fairseq-train` -command-line tool for this, making sure to specify our new Model architecture -(``--arch tutorial_simple_lstm``). - -.. note:: - - Make sure you've already preprocessed the data from the IWSLT example in the - :file:`examples/translation/` directory. - -.. code-block:: console - - > fairseq-train data-bin/iwslt14.tokenized.de-en \ - --arch tutorial_simple_lstm \ - --encoder-dropout 0.2 --decoder-dropout 0.2 \ - --optimizer adam --lr 0.005 --lr-shrink 0.5 \ - --max-tokens 12000 - (...) - | epoch 052 | loss 4.027 | ppl 16.30 | wps 420805 | ups 39.7 | wpb 9841 | bsz 400 | num_updates 20852 | lr 1.95313e-05 | gnorm 0.218 | clip 0% | oom 0 | wall 529 | train_wall 396 - | epoch 052 | valid on 'valid' subset | valid_loss 4.74989 | valid_ppl 26.91 | num_updates 20852 | best 4.74954 - -The model files should appear in the :file:`checkpoints/` directory. While this -model architecture is not very good, we can use the :ref:`fairseq-generate` script to -generate translations and compute our BLEU score over the test set: - -.. code-block:: console - - > fairseq-generate data-bin/iwslt14.tokenized.de-en \ - --path checkpoints/checkpoint_best.pt \ - --beam 5 \ - --remove-bpe - (...) - | Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s) - | Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146) - - -4. Making generation faster ---------------------------- - -While autoregressive generation from sequence-to-sequence models is inherently -slow, our implementation above is especially slow because it recomputes the -entire sequence of Decoder hidden states for every output token (i.e., it is -``O(n^2)``). We can make this significantly faster by instead caching the -previous hidden states. - -In fairseq this is called :ref:`Incremental decoding`. Incremental decoding is a -special mode at inference time where the Model only receives a single timestep -of input corresponding to the immediately 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. - -To implement incremental decoding we will modify our model to implement the -:class:`~fairseq.models.FairseqIncrementalDecoder` interface. Compared to the -standard :class:`~fairseq.models.FairseqDecoder` interface, the incremental -decoder interface allows ``forward()`` methods to take an extra keyword argument -(*incremental_state*) that can be used to cache state across time-steps. - -Let's replace our ``SimpleLSTMDecoder`` with an incremental one:: - - import torch - from fairseq.models import FairseqIncrementalDecoder - - class SimpleLSTMDecoder(FairseqIncrementalDecoder): - - def __init__( - self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128, - dropout=0.1, - ): - # This remains the same as before. - super().__init__(dictionary) - self.embed_tokens = nn.Embedding( - num_embeddings=len(dictionary), - embedding_dim=embed_dim, - padding_idx=dictionary.pad(), - ) - self.dropout = nn.Dropout(p=dropout) - self.lstm = nn.LSTM( - input_size=encoder_hidden_dim + embed_dim, - hidden_size=hidden_dim, - num_layers=1, - bidirectional=False, - ) - self.output_projection = nn.Linear(hidden_dim, len(dictionary)) - - # We now take an additional kwarg (*incremental_state*) for caching the - # previous hidden and cell states. - def forward(self, prev_output_tokens, encoder_out, incremental_state=None): - if incremental_state is not None: - # If the *incremental_state* argument is not ``None`` then we are - # in incremental inference mode. While *prev_output_tokens* will - # still contain the entire decoded prefix, we will only use the - # last step and assume that the rest of the state is cached. - prev_output_tokens = prev_output_tokens[:, -1:] - - # This remains the same as before. - bsz, tgt_len = prev_output_tokens.size() - final_encoder_hidden = encoder_out['final_hidden'] - x = self.embed_tokens(prev_output_tokens) - x = self.dropout(x) - x = torch.cat( - [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)], - dim=2, - ) - - # We will now check the cache and load the cached previous hidden and - # cell states, if they exist, otherwise we will initialize them to - # zeros (as before). We will use the ``utils.get_incremental_state()`` - # and ``utils.set_incremental_state()`` helpers. - initial_state = utils.get_incremental_state( - self, incremental_state, 'prev_state', - ) - if initial_state is None: - # first time initialization, same as the original version - initial_state = ( - final_encoder_hidden.unsqueeze(0), # hidden - torch.zeros_like(final_encoder_hidden).unsqueeze(0), # cell - ) - - # Run one step of our LSTM. - output, latest_state = self.lstm(x.transpose(0, 1), initial_state) - - # Update the cache with the latest hidden and cell states. - utils.set_incremental_state( - self, incremental_state, 'prev_state', latest_state, - ) - - # This remains the same as before - x = output.transpose(0, 1) - x = self.output_projection(x) - return x, None - - # The ``FairseqIncrementalDecoder`` interface also requires implementing a - # ``reorder_incremental_state()`` method, which is used during beam search - # to select and reorder the incremental state. - def reorder_incremental_state(self, incremental_state, new_order): - # Load the cached state. - prev_state = utils.get_incremental_state( - self, incremental_state, 'prev_state', - ) - - # Reorder batches according to *new_order*. - reordered_state = ( - prev_state[0].index_select(1, new_order), # hidden - prev_state[1].index_select(1, new_order), # cell - ) - - # Update the cached state. - utils.set_incremental_state( - self, incremental_state, 'prev_state', reordered_state, - ) - -Finally, we can rerun generation and observe the speedup: - -.. code-block:: console - - # Before - - > fairseq-generate data-bin/iwslt14.tokenized.de-en \ - --path checkpoints/checkpoint_best.pt \ - --beam 5 \ - --remove-bpe - (...) - | Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s) - | Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146) - - # After - - > fairseq-generate data-bin/iwslt14.tokenized.de-en \ - --path checkpoints/checkpoint_best.pt \ - --beam 5 \ - --remove-bpe - (...) - | Translated 6750 sentences (153132 tokens) in 5.5s (1225.54 sentences/s, 27802.94 tokens/s) - | Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146) diff --git a/audio/safeear/safeear_repo/fairseq_ours/dump_mfcc.sh b/audio/safeear/safeear_repo/fairseq_ours/dump_mfcc.sh deleted file mode 100644 index c95b70eec665a4737670c1854a7708dac7af95b3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/dump_mfcc.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash - -rank=4 -# Set the base command -base_command="python examples/hubert/simple_kmeans/dump_mfcc_feature.py examples/hubert/ train ${rank}" - -# Set the output directory -output_dir="examples/hubert/mfcc_feature" - -# Loop from 0 to 3 -for i in {0..3}; do - # Construct the full command with the current value of i - full_command="${base_command} ${i} ${output_dir}" - - # Run the command - echo "Running command: ${full_command}" - ${full_command} -done - -echo "Done." diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/.gitignore b/audio/safeear/safeear_repo/fairseq_ours/examples/.gitignore deleted file mode 100644 index 1ef816f2cd7b4a9aa7adf8bd5635a644834738f1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -!*/*.sh -!*/*.md diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/.gitignore b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/.gitignore deleted file mode 100644 index 70a255dc918bf4a4242dd7ea5ded63e6848264db..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/.gitignore +++ /dev/null @@ -1,139 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ -runs -data -pretrained_models -projects/mmfusion_* -log_test -third-party -python_log -slurm_snapshot_code -lightning_logs -demos diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/CONFIG.md b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/CONFIG.md deleted file mode 100644 index bbd1403dfafc79ab581c691281ca49de28a4656f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/CONFIG.md +++ /dev/null @@ -1,41 +0,0 @@ -### Config Files Explained - -Taking `projects/mfmmlm.yaml` for example, which run pretraining using masked frame model (MFM) and masked language model (MLM) on a single BERT: - -```yaml -project_dir: mfmmlm # specify the project dir for this baseline. -run_task: - - how2.yaml # run pretraining on how2 when launching `projects/taskmfmmlm.yaml` - - [vtt.yaml, vttcap.yaml, vttqa.yaml, youcook.yaml, youcookcap.yaml, crosstask.yaml, coin.yaml] # run fine-tuning tasks. -base_dir: task # a global template folder to specify each training task. -task_group: - pretrain: # section for pretraining. Most baselines differs in this section. - task_list: - - how2.yaml # reconfig `projects/task/how2.yaml` - dataset: - aligner: MFMMLMAligner # overwrite the aligner for MFMMLM training task. - model: - model_cls: MMFusionMFMMLM # overwrite the model, which constructs negative examples for MFM on-the-fly. - loss: - loss_cls: MFMMLM # overwrite the loss as MFMMLM, which combines MFM and MLM together. - fairseq: # all fairseq args can be expecified under this name. - dataset: - batch_size: 128 - finetune: # section for fine-tuning tasks, we don't need to change anything here mostly since we want to see how pretraining can contribute to finetuning. - task_list: # specify the list of downstream tasks, e.g., copy `projects/task/vtt.yaml` to `projects/mfmmlm`. - - vtt.yaml - - vttqa.yaml - - youcook.yaml - - youcookcap.yaml - - crosstask.yaml - - coin.yaml - test: # section for testing. - task_list: - - test_vtt.yaml - - test_vttqa.yaml - - test_youcook.yaml - - test_youcookcap.yaml - - test_crosstask.yaml - - test_crosstask_zs.yaml - - test_coin.yaml -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/DATASET.md b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/DATASET.md deleted file mode 100644 index 930403eb3651a14dfcd015d094383faeb052c8ac..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/DATASET.md +++ /dev/null @@ -1,34 +0,0 @@ -# Dataset - -We understand video data are challenging to download and process. For videos, we provide our preprocessing scripts under `scripts/video_feature_extractor` (deeply adapted from `https://github.com/antoine77340/video_feature_extractor`); for text, we pre-tokenizing scripts under `scripts/text_token_extractor`. - -### S3D Feature Extraction -We use pre-trained [S3D](https://github.com/antoine77340/S3D_HowTo100M) for video feature extraction. Please place the models as `pretrained_models/s3d_dict.npy` and `pretrained_models/s3d_howto100m.pth`. - -We implement a `PathBuilder` to automatically track video ids, source video paths to their feature locations (you may need `conda install -c anaconda pandas`). Decoding may need `pip install ffmpeg-python`. - -### Howto100M -[Howto100M](https://www.di.ens.fr/willow/research/howto100m/) is a large-scale video pre-training datasets. You may download videos by yourself and run preprocessing of our scripts. - -Several key differences of our preprocessing from existing papers: (1) we use `raw_caption.json` instead of `caption.json` to have pure self-supervision on text (`caption.json` has manual removal of stop words); (2) we remove partially duplicated texts that are originally designed for real-time readability (see `mmpt/processors/dedupprocessor.py`); (3) then we shard video/text features using `SharedTensor` in `mmpt/utils/shardedtensor.py` for fast loading during training (faster than `h5py`). - -#### Steps -##### video -To extract video features: edit and run `bash scripts/video_feature_extractor/how2/s3d.sh`. (consider to run this on multiple machines; by default, we store features in fp16 to save space and also for faster training). - -Split available video ids as `data/how2/how2_s3d_train.lst` and `data/how2/how2_s3d_val.lst`. - -Lastly, pack video features into `ShardedTensor` using `python scripts/video_feature_extractor/shard_feature.py`. - -##### text -Clean captions using `python -m mmpt.processors.dedupprocessor`. - -Tokenize dedupped captions `data/how2/raw_caption_dedup.pkl` into sharded numpy arrays: -``` -python scripts/text_token_extractor/pretokenization.py scripts/text_token_extractor/configs/bert-base-uncased.yaml -``` - -### Youcook, MSRVTT etc. -We use the version of Youcook and MSRVTT come with Howto100M and MILNCE. Please download the data to `data/youcook` and `data/msrvtt` accordingly, you can also check `projects/task/youcook.yaml` and `projects/task/vtt.yaml` etc. in details. -We extract features for Youcook, MSRVTT similar to the first step of Howto100M but we read text from meta data directly and perform on-the-fly tokenization. - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/README.md deleted file mode 100644 index 4a84819d9ddc992170296958a9431ff0538a85be..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/README.md +++ /dev/null @@ -1,166 +0,0 @@ -# VideoCLIP and VLM - -You just find this toolkit for multimodal video understanding! It contains implementation of two recent multi-modal video understanding papers [VideoCLIP](https://arxiv.org/pdf/2109.14084.pdf) (EMNLP, 2021) and [VLM](https://aclanthology.org/2021.findings-acl.370.pdf) (ACL Findings, 2021), along with high-performance toolkits that are typically lacking in existing codebase. The toolkit is desigend to contain generic performance-tuned components that can be potentially adapted to other frameworks (we initially use fairseq). - -VideoCLIP is a contrastive learning model for zero-shot transfer to retrieval/classification/sequence labeling style tasks. - - - -VLM is a masked language model style pre-training using only one encoder with masked modality model (MMM) for retrieval/generation/sequence labeling style tasks. - - - -### News -[Oct. 2021] Initial release of implementation for the following papers: -[VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://arxiv.org/pdf/2109.14084.pdf) (Xu et. al., EMNLP 2021) -[VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding](https://aclanthology.org/2021.findings-acl.370.pdf) (Xu et. al., ACL Findings 2021) - - -### Installation -We aim to minimize the dependency of this repo on other packages. -We use fairseq as the main trainer (no models/datasets dependency on fairseq. We will support other trainer in future): -``` -git clone https://github.com/pytorch/fairseq -cd fairseq -pip install -e . # also optionally follow fairseq README for apex installation for fp16 training. -export MKL_THREADING_LAYER=GNU # fairseq may need this for numpy. -``` - -Then install this toolkit: -``` -cd examples/MMPT # MMPT can be in any folder, not necessarily under fairseq/examples. -pip install -e . -``` - -The code is developed under Python=3.8.8, Pytorch=1.8, cuda=11.0 with fairseq=1.0.0a0+af0389f and tested under Python=3.8.8 pytorch=1.9 cuda=11.0 fairseq=1.0.0a0+8e7bc73 during code release. -Most models require `transformers==3.4` for API compatibility `pip install transformers==3.4`. -In addition, some downstream tasks may need `conda install pandas`. - - -### Usage -#### Download Checkpoints -We use pre-trained [S3D](https://github.com/antoine77340/S3D_HowTo100M) for video feature extraction. Please place the models as `pretrained_models/s3d_dict.npy` and `pretrained_models/s3d_howto100m.pth`. - -Download VideoCLIP checkpoint `https://dl.fbaipublicfiles.com/MMPT/retri/videoclip/checkpoint_best.pt` to `runs/retri/videoclip` or VLM checkpoint `https://dl.fbaipublicfiles.com/MMPT/mtm/vlm/checkpoint_best.pt` to `runs/mtm/vlm`. - -#### Demo of Inference -run `python locallaunch.py projects/retri/videoclip.yaml --dryrun` to get all `.yaml`s for VideoCLIP. - -```python -import torch - -from mmpt.models import MMPTModel - - -model, tokenizer, aligner = MMPTModel.from_pretrained( - "projects/retri/videoclip/how2.yaml") - -model.eval() - - -# B, T, FPS, H, W, C (VideoCLIP is trained on 30 fps of s3d) -video_frames = torch.randn(1, 2, 30, 224, 224, 3) -caps, cmasks = aligner._build_text_seq( - tokenizer("some text", add_special_tokens=False)["input_ids"] -) - -caps, cmasks = caps[None, :], cmasks[None, :] # bsz=1 - -with torch.no_grad(): - output = model(video_frames, caps, cmasks, return_score=True) -print(output["score"]) # dot-product -``` - -#### Data Preparation -See [dataset](DATASET.md) for each dataset. - -#### Global Config for Training Pipeline -We organize a global config file for a training/testing pipeline under projects (see a detailed [explanation](CONFIG.md)). For example, VideoCLIP in `projects/retri/videoclip.yaml` and VLM is in `projects/mtm/vlm.yaml`. - -We wrap all cmds into `locallaunch.py` and `mmpt_cli/localjob.py`. You can check concrete cmds by `--dryrun` and then drop it for actual run. - -First, run `python locallaunch.py projects/retri/videoclip.yaml --dryrun` will generate configs for all configs of pre-training, zero-shot evaluation, fine-tuning and testing, for VideoCLIP under `projects/retri/videoclip`. - -Then each (either training or evaluation) process will be configed by a concrete config file (we save all complex arguments into the concrete config file for reproducibility, including fairseq args). For example, run zero-shot evaluation on youcook, -``` -python locallaunch.py projects/retri/videoclip/test_youcook_zs.yaml --jobtype local_predict # zero-shot evaluation. -python locallaunch.py projects/retri/videoclip/youcook_videoclip.yaml --jobtype local_single --dryrun # fine-tuning: use --dryrun to check cmds and drop it to make an actual run; local_small will run on two gpus (as in paper). -python locallaunch.py projects/retri/videoclip/test_youcook_videoclip.yaml --jobtype local_predict # testing on fine-tuned model. -``` - -Pretraining can be run as: -``` -python locallaunch.py projects/retri/videoclip/how2.yaml --jobtype local_single --dryrun # check then drop dryrun; paper is ran on local_big as 8 gpus. -``` -You may need to change `--jobtype`, check/extend `LocalJob` in `mmpt_cli/localjob.py` for multi-gpu/multi-node pre-training. - -The detailed instructions of pretraining and fine-tuning can be found at [pretraining instruction](pretraining.md) and [finetuning instruction](endtask.md). - - -### Development -Several components of this toolkit can be re-used for future research (and also our ongoing research). - -#### Framework Wrapper -We currently only support fairseq, but most components can be easily fit into other frameworks like huggingface. This repo is a `--user-dir` of fairseq with fairseq wrapper. For example, `mmpt/tasks` includes a `FairseqMMTTask`, which manages `mmpt/datasets` with `FairseqDataset`, `mmpt/models` with `FairseqModel`, `mmpt/losses` with `FairseqCriterion`. - -#### Processors -**Multi**modal research introduces the complexity on modality alignment from different input sources to losses. Inspired by [MMF](https://github.com/facebookresearch/mmf), this toolkit leverages `mmpt/processors` to handle various needs of data preprocessing and loading, **alleviating** the needs of multiple `torch.data.utils.Dataset` (that can be tricky for ablation study). -Processors can also be decoupled from `torch.data.utils.Dataset` for offline preprocessing instead of on-the-fly data preprocessing. - -We decouple a `mmpt.MMDataset` as 3 types of processors: `MetaProcessor`, `VideoProcessor`, `TextProcessor` and `Aligner`. They can be configed in `dataset` field of a config file (e.g., see `projects/task/how2.yaml`). -`MetaProcessor` is used to load the meta data about a dataset, aka, all video_ids of how2 dataset. -`VideoProcessor` is used to load the video features about a dataset. For example, S3D features for each second of a video. -`TextProcessor` is used to load the text (feature). For example, BERT pre-tokenized text clips for how2 dataset (with `start`s, `end`s of timestamps and `cap` for `token_ids`). -`Aligner` is the core class for different baselines that prepares the training data. For example, sampling a clip, masking tokens for MLM, etc. - -#### Performance-tuned Components -To speed up pre-training, this toolkit uses sharded features stored in mmaped numpy, backed by `ShardedTensor` in `mmpt/utils/shardedtensor.py` (adopted from MARGE paper). This reduces the loads of IO for multi-GPU training without loading all features for a video into the memory each time and `ShardedTensor` ensure features are stored in continuous disk space for near random access. This is used for both How2 video features and texts in `mmpt/processors/how2processor.py`. - - -### Citation -If this codebase is useful for your work, please cite the following papers: - -```BibTeX -@inproceedings{xu-etal-2021-videoclip, - title = "{VideoCLIP}: Contrastive Pre-training for\\Zero-shot Video-Text Understanding", - author = "Xu, Hu and - Ghosh, Gargi and - Huang, Po-Yao and - Okhonko, Dmytro and - Aghajanyan, Armen and - Metze, Florian and - Zettlemoyer, Luke and - Feichtenhofer, Christoph", - booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", - month = nov, - year = "2021", - address = "Online", - publisher = "Association for Computational Linguistics", -} - -@inproceedings{xu-etal-2021-vlm, - title = "{VLM}: Task-agnostic Video-Language Model Pre-training for Video Understanding", - author = "Xu, Hu and - Ghosh, Gargi and - Huang, Po-Yao and - Arora, Prahal and - Aminzadeh, Masoumeh and - Feichtenhofer, Christoph and - Metze, Florian and - Zettlemoyer, Luke", - booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", - month = aug, - year = "2021", - address = "Online", - publisher = "Association for Computational Linguistics", - url = "https://aclanthology.org/2021.findings-acl.370", - doi = "10.18653/v1/2021.findings-acl.370", - pages = "4227--4239", -} -``` - -### Bug Reports -This repo is in its initial stage, welcome bug reports to huxu@fb.com - -### Copyright -The majority of Multimodal Pre-training (MMPT) is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Evaluation Codes/Models: Howto100M and HuggingFace Transformers are licensed under the Apache2.0 license; COIN and NLG-eval are licensed under the MIT license; CrossTask is licensed under the BSD-3; DiDeMo is licensed under the BSD-2 license. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/endtask.md b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/endtask.md deleted file mode 100644 index 7690955327283ded4b37857a4a7b78463e0eb0f8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/endtask.md +++ /dev/null @@ -1,41 +0,0 @@ -# Zero-shot Transfer and Finetuning - -(If you are new to the ideas of `mmpt.processors`, see [README](README.md) first.) -All finetuning datasets (specifically `processors`) are defined in `mmpt.processors.dsprocessor`. -Given the complexity of different types of finetuning tasks, each task may have their own meta/video/text/aligner processors and `mmpt/evaluators/{Predictor,Metric}`. - -### Tasks - -Currently, we support 5 end datasets: `MSRVTT`, `Youcook`, `COIN`, `Crosstask` and `DiDeMo` with the following tasks: -text-video retrieval: `MSRVTT`, `Youcook`, `DiDeMo`; -video captioning: `Youcook`; -Video Question and Answering: `MSRVTT-QA`. - -To add your own dataset, you can specify the corresponding processors and config them in the `dataset` field of a config file, such as `projects/task/vtt.yaml`. - -### Zero-shot Transfer (no Training) -Zero-shot transfer will run the pre-trained model (e.g., VideoCLIP) directly on testing data. Configs with pattern: `projects/task/*_zs_*.yaml` are dedicated for zero-shot transfer. - -### Fine-tuning - -The training of a downstream task is similar to pretraining, execept you may need to specify the `restore_file` in `fairseq.checkpoint` and reset optimizers, see `projects/task/ft.yaml` that is included by `projects/task/vtt.yaml`. - -We typically do finetuning on 2 gpus (`local_small`). - -### Testing -For each finetuning dataset, you may need to specify a testing config, similar to `projects/task/test_vtt.yaml`. - -We define `mmpt.evaluators.Predictor` for different types of prediction. For example, `MSRVTT` and `Youcook` are video-retrieval tasks and expecting to use `RetrievalPredictor`. You may need to define your new type of predictors and specify that in `predictor` field of a testing config. - -Each task may also have their own metric for evaluation. This can be created in `mmpt.evaluators.Metric` and specified in the `metric` field of a testing config. - -Launching a testing is as simple as training by specifying the path of a testing config: -```python locallaunch.py projects/mfmmlm/test_vtt.yaml``` -Testing will be launched locally by default since prediction is computationally less expensive. - -### Third-party Libraries -We list the following finetuning tasks that require third-party libraries. - -Youcook captioning: `https://github.com/Maluuba/nlg-eval` - -CrossTask: `https://github.com/DmZhukov/CrossTask`'s `dp` under `third-party/CrossTask` (`python setup.py build_ext --inplace`) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/locallaunch.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/locallaunch.py deleted file mode 100644 index bacee3ce6edf11e244733d0e15628f1461bbfcd8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/locallaunch.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 argparse -import os - -from mmpt.utils import overwrite_dir, recursive_config -from mmpt_cli.localjob import LocalJob -from omegaconf import OmegaConf - - -class JobLauncher(object): - JOB_CONFIG = { - "local": LocalJob, - } - - def __init__(self, yaml_file): - self.yaml_file = yaml_file - job_key = "local" - - if yaml_file.endswith(".yaml"): - config = recursive_config(yaml_file) - if config.task_type is not None: - job_key = config.task_type.split("_")[0] - else: - raise ValueError("unknown extension of job file:", yaml_file) - self.job_key = job_key - - def __call__(self, job_type=None, dryrun=False): - if job_type is not None: - self.job_key = job_type.split("_")[0] - print("[JobLauncher] job_key", self.job_key) - job = JobLauncher.JOB_CONFIG[self.job_key]( - self.yaml_file, job_type=job_type, dryrun=dryrun) - return job.submit() - - -class Pipeline(object): - """a job that loads yaml config.""" - - def __init__(self, fn): - """ - load a yaml config of a job and save generated configs as yaml for each task. - return: a list of files to run as specified by `run_task`. - """ - if fn.endswith(".py"): - # a python command. - self.backend = "python" - self.run_yamls = [fn] - return - - job_config = recursive_config(fn) - if job_config.base_dir is None: # single file job config. - self.run_yamls = [fn] - return - - self.project_dir = os.path.join("projects", job_config.project_dir) - self.run_dir = os.path.join("runs", job_config.project_dir) - - if job_config.run_task is not None: - run_yamls = [] - for stage in job_config.run_task: - # each stage can have multiple tasks running in parallel. - if OmegaConf.is_list(stage): - stage_yamls = [] - for task_file in stage: - stage_yamls.append( - os.path.join(self.project_dir, task_file)) - run_yamls.append(stage_yamls) - else: - run_yamls.append(os.path.join(self.project_dir, stage)) - self.run_yamls = run_yamls - configs_to_save = self._overwrite_task(job_config) - self._save_configs(configs_to_save) - - def __getitem__(self, idx): - yaml_files = self.run_yamls[idx] - if isinstance(yaml_files, list): - return [JobLauncher(yaml_file) for yaml_file in yaml_files] - return [JobLauncher(yaml_files)] - - def __len__(self): - return len(self.run_yamls) - - def _save_configs(self, configs_to_save: dict): - # save - os.makedirs(self.project_dir, exist_ok=True) - for config_file in configs_to_save: - config = configs_to_save[config_file] - print("saving", config_file) - OmegaConf.save(config=config, f=config_file) - - def _overwrite_task(self, job_config): - configs_to_save = {} - self.base_project_dir = os.path.join("projects", job_config.base_dir) - self.base_run_dir = os.path.join("runs", job_config.base_dir) - - for config_sets in job_config.task_group: - overwrite_config = job_config.task_group[config_sets] - if ( - overwrite_config.task_list is None - or len(overwrite_config.task_list) == 0 - ): - print( - "[warning]", - job_config.task_group, - "has no task_list specified.") - # we don't want this added to a final config. - task_list = overwrite_config.pop("task_list", None) - for config_file in task_list: - config_file_path = os.path.join( - self.base_project_dir, config_file) - config = recursive_config(config_file_path) - # overwrite it. - if overwrite_config: - config = OmegaConf.merge(config, overwrite_config) - overwrite_dir(config, self.run_dir, basedir=self.base_run_dir) - save_file_path = os.path.join(self.project_dir, config_file) - configs_to_save[save_file_path] = config - return configs_to_save - - -def main(args): - job_type = args.jobtype if args.jobtype else None - # parse multiple pipelines. - pipelines = [Pipeline(fn) for fn in args.yamls.split(",")] - - for pipe_id, pipeline in enumerate(pipelines): - if not hasattr(pipeline, "project_dir"): - for job in pipeline[0]: - job(job_type=job_type, dryrun=args.dryrun) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("yamls", type=str) - parser.add_argument( - "--dryrun", - action="store_true", - help="run config and prepare to submit without launch the job.", - ) - parser.add_argument( - "--jobtype", type=str, default="", - help="force to run jobs as specified.") - args = parser.parse_args() - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/__init__.py deleted file mode 100644 index 6ff86ddd5ce0c454281e6568c628bd3c49ea5024..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/__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. -try: - # fairseq user dir - from .datasets import FairseqMMDataset - from .losses import FairseqCriterion - from .models import FairseqMMModel - from .tasks import FairseqMMTask -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/__init__.py deleted file mode 100644 index 2578235e1771fdc7e6fcfb66a519cbe891d7e254..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .mmdataset import * - -try: - from .fairseqmmdataset import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/fairseqmmdataset.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/fairseqmmdataset.py deleted file mode 100644 index 25258c9987af2db3efe2b34c57eeb798c1c29cfb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/fairseqmmdataset.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. -""" -TODO (huxu): fairseq wrapper class for all dataset you defined: mostly MMDataset. -""" - -from collections import OrderedDict - -from fairseq.data import FairseqDataset, data_utils -from torch.utils.data import Dataset -from torch.utils.data.dataloader import default_collate - - -class FairseqMMDataset(FairseqDataset): - """ - A wrapper class for MMDataset for fairseq. - """ - - def __init__(self, mmdataset): - if not isinstance(mmdataset, Dataset): - raise TypeError("mmdataset must be of type `torch.utils.data.dataset`.") - self.mmdataset = mmdataset - - def set_epoch(self, epoch, **unused): - super().set_epoch(epoch) - self.epoch = epoch - - def __getitem__(self, idx): - with data_utils.numpy_seed(43211, self.epoch, idx): - return self.mmdataset[idx] - - def __len__(self): - return len(self.mmdataset) - - def collater(self, samples): - if hasattr(self.mmdataset, "collator"): - return self.mmdataset.collator(samples) - if len(samples) == 0: - return {} - if isinstance(samples[0], dict): - batch = OrderedDict() - for key in samples[0]: - if samples[0][key] is not None: - batch[key] = default_collate([sample[key] for sample in samples]) - return batch - else: - return default_collate(samples) - - def size(self, index): - """dummy implementation: we don't use --max-tokens""" - return 1 - - def num_tokens(self, index): - """dummy implementation: we don't use --max-tokens""" - return 1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/mmdataset.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/mmdataset.py deleted file mode 100644 index 989992129e4c790cd0ab17da9654a3353a13d2e6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/datasets/mmdataset.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. - -from collections import OrderedDict - -import torch -from torch.utils.data import Dataset -from torch.utils.data.dataloader import default_collate - -from ..utils import set_seed - - -class MMDataset(Dataset): - """ - A generic multi-modal dataset. - Args: - `meta_processor`: a meta processor, - handling loading meta data and return video_id and text_id. - `video_processor`: a video processor, - handling e.g., decoding, loading .np files. - `text_processor`: a text processor, - handling e.g., tokenization. - `aligner`: combine the video and text feature - as one training example. - """ - - def __init__( - self, - meta_processor, - video_processor, - text_processor, - align_processor, - ): - self.split = meta_processor.split - self.meta_processor = meta_processor - self.video_processor = video_processor - self.text_processor = text_processor - self.align_processor = align_processor - - def __len__(self): - return len(self.meta_processor) - - def __getitem__(self, idx): - if self.split == "test": - set_seed(idx) - video_id, text_id = self.meta_processor[idx] - video_feature = self.video_processor(video_id) - text_feature = self.text_processor(text_id) - output = self.align_processor(video_id, video_feature, text_feature) - # TODO (huxu): the following is for debug purpose. - output.update({"idx": idx}) - return output - - def collater(self, samples): - """This collator is deprecated. - set self.collator = MMDataset.collater. - see collator in FairseqMMDataset. - """ - - if len(samples) == 0: - return {} - if isinstance(samples[0], dict): - batch = OrderedDict() - for key in samples[0]: - if samples[0][key] is not None: - batch[key] = default_collate( - [sample[key] for sample in samples]) - # if torch.is_tensor(batch[key]): - # print(key, batch[key].size()) - # else: - # print(key, len(batch[key])) - return batch - else: - return default_collate(samples) - - def print_example(self, output): - print("[one example]", output["video_id"]) - if ( - hasattr(self.align_processor, "subsampling") - and self.align_processor.subsampling is not None - and self.align_processor.subsampling > 1 - ): - for key in output: - if torch.is_tensor(output[key]): - output[key] = output[key][0] - - # search tokenizer to translate ids back. - tokenizer = None - if hasattr(self.text_processor, "tokenizer"): - tokenizer = self.text_processor.tokenizer - elif hasattr(self.align_processor, "tokenizer"): - tokenizer = self.align_processor.tokenizer - if tokenizer is not None: - caps = output["caps"].tolist() - if isinstance(caps[0], list): - caps = caps[0] - print("caps", tokenizer.decode(caps)) - print("caps", tokenizer.convert_ids_to_tokens(caps)) - - for key, value in output.items(): - if torch.is_tensor(value): - if len(value.size()) >= 3: # attention_mask. - print(key, value.size()) - print(key, "first", value[0, :, :]) - print(key, "last", value[-1, :, :]) - else: - print(key, value) - print("[end of one example]") diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/__init__.py deleted file mode 100644 index 5f7ab5b2f20e7e4decd61f9fb1a5deaf3b7b972c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/__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 .evaluator import * -from .metric import * - -# experimental. -try: - from .expmetric import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/evaluator.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/evaluator.py deleted file mode 100644 index b7c365463f35d1e7138f13c0ec804e018f3e2ffc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/evaluator.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 glob -import os - -import numpy as np - -from . import metric as metric_path -from . import predictor as predictor_path - - -class Evaluator(object): - """ - perform evaluation on a single (downstream) task. - make this both offline and online. - TODO(huxu) saving evaluation results. - """ - - def __init__(self, config, eval_dataloader=None): - if config.metric is None: - raise ValueError("config.metric is", config.metric) - metric_cls = getattr(metric_path, config.metric) - self.metric = metric_cls(config) - if config.predictor is None: - raise ValueError("config.predictor is", config.predictor) - predictor_cls = getattr(predictor_path, config.predictor) - self.predictor = predictor_cls(config) - self.eval_dataloader = eval_dataloader - - def __call__(self): - try: - print(self.predictor.pred_dir) - for pred_file in glob.glob( - self.predictor.pred_dir + "/*_merged.npy"): - outputs = np.load(pred_file) - results = self.metric.compute_metrics(outputs) - self.metric.print_computed_metrics(results) - - outputs = np.load(os.path.join( - self.predictor.pred_dir, "merged.npy")) - results = self.metric.compute_metrics(outputs) - return {"results": results, "metric": self.metric} - except FileNotFoundError: - print("\n[missing]", self.predictor.pred_dir) - return {} - - def evaluate(self, model, eval_dataloader=None, output_file="merged"): - if eval_dataloader is None: - eval_dataloader = self.eval_dataloader - outputs = self.predictor.predict_loop( - model, eval_dataloader, output_file) - results = self.metric.compute_metrics(**outputs) - return results diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/metric.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/metric.py deleted file mode 100644 index ac0c8ac6c9605ca54bfece675e9c215e6896303f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/metric.py +++ /dev/null @@ -1,314 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 numpy as np - - -class Metric(object): - def __init__(self, config, metric_names): - self.metric_names = metric_names - - def best_metric(self, metric): - return metric[self.metric_names[0]] - - def save_metrics(self, fn, metrics): - with open(fn, "w") as fw: - json.dump(fw, metrics) - - def print_computed_metrics(self, metrics): - raise NotImplementedError - - -class RetrievalMetric(Metric): - """ - this is modified from `howto100m/metrics.py`. - History of changes: - refactor as a class. - add metric_key in __init__ - """ - - def __init__(self, config, metric_names=["R1", "R5", "R10", "MR"]): - super().__init__(config, metric_names) - self.error = False # TODO(huxu): add to config to print error. - - def compute_metrics(self, outputs, texts, **kwargs): - x = outputs - sx = np.sort(-x, axis=1) - d = np.diag(-x) - d = d[:, np.newaxis] - ind = sx - d - ind = np.where(ind == 0) - ind = ind[1] - metrics = {} - metrics["R1"] = float(np.sum(ind == 0)) / len(ind) - metrics["R5"] = float(np.sum(ind < 5)) / len(ind) - metrics["R10"] = float(np.sum(ind < 10)) / len(ind) - metrics["MR"] = np.median(ind) + 1 - - max_idx = np.argmax(outputs, axis=1) - if self.error: - # print top-20 errors. - error = [] - for ex_idx in range(20): - error.append((texts[ex_idx], texts[max_idx[ex_idx]])) - metrics["error"] = error - return metrics - - def print_computed_metrics(self, metrics): - r1 = metrics["R1"] - r5 = metrics["R5"] - r10 = metrics["R10"] - mr = metrics["MR"] - print( - "R@1: {:.4f} - R@5: {:.4f} - R@10: {:.4f} - Median R: {}".format( - r1, r5, r10, mr - ) - ) - if "error" in metrics: - print(metrics["error"]) - - -class DiDeMoMetric(Metric): - """ - History of changes: - python 2.x to python 3.x. - merge utils.py into eval to save one file. - reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py - Code to evaluate your results on the DiDeMo dataset. - """ - def __init__(self, config, metric_names=["rank1", "rank5", "miou"]): - super().__init__(config, metric_names) - - def compute_metrics(self, outputs, targets, **kwargs): - assert len(outputs) == len(targets) - rank1, rank5, miou = self._eval_predictions(outputs, targets) - metrics = { - "rank1": rank1, - "rank5": rank5, - "miou": miou - } - return metrics - - def print_computed_metrics(self, metrics): - rank1 = metrics["rank1"] - rank5 = metrics["rank5"] - miou = metrics["miou"] - # print("Average rank@1: %f" % rank1) - # print("Average rank@5: %f" % rank5) - # print("Average iou: %f" % miou) - - print( - "Average rank@1: {:.4f} Average rank@5: {:.4f} Average iou: {:.4f}".format( - rank1, rank5, miou - ) - ) - - def _iou(self, pred, gt): - intersection = max(0, min(pred[1], gt[1]) + 1 - max(pred[0], gt[0])) - union = max(pred[1], gt[1]) + 1 - min(pred[0], gt[0]) - return float(intersection)/union - - def _rank(self, pred, gt): - return pred.index(tuple(gt)) + 1 - - def _eval_predictions(self, segments, data): - ''' - Inputs: - segments: For each item in the ground truth data, rank possible video segments given the description and video. - In DiDeMo, there are 21 posible moments extracted for each video so the list of video segments will be of length 21. - The first video segment should be the video segment that best corresponds to the text query. - There are 4180 sentence in the validation data, so when evaluating a model on the val dataset, - segments should be a list of lenght 4180, and each item in segments should be a list of length 21. - data: ground truth data - ''' - average_ranks = [] - average_iou = [] - for s, d in zip(segments, data): - pred = s[0] - ious = [self._iou(pred, t) for t in d['times']] - average_iou.append(np.mean(np.sort(ious)[-3:])) - ranks = [self._rank(s, t) for t in d['times'] if tuple(t) in s] # if t in s] is added for s, e not in prediction. - average_ranks.append(np.mean(np.sort(ranks)[:3])) - rank1 = np.sum(np.array(average_ranks) <= 1)/float(len(average_ranks)) - rank5 = np.sum(np.array(average_ranks) <= 5)/float(len(average_ranks)) - miou = np.mean(average_iou) - - # print("Average rank@1: %f" % rank1) - # print("Average rank@5: %f" % rank5) - # print("Average iou: %f" % miou) - return rank1, rank5, miou - - -class NLGMetric(Metric): - def __init__( - self, - config, - metric_names=[ - "Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4", - "METEOR", "ROUGE_L", "CIDEr" - ] - ): - super().__init__(config, metric_names) - # please install NLGEval from `https://github.com/Maluuba/nlg-eval` - from nlgeval import NLGEval - self.nlg = NLGEval() - - def compute_metrics(self, outputs, targets, **kwargs): - return self.nlg.compute_metrics( - hyp_list=outputs, ref_list=targets) - - def print_computed_metrics(self, metrics): - Bleu_1 = metrics["Bleu_1"] - Bleu_2 = metrics["Bleu_2"] - Bleu_3 = metrics["Bleu_3"] - Bleu_4 = metrics["Bleu_4"] - METEOR = metrics["METEOR"] - ROUGE_L = metrics["ROUGE_L"] - CIDEr = metrics["CIDEr"] - - print( - "Bleu_1: {:.4f} - Bleu_2: {:.4f} - Bleu_3: {:.4f} - Bleu_4: {:.4f} - METEOR: {:.4f} - ROUGE_L: {:.4f} - CIDEr: {:.4f}".format( - Bleu_1, Bleu_2, Bleu_3, Bleu_4, METEOR, ROUGE_L, CIDEr - ) - ) - - -class QAMetric(Metric): - def __init__( - self, - config, - metric_names=["acc"] - ): - super().__init__(config, metric_names) - - def compute_metrics(self, outputs, targets, **kwargs): - from sklearn.metrics import accuracy_score - return {"acc": accuracy_score(targets, outputs)} - - def print_computed_metrics(self, metrics): - print("acc: {:.4f}".format(metrics["acc"])) - - -class COINActionSegmentationMetric(Metric): - """ - COIN dataset listed 3 repos for Action Segmentation. - Action Sets, NeuralNetwork-Viterbi, TCFPN-ISBA. - The first and second are the same. - https://github.com/alexanderrichard/action-sets/blob/master/eval.py - - Future reference for the third: - `https://github.com/Zephyr-D/TCFPN-ISBA/blob/master/utils/metrics.py` - """ - def __init__(self, config, metric_name=["frame_acc"]): - super().__init__(config, metric_name) - - def compute_metrics(self, outputs, targets): - n_frames = 0 - n_errors = 0 - n_errors = sum(outputs != targets) - n_frames = len(targets) - return {"frame_acc": 1.0 - float(n_errors) / n_frames} - - def print_computed_metrics(self, metrics): - fa = metrics["frame_acc"] - print("frame accuracy:", fa) - - -class CrossTaskMetric(Metric): - def __init__(self, config, metric_names=["recall"]): - super().__init__(config, metric_names) - - def compute_metrics(self, outputs, targets, **kwargs): - """refactored from line 166: - https://github.com/DmZhukov/CrossTask/blob/master/train.py""" - - recalls = self._get_recalls(Y_true=targets, Y_pred=outputs) - results = {} - for task, rec in recalls.items(): - results[str(task)] = rec - - avg_recall = np.mean(list(recalls.values())) - results["recall"] = avg_recall - return results - - def print_computed_metrics(self, metrics): - print('Recall: {0:0.3f}'.format(metrics["recall"])) - for task in metrics: - if task != "recall": - print('Task {0}. Recall = {1:0.3f}'.format( - task, metrics[task])) - - def _get_recalls(self, Y_true, Y_pred): - """refactored from - https://github.com/DmZhukov/CrossTask/blob/master/train.py""" - - step_match = {task: 0 for task in Y_true.keys()} - step_total = {task: 0 for task in Y_true.keys()} - for task, ys_true in Y_true.items(): - ys_pred = Y_pred[task] - for vid in set(ys_pred.keys()).intersection(set(ys_true.keys())): - y_true = ys_true[vid] - y_pred = ys_pred[vid] - step_total[task] += (y_true.sum(axis=0) > 0).sum() - step_match[task] += (y_true*y_pred).sum() - recalls = { - task: step_match[task] / n for task, n in step_total.items()} - return recalls - - -class ActionRecognitionMetric(Metric): - def __init__( - self, - config, - metric_names=["acc", "acc_splits", "r1_splits", "r5_splits", "r10_splits"] - ): - super().__init__(config, metric_names) - - def compute_metrics(self, outputs, targets, splits, **kwargs): - all_video_embd = outputs - labels = targets - split1, split2, split3 = splits - accs = [] - r1s = [] - r5s = [] - r10s = [] - for split in range(3): - if split == 0: - s = split1 - elif split == 1: - s = split2 - else: - s = split3 - - X_pred = all_video_embd[np.where(s == 2)[0]] - label_test = labels[np.where(s == 2)[0]] - logits = X_pred - X_pred = np.argmax(X_pred, axis=1) - acc = np.sum(X_pred == label_test) / float(len(X_pred)) - accs.append(acc) - # compute recall. - sorted_pred = (-logits).argsort(axis=-1) - label_test_sp = label_test.reshape(-1, 1) - - r1 = np.mean((sorted_pred[:, :1] == label_test_sp).sum(axis=1), axis=0) - r5 = np.mean((sorted_pred[:, :5] == label_test_sp).sum(axis=1), axis=0) - r10 = np.mean((sorted_pred[:, :10] == label_test_sp).sum(axis=1), axis=0) - r1s.append(r1) - r5s.append(r5) - r10s.append(r10) - - return {"acc": accs[0], "acc_splits": accs, "r1_splits": r1s, "r5_splits": r5s, "r10_splits": r10s} - - def print_computed_metrics(self, metrics): - for split, acc in enumerate(metrics["acc_splits"]): - print("Top 1 accuracy on split {}: {}; r1 {}; r5 {}; r10 {}".format( - split + 1, acc, - metrics["r1_splits"][split], - metrics["r5_splits"][split], - metrics["r10_splits"][split], - ) - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/predictor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/predictor.py deleted file mode 100644 index 21e7d08c446baed977916c0da76e53725427cc14..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/evaluators/predictor.py +++ /dev/null @@ -1,596 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 math -import os -import pickle -import random - -import numpy as np -import torch -from tqdm import tqdm - - -class Predictor(object): - """this base class is used to save predictions to disk - (and being called by a evaluator later). - Predictor has minimum support of single gpu prediction. - """ - def __init__(self, config): - self.pred_dir = None # on-the-fly eval does not save the results. - if hasattr(config, "eval") and config.eval is not None: - self.pred_dir = config.eval.save_path - os.makedirs(self.pred_dir, exist_ok=True) - - def __call__(self, outputs): - """extract the prediction and save it.""" - raise NotImplementedError - - def predict_loop(self, model, eval_dataloader, output_file=None): - """on-the-fly prediction on a single gpu.""" - self.full_scores = [] - model.eval() - model = model.to(0) - with torch.no_grad(): - for data in eval_dataloader: - data = self.to_ctx(data) - outputs = model(**data) - outputs.update(data) - self(outputs) - return self.finalize(output_file) - - def finalize(self, output_file): - pass - - def to_ctx(self, data, ctx=0, dtype=None): - if isinstance(data, dict): - for key in data: - if torch.is_tensor(data[key]): - if dtype is not None and data[key].dtype == torch.float32: - data[key] = data[key].to(dtype) - data[key] = data[key].to(ctx) - return data - else: - raise ValueError("non-dict type of batch is not supported yet.") - - -class NLGPredictor(Predictor): - """Predicting Text from MMFusion models.""" - """TODO: make a context.""" - def __init__(self, config): - super().__init__(config) - from transformers import AutoTokenizer - - self.tokenizer = AutoTokenizer.from_pretrained( - config.dataset.bert_name, - bos_token="[CLS]", eos_token="[SEP]") - self.bos_token_id = self.tokenizer.bos_token_id - self.eos_token_id = self.tokenizer.eos_token_id - - def predict_loop(self, model, eval_dataloader, output_file=None): - """TODO: refactor base classes.""" - ctx = 0 - outputs = {"outputs": [], "targets": [[]]} - model.eval() - model = model.to(ctx) - with torch.no_grad(): - for data in tqdm(eval_dataloader): - data = self.to_ctx(data, ctx) - self(data, model, outputs) - return self.finalize(outputs, output_file) - - def __call__(self, data, model, outputs): - data.update({ - "bos_token_id": self.bos_token_id, - "eos_token_id": self.eos_token_id - }) - - output = model.generate(**data) - assert len(output) == len(data["ref"]) - for idx, _output in enumerate(output): - generated_text = self.tokenizer.decode( - _output, skip_special_tokens=True) - if generated_text == "": - generated_text = "none" - outputs["outputs"].append(generated_text) - outputs["targets"][0].append(data["ref"][idx]) - if random.random() < 0.001: - print("_output", _output) - print("generated_text", generated_text) - print("ref", data["ref"][idx]) - - def finalize(self, outputs, output_file=None): - if output_file is not None: - with open(os.path.join( - self.pred_dir, output_file + ".json"), "w") as fw: - json.dump(outputs, fw, indent=4) - return outputs - - -class RetrievalPredictor(Predictor): - """generated `pooled_video` and `pooled_text`.""" - def __init__(self, config): - super().__init__(config) - from transformers import AutoTokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - config.dataset.bert_name) - - def predict_loop( - self, - model, - eval_dataloader, - output_file="retrieval.npy" - ): - """on-the-fly prediction on a single gpu.""" - full_scores = [] - texts = [] - model.eval() - model = model.cuda() - with torch.no_grad(): - for data in eval_dataloader: - # convert to dict. - if not isinstance(data, dict): - data = { - "caps": data[0], - "cmasks": data[1], - "vfeats": data[2], - "vmasks": data[3], - "video_id": data[4] - } - data = self.to_ctx(data) - outputs = model(**data) - outputs.update(data) - self(outputs, full_scores) - for _cap in data["caps"]: - texts.append( - self.tokenizer.decode(_cap, skip_special_tokens=True) - ) - - return self.finalize(full_scores, texts, output_file) - - def __call__(self, sample, full_scores): - scores = self._get_pooled_outputs(sample) - self._append_scores(scores, full_scores) - - def finalize(self, full_scores, texts, output_file=None): - outputs = self._aggregate_scores(full_scores) - if output_file is not None: - np.save(os.path.join(self.pred_dir, output_file + ".npy"), outputs) - return {"outputs": outputs, "texts": texts} - - def _get_pooled_outputs(self, outputs): - if "pooled_video" in outputs: - return outputs["pooled_video"], outputs["pooled_text"] - else: - raise ValueError("unknown format of outputs.") - - def _append_scores(self, scores, full_scores): - assert len(scores) == 2 - if len(full_scores) == 0: - full_scores.append([]) - full_scores.append([]) - full_scores[0].append(scores[0].cpu().detach().numpy()) - full_scores[1].append(scores[1].cpu().detach().numpy()) - - def _aggregate_scores(self, scores): - assert len(scores) == 2 - video_hidden = np.concatenate(scores[0], axis=0) - text_hidden = np.concatenate(scores[1], axis=0) - # clear up. - self.full_scores = [] - return np.matmul(text_hidden, video_hidden.T) - - -class QAPredictor(Predictor): - """generated `pooled_video` and `pooled_text`.""" - def __init__(self, config): - super().__init__(config) - """predictor maintains scores and aggregate them.""" - - def predict_loop(self, model, eval_dataloader, output_file="qa.npy"): - """on-the-fly prediction on a single gpu.""" - self.full_scores = [] - model.eval() - model = model.cuda() - with torch.no_grad(): - for data in eval_dataloader: - # reshape ans and dup video 5 times. - v_len = data["vfeats"].size(1) - hidden_size = data["vfeats"].size(2) - data["vfeats"] = data["vfeats"].unsqueeze(1).repeat(1, 5, 1, 1).view(-1, v_len, hidden_size) - data["vmasks"] = data["vmasks"].unsqueeze(1).repeat(1, 5, 1).view(-1, v_len) - - t_len = data["caps"].size(-1) - data["caps"] = data["caps"].view(-1, t_len) - data["cmasks"] = data["cmasks"].view(-1, t_len) - - data = self.to_ctx(data) - outputs = model(**data) - outputs.update(data) - self(outputs) - return self.finalize(output_file) - - def __call__(self, sample): - hidden_size = sample["pooled_video"].size(-1) - pooled_video = sample["pooled_video"].view(-1, 5, hidden_size) - pooled_text = sample["pooled_text"].view(-1, 5, hidden_size) - scores = torch.bmm(pooled_video, pooled_text.transpose(2, 1)) - scores = scores.argmax(-1) - self._append_scores(scores[:, 0], sample["answers"], self.full_scores) - - def finalize(self, output_file=None): - outputs, targets = self._aggregate_scores(self.full_scores) - if output_file is not None: - np.save(os.path.join(self.pred_dir, output_file + ".npy"), outputs) - return {"outputs": outputs, "targets": targets} - - def _append_scores(self, scores, answers, full_scores): - if len(full_scores) == 0: - full_scores.append([]) - full_scores.append([]) - full_scores[0].append(scores.cpu().detach().numpy()) - full_scores[1].append(answers.cpu().detach().numpy()) - - def _aggregate_scores(self, scores): - assert len(scores) == 2 - outputs = np.concatenate(scores[0], axis=0) - targets = np.concatenate(scores[1], axis=0) - # clear up. - self.full_scores = [] - return outputs, targets - - -class CrossTaskPredictor(Predictor): - """ - CrossTaskPredictor needs to compute the average of logits - for overlapped sliding-window. - """ - def __init__(self, config): - super().__init__(config) - self.lsm = torch.nn.LogSoftmax(dim=1) - self.max_video_len = config.dataset.max_video_len - self.sliding_window = config.dataset.sliding_window - self.sliding_window_size = config.dataset.sliding_window_size - self.annotation_path = config.dataset.annotation_path - - def predict_loop(self, model, eval_dataloader, output_file="result.pkl"): - """refactored from line 144: - https://github.com/DmZhukov/CrossTask/blob/master/train.py - """ - ctx = 0 - model.eval() - model = model.to(ctx) - # this is not a loss but just compute neg_log_prob. - Y_pred = {} - Y_true = {} - with torch.no_grad(): - for batch in eval_dataloader: - self(batch, model, Y_pred, Y_true) - return self.finalize(Y_pred, Y_true, output_file) - - def __call__(self, sample, model, Y_pred, Y_true): - # please install dp from `https://github.com/DmZhukov/CrossTask` - from dp import dp - vid, task = sample['video_id'][0], sample['task'][0] - sample = self.to_ctx(sample) - # compute the average logits over sliding windows. - output = model(**sample) - batch_logits = output["logits"].cpu() - - video_len = sample["video_len"][0] - - # the following version is slow. - logits = torch.zeros((video_len, batch_logits.size(1))) - logits_counts = torch.zeros((video_len, 1), dtype=torch.long) - # use the same loop as aligner to recover. - batch_logit_idx = 0 - for window_start in range(0, video_len, self.sliding_window): - video_end = min(video_len - window_start, self.sliding_window_size) - logits[window_start: window_start + video_end] += batch_logits[ - batch_logit_idx: batch_logit_idx + video_end] - batch_logit_idx += video_end - logits_counts[window_start: window_start + video_end] += torch.ones((video_end, 1), dtype=torch.long) - - if (video_len - window_start) <= self.sliding_window_size: - break - - logits /= logits_counts - assert logits.size() == (video_len, batch_logits.size(1)), "{}, {}".format(logits.size(), video_len) - - O = self.lsm(logits) - y = np.zeros(O.size(), dtype=np.float32) - dp(y, -O.detach().cpu().numpy()) - if task not in Y_pred: - Y_pred[task] = {} - Y_pred[task][vid] = y - annot_path = os.path.join( - self.annotation_path, task+'_'+vid+'.csv') - if os.path.exists(annot_path): - if task not in Y_true: - Y_true[task] = {} - Y_true[task][vid] = self._read_assignment( - *y.shape, annot_path) - - def finalize(self, Y_pred, Y_true, output_file=None): - if output_file is not None: - with open( - os.path.join(self.pred_dir, output_file + ".pkl"), - "wb") as fw: - pickle.dump( - {"Y_pred": Y_pred, "Y_true": Y_true}, fw, - protocol=pickle.HIGHEST_PROTOCOL) - return {"outputs": Y_pred, "targets": Y_true} - - def _read_assignment(self, T, K, path): - """ - refactored from https://github.com/DmZhukov/CrossTask/blob/master/data.py - Howto interpret contraints on loss that is going to be minimized: - lambd is a big number; - self.lambd * C is a big number for all valid position (csv stores invalids) - - def forward(self, O, Y, C): - return (Y*(self.lambd * C - self.lsm(O))).mean(dim=0).sum() - - This will load the csv file and fill-in the step col from start to end rows. - """ - - Y = np.zeros([T, K], dtype=np.uint8) - with open(path, 'r') as f: - for line in f: - step, start, end = line.strip().split(',') - start = int(math.floor(float(start))) - end = int(math.ceil(float(end))) - step = int(step) - 1 - Y[start:end, step] = 1 - return Y - - -class COINPredictor(Predictor): - """ - COINPredictor is similar to CrossTask on sliding windows. - """ - def __init__(self, config): - super().__init__(config) - self.max_video_len = config.dataset.max_video_len - self.sliding_window = config.dataset.sliding_window - self.sliding_window_size = config.dataset.sliding_window_size - - def predict_loop(self, model, eval_dataloader, output_file="result.pkl"): - """refactored from line 144: - https://github.com/DmZhukov/CrossTask/blob/master/train.py - """ - ctx = 0 - model.eval() - model = model.to(ctx) - # this is not a loss but just compute neg_log_prob. - Y_pred = [] - Y_true = [] - with torch.no_grad(): - for batch in eval_dataloader: - self(batch, model, Y_pred, Y_true) - return self.finalize(Y_pred, Y_true, output_file) - - def __call__(self, sample, model, Y_pred, Y_true): - sample = self.to_ctx(sample) - # compute the average logits over sliding windows. - output = model(**sample) - logits = self._merge_windows(sample, output) - Y_pred.append(logits.argmax(dim=1)) - Y_true.append(sample["video_targets"].squeeze(0).cpu()) - - def _merge_windows(self, sample, output): - targets = sample["targets"].reshape(-1).cpu() - valid_mask = targets != -100 - targets = targets[valid_mask] - batch_logits = output["logits"].cpu() - batch_logits = batch_logits.reshape(-1, batch_logits.size(-1)) - batch_logits = batch_logits[valid_mask] - - video_len = sample["video_len"][0] - - # the following version is slow. - logits = torch.zeros((video_len, batch_logits.size(1))) - logits_counts = torch.zeros((video_len, 1), dtype=torch.long) - # use the same loop as aligner to recover. - batch_logit_idx = 0 - for window_start in range(0, video_len, self.sliding_window): - video_end = min(video_len - window_start, self.sliding_window_size) - logits[window_start: window_start + video_end] += batch_logits[ - batch_logit_idx: batch_logit_idx + video_end] - batch_logit_idx += video_end - logits_counts[window_start: window_start + video_end] += torch.ones((video_end, 1), dtype=torch.long) - if (video_len - window_start) <= self.sliding_window_size: - break - logits /= logits_counts - assert logits.size() == (video_len, batch_logits.size(1)), "{}, {}".format(logits.size(), video_len) - return logits - - def finalize(self, Y_pred, Y_true, output_file=None): - Y_pred = torch.cat(Y_pred, dim=0).numpy() - Y_true = torch.cat(Y_true, dim=0).numpy() - assert len(Y_pred) == len(Y_true) - - error_mask = Y_pred != Y_true - print("sample error", Y_pred[error_mask][:10], Y_true[error_mask][:10]) - print("sample error", Y_pred[error_mask][10:20], Y_true[error_mask][10:20]) - - if output_file is not None: - with open( - os.path.join(self.pred_dir, output_file + ".pkl"), - "wb") as fw: - pickle.dump( - {"Y_pred": Y_pred, "Y_true": Y_true}, fw, - protocol=pickle.HIGHEST_PROTOCOL) - return {"outputs": Y_pred, "targets": Y_true} - - -class COINZSPredictor(COINPredictor): - """ - COINZSPredictor for COIN zero-shot prediction. - """ - - def __init__(self, config): - super().__init__(config) - self.dataset_config = config.dataset - - def predict_loop(self, model, eval_dataloader, output_file="result.pkl"): - """refactored from line 144: - https://github.com/DmZhukov/CrossTask/blob/master/train.py - """ - ctx = 0 - model.eval() - model = model.to(ctx) - - with torch.no_grad(): - outputs = eval_dataloader.dataset.meta_processor.meta_text_labels( - self.dataset_config) - outputs = self.to_ctx(outputs, ctx) - label_hidden_states = model.forward_text(**outputs).cpu() - label_sim = label_hidden_states @ label_hidden_states.t() - num_labels = label_sim.size(0) - eye_mask = ~torch.eye(num_labels, dtype=torch.bool) - label_sim = label_sim.masked_select(eye_mask).view(num_labels, num_labels - 1) - lbd = label_sim.max() - - # this is not a loss but just compute neg_log_prob. - Y_pred = [] - Y_true = [] - with torch.no_grad(): - for batch in eval_dataloader: - self(batch, label_hidden_states, model, lbd, Y_pred, Y_true) - return self.finalize(Y_pred, Y_true, output_file) - - def reshape_subsample(self, sample): - for key in sample: - if torch.is_tensor(sample[key]): - sample[key] = self.flat_subsample(sample[key]) - return sample - - def flat_subsample(self, tensor): - if len(tensor.size()) > 1 and tensor.size(0) == 1: - tensor = tensor.squeeze(0) - return tensor - - def __call__(self, sample, label_hidden_states, model, lbd, Y_pred, Y_true): - sample = self.reshape_subsample(sample) - sample = self.to_ctx(sample) - # compute the average logits over sliding windows. - sample["output_hidden_states"] = True - video_outputs = model.forward_video(**sample).cpu() - output = {"logits": video_outputs[:, 1:sample["vmasks"].size(1)+1] @ label_hidden_states.t()} - logits = self._merge_windows(sample, output) - # logic of zero-shot for sequence labeling. - logits_argmax = logits.argmax(dim=1) + 1 # 0 is "O" label. - logits_max = logits.max(dim=1)[0] - - pred = torch.zeros_like(logits_argmax) - label_select = logits_max > lbd # 73 or 74 - pred[label_select] = logits_argmax[label_select] - - Y_pred.append(pred) - Y_true.append(sample["video_targets"].squeeze(0).cpu()) - - def finalize(self, Y_pred, Y_true, output_file=None): - Y_pred = torch.cat(Y_pred, dim=0).numpy() - Y_true = torch.cat(Y_true, dim=0).numpy() - assert len(Y_pred) == len(Y_true) - - error_mask = Y_pred != Y_true - print("sample error", Y_pred[error_mask][:10], Y_true[error_mask][:10]) - print("sample error", Y_pred[error_mask][10:20], Y_true[error_mask][10:20]) - - if output_file is not None: - with open( - os.path.join(self.pred_dir, output_file + ".pkl"), - "wb") as fw: - pickle.dump( - {"Y_pred": Y_pred, "Y_true": Y_true}, fw, - protocol=pickle.HIGHEST_PROTOCOL) - return {"outputs": Y_pred, "targets": Y_true} - - -class DiDeMoPredictor(Predictor): - """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py - https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py - """ - def __init__(self, config): - super().__init__(config) - # load targets. - with open(config.dataset.test_path) as data_file: - self.test_data = json.load(data_file) - - def predict_loop(self, model, eval_dataloader, output_file="didemo.npy"): - """ - TODO: two solutions here. - """ - import itertools - - # 21 chunks. - self.possible_segments = [(0,0), (1,1), (2,2), (3,3), (4,4), (5,5)] - for i in itertools.combinations(range(6), 2): - self.possible_segments.append(i) - # pick segments from a video. - - """on-the-fly prediction on a single gpu.""" - self.full_scores = [] - model.eval() - model = model.cuda() - with torch.no_grad(): - for data in eval_dataloader: - # TODO special forwarding logic here. - data = self.to_ctx(data) - data["output_hidden_states"] = True - hidden_video = model.forward_video(**data) - data["output_hidden_states"] = False - pooled_text = model.forward_text(**data) - outputs = { - "hidden_video": hidden_video, - "pooled_text": pooled_text - } - outputs.update(data) - self(outputs) - return self.finalize(output_file) - - def __call__(self, sample): - # TODO: make an index select from self.possible_segments. - hidden_video = sample["hidden_video"] - pooled_text = sample["pooled_text"] - vmasks = sample["vmasks"] - # probably maintain valid results here. - - hidden_video = hidden_video[:, 1:-1, :] - # probably maintain valid results here. - pooled_video = [] - for s, e in self.possible_segments: - pooled_video.append( - torch.mean( - hidden_video[:, int(s*5):int((e+1)*5), :], - dim=1, keepdim=True) - ) - pooled_video = torch.cat(pooled_video, dim=1) - scores = torch.bmm( - pooled_video, pooled_text.unsqueeze(-1)).squeeze(-1).cpu() - - ranks = scores.argsort(dim=-1, descending=True) - - for batch_idx, rank in enumerate(ranks): - rank_of_moment = [] - for m_idx, moment in enumerate(rank): - s, e = self.possible_segments[moment.item()] - if torch.any( - vmasks[batch_idx, int(s*5):int((e+1)*5)] - ): - rank_of_moment.append((s, e)) - self.full_scores.append(rank_of_moment) - - def finalize(self, output_file=None): - outputs = self._aggregate_scores(self.full_scores) - if output_file is not None: - np.save(os.path.join(self.pred_dir, output_file + ".npy"), outputs) - return {"outputs": outputs, "targets": self.test_data} - - def _aggregate_scores(self, scores): - self.full_scores = [] - return scores diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/__init__.py deleted file mode 100644 index 8dc32c96d2d8aed25c59e69e9d9a2ff24a9a2a47..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .loss import * -from .nce import * - -try: - from .fairseqmmloss import * -except ImportError: - pass - -try: - from .expnce import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/fairseqmmloss.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/fairseqmmloss.py deleted file mode 100644 index 7f66d53ff8aab0e82feb5ef8b87bcf229cae6656..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/fairseqmmloss.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -TODO (huxu): a general fairseq criterion for all your pre-defined losses. -""" - -from fairseq import metrics -from fairseq.criterions import FairseqCriterion, register_criterion - - -@register_criterion("mmloss") -class MMCriterion(FairseqCriterion): - def __init__(self, task): - super().__init__(task) - # TODO (huxu): wrap forward call of loss_fn and eval_fn into task. - self.mmtask = task.mmtask - - def forward(self, model, sample): - """Compute the loss for the given sample. - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - outputs = self.mmtask(model, sample) - - loss, loss_scalar, max_len, batch_size, sample_size = ( - outputs["loss"], - outputs["loss_scalar"], - outputs["max_len"], - outputs["batch_size"], - outputs["sample_size"], - ) - - logging_output = { - "loss": loss_scalar, - "ntokens": max_len * batch_size, # dummy report. - "nsentences": batch_size, # dummy report. - "sample_size": sample_size, - } - - return loss, 1, logging_output - - @staticmethod - def reduce_metrics(logging_outputs) -> None: - """Aggregate logging outputs from data parallel training.""" - """since we use NCE, our actual batch_size is 1 per GPU. - Then we take the mean of each worker.""" - loss_sum = sum(log.get("loss", 0.0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - metrics.log_scalar("loss", loss_sum / sample_size, round=3) - - @staticmethod - def logging_outputs_can_be_summed() -> bool: - """ - Whether the logging outputs returned by `forward` can be summed - across workers prior to calling `reduce_metrics`. Setting this - to True will improves distributed training speed. - """ - return True diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/loss.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/loss.py deleted file mode 100644 index 694b033761971ab09d358273e0f36da9244fda27..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/loss.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) Facebook, Inc. All Rights Reserved - -import torch -from torch import nn - - -class Loss(object): - def __call__(self, *args, **kwargs): - raise NotImplementedError - - -# Dummy Loss for testing. -class DummyLoss(Loss): - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__(self, logits, targets, **kwargs): - return self.loss(logits, targets) - - -class DummyK400Loss(Loss): - """dummy k400 loss for MViT.""" - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__(self, logits, targets, **kwargs): - return self.loss( - logits, torch.randint(0, 400, (logits.size(0),), device=logits.device)) - - -class CrossEntropy(Loss): - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__(self, logits, targets, **kwargs): - return self.loss(logits.reshape(-1, logits.size(-1)), targets.reshape(-1)) - - -class ArgmaxCrossEntropy(Loss): - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__(self, logits, targets, **kwargs): - return self.loss(logits, targets.argmax(dim=1)) - - -class BCE(Loss): - def __init__(self): - self.loss = nn.BCEWithLogitsLoss() - - def __call__(self, logits, targets, **kwargs): - targets = targets.squeeze(0) - return self.loss(logits, targets) - - -class NLGLoss(Loss): - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__(self, logits, text_label, **kwargs): - targets = text_label[text_label != -100] - return self.loss(logits, targets) - - -class MSE(Loss): - def __init__(self): - self.loss = nn.MSELoss() - - def __call__(self, logits, targets, **kwargs): - return self.loss(logits, targets) - - -class L1(Loss): - def __init__(self): - self.loss = nn.L1Loss() - - def __call__(self, logits, targets, **kwargs): - return self.loss(logits, targets) - - -class SmoothL1(Loss): - def __init__(self): - self.loss = nn.SmoothL1Loss() - - def __call__(self, logits, targets, **kwargs): - return self.loss(logits, targets) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/nce.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/nce.py deleted file mode 100644 index e1704e27b0be60c13c94b8660809d2e5e01ab243..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/losses/nce.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -softmax-based NCE loss, used by this project. -""" - -import torch -from torch import nn - -from .loss import Loss - - -class NCE(Loss): - def __init__(self): - # TODO (huxu): define temperature. - self.loss = nn.CrossEntropyLoss() - - def __call__(self, align_scores, **kargs): - # note: we reuse the same shape as cls head in BERT (batch_size, 2) - # but NCE only needs one logits. - # (so we drop all weights in the second neg logits.) - align_scores = align_scores[:, :1] - # duplicate negative examples - batch_size = align_scores.size(0) // 2 - pos_scores = align_scores[:batch_size] - neg_scores = align_scores[batch_size:].view(1, batch_size).repeat( - batch_size, 1) - scores = torch.cat([pos_scores, neg_scores], dim=1) - return self.loss( - scores, - torch.zeros( - (batch_size,), - dtype=torch.long, - device=align_scores.device), - ) - - -class T2VContraLoss(Loss): - """NCE for MM joint space, on softmax text2video matrix. - """ - def __init__(self): - # TODO (huxu): define temperature. - self.loss = nn.CrossEntropyLoss() - - def __call__(self, pooled_video, pooled_text, **kargs): - batch_size = pooled_video.size(0) - logits = torch.mm(pooled_text, pooled_video.transpose(1, 0)) - targets = torch.arange( - batch_size, - dtype=torch.long, - device=pooled_video.device) - return self.loss(logits, targets) - - -class V2TContraLoss(Loss): - """NCE for MM joint space, with softmax on video2text matrix.""" - - def __init__(self): - # TODO (huxu): define temperature. - self.loss = nn.CrossEntropyLoss() - - def __call__(self, pooled_video, pooled_text, **kargs): - batch_size = pooled_video.size(0) - logits = torch.mm(pooled_video, pooled_text.transpose(1, 0)) - targets = torch.arange( - batch_size, - dtype=torch.long, - device=pooled_video.device) - return self.loss(logits, targets) - - -class MMContraLoss(Loss): - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__(self, pooled_video, pooled_text, **kwargs): - logits_per_video = pooled_video @ pooled_text.t() - logits_per_text = pooled_text @ pooled_video.t() - - targets = torch.arange( - pooled_video.size(0), - dtype=torch.long, - device=pooled_video.device) - loss_video = self.loss(logits_per_video, targets) - loss_text = self.loss(logits_per_text, targets) - return loss_video + loss_text - - -class MTM(Loss): - """Combination of MFM and MLM.""" - - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__( - self, - video_logits, - text_logits, - video_label, - text_label, - **kwargs - ): - text_logits = torch.cat([ - text_logits, - torch.zeros( - (text_logits.size(0), 1), device=text_logits.device) - ], dim=1) - vt_logits = torch.cat([video_logits, text_logits], dim=0) - # loss for video. - video_label = torch.zeros( - (video_logits.size(0),), - dtype=torch.long, - device=video_logits.device - ) - - # loss for text. - text_label = text_label.reshape(-1) - labels_mask = text_label != -100 - selected_text_label = text_label[labels_mask] - - vt_label = torch.cat([video_label, selected_text_label], dim=0) - return self.loss(vt_logits, vt_label) - - -class MFMMLM(Loss): - """Combination of MFM and MLM.""" - - def __init__(self): - self.loss = nn.CrossEntropyLoss() - - def __call__( - self, - video_logits, - text_logits, - video_label, - text_label, - **kwargs - ): - # loss for video. - video_label = torch.zeros( - (video_logits.size(0),), - dtype=torch.long, - device=video_logits.device - ) - masked_frame_loss = self.loss(video_logits, video_label) - - # loss for text. - text_label = text_label.reshape(-1) - labels_mask = text_label != -100 - selected_text_label = text_label[labels_mask] - masked_lm_loss = self.loss(text_logits, selected_text_label) - return masked_frame_loss + masked_lm_loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/__init__.py deleted file mode 100644 index c8a3db3ee25137e3852a51374530f251e63d866a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .mmfusion import * -from .mmfusionnlg import * -from .transformermodel import * - -try: - from .fairseqmmmodel import * -except ImportError: - pass - -try: - from .expmmfusion import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/fairseqmmmodel.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/fairseqmmmodel.py deleted file mode 100644 index bf1c1f682d99cb0836b1bfda6e451ecd28a50d31..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/fairseqmmmodel.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. - -from fairseq.models import BaseFairseqModel, register_model, register_model_architecture - - -@register_model("mmmodel") -class FairseqMMModel(BaseFairseqModel): - """a fairseq wrapper of model built by `task`.""" - - @classmethod - def build_model(cls, args, task): - return FairseqMMModel(task.mmtask.model) - - def __init__(self, mmmodel): - super().__init__() - self.mmmodel = mmmodel - - def forward(self, *args, **kwargs): - return self.mmmodel(*args, **kwargs) - - def upgrade_state_dict_named(self, state_dict, name): - - super().upgrade_state_dict_named(state_dict, name) - - keys_to_delete = [] - - for key in state_dict: - if key not in self.state_dict(): - keys_to_delete.append(key) - for key in keys_to_delete: - print("[INFO]", key, "not used anymore.") - del state_dict[key] - - # copy any newly defined parameters. - for key in self.state_dict(): - if key not in state_dict: - print("[INFO] adding", key) - state_dict[key] = self.state_dict()[key] - - -# a dummy arch, we config the model. -@register_model_architecture("mmmodel", "mmarch") -def mmarch(args): - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/mmfusion.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/mmfusion.py deleted file mode 100644 index d2bc2f4afd853684ce7a204bc86db42ddf158b8e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/mmfusion.py +++ /dev/null @@ -1,926 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) Facebook, Inc. All Rights Reserved - - -import torch -from torch import nn - -try: - from transformers import AutoConfig, AutoTokenizer -except ImportError: - pass - -from . import transformermodel - - -class MMPTModel(nn.Module): - """An e2e wrapper of inference model. - """ - @classmethod - def from_pretrained(cls, config, checkpoint="checkpoint_best.pt"): - import os - - from ..tasks import Task - from ..utils import recursive_config - config = recursive_config(config) - mmtask = Task.config_task(config) - checkpoint_path = os.path.join(config.eval.save_path, checkpoint) - mmtask.build_model(checkpoint=checkpoint_path) - # TODO(huxu): make the video encoder configurable. - from ..processors.models.s3dg import S3D - video_encoder = S3D('pretrained_models/s3d_dict.npy', 512) - video_encoder.load_state_dict( - torch.load('pretrained_models/s3d_howto100m.pth')) - from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained( - config.dataset.bert_name, use_fast=config.dataset.use_fast - ) - from ..processors import Aligner - aligner = Aligner(config.dataset) - return ( - MMPTModel(config, mmtask.model, video_encoder), - tokenizer, - aligner - ) - - def __init__(self, config, model, video_encoder, **kwargs): - super().__init__() - self.max_video_len = config.dataset.max_video_len - self.video_encoder = video_encoder - self.model = model - - def forward(self, video_frames, caps, cmasks, return_score=False): - bsz = video_frames.size(0) - assert bsz == 1, "only bsz=1 is supported now." - seq_len = video_frames.size(1) - video_frames = video_frames.view(-1, *video_frames.size()[2:]) - vfeats = self.video_encoder(video_frames.permute(0, 4, 1, 2, 3)) - vfeats = vfeats['video_embedding'] - vfeats = vfeats.view(bsz, seq_len, vfeats.size(-1)) - padding = torch.zeros( - bsz, self.max_video_len - seq_len, vfeats.size(-1)) - vfeats = torch.cat([vfeats, padding], dim=1) - vmasks = torch.cat([ - torch.ones((bsz, seq_len), dtype=torch.bool), - torch.zeros((bsz, self.max_video_len - seq_len), dtype=torch.bool) - ], - dim=1 - ) - output = self.model(caps, cmasks, vfeats, vmasks) - if return_score: - output = {"score": torch.bmm( - output["pooled_video"][:, None, :], - output["pooled_text"][:, :, None] - ).squeeze(-1).squeeze(-1)} - return output - - -class MMFusion(nn.Module): - """a MMPT wrapper class for MMBert style models. - TODO: move isolated mask to a subclass. - """ - def __init__(self, config, **kwargs): - super().__init__() - transformer_config = AutoConfig.from_pretrained( - config.dataset.bert_name) - self.hidden_size = transformer_config.hidden_size - self.is_train = False - if config.dataset.train_path is not None: - self.is_train = True - # 0 means no iso; 1-12 means iso up to that layer. - self.num_hidden_layers = transformer_config.num_hidden_layers - self.last_iso_layer = 0 - if config.dataset.num_iso_layer is not None: - self.last_iso_layer = config.dataset.num_iso_layer - 1 + 1 - - if config.model.mm_encoder_cls is not None: - mm_encoder_cls = getattr(transformermodel, config.model.mm_encoder_cls) - model_config = AutoConfig.from_pretrained(config.dataset.bert_name) - model_config.max_video_len = config.dataset.max_video_len - # TODO: a general way to add parameter for a model. - model_config.use_seg_emb = config.model.use_seg_emb - self.mm_encoder = mm_encoder_cls.from_pretrained( - config.dataset.bert_name, config=model_config) - elif config.model.video_encoder_cls is not None\ - and config.model.text_encoder_cls is not None: - video_encoder_cls = getattr(transformermodel, config.model.video_encoder_cls) - model_config = AutoConfig.from_pretrained(config.dataset.bert_name) - model_config.max_video_len = config.dataset.max_video_len - # TODO: make each model a set of config class. - if hasattr(model_config, "num_layers"): - model_config.num_layers = config.model.num_hidden_video_layers - else: - model_config.num_hidden_layers = config.model.num_hidden_video_layers - self.video_encoder = video_encoder_cls.from_pretrained( - config.dataset.bert_name, config=model_config) - # exact same NLP model from Huggingface. - text_encoder_cls = getattr(transformermodel, config.model.text_encoder_cls) - self.text_encoder = text_encoder_cls.from_pretrained( - config.dataset.bert_name) - else: - raise ValueError("the encoder must be either MM or two backbones.") - - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - **kwargs - ): - raise NotImplementedError( - "Please derive MMFusion module." - ) - - def _mm_on_the_fly( - self, - cmasks, - vmasks, - attention_mask - ): - """helper function for mask, seg_ids and token_type_ids.""" - if attention_mask is None: - attention_mask = self._mm_attention_mask(cmasks, vmasks) - - """ - 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 - | first sequence | second sequence | - """ - token_type_ids = torch.cat( - [ - torch.zeros( - (vmasks.size(0), vmasks.size(1) + 2), - dtype=torch.long, - device=vmasks.device, - ), - torch.ones( - (cmasks.size(0), cmasks.size(1) - 2), - dtype=torch.long, - device=cmasks.device, - ), - ], - dim=1, - ) - return attention_mask, token_type_ids - - def _mm_attention_mask(self, cmasks, vmasks): - assert cmasks.size(0) == vmasks.size(0), "{}, {}, {}, {}".format( - str(cmasks.size()), - str(vmasks.size()), - str(cmasks.size(0)), - str(vmasks.size(0)), - ) - - mm_mask = torch.cat([cmasks[:, :1], vmasks, cmasks[:, 1:]], dim=1) - if self.last_iso_layer == 0: - # hard attention mask. - return mm_mask - else: - # a gpu iso mask; 0 : num_iso_layer is isolated; - # num_iso_layer: are MM-fused. - # make an iso layer - batch_size = cmasks.size(0) - iso_mask = self._make_iso_mask(batch_size, cmasks, vmasks) - mm_mask = mm_mask[:, None, :].repeat(1, mm_mask.size(-1), 1) - iso_mm_masks = [] - # hard attention mask. - iso_mask = iso_mask[:, None, :, :].repeat( - 1, self.last_iso_layer, 1, 1) - iso_mm_masks.append(iso_mask) - if self.last_iso_layer < self.num_hidden_layers: - mm_mask = mm_mask[:, None, :, :].repeat( - 1, self.num_hidden_layers - self.last_iso_layer, 1, 1 - ) - iso_mm_masks.append(mm_mask) - iso_mm_masks = torch.cat(iso_mm_masks, dim=1) - return iso_mm_masks - - def _make_iso_mask(self, batch_size, cmasks, vmasks): - cls_self_mask = torch.cat( - [ - torch.ones( - (batch_size, 1), dtype=torch.bool, device=cmasks.device), - torch.zeros( - (batch_size, cmasks.size(1) + vmasks.size(1) - 1), - dtype=torch.bool, device=cmasks.device) - ], dim=1) - - iso_video_mask = torch.cat( - [ - # [CLS] is not used. - torch.zeros( - (batch_size, 1), dtype=torch.bool, device=cmasks.device - ), - vmasks, - # assume to be 1. - cmasks[:, 1:2], - # 2 means [CLS] + [SEP] - torch.zeros( - (batch_size, cmasks.size(1) - 2), - dtype=torch.bool, - device=cmasks.device, - ), - ], - dim=1, - ) - iso_text_mask = torch.cat( - [ - torch.zeros( - (batch_size, 2 + vmasks.size(1)), - dtype=torch.bool, - device=cmasks.device, - ), # [CLS] is not used. - cmasks[:, 2:], # assume to be 1. - ], - dim=1, - ) - cls_self_mask = cls_self_mask[:, None, :] - iso_video_mask = iso_video_mask[:, None, :].repeat( - 1, vmasks.size(1) + 1, 1) - iso_text_mask = iso_text_mask[:, None, :].repeat( - 1, cmasks.size(1) - 2, 1) - return torch.cat([cls_self_mask, iso_video_mask, iso_text_mask], dim=1) - - def _pooling_vt_layer( - self, - layered_sequence_output, - cmasks, - vmasks - ): - layer_idx = self.last_iso_layer \ - if self.last_iso_layer > 0 else self.num_hidden_layers - hidden_state = layered_sequence_output[layer_idx] - # also output pooled_video and pooled_text. - batch_size = cmasks.size(0) - # pool the modality. - text_offset = vmasks.size(1) + 2 # [CLS] + [SEP] - # video tokens + [SEP] - video_outputs = hidden_state[:, 1:text_offset] - video_attention_mask = torch.cat( - [ - vmasks, - torch.ones( - (batch_size, 1), dtype=torch.bool, device=vmasks.device), - ], - dim=1, - ) - assert video_outputs.size(1) == video_attention_mask.size(1) - pooled_video = torch.sum( - video_outputs * video_attention_mask.unsqueeze(-1), dim=1 - ) / video_attention_mask.sum(1, keepdim=True) - # pooled_video = torch.mean(video_outputs[0], dim=1) - - # text tokens + [SEP] - text_attention_mask = cmasks[:, 2:] - text_outputs = hidden_state[:, text_offset:] - assert text_outputs.size(1) == text_attention_mask.size(1) - pooled_text = torch.sum( - text_outputs * text_attention_mask.unsqueeze(-1), dim=1 - ) / text_attention_mask.sum(1, keepdim=True) - return pooled_video, pooled_text - - -class MMFusionMFMMLM(MMFusion): - """forward function for MFM and MLM.""" - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask=None, - video_label=None, - text_label=None, - **kwargs - ): - output_hidden_states = False if self.is_train else True - - target_vfeats, non_masked_frame_mask = None, None - if video_label is not None: - target_vfeats = vfeats.masked_select( - video_label.unsqueeze(-1)).view( - -1, vfeats.size(-1) - ) - # mask video token. - vfeats[video_label] = 0.0 - non_masked_frame_mask = vmasks.clone() - non_masked_frame_mask[video_label] = False - - attention_mask, token_type_ids = self._mm_on_the_fly( - cmasks, vmasks, attention_mask) - - outputs = self.mm_encoder( - input_ids=caps, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - masked_frame_labels=video_label, - target_video_hidden_states=target_vfeats, - non_masked_frame_mask=non_masked_frame_mask, - masked_lm_labels=text_label, - output_hidden_states=output_hidden_states, - ) - - video_logits, text_logits = outputs[0], outputs[1] - - if self.is_train: # return earlier for training. - return { - "video_logits": video_logits, - "text_logits": text_logits, - } - - pooled_video, pooled_text = self._pooling_vt_layer( - outputs[2], cmasks, vmasks) - return {"pooled_video": pooled_video, "pooled_text": pooled_text} - - -class MMFusionMTM(MMFusionMFMMLM): - def __init__(self, config, **kwargs): - super().__init__(config) - """ - For reproducibility: - self.mm_encoder will be initialized then discarded. - """ - from .transformermodel import MMBertForMTM - model_config = AutoConfig.from_pretrained(config.dataset.bert_name) - model_config.max_video_len = config.dataset.max_video_len - model_config.use_seg_emb = config.model.use_seg_emb - self.mm_encoder = MMBertForMTM.from_pretrained( - config.dataset.bert_name, config=model_config) - - -class MMFusionShare(MMFusion): - """A retrival wrapper using mm_encoder as both video/text backbone. - TODO: move formally. - """ - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask=None, - video_label=None, - text_label=None, - output_hidden_states=False, - **kwargs - ): - pooled_video = self.forward_video( - vfeats, - vmasks, - caps, - cmasks, - output_hidden_states - ) - - pooled_text = self.forward_text( - caps, - cmasks, - output_hidden_states - ) - - return {"pooled_video": pooled_video, "pooled_text": pooled_text} - - def forward_video( - self, - vfeats, - vmasks, - caps, - cmasks, - output_hidden_states=False, - **kwargs - ): - input_ids = caps[:, :2] - - attention_mask = torch.cat([ - cmasks[:, :1], - vmasks, - cmasks[:, 1:2] - ], dim=1) - - token_type_ids = torch.zeros( - (vmasks.size(0), vmasks.size(1) + 2), - dtype=torch.long, - device=vmasks.device) - - outputs = self.mm_encoder( - input_ids=input_ids, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=True - ) - video_outputs = outputs[0] - - if output_hidden_states: - return video_outputs - - batch_size = cmasks.size(0) - - video_attention_mask = torch.cat( - [ - torch.zeros( - (batch_size, 1), dtype=torch.bool, device=vmasks.device), - vmasks, - torch.ones( - (batch_size, 1), dtype=torch.bool, device=vmasks.device), - ], - dim=1, - ) - assert video_outputs.size(1) == video_attention_mask.size(1) - - video_attention_mask = video_attention_mask.type(video_outputs.dtype) \ - / video_attention_mask.sum(1, keepdim=True) - - pooled_video = torch.bmm( - video_outputs.transpose(2, 1), - video_attention_mask.unsqueeze(2) - ).squeeze(-1) - return pooled_video # video_outputs - - def forward_text( - self, - caps, - cmasks, - output_hidden_states=False, - **kwargs - ): - input_ids = torch.cat([ - caps[:, :1], caps[:, 2:], - ], dim=1) - - attention_mask = torch.cat([ - cmasks[:, :1], - cmasks[:, 2:] - ], dim=1) - - token_type_ids = torch.cat([ - torch.zeros( - (cmasks.size(0), 1), - dtype=torch.long, - device=cmasks.device), - torch.ones( - (cmasks.size(0), cmasks.size(1) - 2), - dtype=torch.long, - device=cmasks.device) - ], dim=1) - - outputs = self.mm_encoder( - input_ids=input_ids, - input_video_embeds=None, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=True - ) - text_outputs = outputs[0] - - if output_hidden_states: - return text_outputs - - batch_size = caps.size(0) - # text tokens + [SEP] - text_attention_mask = torch.cat([ - torch.zeros( - (batch_size, 1), dtype=torch.bool, device=cmasks.device), - cmasks[:, 2:] - ], dim=1) - - assert text_outputs.size(1) == text_attention_mask.size(1) - - text_attention_mask = text_attention_mask.type(text_outputs.dtype) \ - / text_attention_mask.sum(1, keepdim=True) - - pooled_text = torch.bmm( - text_outputs.transpose(2, 1), - text_attention_mask.unsqueeze(2) - ).squeeze(-1) - return pooled_text # text_outputs - - -class MMFusionSeparate(MMFusionShare): - def forward_video( - self, - vfeats, - vmasks, - caps, - cmasks, - output_hidden_states=False, - **kwargs - ): - input_ids = caps[:, :2] - - attention_mask = torch.cat([ - cmasks[:, :1], - vmasks, - cmasks[:, 1:2] - ], dim=1) - - token_type_ids = torch.zeros( - (vmasks.size(0), vmasks.size(1) + 2), - dtype=torch.long, - device=vmasks.device) - - outputs = self.video_encoder( - input_ids=input_ids, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=True - ) - video_outputs = outputs[0] - - if output_hidden_states: - return video_outputs - - batch_size = cmasks.size(0) - - video_attention_mask = torch.cat( - [ - torch.zeros( - (batch_size, 1), dtype=torch.bool, device=vmasks.device), - vmasks, - torch.ones( - (batch_size, 1), dtype=torch.bool, device=vmasks.device), - ], - dim=1, - ) - assert video_outputs.size(1) == video_attention_mask.size(1) - - video_attention_mask = video_attention_mask.type(video_outputs.dtype) \ - / video_attention_mask.sum(1, keepdim=True) - - pooled_video = torch.bmm( - video_outputs.transpose(2, 1), - video_attention_mask.unsqueeze(2) - ).squeeze(-1) - return pooled_video # video_outputs - - def forward_text( - self, - caps, - cmasks, - output_hidden_states=False, - **kwargs - ): - input_ids = torch.cat([ - caps[:, :1], caps[:, 2:], - ], dim=1) - - attention_mask = torch.cat([ - cmasks[:, :1], - cmasks[:, 2:] - ], dim=1) - # different from sharing, we use all-0 type. - token_type_ids = torch.zeros( - (cmasks.size(0), cmasks.size(1) - 1), - dtype=torch.long, - device=cmasks.device) - - outputs = self.text_encoder( - input_ids=input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=True - ) - text_outputs = outputs[0] - - if output_hidden_states: - return text_outputs - - batch_size = caps.size(0) - # text tokens + [SEP] - text_attention_mask = torch.cat([ - torch.zeros( - (batch_size, 1), dtype=torch.bool, device=cmasks.device), - cmasks[:, 2:] - ], dim=1) - - assert text_outputs.size(1) == text_attention_mask.size(1) - - text_attention_mask = text_attention_mask.type(text_outputs.dtype) \ - / text_attention_mask.sum(1, keepdim=True) - - pooled_text = torch.bmm( - text_outputs.transpose(2, 1), - text_attention_mask.unsqueeze(2) - ).squeeze(-1) - return pooled_text # text_outputs - - -class MMFusionJoint(MMFusion): - """fine-tuning wrapper for retrival task.""" - - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask=None, - video_label=None, - text_label=None, - **kwargs - ): - # TODO (huxu): other ways to do negative examples; move the following - # into your criterion forward. - output_hidden_states = True - - attention_mask, token_type_ids = self._mm_on_the_fly( - cmasks, vmasks, attention_mask) - - separate_forward_split = ( - None if self.is_train else vmasks.size(1) + 2 - ) # [CLS] + [SEP] - - outputs = self.mm_encoder( - input_ids=caps, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=output_hidden_states, - separate_forward_split=separate_forward_split, - ) - - pooled_video, pooled_text = self._pooling_vt_layer( - outputs[2], cmasks, vmasks) - return {"pooled_video": pooled_video, "pooled_text": pooled_text} - - -class MMFusionActionSegmentation(MMFusion): - """Fine-tuning wrapper for action segmentation. - TODO: rename this for VLM. - """ - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask=None, - **kwargs - ): - # ActionLocalization assume of batch_size=1, squeeze it. - caps = caps.view(-1, caps.size(-1)) - cmasks = cmasks.view(-1, cmasks.size(-1)) - vfeats = vfeats.view(-1, vfeats.size(2), vfeats.size(3)) - vmasks = vmasks.view(-1, vmasks.size(-1)) - - # this may not cover all shapes of attention_mask. - attention_mask = attention_mask.view( - -1, attention_mask.size(2), attention_mask.size(3)) \ - if attention_mask is not None else None - - # TODO (huxu): other ways to do negative examples; move the following - # into your criterion forward. - output_hidden_states = True - - # video forwarding, text is dummy; never use attention_mask. - attention_mask, token_type_ids = self._mm_on_the_fly( - cmasks, vmasks, attention_mask) - - logits = self.mm_encoder( - input_ids=caps, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=output_hidden_states, - ) - return {"logits": logits[0][:, 1:vmasks.size(1)+1]} - - -class MMFusionActionLocalization(MMFusion): - """fine-tuning model for retrival task.""" - - def __init__(self, config, **kwargs): - super().__init__(config) - tokenizer = AutoTokenizer.from_pretrained( - config.dataset.bert_name) - self.cls_token_id = tokenizer.cls_token_id - self.sep_token_id = tokenizer.sep_token_id - self.pad_token_id = tokenizer.pad_token_id - - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask=None, - **kwargs - ): - # ActionLocalization assume of batch_size=1, squeeze it. - caps = caps.squeeze(0) - cmasks = cmasks.squeeze(0) - vfeats = vfeats.squeeze(0) - vmasks = vmasks.squeeze(0) - attention_mask = attention_mask.squeeze(0) if attention_mask is not None else None - - # TODO (huxu): other ways to do negative examples; move the following - # into your criterion forward. - output_hidden_states = True - - # a len1 dummy video token. - dummy_vfeats = torch.zeros( - (caps.size(0), 1, vfeats.size(-1)), device=vfeats.device, dtype=vfeats.dtype) - dummy_vmasks = torch.ones( - (caps.size(0), 1), dtype=torch.bool, - device=vfeats.device) - - dummy_caps = torch.LongTensor( - [[self.cls_token_id, self.sep_token_id, - self.pad_token_id, self.sep_token_id]], - ).to(caps.device).repeat(vfeats.size(0), 1) - dummy_cmasks = torch.BoolTensor( - [[0, 1, 0, 1]] # pad are valid for attention. - ).to(caps.device).repeat(vfeats.size(0), 1) - - # video forwarding, text is dummy; never use attention_mask. - attention_mask, token_type_ids = self._mm_on_the_fly( - dummy_cmasks, vmasks, None) - - outputs = self.mm_encoder( - input_ids=dummy_caps, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=output_hidden_states, - ) - - layer_idx = self.last_iso_layer \ - if self.last_iso_layer > 0 else self.num_hidden_layers - - video_seq = outputs[2][layer_idx][:, 1:vmasks.size(1)+1].masked_select( - vmasks.unsqueeze(-1) - ).view(-1, self.hidden_size) - - # text forwarding, video is dummy - attention_mask, token_type_ids = self._mm_on_the_fly( - cmasks, dummy_vmasks, None) - - outputs = self.mm_encoder( - input_ids=caps, - input_video_embeds=dummy_vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - output_hidden_states=output_hidden_states, - ) - - _, pooled_text = self._pooling_vt_layer( - outputs[2], cmasks, dummy_vmasks) - # this line is not right. - logits = torch.mm(video_seq, pooled_text.transpose(1, 0)) - return {"logits": logits} - - -# --------------- MMFusionSeparate for end tasks --------------- - -class MMFusionSeparateActionSegmentation(MMFusionSeparate): - """Fine-tuning wrapper for action segmentation.""" - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask=None, - **kwargs - ): - # ActionLocalization assume of batch_size=1, squeeze it. - caps = caps.view(-1, caps.size(-1)) - cmasks = cmasks.view(-1, cmasks.size(-1)) - vfeats = vfeats.view(-1, vfeats.size(2), vfeats.size(3)) - vmasks = vmasks.view(-1, vmasks.size(-1)) - logits = self.forward_video( - vfeats, - vmasks, - caps, - cmasks, - output_hidden_states=True - ) - return {"logits": logits[:, 1:vmasks.size(1)+1]} - - -class MMFusionSeparateActionLocalization(MMFusionSeparate): - def __init__(self, config, **kwargs): - super().__init__(config) - tokenizer = AutoTokenizer.from_pretrained( - config.dataset.bert_name) - self.cls_token_id = tokenizer.cls_token_id - self.sep_token_id = tokenizer.sep_token_id - self.pad_token_id = tokenizer.pad_token_id - - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - **kwargs - ): - # ActionLocalization assume of batch_size=1, squeeze it. - caps = caps.squeeze(0) - cmasks = cmasks.squeeze(0) - vfeats = vfeats.squeeze(0) - vmasks = vmasks.squeeze(0) - - # TODO (huxu): other ways to do negative examples; move the following - # into your criterion forward. - dummy_caps = torch.LongTensor( - [[self.cls_token_id, self.sep_token_id, - self.pad_token_id, self.sep_token_id]], - ).to(caps.device).repeat(vfeats.size(0), 1) - dummy_cmasks = torch.BoolTensor( - [[0, 1, 0, 1]] # pad are valid for attention. - ).to(caps.device).repeat(vfeats.size(0), 1) - - outputs = self.forward_video( - vfeats, - vmasks, - dummy_caps, - dummy_cmasks, - output_hidden_states=True - ) - - video_seq = outputs[:, 1:vmasks.size(1)+1].masked_select( - vmasks.unsqueeze(-1) - ).view(-1, self.hidden_size) - - pooled_text = self.forward_text( - caps, - cmasks, - output_hidden_states=False - ) - - # this line is not right. - logits = torch.mm(video_seq, pooled_text.transpose(1, 0)) - return {"logits": logits} - - -class MMFusionShareActionLocalization(MMFusionShare): - def __init__(self, config, **kwargs): - super().__init__(config) - tokenizer = AutoTokenizer.from_pretrained( - config.dataset.bert_name) - self.cls_token_id = tokenizer.cls_token_id - self.sep_token_id = tokenizer.sep_token_id - self.pad_token_id = tokenizer.pad_token_id - - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - **kwargs - ): - # ActionLocalization assume of batch_size=1, squeeze it. - caps = caps.squeeze(0) - cmasks = cmasks.squeeze(0) - vfeats = vfeats.squeeze(0) - vmasks = vmasks.squeeze(0) - - # TODO (huxu): other ways to do negative examples; move the following - # into your criterion forward. - dummy_caps = torch.LongTensor( - [[self.cls_token_id, self.sep_token_id, - self.pad_token_id, self.sep_token_id]], - ).to(caps.device).repeat(vfeats.size(0), 1) - dummy_cmasks = torch.BoolTensor( - [[0, 1, 0, 1]] # pad are valid for attention. - ).to(caps.device).repeat(vfeats.size(0), 1) - - outputs = self.forward_video( - vfeats, - vmasks, - dummy_caps, - dummy_cmasks, - output_hidden_states=True - ) - - video_seq = outputs[:, 1:vmasks.size(1)+1].masked_select( - vmasks.unsqueeze(-1) - ).view(-1, self.hidden_size) - - pooled_text = self.forward_text( - caps, - cmasks, - output_hidden_states=False - ) - - # this line is not right. - logits = torch.mm(video_seq, pooled_text.transpose(1, 0)) - return {"logits": logits} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/mmfusionnlg.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/mmfusionnlg.py deleted file mode 100644 index de9622447234b56f7507c742217325cdd5f5d85e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/mmfusionnlg.py +++ /dev/null @@ -1,994 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) Facebook, Inc. All Rights Reserved - - -from typing import Iterable, Optional - -import torch -from torch.nn import functional as F - -try: - from transformers import BertPreTrainedModel - from transformers.file_utils import ModelOutput - from transformers.generation_utils import BeamHypotheses, top_k_top_p_filtering - from transformers.modeling_bert import BertOnlyMLMHead - from transformers.modeling_outputs import CausalLMOutput -except ImportError: - pass - -from ..modules import VideoTokenMLP -from .mmfusion import MMFusion -from .transformermodel import MMBertModel - - -class MMFusionNLG(MMFusion): - def __init__(self, config, **kwargs): - super().__init__(config) - if config.model.max_decode_length is not None: - self.max_length = min( - config.model.max_decode_length, - config.dataset.max_len - config.dataset.max_video_len - 3 - ) - else: - self.max_length = \ - config.dataset.max_len - config.dataset.max_video_len - 3 - self.gen_param = config.gen_param if config.gen_param is not None \ - else {} - - def forward( - self, - caps, - cmasks, - vfeats, - vmasks, - attention_mask, - video_label=None, - text_label=None, - **kwargs - ): - """use pre-trained LM header for generation.""" - attention_mask, token_type_ids = self._mm_on_the_fly( - cmasks, vmasks, attention_mask) - - outputs = self.mm_encoder( - input_ids=caps, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - masked_lm_labels=text_label, - ) - return {"logits": outputs[0]} - - @torch.no_grad() - def generate( - self, - caps, cmasks, vfeats, vmasks, - attention_mask=None, - bos_token_id=None, - eos_token_id=None, - **kwargs - ): - # a simplified interface from - # https://huggingface.co/transformers/v3.4.0/_modules/transformers/generation_utils.html#GenerationMixin.generate - - # caps now only have - # [CLS], [SEP] (for video) and [CLS] (as bos_token) - assert caps.size(1) == 3 - - attention_mask, token_type_ids = self._mm_on_the_fly( - cmasks, vmasks, attention_mask) - - output = self.mm_encoder.generate( - input_ids=caps, - input_video_embeds=vfeats, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - bos_token_id=bos_token_id, - eos_token_id=eos_token_id, - max_length=self.max_length, - **self.gen_param - ) - return output - - -class MMBertForNLG(BertPreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.bert = MMBertModel(config) - self.videomlp = VideoTokenMLP(config) - # we do not use `BertGenerationOnlyLMHead` - # because we can reuse pretraining. - self.cls = BertOnlyMLMHead(config) - self.hidden_size = config.hidden_size - self.init_weights() - - def get_output_embeddings(self): - return self.cls.predictions.decoder - - def forward( - self, - input_ids=None, - input_video_embeds=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - masked_lm_labels=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - # similar to MMBertForMFMMLM without MFM. - video_tokens = self.videomlp(input_video_embeds) - outputs = self.bert( - input_ids, - video_tokens, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - prediction_scores = None - if masked_lm_labels is not None: - text_offset = input_video_embeds.size(1) + 1 # [CLS] - # recover caps format: [CLS] [SEP] text [SEP] - text_sequence_output = torch.cat( - [sequence_output[:, :1], sequence_output[:, text_offset:]], - dim=1 - ) - - # only compute select tokens to training to speed up. - hidden_size = text_sequence_output.size(-1) - # masked_lm_labels = masked_lm_labels.reshape(-1) - labels_mask = masked_lm_labels != -100 - - selected_text_output = text_sequence_output.masked_select( - labels_mask.unsqueeze(-1) - ).view(-1, hidden_size) - prediction_scores = self.cls(selected_text_output) - - if not return_dict: - output = ( - prediction_scores, - ) + outputs[2:] - return output - - # for generation. - text_offset = input_video_embeds.size(1) + 2 # [CLS] - text_sequence_output = sequence_output[:, text_offset:] - prediction_scores = self.cls(text_sequence_output) - return CausalLMOutput( - loss=None, - logits=prediction_scores, - ) - - def prepare_inputs_for_generation( - self, - input_ids, - input_video_embeds, - attention_mask=None, - token_type_ids=None, - **model_kwargs - ): - # must return a dictionary. - seq_len = input_ids.size(1) + input_video_embeds.size(1) - if attention_mask is not None: - if len(attention_mask.size()) == 4: - attention_mask = attention_mask[:, :, :seq_len, :seq_len] - elif len(attention_mask.size()) == 3: - attention_mask = attention_mask[:, :seq_len, :seq_len] - else: - attention_mask = attention_mask[:, :seq_len] - if token_type_ids is not None: - token_type_ids = token_type_ids[:, :seq_len] - - return { - "input_ids": input_ids, - "input_video_embeds": input_video_embeds, - "attention_mask": attention_mask, - "token_type_ids": token_type_ids, - } - - @torch.no_grad() - def generate( - self, - input_ids: Optional[torch.LongTensor] = None, - decoder_input_ids: Optional[torch.LongTensor] = None, - max_length: Optional[int] = None, - min_length: Optional[int] = None, - do_sample: Optional[bool] = None, - early_stopping: Optional[bool] = None, - num_beams: Optional[int] = None, - temperature: Optional[float] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - repetition_penalty: Optional[float] = None, - bad_words_ids: Optional[Iterable[int]] = None, - bos_token_id: Optional[int] = None, - pad_token_id: Optional[int] = None, - eos_token_id: Optional[int] = None, - length_penalty: Optional[float] = None, - no_repeat_ngram_size: Optional[int] = None, - num_return_sequences: Optional[int] = None, - attention_mask: Optional[torch.LongTensor] = None, - decoder_start_token_id: Optional[int] = None, - use_cache: Optional[bool] = None, - **model_kwargs - ) -> torch.LongTensor: - r""" - Generates sequences for models with a language modeling head. The method currently supports greedy decoding, - beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling. - Adapted in part from `Facebook's XLM beam search code - `__. - Apart from :obj:`input_ids` and :obj:`attention_mask`, all the arguments below will default to the value of the - attribute of the same name inside the :class:`~transformers.PretrainedConfig` of the model. The default values - indicated are the default values of those config. - Most of these parameters are explained in more detail in `this blog post - `__. - Parameters: - input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - The sequence used as a prompt for the generation. If :obj:`None` the method initializes - it as an empty :obj:`torch.LongTensor` of shape :obj:`(1,)`. - decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - initial input_ids for the decoder of encoder-decoder type models. If :obj:`None` then only - decoder_start_token_id is passed as the first token to the decoder. - max_length (:obj:`int`, `optional`, defaults to 20): - The maximum length of the sequence to be generated. - min_length (:obj:`int`, `optional`, defaults to 10): - The minimum length of the sequence to be generated. - do_sample (:obj:`bool`, `optional`, defaults to :obj:`False`): - Whether or not to use sampling ; use greedy decoding otherwise. - early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`): - Whether to stop the beam search when at least ``num_beams`` sentences are finished per batch or not. - num_beams (:obj:`int`, `optional`, defaults to 1): - Number of beams for beam search. 1 means no beam search. - temperature (:obj:`float`, `optional`, defaults tp 1.0): - The value used to module the next token probabilities. - top_k (:obj:`int`, `optional`, defaults to 50): - The number of highest probability vocabulary tokens to keep for top-k-filtering. - top_p (:obj:`float`, `optional`, defaults to 1.0): - If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or - higher are kept for generation. - repetition_penalty (:obj:`float`, `optional`, defaults to 1.0): - The parameter for repetition penalty. 1.0 means no penalty. See `this paper - `__ for more details. - pad_token_id (:obj:`int`, `optional`): - The id of the `padding` token. - bos_token_id (:obj:`int`, `optional`): - The id of the `beginning-of-sequence` token. - eos_token_id (:obj:`int`, `optional`): - The id of the `end-of-sequence` token. - length_penalty (:obj:`float`, `optional`, defaults to 1.0): - Exponential penalty to the length. 1.0 means no penalty. - Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in - order to encourage the model to produce longer sequences. - no_repeat_ngram_size (:obj:`int`, `optional`, defaults to 0): - If set to int > 0, all ngrams of that size can only occur once. - bad_words_ids(:obj:`List[int]`, `optional`): - List of token ids that are not allowed to be generated. In order to get the tokens of the words that - should not appear in the generated text, use :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`. - num_return_sequences(:obj:`int`, `optional`, defaults to 1): - The number of independently computed returned sequences for each element in the batch. - attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on padding token indices. Mask values are in ``[0, 1]``, 1 for - tokens that are not masked, and 0 for masked tokens. - If not provided, will default to a tensor the same shape as :obj:`input_ids` that masks the pad token. - `What are attention masks? <../glossary.html#attention-mask>`__ - decoder_start_token_id (:obj:`int`, `optional`): - If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token. - use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`): - Whether or not the model should use the past last key/values attentions (if applicable to the model) to - speed up decoding. - model_kwargs: - Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. - Return: - :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: - The generated sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or - shorter if all batches finished early due to the :obj:`eos_token_id`. - Examples:: - tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer - model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache. - outputs = model.generate(max_length=40) # do greedy decoding - print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) - tokenizer = AutoTokenizer.from_pretrained('openai-gpt') # Initialize tokenizer - model = AutoModelWithLMHead.from_pretrained('openai-gpt') # Download model and configuration from S3 and cache. - input_context = 'The dog' - input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context - outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog' - for i in range(3): # 3 output sequences were generated - print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True))) - tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer - model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache. - input_context = 'The dog' - input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context - outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True) # generate 3 candidates using sampling - for i in range(3): # 3 output sequences were generated - print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True))) - tokenizer = AutoTokenizer.from_pretrained('ctrl') # Initialize tokenizer - model = AutoModelWithLMHead.from_pretrained('ctrl') # Download model and configuration from S3 and cache. - input_context = 'Legal My neighbor is' # "Legal" is one of the control codes for ctrl - input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context - outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2) # generate sequences - print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True))) - tokenizer = AutoTokenizer.from_pretrained('gpt2') # Initialize tokenizer - model = AutoModelWithLMHead.from_pretrained('gpt2') # Download model and configuration from S3 and cache. - input_context = 'My cute dog' # "Legal" is one of the control codes for ctrl - bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']] - input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context - outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids) # generate sequences without allowing bad_words to be generated - """ - - # We cannot generate if the model does not have a LM head - if self.get_output_embeddings() is None: - raise AttributeError( - "You tried to generate sequences with a model that does not have a LM Head." - "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )" - ) - - max_length = max_length if max_length is not None else self.config.max_length - min_length = min_length if min_length is not None else self.config.min_length - do_sample = do_sample if do_sample is not None else self.config.do_sample - early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping - use_cache = use_cache if use_cache is not None else self.config.use_cache - num_beams = num_beams if num_beams is not None else self.config.num_beams - temperature = temperature if temperature is not None else self.config.temperature - top_k = top_k if top_k is not None else self.config.top_k - top_p = top_p if top_p is not None else self.config.top_p - repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty - bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id - pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id - eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id - length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty - no_repeat_ngram_size = ( - no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size - ) - bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids - num_return_sequences = ( - num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences - ) - decoder_start_token_id = ( - decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id - ) - - if input_ids is not None: - batch_size = input_ids.shape[0] # overriden by the input batch_size - else: - batch_size = 1 - - assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer." - assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer." - assert isinstance(do_sample, bool), "`do_sample` should be a boolean." - assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean." - assert isinstance(use_cache, bool), "`use_cache` should be a boolean." - assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer." - assert temperature > 0, "`temperature` should be strictly positive." - assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer." - assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1." - assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1." - assert input_ids is not None or ( - isinstance(bos_token_id, int) and bos_token_id >= 0 - ), "If input_ids is not defined, `bos_token_id` should be a positive integer." - assert pad_token_id is None or ( - isinstance(pad_token_id, int) and (pad_token_id >= 0) - ), "`pad_token_id` should be a positive integer." - assert (eos_token_id is None) or ( - isinstance(eos_token_id, int) and (eos_token_id >= 0) - ), "`eos_token_id` should be a positive integer." - assert length_penalty > 0, "`length_penalty` should be strictly positive." - assert ( - isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0 - ), "`no_repeat_ngram_size` should be a positive integer." - assert ( - isinstance(num_return_sequences, int) and num_return_sequences > 0 - ), "`num_return_sequences` should be a strictly positive integer." - assert ( - bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list) - ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated" - - if input_ids is None: - assert isinstance(bos_token_id, int) and bos_token_id >= 0, ( - "you should either supply a context to complete as `input_ids` input " - "or a `bos_token_id` (integer >= 0) as a first token to start the generation." - ) - input_ids = torch.full( - (batch_size, 1), - bos_token_id, - dtype=torch.long, - device=next(self.parameters()).device, - ) - else: - assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)." - - # not allow to duplicate outputs when greedy decoding - if do_sample is False: - if num_beams == 1: - # no_beam_search greedy generation conditions - assert ( - num_return_sequences == 1 - ), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1" - - else: - # beam_search greedy generation conditions - assert ( - num_beams >= num_return_sequences - ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences" - - # create attention mask if necessary - # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140 - if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids): - attention_mask = input_ids.ne(pad_token_id).long() - elif attention_mask is None: - attention_mask = input_ids.new_ones(input_ids.shape) - - # set pad_token_id to eos_token_id if not set. Important that this is done after - # attention_mask is created - if pad_token_id is None and eos_token_id is not None: - print( - "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id) - ) - pad_token_id = eos_token_id - - # vocab size - if hasattr(self.config, "vocab_size"): - vocab_size = self.config.vocab_size - elif ( - self.config.is_encoder_decoder - and hasattr(self.config, "decoder") - and hasattr(self.config.decoder, "vocab_size") - ): - vocab_size = self.config.decoder.vocab_size - else: - raise ValueError("either self.config.vocab_size or self.config.decoder.vocab_size needs to be defined") - - # set effective batch size and effective batch multiplier according to do_sample - if do_sample: - effective_batch_size = batch_size * num_return_sequences - effective_batch_mult = num_return_sequences - else: - effective_batch_size = batch_size - effective_batch_mult = 1 - - if self.config.is_encoder_decoder: - if decoder_start_token_id is None: - # see if BOS token can be used for decoder_start_token_id - if bos_token_id is not None: - decoder_start_token_id = bos_token_id - elif ( - hasattr(self.config, "decoder") - and hasattr(self.config.decoder, "bos_token_id") - and self.config.decoder.bos_token_id is not None - ): - decoder_start_token_id = self.config.decoder.bos_token_id - else: - raise ValueError( - "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation" - ) - - assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self) - assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder) - - # get encoder and store encoder outputs - encoder = self.get_encoder() - encoder_outputs: ModelOutput = encoder(input_ids, attention_mask=attention_mask, return_dict=True) - - # Expand input ids if num_beams > 1 or num_return_sequences > 1 - if num_return_sequences > 1 or num_beams > 1: - # TODO: make this a call-back function. - # input_ids=caps, - # input_video_embeds=vfeats, - # attention_mask=attention_mask, - # token_type_ids=token_type_ids, - input_video_embeds = model_kwargs.pop("input_video_embeds", None) - token_type_ids = model_kwargs.pop("token_type_ids", None) - - input_ids_len = input_ids.shape[-1] - input_ids = input_ids.unsqueeze(1).expand( - batch_size, effective_batch_mult * num_beams, input_ids_len) - - input_video_embeds_len, input_video_embeds_hidden = input_video_embeds.size(1), input_video_embeds.size(2) - input_video_embeds = input_video_embeds.unsqueeze(1).expand( - batch_size, effective_batch_mult * num_beams, input_video_embeds_len, input_video_embeds_hidden) - - attention_mask_from_len, attention_mask_to_len = attention_mask.size(1), attention_mask.size(2) - attention_mask = attention_mask.unsqueeze(1).expand( - batch_size, effective_batch_mult * num_beams, attention_mask_from_len, attention_mask_to_len - ) - - token_type_ids_len = token_type_ids.size(1) - token_type_ids = token_type_ids.unsqueeze(1).expand( - batch_size, effective_batch_mult * num_beams, token_type_ids_len - ) - - # contiguous ... - input_ids = input_ids.contiguous().view( - effective_batch_size * num_beams, input_ids_len - ) # shape: (batch_size * num_return_sequences * num_beams, cur_len) - - input_video_embeds = input_video_embeds.contiguous().view( - effective_batch_size * num_beams, input_video_embeds_len, input_video_embeds_hidden) - - attention_mask = attention_mask.contiguous().view( - effective_batch_size * num_beams, attention_mask_from_len, attention_mask_to_len - ) # shape: (batch_size * num_return_sequences * num_beams, cur_len) - - token_type_ids = token_type_ids.contiguous().view( - effective_batch_size * num_beams, token_type_ids_len - ) - - model_kwargs["input_video_embeds"] = input_video_embeds - model_kwargs["token_type_ids"] = token_type_ids - - if self.config.is_encoder_decoder: - device = next(self.parameters()).device - if decoder_input_ids is not None: - # give initial decoder input ids - input_ids = decoder_input_ids.repeat(effective_batch_size * num_beams, 1).to(device) - else: - # create empty decoder input_ids - input_ids = torch.full( - (effective_batch_size * num_beams, 1), - decoder_start_token_id, - dtype=torch.long, - device=device, - ) - cur_len = input_ids.shape[-1] - - assert ( - batch_size == encoder_outputs.last_hidden_state.shape[0] - ), f"expected encoder_outputs.last_hidden_state to have 1st dimension bs={batch_size}, got {encoder_outputs.last_hidden_state.shape[0]} " - - # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1) - expanded_batch_idxs = ( - torch.arange(batch_size) - .view(-1, 1) - .repeat(1, num_beams * effective_batch_mult) - .view(-1) - .to(input_ids.device) - ) - - # expand encoder_outputs - encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select( - 0, expanded_batch_idxs - ) - - # save encoder_outputs in `model_kwargs` - model_kwargs["encoder_outputs"] = encoder_outputs - - else: - cur_len = input_ids.shape[-1] - - assert ( - cur_len < max_length - ), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`" - - if num_beams > 1: - output = self._generate_beam_search( - input_ids, - cur_len=cur_len, - max_length=max_length, - min_length=min_length, - do_sample=do_sample, - early_stopping=early_stopping, - temperature=temperature, - top_k=top_k, - top_p=top_p, - repetition_penalty=repetition_penalty, - no_repeat_ngram_size=no_repeat_ngram_size, - bad_words_ids=bad_words_ids, - pad_token_id=pad_token_id, - eos_token_id=eos_token_id, - batch_size=effective_batch_size, - num_return_sequences=num_return_sequences, - length_penalty=length_penalty, - num_beams=num_beams, - vocab_size=vocab_size, - attention_mask=attention_mask, - use_cache=use_cache, - model_kwargs=model_kwargs, - ) - else: - output = self._generate_no_beam_search( - input_ids, - cur_len=cur_len, - max_length=max_length, - min_length=min_length, - do_sample=do_sample, - temperature=temperature, - top_k=top_k, - top_p=top_p, - repetition_penalty=repetition_penalty, - no_repeat_ngram_size=no_repeat_ngram_size, - bad_words_ids=bad_words_ids, - pad_token_id=pad_token_id, - eos_token_id=eos_token_id, - batch_size=effective_batch_size, - attention_mask=attention_mask, - use_cache=use_cache, - model_kwargs=model_kwargs, - ) - - return output - - def _generate_beam_search( - self, - input_ids, - cur_len, - max_length, - min_length, - do_sample, - early_stopping, - temperature, - top_k, - top_p, - repetition_penalty, - no_repeat_ngram_size, - bad_words_ids, - pad_token_id, - eos_token_id, - batch_size, - num_return_sequences, - length_penalty, - num_beams, - vocab_size, - attention_mask, - use_cache, - model_kwargs, - ): - """Generate sequences for each example with beam search.""" - - # generated hypotheses - generated_hyps = [ - BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping) - for _ in range(batch_size) - ] - - # scores for each sentence in the beam - beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device) - - # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times - if do_sample is False: - beam_scores[:, 1:] = -1e9 - beam_scores = beam_scores.view(-1) # shape (batch_size * num_beams,) - - # cache compute states - past = None - - # done sentences - done = [False for _ in range(batch_size)] - - while cur_len < max_length: - model_inputs = self.prepare_inputs_for_generation( - input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs - ) - outputs = self(**model_inputs, return_dict=True) # (batch_size * num_beams, cur_len, vocab_size) - next_token_logits = outputs.logits[:, -1, :] # (batch_size * num_beams, vocab_size) - - # if model has past, then set the past variable to speed up decoding - if "past_key_values" in outputs: - past = outputs.past_key_values - elif "mems" in outputs: - past = outputs.mems - - if self.config.is_encoder_decoder and do_sample is False: - # TODO (PVP) still a bit hacky here - there might be a better solution - next_token_logits = self.adjust_logits_during_generation( - next_token_logits, cur_len=cur_len, max_length=max_length - ) - - scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) - - scores = self.postprocess_next_token_scores( - scores=scores, - input_ids=input_ids, - no_repeat_ngram_size=no_repeat_ngram_size, - bad_words_ids=bad_words_ids, - cur_len=cur_len, - min_length=min_length, - max_length=max_length, - eos_token_id=eos_token_id, - repetition_penalty=repetition_penalty, - batch_size=batch_size, - num_beams=num_beams, - ) - - assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format( - scores.shape, (batch_size * num_beams, vocab_size) - ) - - if do_sample: - _scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size) - # Temperature - if temperature != 1.0: - _scores = _scores / temperature - # Top-p/top-k filtering - _scores = top_k_top_p_filtering( - _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2 - ) # (batch_size * num_beams, vocab_size) - # re-organize to group the beam together to sample from all beam_idxs - _scores = _scores.contiguous().view( - batch_size, num_beams * vocab_size - ) # (batch_size, num_beams * vocab_size) - - # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search) - probs = F.softmax(_scores, dim=-1) - next_tokens = torch.multinomial(probs, num_samples=2 * num_beams) # (batch_size, num_beams * 2) - # Compute next scores - next_scores = torch.gather(_scores, -1, next_tokens) # (batch_size, num_beams * 2) - # sort the sampled vector to make sure that the first num_beams samples are the best - next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1) - next_tokens = torch.gather(next_tokens, -1, next_scores_indices) # (batch_size, num_beams * 2) - - else: - next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size) - - # re-organize to group the beam together (we are keeping top hypothesis accross beams) - next_scores = next_scores.view( - batch_size, num_beams * vocab_size - ) # (batch_size, num_beams * vocab_size) - - next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True) - - assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams) - - # next batch beam content - next_batch_beam = [] - - # for each sentence - for batch_idx in range(batch_size): - - # if we are done with this sentence, add a pad token - if done[batch_idx]: - assert ( - len(generated_hyps[batch_idx]) >= num_beams - ), "Batch can only be done if at least {} beams have been generated".format(num_beams) - assert ( - eos_token_id is not None and pad_token_id is not None - ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined" - next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch - continue - - # next sentence beam content, this will get added to next_batch_beam - next_sent_beam = [] - - # next tokens for this sentence - for beam_token_rank, (beam_token_id, beam_token_score) in enumerate( - zip(next_tokens[batch_idx], next_scores[batch_idx]) - ): - # get beam and token IDs - beam_id = beam_token_id // vocab_size - token_id = beam_token_id % vocab_size - - effective_beam_id = batch_idx * num_beams + beam_id - # add to generated hypotheses if end of sentence - if (eos_token_id is not None) and (token_id.item() == eos_token_id): - # if beam_token does not belong to top num_beams tokens, it should not be added - is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams - if is_beam_token_worse_than_top_num_beams: - continue - generated_hyps[batch_idx].add( - input_ids[effective_beam_id].clone(), - beam_token_score.item(), - ) - else: - # add next predicted token since it is not eos_token - next_sent_beam.append((beam_token_score, token_id, effective_beam_id)) - - # once the beam for next step is full, don't add more tokens to it. - if len(next_sent_beam) == num_beams: - break - - # Check if we are done so that we can save a pad step if all(done) - done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done( - next_scores[batch_idx].max().item(), cur_len - ) - - # update next beam content - assert len(next_sent_beam) == num_beams, "Beam should always be full" - next_batch_beam.extend(next_sent_beam) - assert len(next_batch_beam) == num_beams * (batch_idx + 1), "We should have added num_beams each step" - - # stop when we are done with each sentence - if all(done): - break - - # sanity check / prepare next batch - assert len(next_batch_beam) == batch_size * num_beams - beam_scores = beam_scores.new([x[0] for x in next_batch_beam]) - beam_tokens = input_ids.new([x[1] for x in next_batch_beam]) - beam_idx = input_ids.new([x[2] for x in next_batch_beam]) - - # re-order batch and update current length - input_ids = input_ids[beam_idx, :] - input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1) - cur_len = cur_len + 1 - - # re-order internal states - if past is not None: - past = self._reorder_cache(past, beam_idx) - - # extend attention_mask for new generated input if only decoder - # (huxu): move out since we trim attention_mask by ourselves. - # if self.config.is_encoder_decoder is False: - # attention_mask = torch.cat( - # [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 - # ) - - # finalize all open beam hypotheses and add to generated hypotheses - for batch_idx in range(batch_size): - if done[batch_idx]: - continue - - # test that beam scores match previously calculated scores if not eos and batch_idx not done - if eos_token_id is not None and all( - (token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx] - ): - assert torch.all( - next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx] - ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format( - next_scores[:, :num_beams][batch_idx], - beam_scores.view(batch_size, num_beams)[batch_idx], - ) - - # need to add best num_beams hypotheses to generated hyps - for beam_id in range(num_beams): - effective_beam_id = batch_idx * num_beams + beam_id - final_score = beam_scores[effective_beam_id].item() - final_tokens = input_ids[effective_beam_id] - generated_hyps[batch_idx].add(final_tokens, final_score) - - # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch - output_batch_size = batch_size if do_sample else batch_size * num_return_sequences - output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences - - # select the best hypotheses - sent_lengths = input_ids.new(output_batch_size) - best = [] - - # retrieve best hypotheses - for i, hypotheses in enumerate(generated_hyps): - sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0]) - for j in range(output_num_return_sequences_per_batch): - effective_batch_idx = output_num_return_sequences_per_batch * i + j - best_hyp = sorted_hyps.pop()[1] - sent_lengths[effective_batch_idx] = len(best_hyp) - best.append(best_hyp) - - # prepare for adding eos - sent_max_len = min(sent_lengths.max().item() + 1, max_length) - decoded = input_ids.new(output_batch_size, sent_max_len) - # shorter batches are padded if needed - if sent_lengths.min().item() != sent_lengths.max().item(): - assert pad_token_id is not None, "`pad_token_id` has to be defined" - decoded.fill_(pad_token_id) - - # fill with hypotheses and eos_token_id if the latter fits in - for i, hypo in enumerate(best): - decoded[i, : sent_lengths[i]] = hypo - if sent_lengths[i] < max_length: - decoded[i, sent_lengths[i]] = eos_token_id - - return decoded - - def _generate_no_beam_search( - self, - input_ids, - cur_len, - max_length, - min_length, - do_sample, - temperature, - top_k, - top_p, - repetition_penalty, - no_repeat_ngram_size, - bad_words_ids, - pad_token_id, - eos_token_id, - batch_size, - attention_mask, - use_cache, - model_kwargs, - ): - """Generate sequences for each example without beam search (num_beams == 1). - All returned sequence are generated independantly. - """ - # length of generated sentences / unfinished sentences - unfinished_sents = input_ids.new(batch_size).fill_(1) - sent_lengths = input_ids.new(batch_size).fill_(max_length) - - past = None - while cur_len < max_length: - model_inputs = self.prepare_inputs_for_generation( - input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs - ) - - outputs = self(**model_inputs, return_dict=True) - next_token_logits = outputs.logits[:, -1, :] - scores = self.postprocess_next_token_scores( - scores=next_token_logits, - input_ids=input_ids, - no_repeat_ngram_size=no_repeat_ngram_size, - bad_words_ids=bad_words_ids, - cur_len=cur_len, - min_length=min_length, - max_length=max_length, - eos_token_id=eos_token_id, - repetition_penalty=repetition_penalty, - batch_size=batch_size, - num_beams=1, - ) - - # if model has past, then set the past variable to speed up decoding - if "past_key_values" in outputs: - past = outputs.past_key_values - elif "mems" in outputs: - past = outputs.mems - - if do_sample: - # Temperature (higher temperature => more likely to sample low probability tokens) - if temperature != 1.0: - scores = scores / temperature - # Top-p/top-k filtering - next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p) - # Sample - probs = F.softmax(next_token_logscores, dim=-1) - next_token = torch.multinomial(probs, num_samples=1).squeeze(1) - else: - # Greedy decoding - next_token = torch.argmax(next_token_logits, dim=-1) - - # print(next_token_logits[0,next_token[0]], next_token_logits[0,eos_token_id]) - - # update generations and finished sentences - if eos_token_id is not None: - # pad finished sentences if eos_token_id exist - tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents) - else: - tokens_to_add = next_token - - # add token and increase length by one - input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1) - cur_len = cur_len + 1 - - if eos_token_id is not None: - eos_in_sents = tokens_to_add == eos_token_id - # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length - is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool() - sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len) - # unfinished_sents is set to zero if eos in sentence - unfinished_sents.mul_((~eos_in_sents).long()) - - # stop when there is a in each sentence, or if we exceed the maximul length - if unfinished_sents.max() == 0: - break - - - # extend attention_mask for new generated input if only decoder - # if self.config.is_encoder_decoder is False: - # attention_mask = torch.cat( - # [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 - # ) - - return input_ids diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/transformermodel.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/transformermodel.py deleted file mode 100644 index e26ea42194bbc6ef50251b132899919cb7d919e0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/models/transformermodel.py +++ /dev/null @@ -1,733 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) Facebook, Inc. All Rights Reserved - -import torch -from torch import nn - -try: - from transformers.modeling_bert import ( - BertEncoder, - BertModel, - BertPredictionHeadTransform, - BertPreTrainedModel, - ) -except ImportError: - pass - -from ..modules import MMBertEmbeddings, VideoTokenMLP - - -# --------------- fine-tuning models --------------- -class MMBertForJoint(BertPreTrainedModel): - """A BertModel with isolated attention mask to separate modality.""" - - def __init__(self, config): - super().__init__(config) - self.videomlp = VideoTokenMLP(config) - self.bert = MMBertModel(config) - self.init_weights() - - def forward( - self, - input_ids=None, - input_video_embeds=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - next_sentence_label=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - separate_forward_split=None, - ): - return_dict = ( - return_dict if return_dict is not None - else self.config.use_return_dict - ) - video_tokens = self.videomlp(input_video_embeds) - - outputs = self.bert( - input_ids, - video_tokens, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - separate_forward_split=separate_forward_split, - ) - - return outputs - - -class MMBertForTokenClassification(BertPreTrainedModel): - """A BertModel similar to MMJointUni, with extra wrapper layer - to be fine-tuned from other pretrained MMFusion model.""" - - def __init__(self, config): - super().__init__(config) - self.videomlp = VideoTokenMLP(config) - self.bert = MMBertModel(config) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - # TODO(huxu): 779 is the number of classes for COIN: move to config? - self.classifier = nn.Linear(config.hidden_size, 779) - self.init_weights() - - def forward( - self, - input_ids=None, - input_video_embeds=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - next_sentence_label=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - separate_forward_split=None, - ): - return_dict = ( - return_dict if return_dict is not None - else self.config.use_return_dict - ) - - video_tokens = self.videomlp(input_video_embeds) - outputs = self.bert( - input_ids, - video_tokens, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - separate_forward_split=separate_forward_split, - ) - - return (self.classifier(outputs[0]),) - - -# ------------ pre-training models ---------------- - -class MMBertForEncoder(BertPreTrainedModel): - """A BertModel for Contrastive Learning.""" - def __init__(self, config): - super().__init__(config) - self.videomlp = VideoTokenMLP(config) - self.bert = MMBertModel(config) - self.init_weights() - - def forward( - self, - input_ids=None, - input_video_embeds=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - return_dict = ( - return_dict if return_dict is not None - else self.config.use_return_dict - ) - if input_video_embeds is not None: - video_tokens = self.videomlp(input_video_embeds) - else: - video_tokens = None - - outputs = self.bert( - input_ids, - video_tokens, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - return outputs - - -class MMBertForMFMMLM(BertPreTrainedModel): - """A BertModel with shared prediction head on MFM-MLM.""" - def __init__(self, config): - super().__init__(config) - self.videomlp = VideoTokenMLP(config) - self.bert = MMBertModel(config) - self.cls = MFMMLMHead(config) - self.hidden_size = config.hidden_size - self.init_weights() - - def get_output_embeddings(self): - return self.cls.predictions.decoder - - def forward( - self, - input_ids=None, - input_video_embeds=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - masked_frame_labels=None, - target_video_hidden_states=None, - non_masked_frame_mask=None, - masked_lm_labels=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - return_dict = ( - return_dict if return_dict is not None - else self.config.use_return_dict - ) - if input_video_embeds is not None: - video_tokens = self.videomlp(input_video_embeds) - else: - video_tokens = None - - if target_video_hidden_states is not None: - target_video_hidden_states = self.videomlp( - target_video_hidden_states) - - non_masked_frame_hidden_states = video_tokens.masked_select( - non_masked_frame_mask.unsqueeze(-1) - ).view(-1, self.hidden_size) - - outputs = self.bert( - input_ids, - video_tokens, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - mfm_scores, prediction_scores = None, None - if masked_frame_labels is not None and masked_lm_labels is not None: - # split the sequence. - text_offset = masked_frame_labels.size(1) + 1 # [CLS] - video_sequence_output = sequence_output[ - :, 1:text_offset - ] # remove [SEP] as not in video_label. - text_sequence_output = torch.cat( - [sequence_output[:, :1], sequence_output[:, text_offset:]], - dim=1 - ) - - hidden_size = video_sequence_output.size(-1) - selected_video_output = video_sequence_output.masked_select( - masked_frame_labels.unsqueeze(-1) - ).view(-1, hidden_size) - - # only compute select tokens to training to speed up. - hidden_size = text_sequence_output.size(-1) - # masked_lm_labels = masked_lm_labels.reshape(-1) - labels_mask = masked_lm_labels != -100 - - selected_text_output = text_sequence_output.masked_select( - labels_mask.unsqueeze(-1) - ).view(-1, hidden_size) - mfm_scores, prediction_scores = self.cls( - selected_video_output, - target_video_hidden_states, - non_masked_frame_hidden_states, - selected_text_output, - ) - - output = ( - mfm_scores, - prediction_scores, - ) + outputs - return output - - -class BertMFMMLMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.decoder = nn.Linear( - config.hidden_size, config.vocab_size, bias=False) - - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - - # Need a link between the two variables so that the bias is correctly - # resized with `resize_token_embeddings` - self.decoder.bias = self.bias - - def forward( - self, - video_hidden_states=None, - target_video_hidden_states=None, - non_masked_frame_hidden_states=None, - text_hidden_states=None, - ): - video_logits, text_logits = None, None - if video_hidden_states is not None: - video_hidden_states = self.transform(video_hidden_states) - non_masked_frame_logits = torch.mm( - video_hidden_states, - non_masked_frame_hidden_states.transpose(1, 0) - ) - masked_frame_logits = torch.bmm( - video_hidden_states.unsqueeze(1), - target_video_hidden_states.unsqueeze(-1), - ).squeeze(-1) - video_logits = torch.cat( - [masked_frame_logits, non_masked_frame_logits], dim=1 - ) - - if text_hidden_states is not None: - text_hidden_states = self.transform(text_hidden_states) - text_logits = self.decoder(text_hidden_states) - return video_logits, text_logits - - -class MFMMLMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertMFMMLMPredictionHead(config) - - def forward( - self, - video_hidden_states=None, - target_video_hidden_states=None, - non_masked_frame_hidden_states=None, - text_hidden_states=None, - ): - video_logits, text_logits = self.predictions( - video_hidden_states, - target_video_hidden_states, - non_masked_frame_hidden_states, - text_hidden_states, - ) - return video_logits, text_logits - - -class MMBertForMTM(MMBertForMFMMLM): - def __init__(self, config): - BertPreTrainedModel.__init__(self, config) - self.videomlp = VideoTokenMLP(config) - self.bert = MMBertModel(config) - self.cls = MTMHead(config) - self.hidden_size = config.hidden_size - self.init_weights() - - -class BertMTMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - self.decoder = nn.Linear( - config.hidden_size, config.vocab_size, bias=False) - - def forward( - self, - video_hidden_states=None, - target_video_hidden_states=None, - non_masked_frame_hidden_states=None, - text_hidden_states=None, - ): - non_masked_frame_hidden_states = non_masked_frame_hidden_states.transpose(1, 0) - video_logits, text_logits = None, None - if video_hidden_states is not None: - video_hidden_states = self.transform(video_hidden_states) - - masked_frame_logits = torch.bmm( - video_hidden_states.unsqueeze(1), - target_video_hidden_states.unsqueeze(-1), - ).squeeze(-1) - - non_masked_frame_logits = torch.mm( - video_hidden_states, - non_masked_frame_hidden_states - ) - video_on_vocab_logits = self.decoder(video_hidden_states) - video_logits = torch.cat([ - masked_frame_logits, - non_masked_frame_logits, - video_on_vocab_logits], dim=1) - - if text_hidden_states is not None: - text_hidden_states = self.transform(text_hidden_states) - # text first so label does not need to be shifted. - text_on_vocab_logits = self.decoder(text_hidden_states) - text_on_video_logits = torch.mm( - text_hidden_states, - non_masked_frame_hidden_states - ) - text_logits = torch.cat([ - text_on_vocab_logits, - text_on_video_logits - ], dim=1) - - return video_logits, text_logits - - -class MTMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertMTMPredictionHead(config) - - def forward( - self, - video_hidden_states=None, - target_video_hidden_states=None, - non_masked_frame_hidden_states=None, - text_hidden_states=None, - ): - video_logits, text_logits = self.predictions( - video_hidden_states, - target_video_hidden_states, - non_masked_frame_hidden_states, - text_hidden_states, - ) - return video_logits, text_logits - - -class MMBertModel(BertModel): - """MMBertModel has MMBertEmbedding to support video tokens.""" - - def __init__(self, config, add_pooling_layer=True): - super().__init__(config) - # overwrite embedding - self.embeddings = MMBertEmbeddings(config) - self.encoder = MultiLayerAttentionMaskBertEncoder(config) - self.init_weights() - - def forward( - self, - input_ids=None, - input_video_embeds=None, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - separate_forward_split=None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None - else self.config.use_return_dict - ) - - if input_ids is not None and inputs_embeds is not None: - raise ValueError( - "You cannot specify both input_ids " - "and inputs_embeds at the same time" - ) - elif input_ids is not None: - if input_video_embeds is not None: - input_shape = ( - input_ids.size(0), - input_ids.size(1) + input_video_embeds.size(1), - ) - else: - input_shape = ( - input_ids.size(0), - input_ids.size(1), - ) - elif inputs_embeds is not None: - if input_video_embeds is not None: - input_shape = ( - inputs_embeds.size(0), - inputs_embeds.size(1) + input_video_embeds.size(1), - ) - else: - input_shape = ( - input_ids.size(0), - input_ids.size(1), - ) - else: - raise ValueError( - "You have to specify either input_ids or inputs_embeds") - - device = input_ids.device if input_ids is not None \ - else inputs_embeds.device - - if attention_mask is None: - attention_mask = torch.ones(input_shape, device=device) - if token_type_ids is None: - token_type_ids = torch.zeros( - input_shape, dtype=torch.long, device=device) - - # We can provide a self-attention mask of dimensions - # [batch_size, from_seq_length, to_seq_length] - # ourselves in which case - # we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = \ - self.get_extended_attention_mask( - attention_mask, input_shape, device) - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to - # [batch_size, num_heads, seq_length, seq_length] - if self.config.is_decoder and encoder_hidden_states is not None: - ( - encoder_batch_size, - encoder_sequence_length, - _, - ) = encoder_hidden_states.size() - encoder_hidden_shape = ( - encoder_batch_size, encoder_sequence_length) - if encoder_attention_mask is None: - encoder_attention_mask = torch.ones( - encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask( - encoder_attention_mask - ) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or - # [num_hidden_layers x num_heads] - # and head_mask is converted to shape - # [num_hidden_layers x batch x num_heads x seq_length x seq_length] - - head_mask = self.get_head_mask( - head_mask, self.config.num_hidden_layers) - - embedding_output = self.embeddings( - input_ids, - input_video_embeds, - position_ids=position_ids, - token_type_ids=token_type_ids, - inputs_embeds=inputs_embeds, - ) - - if separate_forward_split is not None: - split_embedding_output = \ - embedding_output[:, :separate_forward_split] - split_extended_attention_mask = extended_attention_mask[ - :, :, :, :separate_forward_split, :separate_forward_split - ] - split_encoder_outputs = self.encoder( - split_embedding_output, - attention_mask=split_extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - assert ( - len(split_encoder_outputs) <= 2 - ), "we do not support merge on attention for now." - encoder_outputs = [] - encoder_outputs.append([split_encoder_outputs[0]]) - if len(split_encoder_outputs) == 2: - encoder_outputs.append([]) - for _all_hidden_states in split_encoder_outputs[1]: - encoder_outputs[-1].append([_all_hidden_states]) - - split_embedding_output = \ - embedding_output[:, separate_forward_split:] - split_extended_attention_mask = extended_attention_mask[ - :, :, :, separate_forward_split:, separate_forward_split: - ] - - split_encoder_outputs = self.encoder( - split_embedding_output, - attention_mask=split_extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - assert ( - len(split_encoder_outputs) <= 2 - ), "we do not support merge on attention for now." - encoder_outputs[0].append(split_encoder_outputs[0]) - encoder_outputs[0] = torch.cat(encoder_outputs[0], dim=1) - if len(split_encoder_outputs) == 2: - for layer_idx, _all_hidden_states in enumerate( - split_encoder_outputs[1] - ): - encoder_outputs[1][layer_idx].append(_all_hidden_states) - encoder_outputs[1][layer_idx] = torch.cat( - encoder_outputs[1][layer_idx], dim=1 - ) - encoder_outputs = tuple(encoder_outputs) - else: - encoder_outputs = self.encoder( - embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = encoder_outputs[0] - pooled_output = ( - self.pooler(sequence_output) if self.pooler is not None else None - ) - - return (sequence_output, pooled_output) + encoder_outputs[1:] - - def get_extended_attention_mask(self, attention_mask, input_shape, device): - """This is borrowed from `modeling_utils.py` with the support of - multi-layer attention masks. - The second dim is expected to be number of layers. - See `MMAttentionMaskProcessor`. - Makes broadcastable attention and causal masks so that future - and masked tokens are ignored. - - Arguments: - attention_mask (:obj:`torch.Tensor`): - Mask with ones indicating tokens to attend to, - zeros for tokens to ignore. - input_shape (:obj:`Tuple[int]`): - The shape of the input to the model. - device: (:obj:`torch.device`): - The device of the input to the model. - - Returns: - :obj:`torch.Tensor` The extended attention mask, \ - with a the same dtype as :obj:`attention_mask.dtype`. - """ - # We can provide a self-attention mask of dimensions - # [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable - # to all heads. - if attention_mask.dim() == 4: - extended_attention_mask = attention_mask[:, :, None, :, :] - extended_attention_mask = extended_attention_mask.to( - dtype=self.dtype - ) # fp16 compatibility - extended_attention_mask = (1.0 - extended_attention_mask) \ - * -10000.0 - return extended_attention_mask - else: - return super().get_extended_attention_mask( - attention_mask, input_shape, device - ) - - -class MultiLayerAttentionMaskBertEncoder(BertEncoder): - """extend BertEncoder with the capability of - multiple layers of attention mask.""" - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - output_attentions=False, - output_hidden_states=False, - return_dict=False, - ): - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - for i, layer_module in enumerate(self.layer): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - layer_head_mask = head_mask[i] if head_mask is not None else None - - layer_attention_mask = ( - attention_mask[:, i, :, :, :] - if attention_mask.dim() == 5 - else attention_mask - ) - - if getattr(self.config, "gradient_checkpointing", False): - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - layer_attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - ) - else: - layer_outputs = layer_module( - hidden_states, - layer_attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions, - ) - hidden_states = layer_outputs[0] - if output_attentions: - all_attentions = all_attentions + (layer_outputs[1],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - return tuple( - v - for v in [hidden_states, all_hidden_states, all_attentions] - if v is not None - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/__init__.py deleted file mode 100644 index 4c78594c21d7ccdcb2ab11c918dd211adce27c14..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .mm import * - -try: - from .expmm import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/mm.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/mm.py deleted file mode 100644 index 141020266fef96ca1d771e3303a16e4317aa9b09..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/mm.py +++ /dev/null @@ -1,144 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) Facebook, Inc. All Rights Reserved - - -import torch -from torch import nn - -try: - from transformers.modeling_bert import ( - ACT2FN, - BertEmbeddings, - ) -except ImportError: - pass - - -class VideoTokenMLP(nn.Module): - def __init__(self, config): - super().__init__() - input_dim = config.input_dim if hasattr(config, "input_dim") else 512 - self.linear1 = nn.Linear(input_dim, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size) - self.activation = ACT2FN[config.hidden_act] - self.linear2 = nn.Linear(config.hidden_size, config.hidden_size) - - def forward(self, hidden_states): - hidden_states = self.linear1(hidden_states) - hidden_states = self.activation(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - hidden_states = self.linear2(hidden_states) - return hidden_states - - -class MMBertEmbeddings(BertEmbeddings): - def __init__(self, config): - super().__init__(config) - self.max_video_len = config.max_video_len - if hasattr(config, "use_seg_emb") and config.use_seg_emb: - """the original VLM paper uses seg_embeddings for temporal space. - although not used it changed the randomness of initialization. - we keep it for reproducibility. - """ - self.seg_embeddings = nn.Embedding(256, config.hidden_size) - - def forward( - self, - input_ids, - input_video_embeds, - token_type_ids=None, - position_ids=None, - inputs_embeds=None, - ): - input_tensor = input_ids if input_ids is not None else inputs_embeds - if input_video_embeds is not None: - input_shape = ( - input_tensor.size(0), - input_tensor.size(1) + input_video_embeds.size(1), - ) - else: - input_shape = (input_tensor.size(0), input_tensor.size(1)) - - if position_ids is None: - """ - Auto skip position embeddings for text only case. - use cases: - (1) action localization and segmentation: - feed in len-1 dummy video token needs text part to - skip input_video_embeds.size(1) for the right - position_ids for video [SEP] and rest text tokens. - (2) MMFusionShare for two forward passings: - in `forward_text`: input_video_embeds is None. - need to skip video [SEP] token. - - # video_len + 1: [CLS] + video_embed - # self.max_video_len + 1: [SEP] for video. - # self.max_video_len + 2: [SEP] for video. - # self.max_video_len + input_ids.size(1): rest for text. - """ - if input_video_embeds is not None: - video_len = input_video_embeds.size(1) - starting_offset = self.max_video_len + 1 # video [SEP] - ending_offset = self.max_video_len + input_ids.size(1) - else: - video_len = 0 - starting_offset = self.max_video_len + 2 # first text token. - ending_offset = self.max_video_len + input_ids.size(1) + 1 - position_ids = torch.cat([ - self.position_ids[:, :video_len + 1], - self.position_ids[:, starting_offset:ending_offset] - ], dim=1) - - if token_type_ids is None: - token_type_ids = torch.zeros( - input_shape, dtype=torch.long, device=self.position_ids.device - ) - - """ - the format of input_ids is [CLS] [SEP] caption [SEP] padding. - the goal is to build [CLS] video tokens [SEP] caption [SEP] . - """ - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - if input_video_embeds is not None: - inputs_mm_embeds = torch.cat([ - inputs_embeds[:, :1], input_video_embeds, inputs_embeds[:, 1:] - ], dim=1) - else: - # text only for `MMFusionShare`. - inputs_mm_embeds = inputs_embeds - - position_embeddings = self.position_embeddings(position_ids) - token_type_embeddings = self.token_type_embeddings(token_type_ids) - embeddings = inputs_mm_embeds + position_embeddings - embeddings += token_type_embeddings - - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class AlignHead(nn.Module): - """this will load pre-trained weights for NSP, which is desirable.""" - - def __init__(self, config): - super().__init__() - self.seq_relationship = nn.Linear(config.hidden_size, 2) - - def forward(self, dropout_pooled_output): - logits = self.seq_relationship(dropout_pooled_output) - return logits diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/retri.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/retri.py deleted file mode 100644 index dcf70367f31939e3e42effc0be3b888c421f95ab..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/retri.py +++ /dev/null @@ -1,430 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 pickle -import time - -import numpy as np - -try: - import faiss -except ImportError: - pass - -from collections import defaultdict - -from ..utils import get_local_rank, print_on_rank0 - - -class VectorRetriever(object): - """ - How2 Video Retriver. - Reference usage of FAISS: - https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py - """ - - def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train): - if db_type == "flatl2": - quantizer = faiss.IndexFlatL2(hidden_size) # the other index - self.db = faiss.IndexIVFFlat( - quantizer, hidden_size, cent, faiss.METRIC_L2) - elif db_type == "pq": - self.db = faiss.index_factory( - hidden_size, f"IVF{cent}_HNSW32,PQ32" - ) - else: - raise ValueError("unknown type of db", db_type) - self.train_thres = cent * examples_per_cent_to_train - self.train_cache = [] - self.train_len = 0 - self.videoid_to_vectoridx = {} - self.vectoridx_to_videoid = None - self.make_direct_maps_done = False - - def make_direct_maps(self): - faiss.downcast_index(self.db).make_direct_map() - - def __len__(self): - return self.db.ntotal - - def save(self, out_dir): - faiss.write_index( - self.db, - os.path.join(out_dir, "faiss_idx") - ) - with open( - os.path.join( - out_dir, "videoid_to_vectoridx.pkl"), - "wb") as fw: - pickle.dump( - self.videoid_to_vectoridx, fw, - protocol=pickle.HIGHEST_PROTOCOL - ) - - def load(self, out_dir): - fn = os.path.join(out_dir, "faiss_idx") - self.db = faiss.read_index(fn) - with open( - os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr: - self.videoid_to_vectoridx = pickle.load(fr) - - def add(self, hidden_states, video_ids, last=False): - assert len(hidden_states) == len(video_ids), "{}, {}".format( - str(len(hidden_states)), str(len(video_ids))) - assert len(hidden_states.shape) == 2 - assert hidden_states.dtype == np.float32 - - valid_idx = [] - for idx, video_id in enumerate(video_ids): - if video_id not in self.videoid_to_vectoridx: - valid_idx.append(idx) - self.videoid_to_vectoridx[video_id] = \ - len(self.videoid_to_vectoridx) - - hidden_states = hidden_states[valid_idx] - if not self.db.is_trained: - self.train_cache.append(hidden_states) - self.train_len += hidden_states.shape[0] - if self.train_len < self.train_thres: - return - self.finalize_training() - else: - self.db.add(hidden_states) - - def finalize_training(self): - hidden_states = np.concatenate(self.train_cache, axis=0) - del self.train_cache - local_rank = get_local_rank() - if local_rank == 0: - start = time.time() - print("training db on", self.train_thres, "/", self.train_len) - self.db.train(hidden_states[:self.train_thres]) - if local_rank == 0: - print("training db for", time.time() - start) - self.db.add(hidden_states) - - def search( - self, - query_hidden_states, - orig_dist, - ): - if len(self.videoid_to_vectoridx) != self.db.ntotal: - raise ValueError( - "cannot search: size mismatch in-between index and db", - len(self.videoid_to_vectoridx), - self.db.ntotal - ) - - if self.vectoridx_to_videoid is None: - self.vectoridx_to_videoid = { - self.videoid_to_vectoridx[videoid]: videoid - for videoid in self.videoid_to_vectoridx - } - assert len(self.vectoridx_to_videoid) \ - == len(self.videoid_to_vectoridx) - - # MultilingualFaissDataset uses the following; not sure the purpose. - # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) - queried_dist, index = self.db.search(query_hidden_states, 1) - queried_dist, index = queried_dist[:, 0], index[:, 0] - - outputs = np.array( - [self.vectoridx_to_videoid[_index] - if _index != -1 else (-1, -1, -1) for _index in index], - dtype=np.int32) - outputs[queried_dist <= orig_dist] = -1 - return outputs - - def search_by_video_ids( - self, - video_ids, - retri_factor - ): - if len(self.videoid_to_vectoridx) != self.db.ntotal: - raise ValueError( - len(self.videoid_to_vectoridx), - self.db.ntotal - ) - - if not self.make_direct_maps_done: - self.make_direct_maps() - - if self.vectoridx_to_videoid is None: - self.vectoridx_to_videoid = { - self.videoid_to_vectoridx[videoid]: videoid - for videoid in self.videoid_to_vectoridx - } - assert len(self.vectoridx_to_videoid) \ - == len(self.videoid_to_vectoridx) - - query_hidden_states = [] - vector_ids = [] - for video_id in video_ids: - vector_id = self.videoid_to_vectoridx[video_id] - vector_ids.append(vector_id) - query_hidden_state = self.db.reconstruct(vector_id) - query_hidden_states.append(query_hidden_state) - query_hidden_states = np.stack(query_hidden_states) - - # MultilingualFaissDataset uses the following; not sure the reason. - # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) - _, index = self.db.search(query_hidden_states, retri_factor) - outputs = [] - for sample_idx, sample in enumerate(index): - # the first video_id is always the video itself. - cands = [video_ids[sample_idx]] - for vector_idx in sample: - if vector_idx >= 0 \ - and vector_ids[sample_idx] != vector_idx: - cands.append( - self.vectoridx_to_videoid[vector_idx] - ) - outputs.append(cands) - return outputs - - -class VectorRetrieverDM(VectorRetriever): - """ - with direct map. - How2 Video Retriver. - Reference usage of FAISS: - https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py - """ - - def __init__( - self, - hidden_size, - cent, - db_type, - examples_per_cent_to_train - ): - super().__init__( - hidden_size, cent, db_type, examples_per_cent_to_train) - self.make_direct_maps_done = False - - def make_direct_maps(self): - faiss.downcast_index(self.db).make_direct_map() - self.make_direct_maps_done = True - - def search( - self, - query_hidden_states, - orig_dist, - ): - if len(self.videoid_to_vectoridx) != self.db.ntotal: - raise ValueError( - len(self.videoid_to_vectoridx), - self.db.ntotal - ) - - if not self.make_direct_maps_done: - self.make_direct_maps() - if self.vectoridx_to_videoid is None: - self.vectoridx_to_videoid = { - self.videoid_to_vectoridx[videoid]: videoid - for videoid in self.videoid_to_vectoridx - } - assert len(self.vectoridx_to_videoid) \ - == len(self.videoid_to_vectoridx) - - # MultilingualFaissDataset uses the following; not sure the reason. - # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) - queried_dist, index = self.db.search(query_hidden_states, 1) - outputs = [] - for sample_idx, sample in enumerate(index): - # and queried_dist[sample_idx] < thres \ - if sample >= 0 \ - and queried_dist[sample_idx] < orig_dist[sample_idx]: - outputs.append(self.vectoridx_to_videoid[sample]) - else: - outputs.append(None) - return outputs - - def search_by_video_ids( - self, - video_ids, - retri_factor=8 - ): - if len(self.videoid_to_vectoridx) != self.db.ntotal: - raise ValueError( - len(self.videoid_to_vectoridx), - self.db.ntotal - ) - - if not self.make_direct_maps_done: - self.make_direct_maps() - if self.vectoridx_to_videoid is None: - self.vectoridx_to_videoid = { - self.videoid_to_vectoridx[videoid]: videoid - for videoid in self.videoid_to_vectoridx - } - assert len(self.vectoridx_to_videoid) \ - == len(self.videoid_to_vectoridx) - - query_hidden_states = [] - vector_ids = [] - for video_id in video_ids: - vector_id = self.videoid_to_vectoridx[video_id] - vector_ids.append(vector_id) - query_hidden_state = self.db.reconstruct(vector_id) - query_hidden_states.append(query_hidden_state) - query_hidden_states = np.stack(query_hidden_states) - - # MultilingualFaissDataset uses the following; not sure the reason. - # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) - _, index = self.db.search(query_hidden_states, retri_factor) - outputs = [] - for sample_idx, sample in enumerate(index): - # the first video_id is always the video itself. - cands = [video_ids[sample_idx]] - for vector_idx in sample: - if vector_idx >= 0 \ - and vector_ids[sample_idx] != vector_idx: - cands.append( - self.vectoridx_to_videoid[vector_idx] - ) - outputs.append(cands) - return outputs - - -class MMVectorRetriever(VectorRetrieverDM): - """ - multimodal vector retriver: - text retrieve video or video retrieve text. - """ - - def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train): - super().__init__( - hidden_size, cent, db_type, examples_per_cent_to_train) - video_db = self.db - super().__init__( - hidden_size, cent, db_type, examples_per_cent_to_train) - text_db = self.db - self.db = {"video": video_db, "text": text_db} - self.video_to_videoid = defaultdict(list) - - def __len__(self): - assert self.db["video"].ntotal == self.db["text"].ntotal - return self.db["video"].ntotal - - def make_direct_maps(self): - faiss.downcast_index(self.db["video"]).make_direct_map() - faiss.downcast_index(self.db["text"]).make_direct_map() - - def save(self, out_dir): - faiss.write_index( - self.db["video"], - os.path.join(out_dir, "video_faiss_idx") - ) - faiss.write_index( - self.db["text"], - os.path.join(out_dir, "text_faiss_idx") - ) - - with open( - os.path.join( - out_dir, "videoid_to_vectoridx.pkl"), - "wb") as fw: - pickle.dump( - self.videoid_to_vectoridx, fw, - protocol=pickle.HIGHEST_PROTOCOL - ) - - def load(self, out_dir): - fn = os.path.join(out_dir, "video_faiss_idx") - video_db = faiss.read_index(fn) - fn = os.path.join(out_dir, "text_faiss_idx") - text_db = faiss.read_index(fn) - self.db = {"video": video_db, "text": text_db} - with open( - os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr: - self.videoid_to_vectoridx = pickle.load(fr) - self.video_to_videoid = defaultdict(list) - - def add(self, hidden_states, video_ids): - """hidden_states is a pair `(video, text)`""" - assert len(hidden_states) == len(video_ids), "{}, {}".format( - str(len(hidden_states)), str(len(video_ids))) - assert len(hidden_states.shape) == 3 - assert len(self.video_to_videoid) == 0 - - valid_idx = [] - for idx, video_id in enumerate(video_ids): - if video_id not in self.videoid_to_vectoridx: - valid_idx.append(idx) - self.videoid_to_vectoridx[video_id] = \ - len(self.videoid_to_vectoridx) - - batch_size = hidden_states.shape[0] - hidden_states = hidden_states[valid_idx] - - hidden_states = np.transpose(hidden_states, (1, 0, 2)).copy() - if not self.db["video"].is_trained: - self.train_cache.append(hidden_states) - train_len = batch_size * len(self.train_cache) - if train_len < self.train_thres: - return - - hidden_states = np.concatenate(self.train_cache, axis=1) - del self.train_cache - self.db["video"].train(hidden_states[0, :self.train_thres]) - self.db["text"].train(hidden_states[1, :self.train_thres]) - self.db["video"].add(hidden_states[0]) - self.db["text"].add(hidden_states[1]) - - def get_clips_by_video_id(self, video_id): - if not self.video_to_videoid: - for video_id, video_clip, text_clip in self.videoid_to_vectoridx: - self.video_to_videoid[video_id].append( - (video_id, video_clip, text_clip)) - return self.video_to_videoid[video_id] - - def search( - self, - video_ids, - target_modality, - retri_factor=8 - ): - if len(self.videoid_to_vectoridx) != len(self): - raise ValueError( - len(self.videoid_to_vectoridx), - len(self) - ) - - if not self.make_direct_maps_done: - self.make_direct_maps() - if self.vectoridx_to_videoid is None: - self.vectoridx_to_videoid = { - self.videoid_to_vectoridx[videoid]: videoid - for videoid in self.videoid_to_vectoridx - } - assert len(self.vectoridx_to_videoid) \ - == len(self.videoid_to_vectoridx) - - src_modality = "text" if target_modality == "video" else "video" - - query_hidden_states = [] - vector_ids = [] - for video_id in video_ids: - vector_id = self.videoid_to_vectoridx[video_id] - vector_ids.append(vector_id) - query_hidden_state = self.db[src_modality].reconstruct(vector_id) - query_hidden_states.append(query_hidden_state) - query_hidden_states = np.stack(query_hidden_states) - - # MultilingualFaissDataset uses the following; not sure the reason. - # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10) - _, index = self.db[target_modality].search( - query_hidden_states, retri_factor) - outputs = [] - for sample_idx, sample in enumerate(index): - cands = [] - for vector_idx in sample: - if vector_idx >= 0: - cands.append( - self.vectoridx_to_videoid[vector_idx] - ) - outputs.append(cands) - return outputs diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/vectorpool.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/vectorpool.py deleted file mode 100644 index 249388dd06224bdedc6285cd268410bd61ad29ae..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/modules/vectorpool.py +++ /dev/null @@ -1,247 +0,0 @@ -# Copyright (c) Facebook, Inc. All Rights Reserved - -import os -import pickle - -import numpy as np -import torch - -from ..utils import get_local_rank -from . import retri - - -class VectorPool(object): - """ - Base class of retrieval space. - """ - - def __init__(self, config): - from transformers import AutoConfig - self.hidden_size = AutoConfig.from_pretrained( - config.dataset.bert_name).hidden_size - self.retriever_cls = getattr(retri, config.retriever_cls) - - def __call__(self, sample, **kwargs): - raise NotImplementedError - - def build_retriver( - self, - retriever_cls=None, - hidden_size=None, - centroids=512, - db_type="flatl2", - examples_per_cent_to_train=48 - ): - - """merge results from multiple gpus and return a retriver..""" - self.retriver = retriever_cls( - hidden_size, centroids, db_type, examples_per_cent_to_train) - return self.retriver - - def __repr__(self): - if hasattr(self, "retriver"): - retriver_name = str(len(self.retriver)) - else: - retriver_name = "no retriver field yet" - return self.__class__.__name__ \ - + "(" + retriver_name + ")" - - -class VideoVectorPool(VectorPool): - """ - average clips of a video as video representation. - """ - def __init__(self, config): - super().__init__(config) - self.build_retriver(self.retriever_cls, self.hidden_size) - - def __call__(self, sample, subsampling, **kwargs): - hidden_states = ( - sample["pooled_video"] + sample["pooled_text"]) / 2. - hidden_states = hidden_states.view( - -1, subsampling, - hidden_states.size(-1)) - hidden_states = torch.mean(hidden_states, dim=1) - hidden_states = hidden_states.cpu().detach().numpy() - video_ids = [] - for offset_idx, video_id in enumerate(sample["video_id"]): - if isinstance(video_id, tuple) and len(video_id) == 3: - # a sharded video_id. - video_id = video_id[0] - video_ids.append(video_id) - assert len(video_ids) == len(hidden_states) - self.retriver.add( - hidden_states.astype("float32"), - video_ids - ) - - -class DistributedVectorPool(VectorPool): - """ - support sync of multiple gpus/nodes. - """ - def __init__(self, config): - super().__init__(config) - self.out_dir = os.path.join( - config.fairseq.checkpoint.save_dir, - "retri") - os.makedirs(self.out_dir, exist_ok=True) - self.hidden_states = [] - self.video_ids = [] - - def build_retriver( - self, - retriever_cls=None, - hidden_size=None, - centroids=4096, - db_type="flatl2", - examples_per_cent_to_train=48 - ): - if retriever_cls is None: - retriever_cls = self.retriever_cls - if hidden_size is None: - hidden_size = self.hidden_size - """merge results from multiple gpus and return a retriver..""" - if torch.distributed.is_initialized(): - self.save() - # sync saving. - torch.distributed.barrier() - world_size = torch.distributed.get_world_size() - else: - world_size = 1 - self.retriver = retriever_cls( - hidden_size, centroids, db_type, examples_per_cent_to_train) - # each gpu process has its own retriever. - for local_rank in range(world_size): - if get_local_rank() == 0: - print("load local_rank", local_rank) - hidden_states, video_ids = self.load(local_rank) - hidden_states = hidden_states.astype("float32") - self.retriver.add(hidden_states, video_ids) - return self.retriver - - def load(self, local_rank): - hidden_states = np.load( - os.path.join( - self.out_dir, - "hidden_state" + str(local_rank) + ".npy" - ) - ) - - with open( - os.path.join( - self.out_dir, "video_id" + str(local_rank) + ".pkl"), - "rb") as fr: - video_ids = pickle.load(fr) - return hidden_states, video_ids - - def save(self): - hidden_states = np.vstack(self.hidden_states) - assert len(hidden_states) == len(self.video_ids), "{}, {}".format( - len(hidden_states), - len(self.video_ids) - ) - local_rank = torch.distributed.get_rank() \ - if torch.distributed.is_initialized() else 0 - - np.save( - os.path.join( - self.out_dir, - "hidden_state" + str(local_rank) + ".npy"), - hidden_states) - - with open( - os.path.join( - self.out_dir, - "video_id" + str(local_rank) + ".pkl"), - "wb") as fw: - pickle.dump( - self.video_ids, - fw, - protocol=pickle.HIGHEST_PROTOCOL - ) - - -class DistributedVideoVectorPool(DistributedVectorPool): - """ - average clips of a video as video representation. - """ - def __call__(self, sample, subsampling, **kwargs): - hidden_states = ( - sample["pooled_video"] + sample["pooled_text"]) / 2. - hidden_states = hidden_states.view( - -1, subsampling, - hidden_states.size(-1)) - hidden_states = torch.mean(hidden_states, dim=1) - hidden_states = hidden_states.cpu().detach().numpy() - video_ids = [] - for offset_idx, video_id in enumerate(sample["video_id"]): - if isinstance(video_id, tuple) and len(video_id) == 3: - # a sharded video_id. - video_id = video_id[0] - video_ids.append(video_id) - assert len(video_ids) == len(hidden_states) - self.hidden_states.append(hidden_states) - self.video_ids.extend(video_ids) - - -# ------------ the following are deprecated -------------- - -class TextClipVectorPool(VectorPool): - def __init__(self, config): - from transformers import AutoConfig - hidden_size = AutoConfig.from_pretrained( - config.dataset.bert_name).hidden_size - retriever_cls = getattr(retri, config.retriever_cls) - self.build_retriver(retriever_cls, hidden_size) - - def __call__(self, sample, **kwargs): - clip_meta = sample["clip_meta"].cpu() - assert torch.all(torch.le(clip_meta[:, 4], clip_meta[:, 5])) - text_meta = [tuple(item.tolist()) for item in clip_meta[:, 3:]] - - if hasattr(self, "retriver"): - # build_retriver is called. - self.retriver.add( - sample["pooled_text"].cpu().numpy().astype("float32"), - text_meta - ) - else: - raise NotImplementedError - - -class MMClipVectorPool(VectorPool): - """ - Multimodal Clip-level vector pool. - """ - def __init__(self, out_dir): - """use hidden_states to store `(video, text)`.""" - """use video_ids to store `(video_id, start, end)`.""" - super().__init__(out_dir) - - def __call__(self, sample, **kwargs): - pooled_video = sample["pooled_video"].cpu().unsqueeze(1).numpy() - pooled_text = sample["pooled_text"].cpu().unsqueeze(1).numpy() - - self.hidden_states.append( - np.concatenate([pooled_video, pooled_text], axis=1) - ) - - video_starts = sample["video_start"].cpu() - video_ends = sample["video_end"].cpu() - assert torch.all(torch.le(video_starts, video_ends)) - - text_starts = sample["text_start"].cpu() - text_ends = sample["text_end"].cpu() - assert torch.all(torch.le(text_starts, text_ends)) - subsample_size = sample["pooled_video"].size(0) // len(sample["video_id"]) - video_ids = [video_id for video_id in sample["video_id"] - for _ in range(subsample_size) - ] - for video_id, video_start, video_end, text_start, text_end in zip( - video_ids, video_starts, video_ends, text_starts, text_ends): - self.video_ids.append(( - video_id, - (int(video_start), int(video_end)), - (int(text_start), int(text_end)) - )) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/__init__.py deleted file mode 100644 index 9148ba8cbcf14fc12b8807c23d4938840c3d777d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/__init__.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 .dsprocessor import * -from .how2processor import * -from .how2retriprocessor import * -from .processor import * - -try: - from .codecprocessor import * - from .expcodecprocessor import * - from .expdsprocessor import * - from .expfeatureencoder import * - from .exphow2processor import * - from .exphow2retriprocessor import * - from .expprocessor import * - from .rawvideoprocessor import * - from .webvidprocessor import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/dedupprocessor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/dedupprocessor.py deleted file mode 100644 index 143ba955d4f114af6e19c1fcbbc9496c79b0a411..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/dedupprocessor.py +++ /dev/null @@ -1,243 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 pickle -import random - -import numpy as np -from tqdm import tqdm - - -class CaptionDedupProcessor(object): - """remove overlapping of caption sentences(clip). - Some statistics: - caption: - {'t_clip_len': 246.6448431320854, - 'video_len': 281.09174795676245, - 'clip_tps': 0.8841283727427481, - 'video_tps': 0.7821156477732097, - 'min_clip_len': 0.0, - 'max_clip_len': 398.3, - 'mean_clip_len': 3.196580003006861, - 'num_clip': 77.15897706301081} - - raw_caption: - {'t_clip_len': 238.95908778424115, - 'video_len': 267.5914859862507, - 'clip_tps': 2.4941363624267963, - 'video_tps': 2.258989769647173, - 'min_clip_len': 0.0, - 'max_clip_len': 398.3, - 'mean_clip_len': 3.0537954186814265, - 'num_clip': 78.24986779481756} - """ - - def __init__(self, pkl_file): - with open(pkl_file, "rb") as fd: - self.data = pickle.load(fd) - self.stat = { - "t_clip_len": [], - "video_len": [], - "clip_tps": [], - "video_tps": [], - "clip_len": [], - } - - def __call__(self): - for idx, video_id in enumerate(tqdm(self.data)): - caption = json.loads(self.data[video_id]) - caption = self._dedup(caption) - if idx < 4096: # for the first 4096 examples, compute the statistics. - self.save_stat(video_id, caption) - self.data[video_id] = json.dumps(caption) - self.print_stat() - - def single(self, video_id): - caption = json.loads(self.data[video_id]) - for clip_idx, (start, end, text) in enumerate( - zip(caption["start"], caption["end"], caption["text"]) - ): - print(start, end, text) - print("@" * 100) - caption = self._dedup(caption) - for clip_idx, (start, end, text) in enumerate( - zip(caption["start"], caption["end"], caption["text"]) - ): - print(start, end, text) - print("#" * 100) - self.save_stat(video_id, caption) - self.print_stat() - - def finalize(self, tgt_fn): - with open(tgt_fn, "wb") as fw: - pickle.dump(self.data, fw, pickle.HIGHEST_PROTOCOL) - - def save_stat(self, video_id, caption): - video_fn = os.path.join( - "data/feat/feat_how2_s3d", video_id + ".npy" - ) - if os.path.isfile(video_fn): - with open(video_fn, "rb", 1) as fr: # 24 is the buffer size. buffered - version = np.lib.format.read_magic(fr) - shape, fortran, dtype = np.lib.format._read_array_header(fr, version) - video_len = shape[0] - - t_clip_len = 0.0 - t_tokens = 0 - for idx, (start, end, text) in enumerate( - zip(caption["start"], caption["end"], caption["text"]) - ): - clip_len = ( - (end - max(caption["end"][idx - 1], start)) - if idx > 0 - else end - start - ) - t_clip_len += clip_len - t_tokens += len(text.split(" ")) - self.stat["clip_len"].append(clip_len) - self.stat["t_clip_len"].append(t_clip_len) - self.stat["video_len"].append(video_len) - self.stat["clip_tps"].append(t_tokens / t_clip_len) - self.stat["video_tps"].append(t_tokens / video_len) - - def print_stat(self): - result = { - "t_clip_len": np.mean(self.stat["t_clip_len"]), - "video_len": np.mean(self.stat["video_len"]), - "clip_tps": np.mean(self.stat["clip_tps"]), - "video_tps": np.mean(self.stat["video_tps"]), - "min_clip_len": min(self.stat["clip_len"]), - "max_clip_len": max(self.stat["clip_len"]), - "mean_clip_len": np.mean(self.stat["clip_len"]), - "num_clip": len(self.stat["clip_len"]) / len(self.stat["video_tps"]), - } - print(result) - - def _dedup(self, caption): - def random_merge(end_idx, start, end, text, starts, ends, texts): - if random.random() > 0.5: - # print(clip_idx, "[PARTIAL INTO PREV]", end_idx) - # overlapped part goes to the end of previous. - ends[-1] = max(ends[-1], start) # ? - rest_text = text[end_idx:].strip() - if rest_text: - starts.append(max(ends[-1], start)) - ends.append(max(end, starts[-1])) - texts.append(rest_text) - else: # goes to the beginning of the current. - # strip the previous. - left_text = texts[-1][:-end_idx].strip() - if left_text: - # print(clip_idx, "[PREV PARTIAL INTO CUR]", end_idx) - ends[-1] = min(ends[-1], start) - texts[-1] = left_text - else: - # print(clip_idx, "[PREV LEFT NOTHING ALL INTO CUR]", end_idx) - starts.pop(-1) - ends.pop(-1) - texts.pop(-1) - starts.append(start) - ends.append(end) - texts.append(text) - - starts, ends, texts = [], [], [] - for clip_idx, (start, end, text) in enumerate( - zip(caption["start"], caption["end"], caption["text"]) - ): - if not isinstance(text, str): - continue - text = text.replace("\n", " ").strip() - if len(text) == 0: - continue - starts.append(start) - ends.append(end) - texts.append(text) - break - - for clip_idx, (start, end, text) in enumerate( - zip( - caption["start"][clip_idx + 1:], - caption["end"][clip_idx + 1:], - caption["text"][clip_idx + 1:], - ) - ): - if not isinstance(text, str): - continue - text = text.replace("\n", " ").strip() - if len(text) == 0: - continue - - # print(clip_idx, texts[-5:]) - # print(clip_idx, start, end, text) - if texts[-1].endswith(text): # subset of prev caption -> merge - # print(clip_idx, "[MERGE INTO PREV]") - ends[-1] = max(ends[-1], end) - elif text.startswith(texts[-1]): # superset of prev caption -> merge - # print(clip_idx, "[PREV MERGE INTO CUR]") - texts[-1] = text - starts[-1] = min(starts[-1], start) - ends[-1] = max(ends[-1], end) - else: # overlapping or non-overlapping. - for end_idx in range(1, len(text) + 1): - if texts[-1].endswith(text[:end_idx]): - random_merge(end_idx, start, end, text, starts, ends, texts) - break - else: - starts.append(start) - ends.append(end) - texts.append(text) - - assert (ends[-1] + 0.001) >= starts[-1] and len( - texts[-1] - ) > 0, "{} {} {} <- {} {} {}, {} {} {}".format( - str(starts[-1]), - str(ends[-1]), - texts[-1], - caption["start"][clip_idx - 1], - caption["end"][clip_idx - 1], - caption["text"][clip_idx - 1], - str(start), - str(end), - text, - ) - - return {"start": starts, "end": ends, "text": texts} - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="dedup how2 caption") - parser.add_argument('--how2dir', default="data/how2") - args = parser.parse_args() - - raw_caption_json = os.path.join(args.how2dir, "raw_caption.json") - raw_caption_pickle = os.path.join(args.how2dir, "raw_caption.pkl") - raw_caption_dedup_pickle = os.path.join(args.how2dir, "raw_caption_dedup.pkl") - - def convert_to_pickle(src_fn, tgt_fn): - with open(src_fn) as fd: - captions = json.load(fd) - - for video_id in captions: - captions[video_id] = json.dumps(captions[video_id]) - - with open(tgt_fn, "wb") as fw: - pickle.dump(captions, fw, pickle.HIGHEST_PROTOCOL) - - if not os.path.isfile(raw_caption_pickle): - convert_to_pickle(raw_caption_json, raw_caption_pickle) - - deduper = CaptionDedupProcessor(raw_caption_pickle) - deduper() - deduper.finalize(raw_caption_dedup_pickle) - - """ - # demo - deduper = CaptionDedupProcessor("data/how2/raw_caption.pkl") - deduper.single("HfIeQ9pzL5U") - """ diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/dsprocessor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/dsprocessor.py deleted file mode 100644 index c1c2f56d3ee5e0cb61778406a1fc630e0f46dc05..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/dsprocessor.py +++ /dev/null @@ -1,847 +0,0 @@ -# Copyright (c) Facebook, Inc. All Rights Reserved - -""" -Processors for all downstream (ds) tasks. -""" - -import json -import math -import os -import pickle -import random -from collections import defaultdict - -import numpy as np -import torch - -from .how2processor import TextGenerationProcessor -from .processor import ( - Aligner, - MetaProcessor, - MMAttentionMask2DProcessor, - TextProcessor, - VideoProcessor, -) - -# ------------- A General Aligner for all downstream tasks----------------- - - -class DSAligner(Aligner): - """ - Downstream (DS) aligner shared by all datasets. - """ - - def __call__(self, video_id, video_feature, text_feature, wps=0.7): - # random sample a starting sec for video. - video_start = 0 - video_end = min(len(video_feature), self.max_video_len) - # the whole sequence is a single clip. - video_clips = {"start": [video_start], "end": [video_end]} - - text_feature = { - "cap": [text_feature], - "start": [video_start], - "end": [len(text_feature) / wps], - } - text_clip_indexs = [0] - - vfeats, vmasks = self._build_video_seq( - video_feature, video_clips - ) - caps, cmasks = self._build_text_seq( - text_feature, text_clip_indexs - ) - - return { - "caps": caps, - "cmasks": cmasks, - "vfeats": vfeats, - "vmasks": vmasks, - "video_id": video_id, - } - - -class NLGTextProcessor(TextProcessor): - """ - Also return the original text as ref. - """ - def __call__(self, text_id): - return super().__call__(text_id), text_id - - -class DSNLGAligner(DSAligner): - """extend with the capability of 2d mask for generation.""" - def __init__(self, config): - super().__init__(config) - self.attnmasker = MMAttentionMask2DProcessor() - from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained( - self.bert_name, use_fast=self.use_fast, - bos_token="[CLS]", eos_token="[SEP]" - ) - self.tokenizer = tokenizer - self.bos_token_id = tokenizer.bos_token_id - self.eos_token_id = tokenizer.eos_token_id - self.textgen = TextGenerationProcessor(tokenizer) - - def __call__(self, video_id, video_feature, text_feature): - output = super().__call__(video_id, video_feature, text_feature[0]) - if self.split == "test": - # output.update({"ref": text_feature[1]}) - output.update({"ref": self.tokenizer.decode( - output["caps"], skip_special_tokens=True)}) - text_label = output["caps"] - cmasks = torch.BoolTensor([1] * text_label.size(0)) - caps = torch.LongTensor([ - self.cls_token_id, - self.sep_token_id, - self.bos_token_id]) - else: - caps, text_label = self.textgen(output["caps"]) - cmasks = output["cmasks"] - - attention_mask = self.attnmasker( - output["vmasks"], cmasks, "textgen") - - output.update({ - "caps": caps, - "cmasks": cmasks, - "text_label": text_label, - "attention_mask": attention_mask, - }) - return output - - -# -------------------- MSRVTT ------------------------ - - -class MSRVTTMetaProcessor(MetaProcessor): - """MSRVTT dataset. - reference: `howto100m/msrvtt_dataloader.py` - """ - - def __init__(self, config): - super().__init__(config) - import pandas as pd - data = pd.read_csv(self._get_split_path(config)) - # TODO: add a text1ka flag. - if config.split == "train" \ - and config.full_test_path is not None \ - and config.jsfusion_path is not None: - # add testing videos from full_test_path not used by jfusion. - additional_data = pd.read_csv(config.full_test_path) - jsfusion_data = pd.read_csv(config.jsfusion_path) - - for video_id in additional_data["video_id"]: - if video_id not in jsfusion_data["video_id"].values: - data = data.append( - {"video_id": video_id}, ignore_index=True) - - if config.dup is not None and config.split == "train": - data = data.append([data] * (config.dup - 1), ignore_index=True) - self.data = data - - def __len__(self): - return len(self.data) - - def __getitem__(self, idx): - """slightly modify with if condition to combine train/test.""" - vid, sentence = None, None - vid = self.data["video_id"].values[idx] - if "sentence" in self.data: # for testing. - sentence = self.data["sentence"].values[idx] - else: # for training. - sentence = vid - return vid, sentence - - -class MSRVTTTextProcessor(TextProcessor): - """MSRVTT dataset. - reference: `msrvtt_dataloader.py` `MSRVTT_TrainDataLoader`. - TODO (huxu): add max_words. - """ - - def __init__(self, config): - super().__init__(config) - self.sentences = None - if config.json_path is not None and config.split == "train": - with open(config.json_path) as fd: - self.data = json.load(fd) - self.sentences = defaultdict(list) - for s in self.data["sentences"]: - self.sentences[s["video_id"]].append(s["caption"]) - - def __call__(self, text_id): - if self.sentences is not None: - rind = random.randint(0, len(self.sentences[text_id]) - 1) - sentence = self.sentences[text_id][rind] - else: - sentence = text_id - caption = self.tokenizer(sentence, add_special_tokens=False) - return caption["input_ids"] - - -class MSRVTTNLGTextProcessor(MSRVTTTextProcessor): - """TODO: change dsaligner and merge to avoid any NLG text processor.""" - def __call__(self, text_id): - if self.sentences is not None: - rind = random.randint(0, len(self.sentences[text_id]) - 1) - sentence = self.sentences[text_id][rind] - else: - sentence = text_id - caption = self.tokenizer(sentence, add_special_tokens=False) - return caption["input_ids"], sentence - - -class MSRVTTQAMetaProcessor(MetaProcessor): - """MSRVTT-QA: retrieval-based multi-choice QA from JSFusion dataset. - For simplicity, we use the train retrieval model. - reference: `https://github.com/yj-yu/lsmdc` - """ - - def __init__(self, config): - super().__init__(config) - import pandas as pd - csv_data = pd.read_csv(self._get_split_path(config), sep="\t") - data = [] - for video_id, a1, a2, a3, a4, a5, answer in zip( - csv_data["vid_key"].values, - csv_data["a1"].values, - csv_data["a2"].values, - csv_data["a3"].values, - csv_data["a4"].values, - csv_data["a5"].values, - csv_data["answer"].values): - video_id = video_id.replace("msr", "video") - data.append((video_id, (answer, [a1, a2, a3, a4, a5]))) - self.data = data - - def __len__(self): - return len(self.data) - - def __getitem__(self, idx): - return self.data[idx] - - -class MSRVTTQATextProcessor(TextProcessor): - """MSRVTT-QA dataset. - text_ans is of format `(answer, [a1, a2, a3, a4, a5])`. - """ - - def __call__(self, text_ans): - for ans_idx, ans in enumerate(text_ans[1]): - if isinstance(ans, str): - text_ans[1][ans_idx] = self.tokenizer(ans, add_special_tokens=False)["input_ids"] - return text_ans - - -class MSRVTTQAAligner(DSAligner): - """MSRVTT dataset. - similar to sample in how2. - we call __call__ multiple times. - """ - - def __call__(self, video_id, video_feature, text_feature, wps=0.7): - caps = [] - cmasks = [] - answer = text_feature[0] - for ans_idx, _text_feature in enumerate(text_feature[1]): - output = super().__call__( - video_id, video_feature, _text_feature, wps) - caps.append(output["caps"]) - cmasks.append(output["cmasks"]) - output.update({ - "caps": torch.stack(caps), - "cmasks": torch.stack(cmasks), - "answers": torch.LongTensor([answer]), - }) - return output - - -# -------------------- Youcook ----------------------- - - -class YoucookMetaProcessor(MetaProcessor): - """Youcook dataset. - reference: `howto100m/youcook_dataloader.py` - note that the data can be different as the - (1) some videos already in Howto100m are removed. - (2) stop words are removed from caption - TODO (huxu): make a flag to load the original caption. - (see youcookii_annotations_trainval.json). - - The max_video_len can be 264 and text can be 64 tokens. - In reality we may not need that long. see projects/task/youcook.yaml - """ - - def __init__(self, config): - super().__init__(config) - vfeat_dir = config.vfeat_dir - print(self._get_split_path(config)) - with open(self._get_split_path(config), "rb") as fd: - data = pickle.load(fd) - all_valid_video_ids = set( - [os.path.splitext(fn)[0] for fn in os.listdir(vfeat_dir)] - ) - recs = [] - video_ids = set() - valid_video_ids = set() - for rec in data: # filter videos not available. - udl_idx = rec["id"].rindex("_") - video_id = rec["id"][:udl_idx] - video_ids.add(video_id) - if video_id in all_valid_video_ids: - valid_video_ids.add(video_id) - recs.append(rec) - print("total video_ids in .pkl", len(video_ids)) - print("valid video_ids in .pkl", len(valid_video_ids)) - print("please verify {train,val}_list.txt") - data = recs - self.data = data - - with open(config.trainval_annotation) as fd: - self.youcook_annotation = json.load(fd)["database"] - if config.use_annotation_text is True: - print("using text in annotation.") - self.use_annotation_caption = True - else: - self.use_annotation_caption = False - - def __getitem__(self, idx): - def _get_video_and_caption(rec): - vid = rec["id"] - udl_idx = vid.rindex("_") - video_id, clip_id = vid[:udl_idx], int(vid[udl_idx + 1:]) - clip = self.youcook_annotation[video_id]["annotations"][clip_id] - start, end = clip["segment"] - if self.use_annotation_caption: - caption = clip["sentence"] - else: - caption = rec["caption"] - return (video_id, start, end), caption - - rec = self.data[idx] - video_info, text_info = _get_video_and_caption(rec) - return video_info, text_info - - -class YoucookVideoProcessor(VideoProcessor): - """video_fn is a tuple of (video_id, start, end) now.""" - - def __call__(self, video_fn): - video_id, start, end = video_fn - feat = np.load(os.path.join(self.vfeat_dir, video_id + ".npy")) - return feat[start:end] - - -class YoucookNLGMetaProcessor(MetaProcessor): - """NLG uses the original split: - `train_list.txt` and `val_list.txt` - """ - - def __init__(self, config): - super().__init__(config) - vfeat_dir = config.vfeat_dir - print(self._get_split_path(config)) - with open(self._get_split_path(config)) as fd: - video_ids = [ - line.strip().split("/")[1] for line in fd.readlines()] - print("total video_ids in train/val_list.txt", len(video_ids)) - - all_valid_video_ids = set( - [os.path.splitext(fn)[0] for fn in os.listdir(vfeat_dir)] - ) - video_ids = [ - video_id for video_id in video_ids - if video_id in all_valid_video_ids] - - print("valid video_ids in train/val_list.txt", len(video_ids)) - with open(config.trainval_annotation) as fd: - self.youcook_annotation = json.load(fd)["database"] - - data = [] - for video_id in video_ids: - for clip in self.youcook_annotation[video_id]["annotations"]: - start, end = clip["segment"] - caption = clip["sentence"] - data.append(((video_id, start, end), caption)) - self.data = data - - def __getitem__(self, idx): - return self.data[idx] - - -# --------------------- CrossTask ------------------------- - -class CrossTaskMetaProcessor(MetaProcessor): - def __init__(self, config): - super().__init__(config) - np.random.seed(0) # deterministic random split. - task_vids = self._get_vids( - config.train_csv_path, - config.vfeat_dir, - config.annotation_path) - - val_vids = self._get_vids( - config.val_csv_path, - config.vfeat_dir, - config.annotation_path) - - # filter out those task and vids appear in val_vids. - task_vids = { - task: [ - vid for vid in vids - if task not in val_vids or vid not in val_vids[task]] - for task, vids in task_vids.items()} - - primary_info = self._read_task_info(config.primary_path) - test_tasks = set(primary_info['steps'].keys()) - - # if args.use_related: - related_info = self._read_task_info(config.related_path) - task_steps = {**primary_info['steps'], **related_info['steps']} - n_steps = {**primary_info['n_steps'], **related_info['n_steps']} - # else: - # task_steps = primary_info['steps'] - # n_steps = primary_info['n_steps'] - all_tasks = set(n_steps.keys()) - # filter and keep task in primary or related. - task_vids = { - task: vids for task, vids in task_vids.items() - if task in all_tasks} - # vocab-by-step matrix (A) and vocab (M) - # (huxu): we do not use BoW. - # A, M = self._get_A(task_steps, share="words") - - train_vids, test_vids = self._random_split( - task_vids, test_tasks, config.n_train) - print("train_num_videos", sum(len(vids) for vids in train_vids.values())) - print("test_num_videos", sum(len(vids) for vids in test_vids.values())) - # added by huxu to automatically determine the split. - split_map = { - "train": train_vids, - "valid": test_vids, - "test": test_vids - } - task_vids = split_map[config.split] - - self.vids = [] - for task, vids in task_vids.items(): - self.vids.extend([(task, vid) for vid in vids]) - self.task_steps = task_steps - self.n_steps = n_steps - - def __getitem__(self, idx): - task, vid = self.vids[idx] - n_steps = self.n_steps[task] - steps = self.task_steps[task] - assert len(steps) == n_steps - return (task, vid, steps, n_steps), (task, vid, steps, n_steps) - - def __len__(self): - return len(self.vids) - - def _random_split(self, task_vids, test_tasks, n_train): - train_vids = {} - test_vids = {} - for task, vids in task_vids.items(): - if task in test_tasks and len(vids) > n_train: - train_vids[task] = np.random.choice( - vids, n_train, replace=False).tolist() - test_vids[task] = [ - vid for vid in vids if vid not in train_vids[task]] - else: - train_vids[task] = vids - return train_vids, test_vids - - def _get_vids(self, path, vfeat_dir, annotation_path): - """refactored from - https://github.com/DmZhukov/CrossTask/blob/master/data.py - changes: add `vfeat_dir` to check if the video is available. - add `annotation_path` to check if the video is available. - """ - - task_vids = {} - with open(path, 'r') as f: - for line in f: - task, vid, url = line.strip().split(',') - # double check the video is available. - if not os.path.exists( - os.path.join(vfeat_dir, vid + ".npy")): - continue - # double check the annotation is available. - if not os.path.exists(os.path.join( - annotation_path, - task + "_" + vid + ".csv")): - continue - if task not in task_vids: - task_vids[task] = [] - task_vids[task].append(vid) - return task_vids - - def _read_task_info(self, path): - titles = {} - urls = {} - n_steps = {} - steps = {} - with open(path, 'r') as f: - idx = f.readline() - while idx != '': - idx = idx.strip() - titles[idx] = f.readline().strip() - urls[idx] = f.readline().strip() - n_steps[idx] = int(f.readline().strip()) - steps[idx] = f.readline().strip().split(',') - next(f) - idx = f.readline() - return { - 'title': titles, - 'url': urls, - 'n_steps': n_steps, - 'steps': steps - } - - def _get_A(self, task_steps, share="words"): - raise ValueError("running get_A is not allowed for BERT.") - """Step-to-component matrices.""" - if share == 'words': - # share words - task_step_comps = { - task: [step.split(' ') for step in steps] - for task, steps in task_steps.items()} - elif share == 'task_words': - # share words within same task - task_step_comps = { - task: [[task+'_'+tok for tok in step.split(' ')] for step in steps] - for task, steps in task_steps.items()} - elif share == 'steps': - # share whole step descriptions - task_step_comps = { - task: [[step] for step in steps] for task, steps in task_steps.items()} - else: - # no sharing - task_step_comps = { - task: [[task+'_'+step] for step in steps] - for task, steps in task_steps.items()} - # BERT tokenizer here? - vocab = [] - for task, steps in task_step_comps.items(): - for step in steps: - vocab.extend(step) - vocab = {comp: m for m, comp in enumerate(set(vocab))} - M = len(vocab) - A = {} - for task, steps in task_step_comps.items(): - K = len(steps) - a = torch.zeros(M, K) - for k, step in enumerate(steps): - a[[vocab[comp] for comp in step], k] = 1 - a /= a.sum(dim=0) - A[task] = a - return A, M - - -class CrossTaskVideoProcessor(VideoProcessor): - def __call__(self, video_fn): - task, vid, steps, n_steps = video_fn - video_fn = os.path.join(self.vfeat_dir, vid + ".npy") - feat = np.load(video_fn) - return feat - - -class CrossTaskTextProcessor(TextProcessor): - def __call__(self, text_id): - task, vid, steps, n_steps = text_id - step_ids = [] - for step_str in steps: - step_ids.append( - self.tokenizer(step_str, add_special_tokens=False)["input_ids"] - ) - return step_ids - - -class CrossTaskAligner(Aligner): - """ - TODO: it's not clear yet the formulation of the task; finish this later. - """ - def __init__(self, config): - super().__init__(config) - self.annotation_path = config.annotation_path - self.sliding_window = config.sliding_window - self.sliding_window_size = config.sliding_window_size - - def __call__(self, video_id, video_feature, text_feature): - task, vid, steps, n_steps = video_id - annot_path = os.path.join( - self.annotation_path, task + '_' + vid + '.csv') - video_len = len(video_feature) - - labels = torch.from_numpy(self._read_assignment( - video_len, n_steps, annot_path)).float() - - vfeats, vmasks, targets = [], [], [] - # sliding window on video features and targets. - for window_start in range(0, video_len, self.sliding_window): - video_start = 0 - video_end = min(video_len - window_start, self.sliding_window_size) - video_clip = {"start": [video_start], "end": [video_end]} - - vfeat, vmask = self._build_video_seq( - video_feature[window_start: window_start + video_end], - video_clip - ) - - target = labels[window_start: window_start + video_end] - assert len(vfeat) >= len(target), "{},{}".format(len(vfeat), len(target)) - # TODO: randomly drop all zero targets for training ? - # if self.split == "train" and target.sum() == 0: - # continue - vfeats.append(vfeat) - vmasks.append(vmask) - targets.append(target) - - if (video_len - window_start) <= self.sliding_window_size: - break - - vfeats = torch.stack(vfeats) - vmasks = torch.stack(vmasks) - targets = torch.cat(targets, dim=0) - - caps, cmasks = [], [] - for step in text_feature: - step_text_feature = {"start": [0], "end": [1], "cap": [step]} - step_text_clip_index = [0] - cap, cmask = self._build_text_seq( - step_text_feature, step_text_clip_index - ) - caps.append(cap) - cmasks.append(cmask) - caps = torch.stack(caps) - cmasks = torch.stack(cmasks) - - return { - "caps": caps, - "cmasks": cmasks, - "vfeats": vfeats, # X for original code. - "vmasks": vmasks, - "targets": targets, - "video_id": vid, - "task": task, - "video_len": video_len # for later checking. - } - - def _read_assignment(self, T, K, path): - """ - refactored from https://github.com/DmZhukov/CrossTask/blob/master/data.py - Howto interpret contraints on loss that is going to be minimized: - lambd is a big number; - self.lambd * C is a big number for all valid position (csv stores invalids) - - def forward(self, O, Y, C): - return (Y*(self.lambd * C - self.lsm(O))).mean(dim=0).sum() - - This will load the csv file and fill-in the step col from start to end rows. - """ - - Y = np.zeros([T, K], dtype=np.uint8) - with open(path, 'r') as f: - for line in f: - step, start, end = line.strip().split(',') - start = int(math.floor(float(start))) - end = int(math.ceil(float(end))) - step = int(step) - 1 - Y[start:end, step] = 1 - return Y - - -# --------------------- COIN ------------------------- - -class MetaTextBinarizer(Aligner): - def __call__(self, text_feature): - text_feature = { - "cap": [text_feature], - "start": [0.], - "end": [100.], - } - text_clip_indexs = [0] - - caps, cmasks = self._build_text_seq( - text_feature, text_clip_indexs - ) - return {"caps": caps, "cmasks": cmasks} - - -class COINActionSegmentationMetaProcessor(MetaProcessor): - split_map = { - "train": "training", - "valid": "testing", - "test": "testing", - } - - def __init__(self, config): - super().__init__(config) - with open(self._get_split_path(config)) as fr: - database = json.load(fr)["database"] - id2label = {} - data = [] - # filter the data by split. - for video_id, rec in database.items(): - # always use testing to determine label_set - if rec["subset"] == "testing": - for segment in rec["annotation"]: - id2label[int(segment["id"])] = segment["label"] - # text_labels is used for ZS setting - self.text_labels = ["none"] * len(id2label) - for label_id in id2label: - self.text_labels[label_id-1] = id2label[label_id] - - id2label[0] = "O" - print("num of labels", len(id2label)) - - for video_id, rec in database.items(): - if not os.path.isfile(os.path.join(config.vfeat_dir, video_id + ".npy")): - continue - if rec["subset"] == COINActionSegmentationMetaProcessor.split_map[self.split]: - starts, ends, labels = [], [], [] - for segment in rec["annotation"]: - start, end = segment["segment"] - label = int(segment["id"]) - starts.append(start) - ends.append(end) - labels.append(label) - data.append( - (video_id, {"start": starts, "end": ends, "label": labels})) - self.data = data - - def meta_text_labels(self, config): - from transformers import default_data_collator - - from ..utils import get_local_rank - - text_processor = TextProcessor(config) - binarizer = MetaTextBinarizer(config) - # TODO: add prompts to .yaml. - text_labels = [label for label in self.text_labels] - - if get_local_rank() == 0: - print(text_labels) - - outputs = [] - for text_label in text_labels: - text_feature = text_processor(text_label) - outputs.append(binarizer(text_feature)) - return default_data_collator(outputs) - - def __getitem__(self, idx): - return self.data[idx] - - -class COINActionSegmentationTextProcessor(TextProcessor): - def __call__(self, text_label): - return text_label - - -class COINActionSegmentationAligner(Aligner): - def __init__(self, config): - super().__init__(config) - self.sliding_window = config.sliding_window - self.sliding_window_size = config.sliding_window_size - - def __call__(self, video_id, video_feature, text_feature): - starts, ends, label_ids = text_feature["start"], text_feature["end"], text_feature["label"] - # sliding window. - video_len = len(video_feature) - - vfeats, vmasks, targets = [], [], [] - # sliding window on video features and targets. - for window_start in range(0, video_len, self.sliding_window): - video_start = 0 - video_end = min(video_len - window_start, self.sliding_window_size) - video_clip = {"start": [video_start], "end": [video_end]} - vfeat, vmask = self._build_video_seq( - video_feature[window_start: window_start + video_end], - video_clip - ) - # covers video length only. - target = torch.full_like(vmask, -100, dtype=torch.long) - target[vmask] = 0 - for start, end, label_id in zip(starts, ends, label_ids): - if (window_start < end) and (start < (window_start + video_end)): - start_offset = max(0, math.floor(start) - window_start) - end_offset = min(video_end, math.ceil(end) - window_start) - target[start_offset:end_offset] = label_id - vfeats.append(vfeat) - vmasks.append(vmask) - targets.append(target) - if (video_len - window_start) <= self.sliding_window_size: - break - - vfeats = torch.stack(vfeats) - vmasks = torch.stack(vmasks) - targets = torch.stack(targets) - video_targets = torch.full((video_len,), 0) - for start, end, label_id in zip(starts, ends, label_ids): - start_offset = max(0, math.floor(start)) - end_offset = min(video_len, math.ceil(end)) - video_targets[start_offset:end_offset] = label_id - - caps = torch.LongTensor( - [[self.cls_token_id, self.sep_token_id, - self.pad_token_id, self.sep_token_id]], - ).repeat(vfeats.size(0), 1) - cmasks = torch.BoolTensor( - [[0, 1, 0, 1]] # pad are valid for attention. - ).repeat(vfeats.size(0), 1) - return { - "caps": caps, - "cmasks": cmasks, - "vfeats": vfeats, # X for original code. - "vmasks": vmasks, - "targets": targets, - "video_id": video_id, - "video_len": video_len, # for later checking. - "video_targets": video_targets - } - - -class DiDeMoMetaProcessor(MetaProcessor): - """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py - https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py - """ - def __init__(self, config): - super().__init__(config) - - assert "test" in self._get_split_path(config), "DiDeMo only supports zero-shot testing for now." - - with open(self._get_split_path(config)) as data_file: - json_data = json.load(data_file) - - data = [] - for record in json_data: - data.append((record["video"], record["description"])) - self.data = data - - def __len__(self): - return len(self.data) - - def __getitem__(self, idx): - return self.data[idx] - - -class DiDeMoTextProcessor(TextProcessor): - """reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py - https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py - """ - - def __call__(self, text): - return self.tokenizer(text, add_special_tokens=False)["input_ids"] - - -class DiDeMoAligner(DSAligner): - """ - check video length. - """ - - def __call__(self, video_id, video_feature, text_feature): - # print(video_feature.shape[0]) - return super().__call__(video_id, video_feature, text_feature) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/how2processor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/how2processor.py deleted file mode 100644 index f20055c9ba6be83809166eeb284fc4d8fc33b484..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/how2processor.py +++ /dev/null @@ -1,887 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Copyright (c) Facebook, Inc. All Rights Reserved - - -import math -import os -import pickle -import random -from collections import deque -from typing import List, Optional, Tuple - -import numpy as np -import torch - -from ..utils import ShardedTensor -from .processor import ( - Aligner, - MetaProcessor, - MMAttentionMask2DProcessor, - Processor, - TextProcessor, -) - - -class How2MetaProcessor(MetaProcessor): - def __init__(self, config): - super().__init__(config) - path = self._get_split_path(config) - with open(path) as fd: - self.data = [line.strip() for line in fd] - - def __getitem__(self, idx): - video_id = self.data[idx] - return video_id, video_id - - -class ShardedHow2MetaProcessor(How2MetaProcessor): - def __init__(self, config): - super().__init__(config) - self.split = str(config.split) - self.vfeat_dir = config.vfeat_dir - self._init_shard() - - def _init_shard(self): - if self.split == "train": - meta_fn = os.path.join(self.vfeat_dir, "train" + "_meta.pkl") - with open(meta_fn, "rb") as fr: - meta = pickle.load(fr) - elif self.split == "valid": - meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl") - with open(meta_fn, "rb") as fr: - meta = pickle.load(fr) - elif self.split == "test": - print("use how2 val as test.") - meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl") - with open(meta_fn, "rb") as fr: - meta = pickle.load(fr) - else: - raise ValueError("unsupported for MetaProcessor:", self.split) - video_id_to_shard = {} - for shard_id in meta: - for video_idx, video_id in enumerate(meta[shard_id]): - video_id_to_shard[video_id] = (shard_id, video_idx) - self.video_id_to_shard = video_id_to_shard - - def __getitem__(self, idx): - video_id, video_id = super().__getitem__(idx) - shard_id, shard_idx = self.video_id_to_shard[video_id] - meta = (video_id, idx, shard_id, shard_idx) - return meta, meta - - -class ShardedVideoProcessor(Processor): - """ - mmaped shards of numpy video features. - """ - - def __init__(self, config): - self.split = str(config.split) - self.vfeat_dir = config.vfeat_dir - - def __call__(self, video_id): - _, _, shard_id, video_idx = video_id - if self.split == "train": - shard = ShardedTensor.load( - os.path.join(self.vfeat_dir, "train" + "_" + str(shard_id)), - "r" - ) - elif self.split == "valid": - shard = ShardedTensor.load( - os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)), - "r" - ) - elif self.split == "test": - shard = ShardedTensor.load( - os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)), - "r" - ) - else: - raise ValueError("unknown split", self.split) - feat = shard[video_idx] - return feat - - -class ShardedTextProcessor(Processor): - def __init__(self, config): - self.tfeat_dir = str(config.tfeat_dir) - self.split = str(config.split) - - def __call__(self, video_id): - _, _, shard_id, shard_idx = video_id - if self.split == "train": - target_path = self.tfeat_dir + "train" + "_" + str(shard_id) - elif self.split == "valid": - target_path = self.tfeat_dir + "val" + "_" + str(shard_id) - elif self.split == "test": - target_path = self.tfeat_dir + "val" + "_" + str(shard_id) - else: - raise ValueError("unknown split", self.split) - - startend = ShardedTensor.load( - target_path + ".startends", "r")[shard_idx] - cap_ids = ShardedTensor.load( - target_path + ".caps_ids", "r")[shard_idx] - cap = [] - for clip_idx in range(len(cap_ids)): - clip = cap_ids[clip_idx] - cap.append(clip[clip != -1].tolist()) - start, end = startend[:, 0].tolist(), startend[:, 1].tolist() - return {"start": start, "end": end, "cap": cap} - - -class FixedLenAligner(Aligner): - """ - In the model we assume text is on the left (closer to BERT formulation) - and video is on the right. - We fix the total length of text + video. - max_video_len is in number of secs. - max_text_len is in number of tokens. - - special tokens formats: - we use the format [CLS] [SEP] text tokens [SEP] [PAD] ... - [CLS] will be splitted out into: - [CLS] video tokens [SEP] text tokens [SEP] [PAD] ... - token_type_ids will be generated by the model (for now). - 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 - | first sequence | second sequence | - so each sequence owns a [SEP] token for no-ops. - """ - - def __init__(self, config): - super().__init__(config) - self.text_clip_sampler = TextClipSamplingProcessor( - self.max_len - self.max_video_len - 3 - ) - """ - decide subsampling: - `config.subsampling` will change batch_size in trainer. - `config.clip_per_video` (used by RetriTask) doesn't - change batch_size in trainer. - """ - subsampling = config.subsampling \ - if config.subsampling is not None else None - if config.clip_per_video is not None: - subsampling = config.clip_per_video - self.subsampling = subsampling - - def _get_text_maxlen(self): - # use max text len - return self.text_clip_sampler.max_text_len - - def __call__(self, video_id, video_feature, text_feature): - from transformers import default_data_collator - video_idx = video_id[1] - if self.subsampling is not None and self.subsampling >= 1: - batch = [] - for _ in range(self.subsampling): - centerclip_idx = random.randint( - 0, len(text_feature["start"]) - 1) - batch.append( - self.sampling( - video_idx, - video_feature, - text_feature, - centerclip_idx, - self._get_text_maxlen() - )) - batch = self.batch_post_processing(batch, video_feature) - batch = default_data_collator(batch) - else: - raise ValueError( - "dataset.subsampling must be >= 1 for efficient video loading.") - batch = self.sampling(video_idx, video_feature, text_feature) - batch = self.batch_post_processing(batch, video_feature) - - batch["video_id"] = video_id if isinstance(video_id, str) \ - else video_id[0] - # e2e: make sure frame ids is into tensor. - assert torch.is_tensor(batch["vfeats"]) - return batch - - def sampling( - self, - video_idx, - video_feature, - text_feature, - centerclip_idx=None, - sampled_max_text_len=None, - ): - text_clip_indexs = self.text_clip_sampler( - text_feature, centerclip_idx, - sampled_max_text_len - ) - if isinstance(video_feature, np.ndarray): - video_len = len(video_feature) - else: - video_len = math.ceil(text_feature["end"][-1]) - - video_end = min( - math.ceil(text_feature["end"][text_clip_indexs[-1]]), - video_len - ) - video_start = max( - min( - math.floor(text_feature["start"][text_clip_indexs[0]]), - video_end), - 0 - ) - - video_clips = {"start": [video_start], "end": [video_end]} - - # tensorize. - vfeats, vmasks = self._build_video_seq( - video_feature, video_clips - ) - caps, cmasks = self._build_text_seq( - text_feature, text_clip_indexs - ) - - text_start = text_clip_indexs[0] - text_end = text_clip_indexs[-1] + 1 - - return { - "caps": caps, - "cmasks": cmasks, - "vfeats": vfeats, - "vmasks": vmasks, - "video_start": video_start, - "video_end": video_end, - "text_start": text_start, - "text_end": text_end, - } - - -class VariedLenAligner(FixedLenAligner): - def __init__(self, config): - super().__init__(config) - self.sampled_min_len = config.sampled_min_len - self.sampled_max_len = config.sampled_max_len - - def _get_text_maxlen(self): - return random.randint(self.sampled_min_len, self.sampled_max_len) - - -class StartClipAligner(VariedLenAligner): - def sampling( - self, - video_idx, - video_feature, - text_feature, - centerclip_idx=None, - sampled_max_text_len=None, - ): - return super().sampling( - video_idx, video_feature, text_feature, 0) - - -class OverlappedAligner(VariedLenAligner): - """video clip and text clip has overlappings - but may not be the same start/end.""" - def __init__(self, config): - super().__init__(config) - self.sampled_video_min_len = config.sampled_video_min_len - self.sampled_video_max_len = config.sampled_video_max_len - - self.video_clip_sampler = VideoClipSamplingProcessor() - - def _get_video_maxlen(self): - return random.randint( - self.sampled_video_min_len, self.sampled_video_max_len) - - def sampling( - self, - video_idx, - video_feature, - text_feature, - centerclip_idx=None, - sampled_max_text_len=None, - ): - text_clip_indexs = self.text_clip_sampler( - text_feature, centerclip_idx, - sampled_max_text_len - ) - if isinstance(video_feature, np.ndarray): - video_len = len(video_feature) - else: - video_len = math.ceil(text_feature["end"][-1]) - low = math.floor(text_feature["start"][text_clip_indexs[0]]) - high = math.ceil(text_feature["end"][text_clip_indexs[-1]]) - if low < high: - center = random.randint(low, high) - else: - center = int((low + high) // 2) - center = max(0, min(video_feature.shape[0] - 1, center)) - - assert 0 <= center < video_feature.shape[0] - - video_clips = self.video_clip_sampler( - video_len, self._get_video_maxlen(), center - ) - video_start = video_clips["start"][0] - video_end = video_clips["end"][0] - - # tensorize. - vfeats, vmasks = self._build_video_seq( - video_feature, video_clips - ) - caps, cmasks = self._build_text_seq( - text_feature, text_clip_indexs - ) - - text_start = text_clip_indexs[0] - text_end = text_clip_indexs[-1] + 1 - - return { - "caps": caps, - "cmasks": cmasks, - "vfeats": vfeats, - "vmasks": vmasks, - "video_start": video_start, - "video_end": video_end, - "text_start": text_start, - "text_end": text_end, - } - - -class MFMMLMAligner(FixedLenAligner): - """ - `FixedLenAligner` with Masked Language Model and Masked Frame Model. - """ - - def __init__(self, config): - super().__init__(config) - keep_prob = config.keep_prob if config.keep_prob is not None else 1.0 - self.text_clip_sampler = TextClipSamplingProcessor( - self.max_len - self.max_video_len - 3, keep_prob - ) - self.sampled_min_len = config.sampled_min_len - self.sampled_max_len = config.sampled_max_len - self.masked_token_sampler = TextMaskingProcessor(config) - self.mm_type = config.mm_type \ - if config.mm_type is not None else "full" - self.attnmasker = MMAttentionMask2DProcessor() \ - if self.mm_type == "textgen" else None - self.masked_frame_sampler = FrameMaskingProcessor(config) - self.lazy_vfeat_mask = ( - False if config.lazy_vfeat_mask is None else config.lazy_vfeat_mask - ) - self.mm_prob = config.mm_prob if config.mm_prob is not None else 0. - - def __call__(self, video_id, video_feature, text_feature): - from transformers import default_data_collator - if self.subsampling is not None and self.subsampling > 1: - batch = [] - for _ in range(self.subsampling): - centerclip_idx = random.randint( - 0, len(text_feature["start"]) - 1) - sampled_max_text_len = random.randint( - self.sampled_min_len, self.sampled_max_len - ) - batch.append( - self.sampling( - video_id, - video_feature, - text_feature, - centerclip_idx, - sampled_max_text_len, - ) - ) - batch = self.batch_post_processing(batch, video_feature) - batch = default_data_collator(batch) - else: - batch = self.sampling(video_id, video_feature, text_feature) - batch = self.batch_post_processing(batch, video_feature) - batch["video_id"] = video_id if isinstance(video_id, str) \ - else video_id[0] - return batch - - def sampling( - self, - video_id, - video_feature, - text_feature, - centerclip_idx=None, - sampled_max_text_len=None, - ): - output = FixedLenAligner.sampling(self, - video_id, video_feature, text_feature, - centerclip_idx, sampled_max_text_len) - - masking_text, masking_video = None, None - if random.random() < self.mm_prob: - if random.random() > 0.5: - masking_text, masking_video = self.mm_type, "no" - else: - masking_text, masking_video = "no", "full" - video_feats = output["vfeats"] if not self.lazy_vfeat_mask else None - video_label = self.masked_frame_sampler( - output["vmasks"], masking_video, vfeats=video_feats) - caps, text_label = self.masked_token_sampler( - output["caps"], masking_text) - - output.update({ - "caps": caps, - "video_label": video_label, - "text_label": text_label, - }) - - if self.attnmasker is not None: - attention_mask = self.attnmasker( - output["vmasks"], output["cmasks"], masking_text) - output.update({ - "attention_mask": attention_mask - }) - return output - - -class FrameMaskingProcessor(Processor): - def __init__(self, config): - self.mfm_probability = 0.15 - if config.mfm_probability is not None: - self.mfm_probability = config.mfm_probability - - def __call__(self, vmasks, modality_masking=None, vfeats=None): - """ - We perform lazy masking to save data transfer time. - It only generates video_labels by default and MFM model - will do actualy masking. - Return: `video_label` is a binary mask. - """ - video_label = vmasks.clone() - if modality_masking is not None: - if modality_masking == "full": - probability_matrix = torch.full(video_label.shape, 1.) - elif modality_masking == "no": - probability_matrix = torch.full(video_label.shape, 0.) - elif modality_masking == "inverse": - probability_matrix = torch.full( - video_label.shape, 1. - self.mfm_probability) - else: - raise ValueError("unknown modality masking.", modality_masking) - else: - probability_matrix = torch.full( - video_label.shape, self.mfm_probability) - masked_indices = torch.bernoulli(probability_matrix).bool() - # We only compute loss on masked tokens - video_label[~masked_indices] = 0 - if vfeats is not None: - vfeats[video_label, :] = 0.0 - return video_label - - -class TextGenerationProcessor(Processor): - def __init__(self, tokenizer): - self.bos_token_id = tokenizer.bos_token_id - self.pad_token_id = tokenizer.pad_token_id - - def __call__(self, inputs): - labels = inputs.clone() - # [CLS] [SEP] for video - labels[:2] = -100 - # keep [SEP] for text. - pad_mask = labels == self.pad_token_id - labels[pad_mask] = -100 - inputs[2:] = torch.cat([ - torch.LongTensor([self.bos_token_id]), - inputs[2:-1]]) - inputs[pad_mask] = self.pad_token_id - assert len(inputs) == len(labels) - return inputs, labels - - -class TextMaskingProcessor(Processor): - def __init__(self, config): - """this function is borrowed from - `transformers/data/data_collator.DataCollatorForLanguageModeling`""" - self.mlm_probability = 0.15 - if config.mlm_probability is not None: - self.mlm_probability = config.mlm_probability - self.bert_name = config.bert_name - # [CLS] is used as bos_token and [SEP] is used as eos_token. - # https://huggingface.co/transformers/master/model_doc/bertgeneration.html - from transformers import AutoTokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - self.bert_name, bos_token="[CLS]", eos_token="[SEP]") - self.textgen = TextGenerationProcessor(self.tokenizer) - - def __call__( - self, inputs: torch.Tensor, - modality_masking=None, - special_tokens_mask: Optional[torch.Tensor] = None - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - expand modality_masking into - None: traditional bert masking. - "no": no masking. - "full": all [MASK] token for generation. - "gen": autoregressive generation. - """ - """ - Prepare masked tokens inputs/labels for masked language modeling: - 80% MASK, 10% random, 10% original. - """ - labels = inputs.clone() - # We sample a few tokens in each sequence for MLM training - # (with probability `self.mlm_probability`) - if modality_masking is not None: - if modality_masking == "full": - probability_matrix = torch.full(labels.shape, 1.) - elif modality_masking == "no": - probability_matrix = torch.full(labels.shape, 0.) - elif modality_masking.startswith("textgen"): - # [CLS] [SEP] ... - inputs, labels = self.textgen(inputs) - if "mask" not in modality_masking: - return inputs, labels - inputs = self.mask_input(inputs, special_tokens_mask) - return inputs, labels - elif modality_masking == "mask": - inputs = self.mask_input(inputs, special_tokens_mask) - labels = torch.full(inputs.shape, -100) - return inputs, labels - elif modality_masking == "inverse": - probability_matrix = torch.full(labels.shape, 1. - self.mlm_probability) - else: - raise ValueError("unknown modality masking.", modality_masking) - else: - probability_matrix = torch.full(labels.shape, self.mlm_probability) - - if special_tokens_mask is None: - special_tokens_mask = self.get_special_tokens_mask( - labels.tolist(), already_has_special_tokens=True - ) - special_tokens_mask = torch.tensor( - special_tokens_mask, dtype=torch.bool) - else: - special_tokens_mask = special_tokens_mask.bool() - - probability_matrix.masked_fill_(special_tokens_mask, value=0.0) - masked_indices = torch.bernoulli(probability_matrix).bool() - labels[~masked_indices] = -100 # We only compute loss on masked tokens - - # 80% of the time, - # we replace masked input tokens with tokenizer.mask_token ([MASK]) - indices_replaced = ( - torch.bernoulli( - torch.full(labels.shape, 0.8)).bool() & masked_indices - ) - inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids( - self.tokenizer.mask_token - ) - - # 10% of the time, we replace masked input tokens with random word - indices_random = ( - torch.bernoulli(torch.full(labels.shape, 0.5)).bool() - & masked_indices - & ~indices_replaced - ) - random_words = torch.randint( - len(self.tokenizer), labels.shape, dtype=torch.long - ) - inputs[indices_random] = random_words[indices_random] - - # The rest of the time (10% of the time) we keep the masked input - # tokens unchanged - return inputs, labels - - def mask_input(self, inputs, special_tokens_mask=None): - # the following is new with masked autoregressive. - probability_matrix = torch.full( - inputs.shape, self.mlm_probability) - if special_tokens_mask is None: - special_tokens_mask = self.get_special_tokens_mask( - inputs.tolist(), already_has_special_tokens=True - ) - special_tokens_mask = torch.tensor( - special_tokens_mask, dtype=torch.bool) - else: - special_tokens_mask = special_tokens_mask.bool() - probability_matrix.masked_fill_(special_tokens_mask, value=0.0) - masked_indices = torch.bernoulli(probability_matrix).bool() - indices_replaced = ( - torch.bernoulli( - torch.full(inputs.shape, 0.8)).bool() & masked_indices - ) - inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids( - self.tokenizer.mask_token - ) - - # 10% of the time, we replace masked input tokens with random word - indices_random = ( - torch.bernoulli(torch.full(inputs.shape, 0.5)).bool() - & masked_indices - & ~indices_replaced - ) - random_words = torch.randint( - len(self.tokenizer), inputs.shape, dtype=torch.long - ) - inputs[indices_random] = random_words[indices_random] - return inputs - - def get_special_tokens_mask( - self, token_ids_0: List[int], - token_ids_1: Optional[List[int]] = None, - already_has_special_tokens: bool = False - ) -> List[int]: - """ - Note: the version from transformers do not consider pad - as special tokens. - """ - - if already_has_special_tokens: - if token_ids_1 is not None: - raise ValueError( - "You should not supply a second sequence if" - "the provided sequence of " - "ids is already formated with special tokens " - "for the model." - ) - return list(map(lambda x: 1 if x in [ - self.tokenizer.sep_token_id, - self.tokenizer.cls_token_id, - self.tokenizer.pad_token_id] else 0, token_ids_0)) - - if token_ids_1 is not None: - return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] - return [1] + ([0] * len(token_ids_0)) + [1] - - -class TextClipSamplingProcessor(Processor): - def __init__(self, max_text_len, keep_prob=1.0): - self.max_text_len = max_text_len - self.max_video_len = 256 # always hold. - self.keep_prob = keep_prob - - def __call__( - self, - text_feature, - centerclip_idx=None, - sampled_max_text_len=None, - sampled_max_video_len=None, - ): - # Let's use all caps for now and see if 256 can cover all of them. - if sampled_max_text_len is not None: - max_text_len = sampled_max_text_len - else: - max_text_len = self.max_text_len - if sampled_max_video_len is not None: - max_video_len = sampled_max_video_len - else: - max_video_len = self.max_video_len - - t_num_clips = len(text_feature["start"]) - - if centerclip_idx is None: - centerclip_idx = random.randint(0, t_num_clips - 1) - - start_idx, end_idx = centerclip_idx, centerclip_idx + 1 - text_clip_indexs = deque() - text_clip_indexs.append(start_idx) - text_len = len(text_feature["cap"][start_idx]) - - video_len = max( - 0, - text_feature["end"][start_idx] - - text_feature["start"][start_idx], - ) - - while ( - (start_idx > 0 or end_idx < t_num_clips) - and text_len < max_text_len - and video_len < max_video_len - ): - if random.random() > 0.5 and end_idx < t_num_clips: - # skip the next one? - if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips: - end_idx = end_idx + 1 - text_clip_indexs.append(end_idx) - text_len += len(text_feature["cap"][end_idx]) - end_idx += 1 - elif start_idx > 0: - if random.random() > self.keep_prob and (start_idx - 1) > 0: - start_idx = start_idx - 1 - start_idx -= 1 - text_clip_indexs.insert(0, start_idx) - text_len += len(text_feature["cap"][start_idx]) - else: - if end_idx < t_num_clips: - if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips: - end_idx = end_idx + 1 - text_clip_indexs.append(end_idx) - text_len += len(text_feature["cap"][end_idx]) - end_idx += 1 - else: - return text_clip_indexs - video_len = max( - 0, - text_feature["end"][text_clip_indexs[-1]] - - text_feature["start"][text_clip_indexs[0]], - ) - return text_clip_indexs - - -class VideoClipSamplingProcessor(Processor): - def __call__(self, video_len, max_video_len, center): - """ - `video_len`: length of the video. - `max_video_len`: maximum video tokens allowd in a sequence. - `center`: initial starting index. - """ - assert center >= 0 and center < video_len - t_clip_len = 0 - start, end = center, center - while (start > 0 or end < video_len) and t_clip_len < max_video_len: - # decide the direction to grow. - if start <= 0: - end += 1 - elif end >= video_len: - start -= 1 - elif random.random() > 0.5: - end += 1 - else: - start -= 1 - t_clip_len += 1 - return {"start": [start], "end": [end]} - - -class How2MILNCEAligner(FixedLenAligner): - """reference: `antoine77340/MIL-NCE_HowTo100M/video_loader.py`""" - - def __init__(self, config): - super().__init__(config) - self.num_candidates = 4 - self.min_time = 5.0 - self.num_sec = 3.2 - # self.num_sec = self.num_frames / float(self.fps) num_frames=16 / fps = 5 - # self.num_frames = 16 - - def sampling( - self, - video_id, - video_feature, - text_feature, - centerclip_idx=None, # will be ignored. - sampled_max_text_len=None # will be ignored. - ): - text, start, end = self._get_text(text_feature) - video = self._get_video(video_feature, start, end) - - vfeats = torch.zeros((self.max_video_len, video_feature.shape[1])) - vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool) - vfeats[: video.shape[0]] = torch.from_numpy(np.array(video)) - vmasks[: video.shape[0]] = 1 - - caps, cmasks = [], [] - for words in text: - cap, cmask = self._build_text_seq(text_feature, words) - caps.append(cap) - cmasks.append(cmask) - caps = torch.stack(caps) - cmasks = torch.stack(cmasks) - # video of shape: (video_len) - # text of shape (num_candidates, max_text_len) - - return { - "caps": caps, - "cmasks": cmasks, - "vfeats": vfeats, - "vmasks": vmasks, - # "video_id": video_id, - } - - def _get_video(self, video_feature, start, end): - start_seek = random.randint(start, int(max(start, end - self.num_sec))) - # duration = self.num_sec + 0.1 - return video_feature[start_seek : int(start_seek + self.num_sec)] - - def _get_text(self, cap): - ind = random.randint(0, len(cap["start"]) - 1) - if self.num_candidates == 1: - words = [ind] - else: - words = [] - cap_start = self._find_nearest_candidates(cap, ind) - for i in range(self.num_candidates): - words.append([max(0, min(len(cap["cap"]) - 1, cap_start + i))]) - - start, end = cap["start"][ind], cap["end"][ind] - # TODO: May need to be improved for edge cases. - # expand the min time. - if end - start < self.min_time: - diff = self.min_time - end + start - start = max(0, start - diff / 2) - end = start + self.min_time - return words, int(start), int(end) - - def _find_nearest_candidates(self, caption, ind): - """find the range of the clips.""" - start, end = ind, ind - #diff = caption["end"][end] - caption["start"][start] - n_candidate = 1 - while n_candidate < self.num_candidates: - # the first clip - if start == 0: - return 0 - # we add () in the following condition to fix the bug. - elif end == (len(caption["start"]) - 1): - return start - (self.num_candidates - n_candidate) - elif (caption["end"][end] - caption["start"][start - 1]) < ( - caption["end"][end + 1] - caption["start"][start] - ): - start -= 1 - else: - end += 1 - n_candidate += 1 - return start - - -class PKLJSONStrTextProcessor(TextProcessor): - """`caption.json` from howto100m are preprocessed as a - dict `[video_id, json_str]`. - Json parsing tokenization are conducted on-the-fly and cached into dict. - """ - - def __init__(self, config, max_clip_text_len=96): - print("[Warning] PKLJSONStrTextProcessor is slow for num_workers > 0.") - self.caption_pkl_path = str(config.caption_pkl_path) - with open(self.caption_pkl_path, "rb") as fd: - self.data = pickle.load(fd) - self.max_clip_text_len = max_clip_text_len - from transformers import AutoTokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - str(config.bert_name), use_fast=config.use_fast - ) - - def __call__(self, video_id): - caption = self.data[video_id] - if isinstance(caption, str): - import json - caption = json.loads(caption) - cap = [] - for clip_idx, text_clip in enumerate(caption["text"]): - clip_ids = [] - if isinstance(text_clip, str): - clip_ids = self.tokenizer( - text_clip[: self.max_clip_text_len], - add_special_tokens=False - )["input_ids"] - cap.append(clip_ids) - caption["cap"] = cap - caption.pop("text") # save space. - self.data[video_id] = caption - return caption diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/how2retriprocessor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/how2retriprocessor.py deleted file mode 100644 index 451506a9ff9bebce1fb77f969f8c4c869d08b6c0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/how2retriprocessor.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 .how2processor import ( - OverlappedAligner, - ShardedHow2MetaProcessor, - ShardedTextProcessor, - ShardedVideoProcessor, - VariedLenAligner, -) - - -class ShardedHow2VideoRetriMetaProcessor(ShardedHow2MetaProcessor): - def __init__(self, config): - super().__init__(config) - self.num_video_per_batch = config.num_video_per_batch - self.cands = [ - self.data[batch_offset:batch_offset + self.num_video_per_batch] - for batch_offset in - range(0, (len(self.data) // (8 * self.num_video_per_batch)) * 8 * self.num_video_per_batch, self.num_video_per_batch)] - - def __len__(self): - return len(self.cands) - - def set_candidates(self, cands): - # no changes on num of batches. - print(len(self.cands), "->", len(cands)) - # assert len(self.cands) == len(cands) - self.cands = cands - - def __getitem__(self, idx): - video_ids = self.cands[idx] - assert isinstance(video_ids, list) - sharded_video_idxs = [] - for video_id in video_ids: - shard_id, video_idx = self.video_id_to_shard[video_id] - sharded_video_idxs.append((video_id, -1, shard_id, video_idx)) - return sharded_video_idxs, sharded_video_idxs - - -class ShardedVideoRetriVideoProcessor(ShardedVideoProcessor): - """In retrival case the video_id - is a list of tuples: `(shard_id, video_idx)` .""" - - def __call__(self, sharded_video_idxs): - assert isinstance(sharded_video_idxs, list) - cand_feats = [] - for shared_video_idx in sharded_video_idxs: - feat = super().__call__(shared_video_idx) - cand_feats.append(feat) - return cand_feats - - -class ShardedVideoRetriTextProcessor(ShardedTextProcessor): - """In retrival case the video_id - is a list of tuples: `(shard_id, video_idx)` .""" - - def __call__(self, sharded_video_idxs): - assert isinstance(sharded_video_idxs, list) - cand_caps = [] - for shared_video_idx in sharded_video_idxs: - caps = super().__call__(shared_video_idx) - cand_caps.append(caps) - return cand_caps - - -class VideoRetriAligner(VariedLenAligner): - # Retritask will trim dim-0. - def __call__(self, sharded_video_idxs, video_features, text_features): - from transformers import default_data_collator - batch, video_ids = [], [] - for video_id, video_feature, text_feature in \ - zip(sharded_video_idxs, video_features, text_features): - sub_batch = super().__call__(video_id, video_feature, text_feature) - batch.append(sub_batch) - if isinstance(video_id, tuple): - video_id = video_id[0] - video_ids.append(video_id) - batch = default_data_collator(batch) - batch["video_id"] = video_ids - return batch - - -class VideoRetriOverlappedAligner(OverlappedAligner): - # Retritask will trim dim-0. - def __call__(self, sharded_video_idxs, video_features, text_features): - from transformers import default_data_collator - batch, video_ids = [], [] - for video_id, video_feature, text_feature in \ - zip(sharded_video_idxs, video_features, text_features): - sub_batch = super().__call__(video_id, video_feature, text_feature) - batch.append(sub_batch) - if isinstance(video_id, tuple): - video_id = video_id[0] - video_ids.append(video_id) - batch = default_data_collator(batch) - batch["video_id"] = video_ids - return batch diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/models/s3dg.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/models/s3dg.py deleted file mode 100644 index 2f1a739817bf69e71bac0abf975db8c789fbc691..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/models/s3dg.py +++ /dev/null @@ -1,337 +0,0 @@ -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -"""Contains a PyTorch definition for Gated Separable 3D network (S3D-G) -with a text module for computing joint text-video embedding from raw text -and video input. The following code will enable you to load the HowTo100M -pretrained S3D Text-Video model from: - A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic and A. Zisserman, - End-to-End Learning of Visual Representations from Uncurated Instructional Videos. - https://arxiv.org/abs/1912.06430. - -S3D-G was proposed by: - S. Xie, C. Sun, J. Huang, Z. Tu and K. Murphy, - Rethinking Spatiotemporal Feature Learning For Video Understanding. - https://arxiv.org/abs/1712.04851. - Tensorflow code: https://github.com/tensorflow/models/blob/master/research/slim/nets/s3dg.py - -The S3D architecture was slightly modified with a space to depth trick for TPU -optimization. -""" - -import os -import re - -import numpy as np -import torch as th -import torch.nn as nn -import torch.nn.functional as F - - -class InceptionBlock(nn.Module): - def __init__( - self, - input_dim, - num_outputs_0_0a, - num_outputs_1_0a, - num_outputs_1_0b, - num_outputs_2_0a, - num_outputs_2_0b, - num_outputs_3_0b, - gating=True, - ): - super(InceptionBlock, self).__init__() - self.conv_b0 = STConv3D(input_dim, num_outputs_0_0a, [1, 1, 1]) - self.conv_b1_a = STConv3D(input_dim, num_outputs_1_0a, [1, 1, 1]) - self.conv_b1_b = STConv3D( - num_outputs_1_0a, num_outputs_1_0b, [3, 3, 3], padding=1, separable=True - ) - self.conv_b2_a = STConv3D(input_dim, num_outputs_2_0a, [1, 1, 1]) - self.conv_b2_b = STConv3D( - num_outputs_2_0a, num_outputs_2_0b, [3, 3, 3], padding=1, separable=True - ) - self.maxpool_b3 = th.nn.MaxPool3d((3, 3, 3), stride=1, padding=1) - self.conv_b3_b = STConv3D(input_dim, num_outputs_3_0b, [1, 1, 1]) - self.gating = gating - self.output_dim = ( - num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b + num_outputs_3_0b - ) - if gating: - self.gating_b0 = SelfGating(num_outputs_0_0a) - self.gating_b1 = SelfGating(num_outputs_1_0b) - self.gating_b2 = SelfGating(num_outputs_2_0b) - self.gating_b3 = SelfGating(num_outputs_3_0b) - - def forward(self, input): - """Inception block - """ - b0 = self.conv_b0(input) - b1 = self.conv_b1_a(input) - b1 = self.conv_b1_b(b1) - b2 = self.conv_b2_a(input) - b2 = self.conv_b2_b(b2) - b3 = self.maxpool_b3(input) - b3 = self.conv_b3_b(b3) - if self.gating: - b0 = self.gating_b0(b0) - b1 = self.gating_b1(b1) - b2 = self.gating_b2(b2) - b3 = self.gating_b3(b3) - return th.cat((b0, b1, b2, b3), dim=1) - - -class SelfGating(nn.Module): - def __init__(self, input_dim): - super(SelfGating, self).__init__() - self.fc = nn.Linear(input_dim, input_dim) - - def forward(self, input_tensor): - """Feature gating as used in S3D-G. - """ - spatiotemporal_average = th.mean(input_tensor, dim=[2, 3, 4]) - weights = self.fc(spatiotemporal_average) - weights = th.sigmoid(weights) - return weights[:, :, None, None, None] * input_tensor - - -class STConv3D(nn.Module): - def __init__( - self, input_dim, output_dim, kernel_size, stride=1, padding=0, separable=False - ): - super(STConv3D, self).__init__() - self.separable = separable - self.relu = nn.ReLU(inplace=True) - assert len(kernel_size) == 3 - if separable and kernel_size[0] != 1: - spatial_kernel_size = [1, kernel_size[1], kernel_size[2]] - temporal_kernel_size = [kernel_size[0], 1, 1] - if isinstance(stride, list) and len(stride) == 3: - spatial_stride = [1, stride[1], stride[2]] - temporal_stride = [stride[0], 1, 1] - else: - spatial_stride = [1, stride, stride] - temporal_stride = [stride, 1, 1] - if isinstance(padding, list) and len(padding) == 3: - spatial_padding = [0, padding[1], padding[2]] - temporal_padding = [padding[0], 0, 0] - else: - spatial_padding = [0, padding, padding] - temporal_padding = [padding, 0, 0] - if separable: - self.conv1 = nn.Conv3d( - input_dim, - output_dim, - kernel_size=spatial_kernel_size, - stride=spatial_stride, - padding=spatial_padding, - bias=False, - ) - self.bn1 = nn.BatchNorm3d(output_dim) - self.conv2 = nn.Conv3d( - output_dim, - output_dim, - kernel_size=temporal_kernel_size, - stride=temporal_stride, - padding=temporal_padding, - bias=False, - ) - self.bn2 = nn.BatchNorm3d(output_dim) - else: - self.conv1 = nn.Conv3d( - input_dim, - output_dim, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=False, - ) - self.bn1 = nn.BatchNorm3d(output_dim) - - def forward(self, input): - out = self.relu(self.bn1(self.conv1(input))) - if self.separable: - out = self.relu(self.bn2(self.conv2(out))) - return out - - -class MaxPool3dTFPadding(th.nn.Module): - def __init__(self, kernel_size, stride=None, padding="SAME"): - super(MaxPool3dTFPadding, self).__init__() - if padding == "SAME": - padding_shape = self._get_padding_shape(kernel_size, stride) - self.padding_shape = padding_shape - self.pad = th.nn.ConstantPad3d(padding_shape, 0) - self.pool = th.nn.MaxPool3d(kernel_size, stride, ceil_mode=True) - - def _get_padding_shape(self, filter_shape, stride): - def _pad_top_bottom(filter_dim, stride_val): - pad_along = max(filter_dim - stride_val, 0) - pad_top = pad_along // 2 - pad_bottom = pad_along - pad_top - return pad_top, pad_bottom - - padding_shape = [] - for filter_dim, stride_val in zip(filter_shape, stride): - pad_top, pad_bottom = _pad_top_bottom(filter_dim, stride_val) - padding_shape.append(pad_top) - padding_shape.append(pad_bottom) - depth_top = padding_shape.pop(0) - depth_bottom = padding_shape.pop(0) - padding_shape.append(depth_top) - padding_shape.append(depth_bottom) - return tuple(padding_shape) - - def forward(self, inp): - inp = self.pad(inp) - out = self.pool(inp) - return out - - -class Sentence_Embedding(nn.Module): - def __init__( - self, - embd_dim, - num_embeddings=66250, - word_embedding_dim=300, - token_to_word_path="dict.npy", - max_words=16, - output_dim=2048, - ): - super(Sentence_Embedding, self).__init__() - self.word_embd = nn.Embedding(num_embeddings, word_embedding_dim) - self.fc1 = nn.Linear(word_embedding_dim, output_dim) - self.fc2 = nn.Linear(output_dim, embd_dim) - self.word_to_token = {} - self.max_words = max_words - token_to_word = np.load(token_to_word_path) - for i, t in enumerate(token_to_word): - self.word_to_token[t] = i + 1 - - def _zero_pad_tensor_token(self, tensor, size): - if len(tensor) >= size: - return tensor[:size] - else: - zero = th.zeros(size - len(tensor)).long() - return th.cat((tensor, zero), dim=0) - - def _split_text(self, sentence): - w = re.findall(r"[\w']+", str(sentence)) - return w - - def _words_to_token(self, words): - words = [ - self.word_to_token[word] for word in words if word in self.word_to_token - ] - if words: - we = self._zero_pad_tensor_token(th.LongTensor(words), self.max_words) - return we - else: - return th.zeros(self.max_words).long() - - def _words_to_ids(self, x): - split_x = [self._words_to_token(self._split_text(sent.lower())) for sent in x] - return th.stack(split_x, dim=0) - - def forward(self, x): - x = self._words_to_ids(x) - x = self.word_embd(x) - x = F.relu(self.fc1(x)) - x = th.max(x, dim=1)[0] - x = self.fc2(x) - return {'text_embedding': x} - - -class S3D(nn.Module): - def __init__(self, dict_path, num_classes=512, gating=True, space_to_depth=True): - super(S3D, self).__init__() - self.num_classes = num_classes - self.gating = gating - self.space_to_depth = space_to_depth - if space_to_depth: - self.conv1 = STConv3D( - 24, 64, [2, 4, 4], stride=1, padding=(1, 2, 2), separable=False - ) - else: - self.conv1 = STConv3D( - 3, 64, [3, 7, 7], stride=2, padding=(1, 3, 3), separable=False - ) - self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False) - self.conv_2c = STConv3D(64, 192, [3, 3, 3], padding=1, separable=True) - self.gating = SelfGating(192) - self.maxpool_2a = MaxPool3dTFPadding( - kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME" - ) - self.maxpool_3a = MaxPool3dTFPadding( - kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME" - ) - self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32) - self.mixed_3c = InceptionBlock( - self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64 - ) - self.maxpool_4a = MaxPool3dTFPadding( - kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME" - ) - self.mixed_4b = InceptionBlock( - self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64 - ) - self.mixed_4c = InceptionBlock( - self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64 - ) - self.mixed_4d = InceptionBlock( - self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64 - ) - self.mixed_4e = InceptionBlock( - self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64 - ) - self.mixed_4f = InceptionBlock( - self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128 - ) - self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding( - kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME" - ) - self.mixed_5b = InceptionBlock( - self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128 - ) - self.mixed_5c = InceptionBlock( - self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128 - ) - self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes) - self.text_module = Sentence_Embedding(num_classes, - token_to_word_path=dict_path) - - def _space_to_depth(self, input): - """3D space to depth trick for TPU optimization. - """ - B, C, T, H, W = input.shape - input = input.view(B, C, T // 2, 2, H // 2, 2, W // 2, 2) - input = input.permute(0, 3, 5, 7, 1, 2, 4, 6) - input = input.contiguous().view(B, 8 * C, T // 2, H // 2, W // 2) - return input - - def forward(self, inputs): - """Defines the S3DG base architecture.""" - if self.space_to_depth: - inputs = self._space_to_depth(inputs) - net = self.conv1(inputs) - if self.space_to_depth: - # we need to replicate 'SAME' tensorflow padding - net = net[:, :, 1:, 1:, 1:] - net = self.maxpool_2a(net) - net = self.conv_2b(net) - net = self.conv_2c(net) - if self.gating: - net = self.gating(net) - net = self.maxpool_3a(net) - net = self.mixed_3b(net) - net = self.mixed_3c(net) - net = self.maxpool_4a(net) - net = self.mixed_4b(net) - net = self.mixed_4c(net) - net = self.mixed_4d(net) - net = self.mixed_4e(net) - net = self.mixed_4f(net) - net = self.maxpool_5a(net) - net = self.mixed_5b(net) - net = self.mixed_5c(net) - net = th.mean(net, dim=[2, 3, 4]) - return {'video_embedding': self.fc(net), 'mixed_5c': net} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/processor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/processor.py deleted file mode 100644 index 4c3ee8789cbdc01f4c23495d14ca2bfcc595a7a5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/processors/processor.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright (c) Facebook, Inc. All Rights Reserved - -import os - -import numpy as np -import torch - - -class Processor(object): - """ - A generic processor for video (codec, feature etc.) and text. - """ - - def __call__(self, **kwargs): - raise NotImplementedError - - -class MetaProcessor(Processor): - """ - A meta processor is expected to load the metadata of a dataset: - (e.g., video_ids, or captions). - You must implement the `__getitem__` (meta datasets are rather diverse.). - """ - - def __init__(self, config): - self.split = config.split - - def __len__(self): - return len(self.data) - - def __getitem__(self, idx): - raise NotImplementedError - - def _get_split_path(self, config): - splits = { - "train": config.train_path, - "valid": config.val_path, - "test": config.test_path, - } - if config.split is not None: - return splits[config.split] - return config.train_path - - -class TextProcessor(Processor): - """ - A generic Text processor: rename this as `withTokenizer`. - tokenize a string of text on-the-fly. - Warning: mostly used for end tasks. - (on-the-fly tokenization is slow for how2.) - TODO(huxu): move this class as a subclass. - """ - - def __init__(self, config): - self.bert_name = str(config.bert_name) - self.use_fast = config.use_fast - from transformers import AutoTokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - self.bert_name, use_fast=self.use_fast - ) - - def __call__(self, text_id): - caption = self.tokenizer(text_id, add_special_tokens=False) - return caption["input_ids"] - - -class VideoProcessor(Processor): - """ - A generic video processor: load a numpy video tokens by default. - """ - - def __init__(self, config): - self.vfeat_dir = config.vfeat_dir - - def __call__(self, video_fn): - if isinstance(video_fn, tuple): - video_fn = video_fn[0] - assert isinstance(video_fn, str) - video_fn = os.path.join(self.vfeat_dir, video_fn + ".npy") - feat = np.load(video_fn) - return feat - - -class Aligner(object): - """ - An alignprocessor align video and text and output a dict of tensors (for a model). - """ - def __init__(self, config): - """__init__ needs to be light weight for more workers/threads.""" - self.split = config.split - self.max_video_len = config.max_video_len - self.max_len = config.max_len - from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained( - str(config.bert_name), use_fast=config.use_fast - ) - self.cls_token_id = tokenizer.cls_token_id - self.sep_token_id = tokenizer.sep_token_id - self.pad_token_id = tokenizer.pad_token_id - self.mask_token_id = tokenizer.mask_token_id - - def __call__(self, video_id, video_feature, text_feature): - raise NotImplementedError - - def _build_video_seq(self, video_feature, video_clips=None): - """ - `video_feature`: available video tokens. - `video_clips`: video clip sequence to build. - """ - if not isinstance(video_feature, np.ndarray): - raise ValueError( - "unsupported type of video_feature", type(video_feature) - ) - - if video_clips is None: - # this is borrowed from DSAligner - video_start = 0 - video_end = min(len(video_feature), self.max_video_len) - # the whole sequence is a single clip. - video_clips = {"start": [video_start], "end": [video_end]} - - vfeats = np.zeros( - (self.max_video_len, video_feature.shape[1]), dtype=np.float32 - ) - vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool) - video_len = 0 - for start, end in zip(video_clips["start"], video_clips["end"]): - clip_len = min(self.max_video_len - video_len, (end - start)) - if clip_len > 0: - vfeats[video_len: video_len + clip_len] = video_feature[ - start: start + clip_len - ] - vmasks[video_len: video_len + clip_len] = 1 - video_len += clip_len - vfeats = torch.from_numpy(vfeats) - - return vfeats, vmasks - - def _build_text_seq(self, text_feature, text_clip_indexs=None): - """ - `text_feature`: all available clips. - `text_clip_indexes`: clip sequence to build. - """ - if text_clip_indexs is None: - text_clip_indexs = [0] - - full_caps = [] - if isinstance(text_feature, dict): - for clip_idx in text_clip_indexs: - full_caps.extend(text_feature["cap"][clip_idx]) - else: - full_caps = text_feature - max_text_len = self.max_len - self.max_video_len - 3 - full_caps = full_caps[:max_text_len] - full_caps = ( - [self.cls_token_id, self.sep_token_id] + full_caps + [self.sep_token_id] - ) - text_pad_len = self.max_len - len(full_caps) - self.max_video_len - padded_full_caps = full_caps + [self.pad_token_id] * text_pad_len - caps = torch.LongTensor(padded_full_caps) - cmasks = torch.zeros((len(padded_full_caps),), dtype=torch.bool) - cmasks[: len(full_caps)] = 1 - - return caps, cmasks - - def batch_post_processing(self, batch, video_feature): - return batch - - -class MMAttentionMask2DProcessor(Processor): - """text generation requires 2d mask - that is harder to generate by GPU at this stage.""" - - def __call__(self, vmask, cmask, mtype): - if mtype == "textgen": - return self._build_textgeneration_mask(vmask, cmask) - elif mtype == "videogen": - return self._build_videogeneration_mask(vmask, cmask) - else: - return self._build_mm_mask(vmask, cmask) - - def _build_mm_mask(self, vmask, cmask): - mask_1d = torch.cat([cmask[:1], vmask, cmask[1:]], dim=0) - return mask_1d[None, :].repeat(mask_1d.size(0), 1) - - def _build_videogeneration_mask(self, vmask, cmask): - # cls_mask is only about text otherwise it will leak generation. - cls_text_mask = torch.cat([ - # [CLS] - torch.ones( - (1,), dtype=torch.bool, device=cmask.device), - # video tokens and [SEP] for video. - torch.zeros( - (vmask.size(0) + 1,), dtype=torch.bool, device=cmask.device), - cmask[2:] - ], dim=0) - - # concat horizontially. - video_len = int(vmask.sum()) - video_masks = torch.cat([ - # [CLS] - torch.ones( - (video_len, 1), dtype=torch.bool, device=cmask.device - ), - torch.tril( - torch.ones( - (video_len, video_len), - dtype=torch.bool, device=cmask.device)), - # video_padding - torch.zeros( - (video_len, vmask.size(0) - video_len), - dtype=torch.bool, device=cmask.device - ), - # [SEP] for video (unused). - torch.zeros( - (video_len, 1), dtype=torch.bool, device=cmask.device - ), - cmask[2:].unsqueeze(0).repeat(video_len, 1) - ], dim=1) - - text_masks = cls_text_mask[None, :].repeat( - cmask.size(0) - 2, 1) - video_padding_masks = cls_text_mask[None, :].repeat( - vmask.size(0) - video_len, 1) - - return torch.cat([ - cls_text_mask[None, :], - video_masks, - video_padding_masks, - torch.cat([cmask[:1], vmask, cmask[1:]], dim=0)[None,:], - text_masks - ], dim=0) - - def _build_textgeneration_mask(self, vmask, cmask): - # cls_mask is only about video otherwise it will leak generation. - cls_video_mask = torch.cat([ - # [CLS] - torch.ones( - (1,), dtype=torch.bool, device=cmask.device), - vmask, - # [SEP] - torch.ones((1,), dtype=torch.bool, device=cmask.device), - torch.zeros( - (cmask.size(0)-2,), dtype=torch.bool, device=cmask.device) - ], dim=0) - - # concat horizontially. - text_len = int(cmask[2:].sum()) - text_masks = torch.cat([ - # [CLS] - torch.ones( - (text_len, 1), dtype=torch.bool, device=cmask.device - ), - vmask.unsqueeze(0).repeat(text_len, 1), - # [SEP] for video. - torch.ones( - (text_len, 1), dtype=torch.bool, device=cmask.device - ), - torch.tril( - torch.ones( - (text_len, text_len), - dtype=torch.bool, device=cmask.device)), - # padding. - torch.zeros( - (text_len, cmask.size(0) - text_len - 2), - dtype=torch.bool, device=cmask.device - ) - ], dim=1) - - cls_video_masks = cls_video_mask[None, :].repeat( - vmask.size(0) + 2, 1) - text_padding_masks = cls_video_mask[None, :].repeat( - cmask.size(0) - text_len - 2, 1) - return torch.cat([ - cls_video_masks, text_masks, text_padding_masks], dim=0) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/__init__.py deleted file mode 100644 index 4d6b93b5f1b9267eab9e98cbfbd425ba342716a6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/__init__.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. -from .retritask import * -from .task import * -from .vlmtask import * - -try: - from .fairseqmmtask import * -except ImportError: - pass - -try: - from .milncetask import * -except ImportError: - pass - -try: - from .expretritask import * -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/fairseqmmtask.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/fairseqmmtask.py deleted file mode 100644 index fcb671ceeb033d69bce9dddf650268952148a6b7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/fairseqmmtask.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. -""" -make a general fairseq task for MM pretraining. -""" - -import random - -from fairseq.tasks import LegacyFairseqTask, register_task - -from .. import utils -from ..datasets import FairseqMMDataset -from .retritask import RetriTask -from .task import Task - - -@register_task("mmtask") -class FairseqMMTask(LegacyFairseqTask): - @staticmethod - def add_args(parser): - # Add some command-line arguments for specifying where the data is - # located and the maximum supported input length. - parser.add_argument( - "taskconfig", - metavar="FILE", - help=("taskconfig to load all configurations" "outside fairseq parser."), - ) - - @classmethod - def setup_task(cls, args, **kwargs): - return FairseqMMTask(args) - - def __init__(self, args): - super().__init__(args) - config = utils.load_config(args) - self.mmtask = Task.config_task(config) - self.mmtask.build_dataset() - self.mmtask.build_model() - self.mmtask.build_loss() - - def load_dataset(self, split, **kwargs): - split_map = { - "train": self.mmtask.train_data, - "valid": self.mmtask.val_data, - "test": self.mmtask.test_data, - } - if split not in split_map: - raise ValueError("unknown split type.") - if split_map[split] is not None: - self.datasets[split] = FairseqMMDataset(split_map[split]) - - 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, - ): - random.seed(epoch) - if dataset.mmdataset.split == "train" and isinstance(self.mmtask, RetriTask): - if epoch >= self.mmtask.config.retri_epoch: - if not hasattr(self.mmtask, "retri_dataloader"): - self.mmtask.build_dataloader() - self.mmtask.retrive_candidates(epoch) - - return super().get_batch_iterator( - dataset, - max_tokens, - max_sentences, - max_positions, - ignore_invalid_inputs, - required_batch_size_multiple, - seed, - num_shards, - shard_id, - num_workers, - epoch, - data_buffer_size, - disable_iterator_cache, - grouped_shuffling, - update_epoch_batch_itr, - ) - - @property - def source_dictionary(self): - return None - - @property - def target_dictionary(self): - return None diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/milncetask.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/milncetask.py deleted file mode 100644 index 61b6ab0597f9f3a78bbcf1474613630b20c5a874..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/milncetask.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .task import Task - - -class MILNCETask(Task): - def reshape_subsample(self, sample): - if ( - hasattr(self.config.dataset, "subsampling") - and self.config.dataset.subsampling is not None - and self.config.dataset.subsampling > 1 - ): - for key in sample: - if torch.is_tensor(sample[key]): - tensor = self.flat_subsample(sample[key]) - if key in ["caps", "cmasks"]: - size = tensor.size() - batch_size = size[0] * size[1] - expanded_size = (batch_size,) + size[2:] - tensor = tensor.view(expanded_size) - sample[key] = tensor - return sample diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/retritask.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/retritask.py deleted file mode 100644 index b89a2b225871486fecad06d8eb058a027340b28f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/retritask.py +++ /dev/null @@ -1,252 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 pickle -import random - -import torch -from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler -from tqdm import tqdm - -from ..datasets import MMDataset -from ..evaluators.predictor import Predictor -from ..modules import vectorpool -from ..processors import ( - ShardedHow2MetaProcessor, - ShardedTextProcessor, - ShardedVideoProcessor, - VariedLenAligner, -) -from ..utils import get_local_rank, get_world_size, set_seed -from .task import Task - - -class RetriTask(Task): - """abstract class for task with retrival.""" - - def reshape_subsample(self, sample): - for key in sample: - if torch.is_tensor(sample[key]): - sample[key] = self.flat_subsample(sample[key]) - return sample - - def flat_subsample(self, tensor): - if tensor.size(0) == 1: - tensor = tensor.squeeze(0) - return tensor - - def build_dataloader(self): - """called by `get_batch_iterator` in fairseqmmtask. """ - # TODO: hard-code dataloader for retri for now and configurable in .yaml. - # reuse the `train.lst`. - self.config.dataset.split = "train" - meta_processor = ShardedHow2MetaProcessor(self.config.dataset) - video_processor = ShardedVideoProcessor(self.config.dataset) - text_processor = ShardedTextProcessor(self.config.dataset) - - aligner = VariedLenAligner(self.config.dataset) - aligner.subsampling = self.config.dataset.clip_per_video - - self.retri_data = MMDataset( - meta_processor, video_processor, text_processor, aligner - ) - - retri_sampler = DistributedSampler(self.retri_data) - infer_scale = 16 - batch_size = self.config.dataset.num_video_per_batch \ - * infer_scale - - self.retri_dataloader = DataLoader( - self.retri_data, - collate_fn=self.retri_data.collater, - batch_size=batch_size, - shuffle=False, - sampler=retri_sampler, - num_workers=self.config.fairseq.dataset.num_workers - ) - return self.retri_dataloader - - def retrive_candidates(self, epoch, dataloader=None): - if get_local_rank() == 0: - print("running retrieval model.") - out_dir = os.path.join( - self.config.fairseq.checkpoint.save_dir, "retri") - os.makedirs(out_dir, exist_ok=True) - - if not os.path.isfile( - os.path.join( - out_dir, "batched_e" + str(epoch) + "_videos0.pkl") - ): - if dataloader is None: - dataloader = self.retri_dataloader - - self.model.eval() - self.model.is_train = False - - assert self.retri_data.meta_processor.data == \ - self.train_data.meta_processor.data # video_ids not mutated. - - self._retri_predict(epoch, dataloader) - - self.model.train() - self.model.is_train = True - - torch.distributed.barrier() - output = self._retri_sync(epoch, out_dir) - torch.distributed.barrier() - self.train_data.meta_processor.set_candidates(output) - return output - - -class VideoRetriTask(RetriTask): - """RetriTask on video level.""" - - def reshape_subsample(self, sample): - if ( - hasattr(self.config.dataset, "clip_per_video") - and self.config.dataset.clip_per_video is not None - and self.config.dataset.clip_per_video > 1 - ): - for key in sample: - if torch.is_tensor(sample[key]): - sample[key] = self.flat_subsample(sample[key]) - return sample - - def flat_subsample(self, tensor): - if tensor.size(0) == 1: - tensor = tensor.squeeze(0) - return Task.flat_subsample(self, tensor) - - def _retri_predict(self, epoch, dataloader): - set_seed(epoch) - # save for retrival. - predictor = VideoPredictor(self.config) - predictor.predict_loop( - self.model, dataloader) - set_seed(epoch) # get the same text clips. - # retrival. - retri_predictor = VideoRetriPredictor( - self.config) - retri_predictor.predict_loop( - self.model, predictor.vecpool.retriver, epoch) - del predictor - del retri_predictor - - def _retri_sync(self, epoch, out_dir): - # gpu do the same merge. - batched_videos = [] - for local_rank in range(get_world_size()): - fn = os.path.join( - out_dir, - "batched_e" + str(epoch) + "_videos" + str(local_rank) + ".pkl") - with open(fn, "rb") as fr: - batched_videos.extend(pickle.load(fr)) - print( - "[INFO] batched_videos", - len(batched_videos), len(batched_videos[0])) - return batched_videos - - -class VideoPredictor(Predictor): - def __init__(self, config): - vectorpool_cls = getattr(vectorpool, config.vectorpool_cls) - self.vecpool = vectorpool_cls(config) - - def predict_loop( - self, - model, - dataloader, - early_stop=-1, - ): - with torch.no_grad(): - if get_local_rank() == 0: - dataloader = tqdm(dataloader) - for batch_idx, batch in enumerate(dataloader): - if batch_idx == early_stop: - break - self(batch, model) - return self.finalize() - - def __call__(self, sample, model, **kwargs): - param = next(model.parameters()) - dtype = param.dtype - device = param.device - subsample = sample["vfeats"].size(1) - sample = self.to_ctx(sample, device, dtype) - for key in sample: - if torch.is_tensor(sample[key]): - size = sample[key].size() - if len(size) >= 2: - batch_size = size[0] * size[1] - expanded_size = ( - (batch_size,) + size[2:] if len(size) > 2 - else (batch_size,) - ) - sample[key] = sample[key].view(expanded_size) - - outputs = model(**sample) - sample.update(outputs) - self.vecpool(sample, subsample) - - def finalize(self): - print("[INFO]", self.vecpool) - if not self.vecpool.retriver.db.is_trained: - self.vecpool.retriver.finalize_training() - return self.vecpool.retriver - - -class VideoRetriPredictor(Predictor): - """ - Online Retrieval Predictor for Clips (used by RetriTask). - TODO: merge this with VisPredictor? - """ - - def __init__(self, config): - self.pred_dir = os.path.join( - config.fairseq.checkpoint.save_dir, - "retri") - self.num_cands = config.num_cands - self.num_video_per_batch = config.dataset.num_video_per_batch - - def predict_loop( - self, - model, - retriver, - epoch, - early_stop=-1 - ): - # a fake loop that only try to recover video vector - # from video_id. - batched_videos = [] - # obtain available video_ids. - video_ids = list(retriver.videoid_to_vectoridx.keys()) - - dataloader = random.sample( - video_ids, - len(video_ids) // self.num_video_per_batch - ) - - if get_local_rank() == 0: - dataloader = tqdm(dataloader) - for batch_idx, batch in enumerate(dataloader): - # batch is one video id. - if batch_idx == early_stop: - break - video_ids = retriver.search_by_video_ids( - [batch], self.num_cands)[0] - if len(video_ids) > self.num_video_per_batch: - # we moved the center to make cluster robust. - video_ids = random.sample(video_ids, self.num_video_per_batch) - batched_videos.append(video_ids) - return self.finalize(batched_videos, epoch) - - def finalize(self, batched_videos, epoch): - fn = os.path.join( - self.pred_dir, - "batched_e" + str(epoch) + "_videos" + str(get_local_rank()) + ".pkl") - with open(fn, "wb") as fw: - pickle.dump(batched_videos, fw, pickle.HIGHEST_PROTOCOL) - return batched_videos diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/task.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/task.py deleted file mode 100644 index 458719d35691d0dc48c5b80c80c175c63e294065..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/task.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .. import losses, models, processors, tasks -from ..datasets import MMDataset - - -class Task(object): - """ - A task refers to one generic training task (e.g., training one model). - """ - - @classmethod - def config_task(cls, config): - """ - determine whether to load a hard-coded task or config from a generic one. - via if a task string is available in config. - """ - if config.task is not None: - # TODO (huxu): expand the search scope. - task_cls = getattr(tasks, config.task) - return task_cls(config) - else: - return Task(config) - - def __init__(self, config): - self.config = config - self.train_data = None - self.val_data = None - self.test_data = None - - self.model = None - self.loss_fn = None - self.eval_fn = None - - def build_dataset(self): - """TODO (huxu): move processor breakdown to MMDataset.""" - """fill-in `self.train_data`, `self.val_data` and `self.test_data`.""" - - meta_processor_cls = getattr( - processors, self.config.dataset.meta_processor) - video_processor_cls = getattr( - processors, self.config.dataset.video_processor) - text_processor_cls = getattr( - processors, self.config.dataset.text_processor) - aligner_cls = getattr( - processors, self.config.dataset.aligner) - - if self.config.dataset.train_path is not None: - self.config.dataset.split = "train" - # may be used by meta processor. - # meta_processor controls different dataset. - meta_processor = meta_processor_cls(self.config.dataset) - video_processor = video_processor_cls(self.config.dataset) - text_processor = text_processor_cls(self.config.dataset) - aligner = aligner_cls(self.config.dataset) - self.train_data = MMDataset( - meta_processor, video_processor, text_processor, aligner - ) - print("train_len", len(self.train_data)) - output = self.train_data[0] - self.train_data.print_example(output) - if self.config.dataset.val_path is not None: - self.config.dataset.split = "valid" - # may be used by meta processor. - meta_processor = meta_processor_cls(self.config.dataset) - video_processor = video_processor_cls(self.config.dataset) - text_processor = text_processor_cls(self.config.dataset) - aligner = aligner_cls(self.config.dataset) - self.val_data = MMDataset( - meta_processor, video_processor, text_processor, aligner - ) - print("val_len", len(self.val_data)) - output = self.val_data[0] - self.val_data.print_example(output) - - if self.config.dataset.split == "test": - # the following is run via lauching fairseq-validate. - meta_processor = meta_processor_cls(self.config.dataset) - video_processor = video_processor_cls(self.config.dataset) - text_processor = text_processor_cls(self.config.dataset) - - self.test_data = MMDataset( - meta_processor, video_processor, text_processor, aligner - ) - print("test_len", len(self.test_data)) - output = self.test_data[0] - self.test_data.print_example(output) - - def build_model(self, checkpoint=None): - if self.model is None: - model_cls = getattr(models, self.config.model.model_cls) - self.model = model_cls(self.config) - if checkpoint is not None: - self.load_checkpoint(checkpoint) - return self.model - - def load_checkpoint(self, checkpoint): - if self.model is None: - raise ValueError("model is not initialized.") - state_dict = torch.load(checkpoint) - state_dict = self._trim_state_dict(state_dict) - self.model.load_state_dict(state_dict, strict=False) - # if it's a fp16 model, turn it back. - if next(self.model.parameters()).dtype == torch.float16: - self.model = self.model.float() - return self.model - - def _trim_state_dict(self, state_dict): - from collections import OrderedDict - - if "state_dict" in state_dict: - state_dict = state_dict["state_dict"] - if "model" in state_dict: # fairseq checkpoint format. - state_dict = state_dict["model"] - ret_state_dict = OrderedDict() - for ( - key, - value, - ) in state_dict.items(): - # remove fairseq wrapper since this is a task. - if key.startswith("mmmodel"): - key = key[len("mmmodel."):] - ret_state_dict[key] = value - return ret_state_dict - - def build_loss(self): - if self.loss_fn is None and self.config.loss is not None: - loss_cls = getattr(losses, self.config.loss.loss_cls) - self.loss_fn = loss_cls() - return self.loss_fn - - def flat_subsample(self, tensor): - size = tensor.size() - if len(size) >= 2: - batch_size = size[0] * size[1] - expanded_size = ( - (batch_size,) + size[2:] if len(size) > 2 - else (batch_size,) - ) - tensor = tensor.view(expanded_size) - return tensor - - def reshape_subsample(self, sample): - if ( - hasattr(self.config.dataset, "subsampling") - and self.config.dataset.subsampling is not None - and self.config.dataset.subsampling > 1 - ): - for key in sample: - if torch.is_tensor(sample[key]): - sample[key] = self.flat_subsample(sample[key]) - return sample - - def __call__(self, model, sample): - loss = None - loss_scalar = float("inf") - - sample = self.reshape_subsample(sample) - outputs = self.model(**sample) - sample.update(outputs) - if self.loss_fn is not None: - loss = self.loss_fn(**sample) - loss_scalar = loss.item() - - batch_size = sample["caps"].size(0) - sample_size = 1 - return { - "loss": loss, - "loss_scalar": loss_scalar, - "max_len": self.config.dataset.max_len, - "batch_size": batch_size, - "sample_size": sample_size, - } - - def build_dataloader(self): - """only used for trainer that lacks building loaders.""" - raise NotImplementedError diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/vlmtask.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/vlmtask.py deleted file mode 100644 index 57dc4c91705fdb1292f2f2accbb42acb993eb6aa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/tasks/vlmtask.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 .task import Task - - -class VLMTask(Task): - """A VLM task for reproducibility. - the collator split subsamples into two sub-batches. - This has should have no logic changes. - but changed the randomness in frame masking. - """ - - def flat_subsample(self, tensor): - size = tensor.size() - if len(size) >= 2: - batch_size = size[0] * (size[1] // 2) - expanded_size = ( - (batch_size, 2) + size[2:] if len(size) > 2 - else (batch_size, 2) - ) - tensor = tensor.view(expanded_size) - tensor = torch.cat([tensor[:, 0], tensor[:, 1]], dim=0) - return tensor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/__init__.py deleted file mode 100644 index 629c550cdf8e68ee932b8311a632236fdb6b7b65..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/__init__.py +++ /dev/null @@ -1,69 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 numpy as np -import torch - -from .load_config import * -from .shardedtensor import * - - -def set_seed(seed=43211): - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - if torch.backends.cudnn.enabled: - torch.backends.cudnn.benchmark = False - torch.backends.cudnn.deterministic = True - - -def get_world_size(): - if torch.distributed.is_initialized(): - world_size = torch.distributed.get_world_size() - else: - world_size = 1 - return world_size - - -def get_local_rank(): - return torch.distributed.get_rank() \ - if torch.distributed.is_initialized() else 0 - - -def print_on_rank0(func): - local_rank = get_local_rank() - if local_rank == 0: - print("[INFO]", func) - - -class RetriMeter(object): - """ - Statistics on whether retrieval yields a better pair. - """ - def __init__(self, freq=1024): - self.freq = freq - self.total = 0 - self.replace = 0 - self.updates = 0 - - def __call__(self, data): - if isinstance(data, np.ndarray): - self.replace += data.shape[0] - int((data[:, 0] == -1).sum()) - self.total += data.shape[0] - elif torch.is_tensor(data): - self.replace += int(data.sum()) - self.total += data.size(0) - else: - raise ValueError("unsupported RetriMeter data type.", type(data)) - - self.updates += 1 - if get_local_rank() == 0 and self.updates % self.freq == 0: - print("[INFO]", self) - - def __repr__(self): - return "RetriMeter (" + str(self.replace / self.total) \ - + "/" + str(self.replace) + "/" + str(self.total) + ")" diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/load_config.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/load_config.py deleted file mode 100644 index 1230a098caeacbce3cf10d0e304a1feacd6412a3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/load_config.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 os - -import omegaconf -from omegaconf import OmegaConf - - -def load_config(args=None, config_file=None, overwrite_fairseq=False): - """TODO (huxu): move fairseq overwrite to another function.""" - if args is not None: - config_file = args.taskconfig - config = recursive_config(config_file) - - if config.dataset.subsampling is not None: - batch_size = config.fairseq.dataset.batch_size // config.dataset.subsampling - print( - "adjusting batch_size to {} due to subsampling {}.".format( - batch_size, config.dataset.subsampling - ) - ) - config.fairseq.dataset.batch_size = batch_size - - is_test = config.dataset.split is not None and config.dataset.split == "test" - if not is_test: - if ( - config.fairseq.checkpoint is None - or config.fairseq.checkpoint.save_dir is None - ): - raise ValueError("fairseq save_dir or save_path must be specified.") - - save_dir = config.fairseq.checkpoint.save_dir - os.makedirs(save_dir, exist_ok=True) - if config.fairseq.common.tensorboard_logdir is not None: - tb_run_dir = suffix_rundir( - save_dir, config.fairseq.common.tensorboard_logdir - ) - config.fairseq.common.tensorboard_logdir = tb_run_dir - print( - "update tensorboard_logdir as", config.fairseq.common.tensorboard_logdir - ) - os.makedirs(save_dir, exist_ok=True) - OmegaConf.save(config=config, f=os.path.join(save_dir, "config.yaml")) - - if overwrite_fairseq and config.fairseq is not None and args is not None: - # flatten fields. - for group in config.fairseq: - for field in config.fairseq[group]: - print("overwrite args." + field, "as", config.fairseq[group][field]) - setattr(args, field, config.fairseq[group][field]) - return config - - -def recursive_config(config_path): - """allows for stacking of configs in any depth.""" - config = OmegaConf.load(config_path) - if config.includes is not None: - includes = config.includes - config.pop("includes") - base_config = recursive_config(includes) - config = OmegaConf.merge(base_config, config) - return config - - -def suffix_rundir(save_dir, run_dir): - max_id = -1 - for search_dir in os.listdir(save_dir): - if search_dir.startswith(run_dir): - splits = search_dir.split("_") - cur_id = int(splits[1]) if len(splits) > 1 else 0 - max_id = max(max_id, cur_id) - return os.path.join(save_dir, run_dir + "_" + str(max_id + 1)) - - -def overwrite_dir(config, replace, basedir): - for key in config: - if isinstance(config[key], str) and config[key].startswith(basedir): - config[key] = config[key].replace(basedir, replace) - if isinstance(config[key], omegaconf.dictconfig.DictConfig): - overwrite_dir(config[key], replace, basedir) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/shardedtensor.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/shardedtensor.py deleted file mode 100644 index cbc43eec7ea17181f17854daef3782e917d9a340..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt/utils/shardedtensor.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 os -import pickle - -import numpy as np - - -class ShardedTensor(object): - def __init__(self, data, starts): - self.data = data - self.starts = starts - assert self.starts[0] == 0 - assert self.starts[-1] == len(self.data) - assert (self.starts[1:] >= self.starts[:-1]).all() - assert (self.starts > -1).all() - - @staticmethod - def from_list(xs): - starts = np.full((len(xs) + 1,), -1, dtype=np.long) - data = np.concatenate(xs, axis=0) - starts[0] = 0 - for i, x in enumerate(xs): - starts[i + 1] = starts[i] + x.shape[0] - assert (starts > -1).all() - return ShardedTensor(data, starts) - - def __getitem__(self, i): - return self.data[self.starts[i] : self.starts[i + 1]] - - def __len__(self): - return len(self.starts) - 1 - - def lengths(self): - return self.starts[1:] - self.starts[:-1] - - def save(self, path): - np.save(path + "_starts", self.starts) - np.save(path + "_data", self.data) - - @staticmethod - def load(path, mmap_mode=None): - starts = np.load(path + "_starts.npy", mmap_mode) - data = np.load(path + "_data.npy", mmap_mode) - return ShardedTensor(data, starts) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt_cli/localjob.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt_cli/localjob.py deleted file mode 100644 index 2675d3511a9ca700185d2d7b853dc56ad70c638c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt_cli/localjob.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 os - -from mmpt.utils import recursive_config - - -class BaseJob(object): - def __init__(self, yaml_file, dryrun=False): - self.yaml_file = yaml_file - self.config = recursive_config(yaml_file) - self.dryrun = dryrun - - def submit(self, **kwargs): - raise NotImplementedError - - def _normalize_cmd(self, cmd_list): - cmd_list = list(cmd_list) - yaml_index = cmd_list.index("[yaml]") - cmd_list[yaml_index] = self.yaml_file - return cmd_list - - -class LocalJob(BaseJob): - - CMD_CONFIG = { - "local_single": [ - "fairseq-train", "[yaml]", "--user-dir", "mmpt", - "--task", "mmtask", "--arch", "mmarch", - "--criterion", "mmloss", - ], - "local_small": [ - "fairseq-train", "[yaml]", "--user-dir", "mmpt", - "--task", "mmtask", "--arch", "mmarch", - "--criterion", "mmloss", - "--distributed-world-size", "2" - ], - "local_big": [ - "fairseq-train", "[yaml]", "--user-dir", "mmpt", - "--task", "mmtask", "--arch", "mmarch", - "--criterion", "mmloss", - "--distributed-world-size", "8" - ], - "local_predict": ["python", "mmpt_cli/predict.py", "[yaml]"], - } - - def __init__(self, yaml_file, job_type=None, dryrun=False): - super().__init__(yaml_file, dryrun) - if job_type is None: - self.job_type = "local_single" - if self.config.task_type is not None: - self.job_type = self.config.task_type - else: - self.job_type = job_type - if self.job_type in ["local_single", "local_small"]: - if self.config.fairseq.dataset.batch_size > 32: - print("decreasing batch_size to 32 for local testing?") - - def submit(self): - cmd_list = self._normalize_cmd(LocalJob.CMD_CONFIG[self.job_type]) - if "predict" not in self.job_type: - # append fairseq args. - from mmpt.utils import load_config - - config = load_config(config_file=self.yaml_file) - for field in config.fairseq: - for key in config.fairseq[field]: - if key in ["fp16", "reset_optimizer", "reset_dataloader", "reset_meters"]: # a list of binary flag. - param = ["--" + key.replace("_", "-")] - else: - if key == "lr": - value = str(config.fairseq[field][key][0]) - elif key == "adam_betas": - value = "'"+str(config.fairseq[field][key])+"'" - else: - value = str(config.fairseq[field][key]) - param = [ - "--" + key.replace("_", "-"), - value - ] - cmd_list.extend(param) - - print("launching", " ".join(cmd_list)) - if not self.dryrun: - os.system(" ".join(cmd_list)) - return JobStatus("12345678") - - -class JobStatus(object): - def __init__(self, job_id): - self.job_id = job_id - - def __repr__(self): - return self.job_id - - def __str__(self): - return self.job_id - - def done(self): - return False - - def running(self): - return False - - def result(self): - if self.done(): - return "{} is done.".format(self.job_id) - else: - return "{} is running.".format(self.job_id) - - def stderr(self): - return self.result() - - def stdout(self): - return self.result() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt_cli/predict.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt_cli/predict.py deleted file mode 100644 index cc48ff10fc1e94b1f9f6ed330f99bc31aaf45b1b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/mmpt_cli/predict.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 argparse -import glob -import os -import pprint - -import omegaconf -from mmpt import processors -from mmpt.datasets import MMDataset -from mmpt.evaluators import Evaluator -from mmpt.evaluators import predictor as predictor_path -from mmpt.tasks import Task -from mmpt.utils import load_config, set_seed -from omegaconf import OmegaConf -from torch.utils.data import DataLoader - - -def get_dataloader(config): - meta_processor_cls = getattr(processors, config.dataset.meta_processor) - video_processor_cls = getattr(processors, config.dataset.video_processor) - text_processor_cls = getattr(processors, config.dataset.text_processor) - aligner_cls = getattr(processors, config.dataset.aligner) - - meta_processor = meta_processor_cls(config.dataset) - video_processor = video_processor_cls(config.dataset) - text_processor = text_processor_cls(config.dataset) - aligner = aligner_cls(config.dataset) - - test_data = MMDataset( - meta_processor, - video_processor, - text_processor, - aligner, - ) - print("test_len", len(test_data)) - output = test_data[0] - test_data.print_example(output) - - test_dataloader = DataLoader( - test_data, - batch_size=config.fairseq.dataset.batch_size, - shuffle=False, - num_workers=6, - collate_fn=test_data.collater, - ) - return test_dataloader - - -def main(args): - config = load_config(args) - - if isinstance(config, omegaconf.dictconfig.DictConfig): - print(OmegaConf.to_yaml(config)) - else: - pp = pprint.PrettyPrinter(indent=4) - pp.print(config) - - mmtask = Task.config_task(config) - mmtask.build_model() - - test_dataloader = get_dataloader(config) - checkpoint_search_path = os.path.dirname(config.eval.save_path) - results = [] - - prefix = os.path.basename(args.taskconfig) - if prefix.startswith("test"): - # loop all checkpoint for datasets without validation set. - if "best" not in config.fairseq.common_eval.path: - print("eval each epoch.") - for checkpoint in glob.glob(checkpoint_search_path + "/checkpoint*"): - model = mmtask.load_checkpoint(checkpoint) - ckpt = os.path.basename(checkpoint) - evaluator = Evaluator(config) - output = evaluator.evaluate( - model, test_dataloader, ckpt + "_merged") - results.append((checkpoint, output)) - # use the one specified by the config lastly. - model = mmtask.load_checkpoint(config.fairseq.common_eval.path) - evaluator = Evaluator(config) - output = evaluator.evaluate(model, test_dataloader) - results.append((config.fairseq.common_eval.path, output)) - - best_result = None - best_metric = 0. - for checkpoint, result in results: - print(checkpoint) - evaluator.metric.print_computed_metrics(result) - best_score = evaluator.metric.best_metric(result) - if best_score > best_metric: - best_result = (checkpoint, result) - best_metric = best_score - print("best results:") - print(best_result[0]) - evaluator.metric.print_computed_metrics(best_result[1]) - - elif prefix.startswith("vis"): - model = mmtask.load_checkpoint(config.fairseq.common_eval.path) - predictor_cls = getattr(predictor_path, config.predictor) - predictor = predictor_cls(config) - predictor.predict_loop(model, test_dataloader, mmtask, None) - else: - raise ValueError("unknown prefix of the config file", args.taskconfig) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("taskconfig", type=str) - args = parser.parse_args() - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/pretraining.md b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/pretraining.md deleted file mode 100644 index 8f8e6d0facaa47141342294d7eb26c4232a9677b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/pretraining.md +++ /dev/null @@ -1,29 +0,0 @@ -# Pretraining - -(If you are new to the ideas of `mmpt.processors`, see [README](README.md) first.) -We mostly use [howto100M](https://github.com/antoine77340/howto100m) dataset for pretraining (other datasets are coming). So you are less likely to write a new `MetaProcessor`, `VideoProcessor` or `TextProcessor` but only working on a new `Aligner`, a new model and loss. - -### Data Sharding -Pretraining on Howto100M is heavy on IO since we have millions of videos or captions on the hard disk that cannot be fit into the memory. -It is desirable to have an optimized preprocessing step before the actual dataloading. - -We support data sharding to pack multiple videos into a shards of training data for both videos and captions. (see [dataset](DATASET.md) for preprocessing). -These shards will be mapped into memory to reduce the frequency of IO access on millions of files. See (processors starting with `Sharded*`). -This will be the default config for a how2 dataset `projects/task/how2.yaml`. - -Great thanks to Dmytro Okhonko for sharing the code from MARGE project. - -### Training -Pretraining on Howto100m is expected on one or multiple nodes, where each node has 8 GPUS with 32 GB mem. -launching a pretraing on MFM+MLM can be done, via: -```python locallaunch.py projects/mfmmlm/how2.yaml``` - -### Pre-training with a Retrieval Model (VideoCLIP) -This projects now support alternatively run a retrieval model and pre-training. -We implement a basic retrieval model that is built on the hidden states of a video and faiss. - -You may need to install faiss via `conda install faiss-cpu -c pytorch`. - -Right now, the hidden states of a video is computed as the average of 8 clips of their pooled visual/text hidden states. -See `mmpt/tasks/retritask.py` for more details. -The `.yaml` config for running pre-training with a retrieval model can be found at `projects/retri/videoretri.yaml`. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mfmmlm.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mfmmlm.yaml deleted file mode 100644 index 0f3450a1e091517de19422b0c09cea6a01a02729..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mfmmlm.yaml +++ /dev/null @@ -1,59 +0,0 @@ -project_dir: mfmmlm -run_task: - - how2.yaml - - [vtt.yaml, vttcap.yaml, vttqa.yaml, youcook.yaml, youcookcap.yaml, crosstask.yaml, coin.yaml] -base_dir: task -task_group: - pretrain: - task_list: - - how2.yaml - dataset: - subsampling: 32 - sampled_min_len: 10 - sampled_max_len: 64 - max_video_len: 32 - max_len: 96 - aligner: MFMMLMAligner - lazy_vfeat_mask: True - mfm_probability: 0.15 - mlm_probability: 0.15 - mm_prob: 0.5 - model: - model_cls: MMFusionMFMMLM - mm_encoder_cls: MMFusionForMFMMLM - loss: - loss_cls: MFMMLM - fairseq: - common: - fp16: true - dataset: - batch_size: 256 - optimization: - max_epoch: 15 - finetune: - task_list: - - vtt.yaml - - vttqa.yaml - - youcook.yaml - - youcookcap.yaml - - crosstask.yaml - - coin.yaml - dataset: - max_video_len: 32 - max_len: 96 - fairseq: - common: - fp16: true - # do not write any model or loss here (they are expected to be fixed in mmfusion). - test: - task_list: - - test_vtt.yaml - - test_vttqa.yaml - - test_youcook.yaml - - test_youcookcap.yaml - - test_crosstask.yaml - - test_crosstask_zs.yaml - - test_coin.yaml - dataset: - max_video_len: 32 - max_len: 96 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/mmfusionmtm.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/mmfusionmtm.yaml deleted file mode 100644 index 337d66a2aadcff86da033d6656bdc570a20c32e6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/mmfusionmtm.yaml +++ /dev/null @@ -1,19 +0,0 @@ -includes: projects/mfmmlm.yaml -project_dir: mtm/mmfusionmtm -task_group: - pretrain: - task: VLMTask # reproducible - dataset: - aligner: MFMMLMAligner - model: - use_seg_emb: True # reproducible - model_cls: MMFusionMTM - mm_encoder_cls: MMBertForMFMMLM - loss: - loss_cls: MTM - finetune: - model: - use_seg_emb: True # reproducible - test: - model: - use_seg_emb: True # reproducible diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm.yaml deleted file mode 100644 index 022a2623c5fdee1da51f211bef25a0dfebd7cbb0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm.yaml +++ /dev/null @@ -1,8 +0,0 @@ -includes: projects/mtm/mmfusionmtm.yaml -project_dir: mtm/vlm -task_group: - pretrain: - dataset: - sampled_min_len: 8 - loss: - loss_cls: MTM diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/coin.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/coin.yaml deleted file mode 100644 index 48fd64a5f4996a03b412c418e83c4672b7252e8d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/coin.yaml +++ /dev/null @@ -1,47 +0,0 @@ -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased - meta_processor: COINActionSegmentationMetaProcessor - train_path: data/coin/COIN.json - val_path: data/coin/COIN.json - vfeat_dir: data/feat/feat_coin_s3d - text_processor: COINActionSegmentationTextProcessor - aligner: COINActionSegmentationAligner - num_iso_layer: 12 - sliding_window: 8 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 1 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 8 - checkpoint: - restore_file: runs/mtm/vlm/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/mtm/vlm/coin -task_type: sweep_big -model: - model_cls: MMFusionActionSegmentation - mm_encoder_cls: MMBertForTokenClassification - use_seg_emb: true -loss: - loss_cls: CrossEntropy diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/crosstask.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/crosstask.yaml deleted file mode 100644 index 4e706b549e6085c9823049afaff3285aa2b88f4f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/crosstask.yaml +++ /dev/null @@ -1,53 +0,0 @@ -dataset: - video_processor: CrossTaskVideoProcessor - bert_name: bert-base-uncased - meta_processor: CrossTaskMetaProcessor - train_path: data/crosstask/crosstask_release/videos.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - text_processor: CrossTaskTextProcessor - aligner: CrossTaskAligner - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 1 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 5 - checkpoint: - restore_file: runs/mtm/vlm/checkpoint11.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/mtm/vlm/crosstask -task_type: sweep_small -model: - model_cls: MMFusionActionLocalization - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -loss: - loss_cls: BCE diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/how2.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/how2.yaml deleted file mode 100644 index 7ca40ad815d9393bc9dc4d248cf7ec8810f1011f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/how2.yaml +++ /dev/null @@ -1,55 +0,0 @@ -dataset: - video_processor: ShardedVideoProcessor - bert_name: bert-base-uncased - meta_processor: ShardedHow2MetaProcessor - train_path: data/how2/how2_s3d_train.lst - val_path: data/how2/how2_s3d_val.lst - vfeat_dir: data/feat/feat_how2_s3d_shard_small - text_processor: ShardedTextProcessor - tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. - aligner: MFMMLMAligner - subsampling: 32 - sampled_min_len: 8 - sampled_max_len: 64 - max_video_len: 32 - max_len: 96 - lazy_vfeat_mask: true - mfm_probability: 0.15 - mlm_probability: 0.15 - mm_prob: 0.5 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 256 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 1000 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 15 - checkpoint: - save_dir: runs/mtm/vlm - save_interval_updates: 1024 - keep_interval_updates: 2 - keep_last_epochs: 30 -task_type: sweep_big -slurm_config: big -eval: - save_path: runs/mtm/vlm -model: - model_cls: MMFusionMTM - mm_encoder_cls: MMBertForMFMMLM - use_seg_emb: true -loss: - loss_cls: MTM -task: VLMTask diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_coin.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_coin.yaml deleted file mode 100644 index 8df2e66ad175529e0e86f69ade1e392849507036..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_coin.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: COINActionSegmentationAligner - bert_name: bert-base-uncased - test_path: data/coin/COIN.json - meta_processor: COINActionSegmentationMetaProcessor - vfeat_dir: data/feat/feat_coin_s3d - text_processor: COINActionSegmentationTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/coin/checkpoint_best.pt -model: - model_cls: MMFusionActionSegmentation - mm_encoder_cls: MMBertForTokenClassification - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/coin/eval -metric: COINActionSegmentationMetric -predictor: COINPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml deleted file mode 100644 index d159847875f74050fbae5f0c223fa6d56bbd3d38..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml +++ /dev/null @@ -1,38 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: CrossTaskVideoProcessor - aligner: CrossTaskAligner - bert_name: bert-base-uncased - meta_processor: CrossTaskMetaProcessor - test_path: data/crosstask/crosstask_release/videos_val.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - text_processor: CrossTaskTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/crosstask/checkpoint_best.pt -model: - model_cls: MMFusionActionLocalization - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/crosstask/eval -metric: CrossTaskMetric -predictor: CrossTaskPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_crosstask_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_crosstask_zs.yaml deleted file mode 100644 index 59833c55406ce53225d704b46eb0c141ad4d5e9d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_crosstask_zs.yaml +++ /dev/null @@ -1,38 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: CrossTaskVideoProcessor - aligner: CrossTaskAligner - bert_name: bert-base-uncased - meta_processor: CrossTaskMetaProcessor - test_path: data/crosstask/crosstask_release/videos_val.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - text_processor: CrossTaskTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/checkpoint_best.pt -model: - model_cls: MMFusionActionLocalization - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/crosstask_zs/eval -metric: CrossTaskMetric -predictor: CrossTaskPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_vtt.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_vtt.yaml deleted file mode 100644 index a41557df6af496c755b0b32345a736b1cdc01ce4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_vtt.yaml +++ /dev/null @@ -1,29 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/vtt/checkpoint_last.pt -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/vtt/eval -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml deleted file mode 100644 index abf3309f7072c88a6b7c72e2540d4950d0e55575..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml +++ /dev/null @@ -1,29 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: MSRVTTQAAligner - bert_name: bert-base-uncased - meta_processor: MSRVTTQAMetaProcessor - test_path: data/msrvtt-qa/MSR_MC_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTQATextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/vttqa/checkpoint_last.pt -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/vttqa/eval -metric: QAMetric -predictor: QAPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_youcook.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_youcook.yaml deleted file mode 100644 index 3a57d25c240758cf989ad0ec538562d046446232..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_youcook.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: YoucookVideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased - meta_processor: YoucookMetaProcessor - test_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: true - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: TextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/youcook/checkpoint_last.pt -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/youcook/eval -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml deleted file mode 100644 index b2595d7c3c633d0faac5e4891457067659f7fafc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml +++ /dev/null @@ -1,32 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: YoucookVideoProcessor - aligner: DSNLGAligner - bert_name: bert-base-uncased - meta_processor: YoucookNLGMetaProcessor - test_path: data/youcook/val_list.txt - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: NLGTextProcessor - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/mtm/vlm/youcookcap/checkpoint_best.pt -model: - model_cls: MMFusionNLG - mm_encoder_cls: MMBertForNLG - max_decode_length: 24 - use_seg_emb: true -eval: - save_path: runs/mtm/vlm/youcookcap/eval -metric: NLGMetric -predictor: NLGPredictor -gen_param: - num_beams: 5 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/vtt.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/vtt.yaml deleted file mode 100644 index c6c5b1ab40cbd2256919b42ea6b6a5fa52fe41a1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/vtt.yaml +++ /dev/null @@ -1,49 +0,0 @@ -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - train_path: data/msrvtt/MSRVTT_train.csv - jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - full_test_path: data/msrvtt/MSRVTT_FULL_test.csv - dup: 20 - val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - json_path: data/msrvtt/MSRVTT_data.json - aligner: DSAligner - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 256 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 10 - checkpoint: - restore_file: runs/mtm/vlm/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/mtm/vlm/vtt -task_type: sweep_small -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -loss: - loss_cls: T2VContraLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/vttqa.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/vttqa.yaml deleted file mode 100644 index 0a440c7dd2f5b324d57367456d22ca5aab8f397f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/vttqa.yaml +++ /dev/null @@ -1,47 +0,0 @@ -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - train_path: data/msrvtt/MSRVTT_train.csv - dup: 20 - val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - json_path: data/msrvtt/MSRVTT_data.json - aligner: DSAligner - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 128 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 5 - checkpoint: - restore_file: runs/mtm/vlm/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/mtm/vlm/vttqa -task_type: sweep_small -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -loss: - loss_cls: V2TContraLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/youcook.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/youcook.yaml deleted file mode 100644 index 9ee82b81b8b14a9f689e73bac22f21b780640984..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/youcook.yaml +++ /dev/null @@ -1,47 +0,0 @@ -dataset: - video_processor: YoucookVideoProcessor - bert_name: bert-base-uncased - meta_processor: YoucookMetaProcessor - train_path: data/youcook/youcook_train.pkl - val_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: true - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: TextProcessor - aligner: DSAligner - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 128 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 10 - checkpoint: - restore_file: runs/mtm/vlm/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/mtm/vlm/youcook -task_type: sweep_small -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint - use_seg_emb: true -loss: - loss_cls: T2VContraLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/youcookcap.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/youcookcap.yaml deleted file mode 100644 index d29dfad5cd861cc1e8f587c730938ed491d60289..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/mtm/vlm/youcookcap.yaml +++ /dev/null @@ -1,45 +0,0 @@ -dataset: - video_processor: YoucookVideoProcessor - bert_name: bert-base-uncased - meta_processor: YoucookNLGMetaProcessor - train_path: data/youcook/train_list.txt - val_path: data/youcook/val_list.txt - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: NLGTextProcessor - aligner: DSNLGAligner - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 128 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 10 - checkpoint: - restore_file: runs/mtm/vlm/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/mtm/vlm/youcookcap -task_type: sweep_small -model: - model_cls: MMFusionNLG - mm_encoder_cls: MMBertForNLG - use_seg_emb: true -loss: - loss_cls: NLGLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip.yaml deleted file mode 100644 index afd040ab050554aa0ca9c65ab4cf36cf6f3155dd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip.yaml +++ /dev/null @@ -1,10 +0,0 @@ -includes: projects/retri/videoretri.yaml -project_dir: retri/videoclip -task_group: - pretrain: - model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml deleted file mode 100644 index aaed5e47f62d421e5c434a2f539437831ae387db..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml +++ /dev/null @@ -1,49 +0,0 @@ -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased - meta_processor: COINActionSegmentationMetaProcessor - train_path: data/coin/COIN.json - val_path: data/coin/COIN.json - vfeat_dir: data/feat/feat_coin_s3d - text_processor: COINActionSegmentationTextProcessor - aligner: COINActionSegmentationAligner - num_iso_layer: 12 - sliding_window: 8 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 1 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 8 - checkpoint: - restore_file: runs/retri/videoclip/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/retri/videoclip/coin -task_type: sweep_big -model: - model_cls: MMFusionSeparateActionSegmentation - mm_encoder_cls: null - video_encoder_cls: MMBertForTokenClassification - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -loss: - loss_cls: CrossEntropy diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml deleted file mode 100644 index 758601e3593aa3960fee996dcfdfd71972f9f068..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml +++ /dev/null @@ -1,55 +0,0 @@ -dataset: - video_processor: CrossTaskVideoProcessor - bert_name: bert-base-uncased - meta_processor: CrossTaskMetaProcessor - train_path: data/crosstask/crosstask_release/videos.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - text_processor: CrossTaskTextProcessor - aligner: CrossTaskAligner - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 1 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 5 - checkpoint: - restore_file: runs/retri/videoclip/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/retri/videoclip/crosstask -task_type: sweep_small -model: - model_cls: MMFusionSeparateActionLocalization - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -loss: - loss_cls: BCE diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/how2.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/how2.yaml deleted file mode 100644 index b49581e878390cd8861926988a58a892b5a38606..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/how2.yaml +++ /dev/null @@ -1,65 +0,0 @@ -dataset: - video_processor: ShardedVideoRetriVideoProcessor - bert_name: bert-base-uncased - meta_processor: ShardedHow2VideoRetriMetaProcessor - train_path: data/how2/how2_s3d_train.lst - val_path: data/how2/how2_s3d_val.lst - vfeat_dir: data/feat/feat_how2_s3d_shard_small - text_processor: ShardedVideoRetriTextProcessor - tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. - aligner: VideoRetriOverlappedAligner - subsampling: 1 - sampled_min_len: 8 - sampled_max_len: 64 - max_video_len: 32 - max_len: 96 - lazy_vfeat_mask: true - mfm_probability: 0.15 - mlm_probability: 0.15 - mm_prob: 0.5 - sampled_video_min_len: 3 - sampled_video_max_len: 32 - num_video_per_batch: 32 - clip_per_video: 16 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 1 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 1000 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 25 - checkpoint: - save_dir: runs/retri/videoclip - save_interval_updates: 1024 - keep_interval_updates: 2 - keep_last_epochs: 30 -task_type: sweep_big -slurm_config: big -eval: - save_path: runs/retri/videoclip -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -loss: - loss_cls: MMContraLoss -task: VideoRetriTask -retri_epoch: 1 -vectorpool_cls: VideoVectorPool -retriever_cls: VectorRetriever -num_cands: 64 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml deleted file mode 100644 index 409906203c22d670c361d011d1035874961d4b5b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml +++ /dev/null @@ -1,33 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: COINActionSegmentationAligner - bert_name: bert-base-uncased - test_path: data/coin/COIN.json - meta_processor: COINActionSegmentationMetaProcessor - vfeat_dir: data/feat/feat_coin_s3d - text_processor: COINActionSegmentationTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/coin/checkpoint_best.pt -model: - model_cls: MMFusionSeparateActionSegmentation - mm_encoder_cls: null - video_encoder_cls: MMBertForTokenClassification - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/coin/eval -metric: COINActionSegmentationMetric -predictor: COINPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml deleted file mode 100644 index b33739c7b6d7c4d935c0e4d78354a02f79e21d91..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml +++ /dev/null @@ -1,33 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: COINActionSegmentationAligner - bert_name: bert-base-uncased - test_path: data/coin/COIN.json - meta_processor: COINActionSegmentationMetaProcessor - vfeat_dir: data/feat/feat_coin_s3d - text_processor: COINActionSegmentationTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/checkpoint_best.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/coin_zs/eval -metric: COINActionSegmentationMetric -predictor: COINZSPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml deleted file mode 100644 index e82f54fbe52defa233f1b561415737924dd6ba9c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml +++ /dev/null @@ -1,40 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: CrossTaskVideoProcessor - aligner: CrossTaskAligner - bert_name: bert-base-uncased - meta_processor: CrossTaskMetaProcessor - test_path: data/crosstask/crosstask_release/videos_val.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - text_processor: CrossTaskTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/crosstask/checkpoint_best.pt -model: - model_cls: MMFusionSeparateActionLocalization - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/crosstask/eval -metric: CrossTaskMetric -predictor: CrossTaskPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml deleted file mode 100644 index 6fc357cc1f5285229a508a95dc31c2aae17c977d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml +++ /dev/null @@ -1,40 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: CrossTaskVideoProcessor - aligner: CrossTaskAligner - bert_name: bert-base-uncased - meta_processor: CrossTaskMetaProcessor - test_path: data/crosstask/crosstask_release/videos_val.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - text_processor: CrossTaskTextProcessor - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 1 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/checkpoint_best.pt -model: - model_cls: MMFusionSeparateActionLocalization - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/crosstask_zs/eval -metric: CrossTaskMetric -predictor: CrossTaskPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_didemo_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_didemo_zs.yaml deleted file mode 100644 index 8dc716815d8d4812237c814fc17a90dd7ac3cf1e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_didemo_zs.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: DiDeMoAligner - bert_name: bert-base-uncased - meta_processor: DiDeMoMetaProcessor - test_path: data/didemo/test_data.json - vfeat_dir: data/feat/feat_didemo_s3d - text_processor: DiDeMoTextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/checkpoint_best.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/didemo_zs/eval -metric: DiDeMoMetric -predictor: DiDeMoPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml deleted file mode 100644 index 19321ad5f489121381b1b78506b38ba3705b37e4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/vtt/checkpoint_last.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/vtt/eval -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml deleted file mode 100644 index d149fa3960294f4d9dcac9c4f4f18631eb622435..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/checkpoint_best.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/vtt_zs/eval -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml deleted file mode 100644 index 295aeedbb0f29a28ad9065da0a18e82ac98b6683..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: MSRVTTQAAligner - bert_name: bert-base-uncased - meta_processor: MSRVTTQAMetaProcessor - test_path: data/msrvtt-qa/MSR_MC_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTQATextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/vttqa/checkpoint_last.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/vttqa/eval -metric: QAMetric -predictor: QAPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml deleted file mode 100644 index 7a876c822ae38cadc03c69544e64025d516e8cca..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml +++ /dev/null @@ -1,31 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: MSRVTTQAAligner - bert_name: bert-base-uncased - meta_processor: MSRVTTQAMetaProcessor - test_path: data/msrvtt-qa/MSR_MC_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTQATextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/checkpoint_best.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/vttqa_zs/eval -metric: QAMetric -predictor: QAPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml deleted file mode 100644 index 86a4ab203e097ec156ce02d88ce9c1b2374cec9f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml +++ /dev/null @@ -1,33 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: YoucookVideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased - meta_processor: YoucookMetaProcessor - test_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: true - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: TextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/youcook/checkpoint_last.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/youcook/eval -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_youcook_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_youcook_zs.yaml deleted file mode 100644 index fd2941708be0baacf9e12ca757e0de40676e570d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/test_youcook_zs.yaml +++ /dev/null @@ -1,33 +0,0 @@ -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: YoucookVideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased - meta_processor: YoucookMetaProcessor - test_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: true - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: TextProcessor - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 - common_eval: - path: runs/retri/videoclip/checkpoint_best.pt -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/retri/videoclip/youcook_zs/eval -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/vtt_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/vtt_videoclip.yaml deleted file mode 100644 index d8b4079ac233af17ee16cfed7f3e2316e0e236c5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/vtt_videoclip.yaml +++ /dev/null @@ -1,51 +0,0 @@ -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - train_path: data/msrvtt/MSRVTT_train.csv - jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - full_test_path: data/msrvtt/MSRVTT_FULL_test.csv - dup: 20 - val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - json_path: data/msrvtt/MSRVTT_data.json - aligner: DSAligner - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 224 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 10 - checkpoint: - restore_file: runs/retri/videoclip/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/retri/videoclip/vtt -task_type: sweep_small -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -loss: - loss_cls: T2VContraLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/vttqa_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/vttqa_videoclip.yaml deleted file mode 100644 index f0566d784ab53e0159a1db6b82222ad11ad12add..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/vttqa_videoclip.yaml +++ /dev/null @@ -1,49 +0,0 @@ -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased - meta_processor: MSRVTTMetaProcessor - train_path: data/msrvtt/MSRVTT_train.csv - dup: 20 - val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - json_path: data/msrvtt/MSRVTT_data.json - aligner: DSAligner - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 128 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 5 - checkpoint: - restore_file: runs/retri/videoclip/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/retri/videoclip/vttqa -task_type: sweep_small -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -loss: - loss_cls: V2TContraLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/youcook_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/youcook_videoclip.yaml deleted file mode 100644 index c2b13e5519fe0839c6b3bc681f7ee97b522077d8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoclip/youcook_videoclip.yaml +++ /dev/null @@ -1,49 +0,0 @@ -dataset: - video_processor: YoucookVideoProcessor - bert_name: bert-base-uncased - meta_processor: YoucookMetaProcessor - train_path: data/youcook/youcook_train.pkl - val_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: true - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: TextProcessor - aligner: DSAligner - num_iso_layer: 12 - max_video_len: 32 - max_len: 96 -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - fp16: true - dataset: - num_workers: 4 - batch_size: 128 - optimization: - lr: - - 5.0e-05 - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 - warmup_updates: 122 - weight_decay: 0.0 - ddp_backend: no_c10d - max_epoch: 10 - checkpoint: - restore_file: runs/retri/videoclip/checkpoint_best.pt - reset_optimizer: true - reset_dataloader: true - reset_meters: true - save_dir: runs/retri/videoclip/youcook -task_type: sweep_small -model: - model_cls: MMFusionSeparate - mm_encoder_cls: null - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -loss: - loss_cls: T2VContraLoss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoretri.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoretri.yaml deleted file mode 100644 index 969e1fb2793d5381e52188c4ad78aea240698969..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/retri/videoretri.yaml +++ /dev/null @@ -1,51 +0,0 @@ -includes: projects/mfmmlm.yaml -project_dir: retri/videoretri -run_task: - - how2.yaml -task_group: - pretrain: - task: VideoRetriTask - retri_epoch: 1 - vectorpool_cls: VideoVectorPool - retriever_cls: VectorRetriever - num_cands: 64 - dataset: - train_path: data/how2/how2_s3d_train.lst - meta_processor: ShardedHow2VideoRetriMetaProcessor - video_processor: ShardedVideoRetriVideoProcessor - text_processor: ShardedVideoRetriTextProcessor - aligner: VideoRetriOverlappedAligner - sampled_video_min_len: 3 - sampled_video_max_len: 32 - sampled_min_len: 8 - sampled_max_len: 64 - num_video_per_batch: 32 - # do not use subsampling as it changes fairseq batch_size. - subsampling: 1 # disable subsampling - clip_per_video: 16 - fairseq: - dataset: - batch_size: 1 - optimization: - max_epoch: 25 - model: - model_cls: MMFusionShare - mm_encoder_cls: MMBertForEncoder - loss: - loss_cls: MMContraLoss - finetune: - task_list: [vtt_videoclip.yaml, youcook_videoclip.yaml, vttqa_videoclip.yaml, crosstask_videoclip.yaml, coin_videoclip.yaml] - test: - task_list: - - test_youcook_zs.yaml - - test_vtt_zs.yaml - - test_vttqa_zs.yaml - - test_crosstask_zs_videoclip.yaml - - test_coin_zs.yaml - - test_didemo_zs.yaml - - test_youcook_videoclip.yaml - - test_vtt_videoclip.yaml - - test_vttqa_videoclip.yaml - - test_crosstask_videoclip.yaml - - test_coin_videoclip.yaml - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/coin.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/coin.yaml deleted file mode 100644 index e7772486e166348deff1f7c9deceb68c7e1bb443..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/coin.yaml +++ /dev/null @@ -1,25 +0,0 @@ -includes: projects/task/ft.yaml -task_type: sweep_big -dataset: - meta_processor: COINActionSegmentationMetaProcessor - train_path: data/coin/COIN.json - val_path: data/coin/COIN.json - vfeat_dir: data/feat/feat_coin_s3d - video_processor: VideoProcessor - text_processor: COINActionSegmentationTextProcessor - aligner: COINActionSegmentationAligner - num_iso_layer: 12 - sliding_window: 8 - sliding_window_size: 32 -model: - model_cls: MMFusionActionSegmentation - mm_encoder_cls: MMBertForTokenClassification -loss: - loss_cls: CrossEntropy -fairseq: - dataset: - batch_size: 1 - optimization: - max_epoch: 8 - checkpoint: - save_dir: runs/task/coin diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/coin_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/coin_videoclip.yaml deleted file mode 100644 index 69988bc18a03a8f9bf594266f3bf06aa54f11b6a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/coin_videoclip.yaml +++ /dev/null @@ -1,7 +0,0 @@ -includes: projects/task/coin.yaml -model: - model_cls: MMFusionSeparateActionSegmentation - mm_encoder_cls: - video_encoder_cls: MMBertForTokenClassification - text_encoder_cls: BertModel # dummy, not used. - num_hidden_video_layers: 6 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/crosstask.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/crosstask.yaml deleted file mode 100644 index cb4dbb0cb4a07020d1fbba9a810eb30fd81ae96d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/crosstask.yaml +++ /dev/null @@ -1,31 +0,0 @@ -includes: projects/task/ft.yaml -dataset: - meta_processor: CrossTaskMetaProcessor - train_path: data/crosstask/crosstask_release/videos.csv # dummy - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv # dummy - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - video_processor: CrossTaskVideoProcessor - text_processor: CrossTaskTextProcessor - aligner: CrossTaskAligner - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 -model: - model_cls: MMFusionActionLocalization - mm_encoder_cls: MMBertForJoint -loss: - loss_cls: BCE -fairseq: - dataset: - batch_size: 1 - optimization: - max_epoch: 5 - checkpoint: - save_dir: runs/task/crosstask - restore_file: runs/task/checkpoint11.pt # for VLM diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/crosstask_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/crosstask_videoclip.yaml deleted file mode 100644 index 6ec613c07fcacfa6e6d3c5fc78aaefcb2b33eff5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/crosstask_videoclip.yaml +++ /dev/null @@ -1,10 +0,0 @@ -includes: projects/task/crosstask.yaml -model: - model_cls: MMFusionSeparateActionLocalization - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel # dummy, not used. - num_hidden_video_layers: 6 -fairseq: - checkpoint: - restore_file: runs/task/checkpoint_best.pt # overwrite the default of VLM. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/default.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/default.yaml deleted file mode 100644 index 087fef71a4b94d81b74c5e9ef647db189626ff36..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/default.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# this yaml cannot be run alone. you must use `how2.yaml`, `vtt.yaml` etc for training. -dataset: - video_processor: VideoProcessor - bert_name: bert-base-uncased -fairseq: - common: - tensorboard_logdir: run - log_interval: 1000 - dataset: - num_workers: 4 - optimization: - lr: [ 0.00005 ] - clip_norm: 2.0 - optimizer: adam - adam_betas: (0.9, 0.98) - lr_scheduler: polynomial_decay - total_num_update: 1000000 # backward compatible on fairseq 1.0.0a0+af0389f for reproducibility. - warmup_updates: 1000 - weight_decay: 0.0 - ddp_backend: no_c10d diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/ft.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/ft.yaml deleted file mode 100644 index c93b8a73ea93e00da8a0ee76751c9e1bd34ab8a2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/ft.yaml +++ /dev/null @@ -1,13 +0,0 @@ -includes: projects/task/default.yaml -# all derived config will be run by fairseq-train. -task_type: sweep_small -fairseq: - optimization: - warmup_updates: 122 # copied from roberta glue: https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.glue.md - checkpoint: - # save_interval_updates: 512 - # borrowed from Roberta script. - restore_file: runs/task/checkpoint_best.pt - reset_optimizer: True - reset_dataloader: True - reset_meters: True diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/how2.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/how2.yaml deleted file mode 100644 index 094dd04bfcfdb96699ac80066f874fca954dc56a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/how2.yaml +++ /dev/null @@ -1,22 +0,0 @@ -includes: projects/task/default.yaml -task_type: sweep_big -slurm_config: big -dataset: - meta_processor: ShardedHow2MetaProcessor - train_path: data/how2/how2_s3d_train.lst - val_path: data/how2/how2_s3d_val.lst - video_processor: ShardedVideoProcessor - vfeat_dir: data/feat/feat_how2_s3d_shard_small - text_processor: ShardedTextProcessor - tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased. - aligner: FixedLenAligner -# disable direct running of this yaml -eval: - save_path: runs/task -fairseq: - checkpoint: - save_dir: runs/task - save_interval_updates: 1024 - keep_interval_updates: 2 - keep_last_epochs: 30 - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test.yaml deleted file mode 100644 index 0a98445241a17bbfda84a3b3be0687d31e49d12f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test.yaml +++ /dev/null @@ -1,13 +0,0 @@ -# this yaml cannot be run alone: implement a test_${dataset}.yaml -slurm_config: big -task_type: local_predict -dataset: - split: test - video_processor: VideoProcessor - aligner: DSAligner - bert_name: bert-base-uncased -fairseq: - dataset: - batch_size: 256 - valid_subset: test - num_workers: 2 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin.yaml deleted file mode 100644 index 6d919df7c2a69de33debd03a6d9b1a513da39618..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin.yaml +++ /dev/null @@ -1,24 +0,0 @@ -includes: projects/task/test.yaml -dataset: - split: test - test_path: data/coin/COIN.json - meta_processor: COINActionSegmentationMetaProcessor - vfeat_dir: data/feat/feat_coin_s3d - video_processor: VideoProcessor - text_processor: COINActionSegmentationTextProcessor - aligner: COINActionSegmentationAligner - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 -model: - model_cls: MMFusionActionSegmentation - mm_encoder_cls: MMBertForTokenClassification -eval: - save_path: runs/task/coin/eval -fairseq: - dataset: - batch_size: 1 - common_eval: - path: runs/task/coin/checkpoint_best.pt -metric: COINActionSegmentationMetric -predictor: COINPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin_videoclip.yaml deleted file mode 100644 index b41f5bc4890405993217859c22653fa77a0dd4c3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin_videoclip.yaml +++ /dev/null @@ -1,7 +0,0 @@ -includes: projects/task/test_coin.yaml -model: - model_cls: MMFusionSeparateActionSegmentation - mm_encoder_cls: - video_encoder_cls: MMBertForTokenClassification - text_encoder_cls: BertModel # dummy, not used. - num_hidden_video_layers: 6 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin_zs.yaml deleted file mode 100644 index 5d19b09f1dee1922ffddf5bf030135cb46016dc1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_coin_zs.yaml +++ /dev/null @@ -1,13 +0,0 @@ -includes: projects/task/test_coin.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/task/coin_zs/eval -fairseq: - common_eval: - path: runs/task/checkpoint_best.pt -predictor: COINZSPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask.yaml deleted file mode 100644 index 6dd778e30be543a51997f16e326aa29f6e49e973..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask.yaml +++ /dev/null @@ -1,32 +0,0 @@ -includes: projects/task/test.yaml -dataset: - split: test - meta_processor: CrossTaskMetaProcessor - test_path: data/crosstask/crosstask_release/videos_val.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv # dummy - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - video_processor: CrossTaskVideoProcessor - text_processor: CrossTaskTextProcessor - aligner: CrossTaskAligner - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 -model: - model_cls: MMFusionActionLocalization - mm_encoder_cls: MMBertForJoint -eval: - save_path: runs/task/crosstask/eval -fairseq: - # read code and find what is the checkpoint arg. - dataset: - batch_size: 1 - common_eval: - path: runs/task/crosstask/checkpoint_best.pt -metric: CrossTaskMetric -predictor: CrossTaskPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_videoclip.yaml deleted file mode 100644 index df12535d231ffe6d1759f77f161fe48d0833f4e4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_videoclip.yaml +++ /dev/null @@ -1,7 +0,0 @@ -includes: projects/task/test_crosstask.yaml -model: - model_cls: MMFusionSeparateActionLocalization - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel # dummy, not used. - num_hidden_video_layers: 6 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_zs.yaml deleted file mode 100644 index 19386e495b37ab2570cc1d763277193b28309814..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_zs.yaml +++ /dev/null @@ -1,32 +0,0 @@ -includes: projects/task/test.yaml -dataset: - split: test - meta_processor: CrossTaskMetaProcessor - test_path: data/crosstask/crosstask_release/videos_val.csv - train_csv_path: data/crosstask/crosstask_release/videos.csv - val_path: data/crosstask/crosstask_release/videos_val.csv # dummy - val_csv_path: data/crosstask/crosstask_release/videos_val.csv - primary_path: data/crosstask/crosstask_release/tasks_primary.txt - related_path: data/crosstask/crosstask_release/tasks_related.txt - vfeat_dir: data/feat/feat_crosstask_s3d - annotation_path: data/crosstask/crosstask_release/annotations - n_train: 30 - video_processor: CrossTaskVideoProcessor - text_processor: CrossTaskTextProcessor - aligner: CrossTaskAligner - num_iso_layer: 12 - sliding_window: 16 - sliding_window_size: 32 -model: - model_cls: MMFusionActionLocalization - mm_encoder_cls: MMBertForJoint -eval: - save_path: runs/task/crosstask_zs/eval -fairseq: - # read code and find what is the checkpoint arg. - dataset: - batch_size: 1 - common_eval: - path: runs/task/checkpoint_best.pt # load the best from how2 on ACL submission: runs/task/checkpoint11.pt -metric: CrossTaskMetric -predictor: CrossTaskPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_zs_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_zs_videoclip.yaml deleted file mode 100644 index 7f0198276f74f592413e99fb10672431543a8f67..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_crosstask_zs_videoclip.yaml +++ /dev/null @@ -1,7 +0,0 @@ -includes: projects/task/test_crosstask_zs.yaml -model: - model_cls: MMFusionSeparateActionLocalization - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel # dummy, not used. - num_hidden_video_layers: 6 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_didemo_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_didemo_zs.yaml deleted file mode 100644 index 4b53dca71e4a0e77793483ee971379f0476366f3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_didemo_zs.yaml +++ /dev/null @@ -1,23 +0,0 @@ -includes: projects/task/test.yaml -dataset: - meta_processor: DiDeMoMetaProcessor - test_path: data/didemo/test_data.json - video_processor: VideoProcessor - vfeat_dir: data/feat/feat_didemo_s3d - text_processor: DiDeMoTextProcessor - aligner: DiDeMoAligner - num_iso_layer: 12 -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/task/didemo_zs/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/checkpoint_best.pt -metric: DiDeMoMetric -predictor: DiDeMoPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt.yaml deleted file mode 100644 index 2f809b306d5d2b4d50d83ca3f9fa2db3f64530a5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt.yaml +++ /dev/null @@ -1,19 +0,0 @@ -includes: projects/task/test.yaml -dataset: - meta_processor: MSRVTTMetaProcessor - test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - video_processor: VideoProcessor - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - num_iso_layer: 12 -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint -eval: - save_path: runs/task/vtt/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/vtt/checkpoint_last.pt -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt_videoclip.yaml deleted file mode 100644 index cb6564394c8de5edebe41d9a90e8233d2ef90edf..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt_videoclip.yaml +++ /dev/null @@ -1,8 +0,0 @@ -includes: projects/task/test_vtt.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt_zs.yaml deleted file mode 100644 index 57340924b434338815743d0d2c326737ccfe3818..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vtt_zs.yaml +++ /dev/null @@ -1,13 +0,0 @@ -includes: projects/task/test_vtt.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/task/vtt_zs/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/checkpoint_best.pt diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa.yaml deleted file mode 100644 index ddf813c535c0ecd6e6fd81ba9013a88f8ec4296b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa.yaml +++ /dev/null @@ -1,20 +0,0 @@ -includes: projects/task/test.yaml -dataset: - meta_processor: MSRVTTQAMetaProcessor - test_path: data/msrvtt-qa/MSR_MC_test.csv - video_processor: VideoProcessor - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTQATextProcessor - aligner: MSRVTTQAAligner - num_iso_layer: 12 -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint -eval: - save_path: runs/task/vttqa/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/vttqa/checkpoint_last.pt -metric: QAMetric -predictor: QAPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa_videoclip.yaml deleted file mode 100644 index 32a41e861c2e71642d4e79f8cf9dc314a5a3f621..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa_videoclip.yaml +++ /dev/null @@ -1,8 +0,0 @@ -includes: projects/task/test_vttqa.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa_zs.yaml deleted file mode 100644 index 5e0e29d20729445258f5d07348fd1799f7feecf4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_vttqa_zs.yaml +++ /dev/null @@ -1,13 +0,0 @@ -includes: projects/task/test_vttqa.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/task/vttqa_zs/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/checkpoint_best.pt diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook.yaml deleted file mode 100644 index 092b680fa6400458c447abe4f5cc5e452ffdb54f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook.yaml +++ /dev/null @@ -1,22 +0,0 @@ -includes: projects/task/test.yaml -dataset: - meta_processor: YoucookMetaProcessor - test_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: True - video_processor: YoucookVideoProcessor - vfeat_dir: data/feat/feat_youcook_s3d # /checkpoint/huxu/feat/youcook_vmz # /checkpoint/prarora/berniehuang/feat_youcook_vmz - text_processor: TextProcessor - aligner: DSAligner - num_iso_layer: 12 -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint -eval: - save_path: runs/task/youcook/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/youcook/checkpoint_last.pt -metric: RetrievalMetric -predictor: RetrievalPredictor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook_videoclip.yaml deleted file mode 100644 index b85ea434749009f50eea057146403691bb48c4a9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook_videoclip.yaml +++ /dev/null @@ -1,8 +0,0 @@ -includes: projects/task/test_youcook.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook_zs.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook_zs.yaml deleted file mode 100644 index 0a5875bea4eaca1e46c65dfdc50b586550cc6f36..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcook_zs.yaml +++ /dev/null @@ -1,13 +0,0 @@ -includes: projects/task/test_youcook.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -eval: - save_path: runs/task/youcook_zs/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/checkpoint_best.pt diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcookcap.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcookcap.yaml deleted file mode 100644 index 24f6518b7b4e210289f5200edafdb436d569adf7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/test_youcookcap.yaml +++ /dev/null @@ -1,23 +0,0 @@ -includes: projects/task/test.yaml -dataset: - meta_processor: YoucookNLGMetaProcessor - test_path: data/youcook/val_list.txt - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - video_processor: YoucookVideoProcessor - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: NLGTextProcessor - aligner: DSNLGAligner -model: - model_cls: MMFusionNLG - mm_encoder_cls: MMBertForNLG - max_decode_length: 24 -eval: - save_path: runs/task/youcookcap/eval -fairseq: - # read code and find what is the checkpoint arg. - common_eval: - path: runs/task/youcookcap/checkpoint_best.pt -metric: NLGMetric -predictor: NLGPredictor -gen_param: - num_beams: 5 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vtt.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vtt.yaml deleted file mode 100644 index 395e2ee6fe96899f9c47e39ad33b558358431554..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vtt.yaml +++ /dev/null @@ -1,25 +0,0 @@ -includes: projects/task/ft.yaml -dataset: - meta_processor: MSRVTTMetaProcessor - train_path: data/msrvtt/MSRVTT_train.csv - jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - full_test_path: data/msrvtt/MSRVTT_FULL_test.csv - dup: 20 - val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - json_path: data/msrvtt/MSRVTT_data.json - aligner: DSAligner - num_iso_layer: 12 -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint -loss: - loss_cls: T2VContraLoss -fairseq: - dataset: - batch_size: 256 - optimization: - max_epoch: 10 - checkpoint: - save_dir: runs/task/vtt diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vtt_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vtt_videoclip.yaml deleted file mode 100644 index a9892cab01b810175dcef2ee91f0e64e96bd36d9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vtt_videoclip.yaml +++ /dev/null @@ -1,12 +0,0 @@ -includes: projects/task/vtt.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 -fairseq: - dataset: - batch_size: 224 -# model_cls: MMFusionShare -# mm_encoder_cls: MMBertForEncoder diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vttqa.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vttqa.yaml deleted file mode 100644 index 56d578eff0104e5dcb4cea8720303100a9964db9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vttqa.yaml +++ /dev/null @@ -1,23 +0,0 @@ -includes: projects/task/ft.yaml -dataset: - meta_processor: MSRVTTMetaProcessor - train_path: data/msrvtt/MSRVTT_train.csv - dup: 20 - val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv - vfeat_dir: data/feat/feat_vtt_s3d - text_processor: MSRVTTTextProcessor - json_path: data/msrvtt/MSRVTT_data.json - aligner: DSAligner - num_iso_layer: 12 -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint -loss: - loss_cls: V2TContraLoss -fairseq: - dataset: - batch_size: 128 - optimization: - max_epoch: 5 - checkpoint: - save_dir: runs/task/vttqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vttqa_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vttqa_videoclip.yaml deleted file mode 100644 index 2d484ca8a5de0e96172b49b746f93d052f50dea7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/vttqa_videoclip.yaml +++ /dev/null @@ -1,10 +0,0 @@ -includes: projects/task/vttqa.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 - -# model_cls: MMFusionShare -# mm_encoder_cls: MMBertForEncoder diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcook.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcook.yaml deleted file mode 100644 index e0cd841747a5cfa4c77a94d2478bfcb029517678..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcook.yaml +++ /dev/null @@ -1,25 +0,0 @@ -includes: projects/task/ft.yaml -dataset: - meta_processor: YoucookMetaProcessor - train_path: data/youcook/youcook_train.pkl - val_path: data/youcook/youcook_val.pkl - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - use_annotation_text: True - video_processor: YoucookVideoProcessor - vfeat_dir: data/feat/feat_youcook_s3d # /checkpoint/huxu/feat/youcook_vmz # /checkpoint/prarora/berniehuang/feat_youcook_vmz - text_processor: TextProcessor - aligner: DSAligner - num_iso_layer: 12 -model: - model_cls: MMFusionJoint - mm_encoder_cls: MMBertForJoint -loss: - loss_cls: T2VContraLoss -fairseq: - dataset: - batch_size: 128 - optimization: - max_epoch: 10 - checkpoint: - save_dir: runs/task/youcook - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcook_videoclip.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcook_videoclip.yaml deleted file mode 100644 index e3e901c30ca8ef0e858b9fb0a317aea0b55f6e34..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcook_videoclip.yaml +++ /dev/null @@ -1,9 +0,0 @@ -includes: projects/task/youcook.yaml -model: - model_cls: MMFusionSeparate - mm_encoder_cls: - video_encoder_cls: MMBertForEncoder - text_encoder_cls: BertModel - num_hidden_video_layers: 6 - # model_cls: MMFusionShare - # mm_encoder_cls: MMBertForEncoder diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcookcap.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcookcap.yaml deleted file mode 100644 index 047735f21716777354101ebaa230772244ac79e5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/projects/task/youcookcap.yaml +++ /dev/null @@ -1,23 +0,0 @@ -# finetuning for youcook captioning. -includes: projects/task/ft.yaml -dataset: - meta_processor: YoucookNLGMetaProcessor - train_path: data/youcook/train_list.txt - val_path: data/youcook/val_list.txt - trainval_annotation: data/youcook/youcookii_annotations_trainval.json - video_processor: YoucookVideoProcessor - vfeat_dir: data/feat/feat_youcook_s3d - text_processor: NLGTextProcessor - aligner: DSNLGAligner -model: - model_cls: MMFusionNLG - mm_encoder_cls: MMBertForNLG -loss: - loss_cls: NLGLoss -fairseq: - dataset: - batch_size: 128 - optimization: - max_epoch: 10 - checkpoint: - save_dir: runs/task/youcookcap diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/text_token_extractor/configs/bert-base-uncased.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/text_token_extractor/configs/bert-base-uncased.yaml deleted file mode 100644 index 473dd9b45b78d6d4443921fb5f355356db4f5531..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/text_token_extractor/configs/bert-base-uncased.yaml +++ /dev/null @@ -1,5 +0,0 @@ -dataset: - bert_name: bert-base-uncased - caption_pkl_path: data/how2/raw_caption_dedup.pkl - use_fast: true - target_dir: data/feat/feat_how2_s3d_shard_small diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/text_token_extractor/pretokenization.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/text_token_extractor/pretokenization.py deleted file mode 100644 index c543081945bb3f2084c993bcdd2b2a704d90e133..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/text_token_extractor/pretokenization.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 argparse -import os -import pickle - -import numpy as np -from mmpt.processors import PKLJSONStrTextProcessor -from mmpt.utils import ShardedTensor, recursive_config -from torch.utils.data import DataLoader, Dataset - - -class TokenizerDataset(Dataset): - def __init__(self, config): - self.text_processor = PKLJSONStrTextProcessor(config) - self.video_ids = list(self.text_processor.data.keys()) - - def __getitem__(self, idx): - video_id = self.video_ids[idx] - return video_id, self.text_processor(video_id) - - def __len__(self): - return len(self.video_ids) - - -def numpify(shard_idx, video_ids, captions, target_dir, split, prefix, max_cap_len=32): - startends = [] - caps_ids = [] - for video_id in video_ids: - caption = captions[video_id] - startend = [] - cap_ids = [] - for start, end, cap in zip( - caption["start"], caption["end"], caption["cap"]): - startend.append(np.array([start, end]).astype("float32")) - cap_id = np.full((max_cap_len,), -1, dtype=np.int32) - cap = cap[:max_cap_len] - cap_id[:len(cap)] = cap - cap_ids.append(cap_id) - startends.append(np.stack(startend)) - caps_ids.append(np.stack(cap_ids)) - - startends = ShardedTensor.from_list(startends) - target_path = os.path.join( - target_dir, - prefix + split + "_" + str(shard_idx) - ) - print("save to", target_path) - startends.save(target_path + ".startends") - caps_ids = ShardedTensor.from_list(caps_ids) - caps_ids.save(target_path + ".caps_ids") - - -def sharding(config, out_file): - with open(out_file, "rb") as fr: - captions = pickle.load(fr) - target_dir = config.target_dir - prefix = os.path.basename( - os.path.splitext(config.caption_pkl_path)[0] - ) + "." + config.bert_name + "." - for split in ["train", "val"]: - target_path = os.path.join(target_dir, split + "_meta") - with open(target_path + ".pkl", "rb") as fr: - meta = pickle.load(fr) - print("load meta", target_path, len(meta)) - for shard_id in meta: - numpify( - shard_id, meta[shard_id], captions, - target_dir, split, prefix - ) - - -def tokenize(config, out_file): - def collator(samples): - return samples - dataset = TokenizerDataset(config) - data = {} - for idx, batch in enumerate( - DataLoader(dataset, collate_fn=collator, num_workers=16)): - for video_id, caption in batch: - data[video_id] = caption - if idx % 5000 == 0: - print(idx) - with open(out_file, "wb") as fw: - pickle.dump(data, fw, pickle.HIGHEST_PROTOCOL) - - -def main(args): - config = recursive_config(args.config).dataset - - out_file = os.path.splitext(config.caption_pkl_path)[0] \ - + "." + config.bert_name + ".pkl" - if not os.path.isfile(out_file): - tokenize(config, out_file) - sharding(config, out_file) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="pretokenize (raw_)caption.json into pkl.") - parser.add_argument('config', type=str) - args = parser.parse_args() - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/extract.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/extract.py deleted file mode 100644 index c81177fd320f293c1602b399fc55bcc9ebef6cf8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/extract.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright Howto100M authors. -# Copyright (c) Facebook, Inc. All Rights Reserved - -import argparse -import math - -import numpy as np -import torch as th -import torch.nn.functional as F -from model import get_model -from pathbuilder import PathBuilder -from preprocessing import Preprocessing -from random_sequence_shuffler import RandomSequenceSampler -from torch.utils.data import DataLoader -from tqdm import tqdm -from videoreader import VideoLoader - -parser = argparse.ArgumentParser(description='Easy video feature extractor') - -parser.add_argument('--vdir', type=str) -parser.add_argument('--fdir', type=str) -parser.add_argument('--hflip', type=int, default=0) - -parser.add_argument('--batch_size', type=int, default=64, - help='batch size') -parser.add_argument('--type', type=str, default='2d', - help='CNN type') -parser.add_argument('--half_precision', type=int, default=0, - help='output half precision float') -parser.add_argument('--num_decoding_thread', type=int, default=4, - help='Num parallel thread for video decoding') -parser.add_argument('--l2_normalize', type=int, default=1, - help='l2 normalize feature') -parser.add_argument('--resnext101_model_path', type=str, default='model/resnext101.pth', - help='Resnext model path') -parser.add_argument('--vmz_model_path', type=str, default='model/r2plus1d_34_clip8_ig65m_from_scratch-9bae36ae.pth', - help='vmz model path') - -args = parser.parse_args() - - -# TODO: refactor all args into config. (current code is from different people.) -CONFIGS = { - "2d": { - "fps": 1, - "size": 224, - "centercrop": False, - "shards": 0, - }, - "3d": { - "fps": 24, - "size": 112, - "centercrop": True, - "shards": 0, - }, - "s3d": { - "fps": 30, - "size": 224, - "centercrop": True, - "shards": 0, - }, - "vmz": { - "fps": 24, - "size": 112, - "centercrop": True, - "shards": 0, - }, - "vae": { - "fps": 2, - "size": 256, - "centercrop": True, - "shards": 100, - } -} - -config = CONFIGS[args.type] - - -video_dirs = args.vdir -feature_dir = args.fdir - -video_dict = PathBuilder.build(video_dirs, feature_dir, ".npy", config["shards"]) - -dataset = VideoLoader( - video_dict=video_dict, - framerate=config["fps"], - size=config["size"], - centercrop=config["centercrop"], - hflip=args.hflip -) -n_dataset = len(dataset) -sampler = RandomSequenceSampler(n_dataset, 10) -loader = DataLoader( - dataset, - batch_size=1, - shuffle=False, - num_workers=args.num_decoding_thread, - sampler=sampler if n_dataset > 10 else None, -) -preprocess = Preprocessing(args.type) -model = get_model(args) - -with th.no_grad(): - for k, data in tqdm(enumerate(loader), total=loader.__len__(), ascii=True): - input_file = data['input'][0] - output_file = data['output'][0] - if len(data['video'].shape) > 3: - video = data['video'].squeeze() - if len(video.shape) == 4: - video = preprocess(video) - n_chunk = len(video) - if args.type == 'vmz': - n_chunk = math.ceil(n_chunk/float(3)) - features = th.cuda.FloatTensor(n_chunk, 512).fill_(0) - elif args.type == 's3d': - features = th.cuda.FloatTensor(n_chunk, 512).fill_(0) - elif args.type == "vae": - features = th.cuda.LongTensor(n_chunk, 1024).fill_(0) - else: - features = th.cuda.FloatTensor(n_chunk, 2048).fill_(0) - n_iter = int(math.ceil(n_chunk / float(args.batch_size))) - for i in range(n_iter): - factor = 1 - if args.type == 'vmz': - factor = 3 - min_ind = factor * i * args.batch_size - max_ind = factor * (i + 1) * args.batch_size - video_batch = video[min_ind:max_ind:factor].cuda() - if args.type == '2d': - batch_features = model(video_batch) # (51, 487), (51, 512) - elif args.type == 's3d': - batch_features = model(video_batch) - batch_features = batch_features['video_embedding'] - elif args.type == "vae": - # image_code. - batch_features = model(video_batch) - else: - batch_pred, batch_features = model(video_batch) # (51, 487), (51, 512) - if args.l2_normalize: - batch_features = F.normalize(batch_features, dim=1) - features[i*args.batch_size:(i+1)*args.batch_size] = batch_features - features = features.cpu().numpy() - if args.half_precision: - if args.type == "vae": - features = features.astype(np.int16) - else: - features = features.astype('float16') - else: - if args.type == "vae": - features = features.astype(np.int32) - else: - features = features.astype('float32') - np.save(output_file, features) - else: - print('Video {} error.'.format(input_file)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/how2/s3d.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/how2/s3d.sh deleted file mode 100644 index 90102c89fbb4fb413951dfd049b30b8dfe7cbe99..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/how2/s3d.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash - - -python scripts/video_feature_extractor/extract.py \ - --vdir \ - --fdir data/feat/feat_how2_s3d \ - --type=s3d --num_decoding_thread=4 \ - --batch_size 32 --half_precision 1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/model.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/model.py deleted file mode 100644 index dd3147a6c47fd2141b43a0bf55a475bafa7acc94..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/model.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright (c) Howto100M authors and Facebook, Inc. All Rights Reserved - -import torch as th -from torch import nn - - -class GlobalAvgPool(nn.Module): - def __init__(self): - super(GlobalAvgPool, self).__init__() - - def forward(self, x): - return th.mean(x, dim=[-2, -1]) - - -def get_model(args): - assert args.type in ['2d', '3d', 'vmz', 's3d', 'vae'] - if args.type == '2d': - print('Loading 2D-ResNet-152 ...') - import torchvision.models as models - model = models.resnet152(pretrained=True) - model = nn.Sequential(*list(model.children())[:-2], GlobalAvgPool()) - model = model.cuda() - elif args.type == 'vmz': - print('Loading VMZ ...') - from vmz34 import r2plus1d_34 - model = r2plus1d_34(pretrained_path=args.vmz_model_path, pretrained_num_classes=487) - model = model.cuda() - elif args.type == 's3d': - # we use one copy of s3d instead of dup another one for feature extraction. - from mmpt.processors.models.s3dg import S3D - model = S3D('pretrained_models/s3d_dict.npy', 512) - model.load_state_dict(th.load('pretrained_models/s3d_howto100m.pth')) - model = model.cuda() - - elif args.type == '3d': - print('Loading 3D-ResneXt-101 ...') - from videocnn.models import resnext - model = resnext.resnet101( - num_classes=400, - shortcut_type='B', - cardinality=32, - sample_size=112, - sample_duration=16, - last_fc=False) - model = model.cuda() - model_data = th.load(args.resnext101_model_path) - model.load_state_dict(model_data) - elif args.type == 'vae': - from openaivae import OpenAIParallelDiscreteVAE - model = OpenAIParallelDiscreteVAE() - model = model.cuda() - else: - raise ValueError("model not supported yet.") - - model.eval() - print('loaded') - return model diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/pathbuilder.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/pathbuilder.py deleted file mode 100644 index 2ca4ebfc3d0dd5b387c13721233601f7ca337772..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/pathbuilder.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 json -import os -import urllib.parse - -import pandas as pd -from tqdm import tqdm - -# TODO: extending to other datasets. -supported_formats = {} - - -class PathBuilder(object): - @classmethod - def build(cls, video_dirs, feature_dir, ext, shards=0, split=None): - meta_fn = os.path.join(feature_dir, "meta_plan.json") - os.makedirs(feature_dir, exist_ok=True) - if os.path.isfile(meta_fn): - with open(meta_fn) as fr: - meta = json.load(fr) - return meta - print("searching videos...") - - video_id_to_path = {} - for video_dir in video_dirs.split(","): - # TODO: add supports of recursive listdir. - if video_dir in supported_formats: - supported_formats[video_dir].load(video_dir, video_id_to_path) - else: - for idx, fn in enumerate(tqdm(os.listdir(video_dir))): - video_fn = os.path.join(video_dir, fn) - if os.path.isfile(video_fn): - video_id = os.path.splitext(fn)[0] - video_id_to_path[video_id] = video_fn - elif os.path.isdir(video_fn): - # shards of folders. - shard_dir = video_fn - for idx, fn in enumerate(os.listdir(shard_dir)): - video_fn = os.path.join(shard_dir, fn) - if os.path.isfile(video_fn): - video_id = os.path.splitext(fn)[0] - video_id_to_path[video_id] = video_fn - - video_path, feature_path = [], [] - valid_ext = set() - for idx, video_id in enumerate(video_id_to_path): - video_path.append(video_id_to_path[video_id]) - if ext is None: - # use original file ext for format compatibility. - video_id_to_path[video_id] - path = urllib.parse.urlparse(video_id_to_path[video_id]).path - ext = os.path.splitext(path)[1] - if ext not in valid_ext: - valid_ext.add(ext) - print("adding", ext) - if shards: - shard_id = str(idx % shards) - feature_fn = os.path.join( - feature_dir, shard_id, video_id + ext) - else: - feature_fn = os.path.join( - feature_dir, video_id + ext) - feature_path.append(feature_fn) - - print("targeting", len(feature_path), "videos") - meta = { - "video_path": video_path, "feature_path": feature_path} - with open(meta_fn, "w") as fw: - json.dump(meta, fw) - - if split is not None: - splits = split.split("/") - assert len(splits) == 2 - cur, total = int(splits[0]), int(splits[1]) - assert cur < total - import math - chunk = math.ceil(len(meta["video_path"]) / total) - start = cur * chunk - end = (cur + 1) * chunk - meta = { - "video_path": meta["video_path"][start:end], - "feature_path": meta["feature_path"][start:end] - } - - return meta diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/preprocessing.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/preprocessing.py deleted file mode 100644 index 88df8e6fef31ff70b1628f9fc31f5268a35ad135..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/preprocessing.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright Howto100m authors. -# Copyright (c) Facebook, Inc. All Rights Reserved - -import torch as th - - -class Normalize(object): - - def __init__(self, mean, std): - self.mean = th.FloatTensor(mean).view(1, 3, 1, 1) - self.std = th.FloatTensor(std).view(1, 3, 1, 1) - - def __call__(self, tensor): - tensor = (tensor - self.mean) / (self.std + 1e-8) - return tensor - -class Preprocessing(object): - - def __init__(self, type): - self.type = type - if type == '2d': - self.norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - elif type == '3d': - self.norm = Normalize(mean=[110.6, 103.2, 96.3], std=[1.0, 1.0, 1.0]) - elif type == 'vmz': - self.norm = Normalize(mean=[110.201, 100.64, 95.997], std=[58.1489, 56.4701, 55.3324]) - - def _zero_pad(self, tensor, size): - n = size - len(tensor) % size - if n == size: - return tensor - else: - z = th.zeros(n, tensor.shape[1], tensor.shape[2], tensor.shape[3]) - return th.cat((tensor, z), 0) - - def __call__(self, tensor): - if self.type == '2d': - tensor = tensor / 255.0 - tensor = self.norm(tensor) - elif self.type == 'vmz': - #tensor = self._zero_pad(tensor, 8) - tensor = self._zero_pad(tensor, 10) - tensor = self.norm(tensor) - #tensor = tensor.view(-1, 8, 3, 112, 112) - tensor = tensor.view(-1, 10, 3, 112, 112) - tensor = tensor.transpose(1, 2) - elif self.type == '3d': - tensor = self._zero_pad(tensor, 16) - tensor = self.norm(tensor) - tensor = tensor.view(-1, 16, 3, 112, 112) - tensor = tensor.transpose(1, 2) - elif self.type == 's3d': - tensor = tensor / 255.0 - tensor = self._zero_pad(tensor, 30) - tensor = tensor.view(-1, 30, 3, 224, 224) # N x 30 x 3 x H x W - tensor = tensor.transpose(1, 2) # N x 3 x 30 x H x W - # for vae do nothing - return tensor diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/random_sequence_shuffler.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/random_sequence_shuffler.py deleted file mode 100644 index ad45776165d0afd92205db2afaf8705eef60bb0f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/random_sequence_shuffler.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Facebook, Inc. All Rights Reserved - -import numpy as np -from torch.utils.data.sampler import Sampler - - -class RandomSequenceSampler(Sampler): - - def __init__(self, n_sample, seq_len): - self.n_sample = n_sample - self.seq_len = seq_len - - def _pad_ind(self, ind): - zeros = np.zeros(self.seq_len - self.n_sample % self.seq_len) - ind = np.concatenate((ind, zeros)) - return ind - - def __iter__(self): - idx = np.arange(self.n_sample) - if self.n_sample % self.seq_len != 0: - idx = self._pad_ind(idx) - idx = np.reshape(idx, (-1, self.seq_len)) - np.random.shuffle(idx) - idx = np.reshape(idx, (-1)) - return iter(idx.astype(int)) - - def __len__(self): - return self.n_sample + (self.seq_len - self.n_sample % self.seq_len) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/shard_feature.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/shard_feature.py deleted file mode 100644 index af6bfe90a8b943ab60c26e4829d792ac3d9ae51a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/shard_feature.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 pickle - -import numpy as np -from mmpt.utils import ShardedTensor - - -class Shard(object): - def __init__( - self, - vfeat_dir, - tfeat_dir, - target_dir, - file_paths, - shard_size=4096 - ): - self.vfeat_dir = vfeat_dir - self.tfeat_dir = tfeat_dir - self.target_dir = target_dir - self.video_ids = {} - for split, file_path in zip(["train", "val"], file_paths): - with open(file_path) as fr: - self.video_ids[split] = [ - line.strip() for line in fr.readlines()] - self.shard_size = shard_size - - def __call__(self, split="train"): - for split in ["train", "val"]: - meta = {} - for shard_idx, shard_offset in enumerate( - range(0, len(self.video_ids[split]), self.shard_size) - ): - print(shard_idx) - meta_shard = [] - video_shard = [] - for video_id in self.video_ids[split][shard_offset:shard_offset+self.shard_size]: - meta_shard.append(video_id) - npy_file = os.path.join(self.vfeat_dir, video_id + ".npy") - video_shard.append(np.load(npy_file)) - - meta[shard_idx] = meta_shard - video_shard = ShardedTensor.from_list(video_shard) - target_path = os.path.join( - self.target_dir, split + "_" + str(shard_idx)) - video_shard.save(target_path) - - target_path = os.path.join(self.target_dir, split + "_meta") - with open(target_path + ".pkl", "wb") as fw: - pickle.dump(meta, fw, pickle.HIGHEST_PROTOCOL) - - -if __name__ == "__main__": - shard = Shard( - "data/feat/feat_how2_s3d", - "data/how2/raw_caption_dedup.bert-base-uncased", - "data/feat/feat_how2_s3d_shard_small", - ["data/how2/how2_s3d_train.lst", "data/how2/how2_s3d_val.lst"] - ) - - shard() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/videoreader.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/videoreader.py deleted file mode 100644 index 47aa6d3f48364998d6435062a3593bce907bcc2c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/scripts/video_feature_extractor/videoreader.py +++ /dev/null @@ -1,242 +0,0 @@ -# Copyright Howto100M authors. -# Copyright (c) Facebook, Inc. All Rights Reserved - -import os -import random - -import ffmpeg -import numpy as np -import pandas as pd -import torch as th -from torch.utils.data import Dataset - - -class VideoLoader(Dataset): - """modified from how2's video_feature_extractor.""" - def __init__( - self, - csv=None, - video_dict=None, - framerate=1, - size=112, - centercrop=False, - hflip=False, - **kwargs - ): - if csv is None and video_dict is None: - raise ValueError("csv and video_dict cannot be both None.") - if csv is not None: - self.csv = pd.read_csv(csv) - if video_dict is not None: - self.csv = pd.DataFrame.from_dict(video_dict) - - self.centercrop = centercrop - self.size = size - self.framerate = framerate - self.hflip = hflip - - def __len__(self): - return len(self.csv) - - def _get_video_dim(self, video_path): - probe = ffmpeg.probe(video_path) - video_stream = next((stream for stream in probe['streams'] - if stream['codec_type'] == 'video'), None) - width = int(video_stream['width']) - height = int(video_stream['height']) - return height, width - - def _get_video_info(self, video_path): - probe = ffmpeg.probe(video_path) - video_stream = next((stream for stream in probe['streams'] - if stream['codec_type'] == 'video'), None) - return video_stream - - def _get_output_dim(self, h, w): - if isinstance(self.size, tuple) and len(self.size) == 2: - return self.size - elif h >= w: - return int(h * self.size / w), self.size - else: - return self.size, int(w * self.size / h) - - def __getitem__(self, idx): - video_path = self.csv['video_path'].values[idx] - output_file = self.csv['feature_path'].values[idx] - return self._decode(output_file, video_path) - - def _decode(self, output_file, video_path): - if not(os.path.isfile(output_file)) and os.path.isfile(video_path): - try: - h, w = self._get_video_dim(video_path) - except Exception: - print('ffprobe failed at: {}'.format(video_path)) - return {'video': th.zeros(1), 'input': video_path, - 'output': output_file} - try: - os.makedirs(os.path.dirname(output_file), exist_ok=True) - height, width = self._get_output_dim(h, w) - - cmd = ( - ffmpeg - .input(video_path) - .filter('fps', fps=self.framerate) - .filter('scale', width, height) - ) - if self.hflip: - cmd = cmd.filter('hflip') - - if self.centercrop: - x = int((width - self.size) / 2.0) - y = int((height - self.size) / 2.0) - cmd = cmd.crop(x, y, self.size, self.size) - video = self._run(cmd, output_file) - except Exception: - video = th.zeros(1) - else: - video = th.zeros(1) - - return {'video': video, 'input': video_path, 'output': output_file} - - def _run(self, cmd, output_file): - out, _ = ( - cmd.output('pipe:', format='rawvideo', pix_fmt='rgb24') - .run(capture_stdout=True, quiet=True) - ) - if self.centercrop and isinstance(self.size, int): - height, width = self.size, self.size - video = np.frombuffer(out, np.uint8).reshape([-1, height, width, 3]) - video = th.from_numpy(video.astype('float32')) - return video.permute(0, 3, 1, 2) - - -class VideoVerifier(VideoLoader): - def __getitem__(self, idx): - video_path = self.csv['video_path'].values[idx] - try: - return self._get_video_info(video_path) - except Exception: - # print('ffprobe failed at: {}'.format(video_path)) - return None - - -class VideoCompressor(VideoLoader): - def __init__( - self, - csv=None, - video_dict=None, - framerate=1, - size=112, - centercrop=False, - hflip=False, - crf=32, - **kwargs - ): - super().__init__( - csv, - video_dict, - framerate, - size, - centercrop, - hflip - ) - self.crf = crf - - def _run(self, cmd, output_file): - out, _ = ( - cmd.output(filename=output_file, crf=self.crf) - .run(quiet=True) - ) - video = None - return video - - -class VideoDownloader(VideoCompressor): - """download""" - def __getitem__(self, idx): - video_path = self.csv['video_path'].values[idx] - output_file = self.csv['feature_path'].values[idx] - if not(os.path.isfile(output_file)): - os.makedirs(os.path.dirname(output_file), exist_ok=True) - cmd = "wget -O" + output_file + " " + video_path - # import subprocess - # subprocess.check_output( - # cmd, - # stderr=subprocess.STDOUT, shell=True) - os.system(cmd) - return {'video': None, 'input': video_path, 'output': output_file} - - -class AvKeyframeVideoCompressor(VideoLoader): - """extract keyframes from a video and save it as jpg. - TODO: consider to merge with `CodecProcessor`. - """ - def __init__( - self, - csv=None, - video_dict=None, - framerate=1, - size=112, - centercrop=False, - max_num_frames=5, - **kwargs - ): - super().__init__(csv, video_dict, framerate, size, centercrop) - self.max_num_frames = max_num_frames - - def _get_video_dim(self, video_fn): - """decord cannot probe the size of a video, we use pyav instead.""" - import av - with av.open(video_fn) as container: - height = container.streams.video[0].codec_context.height - width = container.streams.video[0].codec_context.width - return height, width - - def _get_output_dim(self, height, width): - """ - keep the shorter side be `self.size`, strech the other. - """ - if height >= width: - return int(height * self.size / width), self.size - else: - return self.size, int(width * self.size / height) - - def __getitem__(self, idx): - import av - video_path = self.csv['video_path'].values[idx] - output_file = self.csv['feature_path'].values[idx] - if not(os.path.isdir(output_file)) and os.path.isfile(video_path): - try: - h, w = self._get_video_dim(video_path) - except Exception: - print('probe failed at: {}'.format(video_path)) - return {'video': th.zeros(1), 'input': video_path, - 'output': output_file} - - try: - height, width = self._get_output_dim(h, w) - - # new for av. - with av.open(video_path) as container: - container.streams.video[0].thread_type = "AUTO" - container.streams.video[0].codec_context.height = height - container.streams.video[0].codec_context.width = width - if self.framerate == 0: # keyframe. - container.streams.video[0].codec_context.skip_frame = 'NONKEY' - frames = [] - for frame in container.decode(video=0): - frames.append(frame) - frames = random.sample(frames, self.max_num_frames) - - os.makedirs(output_file, exist_ok=True) - for frame in frames: - frame.to_image().save( - os.path.join( - output_file, - "%04d.jpg" % frame.index)) - except Exception: - print('extract failed at: {}'.format(video_path)) - return {'video': th.zeros(1), 'input': video_path, - 'output': output_file} - video = th.zeros(1) - return {'video': video, 'input': video_path, 'output': output_file} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/setup.py b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/setup.py deleted file mode 100644 index a9a82296ea9c3a53f760c29c34020b5a90091a89..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/setup.py +++ /dev/null @@ -1,24 +0,0 @@ -import setuptools - -with open("README.md", "r") as fh: - long_description = fh.read() - -setuptools.setup( - name="mmpt", - version="0.0.1", - author="Hu Xu, Po-yao Huang", - author_email="huxu@fb.com", - description="A package for multimodal pretraining.", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/pytorch/fairseq/examples/MMPT", - packages=setuptools.find_packages(), - install_requires=[ - ], - classifiers=[ - "Programming Language :: Python :: 3", - "License :: CC-BY-NC", - "Operating System :: OS Independent", - ], - python_requires='>=3.6', -) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/videoclip.png b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/videoclip.png deleted file mode 100644 index 2b6f8d959fcfe3ce229df16a52c5c8230950b582..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/videoclip.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1d54fe18d1259ade9332e78fdb74f834fdfbdb0b0486517e6a7cd48956b30663 -size 385871 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/vlm.png b/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/vlm.png deleted file mode 100644 index c702b32b12d7d630c25af96a98ded4f83378bcb4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/MMPT/vlm.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:722852ed6258ac9f7ffd3e3913fa1a370702c4d989ef6d881847432d59ade4e5 -size 418405 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/__init__.py deleted file mode 100644 index 44bb24ae614941f23fea29c56d60167650c39bcb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/__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. - -try: - from fairseq.version import __version__ # noqa -except ImportError: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/README.md deleted file mode 100644 index d5224fb2894606a2a8027e01e224be190776ecfe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/README.md +++ /dev/null @@ -1,90 +0,0 @@ -# Adaptive Span - -Adaptive Span is a novel self-attention mechanism that can learn its optimal -attention span. This allows us to extend significantly the maximum context size -used in Transformer, while maintaining control over their memory footprint -and computational time. It uses the Truncated BPTT technique for training, -as in [transformerXL](https://github.com/pytorch/fairseq/blob/main/examples/truncated_bptt/README.md). - -Adaptive Span was introduced by paper: -[Adaptive Attention Span in Transformers](https://arxiv.org/abs/1905.07799), -which achieved state-of-the-art language modeling results at the time of publication. - -We manage to reproduce their result in fairseq and keep most of the -[original implementation](https://github.com/facebookresearch/adaptive-span) untouched. -You can refer to the their sweep file as well if any combination of hyperparameter is not clear. - -##### 0. Setup - -First you need to process the Enwik8 dataset, we use the pre-tokenized dataset -from [adaptive span paper](https://github.com/facebookresearch/adaptive-span/blob/master/get_data.sh). -You can download the dataset, and then run: -```bash -fairseq-preprocess --only-source --trainpref ~/data/enwik8/train.txt \ - --validpref ~/data/enwik8/valid.txt --testpref ~/data/enwik8/test.txt \ - --destdir ~/data/enwik8/data-bin/ --joined-dictionary --workers 20 -``` - -##### 1. Train a Adaptive Span model on Enwik8 - -We will train a 12-layer Adaptive Span model following the [hyperparameters -used in the original -paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh). - -The following command assumes 4 GPUs, so that the total batch size is 64 -sequences (4 x 16). Training should take 2-3 days on 4 V100 GPUs: -```bash -CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ - --user-dir examples/adaptive_span \ - --data ~/data/enwik8/data-bin/ \ - --fp16 --fp16-no-flatten-grads --max-update 600000 \ - --task truncated_bptt_lm --tokens-per-sample 512 --arch adaptive_span \ - --n-layer 12 --d-model 512 --n-head 8 --d-inner 2048 --dropout 0.3 \ - --attn-span 8192 --optimizer adagrad_with_grad_clip --adagrad-clip 0.03 \ - --validate-interval-updates 1000 \ - --lr-scheduler fixed --warmup-updates 32000 --batch-size-valid 32 \ - --lr 0.07 --criterion adaptive_span_loss --batch-size 16 --update-freq 1 \ - --seed 2 --log-format json --log-interval 25 --aux-loss-scaler 5e-07 -``` -This should land around 1.05 on validation, 1.03 on test. You can lower the ---aux-loss-scaler for better performance (longer span). It gives ~0.03 bpc -improvement to the transformerXL baseline here. -If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients -and simulate training on 4 GPUs. -You can also reproduce the transformerXL result on enwik8 using this code base. -It should land around 1.06 on test,matching the [original paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_enwik8_base.sh). -You can try by -```bash -CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \ - --user-dir examples/truncated_bptt \ - ~/data/enwik8/data-bin/ \ - --task truncated_bptt_lm --fp16 --max-update 400000 \ - --tokens-per-sample 512 --arch transformer_xl --n-layer 12 \ - --d-model 512 --n-head 8 --d-head 64 --d-inner 2048 --dropout 0.1 \ - --dropatt 0.0 --mem-len 512 --optimizer adam --clip-norm 0.25 \ - --lr-scheduler cosine --warmup-updates 0 \ - --lr 0.0 --lr 0.00025 --batch-size 15 \ - --update-freq 1 --seed 2 --log-format json --log-interval 25 \ - --fp16 -``` - -##### 2. Evaluate -For Adaptive Span: -```bash -fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \ - --user-dir examples/adaptive_span \ - --task truncated_bptt_lm --batch-size 8 --tokens-per-sample 512 --gen-subset test -``` -For Transformer-XL evaluation: -```bash -fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \ - --user-dir examples/truncated_bptt/ --task truncated_bptt_lm --batch-size 8 \ - --tokens-per-sample 80 \ - --model-overrides '{"mem_len":2100,"clamp_len":820,"same_length":True}' \ - --gen-subset valid -``` - -*Note:* During training the model saw 512 tokens of context -(``--tokens-per-sample=512``), with batch size 8. These settings match the evaluation -settings from [the original -paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh). diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/__init__.py deleted file mode 100644 index e0a142a769360e1140bf814c532eaf841f1d52d8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/__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 current directory -cur_dir = os.path.dirname(__file__) -for file in os.listdir(cur_dir): - path = os.path.join(cur_dir, file) - if ( - not file.startswith("_") - and not file.startswith(".") - and (file.endswith(".py") or os.path.isdir(path)) - ): - mod_name = file[: file.find(".py")] if file.endswith(".py") else file - module = importlib.import_module(__name__ + "." + mod_name) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adagrad_with_grad_clip.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adagrad_with_grad_clip.py deleted file mode 100644 index df8412f673304b3c749b46095a5784e0da77b1fd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adagrad_with_grad_clip.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. - -from fairseq.optim import LegacyFairseqOptimizer, register_optimizer -from torch.optim import Adagrad - - -@register_optimizer("adagrad_with_grad_clip") -class FairseqAdagradWithGradClip(LegacyFairseqOptimizer): - def __init__(self, args, params): - super().__init__(args) - self._optimizer = AdagradWithGradClip(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') - parser.add_argument('--adagrad-clip', default=0.0, type=float, metavar='D', - help='internal grad clip') - # 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, - "grad_clip": self.args.adagrad_clip, - } - - @property - def supports_flat_params(self): - return False - - -def _clip_grad(clr, grad, group_grad_clip): - if group_grad_clip > 0: - norm = grad.norm(2).item() - if norm > group_grad_clip: - clr *= group_grad_clip / (norm + 1e-10) - return clr - - -class AdagradWithGradClip(Adagrad): - """Adagrad algorithm with custom gradient clipping""" - - def __init__( - self, - params, - lr=1e-2, - lr_decay=0, - weight_decay=0, - initial_accumulator_value=0, - grad_clip=0, - ): - Adagrad.__init__( - self, - params, - lr=lr, - lr_decay=lr_decay, - weight_decay=weight_decay, - initial_accumulator_value=initial_accumulator_value, - ) - self.defaults["grad_clip"] = grad_clip - self.param_groups[0].setdefault("grad_clip", grad_clip) - - def step(self, closure=None): - 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 - state = self.state[p] - - state["step"] += 1 - - if group["weight_decay"] != 0: - if p.grad.data.is_sparse: - raise RuntimeError( - "weight_decay option is " - "not compatible with sparse " - "gradients" - ) - grad = grad.add(group["weight_decay"], p.data) - - clr = group["lr"] / (1 + (state["step"] - 1) * group["lr_decay"]) - - # clip - clr = _clip_grad(clr=clr, grad=grad, group_grad_clip=group["grad_clip"]) - - if grad.is_sparse: - # the update is non-linear so indices must be unique - grad = grad.coalesce() - grad_indices = grad._indices() - grad_values = grad._values() - size = grad.size() - - def make_sparse(values): - constructor = grad.new - if grad_indices.dim() == 0 or values.dim() == 0: - return constructor().resize_as_(grad) - return constructor(grad_indices, values, size) - - state["sum"].add_(make_sparse(grad_values.pow(2))) - std = state["sum"]._sparse_mask(grad) - std_values = std._values().sqrt_().add_(1e-10) - p.data.add_(-clr, make_sparse(grad_values / std_values)) - else: - state["sum"].addcmul_(1, grad, grad) - std = state["sum"].sqrt().add_(1e-10) - p.data.addcdiv_(-clr, grad, std) - - return loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_attention.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_attention.py deleted file mode 100644 index 07f757bb8e1a8a67b1124175ee338c8735aa8d65..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_attention.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 - - -class AdaptiveMask(nn.Module): - """Soft masking function for adaptive size. - It masks out the last K values of an input. The masking value - goes from 1 to 0 gradually, so K can be learned with - back-propagation. - Args: - max_size: maximum size (i.e. input dimension) - ramp_size: size of the ramp going from 0 to 1 - init_val: initial size proportion not to be masked out - shape: learn multiple sizes independent of each other - """ - - def __init__(self, max_size, ramp_size, init_val=0, shape=(1,)): - nn.Module.__init__(self) - self._max_size = max_size - self._ramp_size = ramp_size - self.current_val = nn.Parameter(torch.zeros(*shape) + init_val) - mask_template = torch.linspace(1 - max_size, 0, steps=max_size) - self.register_buffer("mask_template", mask_template) - - def forward(self, x): - mask = self.mask_template.float() + self.current_val.float() * self._max_size - mask = mask / self._ramp_size + 1 - mask = mask.clamp(0, 1) - if x.size(-1) < self._max_size: - # the input could have been trimmed beforehand to save computation - mask = mask.narrow(-1, self._max_size - x.size(-1), x.size(-1)) - x = (x * mask).type_as(x) - return x - - def get_current_max_size(self, include_ramp=True): - current_size = math.ceil(self.current_val.max().item() * self._max_size) - if include_ramp: - current_size += self._ramp_size - current_size = max(0, min(self._max_size, current_size)) - return current_size - - def get_current_avg_size(self, include_ramp=True): - current_size = math.ceil( - self.current_val.float().mean().item() * self._max_size - ) - if include_ramp: - current_size += self._ramp_size - current_size = max(0, min(self._max_size, current_size)) - return current_size - - def clamp_param(self): - """this need to be called after each update""" - self.current_val.data.clamp_(0, 1) - - -class AdaptiveSpan(nn.Module): - """Adaptive attention span for Transformerself. - This module learns an attention span length from data for each - self-attention head. - Args: - attn_span: maximum attention span - adapt_span_loss: loss coefficient for the span length - adapt_span_ramp: length of the masking ramp - adapt_span_init: initial size ratio - adapt_span_cache: adapt cache size to reduce memory usage - """ - - def __init__( - self, - attn_span, - adapt_span_ramp, - adapt_span_init, - n_head, - adapt_span_layer, - **kargs - ): - nn.Module.__init__(self) - self._max_span = attn_span - self._n_head = n_head - self._adapt_span_layer = adapt_span_layer - if self._adapt_span_layer: - self._mask = AdaptiveMask( - max_size=self._max_span, - ramp_size=adapt_span_ramp, - init_val=adapt_span_init, - ) - else: - self._mask = AdaptiveMask( - max_size=self._max_span, - ramp_size=adapt_span_ramp, - init_val=adapt_span_init, - shape=(n_head, 1, 1), - ) - - def forward(self, attn, normalize=True): - """mask attention with the right span""" - # batch and head dimensions are merged together, so separate them first - self.clamp_param() - if self._adapt_span_layer: - attn = self._mask(attn) - else: - B = attn.size(0) # batch size - M = attn.size(1) # block size - attn = attn.reshape(B // self._n_head, self._n_head, M, -1) - attn = self._mask(attn) - attn = attn.view(B, M, -1) - return attn - - def get_trim_len(self): - """how much of memory can be trimmed to reduce computation""" - L = self._max_span - trim_len = min(L - 1, L - self._mask.get_current_max_size()) - # too fine granularity might be bad for the memory management - trim_len = math.floor(trim_len / 64) * 64 - return trim_len - - def trim_memory(self, query, key, value, key_pe): - """trim out unnecessary memory beforehand to reduce computation""" - trim_len = self.get_trim_len() - cache_size = key.size(1) - query.size(1) - trim_len_cache = trim_len - (self._max_span - cache_size) - if trim_len_cache > 0: - key = key[:, trim_len_cache:, :] - value = value[:, trim_len_cache:, :] - elif trim_len_cache < 0: - # cache is too short! this happens when validation resumes - # after a lot of updates. - key = F.pad(key, [0, 0, -trim_len_cache, 0]) - value = F.pad(value, [0, 0, -trim_len_cache, 0]) - if trim_len > 0: - if key_pe is not None: - key_pe = key_pe[:, :, trim_len:] - return key, value, key_pe - - def get_cache_size(self): - """determine how long the cache should be""" - trim_len = self.get_trim_len() - # give a buffer of 64 steps since a span might increase - # in future updates - return min(self._max_span, self._max_span - trim_len + 64) - - def get_loss(self): - """a loss term for regularizing the span length""" - return self._max_span * self._mask.current_val.float().mean() - - def get_current_max_span(self): - return self._mask.get_current_max_size() - - def get_current_avg_span(self): - return self._mask.get_current_avg_size() - - def clamp_param(self): - self._mask.clamp_param() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_loss.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_loss.py deleted file mode 100644 index 056245807e5f8d313a8ad5be68aea4e285f4f580..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_loss.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 math -from dataclasses import dataclass - -import torch.nn.functional as F -from fairseq import metrics, utils -from fairseq.criterions import register_criterion -from fairseq.criterions.cross_entropy import CrossEntropyCriterion -from fairseq.dataclass import FairseqDataclass -from omegaconf import II - - -@dataclass -class AdaptiveSpanCriterionConfig(FairseqDataclass): - sentence_avg: bool = II("optimization.sentence_avg") - - -@register_criterion("adaptive_span_loss", dataclass=AdaptiveSpanCriterionConfig) -class AdaptiveSpanCriterion(CrossEntropyCriterion): - def __init__(self, task, sentence_avg): - super().__init__(task, sentence_avg) - - def forward(self, model, sample, reduce=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss here is summed, different from the adaptive span code - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - net_output = model(**sample["net_input"]) - loss, aux_loss, avg_span, max_span = self.compute_loss( - model, net_output, sample, reduce=reduce - ) - sample_size = ( - sample["target"].size(0) if self.sentence_avg else sample["ntokens"] - ) - loss /= sample_size - total_loss = loss + aux_loss - sample_size = 1 - - logging_output = { - "loss": loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample["target"].size(0), - "sample_size": sample_size, - "total_loss": total_loss.data, - "avg_span": avg_span * sample_size, - "max_span": max_span * sample_size, - } - return total_loss, sample_size, logging_output - - def compute_loss(self, model, net_output, sample, reduce=True): - loss, _ = super().compute_loss(model, net_output, sample, reduce) - aux_loss = model.get_aux_loss() - avg_span = model.get_current_avg_span() - max_span = model.get_current_max_span() - return loss, aux_loss, avg_span, max_span - - @staticmethod - def reduce_metrics(logging_outputs) -> None: - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - total_loss_sum = sum(log.get("total_loss", 0) for log in logging_outputs) - avg_span_sum = sum(log.get("avg_span", 0) for log in logging_outputs) - max_span_sum = sum(log.get("max_span", 0) for log in logging_outputs) - - # we divide by log(2) to convert the loss from base e to base 2 - metrics.log_scalar( - "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 - ) - metrics.log_scalar("avg_span", avg_span_sum / sample_size, sample_size, round=3) - metrics.log_scalar("max_span", max_span_sum / sample_size, sample_size, round=3) - # total loss contains the L1 norm on adaptive-span - metrics.log_scalar( - "total_loss", - total_loss_sum / sample_size / math.log(2), - sample_size, - round=3, - ) - if sample_size != ntokens: - metrics.log_scalar( - "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3 - ) - metrics.log_derived( - "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) - ) - else: - metrics.log_derived( - "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg) - ) - - @staticmethod - def logging_outputs_can_be_summed() -> bool: - """ - Whether the logging outputs returned by `forward` can be summed - across workers prior to calling `reduce_metrics`. Setting this - to True will improves distributed training speed. - """ - return True diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_model.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_model.py deleted file mode 100644 index 698437175a32794b4425e27196933502f773a318..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_model.py +++ /dev/null @@ -1,262 +0,0 @@ -# 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. - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.modules.layer_norm import LayerNorm - -from .adaptive_span_attention import AdaptiveSpan - -# Size notations: -# B = batch_size, H = d_model, M = block_size, L = attn_span - - -def _skew(X, pad_value): - """shift every row 1 step to right""" - # X = B x M x L - B, M, L = X.size() - X = F.pad(X, (0, M + 1), value=pad_value) # B x M x (L+M+1) - X = X.view(B, -1) # B x ML+MM+M - X = X[:, :-M] # B x ML+MM - X = X.view(B, M, M + L) # B x M x L+M - return X - - -def _unskew(X): - """reverse _skew operation""" - # X = B x M x L+M - B, M, L = X.size() - L -= M - X = X.view(B, -1) # B x ML+MM - X = F.pad(X, (0, M)) # B x ML+MM+M - X = X.view(B, M, M + L + 1) # B x M x L+M+1 - X = X[:, :, :L] # B x M x L - return X - - -class SeqAttention(nn.Module): - """Sequential self-attention layer. - Each token will attend to its previous fixed number of steps. - Note that attention doesn't include the current step itself. - """ - - def __init__(self, d_model, n_head, attn_span, dropout, adapt_span_layer, **kargs): - nn.Module.__init__(self) - self.dropout = nn.Dropout(dropout) - self.d_model = d_model # size of a single head - self.attn_span = attn_span - self.adaptive_span = AdaptiveSpan( - attn_span=attn_span, - n_head=n_head, - adapt_span_layer=adapt_span_layer, - **kargs - ) - - def forward(self, query, key, value, key_pe): - # query size = B x M x H - # key, value sizes = B x (M+L) x H - - key, value, key_pe = self.adaptive_span.trim_memory(query, key, value, key_pe) - - # compute attention from context - # B x M (dest) x (M+L) (src) - attn_cont = torch.matmul(query, key.transpose(-1, -2)) - attn_cont = _unskew(attn_cont) # B x M x L - - # compute the effect of position embedding - attn_pos = torch.matmul(query, key_pe) # B x M x L_pos - attn = attn_cont + attn_pos - - attn = attn / math.sqrt(self.d_model) # B x M X L_pos - - attn = F.softmax(attn.float(), dim=-1).type_as(attn) - - # trim attention lengths according to the learned span - attn = self.adaptive_span(attn) - - attn = self.dropout(attn) # B x M X L_pos - - attn_cont = _skew(attn, 0) # B x M X (L+M) - out = torch.matmul(attn_cont, value) # B x M x H - return out - - def get_cache_size(self): - return self.adaptive_span.get_cache_size() - - -class MultiHeadSeqAttention(nn.Module): - def __init__(self, d_model, n_head, **kargs): - nn.Module.__init__(self) - assert d_model % n_head == 0 - self.n_head = n_head - self.head_dim = d_model // n_head - self.attn = SeqAttention(d_model=self.head_dim, n_head=n_head, **kargs) - self.proj_query = nn.Linear(d_model, d_model, bias=False) - nn.init.xavier_normal_(self.proj_query.weight) - self.proj_out = nn.Linear(d_model, d_model, bias=False) - nn.init.xavier_normal_(self.proj_out.weight) - self.proj_val = nn.Linear(d_model, d_model, bias=False) - nn.init.xavier_normal_(self.proj_val.weight) - self.proj_key = nn.Linear(d_model, d_model, bias=False) - nn.init.xavier_normal_(self.proj_key.weight) - - def head_reshape(self, x): - K = self.n_head - D = self.head_dim - x = x.view(x.size()[:-1] + (K, D)) # B x (M+L) x K x D - x = x.transpose(1, 2).contiguous() # B x K x (M+L) x D - x = x.view(-1, x.size(-2), x.size(-1)) # B_K x (M+L) x D - return x - - def forward(self, query, key, value, key_pe): - B = query.size(0) - K = self.n_head - D = self.head_dim - M = query.size(1) - - query = self.proj_query(query) - query = self.head_reshape(query) - value = self.proj_val(value) - value = self.head_reshape(value) - key = self.proj_key(key) - key = self.head_reshape(key) - - out = self.attn(query, key, value, key_pe) # B_K x M x D - out = out.view(B, K, M, D) # B x K x M x D - out = out.transpose(1, 2).contiguous() # B x M x K x D - out = out.view(B, M, -1) # B x M x K_D - out = self.proj_out(out) - return out - - -class FeedForwardLayer(nn.Module): - def __init__(self, d_model, d_inner, dropout, **kargs): - nn.Module.__init__(self) - self.fc1 = nn.Linear(d_model, d_inner) - self.fc2 = nn.Linear(d_inner, d_model) - nn.init.xavier_uniform_(self.fc1.weight) - nn.init.xavier_uniform_(self.fc2.weight) - self.dropout = nn.Dropout(dropout) - - def forward(self, h): - h1 = F.relu(self.fc1(h)) - h1 = self.dropout(h1) - h2 = self.fc2(h1) - return h2 - - -class TransformerSeqLayer(nn.Module): - def __init__(self, d_model, **kargs): - nn.Module.__init__(self) - self.attn = MultiHeadSeqAttention(d_model=d_model, **kargs) - self.norm1 = LayerNorm(d_model) - self.ff = FeedForwardLayer(d_model=d_model, **kargs) - self.norm2 = LayerNorm(d_model) - - def forward(self, h, h_cache, key_pe): - # h = B x M x H - # h_cache = B x L x H - h_all = torch.cat([h_cache, h], dim=1) # B x (M+L) x H - attn_out = self.attn(h, h_all, h_all, key_pe) - h = self.norm1(h + attn_out) # B x M x H - if self.ff is not None: - ff_out = self.ff(h) - out = self.norm2(h + ff_out) # B x M x H - else: - out = h - return out - - def get_cache_size(self): - return self.attn.attn.get_cache_size() - - -class TransformerSeq(nn.Module): - def __init__( - self, - vocab_size, - d_model, - n_head, - n_layer, - attn_span, - emb_dropout, - aux_loss_scaler, - adapt_span_layer, - **kargs - ): - nn.Module.__init__(self) - # token embeddings - self.in_emb = nn.Embedding(vocab_size, d_model) - nn.init.normal_(self.in_emb.weight, mean=0, std=d_model ** -0.5) - self.out_emb = nn.Linear(d_model, vocab_size) - self.aux_loss_scaler = aux_loss_scaler - if emb_dropout > 0: - self.emb_dropout = nn.Dropout(emb_dropout) - else: - self.emb_dropout = None - # position embeddings - self.key_pe = nn.Parameter(torch.randn(1, d_model // n_head, attn_span)) - - self.layers = nn.ModuleList() - self.layers.extend( - TransformerSeqLayer( - d_model=d_model, - n_head=n_head, - attn_span=attn_span, - adapt_span_layer=adapt_span_layer, - **kargs - ) - for _ in range(n_layer) - ) - - def forward(self, x, h_cache, target=None): - # x size = B x M - block_size = x.size(1) - h = self.in_emb(x) # B x M x H - if self.emb_dropout is not None: - h = self.emb_dropout(h) - - h_cache_next = [] - for l, layer in enumerate(self.layers): - cache_size = layer.attn.attn.get_cache_size() - if cache_size > block_size: - h_cache_next_l = torch.cat( - [h_cache[l][:, -cache_size + block_size :, :], h], dim=1 - ).detach() - else: - h_cache_next_l = h[:, -cache_size:, :].detach() - h_cache_next.append(h_cache_next_l) - h = layer(h, h_cache[l], self.key_pe) # B x M x H - - if self.emb_dropout is not None: - h = self.emb_dropout(h) - - out = F.log_softmax(self.out_emb(h).float(), dim=-1).type_as(h) - dummy_loss = None - - return out, h_cache_next, dummy_loss - - def get_aux_loss(self): - loss = 0.0 - for layer in self.layers: - loss += layer.attn.attn.adaptive_span.get_loss() - return self.aux_loss_scaler * loss - - def get_current_max_span(self): - max_span = 0.0 - for layer in self.layers: - max_span = max( - max_span, layer.attn.attn.adaptive_span.get_current_max_span() - ) - return max_span - - def get_current_avg_span(self): - avg_span = 0.0 - for layer in self.layers: - avg_span += layer.attn.attn.adaptive_span.get_current_avg_span() - return avg_span / len(self.layers) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_model_wrapper.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_model_wrapper.py deleted file mode 100644 index ceb3e66e7658a8abb7eb88a29f057ba3dab53bcf..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/adaptive_span_model_wrapper.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 logging -from dataclasses import dataclass -from typing import Dict, List, Optional - -import torch -from fairseq.dataclass import FairseqDataclass -from fairseq.models import ( - FairseqIncrementalDecoder, - FairseqLanguageModel, - register_model, -) - -from .adaptive_span_model import TransformerSeq as AdaptiveSpanTransformerModel - -logger = logging.getLogger(__name__) - - -@dataclass -class AdaptiveSpanSmallConfig(FairseqDataclass): - # defaults come from https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8_small.sh - vocab_size: int = 50 - d_model: int = 256 - n_head: int = 4 - d_inner: int = 1024 - n_layer: int = 8 - attn_span: int = 1024 - dropout: float = 0.0 - emb_dropout: float = 0.0 - adapt_span_ramp: int = 32 - adapt_span_init: float = 0.0 - aux_loss_scaler: float = 0.000002 - adapt_span_layer: bool = False - - -@register_model("adaptive_span", dataclass=AdaptiveSpanSmallConfig) -class AdaptiveSpanTransformer(FairseqLanguageModel): - @classmethod - def build_model(cls, cfg: AdaptiveSpanSmallConfig, task): - return cls(AdaptiveSpanDecoder(cfg, task)) - - def get_aux_loss(self): - return self.decoder.get_aux_loss() - - def get_current_max_span(self): - return self.decoder.get_current_max_span() - - def get_current_avg_span(self): - return self.decoder.get_current_avg_span() - - -class AdaptiveSpanDecoder(FairseqIncrementalDecoder): - def __init__(self, cfg, task): - - super().__init__(task.target_dictionary) - - self.config = cfg - config = AdaptiveSpanSmallConfig( - vocab_size=len(task.target_dictionary), - d_model=cfg.d_model, - n_head=cfg.n_head, - d_inner=cfg.d_inner, - n_layer=cfg.n_layer, - attn_span=cfg.attn_span, - dropout=cfg.dropout, - emb_dropout=cfg.emb_dropout, - adapt_span_ramp=cfg.adapt_span_ramp, - adapt_span_init=cfg.adapt_span_init, - aux_loss_scaler=cfg.aux_loss_scaler, - adapt_span_layer=cfg.adapt_span_layer, - ) - logger.info(config) - self.model = AdaptiveSpanTransformerModel(**config.__dict__) - - self._mems = None - - def forward( - self, - src_tokens, - incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None, - encoder_out=None, - ): - bsz = src_tokens.size(0) - if incremental_state is not None: # used during inference - mems = self.get_incremental_state("mems") - src_tokens = src_tokens[:, -1:] # only keep the most recent token - else: - mems = self._mems - - if mems is None: - # first time init - mems = self.init_hid_cache(bsz) - output = self.model(x=src_tokens, h_cache=mems,) - if incremental_state is not None: - self.set_incremental_state(incremental_state, "mems", output[1]) - else: - self._mems = output[1] - return (output[0],) - - def max_positions(self): - return self.config.attn_span - - def init_hid_cache(self, batch_sz): - hid = [] - for layer in self.model.layers: - param = next(self.model.parameters()) - h = torch.zeros( - batch_sz, - layer.get_cache_size(), - self.config.d_model, - dtype=param.dtype, - device=param.device, - ) - hid.append(h) - return hid - - def get_aux_loss(self): - return self.model.get_aux_loss() - - def get_current_max_span(self): - return self.model.get_current_max_span() - - def get_current_avg_span(self): - return self.model.get_current_avg_span() - - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]], - new_order: torch.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. - """ - raise NotImplementedError("This is required for generation/beam search") - # mems = self.get_incremental_state(incremental_state, "mems") - # if mems is not None: - # new_mems = [mems_i.index_select(1, new_order) for mems_i in mems] - # self.set_incremental_state(incremental_state, "mems", new_mems) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/truncated_bptt_lm_task.py b/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/truncated_bptt_lm_task.py deleted file mode 100644 index e42e243d44b5451293eb1b7ce618da49524aa47a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/adaptive_span/truncated_bptt_lm_task.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 logging -import os -from dataclasses import dataclass, field -from typing import List, Optional, Tuple - -import torch -from fairseq import utils -from fairseq.data import ( - Dictionary, - TokenBlockDataset, - data_utils, - iterators, -) -from fairseq.dataclass import FairseqDataclass -from fairseq.distributed import utils as dist_utils -from fairseq.tasks import FairseqTask, register_task -from omegaconf import II - -logger = logging.getLogger(__name__) - - -@dataclass -class TruncatedBPTTLMConfig(FairseqDataclass): - data: str = field(default="???", metadata={"help": "path to data directory"}) - tokens_per_sample: int = field( - default=1024, metadata={"help": "max number of tokens per sequence"}, - ) - batch_size: int = II("dataset.batch_size") - # Some models use *max_target_positions* to know how many positional - # embeddings to learn. We use II(...) to make it default to - # *tokens_per_sample*, but in principle there could be more positional - # embeddings than tokens in a single batch. This may also be irrelevant for - # custom model implementations. - max_target_positions: int = II("task.tokens_per_sample") - # these will be populated automatically if not provided - data_parallel_rank: Optional[int] = None - data_parallel_size: Optional[int] = None - - -@register_task("truncated_bptt_lm", dataclass=TruncatedBPTTLMConfig) -class TruncatedBPTTLMTask(FairseqTask): - def __init__(self, cfg: TruncatedBPTTLMConfig): - super().__init__(cfg) - - if cfg.data_parallel_rank is None or cfg.data_parallel_size is None: - if torch.distributed.is_initialized(): - cfg.data_parallel_rank = dist_utils.get_data_parallel_rank() - cfg.data_parallel_size = dist_utils.get_data_parallel_world_size() - else: - cfg.data_parallel_rank = 0 - cfg.data_parallel_size = 1 - - # load the dictionary - paths = utils.split_paths(cfg.data) - assert len(paths) > 0 - self.dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt")) - logger.info("dictionary: {} types".format(len(self.dictionary))) - - def load_dataset(self, split, epoch=1, combine=False, **kwargs): - """Load a given dataset split (e.g., train, valid, test)""" - - # support sharded datasets - 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) - - # each element of *data* will be a tensorized line from the original - # text dataset, similar to ``open(split_path).readlines()`` - data = data_utils.load_indexed_dataset( - split_path, self.dictionary, combine=combine - ) - if data is None: - raise FileNotFoundError( - "Dataset not found: {} ({})".format(split, split_path) - ) - - # this is similar to ``data.view(-1).split(tokens_per_sample)`` - data = TokenBlockDataset( - data, - data.sizes, - block_size=self.cfg.tokens_per_sample, - pad=None, # unused - eos=None, # unused - break_mode="none", - ) - - self.datasets[split] = TruncatedBPTTDataset( - data=data, - bsz_per_shard=self.cfg.batch_size, - shard_id=self.cfg.data_parallel_rank, - num_shards=self.cfg.data_parallel_size, - ) - - def dataset(self, split): - return self.datasets[split] - - def get_batch_iterator( - self, - dataset, - num_workers=0, - epoch=1, - data_buffer_size=0, - skip_remainder_batch=False, - **kwargs - ): - return iterators.EpochBatchIterator( - dataset=dataset, - collate_fn=self._collate_fn, - num_workers=num_workers, - epoch=epoch, - buffer_size=data_buffer_size, - # we don't use the batching functionality from EpochBatchIterator; - # instead every item in *dataset* is a whole batch - batch_sampler=[[i] for i in range(len(dataset))], - disable_shuffling=True, - skip_remainder_batch=skip_remainder_batch, - ) - - def _collate_fn(self, items: List[List[torch.Tensor]]): - # we don't use fairseq's batching functionality, so we expect a single - # Tensor of type List[torch.Tensor] - assert len(items) == 1 - - # item will have shape B x T (the last batch may have length < T) - id, item = items[0] - item = data_utils.collate_tokens(item, pad_idx=self.source_dictionary.pad()) - B, T = item.size() - - # shift item one position over and append a padding token for the target - target = torch.nn.functional.pad( - item[:, 1:], (0, 1, 0, 0), value=self.target_dictionary.pad() - ) - - # fairseq expects batches to have the following structure - return { - "id": torch.tensor([id] * item.size(0)), - "net_input": {"src_tokens": item,}, - "target": target, - "nsentences": item.size(0), - "ntokens": item.numel(), - } - - def build_dataset_for_inference( - self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs - ) -> torch.utils.data.Dataset: - eos = self.source_dictionary.eos() - dataset = TokenBlockDataset( - src_tokens, - src_lengths, - block_size=None, # ignored for "eos" break mode - pad=self.source_dictionary.pad(), - eos=eos, - break_mode="eos", - ) - - class Dataset(torch.utils.data.Dataset): - def __getitem__(self, i): - item = dataset[i] - if item[-1] == eos: - # remove eos to support generating with a prefix - item = item[:-1] - return (i, [item]) - - def __len__(self): - return len(dataset) - - return Dataset() - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - if constraints is not None: - raise NotImplementedError - - # 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"] - - # begin generation with the end-of-sentence token - bos_token = self.source_dictionary.eos() - - 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, - context_window: int = 0, - ): - if context_window > 0: - raise NotImplementedError( - "Transformer-XL doesn't need --context-window, try " - "--model-overrides '{\"mem_len\":42}' instead " - ) - 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 self.dictionary - - @property - def target_dictionary(self): - return self.dictionary - - -class TruncatedBPTTDataset(torch.utils.data.Dataset): - def __init__( - self, - data: List[torch.Tensor], # ordered list of items - bsz_per_shard, # number of items processed per GPUs per forward - shard_id, # current GPU ID - num_shards, # number of GPUs - ): - super().__init__() - self.data = data - - def batchify(data, bsz): - # Work out how cleanly we can divide the dataset into bsz parts. - nbatch = data.size(0) // bsz - # Trim off any extra elements that wouldn't cleanly fit (remainders). - data = data.narrow(0, 0, nbatch * bsz) - # Evenly divide the data across the bsz batches. - data = data.view(bsz, -1).contiguous() - return data - - # total number of sequences processed by all GPUs in each forward pass - global_batch_size = bsz_per_shard * num_shards - - """ - With a 16 item dataset, bsz_per_shard=2 and num_shards=3, - *indices* might look like: - - indices = [[0, 1], - [2, 3], - [4, 5], - [6, 7], - [8, 9], - [10, 11]] - - The size of the TruncatedBPTTDataset instance will be 2, - and shard 1 will see items: - - [(0, [data[4], data[6]]), - (1, [data[5], data[7]])] - """ - indices = batchify(torch.arange(len(data)), global_batch_size) - assert indices.size(0) == global_batch_size - - self.my_indices = indices[ - shard_id * bsz_per_shard : (shard_id + 1) * bsz_per_shard - ] - assert self.my_indices.size(0) == bsz_per_shard - - def __len__(self): - return self.my_indices.size(1) - - def __getitem__(self, i) -> Tuple[int, List[torch.Tensor]]: - return (i, [self.data[idx] for idx in self.my_indices[:, i]]) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/README.md deleted file mode 100644 index 2434f1fb212fa075335f97507dc89508f29402ec..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/README.md +++ /dev/null @@ -1,161 +0,0 @@ -# Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling (Gong et al., 2021) - -[https://arxiv.org/pdf/2106.10840.pdf](https://arxiv.org/pdf/2106.10840.pdf) - -## Introduction - -We present attention head selection strategies in multilingual and multi-domain sequence modeling including text translation, speech recognition and speech translation tasks. - -Below is an example of training multilingual/multi-domain speech recognition models. - -## Data Preparation -Prepare mTEDx data as in [mTEDx example](https://github.com/fairinternal/fairseq-py/blob/0d9c5851e6fac40f9e366b3633ccd615c2901788/examples/speech_to_text/docs/mtedx_example.md) and CoVoST data as in [CoVoST example](https://github.com/fairinternal/fairseq-py/blob/0d9c5851e6fac40f9e366b3633ccd615c2901788/examples/speech_to_text/docs/covost_example.md). Similarly prepare EuroParl data. - - -## Training a multilingual ASR model with attention head selection - -```bash -data_dir= -train_subset="train_ar_ar_tedx,train_de_de_tedx,train_el_el_tedx,train_es_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_pt_tedx,train_ru_ru_tedx" -valid_subset="valid_ar_ar_tedx,valid_de_de_tedx,valid_el_el_tedx,valid_es_es_tedx,valid_fr_fr_tedx,valid_it_it_tedx,valid_pt_pt_tedx,valid_ru_ru_tedx" -strateg= - -fairseq-train ${data_dir} \ - --user-dir examples/attention_head_selection/src \ - --train-subset "${train_subset}" \ - --valid-subset "${valid_subset}" \ - --config-yaml 'config_asr.yaml' \ - --arch 'head_selection_s2t_transformer_s' \ - --task 'speech_to_text_head_selection' \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --lr-scheduler 'inverse_sqrt' --stop-min-lr -1.0 --warmup-updates 10000 \ - --lr 5e-4 \ - --clip-norm 10.0 \ - --seed 1 \ - --max-epoch 400 \ - --max-tokens 32000 \ - --ignore-prefix-size 1 \ - --dropout 0.3 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --skip-invalid-size-inputs-valid-test \ - --encoder-attn-head-select \ - --total-encoder-attention-heads 8 \ - --decoder-self-attn-head-select \ - --total-decoder-attention-heads 8 \ - --attn-head-select-strategy ${strategy} \ - --task-type lang \ -``` - -## Training a multi-domain ASR model with attention head selection - -```bash -data_dir= -train_subset="train_es_es_tedx,train_fr_fr_tedx,train_pt_pt_tedx,train_it_it_tedx,train_ru_ru_tedx,train_el_el_tedx,train_ar_ar_tedx,train_de_de_tedx,train_ar_ar_cv,train_de_de_cv,train_es_es_cv,train_fr_fr_cv,train_it_it_cv,train_pt_pt_cv,train_ru_ru_cv,train_de_de_ep,train_es_es_ep,train_fr_fr_ep,train_it_it_ep,train_pt_pt_ep" -valid_subset="dev_es_es_tedx,dev_fr_fr_tedx,dev_pt_pt_tedx,dev_it_it_tedx,dev_ru_ru_tedx,dev_el_el_tedx,dev_ar_ar_tedx,dev_de_de_tedx,dev_ar_ar_cv,dev_de_de_cv,dev_es_es_cv,dev_fr_fr_cv,dev_it_it_cv,dev_pt_pt_cv,dev_ru_ru_cv,dev_de_de_ep,dev_es_es_ep,dev_fr_fr_ep,dev_it_it_ep,dev_pt_pt_ep" -strateg= - -fairseq-train ${data_dir} \ - --user-dir examples/attention_head_selection/src \ - --train-subset "${train_subset}" \ - --valid-subset "${valid_subset}" \ - --config-yaml 'config_asr.yaml' \ - --arch head_selection_s2t_transformer_s \ - --task speech_to_text_head_selection \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --lr-scheduler 'inverse_sqrt' --stop-min-lr -1.0 --warmup-updates 10000 \ - --lr 5e-4 \ - --clip-norm 10.0 \ - --seed 1 \ - --max-epoch 400 \ - --max-tokens 32000 \ - --ignore-prefix-size 1 \ - --dropout 0.3 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --skip-invalid-size-inputs-valid-test \ - --encoder-attn-head-select \ - --total-encoder-attention-heads 8 \ - --decoder-self-attn-head-select \ - --total-decoder-attention-heads 8 \ - --attn-head-select-strategy ${strategy} \ - --task-type domain -``` - -## Inference in multilingual setting - -```bash -MODEL_DIR= -data_dir= -gen_subset= -train_subset="train_ar_ar_tedx,train_de_de_tedx,train_el_el_tedx,train_es_es_tedx,train_fr_fr_tedx,train_it_it_tedx,train_pt_pt_tedx,train_ru_ru_tedx" -last_n=10 -CHECKPOINT_FILENAME="avg_last_${last_n}_checkpoint.pt" -CHECKPOINT="_avg" -RESULTS="${MODEL_DIR}/ckpt${CHECKPOINT}" -if [ ! -d $RESULTS ]; then - mkdir -p $RESULTS -fi; - -python scripts/average_checkpoints.py \ - --inputs ${MODEL_DIR} --num-epoch-checkpoints ${last_n} \ - --output "${MODEL_DIR}/${CHECKPOINT_FILENAME}" - -fairseq-generate ${data_dir} \ - --user-dir examples/attention_head_selection/src \ - --arch 'head_selection_s2t_transformer_s' \ - --task 'speech_to_text_head_selection' \ - --train-subset ${train_subset} \ - --gen-subset ${gen_subset} \ - --path "${MODEL_DIR}/${CHECKPOINT_FILENAME}" \ - --config-yaml 'config_asr.yaml' \ - --prefix-size 1 \ - --max-tokens 40000 --beam 5 \ - --skip-invalid-size-inputs-valid-test \ - --results-path ${RESULTS} \ - --scoring wer --wer-tokenizer 13a \ - --wer-lowercase --wer-remove-punct --remove-bpe -``` - -## Inference in multi-domain setting - -```bash -MODEL_DIR= -data_dir= -gen_subset= -train_subset="train_es_es_tedx,train_fr_fr_tedx,train_pt_pt_tedx,train_it_it_tedx,train_ru_ru_tedx,train_el_el_tedx,train_ar_ar_tedx,train_de_de_tedx,train_ar_ar_cv,train_de_de_cv,train_es_es_cv,train_fr_fr_cv,train_it_it_cv,train_pt_pt_cv,train_ru_ru_cv,train_de_de_ep,train_es_es_ep,train_fr_fr_ep,train_it_it_ep,train_pt_pt_ep" -last_n=10 -CHECKPOINT_FILENAME="avg_last_${last_n}_checkpoint.pt" -CHECKPOINT="_avg" -RESULTS="${MODEL_DIR}/ckpt${CHECKPOINT}" -if [ ! -d $RESULTS ]; then - mkdir -p $RESULTS -fi; - -python scripts/average_checkpoints.py \ - --inputs ${MODEL_DIR} --num-epoch-checkpoints ${last_n} \ - --output "${MODEL_DIR}/${CHECKPOINT_FILENAME}" - -fairseq-generate ${data_dir} \ - --user-dir examples/attention_head_selection/src \ - --arch 'head_selection_s2t_transformer_s' \ - --task 'speech_to_text_head_selection' \ - --train-subset ${train_subset} \ - --gen-subset ${gen_subset} \ - --path "${MODEL_DIR}/${CHECKPOINT_FILENAME}" \ - --config-yaml 'config_asr.yaml' \ - --prefix-size 1 \ - --max-tokens 40000 --beam 5 \ - --skip-invalid-size-inputs-valid-test \ - --results-path ${RESULTS} \ - --scoring wer --wer-tokenizer 13a \ - --wer-lowercase --wer-remove-punct --remove-bpe -``` - -## Citation -```bibtex -@article{gong2021pay, - title={Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling}, - author={Gong, Hongyu and Tang, Yun and Pino, Juan and Li, Xian}, - journal={arXiv preprint arXiv:2106.10840}, - year={2021} -} -''' diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/data/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/data/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/data/speech_to_text_dataset_with_domain.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/data/speech_to_text_dataset_with_domain.py deleted file mode 100644 index b82bc580497ca4a6d6ae6d052380287306e7ed7c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/data/speech_to_text_dataset_with_domain.py +++ /dev/null @@ -1,237 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 -from pathlib import Path -from typing import Dict, List, Optional - -import torch -from fairseq.data import ConcatDataset, Dictionary, FairseqDataset, ResamplingDataset -from fairseq.data.audio.data_cfg import S2TDataConfig -from fairseq.data.audio.speech_to_text_dataset import ( - SpeechToTextDataset, - SpeechToTextDatasetCreator, - SpeechToTextDatasetItem, -) - -logger = logging.getLogger(__name__) - - -@dataclass -class SpeechToTextDatasetItemWithDomain(SpeechToTextDatasetItem): - src_lang_id: Optional[torch.Tensor] = None - tgt_lang_id: Optional[torch.Tensor] = None - domain_id: Optional[torch.Tensor] = None - - -class SpeechToTextDatasetWithDomain(SpeechToTextDataset): - - def __init__( - self, - split: str, - is_train_split: bool, - cfg: S2TDataConfig, - audio_paths: List[str], - n_frames: List[int], - src_texts: Optional[List[str]] = None, - tgt_texts: Optional[List[str]] = None, - speakers: Optional[List[str]] = None, - src_langs: Optional[List[str]] = None, - tgt_langs: Optional[List[str]] = None, - ids: Optional[List[str]] = None, - tgt_dict: Optional[Dictionary] = None, - pre_tokenizer=None, - bpe_tokenizer=None, - n_frames_per_step=1, - speaker_to_id=None, - src_lang_ids: Optional[List[int]] = None, - tgt_lang_ids: Optional[List[int]] = None, - domain_ids: Optional[List[int]] = None - ): - super().__init__( - split, is_train_split, cfg, audio_paths, n_frames, - src_texts, tgt_texts, speakers, src_langs, tgt_langs, - ids, tgt_dict, pre_tokenizer, bpe_tokenizer, - n_frames_per_step, speaker_to_id - ) - assert src_lang_ids is None or len(src_lang_ids) == self.n_samples - assert tgt_lang_ids is None or len(tgt_lang_ids) == self.n_samples - assert domain_ids is None or len(domain_ids) == self.n_samples - - self.src_lang_ids = src_lang_ids - self.tgt_lang_ids = tgt_lang_ids - self.domain_ids = domain_ids - - def __getitem__(self, index: int) -> SpeechToTextDatasetItemWithDomain: - item = super().__getitem__(index) - src_lang_id = self.src_lang_ids[index] - tgt_lang_id = self.tgt_lang_ids[index] - domain_id = self.domain_ids[index] - return SpeechToTextDatasetItemWithDomain( - index=item.index, source=item.source, - target=item.target, speaker_id=item.speaker_id, - src_lang_id=src_lang_id, - tgt_lang_id=tgt_lang_id, - domain_id=domain_id - ) - - def collater( - self, samples: List[SpeechToTextDatasetItem], return_order: bool = False - ) -> Dict: - if len(samples) == 0: - return {} - out = super().collater(samples, return_order=True) - order = out["order"] - src_lang_ids = torch.tensor([x.src_lang_id for x in samples], dtype=torch.long).index_select(0, order) - tgt_lang_ids = torch.tensor([x.tgt_lang_id for x in samples], dtype=torch.long).index_select(0, order) - domain_ids = torch.tensor([x.domain_id for x in samples], dtype=torch.long).index_select(0, order) - - out["src_lang_ids"] = src_lang_ids - out["tgt_lang_ids"] = tgt_lang_ids - out["domain_ids"] = domain_ids - if not return_order: - del out["order"] - return out - - -class SpeechToTextDatasetCreatorWithDomain(SpeechToTextDatasetCreator): - KEY_SRC_LANG_ID, KEY_TGT_LANG_ID = "src_lang_id", "tgt_lang_id" - KEY_DOMAIN_ID = "domain_id" - # default values - DEFAULT_SRC_LANG_ID, DEFAULT_TGT_LANG_ID, DEFAULT_DOMAIN_ID = 0, 0, 0 - - @classmethod - def _from_list( - cls, - split_name: str, - is_train_split, - samples: List[Dict], - cfg: S2TDataConfig, - tgt_dict, - pre_tokenizer, - bpe_tokenizer, - n_frames_per_step, - speaker_to_id - ) -> SpeechToTextDatasetWithDomain: - audio_root = Path(cfg.audio_root) - ids = [s[cls.KEY_ID] for s in samples] - audio_paths = [(audio_root / s[cls.KEY_AUDIO]).as_posix() for s in samples] - n_frames = [int(s[cls.KEY_N_FRAMES]) for s in samples] - tgt_texts = [s[cls.KEY_TGT_TEXT] for s in samples] - src_texts = [s.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for s in samples] - speakers = [s.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for s in samples] - src_langs = [s.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for s in samples] - tgt_langs = [s.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for s in samples] - src_lang_ids = [s.get(cls.KEY_SRC_LANG_ID, cls.DEFAULT_SRC_LANG_ID) for s in samples] - tgt_lang_ids = [s.get(cls.KEY_TGT_LANG_ID, cls.DEFAULT_TGT_LANG_ID) for s in samples] - domain_ids = [s.get(cls.KEY_DOMAIN_ID, cls.DEFAULT_DOMAIN_ID) for s in samples] - return SpeechToTextDatasetWithDomain( - split_name, - is_train_split, - cfg, - audio_paths, - n_frames, - src_texts=src_texts, - tgt_texts=tgt_texts, - speakers=speakers, - src_langs=src_langs, - tgt_langs=tgt_langs, - ids=ids, - tgt_dict=tgt_dict, - pre_tokenizer=pre_tokenizer, - bpe_tokenizer=bpe_tokenizer, - n_frames_per_step=n_frames_per_step, - speaker_to_id=speaker_to_id, - src_lang_ids=src_lang_ids, - tgt_lang_ids=tgt_lang_ids, - domain_ids=domain_ids - ) - - @classmethod - def _load_samples_from_tsv( - cls, - root: str, - split: str, - src_lang_map, - tgt_lang_map, - domain_map - ): - # metadata from split - _, src_lang, tgt_lang, domain = split.split("_") - src_lang_id = src_lang_map[src_lang] - tgt_lang_id = tgt_lang_map[tgt_lang] - domain_id = domain_map[domain] - - samples = SpeechToTextDatasetCreator._load_samples_from_tsv(root, split) - for s in samples: - s.update({ - cls.KEY_SRC_LANG_ID: src_lang_id, - cls.KEY_TGT_LANG_ID: tgt_lang_id, - cls.KEY_DOMAIN_ID: domain_id - }) - return samples - - @classmethod - def _from_tsv( - cls, - root: str, - cfg: S2TDataConfig, - split: str, - tgt_dict, - is_train_split: bool, - pre_tokenizer, - bpe_tokenizer, - n_frames_per_step, - speaker_to_id, - src_lang_map: Dict[str, int], - tgt_lang_map: Dict[str, int], - domain_map: Dict[str, int] - ) -> SpeechToTextDatasetItemWithDomain: - samples = cls._load_samples_from_tsv( - root, split, src_lang_map, - tgt_lang_map, domain_map - ) - return cls._from_list( - split, is_train_split, samples, cfg, tgt_dict, pre_tokenizer, - bpe_tokenizer, n_frames_per_step, speaker_to_id - ) - - @classmethod - def from_tsv( - cls, - root: str, - cfg: S2TDataConfig, - splits: str, - tgt_dict, - pre_tokenizer, - bpe_tokenizer, - is_train_split: bool, - epoch: int, - seed: int, - src_lang_map: Dict[str, int], - tgt_lang_map: Dict[str, int], - domain_map: Dict[str, int], - n_frames_per_step: int = 1, - speaker_to_id=None - ) -> SpeechToTextDatasetWithDomain: - datasets = [ - cls._from_tsv( - root, cfg, split, tgt_dict, is_train_split, pre_tokenizer, bpe_tokenizer, n_frames_per_step, speaker_to_id, src_lang_map, tgt_lang_map, domain_map - ) - for split in splits.split(",") - ] - - if is_train_split and len(datasets) > 1 and cfg.sampling_alpha != 1.0: - # temperature-based sampling - size_ratios = cls.get_size_ratios(datasets, alpha=cfg.sampling_alpha) - datasets = [ - ResamplingDataset( - d, size_ratio=r, seed=seed, epoch=epoch, replace=(r >= 1.0) - ) - for r, d in zip(size_ratios, datasets) - ] - - return ConcatDataset(datasets) if len(datasets) > 1 else datasets[0] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/loss/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/loss/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/loss/attention_head_selection.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/loss/attention_head_selection.py deleted file mode 100644 index 4ba33954d0171572111eca94ef39e0e9a683e0ed..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/loss/attention_head_selection.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 torch.nn.modules.loss import _Loss - - -class HeadSelectionLoss(_Loss): - - def __init__(self, args): - super().__init__() - self.args = args - self.kl_weight = getattr(args, "kl_weight", 0.0) - - def forward(self, head_samples, sample_sizes, prior=0.5, eps=1e-7): - """ - head_scores: (num_tasks, num_layers, num_heads) - sample_sizes: (num_tasks, ) - """ - kl_loss = (head_samples * (torch.log(head_samples + eps) - math.log(prior))).sum(-1).sum(-1) - kl_loss /= (torch.numel(head_samples) / head_samples.size(0)) - kl_loss = self.kl_weight * torch.matmul(kl_loss, sample_sizes) - return kl_loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/head_selection_s2t_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/head_selection_s2t_transformer.py deleted file mode 100644 index 2815f8452585c7fb0c9918af80c44b39cb70612b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/head_selection_s2t_transformer.py +++ /dev/null @@ -1,173 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 Dict, List, Optional - -import torch.nn as nn -from fairseq import checkpoint_utils -from fairseq.models import register_model, register_model_architecture -from fairseq.models.speech_to_text.s2t_transformer import ( - S2TTransformerEncoder, - S2TTransformerModel, - TransformerDecoderScriptable, -) -from fairseq.models.speech_to_text.s2t_transformer import ( - base_architecture as s2t_base_architecture, -) -from fairseq.utils import safe_hasattr -from torch import Tensor - -from ..modules.attn_head_selector import AttnHeadSelector -from ..modules.head_selection_transformer_layer import ( - HeadSelectionTransformerEncoderLayer, -) -from .head_selection_transformer import HeadSelectionTransformerDecoder - -logger = logging.getLogger(__name__) - - -@register_model("head_selection_s2t_transformer") -class HeadSelectionS2TTransformerModel(S2TTransformerModel): - """ - Head selection implemented in S2TTransformer - """ - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - @staticmethod - def add_args(parser): - S2TTransformerModel.add_args(parser) - # encoder head selection - parser.add_argument( - "--encoder-attn-head-select", - action="store_true", - default=False, - help="encoder head selection" - ) - parser.add_argument( - "--total-encoder-attention-heads", - type=int, - help="total number of encoder attention heads" - ) - # decoder self attention selection - parser.add_argument( - "--decoder-self-attn-head-select", - action="store_true", - default=False, - help="decoder self-attention head selection" - ) - # decoder-encoder attention selection - parser.add_argument( - "--dec-enc-attn-head-select", - action="store_true", - default=False, - help="decoder-encoder attention head selection" - ) - parser.add_argument( - "--total-decoder-attention-heads", - type=int, - help="total number of decoder attention heads" - ) - # selection strategy - parser.add_argument( - "--attn-head-select-strategy", - type=str, - help="attention head selection strategy, subset or group" - ) - - @classmethod - def build_encoder(cls, args): - if safe_hasattr(args, "encoder_attn_head_select") and args.encoder_attn_head_select: - encoder = HeadSelectionS2TTransformerEncoder(args) - else: - 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): - if (safe_hasattr(args, "decoder_self_attn_head_select") and args.decoder_self_attn_head_select) or (safe_hasattr(args, "dec_enc_attn_head_select") and args.dec_enc_attn_head_select): - return HeadSelectionTransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) - else: - return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens) - - -class HeadSelectionS2TTransformerEncoder(S2TTransformerEncoder): - - def __init__(self, args): - super().__init__(args) - self.attn_head_selector = AttnHeadSelector( - args.encoder_tasks, - args.encoder_layers, - args.total_encoder_attention_heads, - args.encoder_attention_heads, - args.attn_head_select_strategy, - ) - self.task_ids = None - self.transformer_layers = nn.ModuleList([ - HeadSelectionTransformerEncoderLayer(args, layer_idx, attn_head_selector=self.attn_head_selector) for layer_idx in range(args.encoder_layers) - ]) - - def set_task_ids(self, task_ids): - self.task_ids = task_ids - - def _forward(self, src_tokens, src_lengths, return_all_hiddens=False): - self.attn_head_selector.head_select(self.task_ids) - return super()._forward(src_tokens, src_lengths, return_all_hiddens) - - -class HeadSelectionTransformerDecoderScriptable(HeadSelectionTransformerDecoder): - 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, - ) - return x, None - - -@register_model_architecture(model_name="head_selection_s2t_transformer", arch_name="head_selection_s2t_transformer") -def base_architecture(args): - s2t_base_architecture(args) - args.encoder_attn_head_select = getattr(args, "encoder_attn_head_select", False) - args.decoder_self_attn_head_select = getattr(args, "decoder_self_attn_head_select", False) - args.dec_enc_attn_head_select = getattr(args, "dec_enc_attn_head_select", False) - args.total_encoder_attention_heads = getattr(args, "total_encoder_attention_heads", 8) - args.total_decoder_attention_heads = getattr(args, "total_decoder_attention_heads", 8) - args.attn_head_select_strategy = getattr(args, "attn_head_select_strategy", "group") - - -@register_model_architecture("head_selection_s2t_transformer", "head_selection_s2t_transformer_s") -def head_selection_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) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/head_selection_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/head_selection_transformer.py deleted file mode 100644 index 47b01d1dbf9a682f5b0db7cdc955d63127770569..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/models/head_selection_transformer.py +++ /dev/null @@ -1,215 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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, List, Optional - -import torch -import torch.nn as nn -from fairseq.models.transformer import ( - TransformerDecoder, - TransformerEncoder, - TransformerModel, -) -from fairseq.utils import safe_hasattr -from torch import Tensor - -from ..modules.attn_head_selector import AttnHeadSelector -from ..modules.head_selection_transformer_layer import ( - HeadSelectionTransformerDecoderLayer, - HeadSelectionTransformerEncoderLayer, -) - - -class HeadSelectionTransformerModel(TransformerModel): - def __init__(self, args, encoder, decoder): - super().__init__(args, encoder, decoder) - - @staticmethod - def add_args(parser): - TransformerModel.add_args(parser) - # encoder head selection - parser.add_argument( - "--encoder-attn-head-select", - action="store_true", - default=False, - help="encoder head selection" - ) - parser.add_argument( - "--total-encoder-attention-heads", - type=int, - help="total number of encoder attention heads" - ) - # decoder self attention - parser.add_argument( - "--decoder-self-attn-head-select", - action="store_true", - default=False, - help="decoder self-attention head selection" - ) - # decoder-encoder attention - parser.add_argument( - "--dec-enc-attn-head-select", - action="store_true", - default=False, - help="decoder-encoder attention head selection" - ) - parser.add_argument( - "--total-decoder-attention-heads", - type=int, - help="total number of decoder attention heads" - ) - # selection strategy - parser.add_argument( - "--attn-head-select-strategy", - type=str, - help="attention head selection strategy, subset or group" - ) - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - if safe_hasattr(args, "encoder_attn_head_select") and args.encoder_attn_head_select: - return HeadSelectionTransformerEncoder( - args, src_dict, embed_tokens - ) - else: - return TransformerEncoder(args, src_dict, embed_tokens) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - if (safe_hasattr(args, "decoder_self_attn_head_select") and args.decoder_self_attn_head_select) or (safe_hasattr(args, "dec_enc_attn_head_select") and args.dec_enc_attn_head_select): - return HeadSelectionTransformerDecoder( - args, tgt_dict, embed_tokens - ) - else: - return TransformerDecoder(args, tgt_dict, embed_tokens) - - -class HeadSelectionTransformerEncoder(TransformerEncoder): - - def __init__(self, args, dictionary, embed_tokens): - self.num_tasks = args.encoder_tasks - self.num_layers = args.encoder_layers - self.total_num_heads = args.total_encoder_attention_heads - self.num_heads = args.encoder_attention_heads - self.select_strategy = args.attn_head_select_strategy - - super().__init__(args, dictionary, embed_tokens) - self.attn_head_selector = AttnHeadSelector( - self.num_tasks, - self.num_layers, - self.total_num_heads, - self.num_heads, - self.select_strategy - ) - self.task_ids = None - self.layers = nn.ModuleList( - [self.build_encoder_layer(args, i) for i in range(args.encoder_layers)] - ) - - def set_task_ids(self, task_ids): - self.task_ids = task_ids - - def build_encoder_layer(self, args, layer_idx=None): - return HeadSelectionTransformerEncoderLayer( - args, - layer_idx, - attn_head_selector=self.attn_head_selector - ) - - def forward( - self, - src_tokens, - src_lengths: Optional[torch.Tensor] = None, - return_all_hiddens: bool = False, - token_embeddings: Optional[torch.Tensor] = None, - ): - self.attn_head_selector.head_select(self.task_ids) - return super().forward(src_tokens, src_lengths, return_all_hiddens, token_embeddings) - - -class HeadSelectionTransformerDecoder(TransformerDecoder): - - def __init__( - self, - args, - dictionary, - embed_tokens, - no_encoder_attn=False, - output_projection=None, - ): - self.num_tasks = args.decoder_tasks - self.num_layers = args.decoder_layers - self.total_num_heads = args.total_decoder_attention_heads - self.num_heads = args.decoder_attention_heads - self.select_strategy = args.attn_head_select_strategy - super().__init__( - args, dictionary, embed_tokens, - no_encoder_attn=no_encoder_attn, - output_projection=output_projection - ) - self.self_attn_head_selector = None - self.enc_attn_head_selector = None - if safe_hasattr(args, "decoder_self_attn_head_select") and args.decoder_self_attn_head_select: - self.self_attn_head_selector = AttnHeadSelector( - self.num_tasks, - self.num_layers, - self.total_num_heads, - self.num_heads, - self.select_strategy - ) - if safe_hasattr(args, "dec_enc_attn_head_select") and args.dec_enc_attn_head_select: - self.enc_attn_head_selector = AttnHeadSelector( - self.num_tasks, - self.num_layers, - self.total_num_heads, - self.num_heads, - self.select_strategy - ) - self.task_ids = None - self.layers = nn.ModuleList( - [ - self.build_head_selection_decoder_layer(args, no_encoder_attn, idx) for idx in range(args.decoder_layers) - ] - ) - - def set_task_ids(self, task_ids): - self.task_ids = task_ids - - def build_head_selection_decoder_layer(self, args, no_encoder_attn=False, layer_idx=None): - return HeadSelectionTransformerDecoderLayer( - args, - layer_idx, - self.self_attn_head_selector, - self.enc_attn_head_selector, - no_encoder_attn=no_encoder_attn - ) - - 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, - ): - if self.self_attn_head_selector is not None: - self.self_attn_head_selector.head_select(self.task_ids) - if self.enc_attn_head_selector is not None: - self.enc_attn_head_selector.head_select(self.task_ids) - return super().forward( - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - features_only=features_only, - full_context_alignment=full_context_alignment, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - src_lengths=src_lengths, - return_all_hiddens=return_all_hiddens - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/attn_head_selector.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/attn_head_selector.py deleted file mode 100644 index 713785a99451f1c9a54b31bc595586c50eb3bd7e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/attn_head_selector.py +++ /dev/null @@ -1,82 +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 math - -import torch -import torch.nn as nn - - -class AttnHeadSelector(nn.Module): - """ - Latent variable modeling of attention head selection - """ - def __init__( - self, num_tasks, num_layers, - total_num_heads, num_heads, - select_strategy="group", - head_select_temp=5.0 - ): - super(AttnHeadSelector, self).__init__() - self.num_tasks = num_tasks - self.num_layers = num_layers - self.total_num_heads = total_num_heads - self.num_heads = num_heads - self.select_strategy = select_strategy - self.temp = head_select_temp - - self.head_logits = torch.nn.Parameter( - torch.Tensor(self.num_tasks, self.num_layers, total_num_heads), - requires_grad=True - ) - nn.init.uniform_( - self.head_logits, a=math.log(0.01), - b=math.log(1.0) - ) - - def gumbel_sample(self, logits, tau=1.0): - gumbels1 = -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log() - gumbels2 = -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log() - gumbels1 = (logits + gumbels1 - gumbels2) / tau - y_soft = gumbels1.sigmoid() - return y_soft - - def subset_select(self, y_soft, topk, dim=-1): - top_values, top_inds = torch.topk(y_soft, k=topk, dim=dim) - top_ret = 1.0 - top_values.detach() + top_values - return top_inds.detach(), top_ret - - def group_selet(self, y_soft, topk, dim=-1): - # top_values: (num_tasks, num_layers, topk) - top_values, top_inds = torch.max( - y_soft.view(self.num_tasks, self.num_layers, -1, topk), dim=2 - ) - top_inds = top_inds * topk + torch.arange(topk, device=top_inds.device).unsqueeze(0).unsqueeze(1) - top_ret = 1.0 - top_values.detach() + top_values - return top_inds.detach(), top_ret - - def head_select(self, task_ids=None): - # gumbel_sample - self.head_samples = self.gumbel_sample(self.head_logits, tau=self.temp) - # head select - if self.select_strategy == "subset": - self.subset_heads, self.subset_weights = self.subset_select( - self.head_samples, - topk=self.num_heads, - ) - elif self.select_strategy == "group": - self.subset_heads, self.subset_weights = self.group_selet( - self.head_samples, - topk=self.num_heads, - ) - else: - raise ValueError("{} is not supported".format(self.select_strategy)) - - self.batch_subset = self.subset_heads[task_ids, :, :] - self.batch_weights = self.subset_weights[task_ids, :, :] - - def forward(self, layer_idx): - assert layer_idx is not None - batch_subset = self.batch_subset[:, layer_idx, :] - batch_weights = self.batch_weights[:, layer_idx, :] - return batch_subset, batch_weights diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/head_selection_transformer_layer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/head_selection_transformer_layer.py deleted file mode 100644 index fbf7d57345ea0e4c1f6d624b794feb1a57cd8930..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/head_selection_transformer_layer.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.modules import TransformerDecoderLayer, TransformerEncoderLayer -from fairseq.utils import safe_getattr - -from ..modules.multihead_attention_selection import MultiheadAttentionSelection - - -class HeadSelectionTransformerEncoderLayer(TransformerEncoderLayer): - - def __init__(self, args, layer_idx, attn_head_selector=None): - super().__init__(args) - self.layer_idx = layer_idx - self.self_attn = self.build_self_attention_selection( - self.embed_dim, args, attn_head_selector - ) - - def build_self_attention_selection(self, embed_dim, args, attn_head_selector=None): - return MultiheadAttentionSelection( - embed_dim, - args.total_encoder_attention_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, - layer_idx=self.layer_idx, - attn_head_selector=attn_head_selector - ) - - -class HeadSelectionTransformerDecoderLayer(TransformerDecoderLayer): - - def __init__( - self, - args, - layer_idx, - self_attn_head_selector=None, - enc_attn_head_selector=None, - no_encoder_attn=False, - add_bias_kv=False, - add_zero_attn=False, - ): - self.layer_idx = layer_idx - super().__init__(args, no_encoder_attn, add_bias_kv, add_zero_attn) - if self_attn_head_selector is not None: - self.self_attn = self.build_self_attention_selection( - self.embed_dim, args, - self_attn_head_selector=self_attn_head_selector, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn - ) - if enc_attn_head_selector is not None: - self.encoder_attn = self.build_encoder_attention_selection( - self.embed_dim, args, - enc_attn_head_selector=enc_attn_head_selector - ) - - def build_self_attention_selection( - self, embed_dim, args, self_attn_head_selector=None, - add_bias_kv=False, add_zero_attn=False - ): - return MultiheadAttentionSelection( - embed_dim, - args.total_decoder_attention_heads, - args.decoder_attention_heads, - dropout=args.attention_dropout, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - self_attention=not safe_getattr(args, "cross_self_attention"), - q_noise=self.quant_noise, - qn_block_size=self.quant_noise_block_size, - layer_idx=self.layer_idx, - attn_head_selector=self_attn_head_selector, - ) - - def build_encoder_attention_selection(self, embed_dim, args, enc_attn_head_selector=None): - return MultiheadAttentionSelection( - embed_dim, - args.total_decoder_attention_heads, - args.decoder_attention_heads, - kdim=args.encoder_embed_dim, - vdim=args.encoder_embed_dim, - dropout=args.attention_dropout, - encoder_decoder_attention=True, - q_noise=self.quant_noise, - qn_block_size=self.quant_noise_block_size, - layer_idx=self.layer_idx, - attn_head_selector=enc_attn_head_selector, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/multihead_attention_selection.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/multihead_attention_selection.py deleted file mode 100644 index a646efbfa79da4620c880cce735c33c1bfa421a1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/multihead_attention_selection.py +++ /dev/null @@ -1,356 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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, Tuple - -import torch -from fairseq import utils -from fairseq.modules.multihead_attention import MultiheadAttention -from fairseq.modules.quant_noise import quant_noise -from torch import Tensor, nn -from torch.nn import Parameter - -from ..modules.multihead_functional import multi_head_attention_forward - - -class MultiheadAttentionSelection(MultiheadAttention): - - def __init__( - self, - embed_dim, - total_num_heads, - 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, - layer_idx=0, - attn_head_selector=None - ): - super().__init__( - embed_dim, - num_heads, - kdim=kdim, - vdim=vdim, - dropout=dropout, - bias=bias, - add_bias_kv=add_bias_kv, - add_zero_attn=add_zero_attn, - self_attention=self_attention, - encoder_decoder_attention=encoder_decoder_attention, - q_noise=q_noise, - qn_block_size=qn_block_size, - ) - self.layer_idx = layer_idx - self.attn_head_selector = attn_head_selector - self.total_num_heads = total_num_heads - self.total_embed_dim = self.head_dim * total_num_heads - self.k_proj = quant_noise( - nn.Linear(self.kdim, self.total_embed_dim, bias=bias), q_noise, qn_block_size - ) - self.v_proj = quant_noise( - nn.Linear(self.vdim, self.total_embed_dim, bias=bias), q_noise, qn_block_size - ) - self.q_proj = quant_noise( - nn.Linear(embed_dim, self.total_embed_dim, bias=bias), q_noise, qn_block_size - ) - if add_bias_kv: - self.bias_k = Parameter(torch.Tensor(1, 1, self.total_embed_dim)) - self.bias_v = Parameter(torch.Tensor(1, 1, self.total_embed_dim)) - else: - self.bias_k = self.bias_v = None - self.reset_parameters() - - 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, - # subset_heads: Optional[Tensor] = None, - # subset_weights: Optional[Tensor] = None - ) -> Tuple[Tensor, Optional[Tensor]]: - if need_head_weights: - need_weights = True - - is_tpu = query.device.type == "xla" - - subset_heads, subset_weights = self.attn_head_selector(self.layer_idx) - - tgt_len, bsz, embed_dim = query.size() - src_len = tgt_len - assert list(query.size()) == [tgt_len, bsz, self.embed_dim] - if key is not None: - src_len, key_bsz, _ = key.size() - if not torch.jit.is_scripting(): - assert key_bsz == bsz - assert value is not None - assert src_len, 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() - ): - assert key is not None and value is not None - return multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.total_num_heads, - 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, - subset_heads=subset_heads, - subset_weights=subset_weights - ) - - 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: - 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 = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, self.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, - ) - - q = ( - q.contiguous() - .view(tgt_len, bsz * self.total_num_heads, self.head_dim) - .transpose(0, 1) - ) - if k is not None: - k = ( - k.contiguous() - .view(-1, bsz * self.total_num_heads, self.head_dim) - .transpose(0, 1) - ) - if v is not None: - v = ( - v.contiguous() - .view(-1, bsz * self.total_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 - prev_key = _prev_key.view(bsz * self.total_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 - prev_value = _prev_value.view(bsz * self.total_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=bsz, - src_len=k.size(1), - static_kv=static_kv, - ) - - saved_state["prev_key"] = k.view(bsz, self.total_num_heads, -1, self.head_dim) - saved_state["prev_value"] = v.view(bsz, self.total_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) == bsz - assert key_padding_mask.size(1) == src_len - - if self.add_zero_attn: - assert v is not None - src_len += 1 - k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) - v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=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, - torch.zeros(key_padding_mask.size(0), 1).type_as( - key_padding_mask - ), - ], - dim=1, - ) - - 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.total_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.total_num_heads, tgt_len, src_len) - if not is_tpu: - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2).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 - - # evaluation - if subset_heads is not None and subset_heads.numel() == 1: - subset_heads = subset_heads.repeat(bsz) - subset_weights = subset_weights.repeat(bsz) - - if subset_heads is None: - attn = torch.bmm(attn_probs, v) - else: - # training with head selection - mixed_attn = torch.bmm(attn_probs, v).contiguous().view(bsz, self.total_num_heads, tgt_len, self.head_dim) - attn = torch.stack( - [mixed_attn[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1 - ) - attn = attn * subset_weights.unsqueeze(2).unsqueeze(3) - attn = attn.contiguous().view(bsz * self.num_heads, tgt_len, self.head_dim) - - 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, embed_dim) - else: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - attn = self.out_proj(attn) - attn_weights: Optional[Tensor] = None - if need_weights: - if subset_heads is None: - attn_weights = attn_weights_float.view( - bsz, self.num_heads, tgt_len, src_len - ).transpose(1, 0) - else: - mixed_attn_weights = attn_weights_float.view( - bsz, self.total_num_heads, tgt_len, src_len - ) - attn_weights = torch.stack( - [mixed_attn_weights[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1 - ).transpose(1, 0) - if not need_head_weights: - # average attention weights over heads - attn_weights = attn_weights.mean(dim=0) - - return attn, attn_weights diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/multihead_functional.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/multihead_functional.py deleted file mode 100644 index d0eec6d82b247646afd856dfa5f38fe126898eec..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/modules/multihead_functional.py +++ /dev/null @@ -1,282 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 warnings -from typing import Optional, Tuple - -import torch -from torch import Tensor -from torch.nn.functional import ( - _in_projection_packed, - dropout, - handle_torch_function, - has_torch_function, - linear, - pad, - softmax, -) - - -def _scaled_dot_product_attention( - q: Tensor, - k: Tensor, - v: Tensor, - attn_mask: Optional[Tensor] = None, - dropout_p: float = 0.0, - bsz: int = 1, - subset_heads: Optional[Tensor] = None, - subset_weights: Optional[Tensor] = None, -) -> Tuple[Tensor, Tensor]: - B, Nt, E = q.shape - q = q / math.sqrt(E) - # B: bsz * total_num_heads - # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns) - attn = torch.bmm(q, k.transpose(-2, -1)) - if attn_mask is not None: - attn += attn_mask - attn = softmax(attn, dim=-1) - if dropout_p > 0.0: - attn = dropout(attn, p=dropout_p) - if subset_heads is None: - # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E) - output = torch.bmm(attn, v) - else: - mixed_output = torch.bmm(attn, v).contiguous().view(bsz, -1, Nt, E) - output = torch.stack( - [mixed_output[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], - dim=1 - ) - output = output * subset_weights.unsqueeze(2).unsqueeze(3) - output = output.contiguous().view(-1, Nt, E) - if subset_heads is not None: - _, Nt, Ns = attn.size() - mixed_attn = attn.view(bsz, -1, Nt, Ns) - attn = torch.stack( - [mixed_attn[torch.arange(bsz), subset_heads[:, col], :, :] for col in range(subset_heads.size(1))], dim=1 - ) - return output, attn - - -def _in_projection( - q: Tensor, - k: Tensor, - v: Tensor, - w_q: Tensor, - w_k: Tensor, - w_v: Tensor, - b_q: Optional[Tensor] = None, - b_k: Optional[Tensor] = None, - b_v: Optional[Tensor] = None, -) -> Tuple[Tensor, Tensor, Tensor]: - return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v) - - -def multi_head_attention_forward( - query: Tensor, - key: Tensor, - value: Tensor, - embed_dim_to_check: int, - total_num_heads: int, - num_heads: int, - in_proj_weight: Tensor, - in_proj_bias: Optional[Tensor], - bias_k: Optional[Tensor], - bias_v: Optional[Tensor], - add_zero_attn: bool, - dropout_p: float, - out_proj_weight: Tensor, - out_proj_bias: Optional[Tensor], - training: bool = True, - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - use_separate_proj_weight: bool = False, - q_proj_weight: Optional[Tensor] = None, - k_proj_weight: Optional[Tensor] = None, - v_proj_weight: Optional[Tensor] = None, - static_k: Optional[Tensor] = None, - static_v: Optional[Tensor] = None, - subset_heads: Optional[Tensor] = None, - subset_weights: Optional[Tensor] = None, -): - tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias) - if has_torch_function(tens_ops): - return handle_torch_function( - multi_head_attention_forward, - tens_ops, - query, - key, - value, - embed_dim_to_check, - total_num_heads, - num_heads, - in_proj_weight, - in_proj_bias, - bias_k, - bias_v, - add_zero_attn, - dropout_p, - out_proj_weight, - out_proj_bias, - training=training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - use_separate_proj_weight=use_separate_proj_weight, - q_proj_weight=q_proj_weight, - k_proj_weight=k_proj_weight, - v_proj_weight=v_proj_weight, - static_k=static_k, - static_v=static_v, - subset_heads=subset_heads, - subset_weights=subset_weights - ) - - # set up shape vars - tgt_len, bsz, embed_dim = query.shape - src_len, _, _ = key.shape - assert embed_dim == embed_dim_to_check, \ - f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}" - if isinstance(embed_dim, torch.Tensor): - # embed_dim can be a tensor when JIT tracing - head_dim = embed_dim.div(num_heads, rounding_mode='trunc') - else: - head_dim = embed_dim // num_heads - assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}" - if use_separate_proj_weight: - # allow MHA to have different embedding dimensions when separate projection weights are used - assert key.shape[:2] == value.shape[:2], \ - f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}" - else: - assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}" - - # - # compute in-projection - # - if not use_separate_proj_weight: - q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias) - else: - assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None" - assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None" - assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None" - if in_proj_bias is None: - b_q = b_k = b_v = None - else: - b_q, b_k, b_v = in_proj_bias.chunk(3) - q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v) - - # prep attention mask - if attn_mask is not None: - if attn_mask.dtype == torch.uint8: - warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.") - attn_mask = attn_mask.to(torch.bool) - else: - assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \ - f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}" - # ensure attn_mask's dim is 3 - if attn_mask.dim() == 2: - correct_2d_size = (tgt_len, src_len) - if attn_mask.shape != correct_2d_size: - raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.") - attn_mask = attn_mask.unsqueeze(0) - elif attn_mask.dim() == 3: - correct_3d_size = (bsz * total_num_heads, tgt_len, src_len) - if attn_mask.shape != correct_3d_size: - raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.") - else: - raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported") - - # prep key padding mask - if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8: - warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.") - key_padding_mask = key_padding_mask.to(torch.bool) - - # add bias along batch dimension (currently second) - if bias_k is not None and bias_v is not None: - assert static_k is None, "bias cannot be added to static key." - assert static_v is None, "bias cannot be added to static value." - k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) - if attn_mask is not None: - attn_mask = pad(attn_mask, (0, 1)) - if key_padding_mask is not None: - key_padding_mask = pad(key_padding_mask, (0, 1)) - else: - assert bias_k is None - assert bias_v is None - - # - # reshape q, k, v for multihead attention and make em batch first - # - q = q.contiguous().view(tgt_len, bsz * total_num_heads, head_dim).transpose(0, 1) - if static_k is None: - k = k.contiguous().view(k.shape[0], bsz * total_num_heads, head_dim).transpose(0, 1) - else: - # TODO finish disentangling control flow so we don't do in-projections when statics are passed - assert static_k.size(0) == bsz * total_num_heads, \ - f"expecting static_k.size(0) of {bsz * total_num_heads}, but got {static_k.size(0)}" - assert static_k.size(2) == head_dim, \ - f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}" - k = static_k - if static_v is None: - v = v.contiguous().view(v.shape[0], bsz * total_num_heads, head_dim).transpose(0, 1) - else: - # TODO finish disentangling control flow so we don't do in-projections when statics are passed - assert static_v.size(0) == bsz * total_num_heads, \ - f"expecting static_v.size(0) of {bsz * total_num_heads}, but got {static_v.size(0)}" - assert static_v.size(2) == head_dim, \ - f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}" - v = static_v - - # add zero attention along batch dimension (now first) - if add_zero_attn: - zero_attn_shape = (bsz * total_num_heads, 1, head_dim) - k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1) - v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1) - if attn_mask is not None: - attn_mask = pad(attn_mask, (0, 1)) - if key_padding_mask is not None: - key_padding_mask = pad(key_padding_mask, (0, 1)) - - # update source sequence length after adjustments - src_len = k.size(1) - - # merge key padding and attention masks - if key_padding_mask is not None: - assert key_padding_mask.shape == (bsz, src_len), \ - f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}" - key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \ - expand(-1, total_num_heads, -1, -1).reshape(bsz * total_num_heads, 1, src_len) - if attn_mask is None: - attn_mask = key_padding_mask - elif attn_mask.dtype == torch.bool: - attn_mask = attn_mask.logical_or(key_padding_mask) - else: - attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf")) - - # convert mask to float - if attn_mask is not None and attn_mask.dtype == torch.bool: - new_attn_mask = torch.zeros_like(attn_mask, dtype=torch.float) - new_attn_mask.masked_fill_(attn_mask, float("-inf")) - attn_mask = new_attn_mask - - # adjust dropout probability - if not training: - dropout_p = 0.0 - - # - # (deep breath) calculate attention and out projection - # - attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, bsz, subset_heads, subset_weights) - attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - attn_output = linear(attn_output, out_proj_weight, out_proj_bias) - - if need_weights: - # average attention weights over heads - attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) - return attn_output, attn_output_weights.sum(dim=1) / num_heads - else: - return attn_output, None diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/speech_to_text_head_selection.py b/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/speech_to_text_head_selection.py deleted file mode 100644 index a7bdd52338f1672da6321bcfedfb1f82cdf2b636..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/attention_head_selection/src/speech_to_text_head_selection.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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.optim.amp_optimizer import AMPOptimizer -from fairseq.tasks import register_task -from fairseq.tasks.speech_to_text import SpeechToTextTask - -from .data.speech_to_text_dataset_with_domain import ( - SpeechToTextDatasetCreatorWithDomain, -) -from .loss.attention_head_selection import HeadSelectionLoss - - -@register_task("speech_to_text_head_selection") -class SpeechToTextHeadSelectionTask(SpeechToTextTask): - - @classmethod - def add_args(cls, parser): - SpeechToTextTask.add_args(parser) - parser.add_argument( - "--task-type", - type=str, - default="lang", - help="task type for head selection, lang or domain" - ) - parser.add_argument( - "--kl-weight", - type=float, - default=0.0, - help="the weight of KL loss" - ) - - def __init__(self, args, tgt_dict): - super().__init__(args, tgt_dict) - self.task_type = args.task_type - assert self.task_type in ["lang", "domain"], "invalid task_type: {}, should be either lang or domain".format(self.task_type) - self.map_task_to_id(args.train_subset) - self.encoder_head_prior = float(args.decoder_attention_heads) / args.total_decoder_attention_heads - self.decoder_head_prior = float(args.encoder_attention_heads) / args.total_encoder_attention_heads - self.kl_loss = HeadSelectionLoss(args) - - def map_task_to_id(self, train_subset): - src_lang_set, tgt_lang_set, domain_set = set(), set(), set() - for split in train_subset.split(","): - seq = split.split("_") - assert len(seq) == 4, "subset {} should be in the format of train_src_tgt_domain".format(split) - _, src_lang, tgt_lang, domain = seq - src_lang_set.add(src_lang) - tgt_lang_set.add(tgt_lang) - domain_set.add(domain) - src_langs = sorted(src_lang_set) - tgt_langs = sorted(tgt_lang_set) - domains = sorted(domain_set) - self.src_lang_map = {src_lang: i for (i, src_lang) in enumerate(src_langs)} - self.tgt_lang_map = {tgt_lang: i for (i, tgt_lang) in enumerate(tgt_langs)} - self.domain_map = {domain: i for (i, domain) in enumerate(domains)} - if self.task_type == "lang": - self.encoder_tasks = len(self.src_lang_map) - self.decoder_tasks = len(self.tgt_lang_map) - elif self.task_type == "domain": - self.encoder_tasks = len(self.domain_map) - self.decoder_tasks = len(self.domain_map) - - 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] = SpeechToTextDatasetCreatorWithDomain.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, - src_lang_map=self.src_lang_map, - tgt_lang_map=self.tgt_lang_map, - domain_map=self.domain_map, - speaker_to_id=self.speaker_to_id - ) - - def build_model(self, args): - args.encoder_tasks = self.encoder_tasks - args.decoder_tasks = self.decoder_tasks - return super(SpeechToTextHeadSelectionTask, self).build_model(args) - - def get_sample_sizes(self, sample, task_ids, num_tasks): - """ - task_ids: (bsz,) - get sample sizes for each task - """ - bsz = task_ids.size(0) - mat = torch.zeros((num_tasks, bsz), device=task_ids.device) - mat[task_ids, torch.arange(bsz)] = 1.0 - ntokens = torch.sum(sample['target'] != 1, dim=-1) - sample_sizes = torch.matmul(mat, ntokens.float()) - return sample_sizes - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - model.train() - model.set_num_updates(update_num) - # task ids - if self.task_type == "lang": - encoder_task_ids = sample["src_lang_ids"] - decoder_task_ids = sample["tgt_lang_ids"] - elif self.task_type == "domain": - encoder_task_ids = sample["domain_ids"] - decoder_task_ids = sample["domain_ids"] - model.encoder.set_task_ids(encoder_task_ids) - model.decoder.set_task_ids(decoder_task_ids) - - with torch.autograd.profiler.record_function("forward"): - with torch.cuda.amp.autocast(enabled=(isinstance(optimizer, AMPOptimizer))): - loss, sample_size, logging_output = criterion(model, sample) - # KL loss - if self.args.encoder_attn_head_select: - sample_sizes = self.get_sample_sizes(sample, encoder_task_ids, self.encoder_tasks) - loss += self.kl_loss( - model.encoder.attn_head_selector.head_samples, - sample_sizes, - self.encoder_head_prior - ) - if self.args.decoder_self_attn_head_select: - sample_sizes = self.get_sample_sizes(sample, decoder_task_ids, self.decoder_tasks) - loss += self.kl_loss( - model.decoder.self_attn_head_selector.head_samples, - sample_sizes, - self.decoder_head_prior - ) - if self.args.dec_enc_attn_head_select: - sample_sizes = self.get_sample_sizes(sample, decoder_task_ids, self.decoder_tasks) - loss += self.kl_loss( - model.decoder.enc_attn_head_selector.head_sampes, - sample_sizes, - self.decoder_head_prior - ) - - 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() - # task ids - if self.task_type == "lang": - encoder_task_ids = sample["src_lang_ids"] - decoder_task_ids = sample["tgt_lang_ids"] - elif self.task_type == "domain": - encoder_task_ids = sample["domain_ids"] - decoder_task_ids = sample["domain_ids"] - model.encoder.set_task_ids(encoder_task_ids) - model.decoder.set_task_ids(decoder_task_ids) - with torch.no_grad(): - loss, sample_size, logging_output = criterion(model, sample) - return loss, sample_size, logging_output - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - with torch.no_grad(): - # task ids - if self.task_type == "lang": - encoder_task_ids = sample["src_lang_ids"][:1] - decoder_task_ids = sample["tgt_lang_ids"][:1] - elif self.task_type == "domain": - encoder_task_ids = sample["domain_ids"][:1] - decoder_task_ids = sample["domain_ids"][:1] - for model in models: - model.encoder.set_task_ids(encoder_task_ids) - model.decoder.set_task_ids(decoder_task_ids) - return generator.generate( - models, sample, prefix_tokens=prefix_tokens, constraints=constraints - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/README.md deleted file mode 100644 index 73675f1125d80f58aa824db67d8970504d4d6b2a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/README.md +++ /dev/null @@ -1,297 +0,0 @@ -# Understanding Back-Translation at Scale (Edunov et al., 2018) - -This page includes pre-trained models from the paper [Understanding Back-Translation at Scale (Edunov et al., 2018)](https://arxiv.org/abs/1808.09381). - -## Pre-trained models - -Model | Description | Dataset | Download ----|---|---|--- -`transformer.wmt18.en-de` | Transformer
([Edunov et al., 2018](https://arxiv.org/abs/1808.09381))
WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz)
See NOTE in the archive - -## Example usage (torch.hub) - -We require a few additional Python dependencies for preprocessing: -```bash -pip install subword_nmt sacremoses -``` - -Then to generate translations from the full model ensemble: -```python -import torch - -# List available models -torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt18.en-de', ... ] - -# Load the WMT'18 En-De ensemble -en2de_ensemble = torch.hub.load( - 'pytorch/fairseq', 'transformer.wmt18.en-de', - checkpoint_file='wmt18.model1.pt:wmt18.model2.pt:wmt18.model3.pt:wmt18.model4.pt:wmt18.model5.pt', - tokenizer='moses', bpe='subword_nmt') - -# The ensemble contains 5 models -len(en2de_ensemble.models) -# 5 - -# Translate -en2de_ensemble.translate('Hello world!') -# 'Hallo Welt!' -``` - -## Training your own model (WMT'18 English-German) - -The following instructions can be adapted to reproduce the models from the paper. - - -#### Step 1. Prepare parallel data and optionally train a baseline (English-German) model - -First download and preprocess the data: -```bash -# Download and prepare the data -cd examples/backtranslation/ -bash prepare-wmt18en2de.sh -cd ../.. - -# Binarize the data -TEXT=examples/backtranslation/wmt18_en_de -fairseq-preprocess \ - --joined-dictionary \ - --source-lang en --target-lang de \ - --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ - --destdir data-bin/wmt18_en_de --thresholdtgt 0 --thresholdsrc 0 \ - --workers 20 - -# Copy the BPE code into the data-bin directory for future use -cp examples/backtranslation/wmt18_en_de/code data-bin/wmt18_en_de/code -``` - -(Optionally) Train a baseline model (English-German) using just the parallel data: -```bash -CHECKPOINT_DIR=checkpoints_en_de_parallel -fairseq-train --fp16 \ - data-bin/wmt18_en_de \ - --source-lang en --target-lang de \ - --arch transformer_wmt_en_de_big --share-all-embeddings \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --max-tokens 3584 --update-freq 16 \ - --max-update 30000 \ - --save-dir $CHECKPOINT_DIR -# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a -# different number of GPUs. -``` - -Average the last 10 checkpoints: -```bash -python scripts/average_checkpoints.py \ - --inputs $CHECKPOINT_DIR \ - --num-epoch-checkpoints 10 \ - --output $CHECKPOINT_DIR/checkpoint.avg10.pt -``` - -Evaluate BLEU: -```bash -# tokenized BLEU on newstest2017: -bash examples/backtranslation/tokenized_bleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU4 = 29.57, 60.9/35.4/22.9/15.5 (BP=1.000, ratio=1.014, syslen=63049, reflen=62152) -# compare to 29.46 in Table 1, which is also for tokenized BLEU - -# generally it's better to report (detokenized) sacrebleu though: -bash examples/backtranslation/sacrebleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 29.0 60.6/34.7/22.4/14.9 (BP = 1.000 ratio = 1.013 hyp_len = 62099 ref_len = 61287) -``` - - -#### Step 2. Back-translate monolingual German data - -Train a reverse model (German-English) to do the back-translation: -```bash -CHECKPOINT_DIR=checkpoints_de_en_parallel -fairseq-train --fp16 \ - data-bin/wmt18_en_de \ - --source-lang de --target-lang en \ - --arch transformer_wmt_en_de_big --share-all-embeddings \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --max-tokens 3584 --update-freq 16 \ - --max-update 30000 \ - --save-dir $CHECKPOINT_DIR -# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a -# different number of GPUs. -``` - -Let's evaluate the back-translation (BT) model to make sure it is well trained: -```bash -bash examples/backtranslation/sacrebleu.sh \ - wmt17 \ - de-en \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint_best.py -# BLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 34.9 66.9/41.8/28.5/19.9 (BP = 0.983 ratio = 0.984 hyp_len = 63342 ref_len = 64399) -# compare to the best system from WMT'17 which scored 35.1: http://matrix.statmt.org/matrix/systems_list/1868 -``` - -Next prepare the monolingual data: -```bash -# Download and prepare the monolingual data -# By default the script samples 25M monolingual sentences, which after -# deduplication should be just over 24M sentences. These are split into 25 -# shards, each with 1M sentences (except for the last shard). -cd examples/backtranslation/ -bash prepare-de-monolingual.sh -cd ../.. - -# Binarize each shard of the monolingual data -TEXT=examples/backtranslation/wmt18_de_mono -for SHARD in $(seq -f "%02g" 0 24); do \ - fairseq-preprocess \ - --only-source \ - --source-lang de --target-lang en \ - --joined-dictionary \ - --srcdict data-bin/wmt18_en_de/dict.de.txt \ - --testpref $TEXT/bpe.monolingual.dedup.${SHARD} \ - --destdir data-bin/wmt18_de_mono/shard${SHARD} \ - --workers 20; \ - cp data-bin/wmt18_en_de/dict.en.txt data-bin/wmt18_de_mono/shard${SHARD}/; \ -done -``` - -Now we're ready to perform back-translation over the monolingual data. The -following command generates via sampling, but it's possible to use greedy -decoding (`--beam 1`), beam search (`--beam 5`), -top-k sampling (`--sampling --beam 1 --sampling-topk 10`), etc.: -```bash -mkdir backtranslation_output -for SHARD in $(seq -f "%02g" 0 24); do \ - fairseq-generate --fp16 \ - data-bin/wmt18_de_mono/shard${SHARD} \ - --path $CHECKPOINT_DIR/checkpoint_best.pt \ - --skip-invalid-size-inputs-valid-test \ - --max-tokens 4096 \ - --sampling --beam 1 \ - > backtranslation_output/sampling.shard${SHARD}.out; \ -done -``` - -After BT, use the `extract_bt_data.py` script to re-combine the shards, extract -the back-translations and apply length ratio filters: -```bash -python examples/backtranslation/extract_bt_data.py \ - --minlen 1 --maxlen 250 --ratio 1.5 \ - --output backtranslation_output/bt_data --srclang en --tgtlang de \ - backtranslation_output/sampling.shard*.out - -# Ensure lengths are the same: -# wc -l backtranslation_output/bt_data.{en,de} -# 21795614 backtranslation_output/bt_data.en -# 21795614 backtranslation_output/bt_data.de -# 43591228 total -``` - -Binarize the filtered BT data and combine it with the parallel data: -```bash -TEXT=backtranslation_output -fairseq-preprocess \ - --source-lang en --target-lang de \ - --joined-dictionary \ - --srcdict data-bin/wmt18_en_de/dict.en.txt \ - --trainpref $TEXT/bt_data \ - --destdir data-bin/wmt18_en_de_bt \ - --workers 20 - -# We want to train on the combined data, so we'll symlink the parallel + BT data -# in the wmt18_en_de_para_plus_bt directory. We link the parallel data as "train" -# and the BT data as "train1", so that fairseq will combine them automatically -# and so that we can use the `--upsample-primary` option to upsample the -# parallel data (if desired). -PARA_DATA=$(readlink -f data-bin/wmt18_en_de) -BT_DATA=$(readlink -f data-bin/wmt18_en_de_bt) -COMB_DATA=data-bin/wmt18_en_de_para_plus_bt -mkdir -p $COMB_DATA -for LANG in en de; do \ - ln -s ${PARA_DATA}/dict.$LANG.txt ${COMB_DATA}/dict.$LANG.txt; \ - for EXT in bin idx; do \ - ln -s ${PARA_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train.en-de.$LANG.$EXT; \ - ln -s ${BT_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train1.en-de.$LANG.$EXT; \ - ln -s ${PARA_DATA}/valid.en-de.$LANG.$EXT ${COMB_DATA}/valid.en-de.$LANG.$EXT; \ - ln -s ${PARA_DATA}/test.en-de.$LANG.$EXT ${COMB_DATA}/test.en-de.$LANG.$EXT; \ - done; \ -done -``` - - -#### 3. Train an English-German model over the combined parallel + BT data - -Finally we can train a model over the parallel + BT data: -```bash -CHECKPOINT_DIR=checkpoints_en_de_parallel_plus_bt -fairseq-train --fp16 \ - data-bin/wmt18_en_de_para_plus_bt \ - --upsample-primary 16 \ - --source-lang en --target-lang de \ - --arch transformer_wmt_en_de_big --share-all-embeddings \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.0007 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --max-tokens 3584 --update-freq 16 \ - --max-update 100000 \ - --save-dir $CHECKPOINT_DIR -# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a -# different number of GPUs. -``` - -Average the last 10 checkpoints: -```bash -python scripts/average_checkpoints.py \ - --inputs $CHECKPOINT_DIR \ - --num-epoch-checkpoints 10 \ - --output $CHECKPOINT_DIR/checkpoint.avg10.pt -``` - -Evaluate BLEU: -```bash -# tokenized BLEU on newstest2017: -bash examples/backtranslation/tokenized_bleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU4 = 32.35, 64.4/38.9/26.2/18.3 (BP=0.977, ratio=0.977, syslen=60729, reflen=62152) -# compare to 32.35 in Table 1, which is also for tokenized BLEU - -# generally it's better to report (detokenized) sacrebleu: -bash examples/backtranslation/sacrebleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 31.5 64.3/38.2/25.6/17.6 (BP = 0.971 ratio = 0.971 hyp_len = 59515 ref_len = 61287) -``` - - -## Citation -```bibtex -@inproceedings{edunov2018backtranslation, - title = {Understanding Back-Translation at Scale}, - author = {Edunov, Sergey and Ott, Myle and Auli, Michael and Grangier, David}, - booktitle = {Conference of the Association for Computational Linguistics (ACL)}, - year = 2018, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/deduplicate_lines.py b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/deduplicate_lines.py deleted file mode 100644 index 50e458328c80b71c42a66d473381ca7e98d294da..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/deduplicate_lines.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/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 fileinput -import hashlib -import sys -from multiprocessing import Pool - - -def get_hashes_and_lines(raw_line): - hash = hashlib.md5(raw_line).hexdigest() - return hash, raw_line - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--workers", type=int, default=10) - parser.add_argument("files", nargs="*", help="input files") - args = parser.parse_args() - - seen = set() - with fileinput.input(args.files, mode="rb") as h: - pool = Pool(args.workers) - results = pool.imap_unordered(get_hashes_and_lines, h, 1000) - for i, (hash, raw_line) in enumerate(results): - if hash not in seen: - seen.add(hash) - sys.stdout.buffer.write(raw_line) - 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) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/extract_bt_data.py b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/extract_bt_data.py deleted file mode 100644 index e766391e873d0d9a9561d67d5864934b2fad0681..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/extract_bt_data.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) Facebook, Inc. and 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 fileinput - -from tqdm import tqdm - - -def main(): - parser = argparse.ArgumentParser( - description=( - "Extract back-translations from the stdout of fairseq-generate. " - "If there are multiply hypotheses for a source, we only keep the first one. " - ) - ) - parser.add_argument("--output", required=True, help="output prefix") - parser.add_argument( - "--srclang", required=True, help="source language (extracted from H-* lines)" - ) - parser.add_argument( - "--tgtlang", required=True, help="target language (extracted from S-* lines)" - ) - parser.add_argument("--minlen", type=int, help="min length filter") - parser.add_argument("--maxlen", type=int, help="max length filter") - parser.add_argument("--ratio", type=float, help="ratio filter") - parser.add_argument("files", nargs="*", help="input files") - args = parser.parse_args() - - def validate(src, tgt): - srclen = len(src.split(" ")) if src != "" else 0 - tgtlen = len(tgt.split(" ")) if tgt != "" else 0 - if ( - (args.minlen is not None and (srclen < args.minlen or tgtlen < args.minlen)) - or ( - args.maxlen is not None - and (srclen > args.maxlen or tgtlen > args.maxlen) - ) - or ( - args.ratio is not None - and (max(srclen, tgtlen) / float(min(srclen, tgtlen)) > args.ratio) - ) - ): - return False - return True - - def safe_index(toks, index, default): - try: - return toks[index] - except IndexError: - return default - - with open(args.output + "." + args.srclang, "w") as src_h, open( - args.output + "." + args.tgtlang, "w" - ) as tgt_h: - for line in tqdm(fileinput.input(args.files)): - if line.startswith("S-"): - tgt = safe_index(line.rstrip().split("\t"), 1, "") - elif line.startswith("H-"): - if tgt is not None: - src = safe_index(line.rstrip().split("\t"), 2, "") - if validate(src, tgt): - print(src, file=src_h) - print(tgt, file=tgt_h) - tgt = None - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/prepare-de-monolingual.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/prepare-de-monolingual.sh deleted file mode 100644 index 5e67b2b3bcf27d3436031453e796e58a0ae79ec4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/prepare-de-monolingual.sh +++ /dev/null @@ -1,98 +0,0 @@ -#!/bin/bash - -SCRIPTS=mosesdecoder/scripts -TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl -NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl -BPEROOT=subword-nmt/subword_nmt - - -BPE_CODE=wmt18_en_de/code -SUBSAMPLE_SIZE=25000000 -LANG=de - - -OUTDIR=wmt18_${LANG}_mono -orig=orig -tmp=$OUTDIR/tmp -mkdir -p $OUTDIR $tmp - - -URLS=( - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2007.de.shuffled.gz" - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2008.de.shuffled.gz" - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2009.de.shuffled.gz" - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2010.de.shuffled.gz" - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2011.de.shuffled.gz" - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.de.shuffled.gz" - "http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2013.de.shuffled.gz" - "http://www.statmt.org/wmt15/training-monolingual-news-crawl-v2/news.2014.de.shuffled.v2.gz" - "http://data.statmt.org/wmt16/translation-task/news.2015.de.shuffled.gz" - "http://data.statmt.org/wmt17/translation-task/news.2016.de.shuffled.gz" - "http://data.statmt.org/wmt18/translation-task/news.2017.de.shuffled.deduped.gz" -) -FILES=( - "news.2007.de.shuffled.gz" - "news.2008.de.shuffled.gz" - "news.2009.de.shuffled.gz" - "news.2010.de.shuffled.gz" - "news.2011.de.shuffled.gz" - "news.2012.de.shuffled.gz" - "news.2013.de.shuffled.gz" - "news.2014.de.shuffled.v2.gz" - "news.2015.de.shuffled.gz" - "news.2016.de.shuffled.gz" - "news.2017.de.shuffled.deduped.gz" -) - - -cd $orig -for ((i=0;i<${#URLS[@]};++i)); do - file=${FILES[i]} - if [ -f $file ]; then - echo "$file already exists, skipping download" - else - url=${URLS[i]} - wget "$url" - fi -done -cd .. - - -if [ -f $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} ]; then - echo "found monolingual sample, skipping shuffle/sample/tokenize" -else - gzip -c -d -k $(for FILE in "${FILES[@]}"; do echo $orig/$FILE; done) \ - | shuf -n $SUBSAMPLE_SIZE \ - | perl $NORM_PUNC $LANG \ - | perl $REM_NON_PRINT_CHAR \ - | perl $TOKENIZER -threads 8 -a -l $LANG \ - > $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} -fi - - -if [ -f $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} ]; then - echo "found BPE monolingual sample, skipping BPE step" -else - python $BPEROOT/apply_bpe.py -c $BPE_CODE \ - < $tmp/monolingual.${SUBSAMPLE_SIZE}.${LANG} \ - > $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} -fi - - -if [ -f $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} ]; then - echo "found deduplicated monolingual sample, skipping deduplication step" -else - python deduplicate_lines.py $tmp/bpe.monolingual.${SUBSAMPLE_SIZE}.${LANG} \ - > $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} -fi - - -if [ -f $OUTDIR/bpe.monolingual.dedup.00.de ]; then - echo "found sharded data, skipping sharding step" -else - split --lines 1000000 --numeric-suffixes \ - --additional-suffix .${LANG} \ - $tmp/bpe.monolingual.dedup.${SUBSAMPLE_SIZE}.${LANG} \ - $OUTDIR/bpe.monolingual.dedup. -fi diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/prepare-wmt18en2de.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/prepare-wmt18en2de.sh deleted file mode 100644 index f6fd275307db50ca84c299440ae02dce49064030..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/prepare-wmt18en2de.sh +++ /dev/null @@ -1,135 +0,0 @@ -#!/bin/bash -# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh - -echo 'Cloning Moses github repository (for tokenization scripts)...' -git clone https://github.com/moses-smt/mosesdecoder.git - -echo 'Cloning Subword NMT repository (for BPE pre-processing)...' -git clone https://github.com/rsennrich/subword-nmt.git - -SCRIPTS=mosesdecoder/scripts -TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl -CLEAN=$SCRIPTS/training/clean-corpus-n.perl -NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl -BPEROOT=subword-nmt/subword_nmt -BPE_TOKENS=32000 - -URLS=( - "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" - "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" - "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz" - "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz" - "http://data.statmt.org/wmt17/translation-task/dev.tgz" - "http://statmt.org/wmt14/test-full.tgz" -) -FILES=( - "training-parallel-europarl-v7.tgz" - "training-parallel-commoncrawl.tgz" - "training-parallel-nc-v13.tgz" - "rapid2016.tgz" - "dev.tgz" - "test-full.tgz" -) -CORPORA=( - "training/europarl-v7.de-en" - "commoncrawl.de-en" - "training-parallel-nc-v13/news-commentary-v13.de-en" - "rapid2016.de-en" -) - -if [ ! -d "$SCRIPTS" ]; then - echo "Please set SCRIPTS variable correctly to point to Moses scripts." - exit 1 -fi - -OUTDIR=wmt18_en_de - -src=en -tgt=de -lang=en-de -prep=$OUTDIR -tmp=$prep/tmp -orig=orig - -mkdir -p $orig $tmp $prep - -cd $orig - -for ((i=0;i<${#URLS[@]};++i)); do - file=${FILES[i]} - if [ -f $file ]; then - echo "$file already exists, skipping download" - else - url=${URLS[i]} - wget "$url" - if [ -f $file ]; then - echo "$url successfully downloaded." - else - echo "$url not successfully downloaded." - exit 1 - fi - if [ ${file: -4} == ".tgz" ]; then - tar zxvf $file - elif [ ${file: -4} == ".tar" ]; then - tar xvf $file - fi - fi -done -cd .. - -echo "pre-processing train data..." -for l in $src $tgt; do - rm $tmp/train.tags.$lang.tok.$l - for f in "${CORPORA[@]}"; do - cat $orig/$f.$l | \ - perl $NORM_PUNC $l | \ - perl $REM_NON_PRINT_CHAR | \ - perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l - done -done - -echo "pre-processing test data..." -for l in $src $tgt; do - if [ "$l" == "$src" ]; then - t="src" - else - t="ref" - fi - grep '\s*//g' | \ - sed -e 's/\s*<\/seg>\s*//g' | \ - sed -e "s/\’/\'/g" | \ - perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l - echo "" -done - -echo "splitting train and valid..." -for l in $src $tgt; do - awk '{if (NR%100 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l - awk '{if (NR%100 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l -done - -TRAIN=$tmp/train.de-en -BPE_CODE=$prep/code -rm -f $TRAIN -for l in $src $tgt; do - cat $tmp/train.$l >> $TRAIN -done - -echo "learn_bpe.py on ${TRAIN}..." -python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE - -for L in $src $tgt; do - for f in train.$L valid.$L test.$L; do - echo "apply_bpe.py to ${f}..." - python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f - done -done - -perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250 -perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250 - -for L in $src $tgt; do - cp $tmp/bpe.test.$L $prep/test.$L -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/sacrebleu.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/sacrebleu.sh deleted file mode 100644 index a70da23f48e2699297799611412783d4560dc45a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/sacrebleu.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash - -if [ $# -ne 5 ]; then - echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]" - exit -fi - - -DATASET=$1 -LANGPAIR=$2 -DATABIN=$3 -BPECODE=$4 -MODEL=$5 - -SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1) -TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2) - - -BPEROOT=examples/backtranslation/subword-nmt/subword_nmt -if [ ! -e $BPEROOT ]; then - BPEROOT=subword-nmt/subword_nmt - if [ ! -e $BPEROOT ]; then - echo 'Cloning Subword NMT repository (for BPE pre-processing)...' - git clone https://github.com/rsennrich/subword-nmt.git - fi -fi - - -sacrebleu -t $DATASET -l $LANGPAIR --echo src \ -| sacremoses tokenize -a -l $SRCLANG -q \ -| python $BPEROOT/apply_bpe.py -c $BPECODE \ -| fairseq-interactive $DATABIN --path $MODEL \ - -s $SRCLANG -t $TGTLANG \ - --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \ -| grep ^H- | cut -f 3- \ -| sacremoses detokenize -l $TGTLANG -q \ -| sacrebleu -t $DATASET -l $LANGPAIR diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/tokenized_bleu.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/tokenized_bleu.sh deleted file mode 100644 index c6d6aaa193f6059299bc98909324fe4b9b060372..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/backtranslation/tokenized_bleu.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash - -if [ $# -ne 5 ]; then - echo "usage: $0 [dataset=wmt14/full] [langpair=en-de] [databin] [bpecode] [model]" - exit -fi - - -DATASET=$1 -LANGPAIR=$2 -DATABIN=$3 -BPECODE=$4 -MODEL=$5 - -SRCLANG=$(echo $LANGPAIR | cut -d '-' -f 1) -TGTLANG=$(echo $LANGPAIR | cut -d '-' -f 2) - - -BPEROOT=examples/backtranslation/subword-nmt/subword_nmt -if [ ! -e $BPEROOT ]; then - BPEROOT=subword-nmt/subword_nmt - if [ ! -e $BPEROOT ]; then - echo 'Cloning Subword NMT repository (for BPE pre-processing)...' - git clone https://github.com/rsennrich/subword-nmt.git - fi -fi - - -TMP_REF=$(mktemp) - -sacrebleu -t $DATASET -l $LANGPAIR --echo ref -q \ -| sacremoses normalize -l $TGTLANG -q \ -| sacremoses tokenize -a -l $TGTLANG -q \ -> $TMP_REF - -sacrebleu -t $DATASET -l $LANGPAIR --echo src -q \ -| sacremoses normalize -l $SRCLANG -q \ -| sacremoses tokenize -a -l $SRCLANG -q \ -| python $BPEROOT/apply_bpe.py -c $BPECODE \ -| fairseq-interactive $DATABIN --path $MODEL \ - -s $SRCLANG -t $TGTLANG \ - --beam 5 --remove-bpe --buffer-size 1024 --max-tokens 8000 \ -| grep ^H- | cut -f 3- \ -| fairseq-score --ref $TMP_REF - -rm -f $TMP_REF diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.glue.md b/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.glue.md deleted file mode 100644 index a010934e1e6dec491eb1c704ec02ba7405760510..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.glue.md +++ /dev/null @@ -1,99 +0,0 @@ -# Fine-tuning BART on GLUE tasks - -### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands: -```bash -wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py -python download_glue_data.py --data_dir glue_data --tasks all -``` - -### 2) Preprocess GLUE task data (same as RoBERTa): -```bash -./examples/roberta/preprocess_GLUE_tasks.sh glue_data -``` -`glue_task_name` is one of the following: -`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}` -Use `ALL` for preprocessing all the glue tasks. - -### 3) Fine-tuning on GLUE task: -Example fine-tuning cmd for `RTE` task -```bash -TOTAL_NUM_UPDATES=2036 # 10 epochs through RTE for bsz 16 -WARMUP_UPDATES=61 # 6 percent of the number of updates -LR=1e-05 # Peak LR for polynomial LR scheduler. -NUM_CLASSES=2 -MAX_SENTENCES=16 # Batch size. -BART_PATH=/path/to/bart/model.pt - -CUDA_VISIBLE_DEVICES=0,1 fairseq-train RTE-bin/ \ - --restore-file $BART_PATH \ - --batch-size $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --add-prev-output-tokens \ - --layernorm-embedding \ - --share-all-embeddings \ - --share-decoder-input-output-embed \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 \ - --arch bart_large \ - --criterion sentence_prediction \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --find-unused-parameters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; -``` - -For each of the GLUE task, you will need to use following cmd-line arguments: - -Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B ----|---|---|---|---|---|---|---|--- -`--num-classes` | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 1 -`--lr` | 5e-6 | 1e-5 | 1e-5 | 1e-5 | 5e-6 | 2e-5 | 2e-5 | 2e-5 -`bsz` | 128 | 32 | 32 | 32 | 128 | 64 | 64 | 32 -`--total-num-update` | 30968 | 33112 | 113272 | 1018 | 5233 | 1148 | 1334 | 1799 -`--warmup-updates` | 1858 | 1986 | 6796 | 61 | 314 | 68 | 80 | 107 - -For `STS-B` additionally add `--regression-target --best-checkpoint-metric loss` and remove `--maximize-best-checkpoint-metric`. - -**Note:** - -a) `--total-num-updates` is used by `--polynomial_decay` scheduler and is calculated for `--max-epoch=10` and `--batch-size=32/64/128` depending on the task. - -b) Above cmd-args and hyperparams are tested on Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. - -### Inference on GLUE task -After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: - -```python -from fairseq.models.bart import BARTModel - -bart = BARTModel.from_pretrained( - 'checkpoints/', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='RTE-bin' -) - -label_fn = lambda label: bart.task.label_dictionary.string( - [label + bart.task.label_dictionary.nspecial] -) -ncorrect, nsamples = 0, 0 -bart.cuda() -bart.eval() -with open('glue_data/RTE/dev.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[1], tokens[2], tokens[3] - tokens = bart.encode(sent1, sent2) - prediction = bart.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_fn(prediction) - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.md deleted file mode 100644 index 4050a724ee6a2f20c9998a95df48c58b64764ab1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.md +++ /dev/null @@ -1,228 +0,0 @@ -# BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension - -[https://arxiv.org/abs/1910.13461](https://arxiv.org/abs/1910.13461) - -## Introduction - -BART is sequence-to-sequence model trained with denoising as pretraining objective. We show that this pretraining objective is more generic and show that we can match [RoBERTa](../roberta) results on SQuAD and GLUE and gain state-of-the-art results on summarization (XSum, CNN dataset), long form generative question answering (ELI5) and dialog response genration (ConvAI2). See the associated paper for more details. - -## Pre-trained models - -Model | Description | # params | Download ----|---|---|--- -`bart.base` | BART model with 6 encoder and decoder layers | 140M | [bart.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz) -`bart.large` | BART model with 12 encoder and decoder layers | 400M | [bart.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz) -`bart.large.mnli` | `bart.large` finetuned on `MNLI` | 400M | [bart.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz) -`bart.large.cnn` | `bart.large` finetuned on `CNN-DM` | 400M | [bart.large.cnn.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz) -`bart.large.xsum` | `bart.large` finetuned on `Xsum` | 400M | [bart.large.xsum.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz) - -## Results - -**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)** -_(dev set, single model, single-task finetuning)_ - -Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B ----|---|---|---|---|---|---|---|--- -`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4 -`bart.large` | 89.9 | 94.9 | 92.5 | 87.0 | 96.6 | 90.4 | 62.8 | 91.2 - -**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)** -_(dev set, no additional data used)_ - -Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1 ----|---|--- -`roberta.large` | 88.9/94.6 | 86.5/89.4 -`bart.large` | 88.8/94.6 | 86.1/89.2 - -**[CNN/Daily Mail](http://nlpprogress.com/english/summarization.html)** -_(test set, no additional data used)_ - -Model | R1 | R2 | RL ----|---|---|--- -`BERTSUMEXTABS` | 42.13 | 19.60 | 39.18 -`bart.large` | 44.16 | 21.28 | 40.90 - -## Example usage - -##### Load BART from torch.hub (PyTorch >= 1.1): -```python -import torch -bart = torch.hub.load('pytorch/fairseq', 'bart.large') -bart.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Load BART (for PyTorch 1.0 or custom models): -```python -# Download bart.large model -wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz -tar -xzvf bart.large.tar.gz - -# Load the model in fairseq -from fairseq.models.bart import BARTModel -bart = BARTModel.from_pretrained('/path/to/bart.large', checkpoint_file='model.pt') -bart.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Apply Byte-Pair Encoding (BPE) to input text: -```python -tokens = bart.encode('Hello world!') -assert tokens.tolist() == [0, 31414, 232, 328, 2] -bart.decode(tokens) # 'Hello world!' -``` - -##### Extract features from BART: -```python -# Extract the last layer's features -last_layer_features = bart.extract_features(tokens) -assert last_layer_features.size() == torch.Size([1, 5, 1024]) - -# Extract all layer's features from decoder (layer 0 is the embedding layer) -all_layers = bart.extract_features(tokens, return_all_hiddens=True) -assert len(all_layers) == 13 -assert torch.all(all_layers[-1] == last_layer_features) -``` - -##### Use BART for sentence-pair classification tasks: -```python -# Download BART already finetuned for MNLI -bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli') -bart.eval() # disable dropout for evaluation - -# Encode a pair of sentences and make a prediction -tokens = bart.encode('BART is a seq2seq model.', 'BART is not sequence to sequence.') -bart.predict('mnli', tokens).argmax() # 0: contradiction - -# Encode another pair of sentences -tokens = bart.encode('BART is denoising autoencoder.', 'BART is version of autoencoder.') -bart.predict('mnli', tokens).argmax() # 2: entailment -``` - -##### Register a new (randomly initialized) classification head: -```python -bart.register_classification_head('new_task', num_classes=3) -logprobs = bart.predict('new_task', tokens) -``` - -##### Batched prediction: -```python -import torch -from fairseq.data.data_utils import collate_tokens - -bart = torch.hub.load('pytorch/fairseq', 'bart.large.mnli') -bart.eval() - -batch_of_pairs = [ - ['BART is a seq2seq model.', 'BART is not sequence to sequence.'], - ['BART is denoising autoencoder.', 'BART is version of autoencoder.'], -] - -batch = collate_tokens( - [bart.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1 -) - -logprobs = bart.predict('mnli', batch) -print(logprobs.argmax(dim=1)) -# tensor([0, 2]) -``` - -##### Using the GPU: -```python -bart.cuda() -bart.predict('new_task', tokens) -``` - -#### Filling masks: - -BART can be used to fill multiple `` tokens in the input. -```python -bart = torch.hub.load('pytorch/fairseq', 'bart.base') -bart.eval() -bart.fill_mask(['The cat on the .'], topk=3, beam=10) -# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))]] -``` - -Note that by default we enforce the output length to match the input length. -This can be disabled by setting ``match_source_len=False``: -``` -bart.fill_mask(['The cat on the .'], topk=3, beam=10, match_source_len=False) -# [[('The cat was on the ground.', tensor(-0.6185)), ('The cat was asleep on the couch.', tensor(-0.6276)), ('The cat was on the floor.', tensor(-0.6800))]] -``` - -Example code to fill masks for a batch of sentences using GPU -``` -bart.cuda() -bart.fill_mask(['The cat on the .', 'The dog on the .'], topk=3, beam=10) -# [[('The cat was on the ground.', tensor(-0.6183)), ('The cat was on the floor.', tensor(-0.6798)), ('The cat sleeps on the couch.', tensor(-0.6830))], [('The dog was on the ground.', tensor(-0.6190)), ('The dog lay on the ground.', tensor(-0.6711)), -('The dog was asleep on the couch', tensor(-0.6796))]] -``` - -#### Evaluating the `bart.large.mnli` model: - -Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set. -```python -label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'} -ncorrect, nsamples = 0, 0 -bart.cuda() -bart.eval() -with open('glue_data/MNLI/dev_matched.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[8], tokens[9], tokens[-1] - tokens = bart.encode(sent1, sent2) - prediction = bart.predict('mnli', tokens).argmax().item() - prediction_label = label_map[prediction] - ncorrect += int(prediction_label == target) - nsamples += 1 - print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# Expected output: 0.9010 -``` - -#### Evaluating the `bart.large.cnn` model: -- Follow instructions [here](https://github.com/abisee/cnn-dailymail) to download and process into data-files such that `test.source` and `test.target` has one line for each non-tokenized sample. -- For simpler preprocessing, you can also `wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz`, although there is no guarantee of identical scores -- `huggingface/transformers` has a simpler interface that supports [single-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_eval.py) and [multi-gpu](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/run_distributed_eval.py) beam search. - In `huggingface/transformers`, the BART models' paths are `facebook/bart-large-cnn` and `facebook/bart-large-xsum`. - -In `fairseq`, summaries can be generated using: - -```bash -cp data-bin/cnn_dm/dict.source.txt checkpoints/ -python examples/bart/summarize.py \ - --model-dir pytorch/fairseq \ - --model-file bart.large.cnn \ - --src cnn_dm/test.source \ - --out cnn_dm/test.hypo -``` - -For calculating rouge, install `files2rouge` from [here](https://github.com/pltrdy/files2rouge). - -```bash -export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar - -# Tokenize hypothesis and target files. -cat test.hypo | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.tokenized -cat test.target | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > test.hypo.target -files2rouge test.hypo.tokenized test.hypo.target -# Expected output: (ROUGE-2 Average_F: 0.21238) -``` - - -## Finetuning - -- [Finetuning on GLUE](README.glue.md) -- [Finetuning on CNN-DM](README.summarization.md) - -## Citation - -```bibtex -@article{lewis2019bart, - title = {BART: Denoising Sequence-to-Sequence Pre-training for Natural -Language Generation, Translation, and Comprehension}, - author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and - Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov - and Luke Zettlemoyer }, - journal={arXiv preprint arXiv:1910.13461}, - year = {2019}, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.summarization.md b/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.summarization.md deleted file mode 100644 index 8727584f2b2bdd880c6cd3abbf39b75dfbf4a67c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/README.summarization.md +++ /dev/null @@ -1,102 +0,0 @@ -# Fine-tuning BART on CNN-Dailymail summarization task - -### 1) Download the CNN and Daily Mail data and preprocess it into data files with non-tokenized cased samples. - -Follow the instructions [here](https://github.com/abisee/cnn-dailymail) to download the original CNN and Daily Mail datasets. To preprocess the data, refer to the pointers in [this issue](https://github.com/pytorch/fairseq/issues/1391) or check out the code [here](https://github.com/artmatsak/cnn-dailymail). - -Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to download the original Extreme Summarization datasets, or check out the code [here](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset), Please keep the raw dataset and make sure no tokenization nor BPE on the dataset. - -### 2) BPE preprocess: - -```bash -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' - -TASK=cnn_dm -for SPLIT in train val -do - for LANG in source target - do - python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json encoder.json \ - --vocab-bpe vocab.bpe \ - --inputs "$TASK/$SPLIT.$LANG" \ - --outputs "$TASK/$SPLIT.bpe.$LANG" \ - --workers 60 \ - --keep-empty; - done -done -``` - -### 3) Binarize dataset: -```bash -fairseq-preprocess \ - --source-lang "source" \ - --target-lang "target" \ - --trainpref "${TASK}/train.bpe" \ - --validpref "${TASK}/val.bpe" \ - --destdir "${TASK}-bin/" \ - --workers 60 \ - --srcdict dict.txt \ - --tgtdict dict.txt; -``` - -### 4) Fine-tuning on CNN-DM summarization task: -Example fine-tuning CNN-DM -```bash -TOTAL_NUM_UPDATES=20000 -WARMUP_UPDATES=500 -LR=3e-05 -MAX_TOKENS=2048 -UPDATE_FREQ=4 -BART_PATH=/path/to/bart/model.pt - -CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train cnn_dm-bin \ - --restore-file $BART_PATH \ - --max-tokens $MAX_TOKENS \ - --task translation \ - --source-lang source --target-lang target \ - --truncate-source \ - --layernorm-embedding \ - --share-all-embeddings \ - --share-decoder-input-output-embed \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --arch bart_large \ - --criterion label_smoothed_cross_entropy \ - --label-smoothing 0.1 \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \ - --clip-norm 0.1 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --update-freq $UPDATE_FREQ \ - --skip-invalid-size-inputs-valid-test \ - --find-unused-parameters; -``` -Above is expected to run on `1` node with `8 32gb-V100`. -Expected training time is about `5 hours`. Training time can be reduced with distributed training on `4` nodes and `--update-freq 1`. - -Use TOTAL_NUM_UPDATES=15000 UPDATE_FREQ=2 for Xsum task - -### Inference for CNN-DM test data using above trained checkpoint. -After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using `eval_cnn.py`, for example - -```bash -cp data-bin/cnn_dm/dict.source.txt checkpoints/ -python examples/bart/summarize.py \ - --model-dir checkpoints \ - --model-file checkpoint_best.pt \ - --src cnn_dm/test.source \ - --out cnn_dm/test.hypo -``` -For XSUM, which uses beam=6, lenpen=1.0, max_len_b=60, min_len=10: -```bash -cp data-bin/cnn_dm/dict.source.txt checkpoints/ -python examples/bart/summarize.py \ - --model-dir checkpoints \ - --model-file checkpoint_best.pt \ - --src cnn_dm/test.source \ - --out cnn_dm/test.hypo \ - --xsum-kwargs -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/summarize.py b/audio/safeear/safeear_repo/fairseq_ours/examples/bart/summarize.py deleted file mode 100644 index d0066732bfc81e6a333ce02f00be0873f97406a1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/bart/summarize.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 torch -from fairseq.models.bart import BARTModel - -XSUM_KWARGS = dict(beam=6, lenpen=1.0, max_len_b=60, min_len=10, no_repeat_ngram_size=3) -CNN_KWARGS = dict(beam=4, lenpen=2.0, max_len_b=140, min_len=55, no_repeat_ngram_size=3) - - -@torch.no_grad() -def generate(bart, infile, outfile="bart_hypo.txt", bsz=32, n_obs=None, **eval_kwargs): - count = 1 - - # if n_obs is not None: bsz = min(bsz, n_obs) - - with open(infile) as source, open(outfile, "w") as fout: - sline = source.readline().strip() - slines = [sline] - for sline in source: - if n_obs is not None and count > n_obs: - break - if count % bsz == 0: - hypotheses_batch = bart.sample(slines, **eval_kwargs) - for hypothesis in hypotheses_batch: - fout.write(hypothesis + "\n") - fout.flush() - slines = [] - - slines.append(sline.strip()) - count += 1 - - if slines != []: - hypotheses_batch = bart.sample(slines, **eval_kwargs) - for hypothesis in hypotheses_batch: - fout.write(hypothesis + "\n") - fout.flush() - - -def main(): - """ - Usage:: - - python examples/bart/summarize.py \ - --model-dir $HOME/bart.large.cnn \ - --model-file model.pt \ - --src $HOME/data-bin/cnn_dm/test.source - """ - parser = argparse.ArgumentParser() - parser.add_argument( - "--model-dir", - required=True, - type=str, - default="bart.large.cnn/", - help="path containing model file and src_dict.txt", - ) - parser.add_argument( - "--model-file", - default="checkpoint_best.pt", - help="where in model_dir are weights saved", - ) - parser.add_argument( - "--src", default="test.source", help="text to summarize", type=str - ) - parser.add_argument( - "--out", default="test.hypo", help="where to save summaries", type=str - ) - parser.add_argument("--bsz", default=32, help="where to save summaries", type=int) - parser.add_argument( - "--n", default=None, help="how many examples to summarize", type=int - ) - parser.add_argument( - "--xsum-kwargs", - action="store_true", - default=False, - help="if true use XSUM_KWARGS else CNN_KWARGS", - ) - args = parser.parse_args() - eval_kwargs = XSUM_KWARGS if args.xsum_kwargs else CNN_KWARGS - if args.model_dir == "pytorch/fairseq": - bart = torch.hub.load("pytorch/fairseq", args.model_file) - else: - bart = BARTModel.from_pretrained( - args.model_dir, - checkpoint_file=args.model_file, - data_name_or_path=args.model_dir, - ) - bart = bart.eval() - if torch.cuda.is_available(): - bart = bart.cuda().half() - generate( - bart, args.src, bsz=args.bsz, n_obs=args.n, outfile=args.out, **eval_kwargs - ) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/README.md deleted file mode 100644 index 657092660eae42d20f67647417623b8b8cb7b66c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/README.md +++ /dev/null @@ -1,88 +0,0 @@ -# Neural Machine Translation with Byte-Level Subwords - -https://arxiv.org/abs/1909.03341 - -We provide an implementation of byte-level byte-pair encoding (BBPE), taking IWSLT 2017 Fr-En translation as -example. - -## Data -Get data and generate fairseq binary dataset: -```bash -bash ./get_data.sh -``` - -## Model Training -Train Transformer model with Bi-GRU embedding contextualization (implemented in `gru_transformer.py`): -```bash -# VOCAB=bytes -# VOCAB=chars -VOCAB=bbpe2048 -# VOCAB=bpe2048 -# VOCAB=bbpe4096 -# VOCAB=bpe4096 -# VOCAB=bpe16384 -``` -```bash -fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ - --arch gru_transformer --encoder-layers 2 --decoder-layers 2 --dropout 0.3 --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --log-format 'simple' --log-interval 100 --save-dir "checkpoints/${VOCAB}" \ - --batch-size 100 --max-update 100000 --update-freq 2 -``` - -## Generation -`fairseq-generate` requires bytes (BBPE) decoder to convert byte-level representation back to characters: -```bash -# BPE=--bpe bytes -# BPE=--bpe characters -BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe2048.model -# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe2048.model -# BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe4096.model -# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe4096.model -# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe16384.model -``` - -```bash -fairseq-generate "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ - --source-lang fr --gen-subset test --sacrebleu --path "checkpoints/${VOCAB}/checkpoint_last.pt" \ - --tokenizer moses --moses-target-lang en ${BPE} -``` -When using `fairseq-interactive`, bytes (BBPE) encoder/decoder is required to tokenize input data and detokenize model predictions: -```bash -fairseq-interactive "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \ - --path "checkpoints/${VOCAB}/checkpoint_last.pt" --input data/test.fr --tokenizer moses --moses-source-lang fr \ - --moses-target-lang en ${BPE} --buffer-size 1000 --max-tokens 10000 -``` - -## Results -| Vocabulary | Model | BLEU | -|:-------------:|:-------------:|:-------------:| -| Joint BPE 16k ([Kudo, 2018](https://arxiv.org/abs/1804.10959)) | 512d LSTM 2+2 | 33.81 | -| Joint BPE 16k | Transformer base 2+2 (w/ GRU) | 36.64 (36.72) | -| Joint BPE 4k | Transformer base 2+2 (w/ GRU) | 35.49 (36.10) | -| Joint BBPE 4k | Transformer base 2+2 (w/ GRU) | 35.61 (35.82) | -| Joint BPE 2k | Transformer base 2+2 (w/ GRU) | 34.87 (36.13) | -| Joint BBPE 2k | Transformer base 2+2 (w/ GRU) | 34.98 (35.43) | -| Characters | Transformer base 2+2 (w/ GRU) | 31.78 (33.30) | -| Bytes | Transformer base 2+2 (w/ GRU) | 31.57 (33.62) | - - -## Citation -``` -@misc{wang2019neural, - title={Neural Machine Translation with Byte-Level Subwords}, - author={Changhan Wang and Kyunghyun Cho and Jiatao Gu}, - year={2019}, - eprint={1909.03341}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` - - -## Contact -Changhan Wang ([changhan@fb.com](mailto:changhan@fb.com)), -Kyunghyun Cho ([kyunghyuncho@fb.com](mailto:kyunghyuncho@fb.com)), -Jiatao Gu ([jgu@fb.com](mailto:jgu@fb.com)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/get_bitext.py b/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/get_bitext.py deleted file mode 100644 index 29e4512e0e29bb441d30c154b6577d09d15fcf36..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/get_bitext.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 argparse -import os -import os.path as op -from collections import namedtuple -from multiprocessing import cpu_count -from typing import List, Optional - -import sentencepiece as sp -from fairseq.data.encoders.byte_bpe import ByteBPE -from fairseq.data.encoders.byte_utils import byte_encode -from fairseq.data.encoders.bytes import Bytes -from fairseq.data.encoders.characters import Characters -from fairseq.data.encoders.moses_tokenizer import MosesTokenizer -from fairseq.data.encoders.sentencepiece_bpe import SentencepieceBPE - -SPLITS = ["train", "valid", "test"] - - -def _convert_xml(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - ss = s.strip() - if not ss.startswith("", "").split('">') - assert len(ss) == 2 - f_o.write(ss[1].strip() + "\n") - - -def _convert_train(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - ss = s.strip() - if ss.startswith("<"): - continue - f_o.write(ss.strip() + "\n") - - -def _get_bytes(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - f_o.write(Bytes.encode(s.strip()) + "\n") - - -def _get_chars(in_path: str, out_path: str): - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - f_o.write(Characters.encode(s.strip()) + "\n") - - -def pretokenize(in_path: str, out_path: str, src: str, tgt: str): - Args = namedtuple( - "Args", - [ - "moses_source_lang", - "moses_target_lang", - "moses_no_dash_splits", - "moses_no_escape", - ], - ) - args = Args( - moses_source_lang=src, - moses_target_lang=tgt, - moses_no_dash_splits=False, - moses_no_escape=False, - ) - pretokenizer = MosesTokenizer(args) - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - f_o.write(pretokenizer.encode(s.strip()) + "\n") - - -def _convert_to_bchar(in_path_prefix: str, src: str, tgt: str, out_path: str): - with open(out_path, "w") as f_o: - for lang in [src, tgt]: - with open(f"{in_path_prefix}.{lang}") as f: - for s in f: - f_o.write(byte_encode(s.strip()) + "\n") - - -def _get_bpe(in_path: str, model_prefix: str, vocab_size: int): - arguments = [ - f"--input={in_path}", - f"--model_prefix={model_prefix}", - f"--model_type=bpe", - f"--vocab_size={vocab_size}", - "--character_coverage=1.0", - "--normalization_rule_name=identity", - f"--num_threads={cpu_count()}", - ] - sp.SentencePieceTrainer.Train(" ".join(arguments)) - - -def _apply_bbpe(model_path: str, in_path: str, out_path: str): - Args = namedtuple("Args", ["sentencepiece_model_path"]) - args = Args(sentencepiece_model_path=model_path) - tokenizer = ByteBPE(args) - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - f_o.write(tokenizer.encode(s.strip()) + "\n") - - -def _apply_bpe(model_path: str, in_path: str, out_path: str): - Args = namedtuple("Args", ["sentencepiece_model"]) - args = Args(sentencepiece_model=model_path) - tokenizer = SentencepieceBPE(args) - with open(in_path) as f, open(out_path, "w") as f_o: - for s in f: - f_o.write(tokenizer.encode(s.strip()) + "\n") - - -def _concat_files(in_paths: List[str], out_path: str): - with open(out_path, "w") as f_o: - for p in in_paths: - with open(p) as f: - for r in f: - f_o.write(r) - - -def preprocess_iwslt17( - root: str, - src: str, - tgt: str, - bpe_size: Optional[int], - need_chars: bool, - bbpe_size: Optional[int], - need_bytes: bool, -): - # extract bitext - in_root = op.join(root, f"{src}-{tgt}") - for lang in [src, tgt]: - _convert_train( - op.join(in_root, f"train.tags.{src}-{tgt}.{lang}"), - op.join(root, f"train.{lang}"), - ) - _convert_xml( - op.join(in_root, f"IWSLT17.TED.dev2010.{src}-{tgt}.{lang}.xml"), - op.join(root, f"valid.{lang}"), - ) - _convert_xml( - op.join(in_root, f"IWSLT17.TED.tst2015.{src}-{tgt}.{lang}.xml"), - op.join(root, f"test.{lang}"), - ) - # pre-tokenize - for lang in [src, tgt]: - for split in SPLITS: - pretokenize( - op.join(root, f"{split}.{lang}"), - op.join(root, f"{split}.moses.{lang}"), - src, - tgt, - ) - # tokenize with BPE vocabulary - if bpe_size is not None: - # learn vocabulary - concated_train_path = op.join(root, "train.all") - _concat_files( - [op.join(root, "train.moses.fr"), op.join(root, "train.moses.en")], - concated_train_path, - ) - bpe_model_prefix = op.join(root, f"spm_bpe{bpe_size}") - _get_bpe(concated_train_path, bpe_model_prefix, bpe_size) - os.remove(concated_train_path) - # apply - for lang in [src, tgt]: - for split in SPLITS: - _apply_bpe( - bpe_model_prefix + ".model", - op.join(root, f"{split}.moses.{lang}"), - op.join(root, f"{split}.moses.bpe{bpe_size}.{lang}"), - ) - # tokenize with bytes vocabulary - if need_bytes: - for lang in [src, tgt]: - for split in SPLITS: - _get_bytes( - op.join(root, f"{split}.moses.{lang}"), - op.join(root, f"{split}.moses.bytes.{lang}"), - ) - # tokenize with characters vocabulary - if need_chars: - for lang in [src, tgt]: - for split in SPLITS: - _get_chars( - op.join(root, f"{split}.moses.{lang}"), - op.join(root, f"{split}.moses.chars.{lang}"), - ) - # tokenize with byte-level BPE vocabulary - if bbpe_size is not None: - # learn vocabulary - bchar_path = op.join(root, "train.bchar") - _convert_to_bchar(op.join(root, "train.moses"), src, tgt, bchar_path) - bbpe_model_prefix = op.join(root, f"spm_bbpe{bbpe_size}") - _get_bpe(bchar_path, bbpe_model_prefix, bbpe_size) - os.remove(bchar_path) - # apply - for lang in [src, tgt]: - for split in SPLITS: - _apply_bbpe( - bbpe_model_prefix + ".model", - op.join(root, f"{split}.moses.{lang}"), - op.join(root, f"{split}.moses.bbpe{bbpe_size}.{lang}"), - ) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--root", type=str, default="data") - parser.add_argument( - "--bpe-vocab", - default=None, - type=int, - help="Generate tokenized bitext with BPE of size K." - "Default to None (disabled).", - ) - parser.add_argument( - "--bbpe-vocab", - default=None, - type=int, - help="Generate tokenized bitext with BBPE of size K." - "Default to None (disabled).", - ) - parser.add_argument( - "--byte-vocab", - action="store_true", - help="Generate tokenized bitext with bytes vocabulary", - ) - parser.add_argument( - "--char-vocab", - action="store_true", - help="Generate tokenized bitext with chars vocabulary", - ) - args = parser.parse_args() - - preprocess_iwslt17( - args.root, - "fr", - "en", - args.bpe_vocab, - args.char_vocab, - args.bbpe_vocab, - args.byte_vocab, - ) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/get_data.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/get_data.sh deleted file mode 100644 index c3d55d4925a6e6e23d12d293f093c1ae14acf76e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/get_data.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash - -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -PY_BIN_ROOT= - -# PyPI dependency -${PY_BIN_ROOT}pip install sentencepiece sacremoses - -# Get data -if [ ! -d "data" ]; then - mkdir data -fi - -if [ ! -f "data/fr-en.tgz" ]; then - wget https://wit3.fbk.eu/archive/2017-01-trnted/texts/fr/en/fr-en.tgz -P data - tar xvf data/fr-en.tgz -C data -fi -${PY_BIN_ROOT}python get_bitext.py --bpe-vocab 16384 --byte-vocab --char-vocab -for VOCAB_SIZE in 2048 4096; do - ${PY_BIN_ROOT}python get_bitext.py --bpe-vocab ${VOCAB_SIZE} --bbpe-vocab ${VOCAB_SIZE} -done -rm -r data/fr-en data/fr-en.tgz - -# Generate binary dataset -${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bpe16384 --joined-dictionary \ - --workers "$(nproc)" --trainpref data/train.moses.bpe16384 --validpref data/valid.moses.bpe16384 \ - --testpref data/test.moses.bpe16384 - -${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bytes --joined-dictionary \ - --workers "$(nproc)" --trainpref data/train.moses.bytes --validpref data/valid.moses.bytes \ - --testpref data/test.moses.bytes - -${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_chars --joined-dictionary \ - --workers "$(nproc)" --trainpref data/train.moses.chars --validpref data/valid.moses.chars \ - --testpref data/test.moses.chars - -for VOCAB_SIZE in 2048 4096; do - for TYPE in bbpe bpe; do - ${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir "data/bin_${TYPE}${VOCAB_SIZE}" \ - --joined-dictionary --workers "$(nproc)" --trainpref "data/train.moses.${TYPE}${VOCAB_SIZE}" \ - --validpref "data/valid.moses.${TYPE}${VOCAB_SIZE}" --testpref "data/test.moses.${TYPE}${VOCAB_SIZE}" - done -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/gru_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/gru_transformer.py deleted file mode 100644 index d4efa93a4d75da71c78e786d7f62101ef3266af4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/byte_level_bpe/gru_transformer.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. - -# Copyright (c) Facebook, Inc. and 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 -import torch.nn.functional as F -from fairseq.models import register_model, register_model_architecture -from fairseq.models.transformer import TransformerEncoder, TransformerModel - - -@register_model("gru_transformer") -class GRUTransformerModel(TransformerModel): - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return GRUTransformerEncoder(args, src_dict, embed_tokens) - - -class GRUTransformerEncoder(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): - super().__init__(args, dictionary, embed_tokens) - self.emb_ctx = nn.GRU( - input_size=embed_tokens.embedding_dim, - hidden_size=embed_tokens.embedding_dim // 2, - num_layers=1, - bidirectional=True, - ) - - def forward_embedding(self, src_tokens): - # embed tokens and positions - x = embed = self.embed_scale * self.embed_tokens(src_tokens) - if self.embed_positions is not None: - x = embed + self.embed_positions(src_tokens) - - # contextualize embeddings - x = x.transpose(0, 1) - x = self.dropout_module(x) - x, _ = self.emb_ctx.forward(x) - x = x.transpose(0, 1) - - if self.layernorm_embedding is not None: - x = self.layernorm_embedding(x) - x = self.dropout_module(x) - return x, embed - - -@register_model_architecture("gru_transformer", "gru_transformer") -def gru_transformer_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.layer_wise_attention = getattr(args, "layer_wise_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) - - -@register_model_architecture("gru_transformer", "gru_transformer_big") -def gru_transformer_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) - gru_transformer_base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/camembert/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/camembert/README.md deleted file mode 100644 index 5ef4fe3f151bb468712f3be935ea5bb1b1360bf7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/camembert/README.md +++ /dev/null @@ -1,75 +0,0 @@ -# CamemBERT: a Tasty French Language Model - -## Introduction - -[CamemBERT](https://arxiv.org/abs/1911.03894) is a pretrained language model trained on 138GB of French text based on RoBERTa. - -Also available in [github.com/huggingface/transformers](https://github.com/huggingface/transformers/). - -## Pre-trained models - -| Model | #params | Download | Arch. | Training data | -|--------------------------------|---------|--------------------------------------------------------------------------------------------------------------------------|-------|-----------------------------------| -| `camembert` / `camembert-base` | 110M | [camembert-base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz) | Base | OSCAR (138 GB of text) | -| `camembert-large` | 335M | [camembert-large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz) | Large | CCNet (135 GB of text) | -| `camembert-base-ccnet` | 110M | [camembert-base-ccnet.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz) | Base | CCNet (135 GB of text) | -| `camembert-base-wikipedia-4gb` | 110M | [camembert-base-wikipedia-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz) | Base | Wikipedia (4 GB of text) | -| `camembert-base-oscar-4gb` | 110M | [camembert-base-oscar-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz) | Base | Subsample of OSCAR (4 GB of text) | -| `camembert-base-ccnet-4gb` | 110M | [camembert-base-ccnet-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz) | Base | Subsample of CCNet (4 GB of text) | - -## Example usage - -### fairseq -##### Load CamemBERT from torch.hub (PyTorch >= 1.1): -```python -import torch -camembert = torch.hub.load('pytorch/fairseq', 'camembert') -camembert.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Load CamemBERT (for PyTorch 1.0 or custom models): -```python -# Download camembert model -wget https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz -tar -xzvf camembert.tar.gz - -# Load the model in fairseq -from fairseq.models.roberta import CamembertModel -camembert = CamembertModel.from_pretrained('/path/to/camembert') -camembert.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Filling masks: -```python -masked_line = 'Le camembert est :)' -camembert.fill_mask(masked_line, topk=3) -# [('Le camembert est délicieux :)', 0.4909118115901947, ' délicieux'), -# ('Le camembert est excellent :)', 0.10556942224502563, ' excellent'), -# ('Le camembert est succulent :)', 0.03453322499990463, ' succulent')] -``` - -##### Extract features from Camembert: -```python -# Extract the last layer's features -line = "J'aime le camembert !" -tokens = camembert.encode(line) -last_layer_features = camembert.extract_features(tokens) -assert last_layer_features.size() == torch.Size([1, 10, 768]) - -# Extract all layer's features (layer 0 is the embedding layer) -all_layers = camembert.extract_features(tokens, return_all_hiddens=True) -assert len(all_layers) == 13 -assert torch.all(all_layers[-1] == last_layer_features) -``` - -## Citation -If you use our work, please cite: - -```bibtex -@inproceedings{martin2020camembert, - title={CamemBERT: a Tasty French Language Model}, - author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, - booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, - year={2020} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/README.md deleted file mode 100644 index e04b8b6a018214c8233fa87fd91d46a6dd1519d4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/README.md +++ /dev/null @@ -1,123 +0,0 @@ -# (Vectorized) Lexically constrained decoding with dynamic beam allocation - -This page provides instructions for how to use lexically constrained decoding in Fairseq. -Fairseq implements the code described in the following papers: - -* [Fast Lexically Constrained Decoding With Dynamic Beam Allocation](https://www.aclweb.org/anthology/N18-1119/) (Post & Vilar, 2018) -* [Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://www.aclweb.org/anthology/N19-1090/) (Hu et al., 2019) - -## Quick start - -Constrained search is enabled by adding the command-line argument `--constraints` to `fairseq-interactive`. -Constraints are appended to each line of input, separated by tabs. Each constraint (one or more tokens) -is a separate field. - -The following command, using [Fairseq's WMT19 German--English model](https://github.com/pytorch/fairseq/blob/main/examples/wmt19/README.md), -translates the sentence *Die maschinelle Übersetzung ist schwer zu kontrollieren.* with the constraints -"hard" and "to influence". - - echo -e "Die maschinelle Übersetzung ist schwer zu kontrollieren.\thard\ttoinfluence" \ - | normalize.py | tok.py \ - | fairseq-interactive /path/to/model \ - --path /path/to/model/model1.pt \ - --bpe fastbpe \ - --bpe-codes /path/to/model/bpecodes \ - --constraints \ - -s de -t en \ - --beam 10 - -(tok.py and normalize.py can be found in the same directory as this README; they are just shortcuts around Fairseq's WMT19 preprocessing). -This will generate the following output: - - [snip] - S-0 Die masch@@ in@@ elle Über@@ setzung ist schwer zu kontrollieren . - W-0 1.844 seconds - C-0 hard - C-0 influence - H-0 -1.5333266258239746 Mach@@ ine trans@@ lation is hard to influence . - D-0 -1.5333266258239746 Machine translation is hard to influence . - P-0 -0.5434 -0.1423 -0.1930 -0.1415 -0.2346 -1.8031 -0.1701 -11.7727 -0.1815 -0.1511 - -By default, constraints are generated in the order supplied, with any number (zero or more) of tokens generated -between constraints. If you wish for the decoder to order the constraints, then use `--constraints unordered`. -Note that you may want to use a larger beam. - -## Implementation details - -The heart of the implementation is in `fairseq/search.py`, which adds a `LexicallyConstrainedBeamSearch` instance. -This instance of beam search tracks the progress of each hypothesis in the beam through the set of constraints -provided for each input sentence. It does this using one of two classes, both found in `fairseq/token_generation_contstraints.py`: - -* OrderedConstraintState: assumes the `C` input constraints will be generated in the provided order -* UnorderedConstraintState: tries to apply `C` (phrasal) constraints in all `C!` orders - -## Differences from Sockeye - -There are a number of [differences from Sockeye's implementation](https://awslabs.github.io/sockeye/inference.html#lexical-constraints). - -* Generating constraints in the order supplied (the default option here) is not available in Sockeye. -* Due to an improved beam allocation method, there is no need to prune the beam. -* Again due to better allocation, beam sizes as low as 10 or even 5 are often sufficient. -* [The vector extensions described in Hu et al.](https://github.com/edwardjhu/sockeye/tree/trie_constraints) (NAACL 2019) were never merged - into the main Sockeye branch. - -## Citation - -The paper first describing lexical constraints for seq2seq decoding is: - -```bibtex -@inproceedings{hokamp-liu-2017-lexically, - title = "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search", - author = "Hokamp, Chris and - Liu, Qun", - booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", - month = jul, - year = "2017", - address = "Vancouver, Canada", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/P17-1141", - doi = "10.18653/v1/P17-1141", - pages = "1535--1546", -} -``` - -The fairseq implementation uses the extensions described in - -```bibtex -@inproceedings{post-vilar-2018-fast, - title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation", - author = "Post, Matt and - Vilar, David", - booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", - month = jun, - year = "2018", - address = "New Orleans, Louisiana", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/N18-1119", - doi = "10.18653/v1/N18-1119", - pages = "1314--1324", -} -``` - -and - -```bibtex -@inproceedings{hu-etal-2019-improved, - title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting", - author = "Hu, J. Edward and - Khayrallah, Huda and - Culkin, Ryan and - Xia, Patrick and - Chen, Tongfei and - Post, Matt and - Van Durme, Benjamin", - booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", - month = jun, - year = "2019", - address = "Minneapolis, Minnesota", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/N19-1090", - doi = "10.18653/v1/N19-1090", - pages = "839--850", -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/normalize.py b/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/normalize.py deleted file mode 100644 index 4ae2b5111ba025acb9e1613865c92fdc339a58d5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/normalize.py +++ /dev/null @@ -1,27 +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 - -from sacremoses.normalize import MosesPunctNormalizer - - -def main(args): - normalizer = MosesPunctNormalizer(lang=args.lang, penn=args.penn) - for line in sys.stdin: - print(normalizer.normalize(line.rstrip()), flush=True) - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("--lang", "-l", default="en") - parser.add_argument("--penn", "-p", action="store_true") - args = parser.parse_args() - - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/tok.py b/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/tok.py deleted file mode 100644 index b1f888a8c0d1b8ec7174859476cc3222456e0d2c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/constrained_decoding/tok.py +++ /dev/null @@ -1,34 +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 - -import sacremoses - - -def main(args): - """Tokenizes, preserving tabs""" - mt = sacremoses.MosesTokenizer(lang=args.lang) - - def tok(s): - return mt.tokenize(s, return_str=True) - - for line in sys.stdin: - parts = list(map(tok, line.split("\t"))) - print(*parts, sep="\t", flush=True) - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("--lang", "-l", default="en") - parser.add_argument("--penn", "-p", action="store_true") - parser.add_argument("--fields", "-f", help="fields to tokenize") - args = parser.parse_args() - - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/conv_seq2seq/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/conv_seq2seq/README.md deleted file mode 100644 index 95fe7e7909a77ee0e50fe31d4b8be38daa8f3be7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/conv_seq2seq/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Convolutional Sequence to Sequence Learning (Gehring et al., 2017) - -## Pre-trained models - -Description | Dataset | Model | Test set(s) ----|---|---|--- -Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2)
newstest2012/2013:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2) -Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2) -Convolutional
([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014:
[download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2) - -## Example usage - -See the [translation README](../translation/README.md) for instructions on reproducing results for WMT'14 En-De and -WMT'14 En-Fr using the `fconv_wmt_en_de` and `fconv_wmt_en_fr` model architectures. - -## Citation - -```bibtex -@inproceedings{gehring2017convs2s, - title = {Convolutional Sequence to Sequence Learning}, - author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N}, - booktitle = {Proc. of ICML}, - year = 2017, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/README.md deleted file mode 100644 index 4689ed7c10497a5100b28fe6d6801a7c089da569..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/README.md +++ /dev/null @@ -1,61 +0,0 @@ -# Cross-lingual Retrieval for Iterative Self-Supervised Training - -https://arxiv.org/pdf/2006.09526.pdf - -## Introduction - -CRISS is a multilingual sequence-to-sequnce pretraining method where mining and training processes are applied iteratively, improving cross-lingual alignment and translation ability at the same time. - -## Requirements: - -* faiss: https://github.com/facebookresearch/faiss -* mosesdecoder: https://github.com/moses-smt/mosesdecoder -* flores: https://github.com/facebookresearch/flores -* LASER: https://github.com/facebookresearch/LASER - -## Unsupervised Machine Translation -##### 1. Download and decompress CRISS checkpoints -``` -cd examples/criss -wget https://dl.fbaipublicfiles.com/criss/criss_3rd_checkpoints.tar.gz -tar -xf criss_checkpoints.tar.gz -``` -##### 2. Download and preprocess Flores test dataset -Make sure to run all scripts from examples/criss directory -``` -bash download_and_preprocess_flores_test.sh -``` - -##### 3. Run Evaluation on Sinhala-English -``` -bash unsupervised_mt/eval.sh -``` - -## Sentence Retrieval -##### 1. Download and preprocess Tatoeba dataset -``` -bash download_and_preprocess_tatoeba.sh -``` - -##### 2. Run Sentence Retrieval on Tatoeba Kazakh-English -``` -bash sentence_retrieval/sentence_retrieval_tatoeba.sh -``` - -## Mining -##### 1. Install faiss -Follow instructions on https://github.com/facebookresearch/faiss/blob/master/INSTALL.md -##### 2. Mine pseudo-parallel data between Kazakh and English -``` -bash mining/mine_example.sh -``` - -## Citation -```bibtex -@article{tran2020cross, - title={Cross-lingual retrieval for iterative self-supervised training}, - author={Tran, Chau and Tang, Yuqing and Li, Xian and Gu, Jiatao}, - journal={arXiv preprint arXiv:2006.09526}, - year={2020} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/download_and_preprocess_flores_test.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/download_and_preprocess_flores_test.sh deleted file mode 100644 index ed4b390fbdee3991efeb298050e12065d7fe605b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/download_and_preprocess_flores_test.sh +++ /dev/null @@ -1,64 +0,0 @@ -#!/bin/bash -# 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. - -SPM_ENCODE=flores/scripts/spm_encode.py -DATA=data_tmp -SPM_MODEL=criss_checkpoints/sentence.bpe.model -DICT=criss_checkpoints/dict.txt - -download_data() { - CORPORA=$1 - URL=$2 - - if [ -f $CORPORA ]; then - echo "$CORPORA already exists, skipping download" - else - echo "Downloading $URL" - wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA - if [ -f $CORPORA ]; then - echo "$URL successfully downloaded." - else - echo "$URL not successfully downloaded." - rm -f $CORPORA - fi - fi -} - -if [[ -f flores ]]; then - echo "flores already cloned" -else - git clone https://github.com/facebookresearch/flores -fi - -mkdir -p $DATA -download_data $DATA/wikipedia_en_ne_si_test_sets.tgz "https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz" -pushd $DATA -pwd -tar -vxf wikipedia_en_ne_si_test_sets.tgz -popd - - -for lang in ne_NP si_LK; do - datadir=$DATA/${lang}-en_XX-flores - rm -rf $datadir - mkdir -p $datadir - TEST_PREFIX=$DATA/wikipedia_en_ne_si_test_sets/wikipedia.test - python $SPM_ENCODE \ - --model ${SPM_MODEL} \ - --output_format=piece \ - --inputs ${TEST_PREFIX}.${lang:0:2}-en.${lang:0:2} ${TEST_PREFIX}.${lang:0:2}-en.en \ - --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX - - # binarize data - fairseq-preprocess \ - --source-lang ${lang} --target-lang en_XX \ - --testpref $datadir/test.bpe.${lang}-en_XX \ - --destdir $datadir \ - --srcdict ${DICT} \ - --joined-dictionary \ - --workers 4 -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/download_and_preprocess_tatoeba.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/download_and_preprocess_tatoeba.sh deleted file mode 100644 index 7ed64f017d5e62695ba73745c840507b994abc0f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/download_and_preprocess_tatoeba.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -SPM_ENCODE=flores/scripts/spm_encode.py -DATA=data_tmp -SPM_MODEL=criss_checkpoints/sentence.bpe.model -DICT=criss_checkpoints/dict.txt - -if [[ -f flores ]]; then - echo "flores already cloned" -else - git clone https://github.com/facebookresearch/flores -fi -if [[ -f LASER ]]; then - echo "LASER already cloned" -else - git clone https://github.com/facebookresearch/LASER -fi -mkdir -p data_tmp -declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu" ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn") -for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do - lang_tatoeba=${lang_tatoeba_map[$lang]} - echo $lang_tatoeba - datadir=$DATA/${lang}-en_XX-tatoeba - rm -rf $datadir - mkdir -p $datadir - TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba - python $SPM_ENCODE \ - --model ${SPM_MODEL} \ - --output_format=piece \ - --inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \ - --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX - - # binarize data - fairseq-preprocess \ - --source-lang ${lang} --target-lang en_XX \ - --testpref $datadir/test.bpe.${lang}-en_XX \ - --destdir $datadir \ - --srcdict ${DICT} \ - --joined-dictionary \ - --workers 4 -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/mining/mine.py b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/mining/mine.py deleted file mode 100644 index d420e597a87eac961294581a6a5d9a048ff948f6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/mining/mine.py +++ /dev/null @@ -1,239 +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 argparse -import glob -from subprocess import check_call - -try: - import faiss - - has_faiss = True -except ImportError: - has_faiss = False -import numpy as np - -GB = 1024 * 1024 * 1024 - - -def call(cmd): - print(cmd) - check_call(cmd, shell=True) - - -def get_batches(directory, lang, prefix="all_avg_pool"): - print(f"Finding in {directory}/{prefix}.{lang}*") - files = glob.glob(f"{directory}/{prefix}.{lang}*") - emb_files = [] - txt_files = [] - for emb_fi in files: - emb_files.append(emb_fi) - txt_fi = emb_fi.replace(prefix, "sentences") - txt_files.append(txt_fi) - return emb_files, txt_files - - -def load_batch(emb_file, dim): - embeddings = np.fromfile(emb_file, dtype=np.float32) - num_rows = int(embeddings.shape[0] / dim) - embeddings = embeddings.reshape((num_rows, dim)) - faiss.normalize_L2(embeddings) - return embeddings - - -def knnGPU_sharded(x_batches_f, y_batches_f, dim, k, direction="x2y"): - if not has_faiss: - raise ImportError("Please install Faiss") - sims = [] - inds = [] - xfrom = 0 - xto = 0 - for x_batch_f in x_batches_f: - yfrom = 0 - yto = 0 - x_batch = load_batch(x_batch_f, dim) - xto = xfrom + x_batch.shape[0] - bsims, binds = [], [] - for y_batch_f in y_batches_f: - y_batch = load_batch(y_batch_f, dim) - neighbor_size = min(k, y_batch.shape[0]) - yto = yfrom + y_batch.shape[0] - print("{}-{} -> {}-{}".format(xfrom, xto, yfrom, yto)) - idx = faiss.IndexFlatIP(dim) - idx = faiss.index_cpu_to_all_gpus(idx) - idx.add(y_batch) - bsim, bind = idx.search(x_batch, neighbor_size) - - bsims.append(bsim) - binds.append(bind + yfrom) - yfrom += y_batch.shape[0] - del idx - del y_batch - bsims = np.concatenate(bsims, axis=1) - binds = np.concatenate(binds, axis=1) - aux = np.argsort(-bsims, axis=1) - sim_batch = np.zeros((x_batch.shape[0], k), dtype=np.float32) - ind_batch = np.zeros((x_batch.shape[0], k), dtype=np.int64) - for i in range(x_batch.shape[0]): - for j in range(k): - sim_batch[i, j] = bsims[i, aux[i, j]] - ind_batch[i, j] = binds[i, aux[i, j]] - sims.append(sim_batch) - inds.append(ind_batch) - xfrom += x_batch.shape[0] - del x_batch - sim = np.concatenate(sims, axis=0) - ind = np.concatenate(inds, axis=0) - return sim, ind - - -def score(sim, fwd_mean, bwd_mean, margin): - return margin(sim, (fwd_mean + bwd_mean) / 2) - - -def score_candidates( - sim_mat, candidate_inds, fwd_mean, bwd_mean, margin, verbose=False -): - print(" - scoring {:d} candidates".format(sim_mat.shape[0])) - scores = np.zeros(candidate_inds.shape) - for i in range(scores.shape[0]): - for j in range(scores.shape[1]): - k = int(candidate_inds[i, j]) - scores[i, j] = score(sim_mat[i, j], fwd_mean[i], bwd_mean[k], margin) - return scores - - -def load_text(files): - all_sentences = [] - for fi in files: - with open(fi) as sentence_fi: - for line in sentence_fi: - all_sentences.append(line.strip()) - print(f"Read {len(all_sentences)} sentences") - return all_sentences - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Mine bitext") - parser.add_argument("--src-lang", help="Source language") - parser.add_argument("--tgt-lang", help="Target language") - parser.add_argument( - "--dict-path", help="Path to dictionary file", default="dict.txt" - ) - parser.add_argument( - "--spm-path", help="Path to SPM model file", default="sentence.bpe.model" - ) - parser.add_argument("--dim", type=int, default=1024, help="Embedding dimension") - parser.add_argument("--mem", type=int, default=5, help="Memory in GB") - parser.add_argument("--src-dir", help="Source directory") - parser.add_argument("--tgt-dir", help="Target directory") - parser.add_argument("--output", help="Output path") - parser.add_argument( - "--neighborhood", type=int, default=4, help="Embedding dimension" - ) - parser.add_argument( - "--threshold", type=float, default=1.06, help="Threshold on mined bitext" - ) - parser.add_argument( - "--valid-size", - type=int, - default=2000, - help="Number of sentences used for validation set", - ) - parser.add_argument( - "--min-count", - type=int, - default=50000, - help="Min num sentences used for each language", - ) - args = parser.parse_args() - - x_batches_f, x_sents_f = get_batches(args.src_dir, args.src_lang) - y_batches_f, y_sents_f = get_batches(args.tgt_dir, args.tgt_lang) - margin = lambda a, b: a / b - y2x_sim, y2x_ind = knnGPU_sharded( - y_batches_f, x_batches_f, args.dim, args.neighborhood, direction="y2x" - ) - x2y_sim, x2y_ind = knnGPU_sharded( - x_batches_f, y_batches_f, args.dim, args.neighborhood, direction="x2y" - ) - - x2y_mean = x2y_sim.mean(axis=1) - y2x_mean = y2x_sim.mean(axis=1) - fwd_scores = score_candidates(x2y_sim, x2y_ind, x2y_mean, y2x_mean, margin) - bwd_scores = score_candidates(y2x_sim, y2x_ind, y2x_mean, x2y_mean, margin) - fwd_best = x2y_ind[np.arange(x2y_sim.shape[0]), fwd_scores.argmax(axis=1)] - bwd_best = y2x_ind[np.arange(y2x_sim.shape[0]), bwd_scores.argmax(axis=1)] - indices = np.stack( - ( - np.concatenate((np.arange(x2y_ind.shape[0]), bwd_best)), - np.concatenate((fwd_best, np.arange(y2x_ind.shape[0]))), - ), - axis=1, - ) - scores = np.concatenate((fwd_scores.max(axis=1), bwd_scores.max(axis=1))) - - x_sentences = load_text(x_sents_f) - y_sentences = load_text(y_sents_f) - - threshold = args.threshold - min_count = args.min_count - seen_src, seen_trg = set(), set() - directory = args.output - call(f"mkdir -p {directory}") - src_out = open( - f"{directory}/all.{args.src_lang}", - mode="w", - encoding="utf-8", - errors="surrogateescape", - ) - tgt_out = open( - f"{directory}/all.{args.tgt_lang}", - mode="w", - encoding="utf-8", - errors="surrogateescape", - ) - scores_out = open( - f"{directory}/all.scores", mode="w", encoding="utf-8", errors="surrogateescape" - ) - count = 0 - for i in np.argsort(-scores): - src_ind, trg_ind = indices[i] - if src_ind not in seen_src and trg_ind not in seen_trg: - seen_src.add(src_ind) - seen_trg.add(trg_ind) - if scores[i] > threshold or count < min_count: - if x_sentences[src_ind]: - print(scores[i], file=scores_out) - print(x_sentences[src_ind], file=src_out) - print(y_sentences[trg_ind], file=tgt_out) - count += 1 - else: - print(f"Ignoring sentence: {x_sentences[src_ind]}") - src_out.close() - tgt_out.close() - scores_out.close() - - print(f"Found {count} pairs for threshold={threshold}") - with open(f"{directory}/all.{args.src_lang}") as all_s, open( - f"{directory}/all.{args.tgt_lang}" - ) as all_t, open(f"{directory}/valid.{args.src_lang}", "w") as valid_s, open( - f"{directory}/valid.{args.tgt_lang}", "w" - ) as valid_t, open( - f"{directory}/train.{args.src_lang}", "w" - ) as train_s, open( - f"{directory}/train.{args.tgt_lang}", "w" - ) as train_t: - count = 0 - for s_line, t_line in zip(all_s, all_t): - s_line = s_line.split("\t")[1] - t_line = t_line.split("\t")[1] - if count >= args.valid_size: - train_s.write(s_line) - train_t.write(t_line) - else: - valid_s.write(s_line) - valid_t.write(t_line) - count += 1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/mining/mine_example.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/mining/mine_example.sh deleted file mode 100644 index ace995ac44665f99d904b6a89d7fbbce24103afe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/mining/mine_example.sh +++ /dev/null @@ -1,103 +0,0 @@ -#!/bin/bash -# 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. -# -source_lang=kk_KZ -target_lang=en_XX -MODEL=criss_checkpoints/criss.3rd.pt -SPM=criss_checkpoints/sentence.bpe.model -SPLIT=test -LANG_DICT=criss_checkpoints/lang_dict.txt -SPM_ENCODE=flores/scripts/spm_encode.py -SAVE_ENCODER=save_encoder.py -ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL -DICT=criss_checkpoints/dict.txt -THRESHOLD=1.02 -MIN_COUNT=500 - -DATA_DIR=data_tmp -SAVE_DIR=mining/${source_lang}_${target_lang}_mined -ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang} -INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba - -mkdir -p $ENCODER_SAVE_DIR/${target_lang} -mkdir -p $ENCODER_SAVE_DIR/${source_lang} -mkdir -p $SAVE_DIR - -## Save encoder outputs - -# Save encoder outputs for source sentences -python $SAVE_ENCODER \ - ${INPUT_DIR} \ - --path ${MODEL} \ - --task translation_multi_simple_epoch \ - --lang-pairs ${source_lang}-${target_lang} \ - --lang-dict ${LANG_DICT} \ - --gen-subset ${SPLIT} \ - --bpe 'sentencepiece' \ - -s ${source_lang} -t ${target_lang} \ - --sentencepiece-model ${SPM} \ - --remove-bpe 'sentencepiece' \ - --beam 1 \ - --lang-tok-style mbart \ - --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang} - -## Save encoder outputs for target sentences -python $SAVE_ENCODER \ - ${INPUT_DIR} \ - --path ${MODEL} \ - --lang-pairs ${source_lang}-${target_lang} \ - --lang-dict ${LANG_DICT} \ - --task translation_multi_simple_epoch \ - --gen-subset ${SPLIT} \ - --bpe 'sentencepiece' \ - -t ${source_lang} -s ${target_lang} \ - --sentencepiece-model ${SPM} \ - --remove-bpe 'sentencepiece' \ - --beam 1 \ - --lang-tok-style mbart \ - --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang} - -## Mining -python mining/mine.py \ - --src-lang ${source_lang} \ - --tgt-lang ${target_lang} \ - --dim 1024 \ - --mem 10 \ - --neighborhood 4 \ - --src-dir ${ENCODER_SAVE_DIR}/${source_lang} \ - --tgt-dir ${ENCODER_SAVE_DIR}/${target_lang} \ - --output $SAVE_DIR \ - --threshold ${THRESHOLD} \ - --min-count ${MIN_COUNT} \ - --valid-size 100 \ - --dict-path ${DICT} \ - --spm-path ${SPM} \ - - -## Process and binarize mined data -python $SPM_ENCODE \ - --model ${SPM} \ - --output_format=piece \ - --inputs mining/${source_lang}_${target_lang}_mined/train.${source_lang} mining/${source_lang}_${target_lang}_mined/train.${target_lang} \ - --outputs mining/${source_lang}_${target_lang}_mined/train.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/train.bpe.${target_lang} - -python $SPM_ENCODE \ - --model ${SPM} \ - --output_format=piece \ - --inputs mining/${source_lang}_${target_lang}_mined/valid.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.${target_lang} \ - --outputs mining/${source_lang}_${target_lang}_mined/valid.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.bpe.${target_lang} - - -fairseq-preprocess \ - --source-lang ${source_lang} \ - --target-lang ${target_lang} \ - --trainpref mining/${source_lang}_${target_lang}_mined/train.bpe \ - --validpref mining/${source_lang}_${target_lang}_mined/valid.bpe \ - --destdir mining/${source_lang}_${target_lang}_mined \ - --srcdict ${DICT} \ - --joined-dictionary \ - --workers 8 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/save_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/save_encoder.py deleted file mode 100644 index 24a842e4092663c79c92a299fa85747b7c0bed64..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/save_encoder.py +++ /dev/null @@ -1,214 +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 numpy as np -import torch -from fairseq import checkpoint_utils, options, progress_bar, tasks, utils -from fairseq.sequence_generator import EnsembleModel -from fairseq.utils import safe_hasattr - - -def get_avg_pool( - models, sample, prefix_tokens, src_dict, remove_bpe, has_langtok=False -): - model = EnsembleModel(models) - - # 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 sample["net_input"].items() if k != "prev_output_tokens" - } - - # compute the encoder output for each beam - encoder_outs = model.forward_encoder(encoder_input) - np_encoder_outs = encoder_outs[0].encoder_out.cpu().numpy().astype(np.float32) - encoder_mask = 1 - encoder_outs[0].encoder_padding_mask.cpu().numpy().astype( - np.float32 - ) - encoder_mask = np.expand_dims(encoder_mask.T, axis=2) - if has_langtok: - encoder_mask = encoder_mask[1:, :, :] - np_encoder_outs = np_encoder_outs[1, :, :] - masked_encoder_outs = encoder_mask * np_encoder_outs - avg_pool = (masked_encoder_outs / encoder_mask.sum(axis=0)).sum(axis=0) - return avg_pool - - -def main(args): - assert args.path is not None, "--path required for generation!" - assert ( - not args.sampling or args.nbest == args.beam - ), "--sampling requires --nbest to be equal to --beam" - assert ( - args.replace_unk is None or args.raw_text - ), "--replace-unk requires a raw text dataset (--raw-text)" - - args.beam = 1 - utils.import_user_module(args) - - if args.max_tokens is None: - args.max_tokens = 12000 - print(args) - use_cuda = torch.cuda.is_available() and not args.cpu - - # Load dataset splits - task = tasks.setup_task(args) - task.load_dataset(args.gen_subset) - - # Set dictionaries - try: - src_dict = getattr(task, "source_dictionary", None) - except NotImplementedError: - src_dict = None - tgt_dict = task.target_dictionary - - # Load ensemble - print("| loading model(s) from {}".format(args.path)) - models, _model_args = checkpoint_utils.load_model_ensemble( - args.path.split(":"), - arg_overrides=eval(args.model_overrides), - task=task, - ) - - # Optimize ensemble for generation - for model in models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if args.fp16: - model.half() - if use_cuda: - model.cuda() - - # 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(args.replace_unk) - - # Load dataset (possibly sharded) - itr = task.get_batch_iterator( - dataset=task.dataset(args.gen_subset), - max_tokens=args.max_tokens, - max_positions=utils.resolve_max_positions( - task.max_positions(), - ), - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=args.required_batch_size_multiple, - num_shards=args.num_shards, - shard_id=args.shard_id, - num_workers=args.num_workers, - ).next_epoch_itr(shuffle=False) - - num_sentences = 0 - source_sentences = [] - shard_id = 0 - all_avg_pool = None - encoder_has_langtok = ( - safe_hasattr(task.args, "encoder_langtok") - and task.args.encoder_langtok is not None - and safe_hasattr(task.args, "lang_tok_replacing_bos_eos") - and not task.args.lang_tok_replacing_bos_eos - ) - with progress_bar.build_progress_bar(args, itr) as t: - for sample in t: - if sample is None: - print("Skipping None") - continue - sample = utils.move_to_cuda(sample) if use_cuda else sample - if "net_input" not in sample: - continue - - prefix_tokens = None - if args.prefix_size > 0: - prefix_tokens = sample["target"][:, : args.prefix_size] - - with torch.no_grad(): - avg_pool = get_avg_pool( - models, - sample, - prefix_tokens, - src_dict, - args.post_process, - has_langtok=encoder_has_langtok, - ) - if all_avg_pool is not None: - all_avg_pool = np.concatenate((all_avg_pool, avg_pool)) - else: - all_avg_pool = avg_pool - - if not isinstance(sample["id"], list): - sample_ids = sample["id"].tolist() - else: - sample_ids = sample["id"] - for i, sample_id in enumerate(sample_ids): - # Remove padding - src_tokens = utils.strip_pad( - sample["net_input"]["src_tokens"][i, :], tgt_dict.pad() - ) - - # Either retrieve the original sentences or regenerate them from tokens. - if align_dict is not None: - src_str = task.dataset(args.gen_subset).src.get_original_text( - sample_id - ) - else: - if src_dict is not None: - src_str = src_dict.string(src_tokens, args.post_process) - else: - src_str = "" - - if not args.quiet: - if src_dict is not None: - print("S-{}\t{}".format(sample_id, src_str)) - - source_sentences.append(f"{sample_id}\t{src_str}") - - num_sentences += sample["nsentences"] - if all_avg_pool.shape[0] >= 1000000: - with open( - f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", - "w", - ) as avg_pool_file: - all_avg_pool.tofile(avg_pool_file) - with open( - f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", - "w", - ) as sentence_file: - sentence_file.writelines(f"{line}\n" for line in source_sentences) - all_avg_pool = None - source_sentences = [] - shard_id += 1 - - if all_avg_pool is not None: - with open( - f"{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}", "w" - ) as avg_pool_file: - all_avg_pool.tofile(avg_pool_file) - with open( - f"{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}", "w" - ) as sentence_file: - sentence_file.writelines(f"{line}\n" for line in source_sentences) - return None - - -def cli_main(): - parser = options.get_generation_parser() - parser.add_argument( - "--encoder-save-dir", - default="", - type=str, - metavar="N", - help="directory to save encoder outputs", - ) - args = options.parse_args_and_arch(parser) - main(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/sentence_retrieval/encoder_analysis.py b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/sentence_retrieval/encoder_analysis.py deleted file mode 100644 index a636a6f974544cf0d64e5816750ff32af8e9c72f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/sentence_retrieval/encoder_analysis.py +++ /dev/null @@ -1,91 +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 argparse -import glob - -import numpy as np - -DIM = 1024 - - -def compute_dist(source_embs, target_embs, k=5, return_sim_mat=False): - target_ids = [tid for tid in target_embs] - source_mat = np.stack(source_embs.values(), axis=0) - normalized_source_mat = source_mat / np.linalg.norm( - source_mat, axis=1, keepdims=True - ) - target_mat = np.stack(target_embs.values(), axis=0) - normalized_target_mat = target_mat / np.linalg.norm( - target_mat, axis=1, keepdims=True - ) - sim_mat = normalized_source_mat.dot(normalized_target_mat.T) - if return_sim_mat: - return sim_mat - neighbors_map = {} - for i, sentence_id in enumerate(source_embs): - idx = np.argsort(sim_mat[i, :])[::-1][:k] - neighbors_map[sentence_id] = [target_ids[tid] for tid in idx] - return neighbors_map - - -def load_embeddings(directory, LANGS): - sentence_embeddings = {} - sentence_texts = {} - for lang in LANGS: - sentence_embeddings[lang] = {} - sentence_texts[lang] = {} - lang_dir = f"{directory}/{lang}" - embedding_files = glob.glob(f"{lang_dir}/all_avg_pool.{lang}.*") - for embed_file in embedding_files: - shard_id = embed_file.split(".")[-1] - embeddings = np.fromfile(embed_file, dtype=np.float32) - num_rows = embeddings.shape[0] // DIM - embeddings = embeddings.reshape((num_rows, DIM)) - - with open(f"{lang_dir}/sentences.{lang}.{shard_id}") as sentence_file: - for idx, line in enumerate(sentence_file): - sentence_id, sentence = line.strip().split("\t") - sentence_texts[lang][sentence_id] = sentence - sentence_embeddings[lang][sentence_id] = embeddings[idx, :] - - return sentence_embeddings, sentence_texts - - -def compute_accuracy(directory, LANGS): - sentence_embeddings, sentence_texts = load_embeddings(directory, LANGS) - - top_1_accuracy = {} - - top1_str = " ".join(LANGS) + "\n" - for source_lang in LANGS: - top_1_accuracy[source_lang] = {} - top1_str += f"{source_lang} " - for target_lang in LANGS: - top1 = 0 - top5 = 0 - neighbors_map = compute_dist( - sentence_embeddings[source_lang], sentence_embeddings[target_lang] - ) - for sentence_id, neighbors in neighbors_map.items(): - if sentence_id == neighbors[0]: - top1 += 1 - if sentence_id in neighbors[:5]: - top5 += 1 - n = len(sentence_embeddings[target_lang]) - top1_str += f"{top1/n} " - top1_str += "\n" - - print(top1_str) - print(top1_str, file=open(f"{directory}/accuracy", "w")) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Analyze encoder outputs") - parser.add_argument("directory", help="Source language corpus") - parser.add_argument("--langs", help="List of langs") - args = parser.parse_args() - langs = args.langs.split(",") - compute_accuracy(args.directory, langs) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh deleted file mode 100644 index 0428d8bef9d426ac3e664cd281ce0b688f5f580f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/sentence_retrieval/sentence_retrieval_tatoeba.sh +++ /dev/null @@ -1,59 +0,0 @@ -#!/bin/bash -# 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. -# -source_lang=kk_KZ -target_lang=en_XX -MODEL=criss_checkpoints/criss.3rd.pt -SPM=criss_checkpoints/sentence.bpe.model -SPLIT=test -LANG_DICT=criss_checkpoints/lang_dict.txt -ENCODER_ANALYSIS=sentence_retrieval/encoder_analysis.py -SAVE_ENCODER=save_encoder.py -ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL - - - -DATA_DIR=data_tmp -INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba -ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang} -mkdir -p $ENCODER_SAVE_DIR/${target_lang} -mkdir -p $ENCODER_SAVE_DIR/${source_lang} - -# Save encoder outputs for source sentences -python $SAVE_ENCODER \ - ${INPUT_DIR} \ - --path ${MODEL} \ - --task translation_multi_simple_epoch \ - --lang-dict ${LANG_DICT} \ - --gen-subset ${SPLIT} \ - --bpe 'sentencepiece' \ - --lang-pairs ${source_lang}-${target_lang} \ - -s ${source_lang} -t ${target_lang} \ - --sentencepiece-model ${SPM} \ - --remove-bpe 'sentencepiece' \ - --beam 1 \ - --lang-tok-style mbart \ - --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang} - -# Save encoder outputs for target sentences -python $SAVE_ENCODER \ - ${INPUT_DIR} \ - --path ${MODEL} \ - --lang-dict ${LANG_DICT} \ - --task translation_multi_simple_epoch \ - --gen-subset ${SPLIT} \ - --bpe 'sentencepiece' \ - --lang-pairs ${target_lang}-${source_lang} \ - -t ${source_lang} -s ${target_lang} \ - --sentencepiece-model ${SPM} \ - --remove-bpe 'sentencepiece' \ - --beam 1 \ - --lang-tok-style mbart \ - --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang} - -# Analyze sentence retrieval accuracy -python $ENCODER_ANALYSIS --langs "${source_lang},${target_lang}" ${ENCODER_SAVE_DIR} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/unsupervised_mt/eval.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/criss/unsupervised_mt/eval.sh deleted file mode 100644 index 03b773ed5a522eb82186fea8ffbb6c557e14b6d3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/criss/unsupervised_mt/eval.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -SRC=si_LK -TGT=en_XX -MODEL=criss_checkpoints/criss.3rd.pt - -MULTIBLEU=mosesdecoder/scripts/generic/multi-bleu.perl -MOSES=mosesdecoder -REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl -NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl -TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl -GEN_TMP_DIR=gen_tmp -LANG_DICT=criss_checkpoints/lang_dict.txt - -if [ ! -d "mosesdecoder" ]; then - git clone https://github.com/moses-smt/mosesdecoder -fi -mkdir -p $GEN_TMP_DIR -fairseq-generate data_tmp/${SRC}-${TGT}-flores \ - --task translation_multi_simple_epoch \ - --max-tokens 2000 \ - --path ${MODEL} \ - --skip-invalid-size-inputs-valid-test \ - --beam 5 --lenpen 1.0 --gen-subset test \ - --remove-bpe=sentencepiece \ - --source-lang ${SRC} --target-lang ${TGT} \ - --decoder-langtok --lang-pairs 'en_XX-ar_AR,en_XX-de_DE,en_XX-es_XX,en_XX-fr_XX,en_XX-hi_IN,en_XX-it_IT,en_XX-ja_XX,en_XX-ko_KR,en_XX-nl_XX,en_XX-ru_RU,en_XX-zh_CN,en_XX-tr_TR,en_XX-vi_VN,en_XX-ro_RO,en_XX-my_MM,en_XX-ne_NP,en_XX-si_LK,en_XX-cs_CZ,en_XX-lt_LT,en_XX-kk_KZ,en_XX-gu_IN,en_XX-fi_FI,en_XX-et_EE,en_XX-lv_LV,ar_AR-en_XX,cs_CZ-en_XX,de_DE-en_XX,es_XX-en_XX,et_EE-en_XX,fi_FI-en_XX,fr_XX-en_XX,gu_IN-en_XX,hi_IN-en_XX,it_IT-en_XX,ja_XX-en_XX,kk_KZ-en_XX,ko_KR-en_XX,lt_LT-en_XX,lv_LV-en_XX,my_MM-en_XX,ne_NP-en_XX,nl_XX-en_XX,ro_RO-en_XX,ru_RU-en_XX,si_LK-en_XX,tr_TR-en_XX,vi_VN-en_XX,zh_CN-en_XX,ar_AR-es_XX,es_XX-ar_AR,ar_AR-hi_IN,hi_IN-ar_AR,ar_AR-zh_CN,zh_CN-ar_AR,cs_CZ-es_XX,es_XX-cs_CZ,cs_CZ-hi_IN,hi_IN-cs_CZ,cs_CZ-zh_CN,zh_CN-cs_CZ,de_DE-es_XX,es_XX-de_DE,de_DE-hi_IN,hi_IN-de_DE,de_DE-zh_CN,zh_CN-de_DE,es_XX-hi_IN,hi_IN-es_XX,es_XX-zh_CN,zh_CN-es_XX,et_EE-es_XX,es_XX-et_EE,et_EE-hi_IN,hi_IN-et_EE,et_EE-zh_CN,zh_CN-et_EE,fi_FI-es_XX,es_XX-fi_FI,fi_FI-hi_IN,hi_IN-fi_FI,fi_FI-zh_CN,zh_CN-fi_FI,fr_XX-es_XX,es_XX-fr_XX,fr_XX-hi_IN,hi_IN-fr_XX,fr_XX-zh_CN,zh_CN-fr_XX,gu_IN-es_XX,es_XX-gu_IN,gu_IN-hi_IN,hi_IN-gu_IN,gu_IN-zh_CN,zh_CN-gu_IN,hi_IN-zh_CN,zh_CN-hi_IN,it_IT-es_XX,es_XX-it_IT,it_IT-hi_IN,hi_IN-it_IT,it_IT-zh_CN,zh_CN-it_IT,ja_XX-es_XX,es_XX-ja_XX,ja_XX-hi_IN,hi_IN-ja_XX,ja_XX-zh_CN,zh_CN-ja_XX,kk_KZ-es_XX,es_XX-kk_KZ,kk_KZ-hi_IN,hi_IN-kk_KZ,kk_KZ-zh_CN,zh_CN-kk_KZ,ko_KR-es_XX,es_XX-ko_KR,ko_KR-hi_IN,hi_IN-ko_KR,ko_KR-zh_CN,zh_CN-ko_KR,lt_LT-es_XX,es_XX-lt_LT,lt_LT-hi_IN,hi_IN-lt_LT,lt_LT-zh_CN,zh_CN-lt_LT,lv_LV-es_XX,es_XX-lv_LV,lv_LV-hi_IN,hi_IN-lv_LV,lv_LV-zh_CN,zh_CN-lv_LV,my_MM-es_XX,es_XX-my_MM,my_MM-hi_IN,hi_IN-my_MM,my_MM-zh_CN,zh_CN-my_MM,ne_NP-es_XX,es_XX-ne_NP,ne_NP-hi_IN,hi_IN-ne_NP,ne_NP-zh_CN,zh_CN-ne_NP,nl_XX-es_XX,es_XX-nl_XX,nl_XX-hi_IN,hi_IN-nl_XX,nl_XX-zh_CN,zh_CN-nl_XX,ro_RO-es_XX,es_XX-ro_RO,ro_RO-hi_IN,hi_IN-ro_RO,ro_RO-zh_CN,zh_CN-ro_RO,ru_RU-es_XX,es_XX-ru_RU,ru_RU-hi_IN,hi_IN-ru_RU,ru_RU-zh_CN,zh_CN-ru_RU,si_LK-es_XX,es_XX-si_LK,si_LK-hi_IN,hi_IN-si_LK,si_LK-zh_CN,zh_CN-si_LK,tr_TR-es_XX,es_XX-tr_TR,tr_TR-hi_IN,hi_IN-tr_TR,tr_TR-zh_CN,zh_CN-tr_TR,vi_VN-es_XX,es_XX-vi_VN,vi_VN-hi_IN,hi_IN-vi_VN,vi_VN-zh_CN,zh_CN-vi_VN' \ - --lang-dict ${LANG_DICT} --lang-tok-style 'mbart' --sampling-method 'temperature' --sampling-temperature '1.0' > $GEN_TMP_DIR/${SRC}_${TGT}.gen -cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^T-" | cut -f2 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.hyp -cat $GEN_TMP_DIR/${SRC}_${TGT}.gen | grep -P "^H-" | cut -f3 | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l ${TGT:0:2} | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape ${TGT:0:2} > $GEN_TMP_DIR/${SRC}_${TGT}.ref -${MULTIBLEU} $GEN_TMP_DIR/${SRC}_${TGT}.ref < $GEN_TMP_DIR/${SRC}_${TGT}.hyp diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/cross_lingual_language_model/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/cross_lingual_language_model/README.md deleted file mode 100644 index af9128e39e5925e9411d162c2f24a19e4532d618..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/cross_lingual_language_model/README.md +++ /dev/null @@ -1,77 +0,0 @@ -# Cross-Lingual Language Model Pre-training - -Below are some details for training Cross-Lingual Language Models (XLM) - similar to the ones presented in [Lample & Conneau, 2019](https://arxiv.org/pdf/1901.07291.pdf) - in Fairseq. The current implementation only supports the Masked Language Model (MLM) from the paper above. - -## Downloading and Tokenizing Monolingual Data - -Pointers to the monolingual data from wikipedia, used for training the XLM-style MLM model as well as details on processing (tokenization and BPE) it can be found in the [XLM Github Repository](https://github.com/facebookresearch/XLM#download--preprocess-monolingual-data). - -Let's assume the following for the code snippets in later sections to work -- Processed data is in the folder: monolingual_data/processed -- Each language has 3 files for train, test and validation. For example we have the following files for English: - train.en, valid.en -- We are training a model for 5 languages: Arabic (ar), German (de), English (en), Hindi (hi) and French (fr) -- The vocabulary file is monolingual_data/processed/vocab_mlm - - -## Fairseq Pre-processing and Binarization - -Pre-process and binarize the data with the MaskedLMDictionary and cross_lingual_lm task - -```bash -# Ensure the output directory exists -DATA_DIR=monolingual_data/fairseq_processed -mkdir -p "$DATA_DIR" - -for lg in ar de en hi fr -do - - fairseq-preprocess \ - --task cross_lingual_lm \ - --srcdict monolingual_data/processed/vocab_mlm \ - --only-source \ - --trainpref monolingual_data/processed/train \ - --validpref monolingual_data/processed/valid \ - --testpref monolingual_data/processed/test \ - --destdir monolingual_data/fairseq_processed \ - --workers 20 \ - --source-lang $lg - - # Since we only have a source language, the output file has a None for the - # target language. Remove this - - for stage in train test valid - - sudo mv "$DATA_DIR/$stage.$lg-None.$lg.bin" "$stage.$lg.bin" - sudo mv "$DATA_DIR/$stage.$lg-None.$lg.idx" "$stage.$lg.idx" - - done - -done -``` - -## Train a Cross-lingual Language Model similar to the XLM MLM model - -Use the following command to train the model on 5 languages. - -``` -fairseq-train \ ---task cross_lingual_lm monolingual_data/fairseq_processed \ ---save-dir checkpoints/mlm \ ---max-update 2400000 --save-interval 1 --no-epoch-checkpoints \ ---arch xlm_base \ ---optimizer adam --lr-scheduler reduce_lr_on_plateau \ ---lr-shrink 0.5 --lr 0.0001 --stop-min-lr 1e-09 \ ---dropout 0.1 \ ---criterion legacy_masked_lm_loss \ ---max-tokens 2048 --tokens-per-sample 256 --attention-dropout 0.1 \ ---dataset-impl lazy --seed 0 \ ---masked-lm-only \ ---monolingual-langs 'ar,de,en,hi,fr' --num-segment 5 \ ---ddp-backend=legacy_ddp -``` - -Some Notes: -- Using tokens_per_sample greater than 256 can cause OOM (out-of-memory) issues. Usually since MLM packs in streams of text, this parameter doesn't need much tuning. -- The Evaluation workflow for computing MLM Perplexity on test data is in progress. -- Finetuning this model on a downstream task is something which is not currently available. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/README.md deleted file mode 100644 index 9fd05d804238596b0c75632bced2bf5758e02bfe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/README.md +++ /dev/null @@ -1,139 +0,0 @@ - -# data2vec - -data2vec is a framework for self-supervised representation learning for images, speech, and text as described in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language (Baevski et al., 2022)](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language). The algorithm uses the same learning mechanism for different modalities. - - -## Pre-trained models - -### Vision - -Code and pre-trained models for data2vec visions can be found [here](https://github.com/facebookresearch/data2vec_vision/tree/main/beit). - -### Speech - -| Model | Finetuning split | Dataset | Link -|---|---|---|--- -data2vec Base | No fine-tuning | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls.pt) -data2vec Base | 10 minutes | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls_10m.pt) -data2vec Base | 100 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls_100h.pt) -data2vec Base | 960 hours | [Librispeech](http://www.openslr.org/12) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/audio_base_ls_960h.pt) -data2vec Large | No fine-tuning | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_pretrained.pt) -data2vec Large | 10 minutes | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_10m.pt) -data2vec Large | 100 hours | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_100h.pt) -data2vec Large | 960 hours | [Libri-light](https://github.com/facebookresearch/libri-light) | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/vox_960h.pt) ---- - -### NLP - -Model | Fine-tuning data | Dataset | Link -|---|---|---|---| -data2vec Base | No fine-tuning | Books + Wiki | [download](https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt) - -## Training a new speech model with the CLI tools - -Given a directory containing wav files to be used for pretraining (we recommend splitting each file into separate file 10 to 30 seconds in length) - -### Prepare training data manifest: - -First, install the `soundfile` library: -```shell script -pip install soundfile -``` - -Next, run: - -```shell script -$ python examples/wav2vec/wav2vec_manifest.py /path/to/waves --dest /manifest/path --ext $ext --valid-percent $valid -``` - -$ext should be set to flac, wav, or whatever format your dataset happens to use that soundfile can read. - -$valid should be set to some reasonable percentage (like 0.01) of training data to use for validation. -To use a pre-defined validation set (like dev-other from librispeech), set to it 0 and then overwrite valid.tsv with a -separately pre-processed manifest file. - -### Train a data2vec Base model: - -This configuration was used for the base model trained on the Librispeech dataset in the data2vec paper - -Note that the input is expected to be single channel, sampled at 16 kHz - -```shell script -$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/audio/pretraining \ ---config-name base_librispeech task.data=/path/to/manifests common.user_dir=examples/data2vec -``` - -Note: you can simulate 16 GPUs by using k GPUs and adding command line parameters -`distributed_training.distributed_world_size=k` `+optimization.update_freq='[x]'` where x = 16/k - -### Fine-tune a pre-trained model with CTC: - -Fine-tuning a model requires parallel audio and labels file, as well as a vocabulary file in fairseq format. -A letter vocabulary can be downloaded [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). -An example [script](../wav2vec/libri_labels.py) that generates labels for the Librispeech dataset from the tsv file produced by wav2vec_manifest.py can be used as follows: - -```shell script -split=train -$ python libri_labels.py /path/to/tsv --output-dir /output/dir --output-name $split -``` - -Fine-tuning on 100h of Librispeech with letter targets: -```shell script -$ fairseq-hydra-train \ - distributed_training.distributed_port=$PORT \ - task.data=/path/to/data \ - model.w2v_path=/path/to/model.pt \ - --config-dir /path/to/fairseq-py/examples/wav2vec/config/finetuning \ - --config-name base_100h common.user_dir=examples/data2vec -``` - -There are other config files in the config/finetuning directory that can be used to fine-tune on other splits. -You can specify the right config via the `--config-name` parameter. - -Decoding with a language model during training requires flashlight [python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter). -If you want to use a language model, add `+criterion.wer_args='[/path/to/kenlm, /path/to/lexicon, 2, -1]'` to the command line. - -### Evaluating a CTC model: - -Evaluating a CTC model with a language model requires [flashlight python bindings](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) (previously called [wav2letter](https://github.com/facebookresearch/wav2letter) to be installed. - -Fairseq transformer language model used in the wav2vec 2.0 paper can be obtained from the [wav2letter model repository](https://github.com/facebookresearch/wav2letter/tree/master/recipes/sota/2019). -Be sure to upper-case the language model vocab after downloading it. - -Letter dictionary for pre-trained models can be found [here](https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt). - -Next, run the evaluation command: - -```shell script -python examples/speech_recognition/new/infer.py --config-dir examples/speech_recognition/new/conf \ ---config-name infer task=audio_finetuning task.data=/path/to/manifests common.user_dir=examples/data2vec \ -task.labels=ltr decoding.type=kenlm \ -decoding.lmweight=${lmweight} decoding.wordscore=${wordscore} decoding.silweight=${silscore} \ -decoding.lexicon=/path/to/lexicon \ -decoding.lmpath=/path/to/lm decoding.unique_wer_file=True \ -dataset.gen_subset=dev_clean,dev_other,test_clean,test_other \ -common_eval.path=/path/to/checkpoint.pt decoding.beam=1500 distributed_training.distributed_world_size=${num_gpus} -``` - -To get raw numbers, use decoding.type=viterbi and omit the lexicon. To use the transformer language model, use decoding.type=fairseqlm. - -## Training a new NLP model with the CLI tools - -Please follow the [RoBERTa](../roberta/README.md) instructions to preprocess your data. To train a data2vec model on run: - -```shell script -$ python fairseq_cli/hydra_train.py -m --config-dir examples/data2vec/config/text/pretraining \ ---config-name base task.data=/path/to/data common.user_dir=examples/data2vec -``` - -As for speech models, you can simulate 16 gpus by using the update_freq parameter. - -### Finetuning data2vec-text on GLUE - -Please use a command similar to this: - -```shell -$ python fairseq_cli/hydra_train.py -m --config-dir examples/roberta/config/finetuning \ - --config-name $task task.data=$data_path checkpoint.restore_file="${/path/to/pretrained/model.pt}" -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/config/audio/pretraining/base_librispeech.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/config/audio/pretraining/base_librispeech.yaml deleted file mode 100644 index c332c5a3f8e5e48150a3c0c3649d81234521bc16..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/config/audio/pretraining/base_librispeech.yaml +++ /dev/null @@ -1,83 +0,0 @@ -# @package _group_ - -common: - fp16: true - log_format: json - log_interval: 200 - tensorboard_logdir: tb - -checkpoint: - save_interval: 5 - save_interval_updates: 25000 - keep_interval_updates: 1 - no_epoch_checkpoints: true - -task: - _name: audio_pretraining - data: ??? - max_sample_size: 320000 - min_sample_size: 32000 - normalize: true - -dataset: - num_workers: 6 - max_tokens: 3800000 - skip_invalid_size_inputs_valid_test: true - validate_interval: 5 - required_batch_size_multiple: 1 - disable_validation: true - -distributed_training: - distributed_world_size: 16 - ddp_backend: legacy_ddp - -criterion: - _name: model - log_keys: - - ema_decay - - target_var - - pred_var - -optimization: - max_update: 400000 - lr: [0.0005] - -optimizer: - _name: adam - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - weight_decay: 0.01 - -lr_scheduler: - _name: tri_stage - phase_ratio: [0.03,0.9,0.07] - -model: - _name: data2vec_audio - extractor_mode: layer_norm - encoder_layerdrop: 0.05 - dropout_input: 0.0 - dropout_features: 0.0 - feature_grad_mult: 1.0 - encoder_embed_dim: 768 - - mask_prob: 0.65 - mask_length: 10 - - loss_beta: 0 - loss_scale: null - - instance_norm_target_layer: true - average_top_k_layers: 8 - - pos_conv_depth: 5 - conv_pos: 95 - - ema_decay: 0.999 - ema_end_decay: 0.9999 - ema_anneal_end_step: 30000 - ema_transformer_only: true - ema_layers_only: true - - require_same_masks: true - mask_dropout: 0 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/config/text/pretraining/base.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/config/text/pretraining/base.yaml deleted file mode 100644 index c6b07c4052aaf64d5b73bd983e395fa983b54cd5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/config/text/pretraining/base.yaml +++ /dev/null @@ -1,77 +0,0 @@ -# @package _group_ -common: - fp16: true - log_format: json - log_interval: 200 - tensorboard_logdir: tb - -checkpoint: - no_epoch_checkpoints: true - save_interval_updates: 50000 - keep_interval_updates: 1 - -distributed_training: - distributed_world_size: 16 - ddp_backend: legacy_ddp - -task: - _name: masked_lm - data: ??? - sample_break_mode: complete_doc - tokens_per_sample: 512 - include_target_tokens: true - random_token_prob: 0 - leave_unmasked_prob: 0 - mask_prob: 0.35 - mask_multiple_length: 4 - -criterion: model - -dataset: - max_tokens: 8192 - ignore_unused_valid_subsets: true - skip_invalid_size_inputs_valid_test: true - -optimizer: - _name: adam - weight_decay: 0.01 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: cosine - warmup_updates: 10000 - -optimization: - clip_norm: 5 - lr: [0.0002] - max_update: 1000000 - update_freq: [1] - -model: - _name: data2vec_text - head_layers: 2 - average_top_k_layers: 10 - layer_norm_target_layer: true - loss_scale: 1 - ema_decay: 0.999 - ema_end_decay: 0.9999 - ema_anneal_end_step: 300000 - loss_beta: 4 - ema_transformer_layers_only: true - - transformer: - dropout: 0.1 - attention_dropout: 0.1 - layernorm_embedding: true - activation_fn: gelu - no_scale_embedding: true - max_source_positions: 512 - encoder: - embed_dim: 768 - ffn_embed_dim: 3072 - layers: 12 - attention_heads: 12 - normalize_before: false - learned_pos: true - layerdrop: 0 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/models/data2vec_audio.py b/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/models/data2vec_audio.py deleted file mode 100644 index da4876160729a2c43f85122433db0c461c7ff0a1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/models/data2vec_audio.py +++ /dev/null @@ -1,535 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 dataclasses import dataclass, field -from typing import Optional - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F -from fairseq.data.data_utils import compute_mask_indices -from fairseq.models import BaseFairseqModel, register_model -from fairseq.models.wav2vec import ( - ConvFeatureExtractionModel, - TransformerEncoder, - Wav2Vec2Config, -) -from fairseq.modules import ( - EMAModule, - EMAModuleConfig, - GradMultiply, - LayerNorm, -) -from fairseq.utils import index_put -from omegaconf import II - -logger = logging.getLogger(__name__) - - -@dataclass -class Data2VecAudioConfig(Wav2Vec2Config): - - loss_beta: float = field( - default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} - ) - loss_scale: Optional[float] = field( - default=None, - metadata={ - "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" - }, - ) - average_top_k_layers: int = field( - default=8, metadata={"help": "how many layers to average"} - ) - - layer_norm_target_layer: bool = False - instance_norm_target_layer: bool = False - instance_norm_targets: bool = False - layer_norm_targets: bool = False - batch_norm_target_layer: bool = False - group_norm_target_layer: bool = False - - ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) - ema_end_decay: float = field( - default=0.9999, metadata={"help": "final ema decay rate"} - ) - - # when to finish annealing ema decay rate - ema_anneal_end_step: int = II("optimization.max_update") - - ema_transformer_only: bool = field( - default=True, - metadata={"help": "whether to momentum update only the transformer"}, - ) - ema_layers_only: bool = field( - default=True, - metadata={"help": "whether to momentum update only the transformer layers"}, - ) - - max_update: int = II("optimization.max_update") - - min_target_var: float = field( - default=0.1, metadata={"help": "stop training if target var falls below this"} - ) - min_pred_var: float = field( - default=0.01, - metadata={"help": "stop training if prediction var falls below this"}, - ) - - -def get_annealed_rate(start, end, curr_step, total_steps): - r = end - start - pct_remaining = 1 - curr_step / total_steps - return end - r * pct_remaining - - -@register_model("data2vec_audio", dataclass=Data2VecAudioConfig) -class Data2VecAudioModel(BaseFairseqModel): - def __init__(self, cfg: Data2VecAudioConfig): - super().__init__() - self.cfg = cfg - - feature_enc_layers = eval(cfg.conv_feature_layers) - self.extractor_embed = feature_enc_layers[-1][0] - - self.ema = None - self.embed = cfg.encoder_embed_dim - - self.average_top_k_layers = cfg.average_top_k_layers - self.loss_beta = cfg.loss_beta - self.loss_scale = cfg.loss_scale - - 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.extractor_embed, cfg.encoder_embed_dim) - - 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.mask_emb = nn.Parameter( - torch.FloatTensor(cfg.encoder_embed_dim).uniform_() - ) - - self.encoder = TransformerEncoder(cfg) - self.layer_norm = LayerNorm(self.extractor_embed) - - self.final_proj = nn.Linear(self.embed, self.embed) - - self.num_updates = 0 - - def make_ema_teacher(self): - ema_config = EMAModuleConfig( - ema_decay=self.cfg.ema_decay, - ema_fp32=True, - ) - skip_keys = set() - if self.cfg.ema_layers_only: - self.cfg.ema_transformer_only = True - for k, _ in self.encoder.pos_conv.named_parameters(): - skip_keys.add(f"pos_conv.{k}") - - self.ema = EMAModule( - self.encoder if self.cfg.ema_transformer_only else self, - ema_config, - skip_keys=skip_keys, - ) - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - - if self.ema is None and self.final_proj is not None: - logger.info(f"making ema teacher") - self.make_ema_teacher() - elif self.training and self.ema is not None: - if self.cfg.ema_decay != self.cfg.ema_end_decay: - if num_updates >= self.cfg.ema_anneal_end_step: - decay = self.cfg.ema_end_decay - else: - decay = get_annealed_rate( - self.cfg.ema_decay, - self.cfg.ema_end_decay, - num_updates, - self.cfg.ema_anneal_end_step, - ) - self.ema.set_decay(decay) - if self.ema.get_decay() < 1: - self.ema.step(self.encoder if self.cfg.ema_transformer_only else self) - - self.num_updates = num_updates - - def state_dict(self, destination=None, prefix="", keep_vars=False): - state = super().state_dict(destination, prefix, keep_vars) - - if self.ema is not None: - state[prefix + "_ema"] = self.ema.fp32_params - - return state - - def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): - if self.ema is not None: - k = prefix + "_ema" - assert k in state_dict - self.ema.restore(state_dict[k], True) - del state_dict[k] - return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) - - @classmethod - def build_model(cls, cfg: Data2VecAudioConfig, 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=1, - 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 _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, - ): - features = source - - if self.feature_grad_mult > 0: - features = self.feature_extractor(features) - if self.feature_grad_mult != 1.0: - features = GradMultiply.apply(features, self.feature_grad_mult) - else: - with torch.no_grad(): - features = self.feature_extractor(features) - - features = features.transpose(1, 2) - - features = self.layer_norm(features) - - orig_padding_mask = padding_mask - - 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 - - if self.post_extract_proj is not None: - features = self.post_extract_proj(features) - - pre_encoder_features = None - if self.cfg.ema_transformer_only: - pre_encoder_features = features.clone() - - features = self.dropout_input(features) - - if mask: - x, mask_indices = self.apply_mask( - features, - padding_mask, - mask_indices=mask_indices, - mask_channel_indices=mask_channel_indices, - ) - else: - x = 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, - "layer_results": layer_results, - } - - result = { - "losses": {}, - } - - with torch.no_grad(): - self.ema.model.eval() - - if self.cfg.ema_transformer_only: - y, layer_results = self.ema.model.extract_features( - pre_encoder_features, - padding_mask=padding_mask, - min_layer=self.cfg.encoder_layers - self.average_top_k_layers, - ) - y = { - "x": y, - "padding_mask": padding_mask, - "layer_results": layer_results, - } - else: - y = self.ema.model.extract_features( - source=source, - padding_mask=orig_padding_mask, - mask=False, - ) - - target_layer_results = [l[2] for l in y["layer_results"]] - - permuted = False - if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer: - target_layer_results = [ - tl.permute(1, 2, 0) for tl in target_layer_results # TBC -> BCT - ] - permuted = True - - if self.cfg.batch_norm_target_layer: - target_layer_results = [ - F.batch_norm( - tl.float(), running_mean=None, running_var=None, training=True - ) - for tl in target_layer_results - ] - - if self.cfg.instance_norm_target_layer: - target_layer_results = [ - F.instance_norm(tl.float()) for tl in target_layer_results - ] - - if permuted: - target_layer_results = [ - tl.transpose(1, 2) for tl in target_layer_results # BCT -> BTC - ] - - if self.cfg.group_norm_target_layer: - target_layer_results = [ - F.layer_norm(tl.float(), tl.shape[-2:]) - for tl in target_layer_results - ] - - if self.cfg.layer_norm_target_layer: - target_layer_results = [ - F.layer_norm(tl.float(), tl.shape[-1:]) - for tl in target_layer_results - ] - - y = sum(target_layer_results) / len(target_layer_results) - - if self.cfg.layer_norm_targets: - y = F.layer_norm(y.float(), y.shape[-1:]) - - if self.cfg.instance_norm_targets: - y = F.instance_norm(y.float().transpose(1, 2)).transpose(1, 2) - - if not permuted: - y = y.transpose(0, 1) - - y = y[mask_indices] - - x = x[mask_indices] - x = self.final_proj(x) - - sz = x.size(-1) - - if self.loss_beta == 0: - loss = F.mse_loss(x.float(), y.float(), reduction="none").sum(dim=-1) - else: - loss = F.smooth_l1_loss( - x.float(), y.float(), reduction="none", beta=self.loss_beta - ).sum(dim=-1) - - if self.loss_scale is not None: - scale = self.loss_scale - else: - scale = 1 / math.sqrt(sz) - - result["losses"]["regression"] = loss.sum() * scale - - if "sample_size" not in result: - result["sample_size"] = loss.numel() - - with torch.no_grad(): - result["target_var"] = self.compute_var(y) - result["pred_var"] = self.compute_var(x.float()) - - if self.num_updates > 5000 and result["target_var"] < self.cfg.min_target_var: - logger.error( - f"target var is {result['target_var'].item()} < {self.cfg.min_target_var}, exiting" - ) - raise Exception( - f"target var is {result['target_var'].item()} < {self.cfg.min_target_var}, exiting" - ) - if self.num_updates > 5000 and result["pred_var"] < self.cfg.min_pred_var: - logger.error( - f"pred var is {result['pred_var'].item()} < {self.cfg.min_pred_var}, exiting" - ) - raise Exception( - f"pred var is {result['pred_var'].item()} < {self.cfg.min_pred_var}, exiting" - ) - - if self.ema is not None: - result["ema_decay"] = self.ema.get_decay() * 1000 - - return result - - @staticmethod - def compute_var(y): - y = y.view(-1, y.size(-1)) - if dist.is_initialized(): - zc = torch.tensor(y.size(0)).cuda() - zs = y.sum(dim=0) - zss = (y ** 2).sum(dim=0) - - dist.all_reduce(zc) - dist.all_reduce(zs) - dist.all_reduce(zss) - - var = zss / (zc - 1) - (zs ** 2) / (zc * (zc - 1)) - return torch.sqrt(var + 1e-6).mean() - else: - return torch.sqrt(y.var(dim=0) + 1e-6).mean() - - 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 remove_pretraining_modules(self, last_layer=None): - self.final_proj = None - self.ema = 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 - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/models/data2vec_text.py b/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/models/data2vec_text.py deleted file mode 100644 index ed137553f5d509195804c732e998934916c4403d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/data2vec/models/data2vec_text.py +++ /dev/null @@ -1,515 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 dataclasses import dataclass, field -from typing import Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F -from fairseq.dataclass import FairseqDataclass -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderModel, - register_model, -) -from fairseq.models.roberta.model import RobertaClassificationHead, RobertaLMHead -from fairseq.models.transformer import TransformerConfig, TransformerEncoder -from fairseq.modules import EMAModule, EMAModuleConfig -from fairseq.modules.transformer_sentence_encoder import init_bert_params -from omegaconf import II - -logger = logging.getLogger(__name__) - - -@dataclass -class Data2VecTextConfig(FairseqDataclass): - max_positions: int = II("task.tokens_per_sample") - - head_layers: int = 1 - - transformer: TransformerConfig = TransformerConfig() - - load_checkpoint_heads: bool = field( - default=False, - metadata={"help": "(re-)register and load heads when loading checkpoints"}, - ) - - loss_beta: float = field( - default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} - ) - loss_scale: Optional[float] = field( - default=None, - metadata={ - "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" - }, - ) - average_top_k_layers: int = field( - default=8, metadata={"help": "how many layers to average"} - ) - - layer_norm_target_layer: bool = False - instance_norm_target_layer: bool = False - batch_norm_target_layer: bool = False - instance_norm_targets: bool = False - layer_norm_targets: bool = False - - ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) - ema_end_decay: float = field( - default=0.9999, metadata={"help": "final ema decay rate"} - ) - - # when to finish annealing ema decay rate - ema_anneal_end_step: int = II("optimization.max_update") - - ema_transformer_layers_only: bool = field( - default=True, - metadata={"help": "whether to momentum update only the transformer layers"}, - ) - - -def get_annealed_rate(start, end, curr_step, total_steps): - r = end - start - pct_remaining = 1 - curr_step / total_steps - return end - r * pct_remaining - - -@register_model("data2vec_text", dataclass=Data2VecTextConfig) -class Data2VecTextModel(FairseqEncoderModel): - def __init__(self, cfg: Data2VecTextConfig, encoder): - super().__init__(encoder) - self.cfg = cfg - - # We follow BERT's random weight initialization - self.apply(init_bert_params) - - self.classification_heads = nn.ModuleDict() - - @classmethod - def build_model(cls, cfg, task): - """Build a new model instance.""" - - encoder = Data2VecTextEncoder(cfg, task.source_dictionary, task.cfg.data) - - return cls(cfg, encoder) - - def forward( - self, - src_tokens, - target_tokens=None, - features_only=False, - return_all_hiddens=False, - classification_head_name=None, - **kwargs, - ): - if classification_head_name is not None: - features_only = True - - res = self.encoder( - src_tokens, target_tokens, features_only, return_all_hiddens, **kwargs - ) - - if isinstance(res, tuple): - x, extra = res - else: - return res - - if classification_head_name is not None: - x = self.classification_heads[classification_head_name](x) - return x, extra - - 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.cfg.transformer.encoder.embed_dim, - inner_dim=inner_dim or self.cfg.transformer.encoder.embed_dim, - num_classes=num_classes, - activation_fn="tanh", - pooler_dropout=0, - ) - - @property - def supported_targets(self): - return {"self"} - - 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] - - if self.encoder.regression_head is not None: - if ".lm_head." in k: - new_k = k.replace(".lm_head.", ".regression_head.") - state_dict[new_k] = state_dict[k] - del state_dict[k] - else: - if ".regression_head." in 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") - or self.classification_heads is None - 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 self.cfg.load_checkpoint_heads: - 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") - and self.classification_heads is not None - and len(self.classification_heads) > 0 - ): - 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 - - for k in list(state_dict.keys()): - if k.startswith(prefix + "encoder.lm_head.") or k.startswith( - prefix + "encoder.emb_head." - ): - del state_dict[k] - - self.encoder.lm_head = None - - if self.encoder.target_model is None: - for k in list(state_dict.keys()): - if k.startswith(prefix + "encoder.target_model."): - del state_dict[k] - - if (self.encoder.ema is None) and (prefix + "encoder._ema" in state_dict): - del state_dict[prefix + "encoder._ema"] - - def remove_pretraining_modules(self, last_layer=None): - self.encoder.lm_head = None - self.encoder.regression_head = None - self.encoder.ema = None - self.classification_heads = None - - if last_layer is not None: - self.encoder.sentence_encoder.layers = nn.ModuleList( - l - for i, l in enumerate(self.encoder.sentence_encoder.layers) - if i <= last_layer - ) - self.encoder.sentence_encoder.layer_norm = None - - -class Data2VecTextEncoder(FairseqEncoder): - def __init__(self, cfg: Data2VecTextConfig, dictionary, task_data): - super().__init__(dictionary) - - self.cfg = cfg - - embed_tokens = self.build_embedding( - len(dictionary), cfg.transformer.encoder.embed_dim, dictionary.pad() - ) - - self.sentence_encoder = self.build_encoder(cfg, dictionary, embed_tokens) - self.mask_idx = dictionary.index("") - assert self.mask_idx != dictionary.unk(), dictionary.symbols - - self.ema = None - self.average_top_k_layers = cfg.average_top_k_layers - self.loss_scale = cfg.loss_scale - - assert self.cfg.head_layers >= 1 - - embed_dim = cfg.transformer.encoder.embed_dim - curr_dim = embed_dim - projs = [] - for i in range(self.cfg.head_layers - 1): - next_dim = embed_dim * 2 if i == 0 else curr_dim - projs.append(nn.Linear(curr_dim, next_dim)) - projs.append(nn.GELU()) - curr_dim = next_dim - - projs.append(nn.Linear(curr_dim, embed_dim)) - self.regression_head = nn.Sequential(*projs) - - self.num_updates = 0 - - def build_embedding(self, vocab_size, embedding_dim, padding_idx): - return nn.Embedding(vocab_size, embedding_dim, padding_idx) - - def build_encoder(self, cfg, dictionary, embed_tokens): - encoder = TransformerEncoder(cfg.transformer, dictionary, embed_tokens, return_fc=True) - 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 make_ema_teacher(self): - ema_config = EMAModuleConfig( - ema_decay=self.cfg.ema_decay, - ema_fp32=True, - ) - skip_keys = set() - if self.cfg.ema_transformer_layers_only: - for k, _ in self.sentence_encoder.embed_positions.named_parameters(): - skip_keys.add(f"embed_tokens.{k}") - for k, _ in self.sentence_encoder.embed_positions.named_parameters(): - skip_keys.add(f"embed_positions.{k}") - if self.sentence_encoder.layernorm_embedding is not None: - for ( - k, - _, - ) in self.sentence_encoder.layernorm_embedding.named_parameters(): - skip_keys.add(f"layernorm_embedding.{k}") - if self.sentence_encoder.layer_norm is not None: - for k, _ in self.sentence_encoder.layer_norm.named_parameters(): - skip_keys.add(f"layernorm_embedding.{k}") - - self.ema = EMAModule( - self.sentence_encoder, - ema_config, - skip_keys=skip_keys, - ) - - def set_num_updates(self, num_updates): - super().set_num_updates(num_updates) - - if self.ema is None and self.regression_head is not None: - logger.info(f"making ema teacher") - self.make_ema_teacher() - elif self.training and self.ema is not None: - if self.cfg.ema_decay != self.cfg.ema_end_decay: - if num_updates >= self.cfg.ema_anneal_end_step: - decay = self.cfg.ema_end_decay - else: - decay = get_annealed_rate( - self.cfg.ema_decay, - self.cfg.ema_end_decay, - num_updates, - self.cfg.ema_anneal_end_step, - ) - self.ema.set_decay(decay) - if self.ema.get_decay() < 1: - self.ema.step(self.sentence_encoder) - - def state_dict(self, destination=None, prefix="", keep_vars=False): - state = super().state_dict(destination, prefix, keep_vars) - if self.ema is not None: - state[prefix + "_ema"] = self.ema.fp32_params - return state - - def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): - if self.ema is not None: - k = prefix + "_ema" - assert k in state_dict - self.ema.restore(state_dict[k], True) - del state_dict[k] - return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) - - def forward( - self, - src_tokens, - target_tokens=None, - 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 features_only: - return x, extra - - assert target_tokens is not None - - with torch.no_grad(): - # use EMA parameter as the teacher - self.ema.model.eval() - - encoder_out = self.ema.model( - target_tokens, - return_all_hiddens=True, - ) - y = encoder_out["fc_results"] - - y = y[-self.average_top_k_layers :] - - permuted = False - if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer: - y = [tl.permute(1, 2, 0) for tl in y] # TBC -> BCT - permuted = True - - if self.cfg.batch_norm_target_layer: - y = [ - F.batch_norm( - tl.float(), running_mean=None, running_var=None, training=True - ) - for tl in y - ] - - if self.cfg.instance_norm_target_layer: - y = [F.instance_norm(tl.float()) for tl in y] - - if permuted: - y = [tl.transpose(1, 2) for tl in y] # BCT -> BTC - - if self.cfg.layer_norm_target_layer: - y = [F.layer_norm(tl.float(), tl.shape[-1:]) for tl in y] - - y = sum(y) / len(y) - - if not permuted: - y = y.transpose(0, 1) - - if self.cfg.layer_norm_targets: - y = F.layer_norm(y.float(), y.shape[-1:]) - - if self.cfg.instance_norm_targets: - y = F.instance_norm(y.transpose(1, 2)).transpose(1, 2) - - masked_indices = src_tokens.eq(self.mask_idx) - - x = x[masked_indices] - y = y[masked_indices] - - x = self.regression_head(x) - - sz = x.size(-1) - if self.cfg.loss_beta == 0: - loss = F.mse_loss(x.float(), y.float(), reduction="none").sum(dim=-1) - else: - loss = F.smooth_l1_loss( - x.float(), y.float(), reduction="none", beta=self.cfg.loss_beta - ).sum(dim=-1) - - result = { - "losses": { - "main": loss.sum() / math.sqrt(sz) - if self.loss_scale <= 0 - else loss.sum() * self.loss_scale, - }, - "sample_size": loss.numel(), - } - - # logging other values - other_logs = { - "ema_decay": self.ema.get_decay() * 1000 - } - result["logs"] = other_logs - return result - - 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, - "encoder_embedding": encoder_out["encoder_embedding"][0], - } - - 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.cfg.max_positions diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/README.md deleted file mode 100644 index b155e855f2f94e30ad22262f260008fda8ac1804..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/README.md +++ /dev/null @@ -1,202 +0,0 @@ -# Discriminative Reranking for Neural Machine Translation -https://aclanthology.org/2021.acl-long.563/ - -This folder contains source code for training DrNMT, a discriminatively trained reranker for neural machine translation. - -## Data preparation -1. Follow the instructions under `examples/translation` to build a base MT model. Prepare three files, one with source sentences, one with ground truth target sentences, and one with hypotheses generated from the base MT model. Each line in the file contains one sentence in raw text (i.e. no sentencepiece, etc.). Below is an example of the files with _N_ hypotheses for each source sentence. - -``` -# Example of the source sentence file: (The file should contain L lines.) - -source_sentence_1 -source_sentence_2 -source_sentence_3 -... -source_sentence_L - -# Example of the target sentence file: (The file should contain L lines.) - -target_sentence_1 -target_sentence_2 -target_sentence_3 -... -target_sentence_L - -# Example of the hypotheses file: (The file should contain L*N lines.) - -source_sentence_1_hypo_1 -source_sentence_1_hypo_2 -... -source_sentence_1_hypo_N -source_sentence_2_hypo_1 -... -source_sentence_2_hypo_N -... -source_sentence_L_hypo_1 -... -source_sentence_L_hypo_N -``` - -2. Download the [XLMR model](https://github.com/fairinternal/fairseq-py/tree/main/examples/xlmr#pre-trained-models). -``` -wget https://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz -tar zxvf xlmr.base.tar.gz - -# The folder should contain dict.txt, model.pt and sentencepiece.bpe.model. -``` - -3. Prepare scores and BPE data. -* `N`: Number of hypotheses per each source sentence. We use 50 in the paper. -* `SPLIT`: Name of the data split, i.e. train, valid, test. Use split_name, split_name1, split_name2, ..., if there are multiple datasets for a split, e.g. train, train1, valid, valid1. -* `NUM_SHARDS`: Number of shards. Set this to 1 for non-train splits. -* `METRIC`: The metric for DrNMT to optimize for. We support either `bleu` or `ter`. -``` -# For each data split, e.g. train, valid, test, etc., run the following: - -SOURCE_FILE=/path/to/source_sentence_file -TARGET_FILE=/path/to/target_sentence_file -HYPO_FILE=/path/to/hypo_file -XLMR_DIR=/path/to/xlmr -OUTPUT_DIR=/path/to/output - -python scripts/prep_data.py \ - --input-source ${SOURCE_FILE} \ - --input-target ${TARGET_FILE} \ - --input-hypo ${HYPO_FILE} \ - --output-dir ${OUTPUT_DIR} \ - --split $SPLIT - --beam $N \ - --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ - --metric $METRIC \ - --num-shards ${NUM_SHARDS} - -# The script will create ${OUTPUT_DIR}/$METRIC with ${NUM_SHARDS} splits. -# Under split*/input_src, split*/input_tgt and split*/$METRIC, there will be $SPLIT.bpe and $SPLIT.$METRIC files, respectively. - -``` - -4. Pre-process the data into fairseq format. -``` -# use comma to separate if there are more than one train or valid set -for suffix in src tgt ; do - fairseq-preprocess --only-source \ - --trainpref ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/train.bpe \ - --validpref ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/valid.bpe \ - --destdir ${OUTPUT_DIR}/$METRIC/split1/input_${suffix} \ - --workers 60 \ - --srcdict ${XLMR_DIR}/dict.txt -done - -for i in `seq 2 ${NUM_SHARDS}`; do - for suffix in src tgt ; do - fairseq-preprocess --only-source \ - --trainpref ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix}/train.bpe \ - --destdir ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix} \ - --workers 60 \ - --srcdict ${XLMR_DIR}/dict.txt - - ln -s ${OUTPUT_DIR}/$METRIC/split1/input_${suffix}/valid* ${OUTPUT_DIR}/$METRIC/split${i}/input_${suffix}/. - done - - ln -s ${OUTPUT_DIR}/$METRIC/split1/$METRIC/valid* ${OUTPUT_DIR}/$METRIC/split${i}/$METRIC/. -done -``` - -## Training - -``` -EXP_DIR=/path/to/exp - -# An example of training the model with the config for De-En experiment in the paper. -# The config uses 16 GPUs and 50 hypotheses. -# For training with fewer number of GPUs, set -# distributed_training.distributed_world_size=k +optimization.update_freq='[x]' where x = 16/k -# For training with fewer number of hypotheses, set -# task.mt_beam=N dataset.batch_size=N dataset.required_batch_size_multiple=N - -fairseq-hydra-train -m \ - --config-dir config/ --config-name deen \ - task.data=${OUTPUT_DIR}/$METRIC/split1/ \ - task.num_data_splits=${NUM_SHARDS} \ - model.pretrained_model=${XLMR_DIR}/model.pt \ - common.user_dir=${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ - checkpoint.save_dir=${EXP_DIR} - -``` - -## Inference & scoring -Perform DrNMT reranking (fw + reranker score) -1. Tune weights on valid sets. -``` -# genrate N hypotheses with the base MT model (fw score) -VALID_SOURCE_FILE=/path/to/source_sentences # one sentence per line, converted to the sentencepiece used by the base MT model -VALID_TARGET_FILE=/path/to/target_sentences # one sentence per line in raw text, i.e. no sentencepiece and tokenization -MT_MODEL=/path/to/mt_model -MT_DATA_PATH=/path/to/mt_data - -cat ${VALID_SOURCE_FILE} | \ - fairseq-interactive ${MT_DATA_PATH} \ - --max-tokens 4000 --buffer-size 16 \ - --num-workers 32 --path ${MT_MODEL} \ - --beam $N --nbest $N \ - --post-process sentencepiece &> valid-hypo.out - -# replace "bleu" with "ter" to optimize for TER -python drnmt_rerank.py \ - ${OUTPUT_DIR}/$METRIC/split1/ \ - --path ${EXP_DIR}/checkpoint_best.pt \ - --in-text valid-hypo.out \ - --results-path ${EXP_DIR} \ - --gen-subset valid \ - --target-text ${VALID_TARGET_FILE} \ - --user-dir ${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ - --bpe sentencepiece \ - --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ - --beam $N \ - --batch-size $N \ - --metric bleu \ - --tune - -``` - -2. Apply best weights on test sets -``` -# genrate N hypotheses with the base MT model (fw score) -TEST_SOURCE_FILE=/path/to/source_sentences # one sentence per line, converted to the sentencepiece used by the base MT model - -cat ${TEST_SOURCE_FILE} | \ - fairseq-interactive ${MT_DATA_PATH} \ - --max-tokens 4000 --buffer-size 16 \ - --num-workers 32 --path ${MT_MODEL} \ - --beam $N --nbest $N \ - --post-process sentencepiece &> test-hypo.out - -# replace "bleu" with "ter" to evaluate TER -# Add --target-text for evaluating BLEU/TER, -# otherwise the script will only generate the hypotheses with the highest scores only. -python drnmt_rerank.py \ - ${OUTPUT_DIR}/$METRIC/split1/ \ - --path ${EXP_DIR}/checkpoint_best.pt \ - --in-text test-hypo.out \ - --results-path ${EXP_DIR} \ - --gen-subset test \ - --user-dir ${FAIRSEQ_ROOT}/examples/discriminative_reranking_nmt \ - --bpe sentencepiece \ - --sentencepiece-model ${XLMR_DIR}/sentencepiece.bpe.model \ - --beam $N \ - --batch-size $N \ - --metric bleu \ - --fw-weight ${BEST_FW_WEIGHT} \ - --lenpen ${BEST_LENPEN} -``` - -## Citation -```bibtex -@inproceedings{lee2021discriminative, - title={Discriminative Reranking for Neural Machine Translation}, - author={Lee, Ann and Auli, Michael and Ranzato, Marc'Aurelio}, - booktitle={ACL}, - year={2021} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/__init__.py deleted file mode 100644 index 0278f6a27340c7ff7e207d09348483d1b0d3a100..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import criterions, models, tasks # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/config/deen.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/config/deen.yaml deleted file mode 100644 index 3fc2d5fcf5bacbb842d181fcfcde80e55331fed7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/config/deen.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# @package _group_ - -common: - fp16: true - log_format: json - log_interval: 50 - seed: 2 - -checkpoint: - no_epoch_checkpoints: true - best_checkpoint_metric: bleu - maximize_best_checkpoint_metric: true - -task: - _name: discriminative_reranking_nmt - data: ??? - num_data_splits: ??? - include_src: true - mt_beam: 50 - eval_target_metric: true - target_metric: bleu - -dataset: - batch_size: 50 - num_workers: 6 - required_batch_size_multiple: 50 - valid_subset: ??? - -criterion: - _name: kl_divergence_rereanking - target_dist_norm: minmax - temperature: 0.5 - -optimization: - max_epoch: 200 - lr: [0.00005] - update_freq: [32] - -optimizer: - _name: adam - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 8000 - total_num_update: 320000 - -model: - _name: discriminative_nmt_reranker - pretrained_model: ??? - classifier_dropout: 0.2 - -distributed_training: - ddp_backend: no_c10d - distributed_world_size: 16 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/criterions/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/criterions/__init__.py deleted file mode 100644 index 10b054ea1878ed749b4608ce359da46b296d03e3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/criterions/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .discriminative_reranking_criterion import KLDivergenceRerankingCriterion - -__all__ = [ - "KLDivergenceRerankingCriterion", -] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.py deleted file mode 100644 index 5d8855397970bd36261e52fb4bd54954c666a563..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/criterions/discriminative_reranking_criterion.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. - -import math -from dataclasses import dataclass, field - -import torch -import torch.nn.functional as F -from fairseq import metrics, utils -from fairseq.criterions import FairseqCriterion, register_criterion -from fairseq.dataclass import ChoiceEnum, FairseqDataclass - -_EPSILON = torch.finfo(torch.float32).eps -TARGET_DIST_NORM_CHOICES = ChoiceEnum(["none", "minmax"]) - - -@dataclass -class KLDivergenceRerankingCriterionConfig(FairseqDataclass): - target_dist_norm: TARGET_DIST_NORM_CHOICES = field( - default="none", - metadata={"help": "method to normalize the range of target scores"}, - ) - temperature: float = field( - default=1.0, - metadata={"help": "temperature in softmax for target distributions"}, - ) - forward_batch_size: int = field( - default=32, - metadata={ - "help": "number of hypotheses per batch for model forward (set a value smaller than --mt-beam to avoid OOM when training with a large beam size)" - }, - ) - - -@register_criterion( - "kl_divergence_rereanking", dataclass=KLDivergenceRerankingCriterionConfig -) -class KLDivergenceRerankingCriterion(FairseqCriterion): - def __init__( - self, task, target_dist_norm, temperature, forward_batch_size, - ): - super().__init__(task) - self.target_dist_norm = target_dist_norm - self.temperature = temperature - self.forward_batch_size = forward_batch_size - - def forward(self, model, sample, reduce=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - - sample_size = sample["id"].numel() - assert sample_size % self.task.cfg.mt_beam == 0, ( - f"sample_size ({sample_size}) cannot be divided by beam size ({self.task.cfg.mt_beam})." - f"Please set --required-batch-size-multiple={self.task.cfg.mt_beam}." - ) - - # split into smaller batches for model forward - batch_out = [] - for i in range(0, sample_size, self.forward_batch_size): - j = min(i + self.forward_batch_size, sample_size) - - out = model( - src_tokens=sample["net_input"]["src_tokens"][i:j, :], - src_lengths=sample["net_input"]["src_lengths"][i:j], - ) - - batch_out.append( - model.sentence_forward(out, sample["net_input"]["src_tokens"][i:j, :]) - ) - - batch_out = torch.cat(batch_out, dim=0).view( - self.task.cfg.mt_beam, sample_size // self.task.cfg.mt_beam, -1 - ) # T x B x C - if model.joint_classification == "sent": - batch_out = model.joint_forward(batch_out) - scores = model.classification_forward(batch_out.view(sample_size, 1, -1)).view( - -1, self.task.cfg.mt_beam - ) # input: B x T x C - - loss = self.compute_kl_loss( - scores, sample["target"][:, 0].view(-1, self.task.cfg.mt_beam) - ) - - sample_size = sample_size // self.task.cfg.mt_beam - - logging_output = { - "loss": loss.detach(), - "ntokens": sample["ntokens"], - "nsentences": sample_size * self.task.cfg.mt_beam, - "sample_size": sample_size, - "scores": scores.detach(), - } - - return loss, sample_size, logging_output - - def compute_kl_loss(self, logits, target): - norm_target = target - if self.target_dist_norm == "minmax": - min_v = torch.min(target, 1, keepdim=True).values - max_v = torch.max(target, 1, keepdim=True).values - norm_target = (target - min_v) / (max_v - min_v + _EPSILON) - - target_dist = F.softmax( - norm_target / self.temperature, dim=-1, dtype=torch.float32 - ) - model_dist = F.log_softmax(logits, dim=-1, dtype=torch.float32) - loss = -(target_dist * model_dist - target_dist * target_dist.log()).sum() - return loss - - @staticmethod - def reduce_metrics(logging_outputs) -> None: - """Aggregate logging outputs from data parallel training.""" - loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) - - sample_size = utils.item( - sum(log.get("sample_size", 0) for log in logging_outputs) - ) - - loss = loss_sum / sample_size / math.log(2) - metrics.log_scalar("loss", loss, sample_size, round=3) - - @staticmethod - def logging_outputs_can_be_summed() -> bool: - """ - Whether the logging outputs returned by `forward` can be summed - across workers prior to calling `reduce_metrics`. Setting this - to True will improves distributed training speed. - """ - return True diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/drnmt_rerank.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/drnmt_rerank.py deleted file mode 100644 index f6ba7fd22847d530e0fdab7324312e0809dedbf1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/drnmt_rerank.py +++ /dev/null @@ -1,362 +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. -""" -Score raw text with a trained model. -""" - -import logging -import os -import random -import sys -from collections import namedtuple -from multiprocessing import Pool - -import numpy as np -import sacrebleu -import torch -from fairseq import checkpoint_utils, options, utils - -logger = logging.getLogger("fairseq_cli.drnmt_rerank") -logger.setLevel(logging.INFO) - -Batch = namedtuple("Batch", "ids src_tokens src_lengths") - - -pool_init_variables = {} - - -def init_loaded_scores(mt_scores, model_scores, hyp, ref): - global pool_init_variables - pool_init_variables["mt_scores"] = mt_scores - pool_init_variables["model_scores"] = model_scores - pool_init_variables["hyp"] = hyp - pool_init_variables["ref"] = ref - - -def parse_fairseq_gen(filename, task): - source = {} - hypos = {} - scores = {} - with open(filename, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if line.startswith("S-"): # source - uid, text = line.split("\t", 1) - uid = int(uid[2:]) - source[uid] = text - elif line.startswith("D-"): # hypo - uid, score, text = line.split("\t", 2) - uid = int(uid[2:]) - if uid not in hypos: - hypos[uid] = [] - scores[uid] = [] - hypos[uid].append(text) - scores[uid].append(float(score)) - else: - continue - - source_out = [source[i] for i in range(len(hypos))] - hypos_out = [h for i in range(len(hypos)) for h in hypos[i]] - scores_out = [s for i in range(len(scores)) for s in scores[i]] - - return source_out, hypos_out, scores_out - - -def read_target(filename): - with open(filename, "r", encoding="utf-8") as f: - output = [line.strip() for line in f] - return output - - -def make_batches(args, src, hyp, task, max_positions, encode_fn): - assert len(src) * args.beam == len( - hyp - ), f"Expect {len(src) * args.beam} hypotheses for {len(src)} source sentences with beam size {args.beam}. Got {len(hyp)} hypotheses intead." - hyp_encode = [ - task.source_dictionary.encode_line(encode_fn(h), add_if_not_exist=False).long() - for h in hyp - ] - if task.cfg.include_src: - src_encode = [ - task.source_dictionary.encode_line( - encode_fn(s), add_if_not_exist=False - ).long() - for s in src - ] - tokens = [(src_encode[i // args.beam], h) for i, h in enumerate(hyp_encode)] - lengths = [(t1.numel(), t2.numel()) for t1, t2 in tokens] - else: - tokens = [(h,) for h in hyp_encode] - lengths = [(h.numel(),) for h in hyp_encode] - - itr = task.get_batch_iterator( - dataset=task.build_dataset_for_inference(tokens, lengths), - max_tokens=args.max_tokens, - max_sentences=args.batch_size, - max_positions=max_positions, - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - ).next_epoch_itr(shuffle=False) - - for batch in itr: - yield Batch( - ids=batch["id"], - src_tokens=batch["net_input"]["src_tokens"], - src_lengths=batch["net_input"]["src_lengths"], - ) - - -def decode_rerank_scores(args): - if args.max_tokens is None and args.batch_size is None: - args.batch_size = 1 - - logger.info(args) - - use_cuda = torch.cuda.is_available() and not args.cpu - - # Load ensemble - logger.info("loading model(s) from {}".format(args.path)) - models, _model_args, task = checkpoint_utils.load_model_ensemble_and_task( - [args.path], arg_overrides=eval(args.model_overrides), - ) - - for model in models: - if args.fp16: - model.half() - if use_cuda: - model.cuda() - - # Initialize generator - generator = task.build_generator(args) - - # Handle tokenization and BPE - tokenizer = task.build_tokenizer(args) - bpe = task.build_bpe(args) - - def encode_fn(x): - if tokenizer is not None: - x = tokenizer.encode(x) - if bpe is not None: - x = bpe.encode(x) - return x - - max_positions = utils.resolve_max_positions( - task.max_positions(), *[model.max_positions() for model in models] - ) - - src, hyp, mt_scores = parse_fairseq_gen(args.in_text, task) - model_scores = {} - logger.info("decode reranker score") - for batch in make_batches(args, src, hyp, task, max_positions, encode_fn): - src_tokens = batch.src_tokens - src_lengths = batch.src_lengths - if use_cuda: - src_tokens = src_tokens.cuda() - src_lengths = src_lengths.cuda() - - sample = { - "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}, - } - scores = task.inference_step(generator, models, sample) - - for id, sc in zip(batch.ids.tolist(), scores.tolist()): - model_scores[id] = sc[0] - - model_scores = [model_scores[i] for i in range(len(model_scores))] - - return src, hyp, mt_scores, model_scores - - -def get_score(mt_s, md_s, w1, lp, tgt_len): - return mt_s / (tgt_len ** lp) * w1 + md_s - - -def get_best_hyps(mt_scores, md_scores, hypos, fw_weight, lenpen, beam): - assert len(mt_scores) == len(md_scores) and len(mt_scores) == len(hypos) - hypo_scores = [] - best_hypos = [] - best_scores = [] - offset = 0 - for i in range(len(hypos)): - tgt_len = len(hypos[i].split()) - hypo_scores.append( - get_score(mt_scores[i], md_scores[i], fw_weight, lenpen, tgt_len) - ) - - if (i + 1) % beam == 0: - max_i = np.argmax(hypo_scores) - best_hypos.append(hypos[offset + max_i]) - best_scores.append(hypo_scores[max_i]) - hypo_scores = [] - offset += beam - return best_hypos, best_scores - - -def eval_metric(args, hypos, ref): - if args.metric == "bleu": - score = sacrebleu.corpus_bleu(hypos, [ref]).score - else: - score = sacrebleu.corpus_ter(hypos, [ref]).score - - return score - - -def score_target_hypo(args, fw_weight, lp): - mt_scores = pool_init_variables["mt_scores"] - model_scores = pool_init_variables["model_scores"] - hyp = pool_init_variables["hyp"] - ref = pool_init_variables["ref"] - best_hypos, _ = get_best_hyps( - mt_scores, model_scores, hyp, fw_weight, lp, args.beam - ) - rerank_eval = None - if ref: - rerank_eval = eval_metric(args, best_hypos, ref) - print(f"fw_weight {fw_weight}, lenpen {lp}, eval {rerank_eval}") - - return rerank_eval - - -def print_result(best_scores, best_hypos, output_file): - for i, (s, h) in enumerate(zip(best_scores, best_hypos)): - print(f"{i}\t{s}\t{h}", file=output_file) - - -def main(args): - utils.import_user_module(args) - - src, hyp, mt_scores, model_scores = decode_rerank_scores(args) - - assert ( - not args.tune or args.target_text is not None - ), "--target-text has to be set when tuning weights" - if args.target_text: - ref = read_target(args.target_text) - assert len(src) == len( - ref - ), f"different numbers of source and target sentences ({len(src)} vs. {len(ref)})" - - orig_best_hypos = [hyp[i] for i in range(0, len(hyp), args.beam)] - orig_eval = eval_metric(args, orig_best_hypos, ref) - - if args.tune: - logger.info("tune weights for reranking") - - random_params = np.array( - [ - [ - random.uniform( - args.lower_bound_fw_weight, args.upper_bound_fw_weight - ), - random.uniform(args.lower_bound_lenpen, args.upper_bound_lenpen), - ] - for k in range(args.num_trials) - ] - ) - - logger.info("launching pool") - with Pool( - 32, - initializer=init_loaded_scores, - initargs=(mt_scores, model_scores, hyp, ref), - ) as p: - rerank_scores = p.starmap( - score_target_hypo, - [ - (args, random_params[i][0], random_params[i][1],) - for i in range(args.num_trials) - ], - ) - if args.metric == "bleu": - best_index = np.argmax(rerank_scores) - else: - best_index = np.argmin(rerank_scores) - best_fw_weight = random_params[best_index][0] - best_lenpen = random_params[best_index][1] - else: - assert ( - args.lenpen is not None and args.fw_weight is not None - ), "--lenpen and --fw-weight should be set" - best_fw_weight, best_lenpen = args.fw_weight, args.lenpen - - best_hypos, best_scores = get_best_hyps( - mt_scores, model_scores, hyp, best_fw_weight, best_lenpen, args.beam - ) - - if args.results_path is not None: - os.makedirs(args.results_path, exist_ok=True) - output_path = os.path.join( - args.results_path, "generate-{}.txt".format(args.gen_subset), - ) - with open(output_path, "w", buffering=1, encoding="utf-8") as o: - print_result(best_scores, best_hypos, o) - else: - print_result(best_scores, best_hypos, sys.stdout) - - if args.target_text: - rerank_eval = eval_metric(args, best_hypos, ref) - print(f"before reranking, {args.metric.upper()}:", orig_eval) - print( - f"after reranking with fw_weight={best_fw_weight}, lenpen={best_lenpen}, {args.metric.upper()}:", - rerank_eval, - ) - - -def cli_main(): - parser = options.get_generation_parser(interactive=True) - - parser.add_argument( - "--in-text", - default=None, - required=True, - help="text from fairseq-interactive output, containing source sentences and hypotheses", - ) - parser.add_argument("--target-text", default=None, help="reference text") - parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu") - parser.add_argument( - "--tune", - action="store_true", - help="if set, tune weights on fw scores and lenpen instead of applying fixed weights for reranking", - ) - parser.add_argument( - "--lower-bound-fw-weight", - default=0.0, - type=float, - help="lower bound of search space", - ) - parser.add_argument( - "--upper-bound-fw-weight", - default=3, - type=float, - help="upper bound of search space", - ) - parser.add_argument( - "--lower-bound-lenpen", - default=0.0, - type=float, - help="lower bound of search space", - ) - parser.add_argument( - "--upper-bound-lenpen", - default=3, - type=float, - help="upper bound of search space", - ) - parser.add_argument( - "--fw-weight", type=float, default=None, help="weight on the fw model score" - ) - parser.add_argument( - "--num-trials", - default=1000, - type=int, - help="number of trials to do for random search", - ) - - args = options.parse_args_and_arch(parser) - main(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/models/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/models/__init__.py deleted file mode 100644 index de5b80922917a334360863a9c20519748b2c09e6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/models/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .discriminative_reranking_model import DiscriminativeNMTReranker - -__all__ = [ - "DiscriminativeNMTReranker", -] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py deleted file mode 100644 index 9de5ac72dbfd5ae17855f4131099258bfac5efe8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py +++ /dev/null @@ -1,361 +0,0 @@ -import os -from dataclasses import dataclass, field - -import torch -import torch.nn as nn -from fairseq import utils -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.models import ( - BaseFairseqModel, - register_model, -) -from fairseq.models.roberta.model import RobertaClassificationHead -from fairseq.modules import ( - LayerNorm, - TransformerSentenceEncoder, - TransformerSentenceEncoderLayer, -) - -ACTIVATION_FN_CHOICES = ChoiceEnum(utils.get_available_activation_fns()) -JOINT_CLASSIFICATION_CHOICES = ChoiceEnum(["none", "sent"]) -SENTENCE_REP_CHOICES = ChoiceEnum(["head", "meanpool", "maxpool"]) - - -def update_init_roberta_model_state(state): - """ - update the state_dict of a Roberta model for initializing - weights of the BertRanker - """ - for k in list(state.keys()): - if ".lm_head." in k or "version" in k: - del state[k] - continue - # remove 'encoder/decoder.sentence_encoder.' from the key - assert k.startswith("encoder.sentence_encoder.") or k.startswith( - "decoder.sentence_encoder." - ), f"Cannot recognize parameter name {k}" - if "layernorm_embedding" in k: - new_k = k.replace(".layernorm_embedding.", ".emb_layer_norm.") - state[new_k[25:]] = state[k] - else: - state[k[25:]] = state[k] - del state[k] - - -class BaseRanker(nn.Module): - def __init__(self, args, task): - super().__init__() - - self.separator_token = task.dictionary.eos() - self.padding_idx = task.dictionary.pad() - - def forward(self, src_tokens): - raise NotImplementedError - - def get_segment_labels(self, src_tokens): - segment_boundary = (src_tokens == self.separator_token).long() - segment_labels = ( - segment_boundary.cumsum(dim=1) - - segment_boundary - - (src_tokens == self.padding_idx).long() - ) - - return segment_labels - - def get_positions(self, src_tokens, segment_labels): - segment_positions = ( - torch.arange(src_tokens.shape[1]) - .to(src_tokens.device) - .repeat(src_tokens.shape[0], 1) - ) - segment_boundary = (src_tokens == self.separator_token).long() - _, col_idx = (segment_positions * segment_boundary).nonzero(as_tuple=True) - col_idx = torch.cat([torch.zeros(1).type_as(col_idx), col_idx]) - offset = torch.cat( - [ - torch.zeros(1).type_as(segment_boundary), - segment_boundary.sum(dim=1).cumsum(dim=0)[:-1], - ] - ) - segment_positions -= col_idx[segment_labels + offset.unsqueeze(1)] * ( - segment_labels != 0 - ) - - padding_mask = src_tokens.ne(self.padding_idx) - segment_positions = (segment_positions + 1) * padding_mask.type_as( - segment_positions - ) + self.padding_idx - - return segment_positions - - -class BertRanker(BaseRanker): - def __init__(self, args, task): - super(BertRanker, self).__init__(args, task) - - init_model = getattr(args, "pretrained_model", "") - self.joint_layers = nn.ModuleList() - if os.path.isfile(init_model): - print(f"initialize weight from {init_model}") - - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - os.path.dirname(init_model), - checkpoint_file=os.path.basename(init_model), - ) - - in_state_dict = x["models"][0].state_dict() - init_args = x["args"].model - - num_positional_emb = init_args.max_positions + task.dictionary.pad() + 1 - - # follow the setup in roberta - self.model = TransformerSentenceEncoder( - padding_idx=task.dictionary.pad(), - vocab_size=len(task.dictionary), - num_encoder_layers=getattr( - args, "encoder_layers", init_args.encoder_layers - ), - embedding_dim=init_args.encoder_embed_dim, - ffn_embedding_dim=init_args.encoder_ffn_embed_dim, - num_attention_heads=init_args.encoder_attention_heads, - dropout=init_args.dropout, - attention_dropout=init_args.attention_dropout, - activation_dropout=init_args.activation_dropout, - num_segments=2, # add language embeddings - max_seq_len=num_positional_emb, - offset_positions_by_padding=False, - encoder_normalize_before=True, - apply_bert_init=True, - activation_fn=init_args.activation_fn, - freeze_embeddings=args.freeze_embeddings, - n_trans_layers_to_freeze=args.n_trans_layers_to_freeze, - ) - - # still need to learn segment embeddings as we added a second language embedding - if args.freeze_embeddings: - for p in self.model.segment_embeddings.parameters(): - p.requires_grad = False - - update_init_roberta_model_state(in_state_dict) - print("loading weights from the pretrained model") - self.model.load_state_dict( - in_state_dict, strict=False - ) # ignore mismatch in language embeddings - - ffn_embedding_dim = init_args.encoder_ffn_embed_dim - num_attention_heads = init_args.encoder_attention_heads - dropout = init_args.dropout - attention_dropout = init_args.attention_dropout - activation_dropout = init_args.activation_dropout - activation_fn = init_args.activation_fn - - classifier_embed_dim = getattr( - args, "embed_dim", init_args.encoder_embed_dim - ) - if classifier_embed_dim != init_args.encoder_embed_dim: - self.transform_layer = nn.Linear( - init_args.encoder_embed_dim, classifier_embed_dim - ) - else: - self.model = TransformerSentenceEncoder( - padding_idx=task.dictionary.pad(), - vocab_size=len(task.dictionary), - num_encoder_layers=args.encoder_layers, - embedding_dim=args.embed_dim, - ffn_embedding_dim=args.ffn_embed_dim, - num_attention_heads=args.attention_heads, - dropout=args.dropout, - attention_dropout=args.attention_dropout, - activation_dropout=args.activation_dropout, - max_seq_len=task.max_positions() - if task.max_positions() - else args.tokens_per_sample, - num_segments=2, - offset_positions_by_padding=False, - encoder_normalize_before=args.encoder_normalize_before, - apply_bert_init=args.apply_bert_init, - activation_fn=args.activation_fn, - ) - - classifier_embed_dim = args.embed_dim - ffn_embedding_dim = args.ffn_embed_dim - num_attention_heads = args.attention_heads - dropout = args.dropout - attention_dropout = args.attention_dropout - activation_dropout = args.activation_dropout - activation_fn = args.activation_fn - - self.joint_classification = args.joint_classification - if args.joint_classification == "sent": - if args.joint_normalize_before: - self.joint_layer_norm = LayerNorm(classifier_embed_dim) - else: - self.joint_layer_norm = None - - self.joint_layers = nn.ModuleList( - [ - TransformerSentenceEncoderLayer( - embedding_dim=classifier_embed_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, - ) - for _ in range(args.num_joint_layers) - ] - ) - - self.classifier = RobertaClassificationHead( - classifier_embed_dim, - classifier_embed_dim, - 1, # num_classes - "tanh", - args.classifier_dropout, - ) - - def forward(self, src_tokens, src_lengths): - segment_labels = self.get_segment_labels(src_tokens) - positions = self.get_positions(src_tokens, segment_labels) - - inner_states, _ = self.model( - tokens=src_tokens, - segment_labels=segment_labels, - last_state_only=True, - positions=positions, - ) - - return inner_states[-1].transpose(0, 1) # T x B x C -> B x T x C - - def sentence_forward(self, encoder_out, src_tokens=None, sentence_rep="head"): - # encoder_out: B x T x C - if sentence_rep == "head": - x = encoder_out[:, :1, :] - else: # 'meanpool', 'maxpool' - assert src_tokens is not None, "meanpool requires src_tokens input" - segment_labels = self.get_segment_labels(src_tokens) - padding_mask = src_tokens.ne(self.padding_idx) - encoder_mask = segment_labels * padding_mask.type_as(segment_labels) - - if sentence_rep == "meanpool": - ntokens = torch.sum(encoder_mask, dim=1, keepdim=True) - x = torch.sum( - encoder_out * encoder_mask.unsqueeze(2), dim=1, keepdim=True - ) / ntokens.unsqueeze(2).type_as(encoder_out) - else: # 'maxpool' - encoder_out[ - (encoder_mask == 0).unsqueeze(2).repeat(1, 1, encoder_out.shape[-1]) - ] = -float("inf") - x, _ = torch.max(encoder_out, dim=1, keepdim=True) - - if hasattr(self, "transform_layer"): - x = self.transform_layer(x) - - return x # B x 1 x C - - def joint_forward(self, x): - # x: T x B x C - if self.joint_layer_norm: - x = self.joint_layer_norm(x.transpose(0, 1)) - x = x.transpose(0, 1) - - for layer in self.joint_layers: - x, _ = layer(x, self_attn_padding_mask=None) - return x - - def classification_forward(self, x): - # x: B x T x C - return self.classifier(x) - - -@dataclass -class DiscriminativeNMTRerankerConfig(FairseqDataclass): - pretrained_model: str = field( - default="", metadata={"help": "pretrained model to load"} - ) - sentence_rep: SENTENCE_REP_CHOICES = field( - default="head", - metadata={ - "help": "method to transform the output of the transformer stack to a sentence-level representation" - }, - ) - - 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"} - ) - classifier_dropout: float = field( - default=0.0, metadata={"help": "classifier dropout probability"} - ) - embed_dim: int = field(default=768, metadata={"help": "embedding dimension"}) - ffn_embed_dim: int = field( - default=2048, metadata={"help": "embedding dimension for FFN"} - ) - encoder_layers: int = field(default=12, metadata={"help": "num encoder layers"}) - attention_heads: int = field(default=8, metadata={"help": "num attention heads"}) - encoder_normalize_before: bool = field( - default=False, metadata={"help": "apply layernorm before each encoder block"} - ) - apply_bert_init: bool = field( - default=False, metadata={"help": "use custom param initialization for BERT"} - ) - activation_fn: ACTIVATION_FN_CHOICES = field( - default="relu", metadata={"help": "activation function to use"} - ) - freeze_embeddings: bool = field( - default=False, metadata={"help": "freeze embeddings in the pretrained model"} - ) - n_trans_layers_to_freeze: int = field( - default=0, - metadata={ - "help": "number of layers to freeze in the pretrained transformer model" - }, - ) - - # joint classfication - joint_classification: JOINT_CLASSIFICATION_CHOICES = field( - default="none", - metadata={"help": "method to compute joint features for classification"}, - ) - num_joint_layers: int = field( - default=1, metadata={"help": "number of joint layers"} - ) - joint_normalize_before: bool = field( - default=False, - metadata={"help": "apply layer norm on the input to the joint layer"}, - ) - - -@register_model( - "discriminative_nmt_reranker", dataclass=DiscriminativeNMTRerankerConfig -) -class DiscriminativeNMTReranker(BaseFairseqModel): - @classmethod - def build_model(cls, args, task): - model = BertRanker(args, task) - return DiscriminativeNMTReranker(args, model) - - def __init__(self, args, model): - super().__init__() - - self.model = model - self.sentence_rep = args.sentence_rep - self.joint_classification = args.joint_classification - - def forward(self, src_tokens, src_lengths, **kwargs): - return self.model(src_tokens, src_lengths) - - def sentence_forward(self, encoder_out, src_tokens): - return self.model.sentence_forward(encoder_out, src_tokens, self.sentence_rep) - - def joint_forward(self, x): - return self.model.joint_forward(x) - - def classification_forward(self, x): - return self.model.classification_forward(x) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/scripts/prep_data.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/scripts/prep_data.py deleted file mode 100644 index 7aa7d37edc2c3e4c1d293911b753abf2ef597a7e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/scripts/prep_data.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python - -import argparse -from multiprocessing import Pool -from pathlib import Path - -import sacrebleu -import sentencepiece as spm - - -def read_text_file(filename): - with open(filename, "r") as f: - output = [line.strip() for line in f] - - return output - - -def get_bleu(in_sent, target_sent): - bleu = sacrebleu.corpus_bleu([in_sent], [[target_sent]]) - out = " ".join( - map(str, [bleu.score, bleu.sys_len, bleu.ref_len] + bleu.counts + bleu.totals) - ) - return out - - -def get_ter(in_sent, target_sent): - ter = sacrebleu.corpus_ter([in_sent], [[target_sent]]) - out = " ".join(map(str, [ter.score, ter.num_edits, ter.ref_length])) - return out - - -def init(sp_model): - global sp - sp = spm.SentencePieceProcessor() - sp.Load(sp_model) - - -def process(source_sent, target_sent, hypo_sent, metric): - source_bpe = " ".join(sp.EncodeAsPieces(source_sent)) - hypo_bpe = [" ".join(sp.EncodeAsPieces(h)) for h in hypo_sent] - - if metric == "bleu": - score_str = [get_bleu(h, target_sent) for h in hypo_sent] - else: # ter - score_str = [get_ter(h, target_sent) for h in hypo_sent] - - return source_bpe, hypo_bpe, score_str - - -def main(args): - assert ( - args.split.startswith("train") or args.num_shards == 1 - ), "--num-shards should be set to 1 for valid and test sets" - assert ( - args.split.startswith("train") - or args.split.startswith("valid") - or args.split.startswith("test") - ), "--split should be set to train[n]/valid[n]/test[n]" - - source_sents = read_text_file(args.input_source) - target_sents = read_text_file(args.input_target) - - num_sents = len(source_sents) - assert num_sents == len( - target_sents - ), f"{args.input_source} and {args.input_target} should have the same number of sentences." - - hypo_sents = read_text_file(args.input_hypo) - assert ( - len(hypo_sents) % args.beam == 0 - ), f"Number of hypotheses ({len(hypo_sents)}) cannot be divided by beam size ({args.beam})." - - hypo_sents = [ - hypo_sents[i : i + args.beam] for i in range(0, len(hypo_sents), args.beam) - ] - assert num_sents == len( - hypo_sents - ), f"{args.input_hypo} should contain {num_sents * args.beam} hypotheses but only has {len(hypo_sents) * args.beam}. (--beam={args.beam})" - - output_dir = args.output_dir / args.metric - for ns in range(args.num_shards): - print(f"processing shard {ns+1}/{args.num_shards}") - shard_output_dir = output_dir / f"split{ns+1}" - source_output_dir = shard_output_dir / "input_src" - hypo_output_dir = shard_output_dir / "input_tgt" - metric_output_dir = shard_output_dir / args.metric - - source_output_dir.mkdir(parents=True, exist_ok=True) - hypo_output_dir.mkdir(parents=True, exist_ok=True) - metric_output_dir.mkdir(parents=True, exist_ok=True) - - if args.n_proc > 1: - with Pool( - args.n_proc, initializer=init, initargs=(args.sentencepiece_model,) - ) as p: - output = p.starmap( - process, - [ - (source_sents[i], target_sents[i], hypo_sents[i], args.metric) - for i in range(ns, num_sents, args.num_shards) - ], - ) - else: - init(args.sentencepiece_model) - output = [ - process(source_sents[i], target_sents[i], hypo_sents[i], args.metric) - for i in range(ns, num_sents, args.num_shards) - ] - - with open(source_output_dir / f"{args.split}.bpe", "w") as s_o, open( - hypo_output_dir / f"{args.split}.bpe", "w" - ) as h_o, open(metric_output_dir / f"{args.split}.{args.metric}", "w") as m_o: - for source_bpe, hypo_bpe, score_str in output: - assert len(hypo_bpe) == len(score_str) - for h, m in zip(hypo_bpe, score_str): - s_o.write(f"{source_bpe}\n") - h_o.write(f"{h}\n") - m_o.write(f"{m}\n") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--input-source", type=Path, required=True) - parser.add_argument("--input-target", type=Path, required=True) - parser.add_argument("--input-hypo", type=Path, required=True) - parser.add_argument("--output-dir", type=Path, required=True) - parser.add_argument("--split", type=str, required=True) - parser.add_argument("--beam", type=int, required=True) - parser.add_argument("--sentencepiece-model", type=str, required=True) - parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu") - parser.add_argument("--num-shards", type=int, default=1) - parser.add_argument("--n-proc", type=int, default=8) - - args = parser.parse_args() - - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/tasks/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/tasks/__init__.py deleted file mode 100644 index d8802eeec7546d5a1060002cd59f6c4ea44d4d4c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/tasks/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .discriminative_reranking_task import DiscriminativeRerankingNMTTask - -__all__ = [ - "DiscriminativeRerankingNMTTask", -] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py b/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py deleted file mode 100644 index fc42cdf574ff1029175a14986632a694f5ff7982..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.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. - -import itertools -import logging -import os -from dataclasses import dataclass, field - -import numpy as np -import torch -from fairseq import metrics -from fairseq.data import ( - ConcatDataset, - ConcatSentencesDataset, - Dictionary, - IdDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - PrependTokenDataset, - RawLabelDataset, - RightPadDataset, - SortDataset, - TokenBlockDataset, - TruncateDataset, - data_utils, - indexed_dataset, -) -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.tasks import FairseqTask, register_task -from omegaconf import II, MISSING - -EVAL_BLEU_ORDER = 4 -TARGET_METRIC_CHOICES = ChoiceEnum(["bleu", "ter"]) - -logger = logging.getLogger(__name__) - - -@dataclass -class DiscriminativeRerankingNMTConfig(FairseqDataclass): - data: str = field(default=MISSING, metadata={"help": "path to data directory"}) - num_data_splits: int = field( - default=1, metadata={"help": "total number of data splits"} - ) - no_shuffle: bool = field( - default=False, metadata={"help": "do not shuffle training data"} - ) - max_positions: int = field( - default=512, metadata={"help": "number of positional embeddings to learn"} - ) - include_src: bool = field( - default=False, metadata={"help": "include source sentence"} - ) - mt_beam: int = field(default=50, metadata={"help": "beam size of input hypotheses"}) - eval_target_metric: bool = field( - default=False, - metadata={"help": "evaluation with the target metric during validation"}, - ) - target_metric: TARGET_METRIC_CHOICES = field( - default="bleu", metadata={"help": "name of the target metric to optimize for"} - ) - train_subset: str = field( - default=II("dataset.train_subset"), - metadata={"help": "data subset to use for training (e.g. train, valid, test)"}, - ) - seed: int = field( - default=II("common.seed"), - metadata={"help": "pseudo random number generator seed"}, - ) - - -class RerankerScorer(object): - """Scores the target for a given (source (optional), target) input.""" - - def __init__(self, args, mt_beam): - self.mt_beam = mt_beam - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - """Score a batch of translations.""" - net_input = sample["net_input"] - - assert len(models) == 1, "does not support model ensemble" - model = models[0] - - bs = net_input["src_tokens"].shape[0] - assert ( - model.joint_classification == "none" or bs % self.mt_beam == 0 - ), f"invalid batch size ({bs}) for joint classification with beam size ({self.mt_beam})" - - model.eval() - logits = model(**net_input) - - batch_out = model.sentence_forward(logits, net_input["src_tokens"]) - if model.joint_classification == "sent": - batch_out = model.joint_forward( - batch_out.view(self.mt_beam, bs // self.mt_beam, -1) - ) - scores = model.classification_forward( - batch_out.view(bs, 1, -1) - ) # input: B x T x C - - return scores - - -@register_task( - "discriminative_reranking_nmt", dataclass=DiscriminativeRerankingNMTConfig -) -class DiscriminativeRerankingNMTTask(FairseqTask): - """ - Translation rerank task. - The input can be either (src, tgt) sentence pairs or tgt sentence only. - """ - - cfg: DiscriminativeRerankingNMTConfig - - def __init__(self, cfg: DiscriminativeRerankingNMTConfig, data_dictionary=None): - super().__init__(cfg) - self.dictionary = data_dictionary - self._max_positions = cfg.max_positions - # args.tokens_per_sample = self._max_positions - # self.num_classes = 1 # for model - - @classmethod - def load_dictionary(cls, cfg, filename): - """Load the dictionary from the filename""" - dictionary = Dictionary.load(filename) - dictionary.add_symbol("") # for loading pretrained XLMR model - - return dictionary - - @classmethod - def setup_task(cls, cfg: DiscriminativeRerankingNMTConfig, **kwargs): - # load data dictionary (assume joint dictionary) - data_path = cfg.data - data_dict = cls.load_dictionary( - cfg, os.path.join(data_path, "input_src/dict.txt") - ) - - logger.info("[input] src dictionary: {} types".format(len(data_dict))) - - return DiscriminativeRerankingNMTTask(cfg, data_dict) - - def load_dataset(self, split, epoch=0, combine=False, **kwargs): - """Load a given dataset split (e.g., train, valid, test).""" - if self.cfg.data.endswith("1"): - data_shard = (epoch - 1) % self.cfg.num_data_splits + 1 - data_path = self.cfg.data[:-1] + str(data_shard) - else: - data_path = self.cfg.data - - def get_path(type, data_split): - return os.path.join(data_path, str(type), data_split) - - def make_dataset(type, dictionary, data_split, combine): - split_path = get_path(type, data_split) - - dataset = data_utils.load_indexed_dataset( - split_path, - dictionary, - combine=combine, - ) - return dataset - - def load_split(data_split, metric): - input_src = None - if self.cfg.include_src: - input_src = make_dataset( - "input_src", self.dictionary, data_split, combine=False - ) - assert input_src is not None, "could not find dataset: {}".format( - get_path("input_src", data_split) - ) - - input_tgt = make_dataset( - "input_tgt", self.dictionary, data_split, combine=False - ) - assert input_tgt is not None, "could not find dataset: {}".format( - get_path("input_tgt", data_split) - ) - - label_path = f"{get_path(metric, data_split)}.{metric}" - assert os.path.exists(label_path), f"could not find dataset: {label_path}" - - np_labels = np.loadtxt(label_path) - if self.cfg.target_metric == "ter": - np_labels = -np_labels - label = RawLabelDataset(np_labels) - - return input_src, input_tgt, label - - src_datasets = [] - tgt_datasets = [] - label_datasets = [] - - if split == self.cfg.train_subset: - for k in itertools.count(): - split_k = "train" + (str(k) if k > 0 else "") - prefix = os.path.join(data_path, "input_tgt", split_k) - if not indexed_dataset.dataset_exists(prefix, impl=None): - if k > 0: - break - else: - raise FileNotFoundError(f"Dataset not found: {prefix}") - input_src, input_tgt, label = load_split( - split_k, self.cfg.target_metric - ) - src_datasets.append(input_src) - tgt_datasets.append(input_tgt) - label_datasets.append(label) - else: - input_src, input_tgt, label = load_split(split, self.cfg.target_metric) - src_datasets.append(input_src) - tgt_datasets.append(input_tgt) - label_datasets.append(label) - - if len(tgt_datasets) == 1: - input_tgt, label = tgt_datasets[0], label_datasets[0] - if self.cfg.include_src: - input_src = src_datasets[0] - else: - input_tgt = ConcatDataset(tgt_datasets) - label = ConcatDataset(label_datasets) - if self.cfg.include_src: - input_src = ConcatDataset(src_datasets) - - input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions) - if self.cfg.include_src: - input_src = PrependTokenDataset(input_src, self.dictionary.bos()) - input_src = TruncateDataset(input_src, self.cfg.max_positions) - src_lengths = NumelDataset(input_src, reduce=False) - src_tokens = ConcatSentencesDataset(input_src, input_tgt) - else: - src_tokens = PrependTokenDataset(input_tgt, self.dictionary.bos()) - src_lengths = NumelDataset(src_tokens, reduce=False) - - dataset = { - "id": IdDataset(), - "net_input": { - "src_tokens": RightPadDataset( - src_tokens, - pad_idx=self.source_dictionary.pad(), - ), - "src_lengths": src_lengths, - }, - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_tokens, reduce=True), - "target": label, - } - - dataset = NestedDictionaryDataset( - dataset, - sizes=[src_tokens.sizes], - ) - - assert ( - len(dataset) % self.cfg.mt_beam == 0 - ), "dataset size (%d) is not a multiple of beam size (%d)" % ( - len(dataset), - self.cfg.mt_beam, - ) - - # no need to shuffle valid/test sets - if not self.cfg.no_shuffle and split == self.cfg.train_subset: - - # need to keep all hypothese together - start_idx = np.arange(0, len(dataset), self.cfg.mt_beam) - with data_utils.numpy_seed(self.cfg.seed + epoch): - np.random.shuffle(start_idx) - - idx = np.arange(0, self.cfg.mt_beam) - shuffle = np.tile(idx, (len(start_idx), 1)).reshape(-1) + np.tile( - start_idx, (self.cfg.mt_beam, 1) - ).transpose().reshape(-1) - - dataset = SortDataset( - dataset, - sort_order=[shuffle], - ) - - logger.info(f"Loaded {split} with #samples: {len(dataset)}") - - self.datasets[split] = dataset - return self.datasets[split] - - def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs): - assert not self.cfg.include_src or len(src_tokens[0]) == 2 - input_src = None - if self.cfg.include_src: - input_src = TokenBlockDataset( - [t[0] for t in src_tokens], - [l[0] for l in src_lengths], - block_size=None, # ignored for "eos" break mode - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode="eos", - ) - input_src = PrependTokenDataset(input_src, self.dictionary.bos()) - input_src = TruncateDataset(input_src, self.cfg.max_positions) - - input_tgt = TokenBlockDataset( - [t[-1] for t in src_tokens], - [l[-1] for l in src_lengths], - block_size=None, # ignored for "eos" break mode - pad=self.source_dictionary.pad(), - eos=self.source_dictionary.eos(), - break_mode="eos", - ) - input_tgt = TruncateDataset(input_tgt, self.cfg.max_positions) - if self.cfg.include_src: - src_tokens = ConcatSentencesDataset(input_src, input_tgt) - src_lengths = NumelDataset(input_src, reduce=False) - else: - input_tgt = PrependTokenDataset(input_tgt, self.dictionary.bos()) - src_tokens = input_tgt - src_lengths = NumelDataset(src_tokens, reduce=False) - - dataset = { - "id": IdDataset(), - "net_input": { - "src_tokens": RightPadDataset( - src_tokens, - pad_idx=self.source_dictionary.pad(), - ), - "src_lengths": src_lengths, - }, - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_tokens, reduce=True), - } - - return NestedDictionaryDataset( - dataset, - sizes=[src_tokens.sizes], - ) - - def build_model(self, cfg: FairseqDataclass, from_checkpoint: bool = False): - return super().build_model(cfg) - - def build_generator(self, args): - return RerankerScorer(args, mt_beam=self.cfg.mt_beam) - - def max_positions(self): - return self._max_positions - - @property - def source_dictionary(self): - return self.dictionary - - @property - def target_dictionary(self): - return self.dictionary - - def create_dummy_batch(self, device): - dummy_target = ( - torch.zeros(self.cfg.mt_beam, EVAL_BLEU_ORDER * 2 + 3).long().to(device) - if not self.cfg.eval_ter - else torch.zeros(self.cfg.mt_beam, 3).long().to(device) - ) - - return { - "id": torch.zeros(self.cfg.mt_beam, 1).long().to(device), - "net_input": { - "src_tokens": torch.zeros(self.cfg.mt_beam, 4).long().to(device), - "src_lengths": torch.ones(self.cfg.mt_beam, 1).long().to(device), - }, - "nsentences": 0, - "ntokens": 0, - "target": dummy_target, - } - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - if ignore_grad and sample is None: - sample = self.create_dummy_batch(model.device) - - return super().train_step( - sample, model, criterion, optimizer, update_num, ignore_grad - ) - - def valid_step(self, sample, model, criterion): - if sample is None: - sample = self.create_dummy_batch(model.device) - - loss, sample_size, logging_output = super().valid_step(sample, model, criterion) - - if not self.cfg.eval_target_metric: - return loss, sample_size, logging_output - - scores = logging_output["scores"] - - if self.cfg.target_metric == "bleu": - assert sample["target"].shape[1] == EVAL_BLEU_ORDER * 2 + 3, ( - "target does not contain enough information (" - + str(sample["target"].shape[1]) - + "for evaluating BLEU" - ) - - max_id = torch.argmax(scores, dim=1) - select_id = max_id + torch.arange( - 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam - ).to(max_id.device) - bleu_data = sample["target"][select_id, 1:].sum(0).data - - logging_output["_bleu_sys_len"] = bleu_data[0] - logging_output["_bleu_ref_len"] = bleu_data[1] - - for i in range(EVAL_BLEU_ORDER): - logging_output["_bleu_counts_" + str(i)] = bleu_data[2 + i] - logging_output["_bleu_totals_" + str(i)] = bleu_data[ - 2 + EVAL_BLEU_ORDER + i - ] - - elif self.cfg.target_metric == "ter": - assert sample["target"].shape[1] == 3, ( - "target does not contain enough information (" - + str(sample["target"].shape[1]) - + "for evaluating TER" - ) - - max_id = torch.argmax(scores, dim=1) - select_id = max_id + torch.arange( - 0, sample_size * self.cfg.mt_beam, self.cfg.mt_beam - ).to(max_id.device) - ter_data = sample["target"][select_id, 1:].sum(0).data - - logging_output["_ter_num_edits"] = -ter_data[0] - logging_output["_ter_ref_len"] = -ter_data[1] - - return loss, sample_size, logging_output - - def reduce_metrics(self, logging_outputs, criterion): - super().reduce_metrics(logging_outputs, criterion) - - if not self.cfg.eval_target_metric: - return - - def sum_logs(key): - return sum(log.get(key, 0) for log in logging_outputs) - - if self.cfg.target_metric == "bleu": - 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 - - import sacrebleu - - fn_sig = inspect.getfullargspec(sacrebleu.compute_bleu)[0] - if "smooth_method" in fn_sig: - smooth = {"smooth_method": "exp"} - else: - smooth = {"smooth": "exp"} - bleu = sacrebleu.compute_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) - elif self.cfg.target_metric == "ter": - num_edits = sum_logs("_ter_num_edits") - ref_len = sum_logs("_ter_ref_len") - - if ref_len > 0: - metrics.log_scalar("_ter_num_edits", num_edits) - metrics.log_scalar("_ter_ref_len", ref_len) - - def compute_ter(meters): - score = meters["_ter_num_edits"].sum / meters["_ter_ref_len"].sum - return round(score.item(), 2) - - metrics.log_derived("ter", compute_ter) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/README.md deleted file mode 100644 index f2631a8c34d11bdf7d351c6807b6fe415f5715e1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/README.md +++ /dev/null @@ -1,345 +0,0 @@ -# Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling - -## Introduction -- [Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) introduce a simple and effective noisy channel modeling approach for neural machine translation. However, the noisy channel online decoding approach introduced in this paper is too slow to be practical. -- To address this, [Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 simple approximations to make this approach very fast and practical without much loss in accuracy. -- This README provides intructions on how to run online decoding or generation with the noisy channel modeling approach, including ways to make it very fast without much loss in accuracy. - -## Noisy Channel Modeling - -[Yee et al. (2019)](https://www.aclweb.org/anthology/D19-1571.pdf) applies the Bayes Rule to predict `P(y|x)`, the probability of the target `y` given the source `x`. -```P(y|x) = P(x|y) * P(y) / P(x)``` -- `P(x|y)` predicts the source `x` given the target `y` and is referred to as the **channel model** -- `P(y)` is a **language model** over the target `y` -- `P(x)` is generally not modeled since it is constant for all `y`. - -We use Transformer models to parameterize the direct model `P(y|x)`, the channel model `P(x|y)` and the language model `P(y)`. - -During online decoding with beam search, we generate the top `K2` candidates per beam and score them with the following linear combination of the channel model, the language model as well as the direct model scores. - -```(1 / t) * log(P(y|x) + (1 / s) * ( λ1 * log(P(x|y)) + λ2 * log(P(y) ) )``` -- `t` - Target Prefix Length -- `s` - Source Length -- `λ1` - Channel Model Weight -- `λ2` - Language Model Weight - -The top `beam_size` candidates based on the above combined scores are chosen to continue the beams in beam search. In beam search with a direct model alone, the scores from the direct model `P(y|x)` are used to choose the top candidates in beam search. - -This framework provides a great way to utlize strong target language models trained on large amounts of unlabeled data. Language models can prefer targets unrelated to the source, so we also need a channel model whose role is to ensure that the target preferred by the language model also translates back to the source. - -### Training Translation Models and Language Models - -For training Transformer models in fairseq for machine translation, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/translation) - -For training Transformer models in fairseq for language modeling, refer to instructions [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) - -### Generation with Language Model for German-English translation with fairseq - -Here are instructions to generate using a direct model and a target-side language model. - -Note: -- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) -- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) - -```sh -binarized_data=data_dir/binarized -direct_model=de_en_seed4.pt -lm_model=en_lm.pt -lm_data=lm_data -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} -mkdir -p ${lm_data} -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt - -k2=10 -lenpen=0.16 -lm_wt=0.14 -fairseq-generate ${binarized_data} \ - --user-dir examples/fast_noisy_channel \ - --beam 5 \ - --path ${direct_model} \ - --lm-model ${lm_model} \ - --lm-data ${lm_data} \ - --k2 ${k2} \ - --combine-method lm_only \ - --task noisy_channel_translation \ - --lenpen ${lenpen} \ - --lm-wt ${lm_wt} \ - --gen-subset valid \ - --remove-bpe \ - --fp16 \ - --batch-size 10 -``` -### Noisy Channel Generation for German-English translation with fairseq - -Here are instructions for noisy channel generation with a direct model, channel model and language model as explained in section [Noisy Channel Modeling](#noisy-channel-modeling). - -Note: -- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) -- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) - -```sh -binarized_data=data_dir/binarized -direct_model=de_en_seed4.pt -lm_model=en_lm.pt -lm_data=lm_data -ch_model=en_de.big.seed4.pt -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} -mkdir -p ${lm_data} -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt -O ${ch_model} - -k2=10 -lenpen=0.21 -lm_wt=0.50 -bw_wt=0.30 -fairseq-generate ${binarized_data} \ - --user-dir examples/fast_noisy_channel \ - --beam 5 \ - --path ${direct_model} \ - --lm-model ${lm_model} \ - --lm-data ${lm_data} \ - --channel-model ${ch_model} \ - --k2 ${k2} \ - --combine-method noisy_channel \ - --task noisy_channel_translation \ - --lenpen ${lenpen} \ - --lm-wt ${lm_wt} \ - --ch-wt ${bw_wt} \ - --gen-subset test \ - --remove-bpe \ - --fp16 \ - --batch-size 1 -``` -## Fast Noisy Channel Modeling - -[Bhosale et al. (2020)](http://www.statmt.org/wmt20/pdf/2020.wmt-1.68.pdf) introduces 3 approximations that speed up online noisy channel decoding - -- Smaller channel models (`Tranformer Base` with 1 encoder and decoder layer each vs. `Transformer Big`) - - This involves training a channel model that is possibly smaller and less accurate in terms of BLEU than a channel model of the same size as the direct model. - - Since the role of the channel model is mainly to assign low scores to generations from the language model if they don't translate back to the source, we may not need the most accurate channel model for this purpose. -- Smaller output vocabulary size for the channel model (~30,000 -> ~1000) - - The channel model doesn't need to score the full output vocabulary, it just needs to score the source tokens, which are completely known. - - This is specified using the arguments `--channel-scoring-type src_vocab --top-k-vocab 500` - - This means that the output vocabulary for the channel model will be the source tokens for all examples in the batch and the top-K most frequent tokens in the vocabulary - - This reduces the memory consumption needed to store channel model scores significantly -- Smaller number of candidates (`k2`) scored per beam - - This is specified by reducing the argument `--k2` - - -### Fast Noisy Channel Generation for German-English translation with fairseq - -Here are instructions for **fast** noisy channel generation with a direct model, channel model and language model as explained in section [Fast Noisy Channel Modeling](#fast-noisy-channel-modeling). The main differences are that we use a smaller channel model, reduce `--k2`, set `--channel-scoring-type src_vocab --top-k-vocab 500` and increase the `--batch-size`. - -Note: -- Download and install fairseq as per instructions [here](https://github.com/pytorch/fairseq) -- Preprocess and binarize the dataset as per instructions in section [Test Data Preprocessing](#test-data-preprocessing) - -```sh -binarized_data=data_dir/binarized -direct_model=de_en_seed4.pt -lm_model=en_lm.pt -lm_data=lm_data -small_ch_model=en_de.base_1_1.seed4.pt -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt -O ${direct_model} -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt -O ${lm_model} -mkdir -p ${lm_data} -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/dict.txt -O ${lm_data}/dict.txt -wget https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt -O ${small_ch_model} - -k2=3 -lenpen=0.23 -lm_wt=0.58 -bw_wt=0.26 -fairseq-generate ${binarized_data} \ - --user-dir examples/fast_noisy_channel \ - --beam 5 \ - --path ${direct_model} \ - --lm-model ${lm_model} \ - --lm-data ${lm_data} \ - --channel-model ${small_ch_model} \ - --k2 ${k2} \ - --combine-method noisy_channel \ - --task noisy_channel_translation \ - --lenpen ${lenpen} \ - --lm-wt ${lm_wt} \ - --ch-wt ${bw_wt} \ - --gen-subset test \ - --remove-bpe \ - --fp16 \ - --batch-size 50 \ - --channel-scoring-type src_vocab --top-k-vocab 500 -``` - -## Test Data Preprocessing - -For preprocessing and binarizing the test sets for Romanian-English and German-English translation, we use the following script - - -```sh -FAIRSEQ=/path/to/fairseq -cd $FAIRSEQ -SCRIPTS=$FAIRSEQ/mosesdecoder/scripts -if [ ! -d "${SCRIPTS}" ]; then - echo 'Cloning Moses github repository (for tokenization scripts)...' - git clone https://github.com/moses-smt/mosesdecoder.git -fi -TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl -NORMALIZE=$SCRIPTS/tokenizer/normalize-punctuation.perl - -s=de -t=en -test=wmt18 - -mkdir -p data_dir - -# Tokenization -if [ $s == "ro" ] ; then - # Note: Get normalise-romanian.py and remove-diacritics.py from - # https://github.com/rsennrich/wmt16-scripts/tree/master/preprocess - sacrebleu -t $test -l $s-$t --echo src | \ - $NORMALIZE -l $s | \ - python normalise-romanian.py | \ - python remove-diacritics.py | \ - $TOKENIZER -l $s -a -q > data_dir/$test.$s-$t.$s -else - sacrebleu -t $test -l $s-$t --echo src | perl $NORMALIZE -l $s | perl $TOKENIZER -threads 8 -a -l $s > data_dir/$test.$s-$t.$s -fi - -sacrebleu -t $test -l $s-$t --echo ref | perl $NORMALIZE -l $t | perl $TOKENIZER -threads 8 -a -l $t > data_dir/$test.$s-$t.$t - - -# Applying BPE -src_bpe_code=/path/to/source/language/bpe/code -tgt_bpe_code=/path/to/target/language/bpe/code -src_dict=/path/to/source/language/dict -tgt_dict=/path/to/target/language/dict - -FASTBPE=$FAIRSEQ/fastBPE -if [ ! -d "${FASTBPE}" ] ; then - git clone https://github.com/glample/fastBPE.git - # Follow compilation instructions at https://github.com/glample/fastBPE - g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast -fi - -${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${src_bpe_code} -${FASTBPE}/fast applybpe data_dir/bpe.$test.$s-$t.$s data_dir/$test.$s-$t.$s ${tgt_bpe_code} - -fairseq-preprocess -s $s -t $t \ - --testpref data_dir/bpe.$test.$s-$t \ - --destdir data_dir/binarized \ - --srcdict ${src_dict} \ - --tgtdict ${tgt_dict} -``` - -## Calculating BLEU - -```sh -DETOKENIZER=$SCRIPTS/tokenizer/detokenizer.perl -cat ${generation_output} | grep -P "^H" | sort -V | cut -f 3- | $DETOKENIZER -l $t -q -a | sacrebleu -t $test -l $s-$t -``` - - -## Romanian-English Translation - -The direct and channel models are trained using bitext data (WMT16) combined with backtranslated data (The monolingual data used for backtranslation comes from http://data.statmt.org/rsennrich/wmt16_backtranslations/ (Sennrich et al., 2016c)) - -The backtranslated data is generated using an ensemble of 3 English-Romanian models trained on bitext training data (WMT16) with unrestricted sampling. - -### BPE Codes and Dictionary - -We learn a joint BPE vocabulary of 18K types on the bitext training data which is used for both the source and target. -||Path| -|----------|------| -| BPE Code | [joint_bpe_18k](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/bpe_18k) | -| Dictionary | [dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/dict) | - -### Direct Models -For Ro-En with backtranslation, the direct and channel models use a Transformer-Big architecture. - -| Seed | Model | -|----|----| -| 2 | [ro_en_seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed2.pt) -| 4 | [ro_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed4.pt) -| 6 | [ro_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/direct_models/seed6.pt) - -### Channel Models -For channel models, we follow the same steps as for the direct models. But backtranslated data is generated in the opposite direction using [this Romanian monolingual data](http://data.statmt.org/rsennrich/wmt16_backtranslations/). -The best lenpen, LM weight and CH weight are obtained by sweeping over the validation set (wmt16/dev) using beam 5. -| Model Size | Lenpen | LM Weight | CH Weight | Seed 2 | Seed 4 | Seed 6 | -|----|----|----|----|----|----|----| -| `big` | 0.84 | 0.64 | 0.56 | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | [big.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/big.seed2.pt) | -| `base_1_1` | 0.63 | 0.40 | 0.37 | [base_1_1.seed2.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed2.pt) | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/channel_models/base_1_1.seed6.pt) | - -### Language Model -The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization. -| | Path | -|----|----| -| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/transformer_lm.pt) | -| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/ro_en/lm_model/lm_dict) - -## German-English Translation - -### BPE Codes and Dictionaries - -| | Path| -|----------|------| -| Source BPE Code | [de_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_bpe_code_24K) | -| Target BPE Code | [en_bpe_code_24K](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_bpe_code_24K) -| Source Dictionary | [de_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/de_dict) | -| Target Dictionary | [en_dict](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/en_dict) | - -### Direct Models -We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs. -We use the Transformer-Big architecture for the direct model. - -| Seed | Model | -|:----:|----| -| 4 | [de_en_seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed4.pt) -| 5 | [de_en_seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed5.pt) -| 6 | [de_en_seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/direct_models/seed6.pt) - -### Channel Models - -We train on WMT’19 training data. Following [Ng et al., 2019](http://statmt.org/wmt19/pdf/53/WMT33.pdf), we apply language identification filtering and remove sentences longer than 250 tokens as well as sentence pairs with a source/target length ratio exceeding 1.5. This results in 26.8M sentence pairs. - -| Model Size | Seed 4 | Seed 5 | Seed 6 | -|----|----|----|----| -| `big` | [big.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed4.pt) | [big.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed5.pt) | [big.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big.seed6.pt) | -| `big_1_1` | [big_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed4.pt) | [big_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed5.pt) | [big_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/big_1_1.seed6.pt) | -| `base` | [base.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed4.pt) | [base.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed5.pt) | [base.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base.seed6.pt) | -| `base_1_1` | [base_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed4.pt) | [base_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed5.pt) | [base_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/base_1_1.seed6.pt) | -| `half` | [half.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed4.pt) | [half.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed5.pt) | [half.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half.seed6.pt) | -| `half_1_1` | [half_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed4.pt) | [half_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed5.pt) | [half_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/half_1_1.seed6.pt) | -| `quarter` | [quarter.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed4.pt) | [quarter.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed5.pt) | [quarter.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter.seed6.pt) | -| `quarter_1_1` | [quarter_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed4.pt) | [quarter_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed5.pt) | [quarter_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/quarter_1_1.seed6.pt) | -| `8th` | [8th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed4.pt) | [8th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed5.pt) | [8th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th.seed6.pt) | -| `8th_1_1` | [8th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed4.pt) | [8th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed5.pt) | [8th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/8th_1_1.seed6.pt) | -| `16th` | [16th.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed4.pt) | [16th.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed5.pt) | [16th.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th.seed6.pt) | -| `16th_1_1` | [16th_1_1.seed4.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed4.pt) | [16th_1_1.seed5.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed5.pt) | [16th_1_1.seed6.pt](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/channel_models/16th_1_1.seed6.pt) | - -### Language Model -The model is trained on de-duplicated English Newscrawl data from 2007-2018 comprising 186 million sentences or 4.5B words after normalization and tokenization. -| | Path | -|----|----| -| `--lm-model` | [transformer_en_lm](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/transformer_lm.pt) | -| `--lm-data` | [lm_data](https://dl.fbaipublicfiles.com/fast_noisy_channel/de_en/lm_model/lm_dict/) - - -## Citation - -```bibtex -@inproceedings{bhosale2020language, - title={Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling}, - author={Shruti Bhosale and Kyra Yee and Sergey Edunov and Michael Auli}, - booktitle={Proceedings of the Fifth Conference on Machine Translation (WMT)}, - year={2020}, -} - -@inproceedings{yee2019simple, - title={Simple and Effective Noisy Channel Modeling for Neural Machine Translation}, - author={Yee, Kyra and Dauphin, Yann and Auli, Michael}, - booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, - pages={5700--5705}, - year={2019} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/__init__.py deleted file mode 100644 index a0cd39c108d9726ac5e188b6bcf3bda9d8c70d5d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/__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 . import noisy_channel_beam_search # noqa -from . import noisy_channel_sequence_generator # noqa -from . import noisy_channel_translation # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_beam_search.py b/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_beam_search.py deleted file mode 100644 index 23869ebcd0c438f36e310c8ccddd3b5c07a71182..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_beam_search.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 -from fairseq.search import Search - - -class NoisyChannelBeamSearch(Search): - - def __init__(self, tgt_dict): - super().__init__(tgt_dict) - self.fw_scores_buf = None - self.lm_scores_buf = None - - def _init_buffers(self, t): - # super()._init_buffers(t) - if self.fw_scores_buf is None: - self.scores_buf = t.new() - self.indices_buf = torch.LongTensor().to(device=t.device) - self.beams_buf = torch.LongTensor().to(device=t.device) - self.fw_scores_buf = t.new() - self.lm_scores_buf = t.new() - - def combine_fw_bw(self, combine_method, fw_cum, bw, step): - if combine_method == "noisy_channel": - fw_norm = fw_cum.div(step + 1) - lprobs = bw + fw_norm - elif combine_method == "lm_only": - lprobs = bw + fw_cum - - return lprobs - - def step(self, step, fw_lprobs, scores, bw_lprobs, lm_lprobs, combine_method): - self._init_buffers(fw_lprobs) - bsz, beam_size, vocab_size = fw_lprobs.size() - - if step == 0: - # at the first step all hypotheses are equally likely, so use - # only the first beam - fw_lprobs = fw_lprobs[:, ::beam_size, :].contiguous() - bw_lprobs = bw_lprobs[:, ::beam_size, :].contiguous() - # nothing to add since we are at the first step - fw_lprobs_cum = fw_lprobs - - else: - # make probs contain cumulative scores for each hypothesis - raw_scores = (scores[:, :, step - 1].unsqueeze(-1)) - fw_lprobs_cum = (fw_lprobs.add(raw_scores)) - - combined_lprobs = self.combine_fw_bw(combine_method, fw_lprobs_cum, bw_lprobs, step) - - # choose the top k according to the combined noisy channel model score - torch.topk( - combined_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, - combined_lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad - ), - out=(self.scores_buf, self.indices_buf), - ) - # save corresponding fw and lm scores - self.fw_scores_buf = torch.gather(fw_lprobs_cum.view(bsz, -1), 1, self.indices_buf) - self.lm_scores_buf = torch.gather(lm_lprobs.view(bsz, -1), 1, self.indices_buf) - # Project back into relative indices and beams - self.beams_buf = self.indices_buf // vocab_size - self.indices_buf.fmod_(vocab_size) - return self.scores_buf, self.fw_scores_buf, self.lm_scores_buf, self.indices_buf, self.beams_buf diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_sequence_generator.py b/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_sequence_generator.py deleted file mode 100644 index c0b0e44f5b7fe365e278e4181e4823bf5126c8f5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_sequence_generator.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 math -from typing import Dict, List, Optional - -import numpy as np -import torch -import torch.nn.functional as F -from fairseq.sequence_generator import EnsembleModel -from torch import Tensor - -from .noisy_channel_beam_search import NoisyChannelBeamSearch - - -class NoisyChannelSequenceGenerator(object): - def __init__( - self, - combine_method, - tgt_dict, - src_dict=None, - beam_size=1, - max_len_a=0, - max_len_b=200, - min_len=1, - len_penalty=1.0, - unk_penalty=0.0, - retain_dropout=False, - temperature=1.0, - match_source_len=False, - no_repeat_ngram_size=0, - normalize_scores=True, - channel_models=None, - k2=10, - ch_weight=1.0, - channel_scoring_type='log_norm', - top_k_vocab=0, - lm_models=None, - lm_dict=None, - lm_weight=1.0, - normalize_lm_scores_by_tgt_len=False, - ): - """Generates translations of a given source sentence, - using beam search with noisy channel decoding. - - Args: - combine_method (string, optional): Method to combine direct, LM and - channel model scores (default: None) - tgt_dict (~fairseq.data.Dictionary): target dictionary - src_dict (~fairseq.data.Dictionary): source dictionary - 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 - min_len (int, optional): the minimum length of the generated output - (not including end-of-sentence) - 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) - retain_dropout (bool, optional): use dropout when generating - (default: False) - 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) - no_repeat_ngram_size (int, optional): Size of n-grams that we avoid - repeating in the generation (default: 0) - normalize_scores (bool, optional): normalize scores by the length - of the output (default: True) - channel_models (List[~fairseq.models.FairseqModel]): ensemble of models - translating from the target to the source - k2 (int, optional): Top K2 candidates to score per beam at each step (default:10) - ch_weight (int, optional): Weight associated with the channel model score - assuming that the direct model score has weight 1.0 (default: 1.0) - channel_scoring_type (str, optional): String specifying how to score - the channel model (default: 'log_norm') - top_k_vocab (int, optional): If `channel_scoring_type` is `'src_vocab'` or - `'src_vocab_batched'`, then this parameter specifies the number of - most frequent tokens to include in the channel model output vocabulary, - in addition to the source tokens in the input batch (default: 0) - lm_models (List[~fairseq.models.FairseqModel]): ensemble of models - generating text in the target language - lm_dict (~fairseq.data.Dictionary): LM Model dictionary - lm_weight (int, optional): Weight associated with the LM model score - assuming that the direct model score has weight 1.0 (default: 1.0) - normalize_lm_scores_by_tgt_len (bool, optional): Should we normalize LM scores - by the target length? By default, we normalize the combination of - LM and channel model scores by the source length - """ - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.eos = tgt_dict.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.max_len_a = max_len_a - self.max_len_b = max_len_b - self.min_len = min_len - self.normalize_scores = normalize_scores - self.len_penalty = len_penalty - self.unk_penalty = unk_penalty - self.retain_dropout = retain_dropout - self.temperature = temperature - self.match_source_len = match_source_len - self.no_repeat_ngram_size = no_repeat_ngram_size - self.channel_models = channel_models - self.src_dict = src_dict - self.tgt_dict = tgt_dict - self.combine_method = combine_method - self.k2 = k2 - self.ch_weight = ch_weight - self.channel_scoring_type = channel_scoring_type - self.top_k_vocab = top_k_vocab - self.lm_models = lm_models - self.lm_dict = lm_dict - self.lm_weight = lm_weight - self.log_softmax_fn = torch.nn.LogSoftmax(dim=1) - self.normalize_lm_scores_by_tgt_len = normalize_lm_scores_by_tgt_len - - self.share_tgt_dict = (self.lm_dict == self.tgt_dict) - self.tgt_to_lm = make_dict2dict(tgt_dict, lm_dict) - - self.ch_scoring_bsz = 3072 - - assert temperature > 0, '--temperature must be greater than 0' - - self.search = NoisyChannelBeamSearch(tgt_dict) - - @torch.no_grad() - def generate( - self, - models, - sample, - prefix_tokens=None, - bos_token=None, - **kwargs - ): - """Generate a batch of translations. - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - """ - model = EnsembleModel(models) - 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(model.models_size) - ], - ) - if not self.retain_dropout: - model.eval() - - # 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 sample['net_input'].items() - if k != 'prev_output_tokens' - } - src_tokens = encoder_input['src_tokens'] - src_lengths_no_eos = (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) - input_size = src_tokens.size() - # batch dimension goes first followed by source lengths - bsz = input_size[0] - src_len = input_size[1] - beam_size = self.beam_size - - if self.match_source_len: - max_len = src_lengths_no_eos.max().item() - else: - max_len = min( - int(self.max_len_a * src_len + self.max_len_b), - # exclude the EOS marker - model.max_decoder_positions() - 1, - ) - - # compute the encoder output for each beam - encoder_outs = model.forward_encoder(encoder_input) - 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 = model.reorder_encoder_out(encoder_outs, new_order) - - src_lengths = encoder_input['src_lengths'] - # initialize buffers - scores = src_tokens.new(bsz * beam_size, max_len + 1).float().fill_(0) - lm_prefix_scores = src_tokens.new(bsz * beam_size).float().fill_(0) - - scores_buf = scores.clone() - tokens = src_tokens.new(bsz * beam_size, max_len + 2).long().fill_(self.pad) - tokens_buf = tokens.clone() - tokens[:, 0] = self.eos if bos_token is None else bos_token - - # reorder source tokens so they may be used as a reference in generating P(S|T) - src_tokens = reorder_all_tokens(src_tokens, src_lengths, self.src_dict.eos_index) - - src_tokens = src_tokens.repeat(1, beam_size).view(-1, src_len) - src_lengths = src_lengths.view(bsz, -1).repeat(1, beam_size).view(bsz*beam_size, -1) - - attn, attn_buf = None, None - nonpad_idxs = None - - # The cands_to_ignore indicates candidates that should be ignored. - # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then the cands_to_ignore would mark 2 positions as being ignored, - # so that we only finalize the remaining 3 samples. - cands_to_ignore = src_tokens.new_zeros(bsz, beam_size).eq(-1) # forward and backward-compatible False mask - - # list of completed sentences - finalized = [[] for i in range(bsz)] - finished = [False for i in range(bsz)] - num_remaining_sent = bsz - - # 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) - cand_offsets = torch.arange(0, cand_size).type_as(tokens) - - # helper function for allocating buffers on the fly - buffers = {} - - def buffer(name, type_of=tokens): # noqa - if name not in buffers: - buffers[name] = type_of.new() - return buffers[name] - - def is_finished(sent, step, unfin_idx): - """ - Check whether we've finished generation for a given sentence, by - comparing the worst score among finalized hypotheses to the best - possible score among unfinalized hypotheses. - """ - assert len(finalized[sent]) <= beam_size - if len(finalized[sent]) == beam_size: - return True - return False - - def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores): - """ - Finalize the given hypotheses at this step, while keeping the total - number of finalized hypotheses per sentence <= beam_size. - - Note: the input must be in the desired finalization order, so that - hypotheses that appear earlier in the input are preferred to those - that appear later. - - Args: - step: current time step - bbsz_idx: A vector of indices in the range [0, bsz*beam_size), - indicating which hypotheses to finalize - eos_scores: A vector of the same size as bbsz_idx containing - fw scores for each hypothesis - combined_noisy_channel_eos_scores: A vector of the same size as bbsz_idx containing - combined noisy channel scores for each hypothesis - """ - assert bbsz_idx.numel() == eos_scores.numel() - - # clone relevant token and attention tensors - tokens_clone = tokens.index_select(0, bbsz_idx) - tokens_clone = tokens_clone[:, 1:step + 2] # skip the first index, which is EOS - assert not tokens_clone.eq(self.eos).any() - 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: - combined_noisy_channel_eos_scores /= (step + 1) ** self.len_penalty - - cum_unfin = [] - prev = 0 - for f in finished: - if f: - prev += 1 - else: - cum_unfin.append(prev) - - sents_seen = set() - for i, (idx, score) in enumerate(zip(bbsz_idx.tolist(), combined_noisy_channel_eos_scores.tolist())): - unfin_idx = idx // beam_size - sent = unfin_idx + cum_unfin[unfin_idx] - - sents_seen.add((sent, unfin_idx)) - - if self.match_source_len and step > src_lengths_no_eos[unfin_idx]: - score = -math.inf - - def get_hypo(): - - if attn_clone is not None: - # remove padding tokens from attn scores - hypo_attn = attn_clone[i][nonpad_idxs[sent]] - _, alignment = hypo_attn.max(dim=0) - else: - hypo_attn = None - alignment = None - - return { - 'tokens': tokens_clone[i], - 'score': score, - 'attention': hypo_attn, # src_len x tgt_len - 'alignment': alignment, - 'positional_scores': pos_scores[i], - } - - if len(finalized[sent]) < beam_size: - finalized[sent].append(get_hypo()) - - newly_finished = [] - for sent, unfin_idx in sents_seen: - # check termination conditions for this sentence - if not finished[sent] and is_finished(sent, step, unfin_idx): - finished[sent] = True - newly_finished.append(unfin_idx) - return newly_finished - - def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k): - """Rescore the top k hypothesis from each beam using noisy channel modeling - Returns: - new_fw_lprobs: the direct model probabilities after pruning the top k - new_ch_lm_lprobs: the combined channel and language model probabilities - new_lm_lprobs: the language model probabilities after pruning the top k - """ - with torch.no_grad(): - lprobs_size = lprobs.size() - if prefix_tokens is not None and step < prefix_tokens.size(1): - probs_slice = lprobs.view(bsz, -1, lprobs.size(-1))[:, 0, :] - cand_scores = torch.gather( - probs_slice, dim=1, - index=prefix_tokens[:, step].view(-1, 1).data - ).expand(-1, beam_size).contiguous().view(bsz*beam_size, 1) - cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, beam_size).data.contiguous().view(bsz*beam_size, 1) - - # need to calculate and save fw and lm probs for prefix tokens - fw_top_k = cand_scores - fw_top_k_idx = cand_indices - k = 1 - else: - # take the top k best words for every sentence in batch*beam - fw_top_k, fw_top_k_idx = torch.topk(lprobs.view(beam_size*bsz, -1), k=k) - eos_idx = torch.nonzero(fw_top_k_idx.view(bsz*beam_size*k, -1) == self.eos)[:, 0] - ch_scores = fw_top_k.new_full((beam_size*bsz*k, ), 0) - src_size = torch.sum(src_tokens[:, :] != self.src_dict.pad_index, dim=1, keepdim=True, dtype=fw_top_k.dtype) - - if self.combine_method != "lm_only": - temp_src_tokens_full = src_tokens[:, :].repeat(1, k).view(bsz*beam_size*k, -1) - not_padding = temp_src_tokens_full[:, 1:] != self.src_dict.pad_index - cur_tgt_size = step+2 - - # add eos to all candidate sentences except those that already end in eos - eos_tokens = tokens[:, 0].repeat(1, k).view(-1, 1) - eos_tokens[eos_idx] = self.tgt_dict.pad_index - - if step == 0: - channel_input = torch.cat((fw_top_k_idx.view(-1, 1), eos_tokens), 1) - else: - # move eos from beginning to end of target sentence - channel_input = torch.cat((tokens[:, 1:step + 1].repeat(1, k).view(-1, step), fw_top_k_idx.view(-1, 1), eos_tokens), 1) - - ch_input_lengths = torch.tensor(np.full(channel_input.size(0), cur_tgt_size)) - ch_input_lengths[eos_idx] = cur_tgt_size-1 - if self.channel_scoring_type == "unnormalized": - ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) - ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) - del ch_encoder_output - ch_intermed_scores = channel_model.decoder.unnormalized_scores_given_target(ch_decoder_output, target_ids=temp_src_tokens_full[:, 1:]) - ch_intermed_scores = ch_intermed_scores.float() - ch_intermed_scores *= not_padding.float() - ch_scores = torch.sum(ch_intermed_scores, dim=1) - elif self.channel_scoring_type == "k2_separate": - for k_idx in range(k): - k_eos_tokens = eos_tokens[k_idx::k, :] - if step == 0: - k_ch_input = torch.cat((fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) - else: - # move eos from beginning to end of target sentence - k_ch_input = torch.cat((tokens[:, 1:step + 1], fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) - k_ch_input_lengths = ch_input_lengths[k_idx::k] - k_ch_output = channel_model(k_ch_input, k_ch_input_lengths, src_tokens) - k_ch_lprobs = channel_model.get_normalized_probs(k_ch_output, log_probs=True) - k_ch_intermed_scores = torch.gather(k_ch_lprobs[:, :-1, :], 2, src_tokens[:, 1:].unsqueeze(2)).squeeze(2) - k_ch_intermed_scores *= not_padding.float() - ch_scores[k_idx::k] = torch.sum(k_ch_intermed_scores, dim=1) - elif self.channel_scoring_type == "src_vocab": - ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) - ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) - - del ch_encoder_output - ch_lprobs = normalized_scores_with_batch_vocab( - channel_model.decoder, - ch_decoder_output, src_tokens, k, bsz, beam_size, - self.src_dict.pad_index, top_k=self.top_k_vocab) - ch_scores = torch.sum(ch_lprobs, dim=1) - elif self.channel_scoring_type == "src_vocab_batched": - ch_bsz_size = temp_src_tokens_full.shape[0] - ch_lprobs_list = [None] * len(range(0, ch_bsz_size, self.ch_scoring_bsz)) - for i, start_idx in enumerate(range(0, ch_bsz_size, self.ch_scoring_bsz)): - end_idx = min(start_idx + self.ch_scoring_bsz, ch_bsz_size) - temp_src_tokens_full_batch = temp_src_tokens_full[start_idx:end_idx, :] - channel_input_batch = channel_input[start_idx:end_idx, :] - ch_input_lengths_batch = ch_input_lengths[start_idx:end_idx] - ch_encoder_output_batch = channel_model.encoder(channel_input_batch, src_lengths=ch_input_lengths_batch) - ch_decoder_output_batch, _ = channel_model.decoder(temp_src_tokens_full_batch, encoder_out=ch_encoder_output_batch, features_only=True) - ch_lprobs_list[i] = normalized_scores_with_batch_vocab( - channel_model.decoder, - ch_decoder_output_batch, src_tokens, k, bsz, beam_size, - self.src_dict.pad_index, top_k=self.top_k_vocab, - start_idx=start_idx, end_idx=end_idx) - ch_lprobs = torch.cat(ch_lprobs_list, dim=0) - ch_scores = torch.sum(ch_lprobs, dim=1) - else: - ch_output = channel_model(channel_input, ch_input_lengths, temp_src_tokens_full) - ch_lprobs = channel_model.get_normalized_probs(ch_output, log_probs=True) - ch_intermed_scores = torch.gather(ch_lprobs[:, :-1, :], 2, temp_src_tokens_full[:, 1:].unsqueeze(2)).squeeze().view(bsz*beam_size*k, -1) - ch_intermed_scores *= not_padding.float() - ch_scores = torch.sum(ch_intermed_scores, dim=1) - - else: - cur_tgt_size = 0 - ch_scores = ch_scores.view(bsz*beam_size, k) - expanded_lm_prefix_scores = lm_prefix_scores.unsqueeze(1).expand(-1, k).flatten() - - if self.share_tgt_dict: - lm_scores = get_lm_scores(lm, tokens[:, :step + 1].view(-1, step+1), lm_incremental_states, fw_top_k_idx.view(-1, 1), torch.tensor(np.full(tokens.size(0), step+1)), k) - else: - new_lm_input = dict2dict(tokens[:, :step + 1].view(-1, step+1), self.tgt_to_lm) - new_cands = dict2dict(fw_top_k_idx.view(-1, 1), self.tgt_to_lm) - lm_scores = get_lm_scores(lm, new_lm_input, lm_incremental_states, new_cands, torch.tensor(np.full(tokens.size(0), step+1)), k) - - lm_scores.add_(expanded_lm_prefix_scores) - ch_lm_scores = combine_ch_lm(self.combine_method, ch_scores, lm_scores, src_size, cur_tgt_size) - # initialize all as min value - new_fw_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_ch_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_fw_lprobs[:, self.pad] = -math.inf - new_ch_lm_lprobs[:, self.pad] = -math.inf - new_lm_lprobs[:, self.pad] = -math.inf - - new_fw_lprobs.scatter_(1, fw_top_k_idx, fw_top_k) - new_ch_lm_lprobs.scatter_(1, fw_top_k_idx, ch_lm_scores) - new_lm_lprobs.scatter_(1, fw_top_k_idx, lm_scores.view(-1, k)) - return new_fw_lprobs, new_ch_lm_lprobs, new_lm_lprobs - - def combine_ch_lm(combine_type, ch_scores, lm_scores1, src_size, tgt_size): - if self.channel_scoring_type == "unnormalized": - ch_scores = self.log_softmax_fn( - ch_scores.view(-1, self.beam_size * self.k2) - ).view(ch_scores.shape) - ch_scores = ch_scores * self.ch_weight - lm_scores1 = lm_scores1 * self.lm_weight - - if combine_type == "lm_only": - # log P(T|S) + log P(T) - ch_scores = lm_scores1.view(ch_scores.size()) - elif combine_type == "noisy_channel": - # 1/t log P(T|S) + 1/s log P(S|T) + 1/t log P(T) - if self.normalize_lm_scores_by_tgt_len: - ch_scores.div_(src_size) - lm_scores_norm = lm_scores1.view(ch_scores.size()).div(tgt_size) - ch_scores.add_(lm_scores_norm) - # 1/t log P(T|S) + 1/s log P(S|T) + 1/s log P(T) - else: - ch_scores.add_(lm_scores1.view(ch_scores.size())) - ch_scores.div_(src_size) - - return ch_scores - - if self.channel_models is not None: - channel_model = self.channel_models[0] # assume only one channel_model model - else: - channel_model = None - - lm = EnsembleModel(self.lm_models) - lm_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(lm.models_size) - ], - ) - - reorder_state = None - batch_idxs = None - 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) - model.reorder_incremental_state(incremental_states, reorder_state) - encoder_outs = model.reorder_encoder_out(encoder_outs, reorder_state) - - lm.reorder_incremental_state(lm_incremental_states, reorder_state) - - fw_lprobs, avg_attn_scores = model.forward_decoder( - tokens[:, :step + 1], encoder_outs, incremental_states, temperature=self.temperature, - ) - - fw_lprobs[:, self.pad] = -math.inf # never select pad - fw_lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - fw_lprobs, ch_lm_lprobs, lm_lprobs = noisy_channel_rescoring(fw_lprobs, beam_size, bsz, src_tokens, tokens, self.k2) - - # handle min and max length constraints - if step >= max_len: - fw_lprobs[:, :self.eos] = -math.inf - fw_lprobs[:, self.eos + 1:] = -math.inf - elif step < self.min_len: - fw_lprobs[:, self.eos] = -math.inf - - # handle prefix tokens (possibly with different lengths) - if prefix_tokens is not None and step < prefix_tokens.size(1): - prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) - prefix_mask = prefix_toks.ne(self.pad) - - prefix_fw_lprobs = fw_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - fw_lprobs[prefix_mask] = -math.inf - fw_lprobs[prefix_mask] = fw_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_fw_lprobs - ) - - prefix_ch_lm_lprobs = ch_lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - ch_lm_lprobs[prefix_mask] = -math.inf - ch_lm_lprobs[prefix_mask] = ch_lm_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_ch_lm_lprobs - ) - - prefix_lm_lprobs = lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - lm_lprobs[prefix_mask] = -math.inf - lm_lprobs[prefix_mask] = lm_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lm_lprobs - ) - - # 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() - - def replicate_first_beam(tensor, mask): - tensor = tensor.view(-1, beam_size, tensor.size(-1)) - tensor[mask] = tensor[mask][:, :1, :] - return tensor.view(-1, tensor.size(-1)) - - # copy tokens, scores and lprobs from the first beam to all beams - tokens = replicate_first_beam(tokens, eos_mask_batch_dim) - scores = replicate_first_beam(scores, eos_mask_batch_dim) - - fw_lprobs = replicate_first_beam(fw_lprobs, eos_mask_batch_dim) - ch_lm_lprobs = replicate_first_beam(ch_lm_lprobs, eos_mask_batch_dim) - lm_lprobs = replicate_first_beam(lm_lprobs, eos_mask_batch_dim) - - if self.no_repeat_ngram_size > 0: - # for each beam and batch sentence, generate a list of previous ngrams - gen_ngrams = [{} for bbsz_idx in range(bsz * beam_size)] - for bbsz_idx in range(bsz * beam_size): - gen_tokens = tokens[bbsz_idx].tolist() - for ngram in zip(*[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]): - gen_ngrams[bbsz_idx][tuple(ngram[:-1])] = \ - gen_ngrams[bbsz_idx].get(tuple(ngram[:-1]), []) + [ngram[-1]] - - # Record attention scores - if avg_attn_scores is not None: - if attn is None: - attn = scores.new(bsz * beam_size, src_tokens.size(1), max_len + 2) - attn_buf = attn.clone() - nonpad_idxs = src_tokens.ne(self.pad) - attn[:, :, step + 1].copy_(avg_attn_scores) - - scores = scores.type_as(fw_lprobs) - scores_buf = scores_buf.type_as(fw_lprobs) - - self.search.set_src_lengths(src_lengths_no_eos) - - if self.no_repeat_ngram_size > 0: - def calculate_banned_tokens(bbsz_idx): - # before decoding the next token, prevent decoding of ngrams that have already appeared - ngram_index = tuple(tokens[bbsz_idx, step + 2 - self.no_repeat_ngram_size:step + 1].tolist()) - return gen_ngrams[bbsz_idx].get(ngram_index, []) - - if step + 2 - self.no_repeat_ngram_size >= 0: - # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet - banned_tokens = [calculate_banned_tokens(bbsz_idx) for bbsz_idx in range(bsz * beam_size)] - else: - banned_tokens = [[] for bbsz_idx in range(bsz * beam_size)] - - for bbsz_idx in range(bsz * beam_size): - fw_lprobs[bbsz_idx, banned_tokens[bbsz_idx]] = -math.inf - - combined_noisy_channel_scores, fw_lprobs_top_k, lm_lprobs_top_k, cand_indices, cand_beams = self.search.step( - step, - fw_lprobs.view(bsz, -1, self.vocab_size), - scores.view(bsz, beam_size, -1)[:, :, :step], ch_lm_lprobs.view(bsz, -1, self.vocab_size), - lm_lprobs.view(bsz, -1, self.vocab_size), self.combine_method - ) - - # 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 (except for candidates to be ignored) - eos_mask = cand_indices.eq(self.eos) - eos_mask[:, :beam_size] &= ~cands_to_ignore - - # only consider eos when it's among the top beam_size indices - eos_bbsz_idx = torch.masked_select( - cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents = set() - if eos_bbsz_idx.numel() > 0: - eos_scores = torch.masked_select( - fw_lprobs_top_k[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - combined_noisy_channel_eos_scores = torch.masked_select( - combined_noisy_channel_scores[:, :beam_size], - mask=eos_mask[:, :beam_size], - ) - - # finalize hypo using channel model score - finalized_sents = finalize_hypos( - step, eos_bbsz_idx, eos_scores, combined_noisy_channel_eos_scores) - - num_remaining_sent -= len(finalized_sents) - - assert num_remaining_sent >= 0 - if num_remaining_sent == 0: - break - - 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 = cand_indices.new_ones(bsz) - batch_mask[cand_indices.new(finalized_sents)] = 0 - batch_idxs = torch.nonzero(batch_mask).squeeze(-1) - - 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) - - lm_lprobs_top_k = lm_lprobs_top_k[batch_idxs] - - fw_lprobs_top_k = fw_lprobs_top_k[batch_idxs] - cand_indices = cand_indices[batch_idxs] - if prefix_tokens is not None: - prefix_tokens = prefix_tokens[batch_idxs] - src_lengths_no_eos = src_lengths_no_eos[batch_idxs] - cands_to_ignore = cands_to_ignore[batch_idxs] - - scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - scores_buf.resize_as_(scores) - tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - tokens_buf.resize_as_(tokens) - src_tokens = src_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - src_lengths = src_lengths.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - lm_prefix_scores = lm_prefix_scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1).squeeze() - - if attn is not None: - attn = attn.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, attn.size(1), -1) - attn_buf.resize_as_(attn) - bsz = new_bsz - else: - batch_idxs = None - - # Set active_mask so that values > cand_size indicate eos or - # ignored hypos and values < cand_size indicate candidate - # active hypos. After this, the min values per row are the top - # candidate active hypos. - eos_mask[:, :beam_size] |= cands_to_ignore - 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, new_cands_to_ignore = buffer('active_hypos'), buffer('new_cands_to_ignore') - torch.topk( - active_mask, k=beam_size, dim=1, largest=False, - out=(new_cands_to_ignore, active_hypos) - ) - - # update cands_to_ignore to ignore any finalized hypos - cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] - assert (~cands_to_ignore).any(dim=1).all() - - active_bbsz_idx = buffer('active_bbsz_idx') - torch.gather( - cand_bbsz_idx, dim=1, index=active_hypos, - out=active_bbsz_idx, - ) - active_scores = torch.gather( - fw_lprobs_top_k, dim=1, index=active_hypos, - out=scores[:, step].view(bsz, beam_size), - ) - - active_bbsz_idx = active_bbsz_idx.view(-1) - active_scores = active_scores.view(-1) - - # copy tokens and scores for active hypotheses - torch.index_select( - tokens[:, :step + 1], dim=0, index=active_bbsz_idx, - out=tokens_buf[:, :step + 1], - ) - torch.gather( - cand_indices, dim=1, index=active_hypos, - out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1], - ) - if step > 0: - torch.index_select( - scores[:, :step], dim=0, index=active_bbsz_idx, - out=scores_buf[:, :step], - ) - torch.gather( - fw_lprobs_top_k, dim=1, index=active_hypos, - out=scores_buf.view(bsz, beam_size, -1)[:, :, step], - ) - torch.gather( - lm_lprobs_top_k, dim=1, index=active_hypos, - out=lm_prefix_scores.view(bsz, beam_size) - ) - - # copy attention for active hypotheses - if attn is not None: - torch.index_select( - attn[:, :, :step + 2], dim=0, index=active_bbsz_idx, - out=attn_buf[:, :, :step + 2], - ) - - # swap buffers - tokens, tokens_buf = tokens_buf, tokens - scores, scores_buf = scores_buf, scores - if attn is not None: - attn, attn_buf = attn_buf, attn - - # reorder incremental state in decoder - reorder_state = active_bbsz_idx - - # sort by score descending - for sent in range(len(finalized)): - finalized[sent] = sorted(finalized[sent], key=lambda r: r['score'], reverse=True) - - return finalized - - -def get_lm_scores(model, input_tokens, incremental_states, cand_tokens, input_len, k): - with torch.no_grad(): - lm_lprobs, avg_attn_scores = model.forward_decoder( - input_tokens, encoder_outs=None, incremental_states=incremental_states, - ) - - lm_lprobs_size = lm_lprobs.size(0) - probs_next_wrd = torch.gather(lm_lprobs.repeat(1, k).view(lm_lprobs_size*k, -1), 1, cand_tokens).squeeze().view(-1) - - return probs_next_wrd - - -def make_dict2dict(old_dict, new_dict): - dict2dict_map = {} - for sym in old_dict.symbols: - dict2dict_map[old_dict.index(sym)] = new_dict.index(sym) - return dict2dict_map - - -def dict2dict(tokens, dict2dict_map): - if tokens.device == torch.device('cpu'): - tokens_tmp = tokens - else: - tokens_tmp = tokens.cpu() - return tokens_tmp.map_( - tokens_tmp, - lambda _, val, dict2dict_map=dict2dict_map : dict2dict_map[float(val)] - ).to(tokens.device) - - -def reorder_tokens(tokens, lengths, eos): - # reorder source tokens so they may be used as reference for P(S|T) - return torch.cat((tokens.new([eos]), tokens[-lengths:-1], tokens[:-lengths]), 0) - - -def reorder_all_tokens(tokens, lengths, eos): - # used to reorder src tokens from [ .. ] to [ ...] - # so source tokens can be used to predict P(S|T) - return torch.stack([reorder_tokens(token, length, eos) for token, length in zip(tokens, lengths)]) - - -def normalized_scores_with_batch_vocab( - model_decoder, features, target_ids, k, bsz, beam_size, - pad_idx, top_k=0, vocab_size_meter=None, start_idx=None, - end_idx=None, **kwargs): - """ - Get normalized probabilities (or log probs) from a net's output - w.r.t. vocab consisting of target IDs in the batch - """ - if model_decoder.adaptive_softmax is None: - weight = model_decoder.output_projection.weight - vocab_ids = torch.unique( - torch.cat( - (torch.unique(target_ids), torch.arange(top_k, device=target_ids.device)) - ) - ) - id_map = dict(zip(vocab_ids.tolist(), range(len(vocab_ids)))) - mapped_target_ids = target_ids.cpu().apply_( - lambda x, id_map=id_map: id_map[x] - ).to(target_ids.device) - expanded_target_ids = mapped_target_ids[:, :].repeat(1, k).view(bsz*beam_size*k, -1) - if start_idx is not None and end_idx is not None: - expanded_target_ids = expanded_target_ids[start_idx:end_idx, :] - logits = F.linear(features, weight[vocab_ids, :]) - log_softmax = F.log_softmax(logits, dim=-1, dtype=torch.float32) - intermed_scores = torch.gather( - log_softmax[:, :-1, :], - 2, - expanded_target_ids[:, 1:].unsqueeze(2), - ).squeeze() - not_padding = expanded_target_ids[:, 1:] != pad_idx - intermed_scores *= not_padding.float() - return intermed_scores - else: - raise ValueError("adaptive softmax doesn't work with " + - "`normalized_scores_with_batch_vocab()`") diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_translation.py b/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_translation.py deleted file mode 100644 index 82937a2dfbd54cfbd4c50c2af1574d4e3343b8de..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/fast_noisy_channel/noisy_channel_translation.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. - -import argparse - -import torch -from fairseq import checkpoint_utils -from fairseq.tasks import register_task -from fairseq.tasks.language_modeling import LanguageModelingTask -from fairseq.tasks.translation import TranslationTask - - -@register_task("noisy_channel_translation") -class NoisyChannelTranslation(TranslationTask): - """ - Rescore the top k candidates from each beam using noisy channel modeling - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - TranslationTask.add_args(parser) - # fmt: off - parser.add_argument('--channel-model', metavar='FILE', - help='path to P(S|T) model. P(S|T) and P(T|S) must share source and target dictionaries.') - parser.add_argument('--combine-method', default='lm_only', - choices=['lm_only', 'noisy_channel'], - help="""method for combining direct and channel model scores. - lm_only: decode with P(T|S)P(T) - noisy_channel: decode with 1/t P(T|S) + 1/s(P(S|T)P(T))""") - parser.add_argument('--normalize-lm-scores-by-tgt-len', action='store_true', default=False, - help='normalize lm score by target length instead of source length') - parser.add_argument('--channel-scoring-type', default='log_norm', choices=['unnormalized', 'log_norm', 'k2_separate', 'src_vocab', 'src_vocab_batched'], - help="Normalize bw scores with log softmax or return bw scores without log softmax") - parser.add_argument('--top-k-vocab', default=0, type=int, - help='top k vocab IDs to use with `src_vocab` in channel model scoring') - parser.add_argument('--k2', default=50, type=int, - help='the top k2 candidates to rescore with the noisy channel model for each beam') - parser.add_argument('--ch-wt', default=1, type=float, - help='weight for the channel model') - parser.add_argument('--lm-model', metavar='FILE', - help='path to lm model file, to model P(T). P(T) must share the same vocab as the direct model on the target side') - parser.add_argument('--lm-data', metavar='FILE', - help='path to lm model training data for target language, used to properly load LM with correct dictionary') - parser.add_argument('--lm-wt', default=1, type=float, - help='the weight of the lm in joint decoding') - # fmt: on - - def build_generator( - self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None - ): - if getattr(args, "score_reference", False): - raise NotImplementedError() - else: - from .noisy_channel_sequence_generator import NoisyChannelSequenceGenerator - use_cuda = torch.cuda.is_available() and not self.args.cpu - assert self.args.lm_model is not None, '--lm-model required for noisy channel generation!' - assert self.args.lm_data is not None, '--lm-data required for noisy channel generation to map between LM and bitext vocabs' - if self.args.channel_model is not None: - import copy - ch_args_task = copy.deepcopy(self.args) - tmp = ch_args_task.source_lang - ch_args_task.source_lang = ch_args_task.target_lang - ch_args_task.target_lang = tmp - ch_args_task._name = 'translation' - channel_task = TranslationTask.setup_task(ch_args_task) - - arg_dict = {} - arg_dict['task'] = 'language_modeling' - arg_dict['sample_break_mode'] = 'eos' - arg_dict['data'] = self.args.lm_data - arg_dict['output_dictionary_size'] = -1 - lm_args = argparse.Namespace(**arg_dict) - lm_task = LanguageModelingTask.setup_task(lm_args) - lm_dict = lm_task.output_dictionary - - if self.args.channel_model is not None: - channel_models, _ = checkpoint_utils.load_model_ensemble(self.args.channel_model.split(':'), task=channel_task) - - for model in channel_models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if self.args.fp16: - model.half() - if use_cuda: - model.cuda() - else: - channel_models = None - - lm_models, _ = checkpoint_utils.load_model_ensemble(self.args.lm_model.split(':'), task=lm_task) - - for model in lm_models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if self.args.fp16: - model.half() - if use_cuda: - model.cuda() - return NoisyChannelSequenceGenerator( - combine_method=self.args.combine_method, - tgt_dict=self.target_dictionary, - src_dict=self.source_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), - len_penalty=getattr(args, 'lenpen', 1), - unk_penalty=getattr(args, 'unkpen', 0), - temperature=getattr(args, 'temperature', 1.), - match_source_len=getattr(args, 'match_source_len', False), - no_repeat_ngram_size=getattr(args, 'no_repeat_ngram_size', 0), - normalize_scores=(not getattr(args, 'unnormalized', False)), - channel_models=channel_models, - k2=getattr(self.args, 'k2', 50), - ch_weight=getattr(self.args, 'ch_wt', 1), - channel_scoring_type=self.args.channel_scoring_type, - top_k_vocab=self.args.top_k_vocab, - lm_models=lm_models, - lm_dict=lm_dict, - lm_weight=getattr(self.args, 'lm_wt', 1), - normalize_lm_scores_by_tgt_len=getattr(self.args, 'normalize_lm_scores_by_tgt_len', False), - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/flores101/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/flores101/README.md deleted file mode 100644 index 635c13f40bd0ccab704735bc5c26ea0192ea98cd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/flores101/README.md +++ /dev/null @@ -1,223 +0,0 @@ -

- -

- -# Flores101: Large-Scale Multilingual Machine Translation - -## Introduction - -Baseline pretrained models for small and large tracks of WMT 21 Large-Scale Multilingual Machine Translation competition. - -Flores Task at WMT 21: http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html - -Flores announement blog post: https://ai.facebook.com/blog/flores-researchers-kick-off-multilingual-translation-challenge-at-wmt-and-call-for-compute-grants/ - - - -## Pretrained models - -Model | Num layers | Embed dimension | FFN dimension| Vocab Size | #params | Download ----|---|---|---|---|---|--- -`flores101_mm100_615M` | 12 | 1024 | 4096 | 256,000 | 615M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz -`flores101_mm100_175M` | 6 | 512 | 2048 | 256,000 | 175M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz - - -These models are trained similar to [M2M-100](https://arxiv.org/abs/2010.11125) with additional support for the languages that are part of the WMT Large-Scale Multilingual Machine Translation track. Full list of languages can be found at the bottom. - - -## Example Generation code - -### Download model, sentencepiece vocab - -```bash -fairseq=/path/to/fairseq -cd $fairseq - -# Download 615M param model. -wget https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz - -# Extract -tar -xvzf flores101_mm100_615M.tar.gz -``` - -### Encode using our SentencePiece Model -Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) - - -```bash -fairseq=/path/to/fairseq -cd $fairseq - -# Download example dataset From German to French -sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de -sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr - -for lang in de fr ; do - python scripts/spm_encode.py \ - --model flores101_mm100_615M/sentencepiece.bpe.model \ - --output_format=piece \ - --inputs=raw_input.de-fr.${lang} \ - --outputs=spm.de-fr.${lang} -done -``` - -### Binarization - -```bash -fairseq-preprocess \ - --source-lang de --target-lang fr \ - --testpref spm.de-fr \ - --thresholdsrc 0 --thresholdtgt 0 \ - --destdir data_bin \ - --srcdict flores101_mm100_615M/dict.txt --tgtdict flores101_mm100_615M/dict.txt -``` - -### Generation - - -```bash -fairseq-generate \ - data_bin \ - --batch-size 1 \ - --path flores101_mm100_615M/model.pt \ - --fixed-dictionary flores101_mm100_615M/dict.txt \ - -s de -t fr \ - --remove-bpe 'sentencepiece' \ - --beam 5 \ - --task translation_multi_simple_epoch \ - --lang-pairs flores101_mm100_615M/language_pairs.txt \ - --decoder-langtok --encoder-langtok src \ - --gen-subset test \ - --fp16 \ - --dataset-impl mmap \ - --distributed-world-size 1 --distributed-no-spawn -``` - -### Supported Languages and lang code - -Language | lang code ----|--- -Akrikaans | af -Amharic | am -Arabic | ar -Assamese | as -Asturian | ast -Aymara | ay -Azerbaijani | az -Bashkir | ba -Belarusian | be -Bulgarian | bg -Bengali | bn -Breton | br -Bosnian | bs -Catalan | ca -Cebuano | ceb -Chokwe | cjk -Czech | cs -Welsh | cy -Danish | da -German | de -Dyula| dyu -Greek | el -English | en -Spanish | es -Estonian | et -Persian | fa -Fulah | ff -Finnish | fi -French | fr -Western Frisian | fy -Irish | ga -Scottish Gaelic | gd -Galician | gl -Gujarati | gu -Hausa | ha -Hebrew | he -Hindi | hi -Croatian | hr -Haitian Creole | ht -Hungarian | hu -Armenian | hy -Indonesian | id -Igbo | ig -Iloko | ilo -Icelandic | is -Italian | it -Japanese | ja -Javanese | jv -Georgian | ka -Kachin | kac -Kamba | kam -Kabuverdianu | kea -Kongo | kg -Kazakh | kk -Central Khmer | km -Kimbundu | kmb -Northern Kurdish | kmr -Kannada | kn -Korean | ko -Kurdish | ku -Kyrgyz | ky -Luxembourgish | lb -Ganda | lg -Lingala | ln -Lao | lo -Lithuanian | lt -Luo | luo -Latvian | lv -Malagasy | mg -Maori | mi -Macedonian | mk -Malayalam | ml -Mongolian | mn -Marathi | mr -Malay | ms -Maltese | mt -Burmese | my -Nepali | ne -Dutch | nl -Norwegian | no -Northern Sotho | ns -Nyanja | ny -Occitan | oc -Oromo | om -Oriya | or -Punjabi | pa -Polish | pl -Pashto | ps -Portuguese | pt -Quechua | qu -Romanian | ro -Russian | ru -Sindhi | sd -Shan | shn -Sinhala | si -Slovak | sk -Slovenian | sl -Shona | sn -Somali | so -Albanian | sq -Serbian | sr -Swati | ss -Sundanese | su -Swedish | sv -Swahili | sw -Tamil | ta -Telugu | te -Tajik | tg -Thai | th -Tigrinya | ti -Tagalog | tl -Tswana | tn -Turkish | tr -Ukrainian | uk -Umbundu | umb -Urdu | ur -Uzbek | uz -Vietnamese | vi -Wolof | wo -Xhosa | xh -Yiddish | yi -Yoruba | yo -Chinese| zh -Zulu | zu diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/flores101/flores_logo.png b/audio/safeear/safeear_repo/fairseq_ours/examples/flores101/flores_logo.png deleted file mode 100644 index d4d1455c6eab608ff5317ce885183cd213564273..0000000000000000000000000000000000000000 Binary files a/audio/safeear/safeear_repo/fairseq_ours/examples/flores101/flores_logo.png and /dev/null differ diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/fully_sharded_data_parallel/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/fully_sharded_data_parallel/README.md deleted file mode 100644 index b9e44fef48bee5faeee27b3d1d1b1eb96b6a477f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/fully_sharded_data_parallel/README.md +++ /dev/null @@ -1,177 +0,0 @@ -# Fully Sharded Data Parallel (FSDP) - -## Overview -Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and -[Google](https://arxiv.org/abs/2004.13336) has shown that data parallel -training can be made significantly more efficient by sharding the model -parameters and optimizer state across data parallel workers. These ideas are -encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper provided -by [fairscale](https://github.com/facebookresearch/fairscale/). - -Compared to PyTorch DDP: -* FSDP produces identical results as PyTorch DDP (it's still synchronous data parallel training) -* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs -* FSDP is faster than PyTorch DDP because the optimizer step is sharded, and the communication can be overlapped with the forward pass -* FSDP enables training 13B parameter models on 8 GPUs and 175B parameter models on 128 GPUs - -FSDP is fully supported in fairseq via the following new arguments: -* `--ddp-backend=fully_sharded`: enables full sharding via FSDP -* `--cpu-offload`: offloads the optimizer state and FP32 model copy to CPU (combine with `--optimizer=cpu_adam`) -* `--no-reshard-after-forward`: increases training speed for large models (1B+ params) and is similar to ZeRO stage 2 -* other popular options (`--fp16`, `--update-freq`, `--checkpoint-activations`, `--offload-activations`, etc.) continue to work as normal - -
Limitations

- -FSDP currently has several limitations compared to fairseq's default DDP backend (PyTorch DDP): -* while FSDP is full compatible with pointwise Optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.), it is not currently compatible with non-pointwise Optimizers (e.g., Adagrad, Adafactor, LAMB, etc.) -* FSDP depends on flattening the parameters, so models that currently require `--fp16-no-flatten-grads` may not be supported - -See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed -explanation of these and other limitations. - -

- -
How it works

- -Fully Sharded Data Parallel - -See the [fairscale docs](https://fairscale.readthedocs.io/en/latest/api/nn/fsdp_tips.html) for a more detailed -explanation of how FSDP works. - -

- -## Example usage - -The following examples illustrate how to train a very large language model with -13 billion parameters on 1 GPU by offloading parameters and optimizer states to -CPU, or on 8 GPUs by fully sharding the params and optimizer states across GPUs. - -These examples use the WikiText-103 dataset for demonstration purposes, but -in practice a much larger dataset will be needed to achieve good results. -Follow the [instructions here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.pretraining.md#1-preprocess-the-data) -to preprocess the WikiText-103 dataset using the GPT-2/RoBERTa vocabulary. - -### 13B params on 1 V100 GPU (with CPU offloading) - -The following command trains a 13B parameter GPT-3 model on a single V100 GPU -using the `--cpu-offload` feature to offload parameters and optimizer states to -CPU. In this setting, the optimizer step (Adam) happens on CPU. We also use the -`--checkpoint-activations` feature (sometimes called [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html)), -which further saves memory in exchange for a small increase in computation. - -**Requirements:** -- Install the latest master version of fairscale: `pip install git+https://github.com/facebookresearch/fairscale.git@master` -- You'll need 32GB of GPU memory and ~256GB of system memory to train the 13B param model. -- If you have less system memory, the 6.7B param model can be trained with ~128GB of system memory, just set `--arch transformer_lm_gpt3_6_7` -- We use the CPU Adam optimizer from [DeepSpeed](https://github.com/microsoft/DeepSpeed), so you'll need to `pip install deepspeed` before running the command. - -**Notes:** -- The command will take ~5 minutes to start training, during which time it will appear to be hung, since randomly initializing 13B weights can be slow. -- The `--cpu-offload` feature requires training in mixed precision (`--fp16`). -- Tune the `OMP_NUM_THREADS` env variable for best performance with CPU offloading. -- The example command below stops training after 10 steps (`--max-update 10`) and does not save checkpoints (`--no-save`). - -```bash -OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0 \ - fairseq-train data-bin/wikitext-103-roberta-bpe-bin \ - --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ - --cpu-offload --checkpoint-activations \ - --task language_modeling --tokens-per-sample 2048 --batch-size 8 \ - --arch transformer_lm_gpt3_13 \ - --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 10 --no-save --log-format json --log-interval 1 -``` - -
Example output

- -``` -(...) -2021-03-08 12:29:51 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) -(...) -2021-03-08 12:29:51 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs) -2021-03-08 12:29:51 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 -(...) -Adam Optimizer #0 is created with AVX2 arithmetic capability. -Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 -(...) -2021-03-08 12:31:36 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.475", "ppl": "91120.8", "wps": "0", "ups": "0", "wpb": "16384", "bsz": "8", "num_updates": "1", "lr": "2e-05", "gnorm": "20.751", "loss_scale": "4", "train_wall": "99", "gb_free": "9.3", "wall": "105"} -2021-03-08 12:32:33 | INFO | train_inner | {"epoch": 1, "update": 0.0, "loss": "16.446", "ppl": "89281.6", "wps": "288.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "2", "lr": "4e-05", "gnorm": "19.777", "loss_scale": "4", "train_wall": "57", "gb_free": "9.3", "wall": "161"} -2021-03-08 12:33:12 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 -2021-03-08 12:33:51 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 -2021-03-08 12:34:45 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "25.22", "ppl": "3.90691e+07", "wps": "123.4", "ups": "0.01", "wpb": "16384", "bsz": "8", "num_updates": "3", "lr": "6e-05", "gnorm": "131.281", "loss_scale": "1", "train_wall": "133", "gb_free": "9.3", "wall": "294"} -2021-03-08 12:35:43 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.079", "ppl": "276809", "wps": "285.5", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "4", "lr": "8e-05", "gnorm": "13.776", "loss_scale": "1", "train_wall": "57", "gb_free": "9.3", "wall": "351"} -2021-03-08 12:36:35 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "23.729", "ppl": "1.39088e+07", "wps": "316.7", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "5", "lr": "0.0001", "gnorm": "72.774", "loss_scale": "1", "train_wall": "52", "gb_free": "9.3", "wall": "403"} -2021-03-08 12:37:28 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "20.429", "ppl": "1.41203e+06", "wps": "307.6", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "6", "lr": "8e-05", "gnorm": "60.846", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "456"} -2021-03-08 12:38:27 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.965", "ppl": "511684", "wps": "279.4", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "7", "lr": "6e-05", "gnorm": "22.687", "loss_scale": "1", "train_wall": "59", "gb_free": "9.3", "wall": "515"} -2021-03-08 12:39:18 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "18.345", "ppl": "332887", "wps": "319.1", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "8", "lr": "4e-05", "gnorm": "8.451", "loss_scale": "1", "train_wall": "51", "gb_free": "9.3", "wall": "566"} -2021-03-08 12:40:11 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "18.262", "ppl": "314336", "wps": "305.9", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "9", "lr": "2e-05", "gnorm": "6.457", "loss_scale": "1", "train_wall": "54", "gb_free": "9.3", "wall": "620"} -2021-03-08 12:41:04 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "17.556", "ppl": "192686", "wps": "311.8", "ups": "0.02", "wpb": "16384", "bsz": "8", "num_updates": "10", "lr": "0", "gnorm": "5.796", "loss_scale": "1", "train_wall": "53", "gb_free": "9.3", "wall": "673"} -2021-03-08 12:41:04 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 -2021-03-08 12:41:04 | INFO | fairseq_cli.train | begin validation on "valid" subset -2021-03-08 12:43:15 | INFO | valid | {"epoch": 1, "valid_loss": "17.953", "valid_ppl": "253807", "valid_wps": "1868.4", "valid_wpb": "15400.2", "valid_bsz": "7.6", "valid_num_updates": "10"} -2021-03-08 12:43:15 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) -2021-03-08 12:43:15 | INFO | train | {"epoch": 1, "train_loss": "19.351", "train_ppl": "668509", "train_wps": "210.9", "train_ups": "0.01", "train_wpb": "16384", "train_bsz": "8", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "36.26", "train_loss_scale": "1", "train_train_wall": "667", "train_gb_free": "9.3", "train_wall": "804"} -2021-03-08 12:43:15 | INFO | fairseq_cli.train | done training in 798.6 seconds -``` - -

- -### 13B params on 8 V100 GPUs (with full parameter + optimizer state sharding) - -FSDP can also shard the parameters and optimizer states across multiple GPUs, -reducing memory requirements significantly. On 8 x 32GB GPUs, sharding enables -training the same 13B parameter model *without offloading the parameters to -CPU*. However, without CPU offloading we'd only be able to fit a batch size of -1 per GPU, which would cause training speed to suffer. - -We obtain the best performance on 8 GPUs by combining full sharding and CPU -offloading. The following command trains the same 13B parameter GPT-3 model as -before on 8 x 32GB V100 GPUs; training speed increases superlinearly from ~310 -words per second to ~3200 words per second. - -```bash -OMP_NUM_THREADS=20 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ - fairseq-train data-bin/wikitext-103-roberta-bpe-bin \ - --ddp-backend fully_sharded --fp16 --fp16-init-scale 4 \ - --cpu-offload --checkpoint-activations \ - --task language_modeling --tokens-per-sample 2048 --batch-size 8 \ - --arch transformer_lm_gpt3_13 \ - --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 10 --no-save --log-format json --log-interval 1 -``` - -
Example output

- -``` -(...) -2021-03-08 18:04:09 | INFO | fairseq_cli.train | num. model params: 13,110,865,920 (num. trained: 13,110,865,920) -(...) -2021-03-08 18:04:09 | INFO | fairseq_cli.train | training on 8 devices (GPUs/TPUs) -2021-03-08 18:04:09 | INFO | fairseq_cli.train | max tokens per GPU = None and batch size per GPU = 8 -(...) -Adam Optimizer #0 is created with AVX2 arithmetic capability. -Config: alpha=0.000100, betas=(0.900000, 0.980000), weight_decay=0.000000, adam_w=1 -(...) -2021-03-08 18:05:06 | INFO | train_inner | {"epoch": 1, "update": 0.001, "loss": "16.408", "ppl": "86945.6", "wps": "0", "ups": "0", "wpb": "131072", "bsz": "64", "num_updates": "1", "lr": "2e-05", "gnorm": "18.27", "loss_scale": "4", "train_wall": "47", "gb_free": "9.3", "wall": "56"} -2021-03-08 18:05:45 | INFO | train_inner | {"epoch": 1, "update": 0.002, "loss": "16.352", "ppl": "83644.3", "wps": "3283.4", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "2", "lr": "4e-05", "gnorm": "18.411", "loss_scale": "4", "train_wall": "40", "gb_free": "9.3", "wall": "96"} -2021-03-08 18:06:21 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 2.0 -2021-03-08 18:06:56 | INFO | fairseq.trainer | NOTE: gradient overflow detected, ignoring gradient, setting loss scale to: 1.0 -2021-03-08 18:07:37 | INFO | train_inner | {"epoch": 1, "update": 0.006, "loss": "23.682", "ppl": "1.34537e+07", "wps": "1176.6", "ups": "0.01", "wpb": "131072", "bsz": "64", "num_updates": "3", "lr": "6e-05", "gnorm": "119.682", "loss_scale": "1", "train_wall": "111", "gb_free": "9.3", "wall": "208"} -2021-03-08 18:08:18 | INFO | train_inner | {"epoch": 1, "update": 0.007, "loss": "18.988", "ppl": "519921", "wps": "3189.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "4", "lr": "8e-05", "gnorm": "14.934", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "249"} -2021-03-08 18:08:59 | INFO | train_inner | {"epoch": 1, "update": 0.008, "loss": "20.08", "ppl": "1.10798e+06", "wps": "3223.1", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "5", "lr": "0.0001", "gnorm": "59.92", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "289"} -2021-03-08 18:09:39 | INFO | train_inner | {"epoch": 1, "update": 0.009, "loss": "18.323", "ppl": "327980", "wps": "3256.6", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "6", "lr": "8e-05", "gnorm": "37.425", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "330"} -2021-03-08 18:10:20 | INFO | train_inner | {"epoch": 1, "update": 0.01, "loss": "17.264", "ppl": "157354", "wps": "3188.7", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "7", "lr": "6e-05", "gnorm": "10.824", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "371"} -2021-03-08 18:11:01 | INFO | train_inner | {"epoch": 1, "update": 0.011, "loss": "16.794", "ppl": "113647", "wps": "3230", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "8", "lr": "4e-05", "gnorm": "5.616", "loss_scale": "1", "train_wall": "41", "gb_free": "9.3", "wall": "411"} -2021-03-08 18:11:39 | INFO | train_inner | {"epoch": 1, "update": 0.012, "loss": "16.706", "ppl": "106938", "wps": "3384", "ups": "0.03", "wpb": "131072", "bsz": "64", "num_updates": "9", "lr": "2e-05", "gnorm": "5.318", "loss_scale": "1", "train_wall": "39", "gb_free": "9.3", "wall": "450"} -2021-03-08 18:12:19 | INFO | train_inner | {"epoch": 1, "update": 0.013, "loss": "16.548", "ppl": "95796.2", "wps": "3274.4", "ups": "0.02", "wpb": "131072", "bsz": "64", "num_updates": "10", "lr": "0", "gnorm": "5.22", "loss_scale": "1", "train_wall": "40", "gb_free": "9.3", "wall": "490"} -2021-03-08 18:12:19 | INFO | fairseq_cli.train | Stopping training due to num_updates: 10 >= max_update: 10 -2021-03-08 18:12:19 | INFO | fairseq_cli.train | begin validation on "valid" subset -2021-03-08 18:12:45 | INFO | valid | {"epoch": 1, "valid_loss": "16.624", "valid_ppl": "101000", "valid_wps": "10855.9", "valid_wpb": "123202", "valid_bsz": "60.5", "valid_num_updates": "10"} -2021-03-08 18:12:45 | INFO | fairseq_cli.train | end of epoch 1 (average epoch stats below) -2021-03-08 18:12:45 | INFO | train | {"epoch": 1, "train_loss": "18.114", "train_ppl": "283776", "train_wps": "2567.8", "train_ups": "0.02", "train_wpb": "131072", "train_bsz": "64", "train_num_updates": "10", "train_lr": "0", "train_gnorm": "29.562", "train_loss_scale": "1", "train_train_wall": "480", "train_gb_free": "9.3", "train_wall": "516"} -2021-03-08 18:12:45 | INFO | fairseq_cli.train | done training in 509.9 seconds -``` - -

diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/gottbert/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/gottbert/README.md deleted file mode 100644 index 1d58feb279a4a50222290546c3bb285d3cea98e6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/gottbert/README.md +++ /dev/null @@ -1,64 +0,0 @@ -# GottBERT: a pure German language model - -## Introduction - -[GottBERT](http://arxiv.org/abs/2012.02110) is a pretrained language model trained on 145GB of German text based on RoBERTa. - -## Example usage - -### fairseq -##### Load GottBERT from torch.hub (PyTorch >= 1.1): -```python -import torch -gottbert = torch.hub.load('pytorch/fairseq', 'gottbert-base') -gottbert.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Load GottBERT (for PyTorch 1.0 or custom models): -```python -# Download gottbert model -wget https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz -tar -xzvf gottbert.tar.gz - -# Load the model in fairseq -from fairseq.models.roberta import GottbertModel -gottbert = GottbertModel.from_pretrained('/path/to/gottbert') -gottbert.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Filling masks: -```python -masked_line = 'Gott ist ! :)' -gottbert.fill_mask(masked_line, topk=3) -# [('Gott ist gut ! :)', 0.3642110526561737, ' gut'), -# ('Gott ist überall ! :)', 0.06009674072265625, ' überall'), -# ('Gott ist großartig ! :)', 0.0370681993663311, ' großartig')] -``` - -##### Extract features from GottBERT - -```python -# Extract the last layer's features -line = "Der erste Schluck aus dem Becher der Naturwissenschaft macht atheistisch , aber auf dem Grunde des Bechers wartet Gott !" -tokens = gottbert.encode(line) -last_layer_features = gottbert.extract_features(tokens) -assert last_layer_features.size() == torch.Size([1, 27, 768]) - -# Extract all layer's features (layer 0 is the embedding layer) -all_layers = gottbert.extract_features(tokens, return_all_hiddens=True) -assert len(all_layers) == 13 -assert torch.all(all_layers[-1] == last_layer_features) -``` -## Citation -If you use our work, please cite: - -```bibtex -@misc{scheible2020gottbert, - title={GottBERT: a pure German Language Model}, - author={Raphael Scheible and Fabian Thomczyk and Patric Tippmann and Victor Jaravine and Martin Boeker}, - year={2020}, - eprint={2012.02110}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/README.md deleted file mode 100644 index 6695d819713b3e2077cf0fab30469b237e1cf1be..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/README.md +++ /dev/null @@ -1,116 +0,0 @@ -# HuBERT - -## Pre-trained and fine-tuned (ASR) models -Model | Pretraining Data | Finetuning Dataset | Model | Quantizer -|---|---|---|---|--- -HuBERT Base (~95M params) | [Librispeech](http://www.openslr.org/12) 960 hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt) | [L9 km500](https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960_L9_km500.bin) -HuBERT Large (~316M params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt) -HuBERT Extra Large (~1B params) | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | No finetuning (Pretrained Model) | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt) -HuBERT Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt) -HuBERT Extra Large | [Libri-Light](https://github.com/facebookresearch/libri-light) 60k hr | [Librispeech](http://www.openslr.org/12) 960 hr | [download](https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt) - -## Load a model -``` -ckpt_path = "/path/to/the/checkpoint.pt" -models, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path]) -model = models[0] -``` - -## Train a new model - -### Data preparation - -Follow the steps in `./simple_kmeans` to create: -- `{train,valid}.tsv` waveform list files -- `{train,valid}.km` frame-aligned pseudo label files. -- `dict.km.txt` a dummy dictionary -The `label_rate` is the same as the feature frame rate used for clustering, -which is 100Hz for MFCC features and 50Hz for HuBERT features by default. - -### Pre-train a HuBERT model - -Suppose `{train,valid}.tsv` are saved at `/path/to/data`, `{train,valid}.km` -are saved at `/path/to/labels`, and the label rate is 100Hz. - -To train a base model (12 layer transformer), run: -```sh -$ python fairseq_cli/hydra_train.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/pretrain \ - --config-name hubert_base_librispeech \ - task.data=/path/to/data task.label_dir=/path/to/labels task.labels='["km"]' model.label_rate=100 -``` - -### Fine-tune a HuBERT model with a CTC loss - -Suppose `{train,valid}.tsv` are saved at `/path/to/data`, and their -corresponding character transcripts `{train,valid}.ltr` are saved at -`/path/to/trans`. - -To fine-tune a pre-trained HuBERT model at `/path/to/checkpoint`, run -```sh -$ python fairseq_cli/hydra_train.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/finetune \ - --config-name base_10h \ - task.data=/path/to/data task.label_dir=/path/to/trans \ - model.w2v_path=/path/to/checkpoint -``` - -### Decode a HuBERT model - -Suppose the `test.tsv` and `test.ltr` are the waveform list and transcripts of -the split to be decoded, saved at `/path/to/data`, and the fine-tuned model is -saved at `/path/to/checkpoint`. We support three decoding modes: -- Viterbi decoding: greedy decoding without a language model -- KenLM decoding: decoding with an arpa-format KenLM n-gram language model -- Fairseq-LM deocding: decoding with a Fairseq neural language model - - -#### Viterbi decoding - -`task.normalize` needs to be consistent with the value used during fine-tuning. -Decoding results will be saved at -`/path/to/experiment/directory/decode/viterbi/test`. - -```sh -$ python examples/speech_recognition/new/infer.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ - --config-name infer_viterbi \ - task.data=/path/to/data \ - task.normalize=[true|false] \ - decoding.exp_dir=/path/to/experiment/directory \ - common_eval.path=/path/to/checkpoint - dataset.gen_subset=test \ -``` - -#### KenLM / Fairseq-LM decoding - -Suppose the pronunciation lexicon and the n-gram LM are saved at -`/path/to/lexicon` and `/path/to/arpa`, respectively. Decoding results will be -saved at `/path/to/experiment/directory/decode/kenlm/test`. - -```sh -$ python examples/speech_recognition/new/infer.py \ - --config-dir /path/to/fairseq-py/examples/hubert/config/decode \ - --config-name infer_kenlm \ - task.data=/path/to/data \ - task.normalize=[true|false] \ - decoding.exp_dir=/path/to/experiment/directory \ - common_eval.path=/path/to/checkpoint - dataset.gen_subset=test \ - decoding.decoder.lexicon=/path/to/lexicon \ - decoding.decoder.lmpath=/path/to/arpa -``` - -The command above uses the default decoding hyperparameter, which can be found -in `examples/speech_recognition/hydra/decoder.py`. These parameters can be -configured from the command line. For example, to search with a beam size of -500, we can append the command above with `decoding.decoder.beam=500`. -Important parameters include: -- decoding.decoder.beam -- decoding.decoder.beamthreshold -- decoding.decoder.lmweight -- decoding.decoder.wordscore -- decoding.decoder.silweight - -To decode with a Fairseq LM, use `--config-name infer_fsqlm` instead, and -change the path of lexicon and LM accordingly. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/ax_sweep/ngram.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/ax_sweep/ngram.yaml deleted file mode 100644 index 5a02df1f7da7eebfebe4018ef2758a716fbab646..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/ax_sweep/ngram.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# @package _global_ - -common_eval: - results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}_ax/${dataset.gen_subset} - -hydra: - sweeper: - ax_config: - max_trials: 60 - early_stop: - minimize: true - max_epochs_without_improvement: 10 - epsilon: 0.025 - experiment: - name: ${dataset.gen_subset} - objective_name: wer - minimize: true - parameter_constraints: null - outcome_constraints: null - status_quo: null - client: - verbose_logging: false - random_seed: null - params: - decoding.decoder.lmweight: - type: range - bounds: [0.0, 8.0] - decoding.decoder.wordscore: - type: range - bounds: [-5.0, 5.0] - decoding.decoder.silweight: - type: range - bounds: [-10.0, 0.0] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/ax_sweep/transformer.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/ax_sweep/transformer.yaml deleted file mode 100644 index 85ed3bd1a5a44871260f572786044c28f441add6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/ax_sweep/transformer.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# @package _global_ - -common_eval: - results_path: ${decoding.exp_dir}/decode/${decoding.decoder.name}_ax/${dataset.gen_subset} - -hydra: - sweeper: - ax_config: - max_trials: 60 - early_stop: - minimize: true - max_epochs_without_improvement: 10 - epsilon: 0.025 - experiment: - name: ${dataset.gen_subset} - objective_name: wer - minimize: true - parameter_constraints: null - outcome_constraints: null - status_quo: null - client: - verbose_logging: false - random_seed: null - params: - decoding.decoder.lmweight: - type: range - bounds: [0.0, 4.0] - decoding.decoder.wordscore: - type: range - bounds: [-5.0, 5.0] - decoding.decoder.silweight: - type: range - bounds: [-8.0, 0.0] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_fsqlm.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_fsqlm.yaml deleted file mode 100644 index 026ad8db89a0673969a99fed6e1e84fc41fc7a1a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_fsqlm.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# @package _group_ - -defaults: - - model: null - -hydra: - run: - dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} - sweep: - dir: ${common_eval.results_path} - subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} - -task: - _name: hubert_pretraining - single_target: true - fine_tuning: true - data: ??? - normalize: ??? - -decoding: - type: fairseqlm - lexicon: ??? - lmpath: ??? - beamthreshold: 25 - beam: 500 - lmweight: 2 - wordscore: -1 - silweight: 0 - unique_wer_file: true -common_eval: - results_path: ??? - path: ??? - post_process: letter -dataset: - max_tokens: 1100000 - gen_subset: ??? diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_kenlm.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_kenlm.yaml deleted file mode 100644 index 04642aeb6530133ab44e12e11e3d1661e3b9c32c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_kenlm.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# @package _group_ - -defaults: - - model: null - -hydra: - run: - dir: ${common_eval.results_path}/beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} - sweep: - dir: ${common_eval.results_path} - subdir: beam${decoding.beam}_th${decoding.beamthreshold}_lmw${decoding.lmweight}_wrd${decoding.wordscore}_sil${decoding.silweight} - -task: - _name: hubert_pretraining - single_target: true - fine_tuning: true - data: ??? - normalize: ??? - -decoding: - type: kenlm - lexicon: ??? - lmpath: ??? - beamthreshold: 100 - beam: 500 - lmweight: 2 - wordscore: -1 - silweight: 0 - unique_wer_file: true -common_eval: - results_path: ??? - path: ??? - post_process: letter -dataset: - max_tokens: 1100000 - gen_subset: ??? diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_viterbi.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_viterbi.yaml deleted file mode 100644 index 4afc74c18ca890e1a20c6beabeb9059dd0f480f4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/infer_viterbi.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# @package _group_ - -defaults: - - model: null - -hydra: - run: - dir: ${common_eval.results_path}/viterbi - sweep: - dir: ${common_eval.results_path} - subdir: viterbi - -task: - _name: hubert_pretraining - single_target: true - fine_tuning: true - data: ??? - normalize: ??? - -decoding: - type: viterbi - unique_wer_file: true -common_eval: - results_path: ??? - path: ??? - post_process: letter -dataset: - max_tokens: 1100000 - gen_subset: ??? diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/run/submitit_slurm.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/run/submitit_slurm.yaml deleted file mode 100644 index 0b8065832ecacf9dd4fe4e99c87941e00fb3ef7f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/run/submitit_slurm.yaml +++ /dev/null @@ -1,17 +0,0 @@ -# @package _global_ -hydra: - launcher: - cpus_per_task: ${distributed_training.distributed_world_size} - gpus_per_node: ${distributed_training.distributed_world_size} - tasks_per_node: ${hydra.launcher.gpus_per_node} - nodes: 1 - mem_gb: 200 - timeout_min: 4320 - max_num_timeout: 50 - name: ${hydra.job.config_name} - submitit_folder: ${hydra.sweep.dir}/submitit - -distributed_training: - distributed_world_size: 1 - distributed_no_spawn: true - distributed_port: 29761 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml deleted file mode 100644 index 2f669f376312dbfe4611cc08f4996a314155fb87..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/decode/run/submitit_slurm_8gpu.yaml +++ /dev/null @@ -1,17 +0,0 @@ -# @package _global_ -hydra: - launcher: - cpus_per_task: ${distributed_training.distributed_world_size} - gpus_per_node: ${distributed_training.distributed_world_size} - tasks_per_node: ${hydra.launcher.gpus_per_node} - nodes: 1 - mem_gb: 200 - timeout_min: 4320 - max_num_timeout: 50 - name: ${hydra.job.config_name} - submitit_folder: ${hydra.sweep.dir}/submitit - -distributed_training: - distributed_world_size: 8 - distributed_no_spawn: true - distributed_port: 29761 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/base_10h.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/base_10h.yaml deleted file mode 100644 index a22c7c0347f792221f209bcfba7ba380a69f90a8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/base_10h.yaml +++ /dev/null @@ -1,100 +0,0 @@ -# @package _group_ - -common: - fp16: true - log_format: json - log_interval: 200 - tensorboard_logdir: tblog - seed: 1337 - -checkpoint: - save_interval: 5 - keep_interval_updates: 1 - no_epoch_checkpoints: true - best_checkpoint_metric: wer - -distributed_training: - ddp_backend: c10d - find_unused_parameters: true - distributed_world_size: 1 - distributed_port: 29671 - nprocs_per_node: 8 - -task: - _name: hubert_pretraining - data: ??? - fine_tuning: true - label_dir: ??? - normalize: false # must be consistent with pre-training - labels: ["ltr"] - single_target: true - -dataset: - num_workers: 0 - max_tokens: 3200000 - validate_after_updates: ${model.freeze_finetune_updates} - validate_interval: 5 - train_subset: train - valid_subset: valid - -criterion: - _name: ctc - zero_infinity: true - -optimization: - max_update: 25000 - lr: [2e-5] - sentence_avg: true - update_freq: [1] - -optimizer: - _name: adam - adam_betas: (0.9,0.98) - adam_eps: 1e-08 - -lr_scheduler: - _name: tri_stage - warmup_steps: 8000 - hold_steps: 0 - decay_steps: 72000 - final_lr_scale: 0.05 - -model: - _name: hubert_ctc - w2v_path: ??? - apply_mask: true - mask_selection: static - mask_length: 10 - mask_other: 0 - mask_prob: 0.75 - mask_channel_selection: static - mask_channel_length: 64 - mask_channel_other: 0 - mask_channel_prob: 0.5 - layerdrop: 0.1 - dropout: 0.0 - activation_dropout: 0.1 - attention_dropout: 0.0 - feature_grad_mult: 0.0 - freeze_finetune_updates: 10000 - -hydra: - job: - config: - override_dirname: - kv_sep: '-' - item_sep: '__' - exclude_keys: - - run - - task.data - - task.label_dir - - model.w2v_path - - dataset.train_subset - - dataset.valid_subset - - criterion.wer_kenlm_model - - criterion.wer_lexicon - run: - dir: ??? - sweep: - dir: ??? - subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/ckpt/it1.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/ckpt/it1.yaml deleted file mode 100644 index 2af96b3f72746f85feb13e7efcbdab6602b293de..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/ckpt/it1.yaml +++ /dev/null @@ -1,7 +0,0 @@ -# @package _global_ - -task: - normalize: false - -model: - w2v_path: /checkpoint/wnhsu/w2v/hubert_final/iter1/hubert.km.randcrop.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU400k.s1337.ngpu32/checkpoint_last.pt diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/lm/ls_4gram.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/lm/ls_4gram.yaml deleted file mode 100644 index 8c7728ad29965d3cf18605808a893bc442afd56b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/lm/ls_4gram.yaml +++ /dev/null @@ -1,7 +0,0 @@ -# @package _global_ - -criterion: - wer_kenlm_model: /checkpoint/abdo/old_checkpoint02/datasets/librispeech/4-gram.bin - wer_lexicon: /checkpoint/abdo/old_checkpoint02/datasets/librispeech/10h/raw/lexicon_ltr.lst - wer_lm_weight: 2.0 - wer_word_score: -1.0 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/run/submitit_reg.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/run/submitit_reg.yaml deleted file mode 100644 index 27509503e7b306c07742fbed2fc5726d001bb7df..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/finetune/run/submitit_reg.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package _global_ - -hydra: - launcher: - cpus_per_task: 8 - gpus_per_node: 8 - tasks_per_node: ${hydra.launcher.gpus_per_node} - nodes: 1 - comment: null - mem_gb: 384 - timeout_min: 4320 - max_num_timeout: 100 - constraint: volta32gb - name: ${hydra.job.config_name}/${hydra.job.override_dirname} - submitit_folder: ${hydra.sweep.dir}/submitit/%j - -distributed_training: - distributed_world_size: 8 - distributed_port: 29671 - nprocs_per_node: 8 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/data/iter1.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/data/iter1.yaml deleted file mode 100644 index 0a1b65d802c83128c53f32b21807fa5e51da6cc9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/data/iter1.yaml +++ /dev/null @@ -1,8 +0,0 @@ -# @package _global_ - -task: - label_dir: ??? - labels: ["km"] - -model: - label_rate: 100 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/data/iter2.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/data/iter2.yaml deleted file mode 100644 index 2d4bfe61cc638af9de48e92c58994e435fba2abf..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/data/iter2.yaml +++ /dev/null @@ -1,8 +0,0 @@ -# @package _global_ - -task: - label_dir: ??? - labels: ["km"] - -model: - label_rate: 50 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_base_librispeech.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_base_librispeech.yaml deleted file mode 100644 index bd84461a163866f622b01bf6d36b4de6215f3d97..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_base_librispeech.yaml +++ /dev/null @@ -1,97 +0,0 @@ -# @package _group_ - -common: - fp16: true - log_format: json - log_interval: 200 - seed: 1337 - tensorboard_logdir: tblog - -checkpoint: - save_interval_updates: 25000 - keep_interval_updates: 1 - no_epoch_checkpoints: true - - -distributed_training: - ddp_backend: no_c10d - distributed_backend: 'nccl' - distributed_world_size: 32 - distributed_port: 29671 - nprocs_per_node: 8 - find_unused_parameters: true - -task: - _name: hubert_pretraining - data: ??? - label_dir: ??? - labels: ??? - label_rate: ${model.label_rate} - sample_rate: 16000 - max_sample_size: 250000 - min_sample_size: 32000 - pad_audio: false - random_crop: true - normalize: false # must be consistent with extractor - -dataset: - num_workers: 6 - max_tokens: 1400000 - skip_invalid_size_inputs_valid_test: true - validate_interval: 5 - validate_interval_updates: 10000 - -criterion: - _name: hubert - pred_masked_weight: 1.0 - pred_nomask_weight: 0.0 - loss_weights: [10,] - -optimization: - max_update: 400000 - lr: [0.0005] - clip_norm: 10.0 - -optimizer: - _name: adam - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - weight_decay: 0.01 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 32000 - -model: - _name: hubert - label_rate: ??? - skip_masked: false - skip_nomask: false - mask_prob: 0.80 - extractor_mode: default - conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' - final_dim: 256 - encoder_layerdrop: 0.05 - dropout_input: 0.1 - dropout_features: 0.1 - dropout: 0.1 - attention_dropout: 0.1 - feature_grad_mult: 0.1 - untie_final_proj: true - activation_dropout: 0.0 - -hydra: - job: - config: - override_dirname: - kv_sep: '-' - item_sep: '__' - exclude_keys: - - run - - task.data - - task.label_dir - run: - dir: ??? - sweep: - dir: ??? - subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_large_librivox.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_large_librivox.yaml deleted file mode 100644 index a5192b5f29b53aa8391a0ab67b6238c0d0b4985e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_large_librivox.yaml +++ /dev/null @@ -1,101 +0,0 @@ -# @package _group_ - -common: - fp16: true - log_format: json - log_interval: 200 - seed: 1337 - tensorboard_logdir: tblog - -checkpoint: - save_interval_updates: 25000 - keep_interval_updates: 1 - no_epoch_checkpoints: true - - -distributed_training: - ddp_backend: no_c10d - distributed_backend: 'nccl' - distributed_world_size: 128 - distributed_port: 29671 - nprocs_per_node: 8 - find_unused_parameters: true - -task: - _name: hubert_pretraining - data: ??? - label_dir: ??? - labels: ??? - label_rate: ${model.label_rate} - sample_rate: 16000 - max_sample_size: 250000 - min_sample_size: 32000 - pad_audio: false - random_crop: true - normalize: true # must be consistent with extractor - -dataset: - num_workers: 6 - max_tokens: 900000 - skip_invalid_size_inputs_valid_test: true - validate_interval: 5 - validate_interval_updates: 10000 - -criterion: - _name: hubert - pred_masked_weight: 1.0 - pred_nomask_weight: 0.0 - loss_weights: [10,] - -optimization: - max_update: 400000 - lr: [0.0015] - clip_norm: 1.0 - -optimizer: - _name: adam - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - weight_decay: 0.01 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 32000 - -model: - _name: hubert - label_rate: ??? - encoder_layers: 24 - encoder_embed_dim: 1024 - encoder_ffn_embed_dim: 4096 - encoder_attention_heads: 16 - final_dim: 768 - skip_masked: false - skip_nomask: false - mask_prob: 0.80 - extractor_mode: layer_norm - conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' - encoder_layerdrop: 0.0 - dropout_input: 0.0 - dropout_features: 0.0 - dropout: 0.0 - attention_dropout: 0.0 - layer_norm_first: true - feature_grad_mult: 1.0 - untie_final_proj: true - activation_dropout: 0.0 - -hydra: - job: - config: - override_dirname: - kv_sep: '-' - item_sep: '__' - exclude_keys: - - run - - task.data - run: - dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt - sweep: - dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt - subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml deleted file mode 100644 index 34e8f2bfb93863db122f694785b80857713ceb05..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/hubert_xlarge_librivox.yaml +++ /dev/null @@ -1,101 +0,0 @@ -# @package _group_ - -common: - fp16: true - log_format: json - log_interval: 200 - seed: 1337 - tensorboard_logdir: tblog - -checkpoint: - save_interval_updates: 25000 - keep_interval_updates: 1 - no_epoch_checkpoints: true - - -distributed_training: - ddp_backend: no_c10d - distributed_backend: 'nccl' - distributed_world_size: 256 - distributed_port: 29671 - nprocs_per_node: 8 - find_unused_parameters: true - -task: - _name: hubert_pretraining - data: ??? - label_dir: ??? - labels: ??? - label_rate: ${model.label_rate} - sample_rate: 16000 - max_sample_size: 250000 - min_sample_size: 32000 - pad_audio: false - random_crop: true - normalize: true # must be consistent with extractor - -dataset: - num_workers: 6 - max_tokens: 360000 - skip_invalid_size_inputs_valid_test: true - validate_interval: 5 - validate_interval_updates: 10000 - -criterion: - _name: hubert - pred_masked_weight: 1.0 - pred_nomask_weight: 0.0 - loss_weights: [10,] - -optimization: - max_update: 400000 - lr: [0.003] - clip_norm: 1.0 - -optimizer: - _name: adam - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - weight_decay: 0.01 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 32000 - -model: - _name: hubert - label_rate: ??? - encoder_layers: 48 - encoder_embed_dim: 1280 - encoder_ffn_embed_dim: 5120 - encoder_attention_heads: 16 - final_dim: 1024 - skip_masked: false - skip_nomask: false - mask_prob: 0.80 - extractor_mode: layer_norm - conv_feature_layers: '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2' - encoder_layerdrop: 0.0 - dropout_input: 0.0 - dropout_features: 0.0 - dropout: 0.0 - attention_dropout: 0.0 - layer_norm_first: true - feature_grad_mult: 1.0 - untie_final_proj: true - activation_dropout: 0.0 - -hydra: - job: - config: - override_dirname: - kv_sep: '-' - item_sep: '__' - exclude_keys: - - run - - task.data - run: - dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt - sweep: - dir: /checkpoint/wnhsu/w2v/hubert_final/hydra_pt - subdir: ${hydra.job.config_name}__${hydra.job.override_dirname} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/run/submitit_reg.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/run/submitit_reg.yaml deleted file mode 100644 index 46c979cd2835fe026b0a532a54533904d1001e54..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/config/pretrain/run/submitit_reg.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package _global_ - -hydra: - launcher: - cpus_per_task: 8 - gpus_per_node: 8 - tasks_per_node: ${hydra.launcher.gpus_per_node} - nodes: 4 - comment: null - mem_gb: 384 - timeout_min: 4320 - max_num_timeout: 100 - constraint: volta32gb - name: ${hydra.job.config_name}/${hydra.job.override_dirname} - submitit_folder: ${hydra.sweep.dir}/submitit/%j - -distributed_training: - distributed_world_size: 32 - distributed_port: 29671 - nprocs_per_node: 8 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/create_dummy_dict.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/create_dummy_dict.sh deleted file mode 100644 index 8e710235580fa87c12b7a9fc61c1b03211feb19f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/create_dummy_dict.sh +++ /dev/null @@ -1,5 +0,0 @@ -n_clusters=100 -lab_dir=mfcc_ls960_train -for x in $(seq 0 $((n_clusters - 1))); do - echo "$x 1" -done >> $lab_dir/dict.km.txt \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/measure_teacher_quality.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/measure_teacher_quality.py deleted file mode 100644 index f65cb440b917093e8480c06095afaa854de7404f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/measure_teacher_quality.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 os.path as op -import re -from collections import Counter - -import numpy as np -from tabulate import tabulate - - -def comp_purity(p_xy, axis): - max_p = p_xy.max(axis=axis) - marg_p = p_xy.sum(axis=axis) - indv_pur = max_p / marg_p - aggr_pur = max_p.sum() - return indv_pur, aggr_pur - - -def comp_entropy(p): - return (-p * np.log(p + 1e-8)).sum() - - -def comp_norm_mutual_info(p_xy): - p_x = p_xy.sum(axis=1, keepdims=True) - p_y = p_xy.sum(axis=0, keepdims=True) - pmi = np.log(p_xy / np.matmul(p_x, p_y) + 1e-8) - mi = (p_xy * pmi).sum() - h_x = comp_entropy(p_x) - h_y = comp_entropy(p_y) - return mi, mi / h_x, mi / h_y, h_x, h_y - - -def pad(labs, n): - if n == 0: - return np.array(labs) - return np.concatenate([[labs[0]] * n, labs, [labs[-1]] * n]) - - -def comp_avg_seg_dur(labs_list): - n_frms = 0 - n_segs = 0 - for labs in labs_list: - labs = np.array(labs) - edges = np.zeros(len(labs)).astype(bool) - edges[0] = True - edges[1:] = labs[1:] != labs[:-1] - n_frms += len(edges) - n_segs += edges.astype(int).sum() - return n_frms / n_segs - - -def comp_joint_prob(uid2refs, uid2hyps): - """ - Args: - pad: padding for spliced-feature derived labels - """ - cnts = Counter() - skipped = [] - abs_frmdiff = 0 - for uid in uid2refs: - if uid not in uid2hyps: - skipped.append(uid) - continue - refs = uid2refs[uid] - hyps = uid2hyps[uid] - abs_frmdiff += abs(len(refs) - len(hyps)) - min_len = min(len(refs), len(hyps)) - refs = refs[:min_len] - hyps = hyps[:min_len] - cnts.update(zip(refs, hyps)) - tot = sum(cnts.values()) - - ref_set = sorted({ref for ref, _ in cnts.keys()}) - hyp_set = sorted({hyp for _, hyp in cnts.keys()}) - ref2pid = dict(zip(ref_set, range(len(ref_set)))) - hyp2lid = dict(zip(hyp_set, range(len(hyp_set)))) - # print(hyp_set) - p_xy = np.zeros((len(ref2pid), len(hyp2lid)), dtype=float) - for (ref, hyp), cnt in cnts.items(): - p_xy[ref2pid[ref], hyp2lid[hyp]] = cnt - p_xy /= p_xy.sum() - return p_xy, ref2pid, hyp2lid, tot, abs_frmdiff, skipped - - -def read_phn(tsv_path, rm_stress=True): - uid2phns = {} - with open(tsv_path) as f: - for line in f: - uid, phns = line.rstrip().split("\t") - phns = phns.split(",") - if rm_stress: - phns = [re.sub("[0-9]", "", phn) for phn in phns] - uid2phns[uid] = phns - return uid2phns - - -def read_lab(tsv_path, lab_path, pad_len=0, upsample=1): - """ - tsv is needed to retrieve the uids for the labels - """ - with open(tsv_path) as f: - f.readline() - uids = [op.splitext(op.basename(line.rstrip().split()[0]))[0] for line in f] - with open(lab_path) as f: - labs_list = [pad(line.rstrip().split(), pad_len).repeat(upsample) for line in f] - assert len(uids) == len(labs_list) - return dict(zip(uids, labs_list)) - - -def main_lab_lab( - tsv_dir, - lab_dir, - lab_name, - lab_sets, - ref_dir, - ref_name, - pad_len=0, - upsample=1, - verbose=False, -): - # assume tsv_dir is the same for both the reference and the hypotheses - tsv_dir = lab_dir if tsv_dir is None else tsv_dir - - uid2refs = {} - for s in lab_sets: - uid2refs.update(read_lab(f"{tsv_dir}/{s}.tsv", f"{ref_dir}/{s}.{ref_name}")) - - uid2hyps = {} - for s in lab_sets: - uid2hyps.update( - read_lab( - f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample - ) - ) - _main(uid2refs, uid2hyps, verbose) - - -def main_phn_lab( - tsv_dir, - lab_dir, - lab_name, - lab_sets, - phn_dir, - phn_sets, - pad_len=0, - upsample=1, - verbose=False, -): - uid2refs = {} - for s in phn_sets: - uid2refs.update(read_phn(f"{phn_dir}/{s}.tsv")) - - uid2hyps = {} - tsv_dir = lab_dir if tsv_dir is None else tsv_dir - for s in lab_sets: - uid2hyps.update( - read_lab( - f"{tsv_dir}/{s}.tsv", f"{lab_dir}/{s}.{lab_name}", pad_len, upsample - ) - ) - _main(uid2refs, uid2hyps, verbose) - - -def _main(uid2refs, uid2hyps, verbose): - (p_xy, ref2pid, hyp2lid, tot, frmdiff, skipped) = comp_joint_prob( - uid2refs, uid2hyps - ) - ref_pur_by_hyp, ref_pur = comp_purity(p_xy, axis=0) - hyp_pur_by_ref, hyp_pur = comp_purity(p_xy, axis=1) - (mi, mi_norm_by_ref, mi_norm_by_hyp, h_ref, h_hyp) = comp_norm_mutual_info(p_xy) - outputs = { - "ref pur": ref_pur, - "hyp pur": hyp_pur, - "H(ref)": h_ref, - "H(hyp)": h_hyp, - "MI": mi, - "MI/H(ref)": mi_norm_by_ref, - "ref segL": comp_avg_seg_dur(uid2refs.values()), - "hyp segL": comp_avg_seg_dur(uid2hyps.values()), - "p_xy shape": p_xy.shape, - "frm tot": tot, - "frm diff": frmdiff, - "utt tot": len(uid2refs), - "utt miss": len(skipped), - } - print(tabulate([outputs.values()], outputs.keys(), floatfmt=".4f")) - - -if __name__ == "__main__": - """ - compute quality of labels with respect to phone or another labels if set - """ - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("tsv_dir") - parser.add_argument("lab_dir") - parser.add_argument("lab_name") - parser.add_argument("--lab_sets", default=["valid"], type=str, nargs="+") - parser.add_argument( - "--phn_dir", - default="/checkpoint/wnhsu/data/librispeech/960h/fa/raw_phn/phone_frame_align_v1", - ) - parser.add_argument( - "--phn_sets", default=["dev-clean", "dev-other"], type=str, nargs="+" - ) - parser.add_argument("--pad_len", default=0, type=int, help="padding for hypotheses") - parser.add_argument( - "--upsample", default=1, type=int, help="upsample factor for hypotheses" - ) - parser.add_argument("--ref_lab_dir", default="") - parser.add_argument("--ref_lab_name", default="") - parser.add_argument("--verbose", action="store_true") - args = parser.parse_args() - - if args.ref_lab_dir and args.ref_lab_name: - main_lab_lab( - args.tsv_dir, - args.lab_dir, - args.lab_name, - args.lab_sets, - args.ref_lab_dir, - args.ref_lab_name, - args.pad_len, - args.upsample, - args.verbose, - ) - else: - main_phn_lab( - args.tsv_dir, - args.lab_dir, - args.lab_name, - args.lab_sets, - args.phn_dir, - args.phn_sets, - args.pad_len, - args.upsample, - args.verbose, - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/merge.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/merge.sh deleted file mode 100644 index b576532c2d423bb7156eb54d559f0a65f01caf13..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/merge.sh +++ /dev/null @@ -1,6 +0,0 @@ -nshard=4 -split=train -lab_dir=mfcc_ls960_train -for rank in $(seq 0 $((nshard - 1))); do - cat $lab_dir/${split}_${rank}_${nshard}.km -done > $lab_dir/${split}.km \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/README.md deleted file mode 100644 index 847475c23f8a6a47bb25cba83466ddd9eba167b8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/README.md +++ /dev/null @@ -1,80 +0,0 @@ -# Sharded Feature Extraction and K-means Application - -This folder contains scripts for preparing HUBERT labels from tsv files, the -steps are: -1. feature extraction -2. k-means clustering -3. k-means application - - -## Data preparation - -`*.tsv` files contains a list of audio, where each line is the root, and -following lines are the subpath for each audio: -``` - - - -... -``` - - -## Feature extraction - -### MFCC feature -Suppose the tsv file is at `${tsv_dir}/${split}.tsv`. To extract 39-D -mfcc+delta+ddelta features for the 1st iteration HUBERT training, run: -```sh -python dump_mfcc_feature.py ${tsv_dir} ${split} ${nshard} ${rank} ${feat_dir} -``` -This would shard the tsv file into `${nshard}` and extract features for the -`${rank}`-th shard, where rank is an integer in `[0, nshard-1]`. Features would -be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`. - - -### HUBERT feature -To extract features from the `${layer}`-th transformer layer of a trained -HUBERT model saved at `${ckpt_path}`, run: -```sh -python dump_hubert_feature.py ${tsv_dir} ${split} ${ckpt_path} ${layer} ${nshard} ${rank} ${feat_dir} -``` -Features would also be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`. - -- if out-of-memory, decrease the chunk size with `--max_chunk` - - -## K-means clustering -To fit a k-means model with `${n_clusters}` clusters on 10% of the `${split}` data, run -```sh -python learn_kmeans.py ${feat_dir} ${split} ${nshard} ${km_path} ${n_cluster} --percent 0.1 -``` -This saves the k-means model to `${km_path}`. - -- set `--precent -1` to use all data -- more kmeans options can be found with `-h` flag - - -## K-means application -To apply a trained k-means model `${km_path}` to obtain labels for `${split}`, run -```sh -python dump_km_label.py ${feat_dir} ${split} ${km_path} ${nshard} ${rank} ${lab_dir} -``` -This would extract labels for the `${rank}`-th shard out of `${nshard}` shards -and dump them to `${lab_dir}/${split}_${rank}_${shard}.km` - - -Finally, merge shards for `${split}` by running -```sh -for rank in $(seq 0 $((nshard - 1))); do - cat $lab_dir/${split}_${rank}_${nshard}.km -done > $lab_dir/${split}.km -``` - - -## Create a dummy dict -To create a dummy dictionary, run -```sh -for x in $(seq 0 $((n_clusters - 1))); do - echo "$x 1" -done >> $lab_dir/dict.km.txt -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_avg_feature.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_avg_feature.py deleted file mode 100644 index 25b2e43b6b32379f3c81da18eb35dbb2e2c6f654..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_avg_feature.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 os -import sys -from pathlib import Path - -import fairseq -import soundfile as sf -import torch -import torch.nn.functional as F -import tqdm -from feature_utils import dump_feature, get_path_iterator -from npy_append_array import NpyAppendArray - -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) - - avg_feat = [] - for start in range(0, x.size(1), self.max_chunk): - x_chunk = x[:, start: start + self.max_chunk] - feat_chunk, _, avg_feat_chunk = self.model.extract_features( - source=x_chunk, - padding_mask=None, - mask=False, - output_layer=self.layer, - ) - avg_feat.append(avg_feat_chunk) - return torch.cat(avg_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="/home/lxf/Documents/xinfeng/datasets/ASVSpoof2019") - parser.add_argument("save_dir", default="/home/lxf/Documents/xinfeng/datasets/ASVSpoof2019_Hubert_L9_test") - parser.add_argument("ckpt_path", default="/home/lxf/Documents/xinfeng/model_zoo/hubert") - 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/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_feature.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_feature.py deleted file mode 100644 index 88369aee9777d9b30ea922ca36da55a11585f80f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_feature.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 logging -import os -import sys -from pathlib import Path - -import fairseq -import soundfile as sf -import torch -import torch.nn.functional as F -import tqdm -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(str(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 main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk): -# reader = HubertFeatureReader(ckpt_path, layer, max_chunk) -# generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) -# dump_feature(reader, generator, num, split, nshard, rank, feat_dir) - -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("tsv_dir") - # parser.add_argument("--audio_dir", default="/home/lxf/Documents/xinfeng/dataset/Spoof_data/ASVSpoof2019/LA") - #parser.add_argument("--save_dir", default="/home/lxf/Documents/xinfeng/dataset/Spoof_data/ASVspoof2019_Hubert_L9") - parser.add_argument("--audio_dir", default="/home/lxf/Documents/xinfeng/research/token_spoof/data/Spoof_data/ASVspoof2021_LA_eval/flac") - parser.add_argument("--save_dir", default="/home/lxf/Documents/xinfeng/research/token_spoof/data/Spoof_data/ASVspoof2021_LA_eval/Hubert_L9") - - # parser.add_argument("split") - parser.add_argument("--ckpt_path", default="/home/lxf/Documents/xinfeng/model_zoo/hubert/hubert_base_ls960.pt") - parser.add_argument("--layer", type=int, default=9) - # parser.add_argument("nshard", type=int) - # parser.add_argument("rank", type=int) - # parser.add_argument("feat_dir") - 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/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.py deleted file mode 100644 index 68ed08470b2d934177b47e4844489749e3711653..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_hubert_feature_s2t.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. - -import csv -import io -import logging -import os -import os.path as op -import sys - -from dump_hubert_feature import HubertFeatureReader -from fairseq.data.audio.audio_utils import get_waveform -from fairseq.data.audio.speech_to_text_dataset import ( - read_from_uncompressed_zip, -) -from feature_utils import dump_feature, get_shard_range - -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_s2t") - - -class HubertFeatureReaderS2T(HubertFeatureReader): - def read_audio(self, path, ref_len=None): - path, *extra = path.split(":") - assert len(extra) == 2 - assert path.endswith(".zip") - - data = read_from_uncompressed_zip(path, int(extra[0]), int(extra[1])) - f = io.BytesIO(data) - wav, sr = get_waveform(f) - 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_path_iterator(root, tsv, nshard, rank): - with open(tsv) as f: - reader = csv.DictReader( - f, - delimiter="\t", - quotechar=None, - doublequote=False, - lineterminator="\n", - quoting=csv.QUOTE_NONE, - ) - subpaths = [op.join(root, e["audio"]) for e in reader] - start, end = get_shard_range(len(subpaths), nshard, rank) - subpaths = subpaths[start:end] - def iterate(): - for subpath in subpaths: - yield op.join(root, subpath), None - return iterate, len(subpaths) - - -def main( - root, tsv_path, ckpt_path, layer, nshard, rank, feat_dir, split, max_chunk -): - reader = HubertFeatureReaderS2T(ckpt_path, layer, max_chunk) - generator, num = get_path_iterator(root, tsv_path, nshard, rank) - dump_feature(reader, generator, num, split, nshard, rank, feat_dir) - - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("root") - parser.add_argument("tsv_path") - parser.add_argument("ckpt_path") - parser.add_argument("layer", type=int) - parser.add_argument("nshard", type=int) - parser.add_argument("rank", type=int) - parser.add_argument("feat_dir") - parser.add_argument("split") - 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/fairseq_ours/examples/hubert/simple_kmeans/dump_km_label.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_km_label.py deleted file mode 100644 index f55e88e1f6b1abfddb0eafe19dcc91ec8d172b6e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_km_label.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. - -import logging -import os -import sys - -import joblib -import numpy as np -import torch -import tqdm - -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_km_label") - - -class ApplyKmeans(object): - def __init__(self, km_path): - self.km_model = joblib.load(km_path) - self.C_np = self.km_model.cluster_centers_.transpose() - self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True) - - self.C = torch.from_numpy(self.C_np) - self.Cnorm = torch.from_numpy(self.Cnorm_np) - if torch.cuda.is_available(): - self.C = self.C.cuda() - self.Cnorm = self.Cnorm.cuda() - - def __call__(self, x): - if isinstance(x, torch.Tensor): - dist = ( - x.pow(2).sum(1, keepdim=True) - - 2 * torch.matmul(x, self.C) - + self.Cnorm - ) - return dist.argmin(dim=1).cpu().numpy() - else: - dist = ( - (x ** 2).sum(1, keepdims=True) - - 2 * np.matmul(x, self.C_np) - + self.Cnorm_np - ) - return np.argmin(dist, axis=1) - - -def get_feat_iterator(feat_dir, split, nshard, rank): - feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" - leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" - with open(leng_path, "r") as f: - lengs = [int(line.rstrip()) for line in f] - offsets = [0] + np.cumsum(lengs[:-1]).tolist() - - def iterate(): - feat = np.load(feat_path, mmap_mode="r") - assert feat.shape[0] == (offsets[-1] + lengs[-1]) - for offset, leng in zip(offsets, lengs): - yield feat[offset: offset + leng] - - return iterate, len(lengs) - - -def dump_label(feat_dir, split, km_path, nshard, rank, lab_dir): - apply_kmeans = ApplyKmeans(km_path) - generator, num = get_feat_iterator(feat_dir, split, nshard, rank) - iterator = generator() - - lab_path = f"{lab_dir}/{split}_{rank}_{nshard}.km" - os.makedirs(lab_dir, exist_ok=True) - with open(lab_path, "w") as f: - for feat in tqdm.tqdm(iterator, total=num): - # feat = torch.from_numpy(feat).cuda() - lab = apply_kmeans(feat).tolist() - f.write(" ".join(map(str, lab)) + "\n") - logger.info("finished successfully") - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("feat_dir") - parser.add_argument("split") - parser.add_argument("km_path") - parser.add_argument("nshard", type=int) - parser.add_argument("rank", type=int) - parser.add_argument("lab_dir") - args = parser.parse_args() - logging.info(str(args)) - - dump_label(**vars(args)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_mfcc_feature.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_mfcc_feature.py deleted file mode 100644 index 0d78c6edc4e85742b820ef53fb0e5eaf7477aea3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_mfcc_feature.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 logging -import os -import sys - -import soundfile as sf -import torch -import torchaudio -from feature_utils import dump_feature, get_path_iterator - -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_mfcc_feature") - - -class MfccFeatureReader(object): - def __init__(self, sample_rate): - self.sample_rate = sample_rate - - def read_audio(self, path, ref_len=None): - wav, sr = sf.read(path) - assert sr == self.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() - x = x.view(1, -1) - - mfccs = torchaudio.compliance.kaldi.mfcc( - waveform=x, - sample_frequency=self.sample_rate, - use_energy=False, - ) # (time, freq) - mfccs = mfccs.transpose(0, 1) # (freq, time) - deltas = torchaudio.functional.compute_deltas(mfccs) - ddeltas = torchaudio.functional.compute_deltas(deltas) - concat = torch.cat([mfccs, deltas, ddeltas], dim=0) - concat = concat.transpose(0, 1).contiguous() # (freq, time) - return concat - - -def main(tsv_dir, split, nshard, rank, feat_dir, sample_rate): - reader = MfccFeatureReader(sample_rate) - generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) - dump_feature(reader, generator, num, split, nshard, rank, feat_dir) - - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("tsv_dir") - parser.add_argument("split") - parser.add_argument("nshard", type=int) - parser.add_argument("rank", type=int) - parser.add_argument("feat_dir") - parser.add_argument("--sample_rate", type=int, default=16000) - args = parser.parse_args() - logger.info(args) - - main(**vars(args)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_w2v2_feature.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_w2v2_feature.py deleted file mode 100644 index 84c7e9c4afa1ec7e3cb4a65b211b44dd41ab2803..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/dump_w2v2_feature.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 logging -import os -import sys - -import fairseq -import soundfile as sf -import torch -import torch.nn.functional as F -from feature_utils import dump_feature, get_path_iterator - -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_w2v2_feature") - - -class Wav2Vec2FeatureReader(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 # assume this is 1-based like HuBERT - self.max_chunk = max_chunk - logger.info(f"TASK CONFIG:\n{self.task.cfg}") - logger.info(f" max_chunk = {self.max_chunk}") - logger.info(f" model:\n{self.model}") - - 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] - res = self.model.extract_features( - source=x_chunk, - padding_mask=None, - mask=False, - layer=self.layer - 1, - ) - feat_chunk = res["x"] - feat.append(feat_chunk) - return torch.cat(feat, 1).squeeze(0) - - -def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk): - reader = Wav2Vec2FeatureReader(ckpt_path, layer, max_chunk) - generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) - dump_feature(reader, generator, num, split, nshard, rank, feat_dir) - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("tsv_dir") - parser.add_argument("split") - parser.add_argument("ckpt_path") - parser.add_argument("layer", type=int) - parser.add_argument("nshard", type=int) - parser.add_argument("rank", type=int) - parser.add_argument("feat_dir") - 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/fairseq_ours/examples/hubert/simple_kmeans/feature_utils.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/feature_utils.py deleted file mode 100644 index 1573d3e08192d138164593014366eae4fe9acc6d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/feature_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 logging -import os -import sys - -import tqdm -from npy_append_array import NpyAppendArray - -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("feature_utils") - - -def get_shard_range(tot, nshard, rank): - assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}" - start = round(tot / nshard * rank) - end = round(tot / nshard * (rank + 1)) - assert start < end, f"start={start}, end={end}" - logger.info( - f"rank {rank} of {nshard}, process {end-start} " - f"({start}-{end}) out of {tot}" - ) - return start, end - - -def get_path_iterator(tsv, nshard, rank): - with open(tsv, "r") as f: - root = f.readline().rstrip() - lines = [line.rstrip() for line in f] - start, end = get_shard_range(len(lines), nshard, rank) - lines = lines[start:end] - def iterate(): - for line in lines: - subpath, nsample = line.split("\t") - yield f"{root}/{subpath}", int(nsample) - return iterate, len(lines) - - -def dump_feature(reader, generator, num, split, nshard, rank, feat_dir): - iterator = generator() - - feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" - leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" - - os.makedirs(feat_dir, exist_ok=True) - if os.path.exists(feat_path): - os.remove(feat_path) - - feat_f = NpyAppendArray(feat_path) - with open(leng_path, "w") as leng_f: - for path, nsample in tqdm.tqdm(iterator, total=num): - feat = reader.get_feats(path, nsample) - feat_f.append(feat.cpu().numpy()) - leng_f.write(f"{len(feat)}\n") - logger.info("finished successfully") - - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/learn_kmeans.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/learn_kmeans.py deleted file mode 100644 index 12b96e130641e301341b2907129cddb5efdd9d6f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/learn_kmeans.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 logging -import os -import sys - -import joblib -import numpy as np -from sklearn.cluster import MiniBatchKMeans - -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("learn_kmeans") - - -def get_km_model( - n_clusters, - init, - max_iter, - batch_size, - tol, - max_no_improvement, - n_init, - reassignment_ratio, -): - return MiniBatchKMeans( - n_clusters=n_clusters, - init=init, - max_iter=max_iter, - batch_size=batch_size, - verbose=1, - compute_labels=False, - tol=tol, - max_no_improvement=max_no_improvement, - init_size=None, - n_init=n_init, - reassignment_ratio=reassignment_ratio, - ) - - -def load_feature_shard(feat_dir, split, nshard, rank, percent): - feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" - leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" - with open(leng_path, "r") as f: - lengs = [int(line.rstrip()) for line in f] - offsets = [0] + np.cumsum(lengs[:-1]).tolist() - - if percent < 0: - return np.load(feat_path, mmap_mode="r") - else: - nsample = int(np.ceil(len(lengs) * percent)) - indices = np.random.choice(len(lengs), nsample, replace=False) - feat = np.load(feat_path, mmap_mode="r") - sampled_feat = np.concatenate( - [feat[offsets[i]: offsets[i] + lengs[i]] for i in indices], axis=0 - ) - logger.info( - ( - f"sampled {nsample} utterances, {len(sampled_feat)} frames " - f"from shard {rank}/{nshard}" - ) - ) - return sampled_feat - - -def load_feature(feat_dir, split, nshard, seed, percent): - assert percent <= 1.0 - feat = np.concatenate( - [ - load_feature_shard(feat_dir, split, nshard, r, percent) - for r in range(nshard) - ], - axis=0, - ) - logging.info(f"loaded feature with dimension {feat.shape}") - return feat - - -def learn_kmeans( - feat_dir, - split, - nshard, - km_path, - n_clusters, - seed, - percent, - init, - max_iter, - batch_size, - tol, - n_init, - reassignment_ratio, - max_no_improvement, -): - np.random.seed(seed) - feat = load_feature(feat_dir, split, nshard, seed, percent) - km_model = get_km_model( - n_clusters, - init, - max_iter, - batch_size, - tol, - max_no_improvement, - n_init, - reassignment_ratio, - ) - km_model.fit(feat) - joblib.dump(km_model, km_path) - - inertia = -km_model.score(feat) / len(feat) - logger.info("total intertia: %.5f", inertia) - logger.info("finished successfully") - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("feat_dir", type=str) - parser.add_argument("split", type=str) - parser.add_argument("nshard", type=int) - parser.add_argument("km_path", type=str) - parser.add_argument("n_clusters", type=int) - parser.add_argument("--seed", default=0, type=int) - parser.add_argument( - "--percent", default=-1, type=float, help="sample a subset; -1 for all" - ) - parser.add_argument("--init", default="k-means++") - parser.add_argument("--max_iter", default=100, type=int) - parser.add_argument("--batch_size", default=10000, type=int) - parser.add_argument("--tol", default=0.0, type=float) - parser.add_argument("--max_no_improvement", default=100, type=int) - parser.add_argument("--n_init", default=20, type=int) - parser.add_argument("--reassignment_ratio", default=0.0, type=float) - args = parser.parse_args() - logging.info(str(args)) - - learn_kmeans(**vars(args)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/load_avg_feat_data.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/load_avg_feat_data.py deleted file mode 100644 index 91a83e8131faa8eb90983f1c58ab22ee94890494..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/load_avg_feat_data.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 logging -import os -import sys - -import joblib -import numpy as np -from sklearn.cluster import MiniBatchKMeans - -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("learn_kmeans") - - -def load_feature(feat_path): - feat = np.load(feat_path, mmap_mode="r") - return feat - - -def load_feature(feat_dir, split, nshard, seed, percent): - assert percent <= 1.0 - feat = np.concatenate( - [ - load_feature_shard(feat_dir, split, nshard, r, percent) - for r in range(nshard) - ], - axis=0, - ) - logging.info(f"loaded feature with dimension {feat.shape}") - return feat - - -def load_data( - feat_dir, - split, - nshard, - seed, - percent, -): - np.random.seed(seed) - feat = load_feature(feat_dir, split, nshard, seed, percent) - logger.info("finished successfully") - - -if __name__ == "__main__": - # load_feature_shard( - # feat_dir="../hubert_base_ls960_avg_feature/", - # split='valid', - # nshard=1, - # rank=0 - # ) - load_feature(feat_dir="../hubert_base_ls960_avg_feature/", - split='valid', - nshard=1, - seed=1, - percent=1) - # import argparse - - # parser = argparse.ArgumentParser() - # parser.add_argument("feat_dir", type=str) - # parser.add_argument("split", type=str) - # parser.add_argument("nshard", type=int) - # parser.add_argument("km_path", type=str) - # parser.add_argument("n_clusters", type=int) - # parser.add_argument("--seed", default=0, type=int) - # parser.add_argument( - # "--percent", default=-1, type=float, help="sample a subset; -1 for all" - # ) - # parser.add_argument("--init", default="k-means++") - # parser.add_argument("--max_iter", default=100, type=int) - # parser.add_argument("--batch_size", default=10000, type=int) - # parser.add_argument("--tol", default=0.0, type=float) - # parser.add_argument("--max_no_improvement", default=100, type=int) - # parser.add_argument("--n_init", default=20, type=int) - # parser.add_argument("--reassignment_ratio", default=0.0, type=float) - # args = parser.parse_args() - # logging.info(str(args)) - - # load_data(**vars(args)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/wav2vec_manifest.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/wav2vec_manifest.py deleted file mode 100644 index 9b8aa180e88d9ee98bdca7089aed5046ec0d9cb9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/simple_kmeans/wav2vec_manifest.py +++ /dev/null @@ -1,87 +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 argparse -import glob -import os -import random - -import soundfile - - -def get_parser(): - parser = argparse.ArgumentParser() - parser.add_argument( - "root", metavar="DIR", help="root directory containing flac files to index" - ) - parser.add_argument( - "--valid-percent", - default=0.01, - type=float, - metavar="D", - help="percentage of data to use as validation set (between 0 and 1)", - ) - parser.add_argument( - "--dest", default=".", type=str, metavar="DIR", help="output directory" - ) - parser.add_argument( - "--ext", default="flac", type=str, metavar="EXT", help="extension to look for" - ) - parser.add_argument("--seed", default=42, type=int, metavar="N", help="random seed") - parser.add_argument( - "--path-must-contain", - default=None, - type=str, - metavar="FRAG", - help="if set, path must contain this substring for a file to be included in the manifest", - ) - return parser - - -def main(args): - assert args.valid_percent >= 0 and args.valid_percent <= 1.0 - - if not os.path.exists(args.dest): - os.makedirs(args.dest) - - dir_path = os.path.realpath(args.root) - search_path = os.path.join(dir_path, "**/*." + args.ext) - rand = random.Random(args.seed) - - valid_f = ( - open(os.path.join(args.dest, "valid.tsv"), "w") - if args.valid_percent > 0 - else None - ) - - with open(os.path.join(args.dest, "train.tsv"), "w") as train_f: - print(dir_path, file=train_f) - - if valid_f is not None: - print(dir_path, file=valid_f) - - for fname in glob.iglob(search_path, recursive=True): - file_path = os.path.realpath(fname) - - if args.path_must_contain and args.path_must_contain not in file_path: - continue - - frames = soundfile.info(fname).frames - dest = train_f if rand.random() > args.valid_percent else valid_f - print( - "{}\t{}".format(os.path.relpath(file_path, dir_path), frames), file=dest - ) - if valid_f is not None: - valid_f.close() - - -if __name__ == "__main__": - parser = get_parser() - args = parser.parse_args() - main(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/6313-76958-0021.flac b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/6313-76958-0021.flac deleted file mode 100644 index a3de1e1e9d7ea573e77cbbc33895178908622bb5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/6313-76958-0021.flac +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0dd89e2bf8c60e05264bf2539f0ec35bc65efb55cd952b3510fa243de9cc16ff -size 223912 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.km500.km b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.km500.km deleted file mode 100644 index 656eef96e588b601a7a8c0f2ab8644d4185045fb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.km500.km +++ /dev/null @@ -1 +0,0 @@ -17 17 17 17 296 296 20 20 20 461 461 20 184 20 20 20 184 289 144 445 445 213 213 213 213 252 215 129 401 20 354 180 494 44 416 416 416 192 192 180 180 84 84 84 16 88 88 88 88 319 242 240 348 35 35 117 404 197 226 209 83 55 55 55 322 67 94 199 118 118 118 118 118 118 402 219 219 219 222 222 222 353 59 245 245 251 251 241 241 431 367 367 178 35 35 35 458 192 351 41 324 324 324 252 464 464 139 139 424 424 424 497 497 497 122 90 42 42 147 380 380 499 319 319 319 348 348 33 33 394 90 76 465 74 425 425 386 386 431 319 319 319 319 319 240 203 53 473 34 340 340 340 340 116 64 212 384 377 123 123 123 216 216 216 114 114 57 57 57 203 381 381 117 48 13 47 80 20 80 80 320 7 7 364 345 141 141 141 141 281 281 9 86 221 198 198 22 283 455 236 239 239 107 107 395 286 286 286 468 468 406 406 467 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192 192 135 135 135 135 200 200 464 255 255 255 251 251 241 431 235 235 235 348 348 465 192 44 44 236 8 8 354 319 319 383 348 36 310 107 107 395 462 462 8 32 32 32 354 153 153 153 153 153 387 387 387 387 85 207 318 318 318 49 453 9 168 125 125 125 125 125 466 199 44 44 143 129 144 445 351 351 351 486 486 460 285 285 302 302 497 497 122 239 161 161 79 79 499 499 499 265 265 265 85 85 85 299 299 173 352 352 427 229 170 247 15 15 15 15 15 15 193 193 193 17 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.len b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.len deleted file mode 100644 index 7d3028fa244a2121c51f39dcc92dc15be82823a6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.len +++ /dev/null @@ -1 +0,0 @@ -596 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.npy b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.npy deleted file mode 100644 index 7a2b109228036a1de485a3bb942e1df869999c55..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.base.L9.npy +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b44dc3a0519f6adb670a5da7410c1405f4fdb0e4866e5fc4983b136480212f34 -size 1831104 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.L20.len b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.L20.len deleted file mode 100644 index 7d3028fa244a2121c51f39dcc92dc15be82823a6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.L20.len +++ /dev/null @@ -1 +0,0 @@ -596 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.L20.npy b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.L20.npy deleted file mode 100644 index f5a7dcf1a9ccaa6d8e78a75e3ed749a05775a4e5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.L20.npy +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4c82839134cc2340355eb49b41e7e3517e8e9dfdfaa6fc28f0464cc8ae9569ee -size 2441408 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.hypo.word b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.hypo.word deleted file mode 100644 index d77a4cfddcb93c2e08eb55e630c85fe840fd3cc2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.large.hypo.word +++ /dev/null @@ -1 +0,0 @@ -KEEP A GOING AN IF YOU'RE LUCKY YOU'LL RUN PLUMB INTO THEM WAS THE JEERING ANSWER AS THE SLEEPY COWMEN SPURRED THEIR PONIES ON TOWARD CAMP MUTTERING THEIR DISAPPROVAL OF TAKING ALONG A BUNCH OF BOYS ON A CATTLE DRIVE (None-0) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.L30.len b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.L30.len deleted file mode 100644 index 7d3028fa244a2121c51f39dcc92dc15be82823a6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.L30.len +++ /dev/null @@ -1 +0,0 @@ -596 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.L30.npy b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.L30.npy deleted file mode 100644 index b617c3876654a69d73e3f9bd2ad7ff01b764cf24..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.L30.npy +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bbe9f0929ecd4c58786be55215d83f85787ff0d81196bc2e73414f82a8939806 -size 3051712 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.hypo.word b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.hypo.word deleted file mode 100644 index 53e402d4550c820220e0964654a600dabaca8b1c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/sample.xlarge.hypo.word +++ /dev/null @@ -1 +0,0 @@ -KEEP A GOIN AND IF YOU'RE LUCKY YOU'LL RUN PLUMB INTO THEM WAS THE JEERING ANSWER AS THE SLEEPY COWMEN SPURRED THEIR PONIES ON TOWARD CAMP MUTTERING THEIR DISAPPROVAL OF TAKING ALONG A BUNCH OF BOYS ON A CATTLE DRIVE (None-0) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/test_feature_and_unit.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/test_feature_and_unit.sh deleted file mode 100644 index 8cddb27758c97f0981e7c65696f0ef72fbb97471..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/test_feature_and_unit.sh +++ /dev/null @@ -1,92 +0,0 @@ -#!/bin/bash - -set -e - -sizes="base large xlarge" - -declare -A ckpt_urls -ckpt_urls[base]="https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt" -ckpt_urls[large]="https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k.pt" -ckpt_urls[xlarge]="https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k.pt" - -declare -A km_layers -km_layers[base]=9 -km_layers[large]=20 -km_layers[xlarge]=30 - -declare -A km_urls -km_urls[base]="https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960_L9_km500.bin" - -declare -A km_nunits -km_nunits[base]=500 - -test_dir=./examples/hubert/tests -split=sample - -echo -e "${test_dir}\n6313-76958-0021.flac\t190800" > "${test_dir}/${split}.tsv" - -check_feature () { - echo "checking features..." - - size=$1 - ckpt_url=$2 - km_layer=$3 - ckpt_path="$test_dir/$(basename "$ckpt_url")" - - if [ ! -f "$ckpt_path" ]; then - echo "downloading $ckpt_url to $ckpt_path" - wget "$ckpt_url" -O "$ckpt_path" - fi - - python ./examples/hubert/simple_kmeans/dump_hubert_feature.py \ - "${test_dir}" "${split}" "${ckpt_path}" "${km_layer}" 1 0 "${test_dir}" - - if diff -q "${test_dir}/${split}.${size}.L${km_layer}.npy" "${test_dir}/${split}_0_1.npy" &>/dev/null; then - echo "...passed npy check" - else - echo "...failed npy check" - fi - - if diff -q "${test_dir}/${split}.${size}.L${km_layer}.len" "${test_dir}/${split}_0_1.len" &>/dev/null; then - echo "...passed len check" - else - echo "...failed len check" - fi -} - - -check_unit () { - echo "checking units..." - - size=$1 - km_url=$2 - km_layer=$3 - km_nunit=$4 - km_path="$test_dir/$(basename "$km_url")" - - if [ ! -f "$km_path" ]; then - echo "downloading $km_url to $km_path" - wget "$km_url" -O "$km_path" - fi - - python ./examples/hubert/simple_kmeans/dump_km_label.py \ - "${test_dir}" "${split}" "${km_path}" 1 0 "${test_dir}" - - if diff -q "${test_dir}/${split}.${size}.L${km_layer}.km${km_nunit}.km" "${test_dir}/${split}_0_1.km" &>/dev/null; then - echo "...passed unit check" - else - echo "...failed unit check" - fi -} - - -for size in $sizes; do - echo "=== Running unit test for HuBERT $size ===" - check_feature "$size" "${ckpt_urls[$size]}" "${km_layers[$size]}" - - if [ -n "${km_urls[$size]}" ]; then - check_unit "$size" "${km_urls[$size]}" "${km_layers[$size]}" "${km_nunits[$size]}" - fi - - rm -f $test_dir/${split}_0_1.* -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/test_finetuned_asr.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/test_finetuned_asr.sh deleted file mode 100644 index 3c0538b1f734f8363d0aee6afa31a13e6bbb7306..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/tests/test_finetuned_asr.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash - -set -e - -sizes="large xlarge" - -declare -A ckpt_urls -ckpt_urls[large]="https://dl.fbaipublicfiles.com/hubert/hubert_large_ll60k_finetune_ls960.pt" -ckpt_urls[xlarge]="https://dl.fbaipublicfiles.com/hubert/hubert_xtralarge_ll60k_finetune_ls960.pt" - -test_dir=$(pwd)/examples/hubert/tests -split=sample - -echo -e "${test_dir}\n6313-76958-0021.flac\t190800" > "${test_dir}/${split}.tsv" -echo -e "K E E P | A | G O I N G | A N D | I F | Y O U ' R E | L U C K Y | Y O U ' L L | R U N | P L U M B | I N T O | T H E M | W A S | T H E | J E E R I N G | A N S W E R | A S | T H E | S L E E P Y | C O W M E N | S P U R R E D | T H E I R | P O N I E S | O N | T O W A R D | C A M P | M U T T E R I N G | T H E I R | D I S A P P R O V A L | O F | T A K I N G | A L O N G | A | B U N C H | O F | B O Y S | O N | A | C A T T L E | D R I V E |" > "${test_dir}/${split}.ltr" - -check_asr () { - echo "checking asr outputs..." - - size=$1 - ckpt_url=$2 - ckpt_path="$test_dir/$(basename "$ckpt_url")" - - if [ ! -f "$ckpt_path" ]; then - echo "downloading $ckpt_url to $ckpt_path" - wget "$ckpt_url" -O "$ckpt_path" - fi - - python examples/speech_recognition/new/infer.py \ - --config-dir examples/hubert/config/decode --config-name infer_viterbi \ - common_eval.path="${ckpt_path}" task.data="${test_dir}" task.normalize=true \ - decoding.results_path="${test_dir}/pred" \ - common_eval.results_path="${test_dir}/pred" \ - common_eval.quiet=false dataset.gen_subset="${split}" - - if diff -q "${test_dir}/pred/hypo.word" "${test_dir}/${split}.${size}.hypo.word" &>/dev/null; then - echo "...passed word check" - else - echo "...failed word check" - fi - rm -rf "${test_dir}/pred" -} - -for size in $sizes; do - check_asr "$size" "${ckpt_urls[$size]}" -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/update_ckpt.py b/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/update_ckpt.py deleted file mode 100644 index 53c9e74ea613e30aa5c22614e658f2b7272bac0c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/hubert/update_ckpt.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 torch - -src_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2.pt" -ref_ckpt = "/checkpoint/wnhsu/w2v/hubert_icassp_oss_v3/iter2_km100-400k-grp-L6/oss.km500_p0_1_s334.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU100k.s1337.ngpu32/checkpoint_last.pt" -new_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2_updated.pt" - - -def update_state(state): - state["model"]["label_embs_concat"] = state["model"].pop("label_embs") - state["args"].task = "hubert_pretraining" - state["args"].labels = f"['{state['args'].labels}']" - return state - - -src_state = torch.load(src_ckpt) -src_state = update_state(src_state) -torch.save(src_state, new_ckpt) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/joint_alignment_translation/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/joint_alignment_translation/README.md deleted file mode 100644 index cd9c0ea65f5292198296a8f427b42e01b584e2d9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/joint_alignment_translation/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019) - -This page includes instructions for training models described in [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](https://arxiv.org/abs/1909.02074). - -## Training a joint alignment-translation model on WMT'18 En-De - -##### 1. Extract and preprocess the WMT'18 En-De data -```bash -./prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh -``` - -##### 2. Generate alignments from statistical alignment toolkits e.g. Giza++/FastAlign. -In this example, we use FastAlign. -```bash -git clone git@github.com:clab/fast_align.git -pushd fast_align -mkdir build -cd build -cmake .. -make -popd -ALIGN=fast_align/build/fast_align -paste bpe.32k/train.en bpe.32k/train.de | awk -F '\t' '{print $1 " ||| " $2}' > bpe.32k/train.en-de -$ALIGN -i bpe.32k/train.en-de -d -o -v > bpe.32k/train.align -``` - -##### 3. Preprocess the dataset with the above generated alignments. -```bash -fairseq-preprocess \ - --source-lang en --target-lang de \ - --trainpref bpe.32k/train \ - --validpref bpe.32k/valid \ - --testpref bpe.32k/test \ - --align-suffix align \ - --destdir binarized/ \ - --joined-dictionary \ - --workers 32 -``` - -##### 4. Train a model -```bash -fairseq-train \ - binarized \ - --arch transformer_wmt_en_de_big_align --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --activation-fn relu\ - --lr 0.0002 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ - --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ - --max-tokens 3500 --label-smoothing 0.1 \ - --save-dir ./checkpoints --log-interval 1000 --max-update 60000 \ - --keep-interval-updates -1 --save-interval-updates 0 \ - --load-alignments --criterion label_smoothed_cross_entropy_with_alignment \ - --fp16 -``` - -Note that the `--fp16` flag requires you have CUDA 9.1 or greater and a Volta GPU or newer. - -If you want to train the above model with big batches (assuming your machine has 8 GPUs): -- add `--update-freq 8` to simulate training on 8x8=64 GPUs -- increase the learning rate; 0.0007 works well for big batches - -##### 5. Evaluate and generate the alignments (BPE level) -```bash -fairseq-generate \ - binarized --gen-subset test --print-alignment \ - --source-lang en --target-lang de \ - --path checkpoints/checkpoint_best.pt --beam 5 --nbest 1 -``` - -##### 6. Other resources. -The code for: -1. preparing alignment test sets -2. converting BPE level alignments to token level alignments -3. symmetrizing bidirectional alignments -4. evaluating alignments using AER metric -can be found [here](https://github.com/lilt/alignment-scripts) - -## Citation - -```bibtex -@inproceedings{garg2019jointly, - title = {Jointly Learning to Align and Translate with Transformer Models}, - author = {Garg, Sarthak and Peitz, Stephan and Nallasamy, Udhyakumar and Paulik, Matthias}, - booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)}, - address = {Hong Kong}, - month = {November}, - url = {https://arxiv.org/abs/1909.02074}, - year = {2019}, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh deleted file mode 100644 index e3efeb21d302ef8d9eae8f1d4b06434c593705f6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh +++ /dev/null @@ -1,118 +0,0 @@ -#!/bin/bash - -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -echo 'Cloning Moses github repository (for tokenization scripts)...' -git clone https://github.com/moses-smt/mosesdecoder.git - -SCRIPTS=mosesdecoder/scripts -TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl -CLEAN=$SCRIPTS/training/clean-corpus-n.perl -REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl - -URLS=( - "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz" - "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz" - "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz" - "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz" - "http://data.statmt.org/wmt17/translation-task/dev.tgz" - "http://statmt.org/wmt14/test-full.tgz" -) -CORPORA=( - "training/europarl-v7.de-en" - "commoncrawl.de-en" - "training-parallel-nc-v13/news-commentary-v13.de-en" - "rapid2016.de-en" -) - -if [ ! -d "$SCRIPTS" ]; then - echo "Please set SCRIPTS variable correctly to point to Moses scripts." - exit -fi - -src=en -tgt=de -lang=en-de -prep=wmt18_en_de -tmp=$prep/tmp -orig=orig -dev=dev/newstest2012 -codes=32000 -bpe=bpe.32k - -mkdir -p $orig $tmp $prep $bpe - -cd $orig - -for ((i=0;i<${#URLS[@]};++i)); do - url=${URLS[i]} - file=$(basename $url) - if [ -f $file ]; then - echo "$file already exists, skipping download" - else - wget "$url" - if [ -f $file ]; then - echo "$url successfully downloaded." - else - echo "$url not successfully downloaded." - exit 1 - fi - if [ ${file: -4} == ".tgz" ]; then - tar zxvf $file - elif [ ${file: -4} == ".tar" ]; then - tar xvf $file - fi - fi -done -cd .. - -echo "pre-processing train data..." -for l in $src $tgt; do - rm -rf $tmp/train.tags.$lang.tok.$l - for f in "${CORPORA[@]}"; do - cat $orig/$f.$l | \ - perl $REM_NON_PRINT_CHAR | \ - perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/train.tags.$lang.tok.$l - done -done - -echo "pre-processing test data..." -for l in $src $tgt; do - if [ "$l" == "$src" ]; then - t="src" - else - t="ref" - fi - grep '\s*//g' | \ - sed -e 's/\s*<\/seg>\s*//g' | \ - sed -e "s/\’/\'/g" | \ - perl $TOKENIZER -threads 8 -l $l -no-escape > $tmp/test.$l - echo "" -done - -# apply length filtering before BPE -perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train 1 100 - -# use newstest2012 for valid -echo "pre-processing valid data..." -for l in $src $tgt; do - rm -rf $tmp/valid.$l - cat $orig/$dev.$l | \ - perl $REM_NON_PRINT_CHAR | \ - perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/valid.$l -done - -mkdir output -mv $tmp/{train,valid,test}.{$src,$tgt} output - -#BPE -git clone https://github.com/glample/fastBPE.git -pushd fastBPE -g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast -popd -fastBPE/fast learnbpe $codes output/train.$src output/train.$tgt > $bpe/codes -for split in {train,valid,test}; do for lang in {en,de}; do fastBPE/fast applybpe $bpe/$split.$lang output/$split.$lang $bpe/codes; done; done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.adaptive_inputs.md b/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.adaptive_inputs.md deleted file mode 100644 index 6650d58f37f320aa46402d59ce6494b2dd1c3faa..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.adaptive_inputs.md +++ /dev/null @@ -1,39 +0,0 @@ -# Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018) - -## Pre-trained models - -Description | Parameters | Dataset | Model and Test set(s) ----|---:|---|--- -Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 1026M | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) -Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853)) | 247M | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) - -## Training an LM with adaptive inputs - -First, see the general [language modeling README](README.md) for instructions on -preprocessing the WikiText-103 data. - -Then use the following training command to train a model with adaptive inputs -using the `transformer_lm_wiki103` model architecture: -```bash -fairseq-train --task language_modeling \ - data-bin/wikitext-103 \ - --save-dir checkpoints/transformer_wikitext-103 \ - --arch transformer_lm_wiki103 \ - --max-update 286000 --lr 1.0 --t-mult 2 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 \ - --warmup-updates 16000 --warmup-init-lr 1e-07 --stop-min-lr 1e-09 --optimizer nag --min-lr 0.0001 --clip-norm 0.1 \ - --criterion adaptive_loss --max-tokens 3072 --update-freq 3 --tokens-per-sample 3072 --seed 1 \ - --sample-break-mode none --skip-invalid-size-inputs-valid-test --ddp-backend=legacy_ddp -``` - -## Citation - -```bibtex -@inproceedings{ - baevski2018adaptive, - title={Adaptive Input Representations for Neural Language Modeling}, - author={Alexei Baevski and Michael Auli}, - booktitle={International Conference on Learning Representations}, - year={2019}, - url={https://openreview.net/forum?id=ByxZX20qFQ}, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.conv.md b/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.conv.md deleted file mode 100644 index 1ff8635906cf278208be4714e0ef805a6a6b4da1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.conv.md +++ /dev/null @@ -1,40 +0,0 @@ -# Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017) - -## Example usage - -First download and preprocess the data following the main [language modeling README](README.md). - -Then to train a convolutional LM using the `fconv_lm_dauphin_wikitext103` -architecture: -```bash -fairseq-train --task language_modeling \ - data-bin/wikitext-103 \ - --save-dir checkpoints/fconv_wikitext-103 \ - --arch fconv_lm_dauphin_wikitext103 \ - --adaptive-softmax-cutoff 10000,20000,200000 \ - --dropout 0.2 \ - --criterion adaptive_loss \ - --optimizer nag --clip-norm 0.1 --weight-decay 5e-06 \ - --lr 1.0 --lr-scheduler reduce_lr_on_plateau --lr-shrink 0.5 \ - --max-tokens 1024 --tokens-per-sample 1024 \ - --ddp-backend legacy_ddp \ - --max-epoch 35 -``` - -And evaluate with: -```bash -fairseq-eval-lm data-bin/wikitext-103 --path checkpoints/fconv_wiki103/checkpoint_best.pt -``` - -## Citation - -```bibtex -@inproceedings{dauphin2017language, - title={Language Modeling with Gated Convolutional Networks}, - author={Dauphin, Yann N and Fan, Angela and Auli, Michael and Grangier, David}, - booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70}, - pages={933--941}, - year={2017}, - organization={JMLR} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.md deleted file mode 100644 index e78ea48e08dc99b69751923762107a8f8a9a5e3e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/README.md +++ /dev/null @@ -1,123 +0,0 @@ -# Neural Language Modeling - -## Pre-trained models - -Model | Description | Dataset | Download ----|---|---|--- -`transformer_lm.gbw.adaptive_huge` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
1026M params | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) -`transformer_lm.wiki103.adaptive` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
247M params | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) -`transformer_lm.wmt19.en` | English LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz) -`transformer_lm.wmt19.de` | German LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz) -`transformer_lm.wmt19.ru` | Russian LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz) - -## Example usage - -We require a few additional Python dependencies for preprocessing: -```bash -pip install fastBPE sacremoses -``` - -To sample from a language model using PyTorch Hub: -```python -import torch - -# List available models -torch.hub.list('pytorch/fairseq') # [..., 'transformer_lm.wmt19.en', ...] - -# Load an English LM trained on WMT'19 News Crawl data -en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe') -en_lm.eval() # disable dropout - -# Move model to GPU -en_lm.cuda() - -# Sample from the language model -en_lm.sample('Barack Obama', beam=1, sampling=True, sampling_topk=10, temperature=0.8) -# "Barack Obama is coming to Sydney and New Zealand (...)" - -# Compute perplexity for a sequence -en_lm.score('Barack Obama is coming to Sydney and New Zealand')['positional_scores'].mean().neg().exp() -# tensor(15.1474) - -# The same interface can be used with custom models as well -from fairseq.models.transformer_lm import TransformerLanguageModel -custom_lm = TransformerLanguageModel.from_pretrained('/path/to/model/dir', 'checkpoint100.pt', tokenizer='moses', bpe='fastbpe') -custom_lm.sample('Barack Obama', beam=5) -# "Barack Obama (...)" -``` - -## Training a transformer language model with the CLI tools - -### 1) Preprocess the data - -First download and prepare the [WikiText-103 dataset](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/): -```bash -cd examples/language_model/ -bash prepare-wikitext-103.sh -cd ../.. -``` - -Next preprocess/binarize the data: -```bash -TEXT=examples/language_model/wikitext-103 -fairseq-preprocess \ - --only-source \ - --trainpref $TEXT/wiki.train.tokens \ - --validpref $TEXT/wiki.valid.tokens \ - --testpref $TEXT/wiki.test.tokens \ - --destdir data-bin/wikitext-103 \ - --workers 20 -``` - -### 2) Train a language model - -Next we'll train a basic transformer language model on wikitext-103. For more -advanced usage, see the [adaptive inputs README](README.adaptive_inputs.md). - -To train a basic LM (assumes 2 GPUs): -``` -$ fairseq-train --task language_modeling \ - data-bin/wikitext-103 \ - --save-dir checkpoints/transformer_wikitext-103 \ - --arch transformer_lm --share-decoder-input-output-embed \ - --dropout 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --weight-decay 0.01 --clip-norm 0.0 \ - --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ - --tokens-per-sample 512 --sample-break-mode none \ - --max-tokens 2048 --update-freq 16 \ - --fp16 \ - --max-update 50000 -``` - -If you run out of memory, try reducing `--max-tokens` (max number of tokens per -batch) or `--tokens-per-sample` (max sequence length). You can also adjust -`--update-freq` to accumulate gradients and simulate training on a different -number of GPUs. - -### 3) Evaluate - -```bash -fairseq-eval-lm data-bin/wikitext-103 \ - --path checkpoints/transformer_wiki103/checkpoint_best.pt \ - --batch-size 2 \ - --tokens-per-sample 512 \ - --context-window 400 -# | Evaluated 245569 tokens in 56.1s (4379.02 tokens/s) -# | Loss: 3.4164, Perplexity: 30.46 -``` - -*Note:* The `--context-window` option controls how much context is provided to -each token when computing perplexity. When the window size is 0, the dataset is -chunked into segments of length 512 and perplexity is computed over each segment -normally. However, this results in worse (higher) perplexity since tokens that -appear earlier in each segment have less conditioning. When the maximum window -size is used (511 in this case), then we compute perplexity for each token -fully conditioned on 511 tokens of context. This slows down evaluation -significantly, since we must run a separate forward pass for every token in the -dataset, but results in better (lower) perplexity. - - -## Convolutional language models - -Please see the [convolutional LM README](README.conv.md) for instructions on -training convolutional language models. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/prepare-wikitext-103.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/prepare-wikitext-103.sh deleted file mode 100644 index 751302156f0a6829af9c2ee5e0e2ca62c2cd4187..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/language_model/prepare-wikitext-103.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash -# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh - -URLS=( - "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip" -) -FILES=( - "wikitext-103-v1.zip" -) - -for ((i=0;i<${#URLS[@]};++i)); do - file=${FILES[i]} - if [ -f $file ]; then - echo "$file already exists, skipping download" - else - url=${URLS[i]} - wget "$url" - if [ -f $file ]; then - echo "$url successfully downloaded." - else - echo "$url not successfully downloaded." - exit -1 - fi - if [ ${file: -4} == ".tgz" ]; then - tar zxvf $file - elif [ ${file: -4} == ".tar" ]; then - tar xvf $file - elif [ ${file: -4} == ".zip" ]; then - unzip $file - fi - fi -done -cd .. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/laser/README.md deleted file mode 100644 index 66acada04f58fa235cd312753f144f6f1e5f4a33..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/README.md +++ /dev/null @@ -1,144 +0,0 @@ -# LASER Language-Agnostic SEntence Representations - -LASER is a library to calculate and use multilingual sentence embeddings. - -You can find more information about LASER and how to use it on the official [LASER repository](https://github.com/facebookresearch/LASER). - -This folder contains source code for training LASER embeddings. - - -## Prepare data and configuration file - -Binarize your data with fairseq, as described [here](https://fairseq.readthedocs.io/en/latest/getting_started.html#data-pre-processing). - -Create a json config file with this format: -``` -{ - "src_vocab": "/path/to/spm.src.cvocab", - "tgt_vocab": "/path/to/spm.tgt.cvocab", - "train": [ - { - "type": "translation", - "id": 0, - "src": "/path/to/srclang1-tgtlang0/train.srclang1", - "tgt": "/path/to/srclang1-tgtlang0/train.tgtlang0" - }, - { - "type": "translation", - "id": 1, - "src": "/path/to/srclang1-tgtlang1/train.srclang1", - "tgt": "/path/to/srclang1-tgtlang1/train.tgtlang1" - }, - { - "type": "translation", - "id": 0, - "src": "/path/to/srclang2-tgtlang0/train.srclang2", - "tgt": "/path/to/srclang2-tgtlang0/train.tgtlang0" - }, - { - "type": "translation", - "id": 1, - "src": "/path/to/srclang2-tgtlang1/train.srclang2", - "tgt": "/path/to/srclang2-tgtlang1/train.tgtlang1" - }, - ... - ], - "valid": [ - { - "type": "translation", - "id": 0, - "src": "/unused", - "tgt": "/unused" - } - ] -} -``` -where paths are paths to binarized indexed fairseq dataset files. -`id` represents the target language id. - - -## Training Command Line Example - -``` -fairseq-train \ - /path/to/configfile_described_above.json \ - --user-dir examples/laser/laser_src \ - --log-interval 100 --log-format simple \ - --task laser --arch laser_lstm \ - --save-dir . \ - --optimizer adam \ - --lr 0.001 \ - --lr-scheduler inverse_sqrt \ - --clip-norm 5 \ - --warmup-updates 90000 \ - --update-freq 2 \ - --dropout 0.0 \ - --encoder-dropout-out 0.1 \ - --max-tokens 2000 \ - --max-epoch 50 \ - --encoder-bidirectional \ - --encoder-layers 5 \ - --encoder-hidden-size 512 \ - --decoder-layers 1 \ - --decoder-hidden-size 2048 \ - --encoder-embed-dim 320 \ - --decoder-embed-dim 320 \ - --decoder-lang-embed-dim 32 \ - --warmup-init-lr 0.001 \ - --disable-validation -``` - - -## Applications - -We showcase several applications of multilingual sentence embeddings -with code to reproduce our results (in the directory "tasks"). - -* [**Cross-lingual document classification**](https://github.com/facebookresearch/LASER/tree/master/tasks/mldoc) using the - [*MLDoc*](https://github.com/facebookresearch/MLDoc) corpus [2,6] -* [**WikiMatrix**](https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix) - Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia [7] -* [**Bitext mining**](https://github.com/facebookresearch/LASER/tree/master/tasks/bucc) using the - [*BUCC*](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) corpus [3,5] -* [**Cross-lingual NLI**](https://github.com/facebookresearch/LASER/tree/master/tasks/xnli) - using the [*XNLI*](https://www.nyu.edu/projects/bowman/xnli/) corpus [4,5,6] -* [**Multilingual similarity search**](https://github.com/facebookresearch/LASER/tree/master/tasks/similarity) [1,6] -* [**Sentence embedding of text files**](https://github.com/facebookresearch/LASER/tree/master/tasks/embed) - example how to calculate sentence embeddings for arbitrary text files in any of the supported language. - -**For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.** - - - -## References - -[1] Holger Schwenk and Matthijs Douze, - [*Learning Joint Multilingual Sentence Representations with Neural Machine Translation*](https://aclanthology.info/papers/W17-2619/w17-2619), - ACL workshop on Representation Learning for NLP, 2017 - -[2] Holger Schwenk and Xian Li, - [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), - LREC, pages 3548-3551, 2018. - -[3] Holger Schwenk, - [*Filtering and Mining Parallel Data in a Joint Multilingual Space*](http://aclweb.org/anthology/P18-2037) - ACL, July 2018 - -[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, - [*XNLI: Cross-lingual Sentence Understanding through Inference*](https://aclweb.org/anthology/D18-1269), - EMNLP, 2018. - -[5] Mikel Artetxe and Holger Schwenk, - [*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136) - arXiv, Nov 3 2018. - -[6] Mikel Artetxe and Holger Schwenk, - [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464) - arXiv, Dec 26 2018. - -[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, - [*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791) - arXiv, July 11 2019. - -[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin - [*CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB*](https://arxiv.org/abs/1911.04944) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/__init__.py deleted file mode 100644 index 877985033425562b999c147ee2485cb746f216de..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/__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 .laser_lstm import * # noqa -from .laser_task import * # noqa -from .laser_transformer import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_lstm.py b/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_lstm.py deleted file mode 100644 index f835d2e772a7e018700637652d2c648a4133973a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_lstm.py +++ /dev/null @@ -1,583 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 options, utils -from fairseq.models import ( - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqIncrementalDecoder, - register_model, - register_model_architecture, -) - - -@register_model("laser_lstm") -class LSTMModel(FairseqEncoderDecoderModel): - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens=None, - tgt_tokens=None, - tgt_lengths=None, - target_language_id=None, - dataset_name="", - ): - assert target_language_id is not None - - src_encoder_out = self.encoder(src_tokens, src_lengths, dataset_name) - return self.decoder( - prev_output_tokens, src_encoder_out, lang_id=target_language_id - ) - - @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( - "--encoder-embed-dim", - type=int, - metavar="N", - help="encoder embedding dimension", - ) - parser.add_argument( - "--encoder-embed-path", - default=None, - type=str, - metavar="STR", - help="path to pre-trained encoder embedding", - ) - 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", - default=None, - 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-zero-init", - type=str, - metavar="BOOL", - help="initialize the decoder hidden/cell state to zero", - ) - parser.add_argument( - "--decoder-lang-embed-dim", - type=int, - metavar="N", - help="decoder language embedding dimension", - ) - parser.add_argument( - "--fixed-embeddings", - action="store_true", - help="keep embeddings fixed (ENCODER ONLY)", - ) # TODO Also apply to decoder embeddings? - - # 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", - ) - - @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) - - 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_encoder_embed = None - if args.encoder_embed_path: - pretrained_encoder_embed = load_pretrained_embedding_from_file( - args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim - ) - 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 - ) - - num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 - - 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, - fixed_embeddings=args.fixed_embeddings, - ) - 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, - zero_init=options.eval_bool(args.decoder_zero_init), - encoder_embed_dim=args.encoder_embed_dim, - encoder_output_units=encoder.output_units, - pretrained_embed=pretrained_decoder_embed, - num_langs=num_langs, - lang_embed_dim=args.decoder_lang_embed_dim, - ) - return cls(encoder, decoder) - - -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_value=0.0, - fixed_embeddings=False, - ): - super().__init__(dictionary) - self.num_layers = num_layers - self.dropout_in = dropout_in - self.dropout_out = dropout_out - self.bidirectional = bidirectional - self.hidden_size = hidden_size - - num_embeddings = len(dictionary) - self.padding_idx = dictionary.pad() - if pretrained_embed is None: - self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx) - else: - self.embed_tokens = pretrained_embed - if fixed_embeddings: - self.embed_tokens.weight.requires_grad = False - - self.lstm = LSTM( - input_size=embed_dim, - hidden_size=hidden_size, - num_layers=num_layers, - dropout=self.dropout_out if num_layers > 1 else 0.0, - bidirectional=bidirectional, - ) - self.left_pad = left_pad - self.padding_value = padding_value - - self.output_units = hidden_size - if bidirectional: - self.output_units *= 2 - - def forward(self, src_tokens, src_lengths, dataset_name): - if self.left_pad: - # convert left-padding to right-padding - src_tokens = utils.convert_padding_direction( - src_tokens, - self.padding_idx, - left_to_right=True, - ) - - bsz, seqlen = src_tokens.size() - - # embed tokens - x = self.embed_tokens(src_tokens) - x = F.dropout(x, p=self.dropout_in, training=self.training) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - # pack embedded source tokens into a PackedSequence - try: - packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist()) - except BaseException: - raise Exception(f"Packing failed in dataset {dataset_name}") - - # 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.data.new(*state_size).zero_() - c0 = x.data.new(*state_size).zero_() - 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_value - ) - x = F.dropout(x, p=self.dropout_out, training=self.training) - assert list(x.size()) == [seqlen, bsz, self.output_units] - - if self.bidirectional: - - def combine_bidir(outs): - return torch.cat( - [ - torch.cat([outs[2 * i], outs[2 * i + 1]], dim=0).view( - 1, bsz, self.output_units - ) - for i in range(self.num_layers) - ], - dim=0, - ) - - final_hiddens = combine_bidir(final_hiddens) - final_cells = combine_bidir(final_cells) - - encoder_padding_mask = src_tokens.eq(self.padding_idx).t() - - # Set padded outputs to -inf so they are not selected by max-pooling - padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) - if padding_mask.any(): - x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) - - # Build the sentence embedding by max-pooling over the encoder outputs - sentemb = x.max(dim=0)[0] - - return { - "sentemb": sentemb, - "encoder_out": (x, final_hiddens, final_cells), - "encoder_padding_mask": encoder_padding_mask - if encoder_padding_mask.any() - else None, - } - - def reorder_encoder_out(self, encoder_out_dict, new_order): - encoder_out_dict["sentemb"] = encoder_out_dict["sentemb"].index_select( - 0, new_order - ) - encoder_out_dict["encoder_out"] = tuple( - eo.index_select(1, new_order) for eo in encoder_out_dict["encoder_out"] - ) - if encoder_out_dict["encoder_padding_mask"] is not None: - encoder_out_dict["encoder_padding_mask"] = encoder_out_dict[ - "encoder_padding_mask" - ].index_select(1, new_order) - return encoder_out_dict - - def max_positions(self): - """Maximum input length supported by the encoder.""" - return int(1e5) # an arbitrary large number - - -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, - zero_init=False, - encoder_embed_dim=512, - encoder_output_units=512, - pretrained_embed=None, - num_langs=1, - lang_embed_dim=0, - ): - super().__init__(dictionary) - self.dropout_in = dropout_in - self.dropout_out = dropout_out - self.hidden_size = hidden_size - - 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.layers = nn.ModuleList( - [ - LSTMCell( - input_size=encoder_output_units + embed_dim + lang_embed_dim - if layer == 0 - else hidden_size, - hidden_size=hidden_size, - ) - for layer in range(num_layers) - ] - ) - if hidden_size != out_embed_dim: - self.additional_fc = Linear(hidden_size, out_embed_dim) - self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out) - - if zero_init: - self.sentemb2init = None - else: - self.sentemb2init = Linear( - encoder_output_units, 2 * num_layers * hidden_size - ) - - if lang_embed_dim == 0: - self.embed_lang = None - else: - self.embed_lang = nn.Embedding(num_langs, lang_embed_dim) - nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) - - def forward( - self, prev_output_tokens, encoder_out_dict, incremental_state=None, lang_id=0 - ): - sentemb = encoder_out_dict["sentemb"] - encoder_out = encoder_out_dict["encoder_out"] - - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - bsz, seqlen = prev_output_tokens.size() - - # get outputs from encoder - encoder_outs, _, _ = encoder_out[:3] - srclen = encoder_outs.size(0) - - # embed tokens - x = self.embed_tokens(prev_output_tokens) - x = F.dropout(x, p=self.dropout_in, training=self.training) - - # embed language identifier - if self.embed_lang is not None: - lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) - langemb = self.embed_lang(lang_ids) - # TODO Should we dropout here??? - - # 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, input_feed = cached_state - else: - num_layers = len(self.layers) - if self.sentemb2init is None: - prev_hiddens = [ - x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers) - ] - prev_cells = [ - x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers) - ] - else: - init = self.sentemb2init(sentemb) - prev_hiddens = [ - init[:, (2 * i) * self.hidden_size : (2 * i + 1) * self.hidden_size] - for i in range(num_layers) - ] - prev_cells = [ - init[ - :, - (2 * i + 1) * self.hidden_size : (2 * i + 2) * self.hidden_size, - ] - for i in range(num_layers) - ] - input_feed = x.data.new(bsz, self.hidden_size).zero_() - - attn_scores = x.data.new(srclen, seqlen, bsz).zero_() - outs = [] - for j in range(seqlen): - if self.embed_lang is None: - input = torch.cat((x[j, :, :], sentemb), dim=1) - else: - input = torch.cat((x[j, :, :], sentemb, langemb), dim=1) - - 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 = F.dropout(hidden, p=self.dropout_out, training=self.training) - - # save state for next time step - prev_hiddens[i] = hidden - prev_cells[i] = cell - - out = hidden - out = F.dropout(out, p=self.dropout_out, training=self.training) - - # input feeding - input_feed = out - - # save final output - outs.append(out) - - # cache previous states (no-op except during incremental generation) - utils.set_incremental_state( - self, - incremental_state, - "cached_state", - (prev_hiddens, prev_cells, input_feed), - ) - - # 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) - - # srclen x tgtlen x bsz -> bsz x tgtlen x srclen - attn_scores = attn_scores.transpose(0, 2) - - # project back to size of vocabulary - if hasattr(self, "additional_fc"): - x = self.additional_fc(x) - x = F.dropout(x, p=self.dropout_out, training=self.training) - x = self.fc_out(x) - - return x, attn_scores - - 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) - - def max_positions(self): - """Maximum output length supported by the decoder.""" - return int(1e5) # an arbitrary large number - - -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): - """Weight-normalized 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("laser_lstm", "laser_lstm") -def base_architecture(args): - args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512) - args.encoder_embed_path = getattr(args, "encoder_embed_path", None) - 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_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_dropout_in = getattr(args, "decoder_dropout_in", args.dropout) - args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout) - args.decoder_zero_init = getattr(args, "decoder_zero_init", "0") - args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) - args.fixed_embeddings = getattr(args, "fixed_embeddings", False) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_task.py b/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_task.py deleted file mode 100644 index 78d3a2996d4efa0f766c1985eb7f1cd3acaebd6e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_task.py +++ /dev/null @@ -1,333 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 os -from argparse import ArgumentError -from collections import OrderedDict, defaultdict - -from fairseq import models, options -from fairseq.data import ( - Dictionary, - FairseqDataset, - IndexedDataset, - LanguagePairDataset, - data_utils, -) -from fairseq.tasks import LegacyFairseqTask, register_task - -from .multitask_data_utils import ( - MultidatasetEpochBatchIterator, - MultitaskDatasetWrapper, -) - -logger = logging.getLogger(__name__) - - -@register_task("laser") -class LaserTask(LegacyFairseqTask): - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "configfile", metavar="PATH", help="dataset configuration file in json" - ) - parser.add_argument( - "--weighting-alpha", - type=float, - default=None, - help="alpha for automatic weighting", - ) - parser.add_argument( - "--raw-text", action="store_true", help="load raw text dataset" - ) - 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 - - def __init__(self, args, config, src_dictionary, tgt_dictionary, num_tasks): - super().__init__(args) - self.config = config - self.src_dictionary = src_dictionary - self.tgt_dictionary = tgt_dictionary - self.num_tasks = num_tasks - - @classmethod - def setup_task(cls, args, **kwargs): - with open(args.configfile, "r") as f: - config = json.load(f) - num_tasks = max(dataset["id"] for dataset in config["train"]) + 1 - - args.left_pad_source = options.eval_bool(args.left_pad_source) - args.left_pad_target = options.eval_bool(args.left_pad_target) - - src_dictionary = Dictionary.load(config["src_vocab"]) - tgt_dictionary = Dictionary.load(config["tgt_vocab"]) - - logger.info( - "| src Dictionary {} : {} types".format( - config["src_vocab"], len(src_dictionary) - ) - ) - logger.info( - "| tgt Dictionary {} : {} types".format( - config["tgt_vocab"], len(tgt_dictionary) - ) - ) - - return cls(args, config, src_dictionary, tgt_dictionary, num_tasks) - - # Experimental overriding for backtranslation - def build_model(self, args, from_checkpoint=False): - model = models.build_model(args, self) - return model - - def dataset(self, split): - if split not in self.datasets: - raise KeyError("Dataset not loaded: " + split) - return self.datasets[split] - - def load_dataset(self, split, epoch=1, **kwargs): - """Load a dataset split.""" - - def indexed_dataset(path, dictionary): - if self.args.raw_text: - raise Exception("Unable to handle raw text.") - dataset = IndexedDataset(path, fix_lua_indexing=True) - - return dataset - - pair_datasets = OrderedDict() - - if split == "valid": - self.datasets[split] = pair_datasets - return - - if split not in self.config: - raise FileNotFoundError( - "Dataset not found in config file: {}".format(split) - ) - - size_by_corpus = defaultdict(int) - size_sum = 0 - size_sum_with_subsampling = 0 - init_pair_datasets = {} - - for dataset_config in self.config[split]: - src_path = os.path.dirname(dataset_config["src"]) - corpus_name = src_path.split("/")[-2] - language_pair_name = src_path.split("/")[-1] - pair_datasets_key = corpus_name + "-" + language_pair_name - - logger.info(f"loading... {pair_datasets_key}") - if "src" in dataset_config: - src_dataset = indexed_dataset( - dataset_config["src"], self.src_dictionary - ) - else: - src_dataset = None - - if "tgt" in dataset_config: - tgt_dataset = indexed_dataset( - dataset_config["tgt"], self.tgt_dictionary - ) - else: - tgt_dataset = None - - dataset = LanguagePairDataset( - src_dataset, - src_dataset.sizes, - self.src_dictionary, - tgt_dataset, - tgt_dataset.sizes, - self.tgt_dictionary, - left_pad_source=self.args.left_pad_source, - left_pad_target=self.args.left_pad_target, - ) - - if pair_datasets_key in init_pair_datasets: - logger.warning( - f"Ignoring already added {pair_datasets_key}. " - f"Consider using `sample` key in order to upsample." - ) - else: - init_pair_datasets[pair_datasets_key] = { - "dataset": dataset, - "sample": dataset_config.get("sample", None), - "id": dataset_config.get("id", None), - "len": len(dataset), - } - - length_sum = 0 - weighted_freqs_sum = 0 - freq_per_dataset = {} - vmax = 0 - vmin = 1 - weighted_freq_per_dataset = {} - - if self.args.weighting_alpha: - for key in init_pair_datasets: - if init_pair_datasets[key]["sample"] is None: - length_sum += len(init_pair_datasets[key]["dataset"]) - - for key in init_pair_datasets: - if init_pair_datasets[key]["sample"] is None: - val = float(init_pair_datasets[key]["len"]) / length_sum - freq_per_dataset[key] = val - weighted_freqs_sum += val ** self.args.weighting_alpha - - for key in freq_per_dataset: - val = ( - freq_per_dataset[key] ** self.args.weighting_alpha - / weighted_freqs_sum - ) - vmin = min(vmin, val) - vmax = max(vmax, val) - weighted_freq_per_dataset[key] = val - - for pair_datasets_key in init_pair_datasets: - dataset_config = init_pair_datasets[pair_datasets_key] - dataset = dataset_config["dataset"] - sample = dataset_config["sample"] - if sample is None: - sample = 1.0 - - if pair_datasets_key in weighted_freq_per_dataset: - w = vmax / weighted_freq_per_dataset[pair_datasets_key] - sample = w - - sample = round(sample) - - initial_sample = sample - initial_pair_datasets_key = pair_datasets_key - - while sample >= 1.0: - assert ( - pair_datasets_key not in pair_datasets - ), f"{pair_datasets_key} already in" - size_sum_with_subsampling += len(dataset) - pair_datasets[pair_datasets_key] = MultitaskDatasetWrapper( - dataset, dataset_config.get("id", 0), 1.0, name=pair_datasets_key - ) - size_sum += len(dataset) - sample -= 1.0 - pair_datasets_key += "-up" - - assert sample < 1e-6, f"sample remains > 0 {pair_datasets_key}" - - logger.info( - f"added pair {initial_pair_datasets_key} length {len(dataset)} new_length = {len(dataset)*initial_sample}" - ) - size_by_corpus[corpus_name] += len(dataset) - - self.datasets[split] = pair_datasets - logger.info( - f"Datasets number = {len(self.datasets[split])} size = {size_sum} size_sum_with_subsampling = {size_sum_with_subsampling}" - ) - - @property - def source_dictionary(self): - return self.src_dictionary - - @property - def target_dictionary(self): - return self.tgt_dictionary - - 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, - grouped_shuffling=False, - update_epoch_batch_itr=False, - **kwargs, - ): - - assert isinstance(dataset, OrderedDict) - assert len(dataset) - assert isinstance(dataset[next(iter(dataset))], FairseqDataset) - - # initialize the dataset with the correct starting epoch - for _, dt in dataset.items(): - dt.set_epoch(epoch) - - indices = OrderedDict() - batch_sampler = OrderedDict() - - with data_utils.numpy_seed(seed + epoch): - for key, dt in dataset.items(): - logger.info(f"\t ordered_indices {key}") - indices[key] = dt.ordered_indices() - - # filter examples that are too large - if max_positions is not None: - for key, dt in dataset.items(): - logger.info(f"\t filter_by_size {key}") - indices[key], ignored = dt.filter_indices_by_size( - indices[key], max_positions - ) - - for key, dt in dataset.items(): - logger.info(f"\t batch_by_size {key}") - batch_sampler[key] = data_utils.batch_by_size( - indices[key], - dt.num_tokens, - max_tokens=max_tokens, - max_sentences=max_sentences, - required_batch_size_multiple=required_batch_size_multiple, - ) - - epoch_iter = MultidatasetEpochBatchIterator( - dataset=dataset, - batch_sampler=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/examples/laser/laser_src/laser_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_transformer.py deleted file mode 100644 index 9eb4e070214899a44694d1c541d69abddc6de43a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/laser_transformer.py +++ /dev/null @@ -1,352 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 Any, Dict, List, Optional - -import torch -import torch.nn as nn -from fairseq.models import ( - FairseqEncoderDecoderModel, - register_model, - register_model_architecture, -) -from fairseq.models.transformer import ( - Embedding, - TransformerDecoder, - TransformerEncoder, - TransformerModel, - base_architecture, -) -from fairseq.modules import ( - TransformerDecoderLayer, -) -from torch import Tensor - -logger = logging.getLogger(__name__) - - -@register_model("laser_transformer") -class LaserTransformerModel(FairseqEncoderDecoderModel): - """Train Transformer for LASER task - - Requires --task laser - """ - - def __init__(self, encoder, decoder): - super().__init__(encoder, decoder) - - def forward( - self, - src_tokens, - src_lengths, - prev_output_tokens=None, - tgt_tokens=None, - tgt_lengths=None, - target_language_id=-1, - dataset_name="", - ): - laser_encoder_out = self.encoder(src_tokens, src_lengths) - return self.decoder( - prev_output_tokens, laser_encoder_out, lang_id=target_language_id - ) - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - TransformerModel.add_args(parser) - parser.add_argument( - "--decoder-lang-embed-dim", - type=int, - metavar="N", - help="decoder language embedding dimension", - ) - - @classmethod - def build_model(cls, args, task): - base_laser_transformer_architecture(args) - - num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 - - def load_embed_tokens(dictionary, embed_dim): - num_embeddings = len(dictionary) - padding_idx = dictionary.pad() - - return Embedding(num_embeddings, embed_dim, padding_idx) - - encoder_embed_tokens = load_embed_tokens( - task.source_dictionary, args.encoder_embed_dim - ) - decoder_embed_tokens = load_embed_tokens( - task.target_dictionary, args.decoder_embed_dim - ) - num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0 - - encoder = LaserTransformerEncoder( - args, task.source_dictionary, encoder_embed_tokens - ) - - decoder = LaserTransformerDecoder( - args, - task.target_dictionary, - decoder_embed_tokens, - num_langs=num_langs, - lang_embed_dim=args.decoder_lang_embed_dim, - ) - - return cls(encoder, decoder) - - -class LaserTransformerEncoder(TransformerEncoder): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, src_tokens, *args, **kwargs): - encoder_out = super().forward(src_tokens, *args, **kwargs) - - x = encoder_out["encoder_out"][0] # T x B x C - padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1) - - if padding_mask.any(): - x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x) - - # Build the sentence embedding by max-pooling over the encoder outputs - sentemb = x.max(dim=0)[0] - - # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in - # `foward` 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. - return {"sentemb": [sentemb]} # B x C - - @torch.jit.export - def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order): - """ - Same as the one in transformer.py, with new_sentemb - """ - if len(encoder_out["sentemb"]) == 0: - new_sentemb = [] - else: - new_sentemb = [encoder_out["sentemb"][0].index_select(0, new_order)] - - return { - "sentemb": new_sentemb, # B x C - } - - -class LaserTransformerDecoder(TransformerDecoder): - def __init__(self, args, dictionary, *kargs, **kwargs): - self.num_langs = kwargs.get("num_langs", 1) - self.lang_embed_dim = kwargs.get("lang_embed_dim", 0) - kwargs.pop("num_langs", None) - kwargs.pop("lang_embed_dim", None) - - super().__init__(args, dictionary, *kargs, **kwargs, no_encoder_attn=True) - - if self.lang_embed_dim == 0: - self.embed_lang = None - else: - self.embed_lang = nn.Embedding(self.num_langs, self.lang_embed_dim) - nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1) - - if self.output_projection is not None: - laser_output_embed_dim = ( - self.output_embed_dim + self.lang_embed_dim + args.encoder_embed_dim - ) - self.output_projection = nn.Linear( - laser_output_embed_dim, len(dictionary), bias=False - ) - nn.init.normal_( - self.output_projection.weight, - mean=0, - std=laser_output_embed_dim ** -0.5, - ) - - def build_decoder_layer(self, args, no_encoder_attn=False): - decoder_embed_dim = args.decoder_embed_dim - args.decoder_embed_dim = ( - decoder_embed_dim + self.lang_embed_dim + args.encoder_embed_dim - ) - res = TransformerDecoderLayer(args, no_encoder_attn=True) - args.decoder_embed_dim = decoder_embed_dim - - return res - - 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, - lang_id: 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 - """ - if alignment_layer is None: - alignment_layer = self.num_layers - 1 - - # 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:] - - bsz, seqlen = prev_output_tokens.size() - - # 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) - - if self.embed_lang is not None: - lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id) - langemb = self.embed_lang(lang_ids) - langemb = langemb.unsqueeze(0) - repeat_vals = [x.shape[0] // langemb.shape[0]] + [-1] * ( - len(langemb.shape) - 1 - ) - x = torch.cat((x, langemb.expand(*repeat_vals)), dim=-1) - - sentemb = encoder_out["sentemb"][0] - sentemb = sentemb.unsqueeze(0) - - repeat_vals = [x.shape[0] // sentemb.shape[0]] + [-1] * (len(sentemb.shape) - 1) - x = torch.cat((x, sentemb.expand(*repeat_vals)), dim=-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, - None, - 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) - - return x, {"attn": [attn], "inner_states": inner_states} - - 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, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - src_lengths: Optional[Any] = None, - return_all_hiddens: bool = False, - lang_id: Optional[int] = None, - ): - """ - 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 - 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). - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - - assert lang_id is not None - - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - alignment_layer=alignment_layer, - alignment_heads=alignment_heads, - lang_id=lang_id, - ) - if not features_only: - x = self.output_layer(x) - return x, extra - - -@register_model_architecture("laser_transformer", "laser_transformer") -def base_laser_transformer_architecture(args): - base_architecture(args) - args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/multitask_data_utils.py b/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/multitask_data_utils.py deleted file mode 100644 index db12b303151ecaa0459cf75d0293f9589847847b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/laser/laser_src/multitask_data_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. - -from collections import OrderedDict - -import numpy as np -from fairseq.data import BaseWrapperDataset, FairseqDataset, iterators - - -class MultiItr(object): - def __init__(self, itr): - self.itr = itr - self._counts = [0 for x in itr] - - def __len__(self): - return sum(len(itr) for itr in self.itr) - - def __iter__(self): - return self - - def __next__(self): - ratios = [count / len(itr) for count, itr in zip(self._counts, self.itr)] - idx = ratios.index(min(ratios)) - self._counts[idx] += 1 - return next(self.itr[idx]) - - -class MultidatasetEpochBatchIterator(iterators.EpochBatchIterating): - """A wrapper around multiple epoch batch iterators.""" - - def __init__( - self, - dataset, - batch_sampler, - seed=1, - num_shards=1, - shard_id=0, - num_workers=0, - epoch=1, - ): - - assert isinstance(dataset, OrderedDict) - assert len(dataset) - assert isinstance(dataset[next(iter(dataset))], FairseqDataset) - - self.iterators = [] - - self.epoch = epoch - for key, dt in dataset.items(): - epoch_iter = iterators.EpochBatchIterator( - dataset=dt, - collate_fn=dt.collater, - batch_sampler=batch_sampler[key], - seed=seed, - num_shards=num_shards, - shard_id=shard_id, - num_workers=0, - epoch=epoch, - ) - self.iterators.append(epoch_iter) - - def __len__(self): - return sum(len(itr) for itr in self.iterators) - - def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False): - # `self.epoch += 1` should be handled by underlying `EpochBatchIterator`s. - return MultiItr( - [ - itr.next_epoch_itr( - shuffle=shuffle, fix_batches_to_gpus=fix_batches_to_gpus - ) - for itr in self.iterators - ] - ) - - def end_of_epoch(self): - return all(itr.end_of_epoch() for itr in self.iterators) - - @property - def next_epoch_idx(self): - """Return the epoch index after *next_epoch_itr* is called.""" - - epochs = [itr.next_epoch_idx for itr in self.iterators] - self.epoch = epochs[0] - assert all(epoch == self.epoch for epoch in epochs) - - return self.epoch - - @property - def iterations_in_epoch(self): - return sum(itr.iterations_in_epoch for itr in self.iterators) - - def state_dict(self): - return { - "iterators": [it.state_dict() for it in self.iterators], - "epoch": self.epoch, - } - - def load_state_dict(self, state_dict): - self.epoch = state_dict["epoch"] - for it, d in zip(self.iterators, state_dict["iterators"]): - it.load_state_dict(d) - - -class MultitaskDatasetWrapper(BaseWrapperDataset): - """A wrapper for a multitask dataset.""" - - def __init__(self, dataset, target_language_id, sample=1.0, name=""): - super().__init__(dataset) - self.target_language_id = target_language_id - self.sample = sample - self.name = name - - def collater(self, *args, **kwargs): - ans = self.dataset.collater(*args, **kwargs) - if "net_input" in ans: - ans["net_input"]["target_language_id"] = self.target_language_id - ans["net_input"]["dataset_name"] = self.name - return ans - - def num_tokens(self, *args, **kwargs): - return self.dataset.num_tokens(*args, **kwargs) - - def ordered_indices(self, *args, **kwargs): - indices = self.dataset.ordered_indices(*args, **kwargs) - # Hacky solution for sampling - size = int(self.sample * indices.shape[0]) - - return indices.take(np.sort(np.random.permutation(indices.shape[0])[:size])) - - def size(self, index: int): - return self.dataset.size(index) - - @property - def supports_prefetch(self): - """Whether this dataset supports prefetching.""" - return getattr(self.dataset, "supports_prefetch", False) - - def prefetch(self, indices): - return self.dataset.prefetch(indices) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/README.md deleted file mode 100644 index 7774c333053b95d15b180fdfc3ee3cd817790520..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/README.md +++ /dev/null @@ -1,77 +0,0 @@ -# Deep Transformers with Latent Depth (Li et al., 2020) - -[https://arxiv.org/abs/2009.13102](https://arxiv.org/abs/2009.13102). - -## Introduction - -We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair. - -## Training a multilingual model with latent depth - -Below is an example of training with latent depth in decoder for one-to-many (O2M) related languages. We use the same preprocessed (numberized and binarized) TED8 dataset as in [Balancing Training for Multilingual Neural Machine Translation (Wang et al., 2020)](https://github.com/cindyxinyiwang/multiDDS), which could be generated by [the script](https://github.com/cindyxinyiwang/multiDDS/blob/multiDDS/util_scripts/prepare_multilingual_data.sh) the author provided. -```bash -lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur" -databin_dir= - -fairseq-train ${databin_dir} \ - --user-dir examples/latent_depth/latent_depth_src \ - --lang-pairs "${lang_pairs_str}" \ - --arch multilingual_transformer_iwslt_de_en \ - --task multilingual_translation_latent_depth \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --share-encoders \ - --share-decoders \ - --decoder-langtok \ - --share-decoder-input-output-embed \ - --dropout 0.3 --attention-dropout 0.3 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --lr-scheduler inverse_sqrt --stop-min-lr 1e-9 --warmup-init-lr 1e-7 --warmup-updates 8000 \ - --max-tokens 4096 --update-freq 1 \ - --lr 0.0015 \ - --clip-norm 1.0 \ - --seed 2 \ - --ddp-backend=legacy_ddp \ - --encoder-layers 12 \ - --decoder-layers 24 \ - --decoder-latent-layer \ - --sparsity-weight 0.1 \ - --anneal-updates 5000 \ - --soft-update 500 \ - --target-layers 12 \ - --share-weight 0.1 -``` -## Inference command - -```bash -lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur" -databin_dir= -model_path= -src_lang= -tgt_lang= -gen_data= - -fairseq-generate ${databin_dir} \ - --path ${model_path} \ - --task multilingual_translation_latent_depth \ - --decoder-latent-layer \ - --lang-pairs "${lang_pairs_str}" \ - -s ${src_lang} -t ${tgt_lang} \ - --gen-subset $gen_data \ - --scoring sacrebleu \ - --remove-bpe 'sentencepiece' \ - --lenpen 1.0 \ - --beam 5 \ - --decoder-langtok \ - --max-tokens 4096 -``` - - -## Citation -```bibtex -@article{li2020deep, - title={Deep Transformers with Latent Depth}, - author={Li, Xian and Stickland, Asa Cooper and Tang, Yuqing and Kong, Xiang}, - journal={arXiv preprint arXiv:2009.13102}, - year={2020} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/__init__.py deleted file mode 100644 index c5fa76039ff98c18d3c14b5f4a8f73ffe644de11..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/__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 . import multilingual_translation_latent_depth # noqa -from .loss import latent_depth # noqa -from .models import latent_multilingual_transformer # noqa -from .modules import latent_layers # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/loss/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/loss/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/loss/latent_depth.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/loss/latent_depth.py deleted file mode 100644 index a3b9535ecac3ec403868681a8b50c1fbe1c90dfe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/loss/latent_depth.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 torch.nn.modules.loss import _Loss - - -class LatentLayersKLLoss(_Loss): - def __init__(self, args): - super().__init__() - self.args = args - - def forward(self, layer_samples, lang_idx, update_num, sample_size): - prior = self.args.prior - samples = layer_samples[lang_idx] - eps = 1e-7 - if prior == "uniform": - # uniform prior - kl_loss = (samples * (torch.log(samples + eps) - math.log(0.5))).sum(-1) - elif prior == "agged_posterior": - # aggregated posterior - y_t = torch.stack([x.detach() for x in layer_samples], dim=0) - agged_q = torch.sum(y_t, dim=0) - row_norm = agged_q.sum(-1) - normed_agg_q = agged_q / row_norm - kl_loss = ( - samples * (torch.log(samples + eps) - torch.log(normed_agg_q + eps)) - ).sum(-1) - else: - raise NotImplementedError("The specified prior is not implemented.") - - # normalized by number of layers - kl_loss /= layer_samples[0].size()[0] - kl_weight = min( - self.args.sparsity_weight, - (update_num - self.args.soft_update) - * self.args.sparsity_weight - / self.args.anneal_updates, - ) - kl_loss *= kl_weight * sample_size - return kl_loss - - -class LatentLayersSparsityLoss(_Loss): - def __init__(self, args): - super().__init__() - self.args = args - - def is_valid(self, update_num): - if self.args.target_layers <= 0: - return False - return update_num > (self.args.soft_update + self.args.anneal_updates) - - def forward(self, layer_samples_list, update_num, sample_size): - batch_loss = 0 - share_loss = 0 - global_sparsity_loss = 0 - layer_samples = torch.stack(layer_samples_list, dim=0) - if ( - self.args.target_layers > 0 or self.args.share_weight > 0 - ) and update_num > (self.args.soft_update + self.args.anneal_updates): - # anneal sparsity weight - if update_num < (self.args.anneal_updates + self.args.soft_update): - weight_anneal = 0 - elif update_num < (2 * self.args.anneal_updates + self.args.soft_update): - weight_anneal = ( - (update_num - self.args.soft_update - self.args.anneal_updates) - * self.args.share_weight - / self.args.anneal_updates - ) - else: - weight_anneal = 1 - # compute ratio among languages - layer_utilization = torch.sum(layer_samples, dim=0) - layer_utilization /= layer_samples.size()[0] - if self.args.share_weight > 0: - # encouraging sharing across languages - share_loss = sum( - -1.0 * v * math.log(v) for v in layer_utilization if v > 0 - ) - batch_loss += ( - weight_anneal * self.args.share_weight * sample_size * share_loss - ) - if self.args.target_layers > 0: - # computed expected number of layers selected - expeted_layers = sum(layer_utilization) - # compute l2 loss wrt target number of layers - global_sparsity_loss = (expeted_layers - self.args.target_layers) ** 2 - batch_loss += ( - weight_anneal - * self.args.share_weight - * sample_size - * global_sparsity_loss - ) - return batch_loss diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/latent_multilingual_transformer.py deleted file mode 100644 index 9e7b655feee0042d42ac2b13cec5f1d2a88e201e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/latent_multilingual_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. - -from fairseq.models import register_model, register_model_architecture -from fairseq.models.multilingual_transformer import MultilingualTransformerModel -from fairseq.models.transformer import ( - TransformerDecoder, - TransformerEncoder, - base_architecture, -) -from fairseq.utils import safe_hasattr - -from .latent_transformer import LatentTransformerDecoder, LatentTransformerEncoder - - -@register_model("latent_multilingual_transformer") -class LatentMultilingualTransformerModel(MultilingualTransformerModel): - """A variant of standard multilingual Transformer models which encoder and/or - decoders supports latent depth, as is in "Deep Transformer with Latent Depth" - (https://arxiv.org/abs/2009.13102). - """ - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - MultilingualTransformerModel.add_args(parser) - parser.add_argument( - '--soft-select', - action='store_true', - help='use soft samples in training an inference', - ) - parser.add_argument( - '--sampling-tau', - type=float, - default=5., - help='sampling temperature', - ) - - @classmethod - def _get_module_class(cls, is_encoder, args, lang_dict, embed_tokens, langs): - if is_encoder: - if safe_hasattr(args, "encoder_latent_layer") and args.encoder_latent_layer: - return LatentTransformerEncoder( - args, lang_dict, embed_tokens, num_logits=len(langs) - ) - else: - return TransformerEncoder(args, lang_dict, embed_tokens) - else: - if safe_hasattr(args, "decoder_latent_layer") and args.decoder_latent_layer: - return LatentTransformerDecoder( - args, lang_dict, embed_tokens, num_logits=len(langs) - ) - else: - return TransformerDecoder(args, lang_dict, embed_tokens) - - -@register_model_architecture( - "latent_multilingual_transformer", "latent_multilingual_transformer" -) -def latent_multilingual_architecture(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", 12) - 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", 24) - args.share_encoders = getattr(args, "share_encoders", True) - args.share_decoders = getattr(args, "share_decoders", True) - args.share_encoder_embeddings = getattr(args, "share_encoder_embeddings", True) - args.share_decoder_embeddings = getattr(args, "share_decoder_embeddings", True) - - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/latent_transformer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/latent_transformer.py deleted file mode 100644 index 6a825301a452bd935deafdaf78fa2427ca9a469e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/models/latent_transformer.py +++ /dev/null @@ -1,156 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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, Optional - -import torch.nn as nn -from fairseq.models.fairseq_encoder import EncoderOut -from fairseq.models.transformer import TransformerDecoder, TransformerEncoder -from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer -from torch import Tensor - -from ..modules.latent_layers import LayerSelect - - -class LatentTransformerEncoder(TransformerEncoder): - """Latent depth (https://arxiv.org/abs/2009.13102) implemented in - TransformerEncoder. - """ - - def __init__(self, args, dictionary, embed_tokens, num_logits=1): - self.num_logits = num_logits - self.num_layers = args.encoder_layers - super().__init__(args, dictionary, embed_tokens) - self.layer_select = LayerSelect( - num_layers=self.num_layers, - num_logits=self.num_logits, - soft_select=getattr(args, "soft_select", False), - sampling_tau=getattr(args, "sampling_tau", 5.), - ) - self.lang_idx = None - self.layers = nn.ModuleList( - [self._build_encoder_layer(args, idx) for idx in range(args.encoder_layers)] - ) - - def set_lang_idx(self, lang_idx): - self.lang_idx = lang_idx - - def _build_encoder_layer(self, args, idx=None): - return LatentTransformerEncoderLayer(args, idx, layer_select=self.layer_select) - - def forward(self, src_tokens, src_lengths, return_all_hiddens: bool = False): - self.layer_select.sample(self.lang_idx) - return super().forward(src_tokens, src_lengths, return_all_hiddens) - - -class LatentTransformerEncoderLayer(TransformerEncoderLayer): - """Encoder layer with each (non_residual) block weighted by samples of Bernouli - or Gumbel Signmoid samples. - - Args: - args (argparse.Namespace): parsed command-line arguments from standard - TransformerEncoderLayer. - idx (int): layer index (used to retrieve samples). - layer_select (LayerSelect, optional): instance of LayerSelect module with logits - parameters and sampling method. - """ - - def __init__(self, args, idx, layer_select=None): - super().__init__(args) - self.idx = idx - self.layer_select = layer_select - - def residual_connection(self, x, residual): - return residual + x * self.layer_select(self.idx) - - -class LatentTransformerDecoder(TransformerDecoder): - """Latent depth (https://arxiv.org/abs/2009.13102) implemented in - TransformerDecoder. - """ - - def __init__( - self, args, dictionary, embed_tokens, no_encoder_attn=False, num_logits=1 - ): - self.num_logits = num_logits - self.num_layers = args.decoder_layers - super().__init__( - args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn - ) - self.layer_select = LayerSelect( - num_layers=self.num_layers, - num_logits=self.num_logits, - soft_select=getattr(args, "soft_select", False), - sampling_tau=getattr(args, "sampling_tau", 5.), - ) - self.lang_idx = None - self.layers = nn.ModuleList( - [ - self._build_decoder_layer(args, no_encoder_attn, idx) - for idx in range(args.decoder_layers) - ] - ) - - def set_lang_idx(self, lang_idx): - self.lang_idx = lang_idx - - def _build_decoder_layer(self, args, no_encoder_attn=False, idx=None): - return LatentTransformerDecoderLayer( - args, idx, layer_select=self.layer_select, no_encoder_attn=no_encoder_attn - ) - - def forward( - self, - prev_output_tokens, - encoder_out: Optional[EncoderOut] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - features_only: bool = False, - alignment_layer: Optional[int] = None, - alignment_heads: Optional[int] = None, - src_lengths: Optional[Any] = None, - return_all_hiddens: bool = False, - ): - self.layer_select.sample(self.lang_idx) - return super().forward( - prev_output_tokens=prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - features_only=features_only, - alignment_layer=alignment_layer, - src_lengths=src_lengths, - return_all_hiddens=return_all_hiddens, - ) - - -class LatentTransformerDecoderLayer(TransformerDecoderLayer): - """Decoder layer with each (non_residual) block weighted by samples of Bernouli - or Gumbel Signmoid samples. - - Args: - args (argparse.Namespace): parsed command-line arguments from standard - TransformerDecoderLayer. - idx (int): layer index (used to retrieve samples). - layer_select (LayerSelect, optional): instance of LayerSelect module with logits - parameters and sampling method. - no_encoder_attn (bool, optional): whether to attend to encoder outputs - (default: False). - - """ - - def __init__( - self, - args, - idx, - layer_select=None, - no_encoder_attn=False, - add_bias_kv=False, - add_zero_attn=False, - ): - super().__init__(args, no_encoder_attn, add_bias_kv, add_zero_attn) - self.idx = idx - self.layer_select = layer_select - - def residual_connection(self, x, residual): - return residual + x * self.layer_select(self.idx) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/modules/latent_layers.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/modules/latent_layers.py deleted file mode 100644 index 2be05d5535cb05b16f61603a7356df2326bf2e23..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/modules/latent_layers.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 LayerSelect(nn.Module): - """Compute samples (from a Gumbel-Sigmoid distribution) which is used as - either (soft) weighting or (hard) selection of residual connection. - https://arxiv.org/abs/2009.13102 - """ - def __init__(self, num_layers, num_logits, soft_select=False, sampling_tau=5.): - super(LayerSelect, self).__init__() - self.layer_logits = torch.nn.Parameter( - torch.Tensor(num_logits, num_layers), - requires_grad=True, - ) - self.hard_select = not soft_select - self.tau = sampling_tau - self.detach_grad = False - self.layer_samples = [None] * num_logits - - def sample(self, logit_idx): - """To leverage the efficiency of distributed training, samples for all - layers are computed at once for each logit_idx. Logits are parameters - learnt independent of each other. - - Args: - logit_idx: The index of logit parameters used for sampling. - """ - assert logit_idx is not None - self.samples = self._gumbel_sigmoid( - self.layer_logits[logit_idx, :].detach() - if self.detach_grad - else self.layer_logits[logit_idx, :], - dim=-1, - tau=self.tau, - hard=self.hard_select, - ) - self.layer_samples[logit_idx] = self.samples - - def forward(self, i): - sample = self.samples[i] - return sample - - def _gumbel_sigmoid( - self, logits, tau=1, hard=False, eps=1e-10, dim=-1, threshold=0.5 - ): - # ~Gumbel(0,1) - gumbels1 = ( - -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format) - .exponential_() - .log() - ) - gumbels2 = ( - -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format) - .exponential_() - .log() - ) - # Difference of two gumbels because we apply a sigmoid - gumbels1 = (logits + gumbels1 - gumbels2) / tau - y_soft = gumbels1.sigmoid() - if hard: - # Straight through. - y_hard = torch.zeros_like( - logits, memory_format=torch.legacy_contiguous_format - ).masked_fill(y_soft > threshold, 1.0) - ret = y_hard - y_soft.detach() + y_soft - else: - # Reparametrization trick. - ret = y_soft - return ret diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py b/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.py deleted file mode 100644 index 8cc2a7174b765b7ad8808489196e12082a91a2d7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/latent_depth/latent_depth_src/multilingual_translation_latent_depth.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 fairseq.tasks import register_task -from fairseq.tasks.multilingual_translation import MultilingualTranslationTask -from fairseq.utils import safe_hasattr - -from .loss.latent_depth import LatentLayersKLLoss, LatentLayersSparsityLoss - - -@register_task("multilingual_translation_latent_depth") -class MultilingualTranslationTaskLatentDepth(MultilingualTranslationTask): - """A task for multiple translation with latent depth. - - See `"Deep Transformer with Latent Depth" - (Li et al., 2020) `_. - """ - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - # fmt: off - MultilingualTranslationTask.add_args(parser) - parser.add_argument('--encoder-latent-layer', action='store_true', help='latent layer selection in encoder') - parser.add_argument('--decoder-latent-layer', action='store_true', help='latent layer selection in decoder') - parser.add_argument('--target-layers', default=-1, type=int, - help='number of effective layers to learn; -1 means no constraint') - parser.add_argument('--sparsity-weight', default=0.0, type=float, - help='weight for sparsity loss') - parser.add_argument('--share-weight', default=0.0, type=float, - help='weight for sharing loss') - parser.add_argument('--soft-update', default=1, type=int, - help='number of updates with soft sampling') - parser.add_argument('--anneal-updates', default=1, type=int, - help='number of updates to anneal the KL loss weight') - parser.add_argument('--prior', default="uniform", type=str, - help='prior used for computing KL loss') - # fmt: on - - def __init__(self, args, dicts, training): - super().__init__(args, dicts, training) - self.src_langs, self.tgt_langs = zip( - *[(lang.split("-")[0], lang.split("-")[1]) for lang in args.lang_pairs] - ) - if self.training and self.encoder_latent_layer: - assert self.args.share_encoders - if self.training and self.decoder_latent_layer: - assert self.args.share_decoders - if training or self.encoder_latent_layer or self.decoder_latent_layer: - self.lang_pairs = args.lang_pairs - else: - self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)] - self.eval_lang_pairs = self.lang_pairs - self.model_lang_pairs = self.lang_pairs - if self.training and (self.encoder_latent_layer or self.decoder_latent_layer): - self.kl_loss = LatentLayersKLLoss(self.args) - self.sparsity_loss = LatentLayersSparsityLoss(self.args) - - def _per_lang_pair_train_loss( - self, lang_pair, model, update_num, criterion, sample, optimizer, ignore_grad - ): - src, tgt = lang_pair.split("-") - if self.encoder_latent_layer: - src_lang_idx = self.src_lang_idx_dict[src] - model.models[lang_pair].encoder.set_lang_idx(src_lang_idx) - model.models[lang_pair].encoder.layer_select.hard_select = ( - update_num > self.args.soft_update - ) - if self.decoder_latent_layer: - tgt_lang_idx = self.tgt_lang_idx_dict[tgt] - model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx) - model.models[lang_pair].decoder.layer_select.hard_select = ( - update_num > self.args.soft_update - ) - - loss, sample_size, logging_output = criterion( - model.models[lang_pair], sample[lang_pair] - ) - if self.encoder_latent_layer: - none_samples = sum( - 1 if x is None else 0 - for x in model.models[lang_pair].encoder.layer_select.layer_samples - ) - if none_samples == 0 or self.args.prior != "agged_posterior": - loss += self.kl_loss( - model.models[lang_pair].encoder.layer_select.layer_samples, - src_lang_idx, - update_num, - sample_size, - ) - if self.decoder_latent_layer: - none_samples = sum( - 1 if x is None else 0 - for x in model.models[lang_pair].decoder.layer_select.layer_samples - ) - if none_samples == 0 or self.args.prior != "agged_posterior": - loss += self.kl_loss( - model.models[lang_pair].decoder.layer_select.layer_samples, - tgt_lang_idx, - update_num, - sample_size, - ) - if ignore_grad: - loss *= 0 - - if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num): - # need to retain the graph if sparsity loss needs to be added - loss.backward(retain_graph=True) - else: - optimizer.backward(loss) - - return loss, sample_size, logging_output - - def train_step( - self, sample, model, criterion, optimizer, update_num, ignore_grad=False - ): - agg_loss, agg_sample_size, agg_logging_output = super().train_step( - sample, model, criterion, optimizer, update_num, ignore_grad - ) - # compute auxiliary loss from layere sparsity, based on all samples from all languages - if hasattr(self, "sparsity_loss") and self.sparsity_loss.is_valid(update_num): - sparsity_loss = 0 - if self.encoder_latent_layer: - sparsity_loss += self.sparsity_loss( - next( - iter(model.models.values()) - ).encoder.layer_select.layer_samples, - update_num, - agg_sample_size, - ) - if self.decoder_latent_layer: - sparsity_loss += self.sparsity_loss( - next( - iter(model.models.values()) - ).decoder.layer_select.layer_samples, - update_num, - agg_sample_size, - ) - if sparsity_loss > 0: - optimizer.backward(sparsity_loss) - return agg_loss, agg_sample_size, agg_logging_output - - def _per_lang_pair_valid_loss(self, lang_pair, model, criterion, sample): - src, tgt = lang_pair.split("-") - if self.encoder_latent_layer: - src_lang_idx = self.src_lang_idx_dict[src] - model.models[lang_pair].encoder.set_lang_idx(src_lang_idx) - if self.decoder_latent_layer: - tgt_lang_idx = self.tgt_lang_idx_dict[tgt] - model.models[lang_pair].decoder.set_lang_idx(tgt_lang_idx) - loss, sample_size, logging_output = criterion( - model.models[lang_pair], sample[lang_pair] - ) - return loss, sample_size, logging_output - - def inference_step( - self, generator, models, sample, prefix_tokens=None, constraints=None - ): - if self.encoder_latent_layer or self.decoder_latent_layer: - for model in models: - if self.encoder_latent_layer: - assert model.encoder.layer_select is not None - src_lang_idx = self.src_lang_idx_dict[self.args.source_lang] - model.encoder.set_lang_idx(src_lang_idx) - if self.decoder_latent_layer: - assert model.decoder.layer_select is not None - tgt_lang_idx = self.tgt_lang_idx_dict[self.args.target_lang] - model.decoder.set_lang_idx(tgt_lang_idx) - return super().inference_step( - generator, models, sample, prefix_tokens, constraints - ) - - @property - def encoder_latent_layer(self): - return ( - safe_hasattr(self.args, "encoder_latent_layer") - and self.args.encoder_latent_layer - ) - - @property - def decoder_latent_layer(self): - return ( - safe_hasattr(self.args, "decoder_latent_layer") - and self.args.decoder_latent_layer - ) - - @property - def src_lang_idx_dict(self): - return {lang: lang_idx for lang_idx, lang in enumerate(self.src_langs)} - - @property - def tgt_lang_idx_dict(self): - return {lang: lang_idx for lang_idx, lang in enumerate(self.tgt_langs)} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/layerdrop/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/layerdrop/README.md deleted file mode 100644 index 4d48ee9615e1458e1e889635dc9938e427a7f64a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/layerdrop/README.md +++ /dev/null @@ -1,154 +0,0 @@ -# Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019) -This page contains information for how to train models with LayerDrop, based on this [paper](https://arxiv.org/abs/1909.11556). - -## Citation: -If you found this technique useful, please cite our paper: -```bibtex -@article{fan2019reducing, - title={Reducing Transformer Depth on Demand with Structured Dropout}, - author={Fan, Angela and Grave, Edouard and Joulin, Armand}, - journal={arXiv preprint arXiv:1909.11556}, - year={2019} -} -``` - -## Pre-trained models - -Model | Description | Download ----|---|--- -`layerdrop_wmt_en_de_12_6` | Transformer + LayerDrop 0.2 trained on WMT16 en-de with 12 encoder and 6 decoder layers | [layerdrop_wmt_en_de_12_6.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/layerdrop_wmt_en_de_12_6.tar.gz) -`roberta_layerdrop.base` | RoBERTa Base + LayerDrop 0.2 | [roberta_layerdrop.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.base.qnli.tar.gz) -`roberta_layerdrop.large` | RoBERTa Large + LayerDrop 0.2 | [roberta_layerdrop.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.tar.gz) -`roberta_layerdrop.large.mnli` | `roberta_layerdrop.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.mnli.tar.gz) -`roberta_layerdrop.large.qnli` | `roberta_layerdrop.large` finetuned on [QNLI](https://arxiv.org/abs/1804.07461) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.qnli.tar.gz) - - -Evaluate performance of these pre-trained models: -```bash -# Example for Machine Translation -fairseq-generate /path/to/bped/wmt/data --path nmt_checkpoint.pt \ - --beam 8 --lenpen 0.4 \ - --batch-size 64 \ - --remove-bpe \ - --gen-subset test > wmt16_gen.txt -bash scripts/compound_split_bleu.sh wmt16_gen.txt -# prints BLEU4 = 30.17 -``` - -```python -# Example for RoBERTa + LayerDrop finetuned on MNLI: -from fairseq.models.roberta import RobertaModel - -roberta_layerdrop = RobertaModel.from_pretrained( - '/path/to/MNLI/model', - checkpoint_file='mnli_checkpoint.pt', - data_name_or_path='/path/to/MNLI/data/MNLI-bin' -) -label_map = {0: 'contradiction', 2: 'neutral', 1: 'entailment'} -ncorrect, nsamples = 0, 0 -roberta_layerdrop.cuda() -roberta_layerdrop.eval() -with open('/path/to/MNLI/data/dev_matched.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[8], tokens[9], tokens[-1] - tokens = roberta_layerdrop.encode(sent1, sent2) - prediction = roberta_layerdrop.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_map[prediction] - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# prints | Accuracy: 0.9026999490575649 - - -# Example for RoBERTa + LayerDrop finetuned on QNLI: -roberta = RobertaModel.from_pretrained( - '/path/to/QNLI/model', - checkpoint_file='qnli_checkpoint.pt', - data_name_or_path='/path/to/QNLI/data/QNLI-bin' -) - -label_fn = lambda label: roberta.task.label_dictionary.string( - [label + roberta.task.target_dictionary.nspecial] -) -ncorrect, nsamples = 0, 0 -roberta.cuda() -roberta.eval() -with open('/path/to/QNLI/data/dev.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[1], tokens[2], tokens[3] - tokens = roberta.encode(sent1, sent2) - prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_fn(prediction) - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# prints | Accuracy: 0.9480139117700896 -``` - - -## Example usage - -To train a model with LayerDrop, add the following flags. We recommend 0.2, a value that worked well in our experiments. For Language Models that are decoder-only, you need only the decoder flag. For RoBERTa, an encoder, you need only the encoder flag. The encoder and decoder LayerDrop values can be set differently. -``` ---encoder-layerdrop 0.2 --decoder-layerdrop 0.2 -``` - -To prune a model that has been trained with LayerDrop, add the following flags followed by a comma separated list of which layers you would like to keep. -``` ---encoder-layers-to-keep 0,2,4,6,8,10,12,14 --decoder-layers-to-keep 0,2,4,6,8,10,12,14 -``` -Setting these flags should print a message such as: -``` -| Pruning model to specified layer configuration -``` -You should also see a smaller number of parameters in the model, for example the 16-Layer Transformer Language Model prints: -``` -num. model params: 246933504 -``` -while a model pruned to 8 Layers prints: -``` -num. model params: 146163712 -``` - -If you would like to pick up training with a model that has been pruned, simply adding these flags is sufficient. If you would like to use a script that only does evaluation (no training), you may need to pass an override command. A specific example would be for language modeling: -```bash -fairseq-eval-lm /path/to/wikitext-103 \ - --path /path/to/model/checkpoint.pt \ - --model-overrides "{'decoder_layers_to_keep':'0,2,4,6,8,10,12,14'}" -``` -This model override command overrides the training parameters and updates the model arguments so that the pruned model is run instead of the full model. - -## Reproduce Paper Results - -Looking to reproduce the results in the paper? - -1. For Translation on WMT16 en-de, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/scaling_nmt/README.md) -2. To train RoBERTa, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta) -3. To train Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model) - - -## Tips - -1. If you would like to train large models with better performance, LayerDrop should be set to a smaller value such as 0.1 or 0.2. Too much LayerDrop will mean the model has too much regularization, so may not reach the best performance. Since LayerDrop adds regularization, you may achieve the best performance by slightly reducing the amount of standard dropout (for example, reduce by 0.1). - -2. If you would like to train large models to be pruned and made smaller, LayerDrop should be set to a larger value such as 0.5 if you want to prune very aggressively (such as removing half the network or more). If you would like to prune fewer layers away, LayerDrop can be set to a smaller value such as 0.2. Our experiments were conducted with low values of LayerDrop (such as 0.1 and 0.2), for reference. - -3. When pruning layers at inference time, it is best to spread out the layers remaining so they are evenly spaced throughout the network. For example, if you want to remove 50% of the network, keeping every other layer is good. - - -## FAQ - -1. How did the sharing layers experiment work? In an appendix (https://openreview.net/pdf?id=SylO2yStDr) we added an experiment on Wikitext-103 language modeling that combined LayerDrop with Weight Sharing. We shared chunks of 2 layers such that every other layer had shared weights. For example, if our network has layers 1 through 6, then layer 1 and 2 are shared, layer 3 and 4 are shared, and layer 5 and 6 are shared. - -2. LayerDrop hasn't been helping in my setting? During training time, LayerDrop can help regularize your network. This is most important if your network is already overfitting - if your network is underfitting, it is possible LayerDrop is adding too much regularization. We recommend using smaller values (such as 0.1 or 0.2) and also decreasing the quantity of standard dropout (for example, reduce by 0.1). - -3. Can you train a model without LayerDrop and finetune with LayerDrop (e.g. for BERT)? In our experiments, we did not see great performance. Models such as RoBERTa have trained for a long time in the pre-training setting, so only finetuning with LayerDrop for a few epochs on a downstream task such as MNLI does not achieve the robustness required for successful pruning. - - -## Having an issue or have a question? - -Please open an issue in this repository with the details of your question. Thanks! diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/README.md deleted file mode 100644 index f8b36bc691cb8f5bf82942e07b6d9c014387bdd8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# Linformer: Self-Attention with Linear Complexity (Wang et al., 2020) - -This example contains code to train Linformer models as described in our paper -[Linformer: Self-Attention with Linear Complexity](https://arxiv.org/abs/2006.04768). - -## Training a new Linformer RoBERTa model - -You can mostly follow the [RoBERTa pretraining README](/examples/roberta/README.pretraining.md), -updating your training command with `--user-dir examples/linformer/linformer_src --arch linformer_roberta_base`. - -## Citation - -If you use our work, please cite: - -```bibtex -@article{wang2020linformer, - title={Linformer: Self-Attention with Linear Complexity}, - author={Wang, Sinong and Li, Belinda and Khabsa, Madian and Fang, Han and Ma, Hao}, - journal={arXiv preprint arXiv:2006.04768}, - year={2020} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/__init__.py deleted file mode 100644 index 1c52f135ea6f99d0effe8ce1f7d77cbd66be3745..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/__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 .models import linformer_roberta # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/models/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/models/linformer_roberta.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/models/linformer_roberta.py deleted file mode 100644 index 53088cf0712909a40f18695f4b3de001dbb52dd3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/models/linformer_roberta.py +++ /dev/null @@ -1,119 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Linformer: Self-Attention with Linear Complexity -""" - -import logging - -import torch -from fairseq import utils -from fairseq.models import register_model, register_model_architecture -from fairseq.models.roberta import ( - RobertaEncoder, - RobertaModel, - init_bert_params, - roberta_base_architecture, - roberta_large_architecture, -) -from fairseq.utils import safe_hasattr - -from ..modules.linformer_sentence_encoder import LinformerTransformerEncoder - -logger = logging.getLogger(__name__) - - -@register_model("linformer_roberta") -class LinformerModel(RobertaModel): - @staticmethod - def add_args(parser): - RobertaModel.add_args(parser) - - # add args for Linformer - parser.add_argument( - "--compressed", type=int, help="compressed ratio of sequence length" - ) - parser.add_argument( - "--shared-kv-compressed", - type=int, - help="share compressed matrix between k and v, in each layer", - ) - parser.add_argument( - "--shared-layer-kv-compressed", - type=int, - help="share compressed matrix between k and v and across all layers", - ) - parser.add_argument( - "--freeze-compress", - type=int, - help="freeze the parameters in compressed layer", - ) - - @classmethod - def build_model(cls, args, task): - """Build a new model instance.""" - - # make sure all arguments are present - base_architecture(args) - - if not safe_hasattr(args, "max_positions"): - args.max_positions = args.tokens_per_sample - - encoder = LinformerEncoder(args, task.source_dictionary) - return cls(args, encoder) - - -class LinformerEncoder(RobertaEncoder): - """Linformer encoder.""" - - def __init__(self, args, dictionary): - super().__init__(args, dictionary) - self.register_buffer("version", torch.tensor(2)) - - def build_encoder(self, args, dictionary, embed_tokens): - encoder = LinformerTransformerEncoder(args, dictionary, embed_tokens) - encoder.apply(init_bert_params) - return encoder - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - prefix = name + "." if name != "" else "" - - # some old checkpoints had weight sharing implemented incorrectly - # (note: this was correct in the original paper code) - if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: - state_dict[f"{prefix}version"] = torch.tensor(1) - # check if input embeddings and output embeddings were tied - if not torch.allclose( - state_dict[f"{prefix}sentence_encoder.embed_tokens.weight"], - state_dict[f"{prefix}lm_head.weight"], - ): - # they weren't tied, re-init the LM head without weight sharing - self.lm_head = self.build_lm_head( - embed_dim=self.args.encoder_embed_dim, - output_dim=len(self.dictionary), - activation_fn=self.args.activation_fn, - weight=None, # don't share weights - ) - - -@register_model_architecture("linformer_roberta", "linformer_roberta") -def base_architecture(args): - args.compressed = getattr(args, "compressed", 4) - args.shared_kv_compressed = getattr(args, "shared_kv_compressed", 0) - args.shared_layer_kv_compressed = getattr(args, "shared_layer_kv_compressed", 0) - args.freeze_compress = getattr(args, "freeze_compress", 0) - roberta_base_architecture(args) - - -@register_model_architecture("linformer_roberta", "linformer_roberta_base") -def linformer_roberta_base_architecture(args): - base_architecture(args) - - -@register_model_architecture("linformer_roberta", "linformer_roberta_large") -def linformer_roberta_large_architecture(args): - roberta_large_architecture(args) - base_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/linformer_sentence_encoder.py deleted file mode 100644 index 44f7989bd863329f763aa62b78df2eb42b3084ea..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/linformer_sentence_encoder.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 math - -import torch.nn as nn -from fairseq.models.transformer import TransformerEncoder - -from .linformer_sentence_encoder_layer import LinformerTransformerEncoderLayer - - -class LinformerTransformerEncoder(TransformerEncoder): - """ - Implementation for a Bi-directional Linformer 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 - LinformerEncoderLayers, 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, args, dictionary, embed_tokens): - self.compress_layer = None - super().__init__(args, dictionary, embed_tokens) - - def build_encoder_layer(self, args): - if self.args.shared_layer_kv_compressed == 1 and self.compress_layer is None: - compress_layer = nn.Linear( - self.args.max_positions, - self.args.max_positions // self.args.compressed, - ) - # intialize parameters for compressed layer - nn.init.xavier_uniform_(compress_layer.weight, gain=1 / math.sqrt(2)) - if self.args.freeze_compress == 1: - compress_layer.weight.requires_grad = False - self.compress_layer = compress_layer - - return LinformerTransformerEncoderLayer(args, self.compress_layer) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py deleted file mode 100644 index 7e2caa03400129ac0bb34ae35274cdf46f27a055..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.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 torch -from fairseq import utils -from fairseq.modules import TransformerEncoderLayer - -from .multihead_linear_attention import MultiheadLinearAttention - - -class LinformerTransformerEncoderLayer(TransformerEncoderLayer): - """ - Implements a Linformer Encoder Layer used in BERT/XLM style pre-trained - models. - """ - - def __init__(self, args, shared_compress_layer): - # wrap in a list so it's not automatically registered by PyTorch - self.shared_compress_layer = [shared_compress_layer] - - super().__init__(args) - - self.register_buffer("version", torch.tensor(2)) - - def build_self_attention(self, embed_dim, args): - return MultiheadLinearAttention( - embed_dim, - args.encoder_attention_heads, - dropout=args.dropout, - self_attention=True, - q_noise=args.quant_noise_pq, - qn_block_size=args.quant_noise_pq_block_size, - compressed=args.compressed, - max_seq_len=args.max_positions, - shared_kv_compressed=args.shared_kv_compressed, - shared_compress_layer=self.shared_compress_layer[0], - freeze_compress=args.freeze_compress, - ) - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - prefix = name + "." if name != "" else "" - - # some old checkpoints had weight sharing implemented incorrectly - # (note: this was correct in the original paper code) - if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: - state_dict[f"{prefix}version"] = torch.tensor(1) - # check compression layer sharing - if f"{prefix}shared_compress_layer.weight" in state_dict: - # reinitialize block without sharing compression layer to match - # old behavior - self.shared_compress_layer = [ - torch.nn.Linear( - self.shared_compress_layer[0].weight.size(1), - self.shared_compress_layer[0].weight.size(0), - ) - ] - self.self_attn = self.build_self_attention(self.embed_dim, self.args) - # delete shared_compress_layer, since it's already copied to - # self_attn.compress_k.weight - del state_dict[f"{prefix}shared_compress_layer.weight"] - if f"{prefix}shared_compress_layer.bias" in state_dict: - del state_dict[f"{prefix}shared_compress_layer.bias"] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/multihead_linear_attention.py b/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/multihead_linear_attention.py deleted file mode 100644 index 6be1007279217c5de644e8b054f5d14a19f06c55..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/linformer/linformer_src/modules/multihead_linear_attention.py +++ /dev/null @@ -1,481 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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, Optional, Tuple - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.quant_noise import quant_noise -from torch import Tensor, nn -from torch.nn import Parameter - - -@with_incremental_state -class MultiheadLinearAttention(nn.Module): - """Multi-headed linformer attention. - - Projects the key and values down to the compressed dimension, before computing self-attention. - - See "Linformer: Self-Attention with Linear Complexity" 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, - compressed=1, - max_seq_len=256, - shared_kv_compressed=0, - shared_compress_layer=None, - freeze_compress=0, - ): - super().__init__() - 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 = dropout - 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 - ) - - # used for compress sequence to subsequence - if shared_compress_layer is None: - self.compress_seq_len = max_seq_len // compressed - self.compress_k = nn.Linear(max_seq_len, self.compress_seq_len, bias=False) - if shared_kv_compressed == 0: - self.compress_v = nn.Linear( - max_seq_len, self.compress_seq_len, bias=False - ) - self.layerwise_sharing = False - else: - self.compress_k = shared_compress_layer - if shared_kv_compressed == 0: - self.compress_v = shared_compress_layer - self.layerwise_sharing = True - self.shared_kv_compressed = shared_kv_compressed - - 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.reset_parameters() - - if freeze_compress == 1: - self.compress_k.weight.requires_grad = False - if shared_kv_compressed == 0: - self.compress_v.weight.requires_grad = False - - self.onnx_trace = 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)) - if ( - not self.layerwise_sharing - ): # otherwise, we already initialize the parameters - nn.init.xavier_uniform_(self.compress_k.weight, gain=1 / math.sqrt(2)) - if self.shared_kv_compressed == 0: - nn.init.xavier_uniform_( - self.compress_v.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) - if ( - not self.layerwise_sharing - ): # otherwise, we already initialize the parameters - nn.init.xavier_uniform_(self.compress_k.weight) - if self.shared_kv_compressed == 0: - nn.init.xavier_uniform_(self.compress_v.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 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 - - tgt_len, bsz, embed_dim = query.size() - assert embed_dim == self.embed_dim - assert list(query.size()) == [tgt_len, bsz, embed_dim] - - 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_input = query.permute(1, 2, 0).contiguous() # B * C * T - k_input = ( - F.linear(k_input, self.compress_k.weight[:, 0:tgt_len]) - .permute(2, 0, 1) - .contiguous() - ) - k = self.k_proj(k_input) - - v_input = query.permute(1, 2, 0).contiguous() # B * C * T - if self.shared_kv_compressed == 0: - v_input = ( - F.linear(v_input, self.compress_v.weight[:, 0:tgt_len]) - .permute(2, 0, 1) - .contiguous() - ) - if self.shared_kv_compressed == 1: # use shared kv compressed linear layer - v_input = ( - F.linear(v_input, self.compress_k.weight[:, 0:tgt_len]) - .permute(2, 0, 1) - .contiguous() - ) - v = self.v_proj(v_input) - 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: - 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 = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, self.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, - ) - - q = ( - q.contiguous() - .view(tgt_len, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if k is not None: - k = ( - k.contiguous() - .view(-1, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if v is not None: - v = ( - v.contiguous() - .view(-1, 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 - prev_key = _prev_key.view(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) - if "prev_value" in saved_state: - _prev_value = saved_state["prev_value"] - assert _prev_value is not None - prev_value = _prev_value.view(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 = MultiheadLinearAttention._append_prev_key_padding_mask( - key_padding_mask=key_padding_mask, - prev_key_padding_mask=prev_key_padding_mask, - batch_size=bsz, - src_len=k.size(1), - static_kv=static_kv, - ) - - saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) - saved_state["prev_value"] = v.view(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 - src_len = k.size(1) - - if self.add_zero_attn: - assert v is not None - src_len += 1 - k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) - v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - - attn_weights = torch.bmm(q, k.transpose(1, 2)) - attn_weights = MultiheadLinearAttention.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 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 = F.dropout( - attn_weights, - p=self.dropout, - training=self.training, - ) - assert v is not None - 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, embed_dim) - else: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, 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: - 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 - ) - elif key_padding_mask is not None: - 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 = 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 and input_buffer_k.size( - 0 - ) == new_order.size(0): - break - 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 _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(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/examples/m2m_100/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/README.md deleted file mode 100644 index 02a68a5f0919a26a0468069bed46a5b1abc78941..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/README.md +++ /dev/null @@ -1,241 +0,0 @@ -# Beyond English-Centric Multilingual Machine Translation - -## Introduction -In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively with the best single systems of WMT. - -If you are new to using fairseq, read the following walkthrough. Otherwise, skip to the sections below. - -0. **Generation Data** - -To download the generation data, follow the below commands. Note that all datasets need to be detokenized *before* applying SPM in the data preprocessing step. If you use these evaluation datasets, please cite their associated papers. -```bash -# WMT - use sacrebleu, example here: -sacrebleu -t wmt14 -l fr-en --echo src > wmt.test.fr-en.fr -sacrebleu -t wmt14 -l fr-en --echo ref > wmt.test.fr-en.en - -# WAT -wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip -unzip wat2020.my-en.zip - -# FLORES -# download from: https://github.com/facebookresearch/flores - -# TED - need to detokenize with Moses! -# from: https://github.com/neulab/word-embeddings-for-nmt -wget http://phontron.com/data/ted_talks.tar.gz - -# Autshumato -# request to download: https://repo.sadilar.org/handle/20.500.12185/397 - -# Tatoeba Challenge -# available here: https://github.com/Helsinki-NLP/Tatoeba-Challenge -``` - -1. **Training Data** - -To produce the training data, we use a combination of [CCMatrix](https://arxiv.org/abs/1911.04944) and [CCAligned](https://arxiv.org/abs/1911.06154). Check out the instructions [here](https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix) to download the raw data. - -2. **Preprocess Data** - -After downloading raw data, you will need to postprocess the data, then apply SPM, then binarize. Note that it is very important you run the postprocessing script, because this removes any instance of the evaluation data in the mined training data. - -```bash -# preprocess data - -# remove sentences with more than 50% punctuation -python /path/to/fairseq/examples/m2m_100/process_data/remove_too_much_punc.py - -# deduplicate training data -paste /path/to/datadir/train.$src /path/to/datadir/train.$tgt | awk '!x[$0]++' > /path/to/datadir/train.dedup -echo "keeping $(wc -l /path/to/datadir/train.dedup) bitext out of $(wc -l /path/to/datadir/train.$src)" -cut -f1 /path/to/datadir/train.dedup > /path/to/datadir/train.$src -cut -f2 /path/to/datadir/train.dedup > /path/to/datadir/train.$tgt - -# remove all instances of evaluation data from the training data -python /path/to/fairseq/examples/m2m_100/process_data/dedup_data.py - -# frequency cleaning -wget https://dl.fbaipublicfiles.com/m2m_100/histograms.tar.gz -tar -xvzf histograms.tar.gz -python /path/to/fairseq/examples/m2m_100/process_data/clean_histogram.py --src $src --tgt $tgt --src-file /path/to/source/file --tgt-file /path/to/output/file --src-output-file source_output.$src --tgt-output-file target_output.$tgt --histograms /path/to/histograms - -# apply SPM -wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model -python /path/to/fairseq/scripts/spm_encode.py \ - --model spm.128k.model \ - --output_format=piece \ - --inputs=/path/to/input/file/here \ - --outputs=/path/to/output/file/here - -# length ratio cleaning -perl mosesdecoder/scripts/training/clean-corpus-n.perl --ratio 3 /path/to/training/data/train.spm.$src-$tgt $src $tgt /path/to/output/directory/train.spm.$src-$tgt 1 250 - -# binarize data -wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt -fairseq-preprocess \ - --source-lang $src --target-lang $tgt \ - --testpref spm.$src.$tgt \ - --thresholdsrc 0 --thresholdtgt 0 \ - --destdir data_bin \ - --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt -``` - -3. **Training Scripts** - -To reproduce the training of our models, we train with fairseq-py's multilingual translation [task](https://github.com/pytorch/fairseq/tree/main/examples/multilingual). If you are interested in model parallel training, also check out [fairscale](https://github.com/facebookresearch/fairscale). - -4. **Generation** - -To generate from our models, follow the the commands in the generation section below. - - -If you use any of the resources listed here, please cite: -```bibtex -@article{fan2020beyond, - title={Beyond English-Centric Multilingual Machine Translation}, - author={Fan, Angela and Bhosale, Shruti and Schwenk, Holger and Ma, Zhiyi and El-Kishky, Ahmed and Goyal, Siddharth and Baines, Mandeep and Celebi, Onur and Wenzek, Guillaume and Chaudhary, Vishrav and Goyal, Naman and Birch, Tom and Liptchinsky, Vitaliy and Edunov, Sergey and Grave, Edouard and Auli, Michael and Joulin, Armand}, - journal={arXiv preprint}, - year={2020} -} - -@article{schwenk2019ccmatrix, - title={Ccmatrix: Mining billions of high-quality parallel sentences on the web}, - author={Schwenk, Holger and Wenzek, Guillaume and Edunov, Sergey and Grave, Edouard and Joulin, Armand}, - journal={arXiv preprint arXiv:1911.04944}, - year={2019} -} - -@article{el2019massive, - title={A Massive Collection of Cross-Lingual Web-Document Pairs}, - author={El-Kishky, Ahmed and Chaudhary, Vishrav and Guzman, Francisco and Koehn, Philipp}, - journal={arXiv preprint arXiv:1911.06154}, - year={2019} -} -``` - - -## Trained Models - -### 418M and 1.2B Model -We include the last checkpoint for both of these models. - -```bash -wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt -wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs_small_models.txt - -# 418M parameter model -wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt - -# 1.2B parameter model -wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt - -# Generation: -fairseq-generate $binarized_data_path --batch-size 32 --path $path_to_model --fixed-dictionary model_dict.128k.txt -s en -t fr --remove-bpe 'sentencepiece' --beam 5 --task translation_multi_simple_epoch --lang-pairs language_pairs_small_models.txt --decoder-langtok --encoder-langtok src --gen-subset test > gen_out -``` - -### 12B Model -12B parameter model trained on many-to-many training data for 100 languages. We include the last checkpoint, average of last 5 checkpoints, average of last 10 checkpoints. There isn't a universally best choice out of these three, but all three versions are pretty close in accuracy. You can either sweep over the 3 checkpoints on a dev test and use the best performing checkpoint for final testing. Or the last checkpoint can be a good default choice. - -**Model Download Links** -Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs -:--|:--|:--|:--|:-- -Last Checkpoint | [12b_last_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_2_gpus.pt) | [12b_last_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt) | [12b_last_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_6_gpus.pt) | [12b_last_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_8_gpus.pt) -Average of last 5 checkpoints | [12b_avg5_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_2_gpus.pt) | [12b_avg5_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_4_gpus.pt) | [12b_avg5_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_6_gpus.pt) | [12b_avg5_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg5_chk_8_gpus.pt) -Average of last 10 checkpoints | [12b_avg10_chk_2_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_2_gpus.pt) | [12b_avg10_chk_4_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_4_gpus.pt) | [12b_avg10_chk_6_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_6_gpus.pt) | [12b_avg10_chk_8_gpus.pt](https://dl.fbaipublicfiles.com/m2m_100/12b_avg10_chk_8_gpus.pt) - -**Generation Arguments** -Configuration | 2 32GB GPUs | 4 16GB GPUs | 6 12GB GPUs | 8 8GB GPUs -:--|:--|:--|:--|:-- -`--pipeline-encoder-balance` | `[26]` | `[1,15,10]` | `[1,9,9,7]` | `[1,6,6,6,7]` -`--pipeline-encoder-devices` | `[0]` | `[0,1,0]` | `[0,1,2,0]` | `[0,4,5,1,0]` -`--pipeline-decoder-balance` | `[3,22,1]` | `[3,11,11,1]` | `[3,7,7,8,1]` | `[1,6,6,6,6,1]` -`--pipeline-decoder-devices` | `[0,1,0]` | `[0,2,3,0]` | `[0,3,4,5,0]` | `[0,2,6,7,3,0]` - - -## SentencePiece Model - -```bash -wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model -``` - -## Generation with M2M-100 - -### Encode using our SentencePiece Model - -Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) - -```bash -fairseq=/path/to/fairseq -cd $fairseq -sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de -sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr -wget https://dl.fbaipublicfiles.com/m2m_100/spm.128k.model -for lang in de fr ; do - python scripts/spm_encode.py \ - --model spm.128k.model \ - --output_format=piece \ - --inputs=raw_input.de-fr.${lang} \ - --outputs=spm.de-fr.${lang} -done -``` - -### Binarization - -```bash -wget https://dl.fbaipublicfiles.com/m2m_100/data_dict.128k.txt -fairseq-preprocess \ - --source-lang de --target-lang fr \ - --testpref spm.de-fr \ - --thresholdsrc 0 --thresholdtgt 0 \ - --destdir data_bin \ - --srcdict data_dict.128k.txt --tgtdict data_dict.128k.txt -``` - -### Generation for the 12B model - -Note that generation can currently be run using 2 32GB / 4 16GB / 6 12GB / 8 8GB GPUs, and the corresponding model checkpoints and pipeline arguments can be found in the [12B Model Section](#12b-model). -Generation on CPUs will be added in the future. - -```bash -wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt -wget https://dl.fbaipublicfiles.com/m2m_100/language_pairs.txt -wget https://dl.fbaipublicfiles.com/m2m_100/12b_last_chk_4_gpus.pt -fairseq-generate \ - data_bin \ - --batch-size 1 \ - --path 12b_last_chk_4_gpus.pt \ - --fixed-dictionary model_dict.128k.txt \ - -s de -t fr \ - --remove-bpe 'sentencepiece' \ - --beam 5 \ - --task translation_multi_simple_epoch \ - --lang-pairs language_pairs.txt \ - --decoder-langtok --encoder-langtok src \ - --gen-subset test \ - --fp16 \ - --dataset-impl mmap \ - --distributed-world-size 1 --distributed-no-spawn \ - --pipeline-model-parallel \ - --pipeline-chunks 1 \ - --pipeline-encoder-balance '[1,15,10]' \ - --pipeline-encoder-devices '[0,1,0]' \ - --pipeline-decoder-balance '[3,11,11,1]' \ - --pipeline-decoder-devices '[0,2,3,0]' > gen_out -``` -## Evaluation with M2M-100 - -### Tokenization - -Note: Refer to tokenizers/README.md for more details on tokenization. - -```bash -cd ${fairseq}/examples/m2m_100 -cat ${fairseq}/gen_out | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh fr > hyp -cat ${fairseq}/raw_input.de-fr.fr | sh tok.sh fr > ref -``` - -### BLEU - -```bash -sacrebleu -tok 'none' ref < hyp -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/install_dependecies.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/install_dependecies.sh deleted file mode 100644 index 82a1054745264a56fbec4a8eb593884f8a42bd08..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/install_dependecies.sh +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env bash -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -CWD=`pwd` -INSTALL_PATH=$CWD/tokenizers/thirdparty - -MOSES=$INSTALL_PATH/mosesdecoder -if [ ! -d $MOSES ]; then - echo 'Cloning Moses github repository (for tokenization scripts)...' - git clone https://github.com/moses-smt/mosesdecoder.git $MOSES - cd $MOSES - # To deal with differences in handling ' vs " - git checkout 03578921cc1a03402 - cd - -fi - -WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts -if [ ! -d $WMT16_SCRIPTS ]; then - echo 'Cloning Romanian tokenization scripts' - git clone https://github.com/rsennrich/wmt16-scripts.git $WMT16_SCRIPTS -fi - -KYTEA=$INSTALL_PATH/kytea -if [ ! -f $KYTEA/bin/kytea ]; then - git clone https://github.com/neubig/kytea.git $KYTEA - cd $KYTEA - autoreconf -i - ./configure --prefix=`pwd` - make - make install - cd .. -fi - -export MECAB=$INSTALL_PATH/mecab-0.996-ko-0.9.2 -if [ ! -f $MECAB/bin/mecab ]; then - cd $INSTALL_PATH - curl -LO https://bitbucket.org/eunjeon/mecab-ko/downloads/mecab-0.996-ko-0.9.2.tar.gz - tar zxfv mecab-0.996-ko-0.9.2.tar.gz - cd mecab-0.996-ko-0.9.2/ - ./configure --prefix=`pwd` - make - make install - - cd .. - curl -LO https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.1.1-20180720.tar.gz - tar zxfv mecab-ko-dic-2.1.1-20180720.tar.gz - cd mecab-ko-dic-2.1.1-20180720/ - ./autogen.sh - ./configure --prefix=`pwd` --with-dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic --with-mecab-config=$MECAB/bin/mecab-config - make - sh -c 'echo "dicdir=$MECAB/lib/mecab/dic/mecab-ko-dic" > $MECAB/etc/mecabrc' - make install - cd $CWD -fi - -INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources -if [ ! -d $INDIC_RESOURCES_PATH ]; then - echo 'Cloning indic_nlp_resources' - git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git $INDIC_RESOURCES_PATH -fi - - -if [ ! -f $INSTALL_PATH/seg_my.py ]; then - cd $INSTALL_PATH - wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/wat2020.my-en.zip - unzip wat2020.my-en.zip - # switch to python3 - cat wat2020.my-en/myseg.py |sed 's/^sys.std/###sys.std/g' | sed 's/### sys/sys/g' | sed 's/unichr/chr/g' > seg_my.py - cd $CWD -fi - - -pip install pythainlp sacrebleu indic-nlp-library - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/clean_histogram.py b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/clean_histogram.py deleted file mode 100644 index e24e073dc0eb43c76e2ce717f52bb848c5b026b8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/clean_histogram.py +++ /dev/null @@ -1,52 +0,0 @@ -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument('--src', type=str, help='Source language') -parser.add_argument('--tgt', type=str, help='Target language') -parser.add_argument('--src-file', type=str, help='Input source file') -parser.add_argument('--tgt-file', type=str, help='Input target file') -parser.add_argument('--src-output-file', type=str, help='Output source file') -parser.add_argument('--tgt-output-file', type=str, help='Output target file') -parser.add_argument('--threshold', type=float, default=0.5, help='Threshold') -parser.add_argument('--threshold-character', type=str, default=']', help='Threshold character') -parser.add_argument('--histograms', type=str, help='Path to histograms') - -args = parser.parse_args() - - -def read_hist(f): - ch = [] - for line in f: - c = line[0] - if c == args.threshold_character: - break - ch.append(c) - return ch - - -with(open("{}/{}".format(args.histograms, args.src), 'r', encoding='utf8')) as f: - ch1 = read_hist(f) - -with(open("{}/{}".format(args.histograms, args.tgt), 'r', encoding='utf8')) as f: - ch2 = read_hist(f) - -print("Accepted characters for {}: {}".format(args.src, ch1)) -print("Accepted characters for {}: {}".format(args.tgt, ch2)) - -with open(args.src_file, 'r', encoding='utf8') as fs1, open(args.tgt_file, 'r', encoding='utf8') as fs2, open(args.src_output_file, 'w', encoding='utf8') as fos1, open(args.tgt_output_file, 'w', encoding='utf8') as fos2: - ls1 = fs1.readline() - ls2 = fs2.readline() - - while ls1 or ls2: - cnt1 = len([c for c in ls1.strip() if c in ch1]) - cnt2 = len([c for c in ls2.strip() if c in ch2]) - - if cnt1 / len(ls1) > args.threshold and cnt2 / len(ls2) > args.threshold: - fos1.write(ls1) - fos2.write(ls2) - else: - print("{} {} {} \n{} {} {}".format(args.src, cnt1 / len(ls1), ls1.strip(), args.tgt, cnt2 / len(ls2), ls2.strip())) - - ls1 = fs1.readline() - ls2 = fs2.readline() - \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/dedup_data.py b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/dedup_data.py deleted file mode 100644 index 4a818e4a6ba6253f5e34f796648c9050c44926f7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/dedup_data.py +++ /dev/null @@ -1,91 +0,0 @@ -import argparse -import os -from collections import namedtuple - -DATADIR = "/path/to/train_data" -DEDUP_FROM_DIR = "/path/to/eval/data" -OUTPUT_DIR = "/path/to/output/data" - - -def main(args): - languages = set() - for language_directory in os.listdir(DATADIR): - if "_" in language_directory: - src, tgt = language_directory.split("_") - languages.add(LanguagePair(src=src, tgt=tgt)) - - data = existing_data() - train_languages = sorted(languages) - for language_pair in train_languages[args.start_index:args.start_index + args.size]: - print(language_pair) - dedup(language_pair, data) - - -LanguagePair = namedtuple("LanguagePair", ["src", "tgt"]) - - -def existing_data(): - data = set() - for file in os.listdir(DEDUP_FROM_DIR): - with open(os.path.join(DEDUP_FROM_DIR, file)) as f: - data |= set(f.readlines()) - return data - -def dedup(language_pair, data, verbose=True, output=True): - train_filenames = LanguagePair( - src=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.src}", - tgt=f"{DATADIR}/{language_pair.src}_{language_pair.tgt}/train.{language_pair.tgt}", - ) - - output_filenames = LanguagePair( - src=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.src}", - tgt=f"{OUTPUT_DIR}/train.dedup.{language_pair.src}-{language_pair.tgt}.{language_pair.tgt}" - ) - - # If output exists, skip this pair. It has already been done. - if (os.path.exists(output_filenames.src) and - os.path.exists(output_filenames.tgt)): - if verbose: - print(f"{language_pair.src}-{language_pair.tgt} already done.") - return - - if verbose: - print(f"{language_pair.src}-{language_pair.tgt} ready, will check dups.") - - # If there is no output, no need to actually do the loop. - if not output: - return - - if os.path.exists(train_filenames.src) and os.path.exists(train_filenames.tgt): - with open(train_filenames.src) as f: - train_source = f.readlines() - - with open(train_filenames.tgt) as f: - train_target = f.readlines() - - # do dedup - new_train_source = [] - new_train_target = [] - for i, train_line in enumerate(train_source): - if train_line not in data and train_target[i] not in data: - new_train_source.append(train_line) - new_train_target.append(train_target[i]) - - assert len(train_source) == len(train_target) - assert len(new_train_source) == len(new_train_target) - assert len(new_train_source) <= len(train_source) - - with open(output_filenames.src, "w") as o: - for line in new_train_source: - o.write(line) - - with open(output_filenames.tgt, "w") as o: - for line in new_train_target: - o.write(line) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("-s", "--start-index", required=True, type=int) - parser.add_argument("-n", "--size", required=True, type=int) - main(parser.parse_args()) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/remove_too_much_punc.py b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/remove_too_much_punc.py deleted file mode 100644 index 427abe7d2f425eb4643417dff66dd3fd206db325..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/process_data/remove_too_much_punc.py +++ /dev/null @@ -1,37 +0,0 @@ -import argparse -import gzip -from string import punctuation - - -def len_no_punc(s, punc): - return len([ch for ch in s if ch in punc]) - -def filter_overpunc(len_npunc, len_sen): - return len_npunc < 0.5*len_sen - -def main(args): - punc = punctuation + "—|–" - print('Processing file {}'.format(args.input)) - with gzip.open(args.input, 'rt', encoding=args.encoding) as tsv: - with open(args.bitext + '.' + args.src_lang, 'wt', encoding=args.encoding) as fsrc: - with open(args.bitext + '.' + args.tgt_lang, 'wt', encoding=args.encoding) as ftgt: - line = tsv.readline() - fields = line.split('\t') - - src, tgt = fields[1], fields[2] - - nchar_npunc_src = len_no_punc(src, punc) - nchar_npunc_tgt = len_no_punc(tgt, punc) - - if filter_overpunc(nchar_npunc_src, len(src)) and filter_overpunc(nchar_npunc_tgt, len(tgt)): - fsrc.write(src.strip() + '\n') - ftgt.write(tgt.strip() + '\n') - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--input", required=True, type=str) - parser.add_argument('--encoding', default='utf-8', help='character encoding for input/output') - parser.add_argument('--bitext', type=str, required=True, help='language direction') - parser.add_argument('--src-lang', type=str, required=True, help='Source language') - parser.add_argument('--tgt-lang', type=str, required=True, help='Target language') - main(parser.parse_args()) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tok.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tok.sh deleted file mode 100644 index ba2ec5a2f3f4794d2e528d3a6574bf05abe1d043..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tok.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/usr/bin/env bash -# Copyright (c) 2019-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. -# - -set -e - -TOKENIZERS_SCRIPTS=tokenizers -INSTALL_PATH=$TOKENIZERS_SCRIPTS/thirdparty - -N_THREADS=8 - -lg=$1 - -MOSES=$INSTALL_PATH/mosesdecoder -REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl -NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl -TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl - -# special tokenization for Romanian -WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts - -NORMALIZE_ROMANIAN=$WMT16_SCRIPTS/preprocess/normalise-romanian.py -REMOVE_DIACRITICS=$WMT16_SCRIPTS/preprocess/remove-diacritics.py - -# Burmese -MY_SEGMENT=$INSTALL_PATH/seg_my.py - -# Arabic -AR_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenizer_ar.sh - -# Korean -KO_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ko.sh - -# Japanese -JA_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ja.sh - -# Indic -IN_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_indic.py -INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources - -# Thai -THAI_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_thai.py - -# Chinese -CHINESE_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_zh.py - -# Chinese -if [ "$lg" = "zh" ]; then - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | python $CHINESE_TOKENIZER -# Thai -elif [ "$lg" = "th" ]; then - cat - | python $THAI_TOKENIZER -# Japanese -elif [ "$lg" = "ja" ]; then - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | ${JA_SEGMENT} -# Korean -elif [ "$lg" = "ko" ]; then - cat - | $REM_NON_PRINT_CHAR | ${KO_SEGMENT} -# Romanian -elif [ "$lg" = "ro" ]; then - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $NORMALIZE_ROMANIAN | $REMOVE_DIACRITICS | $TOKENIZER -no-escape -threads $N_THREADS -l $lg -# Burmese -elif [ "$lg" = "my" ]; then - cat - | python ${MY_SEGMENT} -# Arabic -elif [ "$lg" = "ar" ]; then - cat - | ${AR_TOKENIZER} -# Indic -elif [ "$lg" = "ne" ]; then - cat - | python ${IN_TOKENIZER} $lg -elif [ "$lg" = "si" ]; then - cat - | python ${IN_TOKENIZER} $lg -elif [ "$lg" = "hi" ]; then - cat - | python ${IN_TOKENIZER} $lg -# other languages -else - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape -threads $N_THREADS -l $lg -fi diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/README.md deleted file mode 100644 index e116932bc80572f221cff6472a7b1eea7032925d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# M2M-100 Tokenization - -We apply different tokenization strategies for different languages following the existing literature. Here we provide tok.sh a tokenizer that can be used to reproduce our results. - -To reproduce the results, follow these steps: - -``` -tgt_lang=... -reference_translation=... -cat generation_output | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh $tgt_lang > hyp -cat $reference_translation |sh tok.sh $tgt_lang > ref -sacrebleu -tok 'none' ref < hyp -``` - -## Installation - -Tools needed for all the languages except Arabic can be installed by running install_dependencies.sh -If you want to evaluate Arabic models, please follow the instructions provided here: http://alt.qcri.org/tools/arabic-normalizer/ to install diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/seg_ja.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/seg_ja.sh deleted file mode 100644 index be6f5ca5fe4ac8e8c786a439caaed1d1314f1aef..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/seg_ja.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/env bash -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -SCRIPT=`realpath $0` -KYTEA=`dirname $SCRIPT`/thirdparty/kytea -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$KYTEA/lib:/usr/local/lib -export PATH=$PATH:"$KYTEA/bin" - -cat - | tr -d "[:blank:]" | kytea -notags diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/seg_ko.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/seg_ko.sh deleted file mode 100644 index c523d92634d9b61b97bbcdbfd17dfc33465bfc09..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/seg_ko.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/usr/bin/env bash -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -SCRIPT=`realpath $0` -MECAB=`dirname $SCRIPT`/thirdparty/mecab-0.996-ko-0.9.2 - -export PATH=$PATH:"$MECAB/bin":"$MECAB/lib" -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"$MECAB/lib" - -cat - | mecab -O wakati diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/thirdparty/.gitignore b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/thirdparty/.gitignore deleted file mode 100644 index 19eb6a9dd705ac583f22ecb60d9b744987e27ff1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/thirdparty/.gitignore +++ /dev/null @@ -1,12 +0,0 @@ -seg_my.py -indic_nlp_library/ -indic_nlp_resources/ -kytea/ -mecab-0.996-ko-0.9.2.tar.gz -mecab-0.996-ko-0.9.2/ -mosesdecoder/ -wat2020.my-en.zip -wat2020.my-en/ -wmt16-scripts/ -mecab-ko-dic-2.1.1-20180720/ -mecab-ko-dic-2.1.1-20180720.tar.gz \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_indic.py b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_indic.py deleted file mode 100644 index 8b56e667581a5a299fa8f77a11660cdc408fa5cc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_indic.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. - -# Use: echo {text} | python tokenize_indic.py {language} - -import sys - -from indicnlp.normalize.indic_normalize import IndicNormalizerFactory -from indicnlp.tokenize.indic_tokenize import trivial_tokenize - -factory = IndicNormalizerFactory() -normalizer = factory.get_normalizer( - sys.argv[1], remove_nuktas=False, nasals_mode="do_nothing" -) - -for line in sys.stdin: - normalized_line = normalizer.normalize(line.strip()) - tokenized_line = " ".join(trivial_tokenize(normalized_line, sys.argv[1])) - print(tokenized_line) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_thai.py b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_thai.py deleted file mode 100644 index 7c7b7ebfaad7cc5c07c7a879ea233e4beae84d84..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_thai.py +++ /dev/null @@ -1,12 +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 - -from pythainlp import word_tokenize - -for line in sys.stdin: - print(" ".join(word_tokenize(line.strip()))) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_zh.py b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_zh.py deleted file mode 100644 index f28db1c225092966c81c8cd584385f15a4c7d2c2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenize_zh.py +++ /dev/null @@ -1,13 +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 fileinput - -import sacrebleu - -for line in fileinput.input(): - print(sacrebleu.tokenize_zh(line)) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenizer_ar.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenizer_ar.sh deleted file mode 100644 index ad35d7adf28dc9b23d13a6a3fec0b12cb760e855..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/m2m_100/tokenizers/tokenizer_ar.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/usr/bin/env sh -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# -# Please follow the instructions here http://alt.qcri.org/tools/arabic-normalizer/ -# to install tools needed for Arabic - -echo "Please install Arabic tools: http://alt.qcri.org/tools/arabic-normalizer/" -echo "Then update environment variables in tokenizer_ar.sh" -exit 1 - -SVMTOOL=... -GOMOSESGO=... -QCRI_ARABIC_NORMALIZER=... - -export PERL5LIB="$SVMTOOL/lib":"$GOMOSESGO/bin/MADA-3.2":$PERL5LIB - - -tempfile=$(mktemp) -cat - > $tempfile - -cd $QCRI_ARABIC_NORMALIZER - -bash qcri_normalizer_mada3.2_aramorph1.2.1.sh $tempfile -cat $tempfile.mada_norm-aramorph.europarl_tok diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/mbart/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/mbart/README.md deleted file mode 100644 index a45e37243c2c5d4027f79cf71498ca58bbac7d98..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/mbart/README.md +++ /dev/null @@ -1,123 +0,0 @@ -# MBART: Multilingual Denoising Pre-training for Neural Machine Translation -[https://arxiv.org/abs/2001.08210] - -## Introduction - -MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. - -## Pre-trained models - -Model | Description | # params | Download ----|---|---|--- -`mbart.CC25` | mBART model with 12 encoder and decoder layers trained on 25 languages' monolingual corpus | 610M | [mbart.CC25.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.v2.tar.gz) -`mbart.ft.ro_en` | finetune mBART cc25 model on ro-en language pairs | 610M | [mbart.cc25.ft.enro.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.ft.enro.tar.gz) - -## Results - -**[WMT16 EN-RO](https://www.statmt.org/wmt16/translation-task.html)** - -_(test set, no additional data used)_ - -Model | en-ro | ro-en ----|---|--- -`Random` | 34.3 | 34.0 -`mbart.cc25` | 37.7 | 37.8 -`mbart.enro.bilingual` | 38.5 | 38.5 - -## BPE data -# download model -wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.v2.tar.gz -tar -xzvf mbart.CC25.tar.gz -# bpe data -install SPM [here](https://github.com/google/sentencepiece) -```bash -SPM=/path/to/sentencepiece/build/src/spm_encode -MODEL=sentence.bpe.model -${SPM} --model=${MODEL} < ${DATA}/${TRAIN}.${SRC} > ${DATA}/${TRAIN}.spm.${SRC} & -${SPM} --model=${MODEL} < ${DATA}/${TRAIN}.${TGT} > ${DATA}/${TRAIN}.spm.${TGT} & -${SPM} --model=${MODEL} < ${DATA}/${VALID}.${SRC} > ${DATA}/${VALID}.spm.${SRC} & -${SPM} --model=${MODEL} < ${DATA}/${VALID}.${TGT} > ${DATA}/${VALID}.spm.${TGT} & -${SPM} --model=${MODEL} < ${DATA}/${TEST}.${SRC} > ${DATA}/${TEST}.spm.${SRC} & -${SPM} --model=${MODEL} < ${DATA}/${TEST}.${TGT} > ${DATA}/${TEST}.spm.${TGT} & -``` - -## Preprocess data - -```bash -DICT=dict.txt -fairseq-preprocess \ - --source-lang ${SRC} \ - --target-lang ${TGT} \ - --trainpref ${DATA}/${TRAIN}.spm \ - --validpref ${DATA}/${VALID}.spm \ - --testpref ${DATA}/${TEST}.spm \ - --destdir ${DEST}/${NAME} \ - --thresholdtgt 0 \ - --thresholdsrc 0 \ - --srcdict ${DICT} \ - --tgtdict ${DICT} \ - --workers 70 -``` - -## Finetune on EN-RO -Finetune on mbart CC25 - -```bash -PRETRAIN=mbart.cc25 # fix if you moved the downloaded checkpoint -langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN - -fairseq-train path_2_data \ - --encoder-normalize-before --decoder-normalize-before \ - --arch mbart_large --layernorm-embedding \ - --task translation_from_pretrained_bart \ - --source-lang en_XX --target-lang ro_RO \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --lr-scheduler polynomial_decay --lr 3e-05 --warmup-updates 2500 --total-num-update 40000 \ - --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ - --max-tokens 1024 --update-freq 2 \ - --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ - --seed 222 --log-format simple --log-interval 2 \ - --restore-file $PRETRAIN \ - --reset-optimizer --reset-meters --reset-dataloader --reset-lr-scheduler \ - --langs $langs \ - --ddp-backend legacy_ddp -``` -## Generate on EN-RO -Get sacrebleu on finetuned en-ro model - -get tokenizer [here](https://github.com/rsennrich/wmt16-scripts) -```bash -wget https://dl.fbaipublicfiles.com/fairseq/models/mbart/mbart.cc25.ft.enro.tar.gz -tar -xzvf mbart.cc25.ft.enro.tar.gz -``` - -```bash -model_dir=MBART_finetuned_enro # fix if you moved the checkpoint - -fairseq-generate path_2_data \ - --path $model_dir/model.pt \ - --task translation_from_pretrained_bart \ - --gen-subset test \ - -t ro_RO -s en_XX \ - --bpe 'sentencepiece' --sentencepiece-model $model_dir/sentence.bpe.model \ - --sacrebleu --remove-bpe 'sentencepiece' \ - --batch-size 32 --langs $langs > en_ro - -cat en_ro | grep -P "^H" |sort -V |cut -f 3- | sed 's/\[ro_RO\]//g' |$TOKENIZER ro > en_ro.hyp -cat en_ro | grep -P "^T" |sort -V |cut -f 2- | sed 's/\[ro_RO\]//g' |$TOKENIZER ro > en_ro.ref -sacrebleu -tok 'none' -s 'none' en_ro.ref < en_ro.hyp -``` - -## Citation - -```bibtex -@article{liu2020multilingual, - title={Multilingual Denoising Pre-training for Neural Machine Translation}, - author={Yinhan Liu and Jiatao Gu and Naman Goyal and Xian Li and Sergey Edunov and Marjan Ghazvininejad and Mike Lewis and Luke Zettlemoyer}, - year={2020}, - eprint={2001.08210}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/megatron_11b/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/megatron_11b/README.md deleted file mode 100644 index 945c96c91e2e2d93466abc28d90bc25a1e7dd471..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/megatron_11b/README.md +++ /dev/null @@ -1,161 +0,0 @@ -# Megatron-11b - -Megatron-11b is a unidirectional language model with `11B` parameters based on [Megatron-LM](https://arxiv.org/pdf/1909.08053.pdf). Following the original Megatron work, we trained the model using intra-layer model parallelism with each layer's parameters split across 8 GPUs. - -Megatron-11b is trained on the same data and uses the same byte-pair encoding (BPE) as [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf). - -## Pre-trained models - -Model | Description | # params | # filesize | Download ----|---|---|---|--- -`megatron_11b` | megatron_11b unidirectional language model | 11B | 19Gb | [megatron_11b.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz) - -#### Architecture: - -Param | Value ----|--- -embed_dim | 3072 -ffn_dim | 3072 * 6 -layers | 72 -attention heads | 32 - -#### Training details: - -Param | value ----|--- -bsz | 512 -num_updates | 300,000 -peak_lr | 1.5e-04 -lr scheduler | inverse_sqrt -clip norm | 0.0 - - -## Example training command (model parallel) - -Megatron-11b contains too many parameters to train on a single GPU. Following -the original Megatron work, we adopt an intra-layer model parallel training -approach in which each layer's parameters are split across multiple GPUs and -activations and gradients are communicated during the forward/backward pass, -respectively. We similarly split the loss computation using the -`vocab_parallel_cross_entropy` criterion. - -The following training command illustrates how to do model parallel training in -fairseq. We assume that each machine (node) has 8 GPUs among which to split the -model parameters (`--model-parallel-size 8`). If you have access to multiple -nodes, you may combine this with data parallel training by increasing -`--distributed-world-size`. - -To train Megatron-11b on a single node: - - -```bash -fairseq-train \ - --distributed-world-size 8 \ - --memory-efficient-fp16 \ - --num-workers 2 \ - --model-parallel-size 8 \ - --criterion vocab_parallel_cross_entropy \ - --task language_modeling \ - --sample-break-mode none \ - --tokens-per-sample 1024 \ - --arch transformer_lm_megatron_11b \ - --share-decoder-input-output-embed \ - --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 --clip-norm 0.0 \ - --lr-scheduler inverse_sqrt --lr 0.00015 \ - --warmup-updates 3000 --weight-decay 0.01 \ - --dropout 0.1 --attention-dropout 0.1 \ - --batch-size 2 \ - --max-update 300000; -``` - -Note: Above was tested on `DGX-1` box, with `8xV100-32Gb` GPUs. - -## Results - -**[Wikitext103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)** - -Model | Valid perplexity | Test perplexity ----|---|--- -`megatron_11b` | 10.64 | 10.54 - - -## Evaluating `megatron_11b` on Wikitext-103 - -#### 1. Downloading Megatron-11b -```bash -# WARNING: this file is 19GB -wget https://dl.fbaipublicfiles.com/fairseq/models/model_parallel/megatron_11b.tar.gz -tar -xzvf megatron_11b.tar.gz -``` - -#### 2. Download Wikitext-103 -```bash -wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip -unzip wikitext-103-raw-v1.zip -``` - -#### 3. Detokenize test tokens -Megatron-11b uses a byte-level BPE that expects raw (untokenized) input. Since -the wikitext-103 dataset comes tokenized, we apply a simple detokenization -process to restore the untokenized test set: - -```bash -python -m examples.megatron_11b.detok wikitext-103-raw/wiki.test.raw > wikitext-103-raw/wiki.test.detok -``` - -#### 4. BPE encoding -```bash -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' - -python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json encoder.json \ - --vocab-bpe vocab.bpe \ - --inputs "wikitext-103-raw/wiki.test.detok" \ - --outputs "wikitext-103-raw/wiki.test.bpe" \ - --workers 60; -``` - -#### 5. Fairseq binarize -```bash -fairseq-preprocess \ - --only-source \ - --testpref wikitext-103-raw/wiki.test.bpe \ - --srcdict megatron_11b/dict.txt \ - --destdir wikitext103-bin; -``` - -#### 6. Evaluating perplexity. -We can now evaluate perplexity on the test set. Note that because we've modified -the test set (via detokenization and BPE), the perplexity reported by -`fairseq-eval-lm` needs to be renormalized. - -Compute unnormalized perplexity: - -```bash -DATA_PATH=wikitext103-bin/ -fairseq-eval-lm \ - $DATA_PATH \ - --path megatron_11b/model.pt \ - --task language_modeling \ - --gen-subset test \ - --batch-size 8 \ - --criterion cross_entropy \ - --context-window 992 \ - --distributed-world-size 8 \ - --model-parallel-size 8; -# Expected PPL (unnormalized_ppl): [8.46] -# Note: the eval command needs to run on 8 GPUs for the released model -``` -Renormalizing formula: `2 ^ ( log_2(unnormalized_PPL) * (270847 / 245566))`. -PPL After normalization: `10.54` - -To renormalize the perplexity, we must account for the change in token count -after detokenizing and appling BPE. The formula for this is: -`2 ^ ( log_2(unnormalized_PPL) * (new_token_cnt / orig_token_cnt))` - -For the wikitext-103 test set, the original token count is `245566` and the -token count after detokenization and applying BPE is `270847`. - -The perplexity after renormalization is: -`2 ^ ( log_2(8.46) * (270847 / 245566)) = 10.54` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/megatron_11b/detok.py b/audio/safeear/safeear_repo/fairseq_ours/examples/megatron_11b/detok.py deleted file mode 100644 index 49921b28a1f35c6216b5ed85729453524e7a049d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/megatron_11b/detok.py +++ /dev/null @@ -1,32 +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 argparse -import fileinput - -import sacremoses - - -def main(): - parser = argparse.ArgumentParser(description="") - parser.add_argument("files", nargs="*", help="input files") - args = parser.parse_args() - - detok = sacremoses.MosesDetokenizer() - - for line in fileinput.input(args.files, openhook=fileinput.hook_compressed): - print( - detok.detokenize(line.strip().split(" ")) - .replace(" @", "") - .replace("@ ", "") - .replace(" =", "=") - .replace("= ", "=") - .replace(" – ", "–") - ) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/README.md deleted file mode 100644 index 64ee57ec22092cfd2455622f4daf7681fd034be3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/README.md +++ /dev/null @@ -1,126 +0,0 @@ -# Efficient Large Scale Language Modeling with Mixtures of Experts - -## Introduction - -Mixture of Experts layers (MoEs) enable efficient scaling of language models -through conditional computation. This work empirically compares how -autoregressive MoE language models scale in comparison with dense models in a -wide range of settings: in- and out-of-domain language modeling, zero- and -few-shot priming, and full fine-tuning. See the associated paper for more -details. - -This repo contains instructions for reproducing results from the paper. - -## Pre-trained models - -These models are intended for research purposes only in order to reproduce the -results from the paper, and to enable further research on the capabilities and -limitations of language models. Please see the [model card](model_card.md) for -more details about how the models were trained and evaluated, as well as their -limitations and intended use. - -#### Dense models - -Dense models can be run directly from the `main` branch. - -Model | Layers | Model Dim | Languages | Download ----|---|---|---|--- -`dense_125m` | 12 | 768 | English | [en_dense_lm_125m.tar.gz (0.2GB)](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_dense_lm_125m.tar.gz) -`dense_355m` | 24 | 1024 | English | [en_dense_lm_355m.tar.gz (0.6GB)](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_dense_lm_355m.tar.gz) -`dense_1_3b` | 24 | 2048 | English | [en_dense_lm_1_3b.tar.gz (2.3GB)](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_dense_lm_1_3b.tar.gz) -`dense_2_7b` | 32 | 2560 | English | [en_dense_lm_2_7b.tar.gz (4.6GB)](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_dense_lm_2_7b.tar.gz) -`dense_6_7b` | 32 | 4096 | English | [en_dense_lm_6_7b.tar.gz (12GB)](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_dense_lm_6_7b.tar.gz) -`dense_13b` | 40 | 5120 | English | [en_dense_lm_13b.tar.gz (23GB)](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_dense_lm_13b.tar.gz) - -#### Mixture of expert models - -MoE models must be run from the `moe` branch. Please see the -[MoE README](https://github.com/pytorch/fairseq/tree/moe#evaluating-moe-language-models) -for more details about how to load and evaluate MoE models. - -Model | Layers | Model Dim | Languages | Download ----|---|---|---|--- -`moe_15b` | 12 | 768 | English | [en_moe_lm_15b.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_moe_lm_15b.tar.gz) -`moe_52b` | 24 | 1024 | English | [en_moe_lm_52b.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/lm/en_moe_lm_52b.tar.gz) -`moe_207b` | 24 | 2048 | English | Available by request -`moe_1_1t` | 32 | 4096 | English | Available by request - -## Evaluation - -### Example (COPA) - -The following snippet shows how to evaluate our dense models on the [Choice of -Plausible Alternatives (COPA)](https://people.ict.usc.edu/~gordon/copa.html) task. - -```python -from fairseq.models.transformer_lm import TransformerLanguageModel -model_dir = '/path/to/en_dense_lm_125m' -lm = TransformerLanguageModel.from_pretrained(model_dir, bpe='gpt2') -lm = lm.eval(); # disable dropout -lm = lm.half(); # use FP16 for evaluation -lm = lm.cuda(); # move to GPU - -def get_logprobs(prompt): - import re - prompt = re.sub('\n+' , '\n', prompt) # collapse repeated newlines, which indicate separate documents - return lm.score(prompt, replace_newlines_with_eos=True)['positional_scores'] - -# Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task. -# A return value of 1 indicates that the first alternative is more plausible, -# while 2 indicates that the second alternative is more plausible. -def COPA_eval(prompt, alternative1, alternative2): - lprob1 = get_logprobs(prompt + "\n" + alternative1).sum() - lprob2 = get_logprobs(prompt + "\n" + alternative2).sum() - return 1 if lprob1 > lprob2 else 2 - -COPA_eval("The man broke his toe. What was the CAUSE of this?", "He got a hole in his sock.", "He dropped a hammer on his foot.") -# 2 -COPA_eval("I tipped the bottle. What happened as a RESULT?", "The liquid in the bottle froze.", "The liquid in the bottle poured out.") -# 2 -COPA_eval("I knocked on my neighbor's door. What happened as a RESULT?", "My neighbor invited me in.", "My neighbor left his house.") -# 1 -``` - -### Data format - -Few-shot prompting is known to be sensitive to the input formatting, and it is usually best to match the formatting used in pretraining. - -During pretraining our models were presented with data in the following format (i.e., one paragraph per line, with a blank line separating documents): -``` - ... - ... - - ... -... -``` - -#### Newlines - -While we use the byte-level BPE from GPT-2/3, fairseq's preprocessing replaces newlines with the end-of-sentence symbol (``), which corresponds to embedding index `2`. -Thus **the model never saw newline characters during pretraining** and newlines should not be used during few-shot prompting. - -This is more clearly illustrated in the following example, which uses fairseq's Hub Interface to tokenize two documents in the desired format: -```python -from fairseq.models.transformer_lm import TransformerLanguageModel -model_dir = '/path/to/en_dense_lm_125m' -lm = TransformerLanguageModel.from_pretrained(model_dir, bpe='gpt2') - -data = """\ -This is the first paragraph of the first document. -This is the second paragraph of the first document. - -This is the first paragraph of the second document.\ -""" - -# The following is wrong, since it will encode newlines present in `data`. -tokens_bad = lm.score(data)['tokens'] -assert '\n' in lm.decode(tokens_bad) # oops, we encoded a newline - -# Instead pass the replace_newlines_with_eos option to get the correct behavior. -tokens_good = lm.score(data, replace_newline_with_eos=True)['tokens'] -assert '\n' not in lm.decode(tokens_good) # no newlines were encoded -``` - -## Citation - -Coming soon. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/data_card.md b/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/data_card.md deleted file mode 100644 index a69cdc974bcca1404bdd86e923380fbf2d6aca6d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/data_card.md +++ /dev/null @@ -1,221 +0,0 @@ -# Data card for the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" -## Version 1.0.0 - -We follow the recommendations of Gebru et al. (2018) and provide a datacard for the dataset used to train the 1.1T parameter model. - -## Motivation -* **For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.** -The pre-training data for training the 1.1 T model was created by a union of six English language datasets, including five datasets used by RoBERTa (Liu et al 2019) and the English subset of CC 100. These purpose of creating this dataset was to pre-train the language model. - -* **Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?** -Meta AI. - -* **Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.** -Meta AI. - -* **Any other comments?** -No. - -## Composition - -* **What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.** -The instances are textual documents. The overall dataset is composed from a union of the following datasets - - * BookCorpus (Zhu et al., 2019) consists of more than 10K unpublished books (4GB); - * English Wikipedia, excluding lists, tables and headers (12GB); - * CC-News (Nagel,2016) contains 63 million English news articles crawled between September 2016 and February 2019 (76GB); - * OpenWebText (Gokaslan and Cohen, 2019), an open source recreation of the WebText dataset used to train GPT-2 (38GB); - * CC-Stories (Trinh and Le, 2018) contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas (31GB); - * English CC100 (Wenzek et al., 2020), a dataset extracted from CommonCrawl snapshots between January 2018 and December 2018, filtered to match the style of Wikipedia (292GB). - -* **How many instances are there in total (of each type, if appropriate)?** -The training data contains 112B tokens corresponding to 453 GB of data. - -* **Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).** -The English CC100 section of the dataset is a subset of CommonCrawl snapshots extracted between January 2018 to December 2018, filtered to match the style of Wikipedia. The CC-stories dataset contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. - -* **What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.** -Each instance consists of raw text data. - -* **Is there a label or target associated with each instance? If so, please provide a description.** -No. - -* **Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.** -No. - -* **Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? If so, please describe how these relationships are made explicit.** -There are no explicit relationships between individual instances. - -* **Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.** -We hold out a random validation set of approximately 150MB from the pretraining data, sampled proportionally to each dataset's size in the pretraining corpus. - -* **Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.** -N/A - -* **Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?** -It's self-contained. - -* **Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals' non-public communications)? If so, please provide a description.** -The datasets used are publicly available, and the information in them is not considered confidential. - -* **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.** -Parts of the dataset are a subset of public Common Crawl data, which could contain sentences that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety. - -* **Does the dataset relate to people? If not, you may skip the remaining questions in this section.** -Some documents of this data relate to people, such as news articles, Wikipedia descriptions, etc. - -* **Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.** -No. - -* **Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how** -In addition to individuals who have Wikipedia pages (celebrities, politicians, etc.), it may be possible to identify other individuals by their names, Twitter account names, etc. if that information is present in Common Crawl. - -* **Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.** -The training dataset is partially derived from Common Crawl, which may contain some sensitive information. - -* **Any other comments?** -No - - -## Collection Process - -* **How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/ derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.** -N/A. The dataset is a union of six publicly available datasets. - -* **What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated?** -N/A - -* **If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?** -Please refer to the main document for details. - -* **Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?** -This data is mined, filtered and sampled by machines. - -* **Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.** -Different parts of the dataset were mined over different time periods. -1. The CC-News dataset contains English news articles crawled between September 2016 and February 2019. -2. The English CC-100 dataset was extracted from CommonCrawl snapshots between January 2018 and December 2018. - -* **Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.** -No. - -* **Does the dataset relate to people? If not, you may skip the remainder of the questions in this section.** -No. - -* **Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?** -N/A - -* **Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.** -N/A - -* **Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.** -N/A - -* **If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).** -N/A - -* **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.** -Some responsible AI related evaluations were performed. Please refer to the main document and the model card for the paper. - -* **Any other comments?** -No - - -## Preprocessing/cleaning/labeling - - -* **Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section.** -The component datasets went through standard cleaning and re-formatting practices, including removing repetitive/non informative text like "Chapter One", or "This ebook by Project Gutenberg". - -* **Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data.** -The "raw" component datasets is publicly available in their respective locations (more details can be seen in the respective papers linked in references). - -* **Is the software used to preprocess/clean/label the instances available? If so, please provide a link or other access point.** -The software is proprietary to Meta Platforms and currently unavailable publicly. - -* **Any other comments?** -No - - -## Uses - -* **Has the dataset been used for any tasks already? If so, please provide a description.** -Yes, this dataset was used to pre-train the models described in the paper. - -* **Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point.** -No. - -* **What (other) tasks could the dataset be used for?** -This data can be used to pretrain English language models, which are foundation to many current and future language tasks. - -* **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?** -The pipeline for creating this dataset paves a way for building a scalable infrastructure for mining datasets to be be used for training large-scale models. - -* **Are there tasks for which the dataset should not be used? If so, please provide a description.** -No. - -* **Any other comments?** -No. - -## Distribution - - -* **Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description.** -No. - -* **How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?** -N/A - -* **When will the dataset be distributed?** -No. - -* **Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.** -No. - -* **Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.** -No. - -* **Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation.** -N/A - -* **Any other comments?** -No. - -## Maintenance - -* **Who is supporting/hosting/maintaining the dataset?** -Meta AI. - -* **How can the owner/curator/manager of the dataset be contacted (e.g., email address)?** -Refer to the main document. - -* **Is there an erratum? If so, please provide a link or other access point.** -N/A - -* **Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?** -No plan for updating. - -* **If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced.** -N/A - -* **Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users.** -N/A - -* **If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/ verified? If so, please describe how. If not, why not? Is there a process for communicating/ distributing these contributions to other users? If so, please provide a description.** -No. - -* **Any other comments?** -No. - -## References -Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. - -Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2019. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. arXiv:1506.06724. - -Sebastian Nagel. 2016. Cc-news. http: //web.archive.org/save/http: //commoncrawl.org/2016/10/news-dataset-available. - -Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus. http://web.archive.org/save/http://Skylion007.github.io/OpenWebTextCorpus - -Trieu H Trinh and Quoc V Le. 2018. A simple method for commonsense reasoning. arXiv preprint arXiv:1806.02847. - -Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020. CCNet: Extracting high quality monolingual datasets from web crawl data. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4003–4012, Marseille, France. European Language Resources Association. - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/model_card.md b/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/model_card.md deleted file mode 100644 index 22cd551f8d48e7e1ad0cd9d0cc14686675388a29..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/moe_lm/model_card.md +++ /dev/null @@ -1,170 +0,0 @@ -# Model card for the paper ``Efficient Large Scale Language Modeling with Mixtures of Experts" -## Version 1.0.0 - -### Model developer -Meta AI - -### Model type -An autoregressive English language model trained on a union of six English language models. We explore dense and sparse (MoE based) architectures in the paper. -* Dense models - Our dense models range from 125M parameters to 13B parameters. -* Sparse (MoE) models - Our MoE based models range from 15B parameters to 1.1 Trillion parameters. -This model card focuses on the 1.1 Trillion parameter model, but the discussion -applies to all of the models explored in this work. - -### Citation details -Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts - -### Model Feedback Channel -fairseq - -## Intended use -### Primary intended use -For research purposes only, e.g. reproducing model evaluation results. Generation is only used in a limited capacity for explanation/justification or for prompting/probing/priming for class labels. - -### Out of scope uses -The primary purpose of the model is not to generate language, although the model is capable of doing that. - -## Factors influencing model performance -This section discusses potential risks associated with using the model. - -### Relevant factors -Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). - -### Evaluation factors -The 1.1T model was evaluated on StereoSet and CrowS-Pairs datasets to quantify encoded bias in the model. - -## Metrics -### Model performance measures -The 1.1T parameter model was primarily evaluated on -1. In-domain and out-of-domain language modeling perplexity. -2. Zero-shot and few-shot priming. -3. Fully supervised finetuning. - -### Approaches to handle uncertainty -For few-shot learning, we report the average results across 25 runs, randomly sampling a different set of few-shot examples from the training set each time. - -## Evaluation data -## Zero Shot evaluation - -### HellaSwag -#### Description -HellaSwag is a dataset for evaluating commonsense reasoning. - -### PIQA -#### Description -PIQA is a dataset designed to evaluate reasoning about Physical Commonsense in Natural Language - -### ReCoRd -#### Description -Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) is a large-scale reading comprehension dataset which requires commonsense reasoning. ReCoRD consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of ReCoRD is to evaluate a machine's ability of commonsense reasoning in reading comprehension. - -## Few Shot evaluation -### Winogrande -#### Description -Winogrande is a benchmark for commonsense reasoning. The dataset contains pronoun resolution problems originally designed to be unsolvable for statistical models that rely on selectional preferences or word associations. - -### StoryCloze -#### Description -StoryCloze is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning. This test requires a system to choose the correct ending to a four-sentence story. - -### OpenBookQA -#### Description -OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test), which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel situations. - -## Fully supervised evaluation - -### BoolQ -#### Description -BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring – they are generated in unprompted and unconstrained settings. Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. - -### SST-2 -#### Description -SST-2 (or SST-binary) is a binary classification dataset where the goal is to differentiate between negative or somewhat negative vs somewhat positive or positive. - -### MNLI -#### Description -The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. - -## Responsible AI (RAI) evaluation -### StereoSet -#### Description -A large-scale natural dataset in English to measure stereotypical biases in four domains: gender, profession, race, and religion - -#### Motivation for dataset use -The motivation for evaluating the 1.1T parameter model on this dataset is to evaluate the model's stereotype bias in gender, profession, race, and religion - -### CrowS -#### Description -Challenge Dataset for Measuring Social Biases in Masked Language Models - -#### Motivation for dataset use -The motivation for evaluating the 1.1T parameter model on this dataset is to evaluate the model’s bias in the domains of race, religion and age - ----- - -## Training data -### BookCorpus -#### Description -A dataset consisting of more than 10K unpublished books. 4GB in size. (Zhu et al., 2019) - -### English Wikipedia -#### Description -Data from English wikipedia, excluding lists, tables and headers. 12GB in size. - -### CC-News -#### Description -A dataset containing 63 millions English news articles crawled between September 2016 and February 2019. 76GB in size. (Nagel,2016) - -### OpenWebText -#### Description -An open source recreation of the WebText dataset used to train GPT-2. 38GB in size. (Gokaslan and Cohen, 2019) - -### CC-Stories -#### Description -A dataset containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. 31GB in size. (Trinh and Le, 2018) - -### English CC100 -#### Description -A dataset extracted from CommonCrawl snapshots between January 2018 and December 2018, filtered to match the style of Wikipedia following the methodology introduced in CCNet (https://arxiv.org/abs/1911.00359). 292GB in size. (Wenzek et al., 2020) - -## Responsible AI (RAI) Dimensions -### Fairness (Bias and inclusion) -The 1.1T parameter model was evaluated on the StereoSet and CrowS pairs dataset for inherent bias in the model, and bias as a result of the data. Similar to StereoSet, we observe that both the dense and MoE models get worse in terms of the Stereotype Score (SS) with scale. - -### Privacy and security -The 1.1T model did not have any special Privacy and Security considerations. The training data and evaluation data were both public and went through standard Meta AI Privacy and licensing procedures. - -### Transparency and control -In the spirit of transparency and accountability we have created this model card for the 1.1T parameter model and a data card for the training data (referenced in Artetxe et al. (2021)). - -### Efficiency (Green AI) -The 1.1T parameter model is trained as a Mixture of Experts (MoE) model. Mixture of expert (MoE) models are efficient because they leverage sparse computation, i.e., only a small fraction of parameters are active for any given input. For instance, our 1.1T parameter MoE model requires only 30% more FLOPS compared to a 6.7B parameter dense model, i.e., a 160x increase in parameters with only a 30% increase in FLOPS. Notably, MoE models achieve much better validation perplexity for a given compute budget compared to dense models. - -## References -Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. HellaSwag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791– 4800, Florence, Italy. Association for Computational Linguistics. - -Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020. Piqa: Reasoning about physical commonsense in natural language. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):7432–7439. - -Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018. ReCoRD: Bridging the gap between human and machine commonsense reading comprehension. arXiv preprint 1810.12885. - -Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020. Winogrande: An adversarial winograd schema challenge at scale. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8732–8740. - -Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A corpus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 839–849, San Diego, California. Association for Computational Linguistics. - -Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. Can a suit of armor conduct electricity? a new dataset for open book question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2381–2391, Brussels, Belgium. Association for Computational Linguistics. - -Christopher Clark and Kenton Lee and Ming-Wei Chang and Tom Kwiatkowski and Michael Collins and Kristina Toutanova. 2019. BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions - -Moin Nadeem, Anna Bethke, and Siva Reddy. 2021. StereoSet: Measuring stereotypical bias in pretrained language models. In Association for Computational Linguistics (ACL). - -Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020. CrowS-pairs: A challenge dataset for measuring social biases in masked language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1953–1967, Online. Association for Computational Linguistics. - -Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2019. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. arXiv:1506.06724. - -Sebastian Nagel. 2016. Cc-news. http: //web.archive.org/save/http: //commoncrawl.org/2016/10/news-dataset-available. - -Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus. http://web.archive.org/save/http://Skylion007.github.io/OpenWebTextCorpus - -Trieu H Trinh and Quoc V Le. 2018. A simple method for commonsense reasoning. arXiv preprint arXiv:1806.02847. - -Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020. CCNet: Extracting high quality monolingual datasets from web crawl data. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4003–4012, Marseille, France. European Language Resources Association. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/ML50_langs.txt b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/ML50_langs.txt deleted file mode 100644 index 558abbc785072629de8000e343fc02a32c0afb97..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/ML50_langs.txt +++ /dev/null @@ -1,52 +0,0 @@ -ar_AR -cs_CZ -de_DE -en_XX -es_XX -et_EE -fi_FI -fr_XX -gu_IN -hi_IN -it_IT -ja_XX -kk_KZ -ko_KR -lt_LT -lv_LV -my_MM -ne_NP -nl_XX -ro_RO -ru_RU -si_LK -tr_TR -vi_VN -zh_CN -af_ZA -az_AZ -bn_IN -fa_IR -he_IL -hr_HR -id_ID -ka_GE -km_KH -mk_MK -ml_IN -mn_MN -mr_IN -pl_PL -ps_AF -pt_XX -sv_SE -sw_KE -ta_IN -te_IN -th_TH -tl_XX -uk_UA -ur_PK -xh_ZA -gl_ES -sl_SI \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/README.md deleted file mode 100644 index 46ff9c351b1030e0729f89f246e0cd86444c1633..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/README.md +++ /dev/null @@ -1,158 +0,0 @@ -# Multilingual Translation - -[[Multilingual Translation with Extensible Multilingual Pretraining and Finetuning, https://arxiv.org/abs/2008.00401]](https://arxiv.org/abs/2008.00401) - -## Introduction - -This work is for training multilingual translation models with multiple bitext datasets. This multilingual translation framework supports (see [[training section]](#Training) and [[finetuning section]](#Finetuning) for examples) - -* temperature based sampling over unbalancing datasets of different translation directions - - --sampling-method' with - choices=['uniform', 'temperature', 'concat'] - - --sampling-temperature -* configurable to automatically add source and/or target language tokens to source/target sentences using data which are prepared in the same way as bilignual training - - --encoder-langtok with choices=['src', 'tgt', None] to specify whether to add source or target language tokens to the source sentences - - --decoder-langtok (binary option) to specify whether to add target language tokens to the target sentences or not -* finetuning mBART pretrained models for multilingual translation - - --finetune-from-model to specify the path from which to load the pretrained model - -## Preprocessing data -Multilingual training requires a joint BPE vocab. Please follow [mBART's preprocessing steps](https://github.com/pytorch/fairseq/tree/main/examples/mbart#bpe-data) to reuse our pretrained sentence-piece model. - -You can also train a joint BPE model on your own dataset and then follow the steps in [[link]](https://github.com/pytorch/fairseq/tree/main/examples/translation#multilingual-translation). - -## Training - - -```bash -lang_pairs= -path_2_data= -lang_list= - -fairseq-train $path_2_data \ - --encoder-normalize-before --decoder-normalize-before \ - --arch transformer --layernorm-embedding \ - --task translation_multi_simple_epoch \ - --sampling-method "temperature" \ - --sampling-temperature 1.5 \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" \ - --lang-pairs "$lang_pairs" \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ - --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ - --max-tokens 1024 --update-freq 2 \ - --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ - --seed 222 --log-format simple --log-interval 2 -``` - -## Finetuning -We can also finetune multilingual models from a monolingual pretrained models, e.g. [mMBART](https://github.com/pytorch/fairseq/tree/main/examples/mbart). -```bash -lang_pairs= -path_2_data= -lang_list= -pretrained_model= - -fairseq-train $path_2_data \ - --finetune-from-model $pretrained_model \ - --encoder-normalize-before --decoder-normalize-before \ - --arch transformer --layernorm-embedding \ - --task translation_multi_simple_epoch \ - --sampling-method "temperature" \ - --sampling-temperature 1.5 \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" \ - --lang-pairs "$lang_pairs" \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ - --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ - --max-tokens 1024 --update-freq 2 \ - --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ - --seed 222 --log-format simple --log-interval 2 -``` -## Generate -The following command uses the multilingual task (translation_multi_simple_epoch) to generate translation from $source_lang to $target_lang on the test dataset. During generaton, the source language tokens are added to source sentences and the target language tokens are added as the starting token to decode target sentences. Options --lang-dict and --lang-pairs are needed to tell the generation process the ordered list of languages and translation directions that the trained model are awared of; they will need to be consistent with the training. - -```bash -model= -source_lang= -target_lang= - -fairseq-generate $path_2_data \ - --path $model \ - --task translation_multi_simple_epoch \ - --gen-subset test \ - --source-lang $source_lang \ - --target-lang $target_lang - --sacrebleu --remove-bpe 'sentencepiece'\ - --batch-size 32 \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" \ - --lang-pairs "$lang_pairs" > ${source_lang}_${target_lang}.txt -``` -Fairseq will generate translation into a file {source_lang}_${target_lang}.txt with sacreblue at the end. - -You can also use costomized tokenizer to compare the performance with the literature. For example, you get a tokenizer [here](https://github.com/rsennrich/wmt16-scripts) and do the following: -```bash -TOKENIZER= -TOK_CMD=<"$TOKENIZER $target_lang" or cat for sacrebleu> - -cat {source_lang}_${target_lang}.txt | grep -P "^H" |sort -V |cut -f 3- |$TOK_CMD > ${source_lang}_${target_lang}.hyp -cat {source_lang}_${target_lang}.txt | grep -P "^T" |sort -V |cut -f 2- |$TOK_CMD > ${source_lang}_${target_lang}.ref -sacrebleu -tok 'none' -s 'none' ${source_lang}_${target_lang}.ref < ${source_lang}_${target_lang}.hyp -``` - -# mBART50 models - -* [mMBART 50 pretrained model](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.pretrained.tar.gz). -* [mMBART 50 finetuned many-to-one](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.n1.tar.gz). -* [mMBART 50 finetuned one-to-many](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.1n.tar.gz). -* [mMBART 50 finetuned many-to-many](https://dl.fbaipublicfiles.com/fairseq/models/mbart50/mbart50.ft.nn.tar.gz). - -Please download and extract from the above tarballs. Each tarball contains -* The fairseq model checkpoint: model.pt -* The list of supported languages: ML50_langs.txt -* Sentence piece model: sentence.bpe.model -* Fairseq dictionary of each language: dict.{lang}.txt (please replace lang with a language specified in ML50_langs.txt) - -To use the trained models, -* use the tool [binarize.py](./data_scripts/binarize.py) to binarize your data using sentence.bpe.model and dict.{lang}.txt, and copy the dictionaries to your data path -* then run the generation command: -```bash -path_2_data= -model=/model.pt -lang_list=/ML50_langs.txt -source_lang= -target_lang= - -fairseq-generate $path_2_data \ - --path $model \ - --task translation_multi_simple_epoch \ - --gen-subset test \ - --source-lang $source_lang \ - --target-lang $target_lang - --sacrebleu --remove-bpe 'sentencepiece'\ - --batch-size 32 \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" -``` - -## Citation - -```bibtex -@article{tang2020multilingual, - title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, - author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, - year={2020}, - eprint={2008.00401}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/README.md deleted file mode 100644 index cc610c0c9e936a5ae4659ceda691c6db6d387296..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/README.md +++ /dev/null @@ -1,24 +0,0 @@ - -# Install dependency -```bash -pip install -r requirement.txt -``` - -# Download the data set -```bash -export WORKDIR_ROOT= - -``` -The downloaded data will be at $WORKDIR_ROOT/ML50 - -# preprocess the data -Install SPM [here](https://github.com/google/sentencepiece) -```bash -export WORKDIR_ROOT= -export SPM_PATH= -``` -* $WORKDIR_ROOT/ML50/raw: extracted raw data -* $WORKDIR_ROOT/ML50/dedup: dedup data -* $WORKDIR_ROOT/ML50/clean: data with valid and test sentences removed from the dedup data - - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/binarize.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/binarize.py deleted file mode 100644 index 29107df3d64858058c3da328725cf45be3f2392f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/binarize.py +++ /dev/null @@ -1,201 +0,0 @@ -import argparse -import glob -import itertools -import os -import pathlib -import shutil -import sys -from subprocess import check_call, check_output - - -def call_output(cmd): - print(f"Executing: {cmd}") - ret = check_output(cmd, shell=True) - print(ret) - return ret - -def call(cmd): - print(cmd) - check_call(cmd, shell=True) - - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - -SPM_PATH = os.environ.get('SPM_PATH', None) - -if SPM_PATH is None or not SPM_PATH.strip(): - print("Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting...") - sys.exit(-1) - - -SPM_MODEL = f'{WORKDIR_ROOT}/sentence.bpe.model' -SPM_VOCAB = f'{WORKDIR_ROOT}/dict_250k.txt' - -SPM_ENCODE = f'{SPM_PATH}' - -if not os.path.exists(SPM_MODEL): - call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/sentence.bpe.model -O {SPM_MODEL}") - - -if not os.path.exists(SPM_VOCAB): - call(f"wget https://dl.fbaipublicfiles.com/fairseq/models/mbart50/dict_250k.txt -O {SPM_VOCAB}") - - - -def get_data_size(raw): - cmd = f'wc -l {raw}' - ret = call_output(cmd) - return int(ret.split()[0]) - -def encode_spm(model, direction, prefix='', splits=['train', 'test', 'valid'], pairs_per_shard=None): - src, tgt = direction.split('-') - - for split in splits: - src_raw, tgt_raw = f'{RAW_DIR}/{split}{prefix}.{direction}.{src}', f'{RAW_DIR}/{split}{prefix}.{direction}.{tgt}' - if os.path.exists(src_raw) and os.path.exists(tgt_raw): - cmd = f"""python {SPM_ENCODE} \ - --model {model}\ - --output_format=piece \ - --inputs {src_raw} {tgt_raw} \ - --outputs {BPE_DIR}/{direction}{prefix}/{split}.bpe.{src} {BPE_DIR}/{direction}{prefix}/{split}.bpe.{tgt} """ - print(cmd) - call(cmd) - - -def binarize_( - bpe_dir, - databin_dir, - direction, spm_vocab=SPM_VOCAB, - splits=['train', 'test', 'valid'], -): - src, tgt = direction.split('-') - - try: - shutil.rmtree(f'{databin_dir}', ignore_errors=True) - os.mkdir(f'{databin_dir}') - except OSError as error: - print(error) - cmds = [ - "fairseq-preprocess", - f"--source-lang {src} --target-lang {tgt}", - f"--destdir {databin_dir}/", - f"--workers 8", - ] - if isinstance(spm_vocab, tuple): - src_vocab, tgt_vocab = spm_vocab - cmds.extend( - [ - f"--srcdict {src_vocab}", - f"--tgtdict {tgt_vocab}", - ] - ) - else: - cmds.extend( - [ - f"--joined-dictionary", - f"--srcdict {spm_vocab}", - ] - ) - input_options = [] - if 'train' in splits and glob.glob(f"{bpe_dir}/train.bpe*"): - input_options.append( - f"--trainpref {bpe_dir}/train.bpe", - ) - if 'valid' in splits and glob.glob(f"{bpe_dir}/valid.bpe*"): - input_options.append(f"--validpref {bpe_dir}/valid.bpe") - if 'test' in splits and glob.glob(f"{bpe_dir}/test.bpe*"): - input_options.append(f"--testpref {bpe_dir}/test.bpe") - if len(input_options) > 0: - cmd = " ".join(cmds + input_options) - print(cmd) - call(cmd) - - -def binarize( - databin_dir, - direction, spm_vocab=SPM_VOCAB, prefix='', - splits=['train', 'test', 'valid'], - pairs_per_shard=None, -): - def move_databin_files(from_folder, to_folder): - for bin_file in glob.glob(f"{from_folder}/*.bin") \ - + glob.glob(f"{from_folder}/*.idx") \ - + glob.glob(f"{from_folder}/dict*"): - try: - shutil.move(bin_file, to_folder) - except OSError as error: - print(error) - bpe_databin_dir = f"{BPE_DIR}/{direction}{prefix}_databin" - bpe_dir = f"{BPE_DIR}/{direction}{prefix}" - if pairs_per_shard is None: - binarize_(bpe_dir, bpe_databin_dir, direction, spm_vocab=spm_vocab, splits=splits) - move_databin_files(bpe_databin_dir, databin_dir) - else: - # binarize valid and test which will not be sharded - binarize_( - bpe_dir, bpe_databin_dir, direction, - spm_vocab=spm_vocab, splits=[s for s in splits if s != "train"]) - for shard_bpe_dir in glob.glob(f"{bpe_dir}/shard*"): - path_strs = os.path.split(shard_bpe_dir) - shard_str = path_strs[-1] - shard_folder = f"{bpe_databin_dir}/{shard_str}" - databin_shard_folder = f"{databin_dir}/{shard_str}" - print(f'working from {shard_folder} to {databin_shard_folder}') - os.makedirs(databin_shard_folder, exist_ok=True) - binarize_( - shard_bpe_dir, shard_folder, direction, - spm_vocab=spm_vocab, splits=["train"]) - - for test_data in glob.glob(f"{bpe_databin_dir}/valid.*") + glob.glob(f"{bpe_databin_dir}/test.*"): - filename = os.path.split(test_data)[-1] - try: - os.symlink(test_data, f"{databin_shard_folder}/{filename}") - except OSError as error: - print(error) - move_databin_files(shard_folder, databin_shard_folder) - - -def load_langs(path): - with open(path) as fr: - langs = [l.strip() for l in fr] - return langs - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--data_root", default=f"{WORKDIR_ROOT}/ML50") - parser.add_argument("--raw-folder", default='raw') - parser.add_argument("--bpe-folder", default='bpe') - parser.add_argument("--databin-folder", default='databin') - - args = parser.parse_args() - - DATA_PATH = args.data_root #'/private/home/yuqtang/public_data/ML50' - RAW_DIR = f'{DATA_PATH}/{args.raw_folder}' - BPE_DIR = f'{DATA_PATH}/{args.bpe_folder}' - DATABIN_DIR = f'{DATA_PATH}/{args.databin_folder}' - os.makedirs(BPE_DIR, exist_ok=True) - - raw_files = itertools.chain( - glob.glob(f'{RAW_DIR}/train*'), - glob.glob(f'{RAW_DIR}/valid*'), - glob.glob(f'{RAW_DIR}/test*'), - ) - - directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] - - for direction in directions: - prefix = "" - splits = ['train', 'valid', 'test'] - try: - shutil.rmtree(f'{BPE_DIR}/{direction}{prefix}', ignore_errors=True) - os.mkdir(f'{BPE_DIR}/{direction}{prefix}') - os.makedirs(DATABIN_DIR, exist_ok=True) - except OSError as error: - print(error) - spm_model, spm_vocab = SPM_MODEL, SPM_VOCAB - encode_spm(spm_model, direction=direction, splits=splits) - binarize(DATABIN_DIR, direction, spm_vocab=spm_vocab, splits=splits) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_iswlt_test_data.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_iswlt_test_data.py deleted file mode 100644 index e519b6b4da49d9c326a4916b7e8f41d59af7798e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_iswlt_test_data.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 subprocess -import sys -from subprocess import check_call, check_output - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - - -BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ") -def run_eval_bleu(cmd): - output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip() - print(output) - bleu = -1.0 - for line in output.strip().split('\n'): - m = BLEU_REGEX.search(line) - if m is not None: - bleu = m.groups()[0] - bleu = float(bleu) - break - return bleu - -def check_data_test_bleu(raw_folder, data_lang_pairs): - not_matchings = [] - for sacrebleu_set, src_tgts in data_lang_pairs: - for src_tgt in src_tgts: - print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}') - src, tgt = src_tgt.split('-') - ssrc, stgt = src[:2], tgt[:2] - if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'): - # reversed direction may have different test set - test_src = f'{raw_folder}/test.{tgt}-{src}.{src}' - else: - test_src = f'{raw_folder}/test.{src}-{tgt}.{src}' - cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""' - test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}' - cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""' - bleu1 = run_eval_bleu(cmd1) - if bleu1 != 100.0: - not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}') - bleu2 = run_eval_bleu(cmd2) - if bleu2 != 100.0: - not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}') - return not_matchings - -if __name__ == "__main__": - to_data_path = f'{WORKDIR_ROOT}/iwsltv2' - not_matching = check_data_test_bleu( - f'{to_data_path}/raw', - [ - ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']), - ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']), - ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]), - ] - ) - if len(not_matching) > 0: - print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching)) - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_self_overlaps.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_self_overlaps.py deleted file mode 100644 index 09c39c3b5d0f02001cb78a0c4726ec4b3cb15690..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_self_overlaps.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. - - -import argparse -import glob -import os -import sys - -from utils.dedup import deup - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - -def get_directions(folder): - raw_files = glob.glob(f'{folder}/train*') - directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] - return directions - -def diff_list(lhs, rhs): - return set(lhs).difference(set(rhs)) - -def check_diff( - from_src_file, from_tgt_file, - to_src_file, to_tgt_file, -): - seen_in_from = set() - seen_src_in_from = set() - seen_tgt_in_from = set() - from_count = 0 - with open(from_src_file, encoding='utf-8') as fsrc, \ - open(from_tgt_file, encoding='utf-8') as ftgt: - for s, t in zip(fsrc, ftgt): - seen_in_from.add((s, t)) - seen_src_in_from.add(s) - seen_tgt_in_from.add(t) - from_count += 1 - common = 0 - common_src = 0 - common_tgt = 0 - to_count = 0 - seen = set() - - with open(to_src_file, encoding='utf-8') as fsrc, \ - open(to_tgt_file, encoding='utf-8') as ftgt: - for s, t in zip(fsrc, ftgt): - to_count += 1 - if (s, t) not in seen: - if (s, t) in seen_in_from: - common += 1 - if s in seen_src_in_from: - common_src += 1 - seen_src_in_from.remove(s) - if t in seen_tgt_in_from: - common_tgt += 1 - seen_tgt_in_from.remove(t) - seen.add((s, t)) - return common, common_src, common_tgt, from_count, to_count - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--folder", type=str, required=True, - help="the data folder ") - parser.add_argument("--split", type=str, default='test', - help="split (valid, test) to check against training data") - parser.add_argument('--directions', type=str, default=None, required=False) - - args = parser.parse_args() - - if args.directions is None: - directions = set(get_directions(args.folder)) - directions = sorted(directions) - else: - directions = args.directions.split(',') - directions = sorted(set(directions)) - - results = [] - print(f'checking where {args.split} split data are in training') - print(f'direction\tcommon_count\tsrc common\ttgt common\tfrom_size\tto_size') - - for direction in directions: - src, tgt = direction.split('-') - from_src_file = f'{args.folder}/{args.split}.{src}-{tgt}.{src}' - from_tgt_file = f'{args.folder}/{args.split}.{src}-{tgt}.{tgt}' - if not os.path.exists(from_src_file): - # some test/valid data might in reverse directinos: - from_src_file = f'{args.folder}/{args.split}.{tgt}-{src}.{src}' - from_tgt_file = f'{args.folder}/{args.split}.{tgt}-{src}.{tgt}' - to_src_file = f'{args.folder}/train.{src}-{tgt}.{src}' - to_tgt_file = f'{args.folder}/train.{src}-{tgt}.{tgt}' - if not os.path.exists(to_src_file) or not os.path.exists(from_src_file): - continue - r = check_diff(from_src_file, from_tgt_file, to_src_file, to_tgt_file) - results.append(r) - print(f'{direction}\t', '\t'.join(map(str, r))) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_valid_test_overlaps.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_valid_test_overlaps.py deleted file mode 100644 index 2359227b5a2e892737d29df3158b3b23339034e6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/check_valid_test_overlaps.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 os -import sys - -import pandas as pd - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - -def load_langs(path): - with open(path) as fr: - langs = [l.strip() for l in fr] - return langs - - - -def load_sentences(raw_data, split, direction): - src, tgt = direction.split('-') - src_path = f"{raw_data}/{split}.{direction}.{src}" - tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" - if os.path.exists(src_path) and os.path.exists(tgt_path): - return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] - else: - return [] - -def swap_direction(d): - src, tgt = d.split('-') - return f'{tgt}-{src}' - -def get_all_test_data(raw_data, directions, split='test'): - test_data = [ - x - for dd in directions - for d in [dd, swap_direction(dd)] - for x in load_sentences(raw_data, split, d) - ] - # all_test_data = {s for _, d in test_data for s in d} - all_test_data = {} - for lang, d in test_data: - for s in d: - s = s.strip() - lgs = all_test_data.get(s, set()) - lgs.add(lang) - all_test_data[s] = lgs - return all_test_data, test_data - - -def check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train={}): - # src, tgt = direction.split('-') - print(f'check training data for {direction} in {src_path} and {tgt_path}') - size = 0 - overlapped_size_counted_dup = 0 - if not os.path.exists(tgt_path) or not os.path.exists(src_path): - return mess_up_train, size, overlapped_size_counted_dup - - with open(src_path) as f, open(tgt_path) as g: - for src_line, tgt_line in zip(f, g): - s = src_line.strip() - t = tgt_line.strip() - size += 1 - if s in all_test_data: - langs = mess_up_train.get(s, set()) - langs.add(direction) - mess_up_train[s] = langs - overlapped_size_counted_dup += 1 - if t in all_test_data: - langs = mess_up_train.get(t, set()) - langs.add(direction) - mess_up_train[t] = langs - overlapped_size_counted_dup += 1 - print(f'{direction}: size={size}, overlapped={overlapped_size_counted_dup}') - return mess_up_train, size, overlapped_size_counted_dup - -def check_train_all(raw_data, directions, all_test_data): - mess_up_train = {} - data_sizes = {} - # raw_data = '~chau/data-bin/MineBART/multilingual_mined_100M/en_XX/et_EE-en_XX/all.{en_XX, et_EE}' - print(f'checking training data againsts # {len(all_test_data)} sentences') - print(f'example test data: ', [s for i, s in enumerate(all_test_data.keys()) if i < 10]) - for direction in directions: - src, tgt = direction.split('-') - path = f'{raw_data}/en_XX/{direction}/all' - src_path = f'{path}.{src}' - tgt_path = f'{path}.{tgt}' - print(f'checking {src_path} {tgt_path}') - _, size, overlapped_size_counted_dup = check_train_sentences(src_path, tgt_path, direction, all_test_data, mess_up_train) - data_sizes[direction] = (size, overlapped_size_counted_dup) - return mess_up_train, data_sizes - - - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--folder", type=str, required=True, - help="the data folder ") - parser.add_argument("--test-data", type=str, required=True, - help="the test data folder ") - parser.add_argument('--directions', type=str, default=None, required=False) - - args = parser.parse_args() - directions = args.directions.split(',') - directions = sorted(set(directions)) - - results = [] - # print(f'checking where {args.split} split data are in training') - # print(f'direction\tcommon_count\tsrc common\ttgt common\tfrom_size\tto_size') - raw_data = args.folder - all_test_data, test_data = get_all_test_data(args.test_data, directions, split='test') - mess_up_train, data_sizes = check_train_all(raw_data, directions, all_test_data) - print(data_sizes) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/dedup_all.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/dedup_all.py deleted file mode 100644 index 49a6a6a5fd83fd1b5aa82df254e7ccc3edab1ca7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/dedup_all.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 argparse -import glob -import os -import sys - -from utils.dedup import deup - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--from-folder", type=str, required=True, - help="the data folder to be dedup") - parser.add_argument("--to-folder", type=str, required=True, - help="the data folder to save deduped data") - parser.add_argument('--directions', type=str, default=None, required=False) - - args = parser.parse_args() - - if args.directions is None: - raw_files = glob.glob(f'{args.from_folder}/train*') - - directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] - else: - directions = args.directions.split(',') - directions = sorted(set(directions)) - - for direction in directions: - src, tgt = direction.split('-') - src_file = f'{args.from_folder}/train.{src}-{tgt}.{src}' - tgt_file = f'{args.from_folder}/train.{src}-{tgt}.{tgt}' - src_file_out = f'{args.to_folder}/train.{src}-{tgt}.{src}' - tgt_file_out = f'{args.to_folder}/train.{src}-{tgt}.{tgt}' - assert src_file != src_file_out - assert tgt_file != tgt_file_out - print(f'deduping {src_file}, {tgt_file}') - deup(src_file, tgt_file, src_file_out, tgt_file_out) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_ML50_v1.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_ML50_v1.sh deleted file mode 100644 index 99fbc75920836a4b4bbdbd6b523749843288e450..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_ML50_v1.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/bin/bash -# 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. - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - -# first run download_wmt20.sh; it will install a few useful tools for other scripts -# TODO: need to print out instructions on downloading a few files which requires manually authentication from the websites -bash ./download_wmt20.sh - -python ./download_wmt19_and_before.py -bash ./download_wat19_my.sh -python ./download_ted_and_extract.py -bash ./download_lotus.sh -bash ./download_iitb.sh -bash ./download_af_xh.sh - - -# IWSLT downloading URLs have changed in between; TODO: fix them: -bash ./download_iwslt_and_extract.sh - -# TODO: globalvoices URLs changed; need to be fixed -bash ./download_flores_data.sh diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_af_xh.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_af_xh.sh deleted file mode 100644 index a78fbbbbccb6f6ae005a1f03b97f083a2d958ebe..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_af_xh.sh +++ /dev/null @@ -1,164 +0,0 @@ -#!/bin/bash -# 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. - -# set -x -e - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - -# put intermediate files -TMP_DIR=$WORKDIR_ROOT/temp/af_xhv2 -# output {train,valid,test} files to dest -DEST=${WORKDIR_ROOT}/ML50/raw - - - -ROOT=${WORKDIR_ROOT} -UTILS=$PWD/utils -TMX2CORPUS="${UTILS}/tmx2corpus" -TMX_TOOL="python ${TMX2CORPUS}/tmx2corpus.py" - -mkdir -p $TMP_DIR -mkdir -p $DEST -mkdir -p $UTILS - -function download_opus(){ - src=$1 - tgt=$2 - subset=$3 - ulr=$4 - - mkdir extract_$subset.$src-$tgt - pushd extract_$subset.$src-$tgt - if [ ! -f "$subset.$src-$tgt.tmx.gz" ]; then - wget $url -O "$subset.$src-$tgt.tmx.gz" - gzip -d "$subset.$src-$tgt.tmx.gz" - f=$subset.$src-$tgt.tmx - $TMX_TOOL $f - mv bitext.$src ../$subset.$src-$tgt.$src - mv bitext.$tgt ../$subset.$src-$tgt.$tgt - fi - popd -} - -function concat_subsets(){ - src=$1 - tgt=$2 - subsets=$3 - src_train=raw_train.$src-$tgt.$src - tgt_train=raw_train.$src-$tgt.$tgt - > $src_train - > $tgt_train - for subset in $subsets; do - cat $subset.$src-$tgt.$src >> $src_train - cat $subset.$src-$tgt.$tgt >> $tgt_train - done -} - - - -function get_seeded_random() -{ - seed="$1" - openssl enc -aes-256-ctr -pass pass:"$seed" -nosalt \ - /dev/null -} - -function split_train_valid(){ - src=$1 - tgt=$2 - raw_src_train=raw_train.$src-$tgt.$src - raw_tgt_train=raw_train.$src-$tgt.$tgt - - shuf --random-source=<(get_seeded_random 43) $raw_src_train > shuffled.$src-$tgt.$src - shuf --random-source=<(get_seeded_random 43) $raw_tgt_train > shuffled.$src-$tgt.$tgt - - head -n 1500 shuffled.$src-$tgt.$src > valid.$src-$tgt.$src - head -n 1500 shuffled.$src-$tgt.$tgt > valid.$src-$tgt.$tgt - - tail +1501 shuffled.$src-$tgt.$src > train.$src-$tgt.$src - tail +1501 shuffled.$src-$tgt.$tgt > train.$src-$tgt.$tgt -} - -function copy2dst(){ - lsrc=$1 - ltgt=$2 - src=${lsrc:0:2} - tgt=${ltgt:0:2} - - - cp valid.$src-$tgt.$src $DEST/valid.$lsrc-$ltgt.$lsrc - cp valid.$src-$tgt.$tgt $DEST/valid.$lsrc-$ltgt.$ltgt - - cp train.$src-$tgt.$src $DEST/train.$lsrc-$ltgt.$lsrc - cp train.$src-$tgt.$tgt $DEST/train.$lsrc-$ltgt.$ltgt -} - - - - -#for xh-en -declare -A xh_en_urls -xh_en_urls=( - [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/en-xh.tmx.gz - [wikimedia]=https://object.pouta.csc.fi/OPUS-wikimedia/v20190628/tmx/en-xh.tmx.gz - [memat]=https://object.pouta.csc.fi/OPUS-memat/v1/tmx/en-xh.tmx.gz - [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/en-xh.tmx.gz - [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/en-xh.tmx.gz - [XhosaNavy]=https://object.pouta.csc.fi/OPUS-XhosaNavy/v1/tmx/en-xh.tmx.gz - [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/en-xh.tmx.gz - [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/en-xh.tmx.gz -) - -mkdir $TMP_DIR/xh-en -pushd $TMP_DIR/xh-en -for k in "${!xh_en_urls[@]}" -do - name=$k - url=${xh_en_urls[$k]} - echo "$name: $url" - download_opus xh en $name $ulr -done -concat_subsets xh en "${!xh_en_urls[@]}" -split_train_valid xh en -copy2dst xh_ZA en_XX -popd - - -## -#for af-en -declare -A af_en_urls -af_en_urls=( - [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/af-en.tmx.gz - [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/af-en.tmx.gz - [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/af-en.tmx.gz - [QED]=https://object.pouta.csc.fi/OPUS-QED/v2.0a/tmx/af-en.tmx.gz - [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/af-en.tmx.gz - [OpenSubtitles]=https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/tmx/af-en.tmx.gz - [SPC]=https://object.pouta.csc.fi/OPUS-SPC/v1/tmx/af-en.tmx.gz - [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/af-en.tmx.gz -) - -mkdir $TMP_DIR/af-en -pushd $TMP_DIR/af-en -for k in "${!af_en_urls[@]}" -do - name=$k - url=${af_en_urls[$k]} - echo "$name: $url" - download_opus af en $name $ulr -done -concat_subsets af en "${!af_en_urls[@]}" -split_train_valid af en -copy2dst af_ZA en_XX -popd - - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_flores_data.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_flores_data.sh deleted file mode 100644 index e6175ce0c38b06a1ebddaeca808f71b47f77f500..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_flores_data.sh +++ /dev/null @@ -1,246 +0,0 @@ -#!/bin/bash - -# 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. -# - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - -set -e -set -o pipefail - -SRC=en -SI_TGT=si -NE_TGT=ne - -DESTDIR=${WORKDIR_ROOT}/ML50/raw/ - -ROOT=${WORKDIR_ROOT}/tmp -mkdir -p $ROOT -DATA=$ROOT/data -NE_ROOT=$DATA/all-clean-ne -SI_ROOT=$DATA/all-clean-si - -mkdir -p $DATA $NE_ROOT $SI_ROOT - -SI_OPUS_DATASETS=( - "$SI_ROOT/GNOME.en-si" - "$SI_ROOT/Ubuntu.en-si" - "$SI_ROOT/KDE4.en-si" - "$SI_ROOT/OpenSubtitles.en-si" -) - -SI_OPUS_URLS=( - "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-si.txt.zip" - "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-si.txt.zip" - "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-si.txt.zip" - "https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/moses/en-si.txt.zip" -) - -NE_OPUS_DATASETS=( - "$NE_ROOT/GNOME.en-ne" - "$NE_ROOT/Ubuntu.en-ne" - "$NE_ROOT/KDE4.en-ne" -) - -NE_OPUS_URLS=( - "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-ne.txt.zip" - "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-ne.txt.zip" - "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-ne.txt.zip" -) - -REMOVE_FILE_PATHS=() - -# Download data -download_data() { - CORPORA=$1 - URL=$2 - - if [ -f $CORPORA ]; then - echo "$CORPORA already exists, skipping download" - else - echo "Downloading $URL" - wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA - if [ -f $CORPORA ]; then - echo "$URL successfully downloaded." - else - echo "$URL not successfully downloaded." - rm -f $CORPORA - exit -1 - fi - fi -} - -# Example: download_opus_data $LANG_ROOT $TGT -download_opus_data() { - LANG_ROOT=$1 - TGT=$2 - - if [ "$TGT" = "si" ]; then - URLS=("${SI_OPUS_URLS[@]}") - DATASETS=("${SI_OPUS_DATASETS[@]}") - else - URLS=("${NE_OPUS_URLS[@]}") - DATASETS=("${NE_OPUS_DATASETS[@]}") - fi - - # Download and extract data - for ((i=0;i<${#URLS[@]};++i)); do - URL=${URLS[i]} - CORPORA=${DATASETS[i]} - - download_data $CORPORA $URL - unzip -o $CORPORA -d $LANG_ROOT - REMOVE_FILE_PATHS+=( $CORPORA $CORPORA.xml $CORPORA.ids $LANG_ROOT/README $LANG_ROOT/LICENSE ) - done - - cat ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$SRC - cat ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$TGT - - REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC ) - REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT ) -} - -download_opus_data $SI_ROOT $SI_TGT -cp ${SI_OPUS_DATASETS[3]}.$SRC $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SRC -cp ${SI_OPUS_DATASETS[3]}.$SI_TGT $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SI_TGT -REMOVE_FILE_PATHS+=( ${SI_OPUS_DATASETS[3]}.$SRC ${SI_OPUS_DATASETS[3]}.$SI_TGT ) - -download_opus_data $NE_ROOT $NE_TGT - - -# Download and extract Global Voices data -GLOBAL_VOICES="$NE_ROOT/globalvoices.2018q4.ne-en" -GLOBAL_VOICES_URL="http://www.casmacat.eu/corpus/global-voices/globalvoices.ne-en.xliff.gz" - -download_data $GLOBAL_VOICES.gz $GLOBAL_VOICES_URL -gunzip -Nf $GLOBAL_VOICES.gz - -sed -ne 's?.*\(.*\).*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$NE_TGT -sed -ne 's?.*]*>\(.*\).*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$SRC - -REMOVE_FILE_PATHS+=( $GLOBAL_VOICES ) - -# Download and extract the bible dataset -BIBLE_TOOLS=bible-corpus-tools -XML_BIBLES=XML_Bibles -XML_BIBLES_DUP=XML_Bibles_dup - -if [ ! -e $BIBLE_TOOLS ]; then - echo "Cloning bible-corpus-tools repository..." - git clone https://github.com/christos-c/bible-corpus-tools.git -fi - -mkdir -p $BIBLE_TOOLS/bin $XML_BIBLES $XML_BIBLES_DUP -javac -cp "$BIBLE_TOOLS/lib/*" -d $BIBLE_TOOLS/bin $BIBLE_TOOLS/src/bible/readers/*.java $BIBLE_TOOLS/src/bible/*.java - -download_data bible.tar.gz "https://github.com/christos-c/bible-corpus/archive/v1.2.1.tar.gz" -tar xvzf bible.tar.gz - -cp bible-corpus-1.2.1/bibles/{Greek.xml,English.xml,Nepali.xml} $XML_BIBLES/ -cp bible-corpus-1.2.1/bibles/{Greek.xml,English-WEB.xml,Nepali.xml} $XML_BIBLES_DUP/ - -java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES -java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES_DUP -java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES -java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES_DUP - -cat $XML_BIBLES/aligned/*/English.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$SRC -cat $XML_BIBLES/aligned/*/Nepali.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$NE_TGT -cat $XML_BIBLES_DUP/aligned/*/English-WEB.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$SRC -cat $XML_BIBLES_DUP/aligned/*/Nepali.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$NE_TGT -REMOVE_FILE_PATHS+=( bible-corpus-1.2.1 bible.tar.gz $BIBLE_TOOLS $XML_BIBLES $XML_BIBLES_DUP ) - -# Download and extract the Penn Treebank dataset -NE_TAGGED=$ROOT/new_submissions_parallel_corpus_project_Nepal -NE_TAGGED_URL="http://www.cle.org.pk/Downloads/ling_resources/parallelcorpus/NepaliTaggedCorpus.zip" -EN_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.en.patch" -NE_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.ne.patch" -MOSES=mosesdecoder -MOSES_TOK=$MOSES/scripts/tokenizer -EN_PATCH_REGEX="{s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}" -NE_PATCH_REGEX="{s:\p{Cf}::g;s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}" - -download_data $DATA/nepali-penn-treebank.$SRC.patch $EN_TAGGED_PATCH_URL -download_data $DATA/nepali-penn-treebank.$NE_TGT.patch $NE_TAGGED_PATCH_URL -download_data original.zip $NE_TAGGED_URL -unzip -o original.zip -d $ROOT - -cat $NE_TAGGED/00.txt $NE_TAGGED/01.txt $NE_TAGGED/02.txt > $NE_TAGGED/nepali-penn-treebank.$SRC -cat $NE_TAGGED/00ne_revised.txt $NE_TAGGED/01ne_revised.txt $NE_TAGGED/02ne_revised.txt > $NE_TAGGED/nepali-penn-treebank.$NE_TGT - -patch $NE_TAGGED/nepali-penn-treebank.$SRC -i $DATA/nepali-penn-treebank.$SRC.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$SRC -patch $NE_TAGGED/nepali-penn-treebank.$NE_TGT -i $DATA/nepali-penn-treebank.$NE_TGT.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT - -if [ ! -e $MOSES ]; then - echo "Cloning moses repository..." - git clone https://github.com/moses-smt/mosesdecoder.git -fi - -cat $NE_TAGGED/nepali-penn-treebank-patched.$SRC | \ - perl -anpe "$EN_PATCH_REGEX" | \ - $MOSES_TOK/tokenizer.perl -l $SRC | \ - $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$SRC - -cat $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT | \ - perl -CIO -anpe "$NE_PATCH_REGEX" | \ - $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$NE_TGT - - -# Download nepali dictionary data -NE_DICT=$NE_ROOT/dictionaries -download_data $NE_DICT "http://www.seas.upenn.edu/~nlp/resources/TACL-data-release/dictionaries.tar.gz" -tar xvzf $NE_DICT -cp dictionaries/dict.ne $NE_ROOT/dictionary.$NE_TGT-$SRC -REMOVE_FILE_PATHS+=( $NE_DICT dictionaries ) - -REMOVE_FILE_PATHS+=( $MOSES $NE_TAGGED original.zip $DATA/nepali-penn-treebank.$SRC.patch $DATA/nepali-penn-treebank.$NE_TGT.patch ) - - -# Remove the temporary files -for ((i=0;i<${#REMOVE_FILE_PATHS[@]};++i)); do - rm -rf ${REMOVE_FILE_PATHS[i]} -done - -# Copy the training data -si=si_LK -ne=ne_NP -en=en_XX -cat $SI_ROOT/GNOMEKDEUbuntu.en-si.si $SI_ROOT/OpenSubtitles2018.en-si.si > $DESTDIR/train.$si-$en.$si -cat $SI_ROOT/GNOMEKDEUbuntu.en-si.en $SI_ROOT/OpenSubtitles2018.en-si.en > $DESTDIR/train.$si-$en.$en - -cat $NE_ROOT/bible_dup.en-ne.ne $NE_ROOT/bible.en-ne.ne $NE_ROOT/globalvoices.2018q4.ne-en.ne $NE_ROOT/GNOMEKDEUbuntu.en-ne.ne $NE_ROOT/nepali-penn-treebank.ne > $DESTDIR/train.$ne-$en.$ne -cat $NE_ROOT/bible_dup.en-ne.en $NE_ROOT/bible.en-ne.en $NE_ROOT/globalvoices.2018q4.ne-en.en $NE_ROOT/GNOMEKDEUbuntu.en-ne.en $NE_ROOT/nepali-penn-treebank.en > $DESTDIR/train.$ne-$en.$en - - -#Download the test sets -wget https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz -tar -xvzf wikipedia_en_ne_si_test_sets.tgz - -cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.ne $DESTDIR/valid.$ne-$en.$ne -cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.en $DESTDIR/valid.$ne-$en.$en - -cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.si $DESTDIR/valid.$si-$en.$si -cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.en $DESTDIR/valid.$si-$en.$en - -cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.ne $DESTDIR/devtest.$ne-$en.$ne -cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.en $DESTDIR/devtest.$ne-$en.$en - -cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.si $DESTDIR/devtest.$si-$en.$si -cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.en $DESTDIR/devtest.$si-$en.$en - -cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.ne $DESTDIR/test.$ne-$en.$ne -cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.en $DESTDIR/test.$ne-$en.$en - -cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.si $DESTDIR/test.$si-$en.$si -cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.en $DESTDIR/test.$si-$en.$en - -rm -rf wikipedia_en_ne_si_test_sets.tgz wikipedia_en_ne_si_test_sets diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_iitb.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_iitb.sh deleted file mode 100644 index a884e20839e2a41a57405cb6af362e37bd16ab6f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_iitb.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash -# 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. - - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - -IITB=$WORKDIR_ROOT/IITB -mkdir -p $IITB -pushd $IITB - -wget http://www.cfilt.iitb.ac.in/~moses/iitb_en_hi_parallel/iitb_corpus_download/parallel.tgz -tar -xvzf parallel.tgz - -wget http://www.cfilt.iitb.ac.in/~moses/iitb_en_hi_parallel/iitb_corpus_download/dev_test.tgz -tar -xvzf dev_test.tgz - -DESTDIR=${WORKDIR_ROOT}/ML50/raw/ - -cp parallel/IITB.en-hi.en $DESTDIR/train.hi_IN-en_XX.en_XX -cp parallel/IITB.en-hi.hi $DESTDIR/train.hi_IN-en_XX.hi_IN - -cp dev_test/dev.en $DESTDIR/valid.hi_IN-en_XX.en_XX -cp dev_test/dev.hi $DESTDIR/valid.hi_IN-en_XX.hi_IN - -cp dev_test/test.en $DESTDIR/test.hi_IN-en_XX.en_XX -cp dev_test/test.hi $DESTDIR/test.hi_IN-en_XX.hi_IN -popd \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_iwslt_and_extract.sh deleted file mode 100644 index ca3591b3db1715f136773d62e4b9b9ede97d436c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_iwslt_and_extract.sh +++ /dev/null @@ -1,225 +0,0 @@ -#!/bin/bash -# 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. - -#echo 'Cloning Moses github repository (for tokenization scripts)...' -#git clone https://github.com/moses-smt/mosesdecoder.git - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - - -data_root=${WORKDIR_ROOT}/iwsltv2 -DESTDIR=${WORKDIR_ROOT}/ML50/raw - - -langs="ar_AR it_IT nl_XX ko_KR vi_VN" -echo "data_root: $data_root" - -download_path=${data_root}/downloads -raw=${DESTDIR} -tmp=${data_root}/tmp -orig=${data_root}/orig - -mkdir -p $download_path $orig $raw $tmp -####################### -download_iwslt(){ - iwslt_key=$1 - src=$2 - tgt=$3 - save_prefix=$4 - pushd ${download_path} - if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then - wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz - [ $? -eq 0 ] && return 0 - fi - popd -} - -extract_iwslt(){ - src=$1 - tgt=$2 - prefix=$3 - pushd $orig - tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz - popd -} - -generate_train(){ - lsrc=$1 - ltgt=$2 - src=${lsrc:0:2} - tgt=${ltgt:0:2} - for ll in $lsrc $ltgt; do - l=${ll:0:2} - f="$orig/*/train.tags.$src-$tgt.$l" - f_raw=$raw/train.$lsrc-$ltgt.$ll - cat $f \ - | grep -v '' \ - | grep -v '' \ - | grep -v '' \ - | grep -v '' \ - | grep -v '' \ - | sed -e 's///g' \ - | sed -e 's/<\/title>//g' \ - | sed -e 's/<description>//g' \ - | sed -e 's/<\/description>//g' \ - | sed 's/^\s*//g' \ - | sed 's/\s*$//g' \ - > $f_raw - [ $? -eq 0 ] && echo "extracted $f to $f_raw" - done - return 0 -} - -convert_valid_test(){ - src=$1 - tgt=$2 - for l in $src $tgt; do - echo "lang: ${l}" - for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do - fname=${o##*/} - f=$tmp/${fname%.*} - echo "$o => $f" - grep '<seg id' $o \ - | sed -e 's/<seg id="[0-9]*">\s*//g' \ - | sed -e 's/\s*<\/seg>\s*//g' \ - | sed -e "s/\’/\'/g" \ - > $f - echo "" - done - done -} - -generate_subset(){ - lsrc=$1 - ltgt=$2 - src=${lsrc:0:2} - tgt=${ltgt:0:2} - subset=$3 - prefix=$4 - for ll in $lsrc $ltgt; do - l=${ll:0:2} - f=$tmp/$prefix.${src}-${tgt}.$l - if [[ -f $f ]]; then - cp $f $raw/$subset.${lsrc}-$ltgt.${ll} - fi - done -} -################# - -echo "downloading iwslt training and dev data" -# using multilingual for it, nl -download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo -download_iwslt "2017-01-trnted" ar en -download_iwslt "2017-01-trnted" en ar -download_iwslt "2017-01-trnted" ko en -download_iwslt "2017-01-trnted" en ko -download_iwslt "2015-01" vi en -download_iwslt "2015-01" en vi - -echo "donwloading iwslt test data" -download_iwslt "2017-01-mted-test" it en "test." -download_iwslt "2017-01-mted-test" en it "test." -download_iwslt "2017-01-mted-test" nl en "test." -download_iwslt "2017-01-mted-test" en nl "test." - -download_iwslt "2017-01-ted-test" ar en "test." -download_iwslt "2017-01-ted-test" en ar "test." -download_iwslt "2017-01-ted-test" ko en "test." -download_iwslt "2017-01-ted-test" en ko "test." -download_iwslt "2015-01-test" vi en "test." -download_iwslt "2015-01-test" en vi "test." - -echo "extract training data tar balls" -extract_iwslt DeEnItNlRo DeEnItNlRo -extract_iwslt ar en -extract_iwslt en ar -extract_iwslt ko en -extract_iwslt en ko -extract_iwslt vi en -extract_iwslt en vi - - -echo "extracting iwslt test data" -for lang in $langs; do - l=${lang:0:2} - extract_iwslt $l en "test." - extract_iwslt en $l "test." -done - -echo "convert dev and test data" -for lang in $langs; do - s_lang=${lang:0:2} - convert_valid_test $s_lang en - convert_valid_test en $s_lang -done - - - -echo "creating training data into $raw" -for lang in $langs; do - generate_train $lang en_XX - generate_train en_XX $lang -done - -echo "creating iwslt dev data into raw" -generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013" -generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013" - -generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016" -generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016" -generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016" -generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016" - - -generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010" -generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010" -generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010" -generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010" - -echo "creating iswslt test data into raw" -generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015" -generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015" - -generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017" -generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017" -generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017" -generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017" - -generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng" -generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng" -generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng" -generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng" - -# normalze iwslt directions into x-en -pushd $raw -for lang in $langs; do - for split in test valid; do - x_en_f1=$split.$lang-en_XX.en_XX - x_en_f2=$split.$lang-en_XX.${lang} - - en_x_f1=$split.en_XX-$lang.en_XX - en_x_f2=$split.en_XX-$lang.${lang} - - if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then - echo "cp $en_x_f1 $x_en_f1" - cp $en_x_f1 $x_en_f1 - fi - if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then - echo "cp $en_x_f2 $x_en_f2" - cp $en_x_f2 $x_en_f2 - fi - done -done -popd \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_lotus.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_lotus.sh deleted file mode 100644 index c08c701314a8e575637deff78381ab02c2ef6728..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_lotus.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash -# 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. - - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - -SRCDIR=$WORKDIR_ROOT/indic_languages_corpus -DESTDIR=${WORKDIR_ROOT}/ML50/raw/ -mkdir -p $SRCDIR -mkdir -p $DESTDIR - -cd $SRCDIR -wget http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/indic_languages_corpus.tar.gz -tar -xvzf indic_languages_corpus.tar.gz - -SRC_EXTRACT_DIR=$SRCDIR/indic_languages_corpus/bilingual - -cp $SRC_EXTRACT_DIR/ml-en/train.ml $DESTDIR/train.ml_IN-en_XX.ml_IN -cp $SRC_EXTRACT_DIR/ml-en/train.en $DESTDIR/train.ml_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ml-en/dev.ml $DESTDIR/valid.ml_IN-en_XX.ml_IN -cp $SRC_EXTRACT_DIR/ml-en/dev.en $DESTDIR/valid.ml_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ml-en/test.ml $DESTDIR/test.ml_IN-en_XX.ml_IN -cp $SRC_EXTRACT_DIR/ml-en/test.en $DESTDIR/test.ml_IN-en_XX.en_XX - -cp $SRC_EXTRACT_DIR/ur-en/train.ur $DESTDIR/train.ur_PK-en_XX.ur_PK -cp $SRC_EXTRACT_DIR/ur-en/train.en $DESTDIR/train.ur_PK-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ur-en/dev.ur $DESTDIR/valid.ur_PK-en_XX.ur_PK -cp $SRC_EXTRACT_DIR/ur-en/dev.en $DESTDIR/valid.ur_PK-en_XX.en_XX -cp $SRC_EXTRACT_DIR/ur-en/test.ur $DESTDIR/test.ur_PK-en_XX.ur_PK -cp $SRC_EXTRACT_DIR/ur-en/test.en $DESTDIR/test.ur_PK-en_XX.en_XX - -cp $SRC_EXTRACT_DIR/te-en/train.te $DESTDIR/train.te_IN-en_XX.te_IN -cp $SRC_EXTRACT_DIR/te-en/train.en $DESTDIR/train.te_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/te-en/dev.te $DESTDIR/valid.te_IN-en_XX.te_IN -cp $SRC_EXTRACT_DIR/te-en/dev.en $DESTDIR/valid.te_IN-en_XX.en_XX -cp $SRC_EXTRACT_DIR/te-en/test.te $DESTDIR/test.te_IN-en_XX.te_IN -cp $SRC_EXTRACT_DIR/te-en/test.en $DESTDIR/test.te_IN-en_XX.en_XX diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_ted_and_extract.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_ted_and_extract.py deleted file mode 100644 index 123a5d1f8cd4895ba9711b21f22b082177b993d4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_ted_and_extract.py +++ /dev/null @@ -1,338 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 csv -import io -import itertools -import os -import sys -from collections import defaultdict -from subprocess import check_call, check_output - -import wget -from six.moves import zip - -# scripts and data locations -CWD = os.getcwd() -UTILS = f"{CWD}/utils" - -MOSES = f"{UTILS}/mosesdecoder" - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - - -# please donwload mosesdecoder here: -detok_cmd = f'{MOSES}/scripts/tokenizer/detokenizer.perl' - - -def call(cmd): - print(f"Executing: {cmd}") - check_call(cmd, shell=True) - -class MultiLingualAlignedCorpusReader(object): - """A class to read TED talk dataset - """ - - def __init__(self, corpus_path, delimiter='\t', - target_token=True, bilingual=True, corpus_type='file', - lang_dict={'source': ['fr'], 'target': ['en']}, - eval_lang_dict=None, zero_shot=False, - detok=True, - ): - - self.empty_line_flag = 'NULL' - self.corpus_path = corpus_path - self.delimiter = delimiter - self.bilingual = bilingual - self.lang_dict = lang_dict - self.lang_set = set() - self.target_token = target_token - self.zero_shot = zero_shot - self.eval_lang_dict = eval_lang_dict - self.corpus_type = corpus_type - self.detok = detok - - for list_ in self.lang_dict.values(): - for lang in list_: - self.lang_set.add(lang) - - self.data = dict() - self.data['train'] = self.read_aligned_corpus(split_type='train') - self.data['test'] = self.read_aligned_corpus(split_type='test') - self.data['dev'] = self.read_aligned_corpus(split_type='dev') - - def read_data(self, file_loc_): - data_list = list() - with io.open(file_loc_, 'r', encoding='utf8') as fp: - for line in fp: - try: - text = line.strip() - except IndexError: - text = self.empty_line_flag - data_list.append(text) - return data_list - - def filter_text(self, dict_): - if self.target_token: - field_index = 1 - else: - field_index = 0 - data_dict = defaultdict(list) - list1 = dict_['source'] - list2 = dict_['target'] - for sent1, sent2 in zip(list1, list2): - try: - src_sent = ' '.join(sent1.split()[field_index: ]) - except IndexError: - src_sent = 'NULL' - - if src_sent.find(self.empty_line_flag) != -1 or len(src_sent) == 0: - continue - - elif sent2.find(self.empty_line_flag) != -1 or len(sent2) == 0: - continue - - else: - data_dict['source'].append(sent1) - data_dict['target'].append(sent2) - return data_dict - - def read_file(self, split_type, data_type): - return self.data[split_type][data_type] - - def save_file(self, path_, split_type, data_type, lang): - tok_file = tok_file_name(path_, lang) - with io.open(tok_file, 'w', encoding='utf8') as fp: - for line in self.data[split_type][data_type]: - fp.write(line + '\n') - if self.detok: - de_tok(tok_file, lang) - - def add_target_token(self, list_, lang_id): - new_list = list() - token = '__' + lang_id + '__' - for sent in list_: - new_list.append(token + ' ' + sent) - return new_list - - def read_from_single_file(self, path_, s_lang, t_lang): - data_dict = defaultdict(list) - with io.open(path_, 'r', encoding='utf8') as fp: - reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) - for row in reader: - data_dict['source'].append(row[s_lang]) - data_dict['target'].append(row[t_lang]) - - if self.target_token: - text = self.add_target_token(data_dict['source'], t_lang) - data_dict['source'] = text - - return data_dict['source'], data_dict['target'] - - def read_aligned_corpus(self, split_type='train'): - data_dict = defaultdict(list) - iterable = [] - s_list = [] - t_list = [] - - if self.zero_shot: - if split_type == "train": - iterable = zip(self.lang_dict['source'], self.lang_dict['target']) - else: - iterable = zip(self.eval_lang_dict['source'], self.eval_lang_dict['target']) - - elif self.bilingual: - iterable = itertools.product(self.lang_dict['source'], self.lang_dict['target']) - - for s_lang, t_lang in iterable: - if s_lang == t_lang: - continue - if self.corpus_type == 'file': - split_type_file_path = os.path.join(self.corpus_path, - "all_talks_{}.tsv".format(split_type)) - s_list, t_list = self.read_from_single_file(split_type_file_path, - s_lang=s_lang, - t_lang=t_lang) - data_dict['source'] += s_list - data_dict['target'] += t_list - new_data_dict = self.filter_text(data_dict) - return new_data_dict - - -def read_langs(corpus_path): - split_type_file_path = os.path.join(corpus_path, 'extracted', - "all_talks_dev.tsv") - with io.open(split_type_file_path, 'r', encoding='utf8') as fp: - reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) - header = next(reader) - return [k for k in header.keys() if k != 'talk_name'] - -def extra_english(corpus_path, split): - split_type_file_path = os.path.join(corpus_path, - f"all_talks_{split}.tsv") - output_split_type_file_path = os.path.join(corpus_path, - f"all_talks_{split}.en") - with io.open(split_type_file_path, 'r', encoding='utf8') as fp, io.open(output_split_type_file_path, 'w', encoding='utf8') as fw: - reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE) - for row in reader: - line = row['en'] - fw.write(line + '\n') - de_tok(output_split_type_file_path, 'en') - - - -def tok_file_name(filename, lang): - seps = filename.split('.') - seps.insert(-1, 'tok') - tok_file = '.'.join(seps) - return tok_file - -def de_tok(tok_file, lang): - # seps = tok_file.split('.') - # seps.insert(-1, 'detok') - # de_tok_file = '.'.join(seps) - de_tok_file = tok_file.replace('.tok.', '.') - cmd = 'perl {detok_cmd} -l {lang} < {tok_file} > {de_tok_file}'.format( - detok_cmd=detok_cmd, tok_file=tok_file, - de_tok_file=de_tok_file, lang=lang[:2]) - call(cmd) - -def extra_bitex( - ted_data_path, - lsrc_lang, - ltrg_lang, - target_token, - output_data_path, -): - def get_ted_lang(lang): - long_langs = ['pt-br', 'zh-cn', 'zh-tw', 'fr-ca'] - if lang[:5] in long_langs: - return lang[:5] - elif lang[:4] =='calv': - return lang[:5] - elif lang in ['pt_BR', 'zh_CN', 'zh_TW', 'fr_CA']: - return lang.lower().replace('_', '-') - return lang[:2] - src_lang = get_ted_lang(lsrc_lang) - trg_lang = get_ted_lang(ltrg_lang) - train_lang_dict={'source': [src_lang], 'target': [trg_lang]} - eval_lang_dict = {'source': [src_lang], 'target': [trg_lang]} - - obj = MultiLingualAlignedCorpusReader(corpus_path=ted_data_path, - lang_dict=train_lang_dict, - target_token=target_token, - corpus_type='file', - eval_lang_dict=eval_lang_dict, - zero_shot=False, - bilingual=True) - - os.makedirs(output_data_path, exist_ok=True) - lsrc_lang = lsrc_lang.replace('-', '_') - ltrg_lang = ltrg_lang.replace('-', '_') - obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", - split_type='train', data_type='source', lang=src_lang) - obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", - split_type='train', data_type='target', lang=trg_lang) - - obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", - split_type='test', data_type='source', lang=src_lang) - obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", - split_type='test', data_type='target', lang=trg_lang) - - obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}", - split_type='dev', data_type='source', lang=src_lang) - obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}", - split_type='dev', data_type='target', lang=trg_lang) - - -def bar_custom(current, total, width=80): - print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r') - - -def download_and_extract(download_to, extract_to): - url = 'http://phontron.com/data/ted_talks.tar.gz' - filename = f"{download_to}/ted_talks.tar.gz" - if os.path.exists(filename): - print(f'{filename} has already been downloaded so skip') - else: - filename = wget.download(url, filename, bar=bar_custom) - if os.path.exists(f'{extract_to}/all_talks_train.tsv'): - print(f'Already extracted so skip') - else: - extract_cmd = f'tar xzfv "{filename}" -C "{extract_to}"' - call(extract_cmd) - - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--ted_data_path', type=str, default=WORKDIR_ROOT, required=False) - parser.add_argument( - '--direction-list', - type=str, - # default=None, - #for ML50 - default=( - "bn_IN-en_XX,he_IL-en_XX,fa_IR-en_XX,id_ID-en_XX,sv_SE-en_XX,pt_XX-en_XX,ka_GE-en_XX,ka_GE-en_XX,th_TH-en_XX," - "mr_IN-en_XX,hr_HR-en_XX,uk_UA-en_XX,az_AZ-en_XX,mk_MK-en_XX,gl_ES-en_XX,sl_SI-en_XX,mn_MN-en_XX," - #non-english directions - # "fr_XX-de_DE," # replaced with wmt20 - # "ja_XX-ko_KR,es_XX-pt_XX,ru_RU-sv_SE,hi_IN-bn_IN,id_ID-ar_AR,cs_CZ-pl_PL,ar_AR-tr_TR" - ), - required=False) - parser.add_argument('--target-token', action='store_true', default=False) - parser.add_argument('--extract-all-english', action='store_true', default=False) - - args = parser.parse_args() - - import json - import sys - - # TED Talks data directory - ted_data_path = args.ted_data_path - - download_to = f'{ted_data_path}/downloads' - extract_to = f'{ted_data_path}/extracted' - - #DESTDIR=${WORKDIR_ROOT}/ML50/raw/ - output_path = f'{ted_data_path}/ML50/raw' - os.makedirs(download_to, exist_ok=True) - os.makedirs(extract_to, exist_ok=True) - os.makedirs(output_path, exist_ok=True) - download_and_extract(download_to, extract_to) - - - if args.extract_all_english: - for split in ['train', 'dev', 'test']: - extra_english(ted_data_path, split) - exit(0) - if args.direction_list is not None: - directions = args.direction_list.strip().split(',') - directions = [tuple(d.strip().split('-', 1)) for d in directions if d] - else: - langs = read_langs(ted_data_path) - # directions = [ - # '{}.{}'.format(src, tgt) - # for src in langs - # for tgt in langs - # if src < tgt - # ] - directions = [('en', tgt) for tgt in langs if tgt != 'en'] - print(f'num directions={len(directions)}: {directions}') - - for src_lang, trg_lang in directions: - print('--working on {}-{}'.format(src_lang, trg_lang)) - extra_bitex( - extract_to, - src_lang, - trg_lang, - target_token=args.target_token, - output_data_path=output_path - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wat19_my.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wat19_my.sh deleted file mode 100644 index c1e2d47287a29af4576e7a63641e8152ecb63c44..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wat19_my.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash -# 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. - - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - -SRCDIR=$WORKDIR_ROOT/indic_languages_corpus -DESTDIR=$WORKDIR_ROOT/ML50/raw -mkdir -p $SRCDIR -mkdir -p $DESTDIR - -WAT_MY_EN=wat2020.my-en.zip -cd $SRCDIR -# please refer to http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/ for latest URL if the following url expired -#- The data used for WAT2020 are identical to those used in WAT2019. -wget http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/$WAT_MY_EN -unzip $WAT_MY_EN - - -SRC_EXTRACT_DIR=$SRCDIR/wat2020.my-en/alt - -cp $SRC_EXTRACT_DIR/train.alt.en $DESTDIR/train.my_MM-en_XX.en_XX -cp $SRC_EXTRACT_DIR/train.alt.my $DESTDIR/train.my_MM-en_XX.my_MM -cp $SRC_EXTRACT_DIR/dev.alt.en $DESTDIR/valid.my_MM-en_XX.en_XX -cp $SRC_EXTRACT_DIR/dev.alt.my $DESTDIR/valid.my_MM-en_XX.my_MM -cp $SRC_EXTRACT_DIR/test.alt.en $DESTDIR/test.my_MM-en_XX.en_XX -cp $SRC_EXTRACT_DIR/test.alt.my $DESTDIR/test.my_MM-en_XX.my_MM diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wmt19_and_before.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wmt19_and_before.py deleted file mode 100644 index ad05e974dccb02273fb53a7a48de2a6de3fbdbd4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wmt19_and_before.py +++ /dev/null @@ -1,901 +0,0 @@ -import glob -import multiprocessing as mp -import os -import pathlib -import re -import subprocess -import sys -from collections import OrderedDict -from functools import partial -from subprocess import check_call, check_output -from typing import List, NamedTuple -from urllib.parse import urlparse - -import wget - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - -# scripts and data locations -CWD = os.getcwd() -UTILS = f"{CWD}/utils" - -MOSES = f"{UTILS}/mosesdecoder" -SGM_TOOL = f'{MOSES}/scripts/ems/support/input-from-sgm.perl' - -TMX2CORPUS = f"{UTILS}/tmx2corpus" -TMX_TOOL = f'python {TMX2CORPUS}/tmx2corpus.py' - -to_data_path = f'{WORKDIR_ROOT}/wmt' -download_to = f'{to_data_path}/downloads' -manually_downloads = f'{to_data_path}/downloads' -extract_to = f'{to_data_path}/extracted' -#DESTDIR=${WORKDIR_ROOT}/ML50/raw/ -raw_data = f'{WORKDIR_ROOT}/ML50/raw' -#### - -class DLDataset(NamedTuple): - name: str - train_urls: List[str] - valid_urls: List[str] - test_urls: List[str] - train_files_patterns: List[str] = [] - valid_files_patterns: List[str] = [] - test_files_patterns: List[str] = [] - - - -def bar_custom(current, total, width=80): - print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r') - -def get_downloaded_file(dl_folder, url): - if isinstance(url, tuple): - url, f = url - else: - url_f = urlparse(url) - # f = os.path.split(url_f.path)[-1] - f = '_'.join(url_f.path.split('/')[1:]) - return url, f"{dl_folder}/{f}" - -def download_parts_and_combine(dl_folder, urls, filename): - parts = [] - for url_record in urls: - url, part_file = get_downloaded_file(dl_folder, url_record) - if os.path.exists(part_file): - print(f'{part_file} has already been downloaded so skip') - else: - part_file = wget.download(url, part_file, bar=bar_custom) - parts.append(part_file) - - def get_combine_cmd(parts): - #default as tar.gz.?? - return f'cat {" ".join(parts)} > {filename}' - - combine_cmd = get_combine_cmd(parts) - call(combine_cmd, debug=True) - return filename - -def download_a_url(dl_folder, url): - url, filename = get_downloaded_file(dl_folder, url) - if os.path.exists(filename): - print(f'{filename} has already been downloaded so skip') - return filename - - print(f'downloading {url} to {filename}') - if isinstance(url, list) or isinstance(url, tuple): - download_parts_and_combine(dl_folder, url, filename) - else: - wget.download(url, filename, bar=bar_custom) - print(f'dowloaded: {filename}') - return filename - -def download_files(dl_folder, urls, completed_urls={}): - for url_record in urls: - url, _ = get_downloaded_file(dl_folder, url_record) - filename = download_a_url(dl_folder, url_record) - completed_urls[str(url)] = filename - return completed_urls - -def check_need_manual_downalod(dl_folder, to_manually_download_urls): - to_be_manually_dowloaded = [] - manually_completed_urls = {} - for url_record, instruction in to_manually_download_urls: - url, filename = get_downloaded_file(dl_folder, url_record) - if not os.path.exists(filename): - print(f'{url} need to be download manually, please download it manually following {instruction}; and copy it to {filename}') - to_be_manually_dowloaded.append((url, filename)) - else: - manually_completed_urls[url] = filename - # if len(to_be_manually_dowloaded) > 0: - # raise ValueError('Missing files that need to be downloaded manually; stop the process now.') - return to_be_manually_dowloaded - -def download_dataset(to_folder, dl_dataset, completed_urls={}): - download_files(to_folder, dl_dataset.train_urls, completed_urls) - download_files(to_folder, dl_dataset.valid_urls, completed_urls) - download_files(to_folder, dl_dataset.test_urls, completed_urls) - print('completed downloading') - return completed_urls - -def call(cmd, debug=False): - if debug: - print(cmd) - check_call(cmd, shell=True) - - -def get_extract_name(file_path): - path = os.path.split(file_path) - return path[-1] + '_extract' #.split('.')[0] - -def extract_file(downloaded_file, extract_folder, get_extract_name=get_extract_name, debug=False): - extract_name = get_extract_name(downloaded_file) - extract_to = f'{extract_folder}/{extract_name}' - os.makedirs(extract_to, exist_ok=True) - if os.path.exists(f'{extract_to}/DONE'): - print(f'{downloaded_file} has already been extracted to {extract_to} so skip') - return extract_to - def get_extract_cmd(filename): - if filename.endswith('.tgz') or filename.endswith('tar.gz'): - return f'tar xzfv {filename} -C {extract_to}' - elif filename.endswith('.gz.tar'): - return f'tar xfv {filename} -C {extract_to}; (cd {extract_to}; gzip -d *.gz; [ $? -eq 0 ] || gzip -d */*.gz)' - elif filename.endswith('.tar'): - return f'tar xfv {filename} -C {extract_to}' - elif filename.endswith('.gz'): - return f'cp {filename} {extract_to}; (cd {extract_to}; gzip -d *.gz)' - elif filename.endswith('.zip'): - return f'unzip {filename} -d {extract_to}' - extract_cmd = get_extract_cmd(downloaded_file) - print(f'extracting {downloaded_file}') - if isinstance(extract_cmd, list): - for c in extract_cmd: - call(c, debug=debug) - else: - call(extract_cmd, debug=debug) - call(f'echo DONE > {extract_to}/DONE') - return extract_to - - -def extract_all_files( - completed_urls, extract_folder, - get_extract_name=get_extract_name, - completed_extraction={}, - debug=False): - extracted_folders = OrderedDict() - for url, downloaded_file in set(completed_urls.items()): - if downloaded_file in completed_extraction: - print(f'{downloaded_file} is already extracted; so skip') - continue - folder = extract_file(downloaded_file, extract_folder, get_extract_name, debug) - extracted_folders[url] = folder - return extracted_folders - - -def my_glob(folder): - for p in [f'{folder}/*', f'{folder}/*/*', f'{folder}/*/*/*']: - for f in glob.glob(p): - yield f - - -def sgm2raw(sgm, debug): - to_file = sgm[0:len(sgm) - len('.sgm')] - if os.path.exists(to_file): - debug and print(f'{sgm} already converted to {to_file}; so skip') - return to_file - cmd = f'{SGM_TOOL} < {sgm} > {to_file}' - call(cmd, debug) - return to_file - -def tmx2raw(tmx, debug): - to_file = tmx[0:len(tmx) - len('.tmx')] - to_folder = os.path.join(*os.path.split(tmx)[:-1]) - if os.path.exists(f'{to_folder}/bitext.en'): - debug and print(f'{tmx} already extracted to {to_file}; so skip') - return to_file - cmd = f'(cd {to_folder}; {TMX_TOOL} {tmx})' - call(cmd, debug) - return to_file - -CZENG16_REGEX = re.compile(r'.*?data.plaintext-format/0[0-9]train$') -WMT19_WIKITITLES_REGEX = re.compile(r'.*?wikititles-v1.(\w\w)-en.tsv.gz') -TSV_REGEX = re.compile(r'.*?(\w\w)-(\w\w).tsv$') - - - -def cut_wikitles(wiki_file, debug): - # different languages have different file names: - if wiki_file.endswith('wiki/fi-en/titles.fi-en'): - to_file1 = f'{wiki_file}.fi' - to_file2 = f'{wiki_file}.en' - BACKSLASH = '\\' - cmd1 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f1 |awk '{{$1=$1}};1' > {to_file1}" - cmd2 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f2 |awk '{{$1=$1}};1' > {to_file2}" -# elif WMT19_WIKITITLES_REGEX.match(wiki_file): -# src = WMT19_WIKITITLES_REGEX.match(wiki_file).groups()[0] -# to_file1 = f'{wiki_file}.{src}' -# to_file2 = f'{wiki_file}.en' -# cmd1 = f"cat {wiki_file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}" -# cmd2 = f"cat {wiki_file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}" - else: - return None - if os.path.exists(to_file1) and os.path.exists(to_file2): - debug and print(f'{wiki_file} already processed to {to_file1} and {to_file2}; so skip') - return wiki_file - - call(cmd1, debug=debug) - call(cmd2, debug=debug) - return wiki_file - -def cut_tsv(file, debug): - m = TSV_REGEX.match(file) - if m is None: - raise ValueError(f'{file} is not matching tsv pattern') - src = m.groups()[0] - tgt = m.groups()[1] - - to_file1 = f'{file}.{src}' - to_file2 = f'{file}.{tgt}' - cmd1 = f"cat {file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}" - cmd2 = f"cat {file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}" - if os.path.exists(to_file1) and os.path.exists(to_file2): - debug and print(f'{file} already processed to {to_file1} and {to_file2}; so skip') - return file - - call(cmd1, debug=debug) - call(cmd2, debug=debug) - return file - - -def convert_file_if_needed(file, debug): - if file.endswith('.sgm'): - return sgm2raw(file, debug) - elif file.endswith('.tmx'): - return tmx2raw(file, debug) - elif file.endswith('wiki/fi-en/titles.fi-en'): - return cut_wikitles(file, debug) -# elif WMT19_WIKITITLES_REGEX.match(file): -# return cut_wikitles(file, debug) - elif file.endswith('.tsv'): - return cut_tsv(file, debug) - elif CZENG16_REGEX.match(file): - return convert2czeng17(file, debug) - else: - return file - - -def convert_files_if_needed(extracted_foldrs, my_glob=my_glob, debug=False): - return { - url: list(sorted(set(convert_file_if_needed(f, debug)) for f in sorted(set(my_glob(folder))))) - for url, folder in extracted_foldrs.items() - } - -def match_patt(file_path, file_pattern, src, tgt, lang): - return file_pattern.format(src=src, tgt=tgt, lang=lang) in file_path - -def match_patts(file_path, file_patterns, src, tgt, lang): - for file_pattern in file_patterns: - params = { k: v for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] if k in file_pattern} - matching = file_pattern.format(**params) - - if isinstance(file_pattern, tuple): - pattern, directions = file_pattern - if f'{src}-{tgt}' in directions and matching in file_path: - return True - else: - if matching in file_path: - return True - return False - -def extracted_glob(extracted_folder, file_patterns, src, tgt, lang): - def get_matching_pattern(file_pattern): - params = { - k: v - for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] - if '{' + k + '}' in file_pattern - } - file_pattern = re.sub(r'{src:(.*?)}', r'\1' if lang == src else '', file_pattern) - file_pattern = re.sub(r'{tgt:(.*?)}', r'\1' if lang == tgt else '', file_pattern) - file_pattern = file_pattern.format(**params) - return file_pattern - for file_pattern in file_patterns: - if isinstance(file_pattern, tuple): - file_pattern, lang_pairs = file_pattern - if f'{src}-{tgt}' not in lang_pairs: - continue -# print('working on pattern: ', file_pattern, lang_pairs ) - matching_pattern = get_matching_pattern(file_pattern) - if matching_pattern is None: - continue - glob_patterns = f'{extracted_folder}/{matching_pattern}' -# print('glob_patterns: ', glob_patterns) - for f in glob.glob(glob_patterns): - yield f - -# for debug usage -def all_extracted_files(split, src, tgt, extracted_folders, split_urls): - def get_url(url): - if isinstance(url, tuple): - url, downloaded_file = url - return url - return [ - f - for url in split_urls - for f in my_glob(extracted_folders[str(get_url(url))]) - ] - -def concat_files(split, src, tgt, extracted_folders, split_urls, path_patterns, to_folder, debug=False): -# if debug: -# print('extracted files to be filtered by patterns: ', -# '\n\t'.join(sorted(all_extracted_files(split, src, tgt, extracted_folders, split_urls)))) - for lang in [src, tgt]: - to_file = f'{to_folder}/{split}.{src}-{tgt}.{lang}' - s_src, s_tgt, s_lang = src.split('_')[0], tgt.split('_')[0], lang.split('_')[0] - files = [] - for url in split_urls: - if isinstance(url, tuple): - url, downloaded_file = url - if str(url) not in extracted_folders: - print(f'warning: {url} not in extracted files') - for extracted_file in set( - extracted_glob( - extracted_folders[str(url)], path_patterns, - s_src, s_tgt, s_lang)): - files.append(extracted_file) - if len(files) == 0: - print('warning: ', f'No files found for split {to_file}') - continue - files = sorted(set(files)) - print(f'concating {len(files)} files into {to_file}') - cmd = ['cat'] + [f'"{f}"' for f in files] + [f'>{to_file}'] - cmd = " ".join(cmd) - call(cmd, debug=debug) - -UTILS = os.path.join(pathlib.Path(__file__).parent, 'utils') -LID_MODEL = f'{download_to}/lid.176.bin' -LID_MULTI = f'{UTILS}/fasttext_multi_filter.py' - -def lid_filter(split, src, tgt, from_folder, to_folder, debug=False): - if not os.path.exists(LID_MODEL): - call(f'wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O {LID_MODEL}') - from_prefix = f'{from_folder}/{split}.{src}-{tgt}' - to_prefix = f'{to_folder}/{split}.{src}-{tgt}' - if os.path.exists(f'{from_prefix}.{src}') and os.path.exists(f'{from_prefix}.{tgt}'): - s_src, s_tgt = src.split('_')[0], tgt.split('_')[0] - cmd = ( - f'python {LID_MULTI} --model {LID_MODEL} --inputs {from_prefix}.{src} {from_prefix}.{tgt} ' - f'--langs {s_src} {s_tgt} --outputs {to_prefix}.{src} {to_prefix}.{tgt}' - ) - print(f'filtering {from_prefix}') - call(cmd, debug=debug) - -def concat_into_splits(dl_dataset, src, tgt, extracted_folders, to_folder, debug): - to_folder_tmp = f"{to_folder}_tmp" - os.makedirs(to_folder_tmp, exist_ok=True) - concat_files('train', src, tgt, - extracted_folders, - split_urls=dl_dataset.train_urls, - path_patterns=dl_dataset.train_files_patterns, - to_folder=to_folder_tmp, debug=debug) - lid_filter('train', src, tgt, to_folder_tmp, to_folder, debug) - - concat_files('valid', src, tgt, - extracted_folders, - split_urls=dl_dataset.valid_urls, - path_patterns=dl_dataset.valid_files_patterns, - to_folder=to_folder, debug=debug) - concat_files('test', src, tgt, - extracted_folders, - split_urls=dl_dataset.test_urls, - path_patterns=dl_dataset.test_files_patterns, - to_folder=to_folder, debug=debug) - - -def download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=False): - pool = mp.Pool(processes=num_processes) - download_f = partial(download_a_url, dl_folder) - downloaded_files = pool.imap_unordered(download_f, urls) - pool.close() - pool.join() - -BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ") -def run_eval_bleu(cmd): - output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip() - print(output) - bleu = -1.0 - for line in output.strip().split('\n'): - m = BLEU_REGEX.search(line) - if m is not None: - bleu = m.groups()[0] - bleu = float(bleu) - break - return bleu - -def check_wmt_test_bleu(raw_folder, wmt_lang_pairs): - not_matchings = [] - for wmt, src_tgts in wmt_lang_pairs: - for src_tgt in src_tgts: - print(f'checking test bleus for: {src_tgt} at {wmt}') - src, tgt = src_tgt.split('-') - ssrc, stgt = src[:2], tgt[:2] - if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'): - # reversed direction may have different test set - test_src = f'{raw_folder}/test.{tgt}-{src}.{src}' - else: - test_src = f'{raw_folder}/test.{src}-{tgt}.{src}' - cmd1 = f'cat {test_src} | sacrebleu -t "{wmt}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""' - test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}' - cmd2 = f'cat {test_tgt} | sacrebleu -t "{wmt}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""' - bleu1 = run_eval_bleu(cmd1) - if bleu1 != 100.0: - not_matchings.append(f'{wmt}:{src_tgt} source side not matching: {test_src}') - bleu2 = run_eval_bleu(cmd2) - if bleu2 != 100.0: - not_matchings.append(f'{wmt}:{src_tgt} target side not matching: {test_tgt}') - return not_matchings - -def download_and_extract( - to_folder, lang_pairs, dl_dataset, - to_manually_download_urls, - completed_urls={}, completed_extraction={}, - debug=False): - - dl_folder = f'{to_folder}/downloads' - extract_folder = f'{to_folder}/extracted' - raw_folder = f'{to_folder}/raw' - lid_filtered = f'{to_folder}/lid_filtered' - - os.makedirs(extract_folder, exist_ok=True) - os.makedirs(raw_folder, exist_ok=True) - os.makedirs(lid_filtered, exist_ok=True) - - - to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls) - - completed_urls = download_dataset( - dl_folder, dl_dataset, completed_urls) - if debug: - print('completed urls: ', completed_urls) - - - extracted_folders = extract_all_files( - completed_urls, - extract_folder=extract_folder, - completed_extraction=completed_extraction, - debug=debug) - if debug: - print('download files have been extracted to folders: ', extracted_folders) - - converted_files = convert_files_if_needed(extracted_folders, debug=False) - for src_tgt in lang_pairs: - print(f'working on {dl_dataset.name}: {src_tgt}') - src, tgt = src_tgt.split('-') - concat_into_splits(dl_dataset, - src=src, tgt=tgt, - extracted_folders=extracted_folders, - to_folder=raw_folder, debug=debug) - print('completed data into: ', raw_folder) - -def download_czang16(download_to, username=None): - wgets = [ - f'wget --user={username} --password=czeng -P {download_to} http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar' - for i in range(10)] - cmds = [] - for i, cmd in enumerate(wgets): - filename = f'{download_to}/data-plaintext-format.{i}.tar' - if os.path.exists(filename): - print(f'{filename} has already been downloaded; so skip') - continue - cmds.append(cmd) - if cmds and username is None: - raise ValueError('No czeng username is given; please register at http://ufal.mff.cuni.cz/czeng/czeng16 to obtain username to download') - for cmd in cmds: - call(cmd) - print('done with downloading czeng1.6') - -def download_czeng17_script(download_to, extract_folder, debug=False): - url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip' - filename = f'{download_to}/convert_czeng16_to_17.pl.zip' - extract_to = f'{extract_folder}/{get_extract_name(filename)}' - script_path = f'{extract_to}/convert_czeng16_to_17.pl' - - if not os.path.exists(script_path): - wget.download(url, filename, bar=bar_custom) - extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug) - return script_path - -czeng17_script_path = "" -def convert2czeng17(file, debug): - en_file = f'{file}.en' - cs_file = f'{file}.cs' - - if not os.path.exists(en_file) or not os.path.exists(cs_file): - cs_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f3 > {cs_file}' - en_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f4 > {en_file}' - call(cs_cmd, debug) - call(en_cmd, debug) - else: - print(f'already extracted: {en_file} and {cs_file}') - return file - -def extract_czeng17(extract_folder, debug=False): - url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip' - filename = f'{download_to}/convert_czeng16_to_17.pl.zip' - extract_to = f'{extract_folder}/{get_extract_name(filename)}' - script_path = f'{extract_to}/convert_czeng16_to_17.pl' - - if not os.path.exists(script_path): - wget.download(url, filename, bar=bar_custom) - extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug) - return script_path - -######### -# definitions of wmt data sources -# for es-en -# Punctuation in the official test sets will be encoded with ASCII characters (not complex Unicode characters) as much as possible. You may want to normalize your system's output before submission. You are able able to use a rawer version of the test sets that does not have this normalization. -# script to normalize punctuation: http://www.statmt.org/wmt11/normalize-punctuation.perl -wmt13_es_en = DLDataset( - name='wmt13_es-en', - train_urls=[ - 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', - 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', - 'http://www.statmt.org/wmt13/training-parallel-un.tgz', - 'http://www.statmt.org/wmt13/training-parallel-nc-v8.tgz', - ], - valid_urls=[ - ('http://www.statmt.org/wmt13/dev.tgz', 'wmt13_dev.tgz') - ], - test_urls=[ - ('http://www.statmt.org/wmt13/test.tgz', 'wmt13_test.tgz') - ], - train_files_patterns=[ - ('*/europarl-v7.{src}-{tgt}.{lang}', ['es-en']), - ('*commoncrawl.{src}-{tgt}.{lang}', ['es-en']), - ('*/news-commentary-v8.{src}-{tgt}.{lang}', ['es-en']), - ('un/*undoc.2000.{src}-{tgt}.{lang}', ['es-en']), - ] , - valid_files_patterns=[ - ('dev/newstest2012.{lang}', ['es-en']) - ], - test_files_patterns=[ - ('test/newstest*.{lang}', ['es-en']) - ], -) - -wmt14_de_fr_en = DLDataset( - name='wmt14_de_fr_en', - train_urls=[ - 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', - 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', - 'http://www.statmt.org/wmt13/training-parallel-un.tgz', - 'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz', - ('http://www.statmt.org/wmt10/training-giga-fren.tar', 'training-giga-fren.gz.tar'), #it is actuall a gz.tar - ], - valid_urls=[ - ('http://www.statmt.org/wmt14/dev.tgz', 'wmt14_dev.tgz'), - ], - test_urls=[ - ('http://www.statmt.org/wmt14/test-full.tgz', 'wmt14_test_full.tgz'), # cleaned test sets - ], - train_files_patterns=[ - ('*/europarl-v7.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), - ('*commoncrawl.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), - ('*/*news-commentary-v9.{src}-{tgt}.{lang}', ['fr-en', 'de-en']), - ('un/undoc.2000.{src}-{tgt}.{lang}', ['fr-en']), - ('*giga-{src}{tgt}*{lang}', ['fr-en']) - ], - valid_files_patterns=[ - ('dev/newstest2013.{lang}', ['fr-en', 'de-en']) - ], - test_files_patterns=[ - ('test-full/newstest*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['en-de', 'de-en', 'fr-en', 'en-fr']), - ], -) - -# pip install git+https://github.com/amake/tmx2corpus.git -wmt16_ro_en = DLDataset( - name='wmt16_ro-en', - train_urls=[ - ('http://data.statmt.org/wmt16/translation-task/training-parallel-ep-v8.tgz', 'wmt16_training-parallel-ep-v8.tgz'), - ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz', 'en-ro.tmx.gz'), - ], - valid_urls=[ - ('http://data.statmt.org/wmt16/translation-task/dev-romanian-updated.tgz', 'wmt16_dev.tgz') - ], - test_urls=[ - ('http://data.statmt.org/wmt16/translation-task/test.tgz', 'wmt16_test.tgz') - ], - train_files_patterns=[ - ('*/*europarl-v8.{src}-{tgt}.{lang}', ['ro-en']), - ('bitext.{lang}', ['ro-en']) #setimes from tmux - ] , - valid_files_patterns=[ - ('dev/newsdev2016*{src}{tgt}*.{lang}', ['ro-en', 'ro-en']) - ], - test_files_patterns=[ - ('test/newstest*{src}{tgt}*.{lang}', ['ro-en', 'en-ro']) - ], -) - -cwmt_wmt_instruction = 'cwmt download instruction at: http://nlp.nju.edu.cn/cwmt-wmt' -wmt17_fi_lv_tr_zh_en_manual_downloads = [ - # fake urls to have unique keys for the data - ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), cwmt_wmt_instruction), - ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), cwmt_wmt_instruction), - ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), cwmt_wmt_instruction), - ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), cwmt_wmt_instruction), - ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), cwmt_wmt_instruction), - ( ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), cwmt_wmt_instruction), -] -wmt17_fi_lv_tr_zh_en = DLDataset( - name='wmt17_fi_lv_tr_zh_en', - train_urls=[ - ('http://data.statmt.org/wmt17/translation-task/training-parallel-ep-v8.tgz', 'wmt17_training-parallel-ep-v8.tgz'), - 'http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz', - 'http://www.statmt.org/wmt15/wiki-titles.tgz', - ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-tr.tmx.gz', 'en-tr.tmx.gz'), - ('http://data.statmt.org/wmt17/translation-task/rapid2016.tgz', 'wmt17_rapid2016.tgz'), - 'http://data.statmt.org/wmt17/translation-task/leta.v1.tgz', - 'http://data.statmt.org/wmt17/translation-task/dcep.lv-en.v1.tgz', - 'http://data.statmt.org/wmt17/translation-task/books.lv-en.v1.tgz', - (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00', - 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01',), 'UNv1.0.en-zh.tar.gz'), - #manually download files: - ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), - ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), - ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), - ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), - ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), - ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), - ], - valid_urls=[ - ('http://data.statmt.org/wmt17/translation-task/dev.tgz', 'wmt17_dev.tgz'), - ], - test_urls=[ - #NEW: Improved translations for zh test sets - ('http://data.statmt.org/wmt17/translation-task/test-update-1.tgz', 'wmt17_test_zh_en.tgz'), - ('http://data.statmt.org/wmt17/translation-task/test.tgz', 'wmt17_test_others.tgz') - ], - train_files_patterns=[ - ('casict*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ), - ('casia*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ), - ('dataum*/Book*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en']), - ('neu*/NEU*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en'] ), - ('*/*UNv1.0.en-zh.{src:zh}{tgt:en}', ['zh-en']), - ('training/*news-commentary-v12.{src}-{tgt}.{lang}', ['zh-en', ]), - - ('*/*europarl-v8.{src}-{tgt}.{lang}', ['fi-en', 'lv-en']), - ('wiki/fi-en/titles.{src}-{tgt}.{lang}', ['fi-en', ]), - ('rapid2016.{tgt}-{src}.{lang}', ['fi-en', 'lv-en']), - ('*/leta.{lang}', ['lv-en']), - ('*/dcep.{lang}', ['lv-en']), - ('*/farewell.{lang}', ['lv-en']), - ('bitext.{lang}', ['tr-en']), - ] , - valid_files_patterns=[ - ('dev/newsdev2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', - [ - 'fi-en', 'lv-en', 'tr-en', 'zh-en', - 'en-fi', 'en-lv', 'en-tr', 'en-zh' - ]), - ('dev/newstest2016*{src}{tgt}-{src:src}{tgt:ref}.{lang}', - [ - 'fi-en', 'tr-en', - 'en-fi', 'en-tr', - ]), - ], - test_files_patterns=[ - ('test/newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', - [ - 'fi-en', 'lv-en', 'tr-en', - 'en-fi', 'en-lv', 'en-tr', - ]), - ('newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}', - [ - 'zh-en', - 'en-zh' - ]), - ], -) - -czeng_instruction = 'download instruction at: http://ufal.mff.cuni.cz/czeng/czeng16' -#alternative: use the prepared data but detokenize it? -wmt18_cs_et_en_manual_downloads = [ -#for cs, need to register and download; Register and download CzEng 1.6. -#Better results can be obtained by using a subset of sentences, released under a new version name CzEng 1.7. - # ((f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', - # f'data-plaintext-format.{i}.tar'), czeng_instruction) - # for i in range(10) -] - -wmt18_cs_et_en = DLDataset( - name='wmt18_cs_et_en', - train_urls=[ - 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz', - 'http://data.statmt.org/wmt18/translation-task/training-parallel-ep-v8.tgz', - 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-cs.zipporah0-dedup-clean.tgz', - 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-et.zipporah0-dedup-clean.tgz', - 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', - 'http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz', - ('http://data.statmt.org/wmt18/translation-task/rapid2016.tgz', 'wmt18_rapid2016.tgz'), - # (tuple( - # (f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar', - # f'data-plaintext-format.{i}.tar') - # for i in range(10) - # ), - # 'czeng16_data_plaintext.gz.tar'), - ], - valid_urls=[ - ('http://data.statmt.org/wmt18/translation-task/dev.tgz', 'wmt18_dev.tgz'), - ], - test_urls=[ - ('http://data.statmt.org/wmt18/translation-task/test.tgz', 'wmt18_test.tgz'), - ], - train_files_patterns=[ - # ('*/*europarl-v7.{src}-{tgt}.{lang}', ['cs-en']), - ('*/*europarl-v8.{src}-{tgt}.{lang}', ['et-en']), - # ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['cs-en', 'et-en']), - ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['et-en']), - # ('*commoncrawl.{src}-{tgt}.{lang}', ['cs-en']), - # ('*/news-commentary-v13.{src}-{tgt}.{lang}', ['cs-en']), - # ('data.plaintext-format/*train.{lang}', ['cs-en']), - ('rapid2016.{tgt}-{src}.{lang}', ['et-en']), - ] , - valid_files_patterns=[ - ('dev/newsdev2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['et-en']), - # ('dev/newstest2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['cs-en']) - ], - test_files_patterns=[ - ('test/newstest2018-{src}{tgt}-{src:src}{tgt:ref}.{lang}', - # ['cs-en', 'et-en']), - ['et-en']), - ] -) - -ru_en_yandex_instruction = 'Yandex Corpus download instruction at: https://translate.yandex.ru/corpus?lang=en' -wmt19_ru_gu_kk_lt_manual_downloads = [ - (('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), ru_en_yandex_instruction) -] -wmt19_ru_gu_kk_lt = DLDataset( - name='wmt19_ru_gu_kk_lt', - train_urls=[ - 'http://www.statmt.org/europarl/v9/training/europarl-v9.lt-en.tsv.gz', - 'https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-lt.bicleaner07.tmx.gz', - 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz', - 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz', - 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14-wmt19.en-kk.tsv.gz', - 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.en-ru.tsv.gz', - 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz', - 'http://data.statmt.org/wikititles/v1/wikititles-v1.ru-en.tsv.gz', - 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz', - 'http://data.statmt.org/wikititles/v1/wikititles-v1.lt-en.tsv.gz', - 'http://data.statmt.org/wikititles/v1/wikititles-v1.gu-en.tsv.gz', - (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00', - 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01', - 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02',), - 'wmt19_UNv1.0.en-ru.tar.gz'), - 'https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-lt.tmx.zip', - ('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), - ], - valid_urls=[ - ('http://data.statmt.org/wmt19/translation-task/dev.tgz', 'wmt19_dev.tgz'), - ], - test_urls=[ - ('http://data.statmt.org/wmt19/translation-task/test.tgz', 'wmt19_test.tgz'), - ], - train_files_patterns=[ - ('*europarl-v9.{src}-{tgt}.tsv.{lang}', ['lt-en']), - #paracrawl - ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['ru-en']), - ('bitext.{lang}', ['lt-en',]), - ('*commoncrawl.{src}-{tgt}.{lang}', ['ru-en',]), - ('*news-commentary-v14-wmt19.{tgt}-{src}.tsv.{lang}', ['kk-en', ]), - ('*news-commentary-v14.{tgt}-{src}.tsv.{lang}', ['ru-en']), - #yandex - ('corpus.{tgt}_{src}.1m.{lang}', ['ru-en']), - ('wikititles_v1_wikititles-v1.{src}-{tgt}.tsv.{lang}', ['ru-en', 'kk-en', 'lt-en', 'gu-en']), - ('*/UNv1.0.{tgt}-{src}.{lang}', ['ru-en']), - #rapid - ('bitext.{lang}', ['lt-en']) - ], - valid_files_patterns=[ - ('dev/newsdev2019*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['gu-en', 'kk-en', 'lt-en']), - ('dev/newstest2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['ru-en']), - ], - test_files_patterns=[ - ('sgm/newstest2019-{src}{tgt}-{src:src}{tgt:ref}.{lang}', - ['ru-en', 'gu-en', 'kk-en', 'lt-en', 'en-ru', 'en-gu', 'en-kk', 'en-lt']), - ] -) - - -######### - -if __name__ == "__main__": - # speed up the downloads with multiple processing - dl_folder = f'{to_data_path}/downloads' - extract_folder = f'{to_data_path}/extracted' - - urls = [ - url - for dataset in [wmt13_es_en, wmt14_de_fr_en, wmt16_ro_en, wmt18_cs_et_en, wmt19_ru_gu_kk_lt] - for urls in [dataset.train_urls, dataset.valid_urls, dataset.test_urls] - for url in urls - ] - urls = set(urls) - download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=True) - - # check manually downlaods - to_manually_download_urls = ( - wmt17_fi_lv_tr_zh_en_manual_downloads + wmt18_cs_et_en_manual_downloads + wmt19_ru_gu_kk_lt_manual_downloads - ) - to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls) - if len(to_be_manually_dowloaded) > 0: - print('Missing files that need to be downloaded manually; stop the process now.') - exit(-1) - - completed_urls = {} - completed_extraction = {} - def work_on_wmt(directions, wmt_data): - download_and_extract( - to_data_path, - directions, - wmt_data, - to_manually_download_urls=to_manually_download_urls, - completed_urls=completed_urls, completed_extraction=completed_extraction, debug=True) - - work_on_wmt( - ['es_XX-en_XX'], - wmt13_es_en,) - work_on_wmt( - [ - 'fr_XX-en_XX', 'en_XX-fr_XX', - # 'en_XX-de_DE', 'de_DE-en_XX', - ], - wmt14_de_fr_en,) - work_on_wmt( - ['ro_RO-en_XX', 'en_XX-ro_XX'], - wmt16_ro_en,) - work_on_wmt( - [ - # 'zh_CN-en_XX', - 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX', - #in case the reversed directions have different train/valid/test data - # 'en_XX-zh_CN', - 'en_XX-lv_LV', 'en_XX-fi_FI', 'en_XX-tr_TR', - ], - wmt17_fi_lv_tr_zh_en, ) - # czeng17_script_path = download_czeng17_script(download_to, extract_to, debug=False) - # cz_username = None - work_on_wmt( - [ - # 'cs_CZ-en_XX', - 'et_EE-en_XX'], - wmt18_cs_et_en,) - work_on_wmt( - [ - # 'ru_RU-en_XX', 'en_XX-ru_RU', - 'gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX', - #in case the reversed directions have different train/valid/test data - 'en_XX-gu_IN', 'en_XX-kk_KZ', 'en_XX-lt_LT' - ], - wmt19_ru_gu_kk_lt,) - - not_matching = check_wmt_test_bleu( - f'{to_data_path}/raw', - [ - ('wmt13', ['es_XX-en_XX']), - ('wmt14/full', ['fr_XX-en_XX',]), - ('wmt16', ['ro_RO-en_XX',]), - # ('wmt17/improved', ['zh_CN-en_XX']), - ('wmt17', [ 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX']), - ('wmt18', ['cs_CZ-en_XX', 'et_EE-en_XX']), - ('wmt19', ['gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX']), - #'ru_RU-en_XX', - ] - ) - if len(not_matching) > 0: - print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching)) - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wmt20.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wmt20.sh deleted file mode 100644 index 31cd5c76b75081331ae03c5ea70ea7ddebaa06e1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/download_wmt20.sh +++ /dev/null @@ -1,547 +0,0 @@ -#!/bin/bash -# 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. - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - - - -set -x -e - -# TODO update the workdir and dest dir name -# put fasttext model -WORKDIR=$WORKDIR_ROOT -# put intermediate files -TMP_DIR=$WORKDIR_ROOT/tmp/tmp_wmt20_lowres_download -# output {train,valid,test} files to dest -DEST=$WORKDIR_ROOT/ML50/raw - -UTILS=$PWD/utils - -# per dataset locations -COMMONCRAWL_DIR=$TMP_DIR/commoncrawl -YANDEX_CORPUS=$WORKDIR_ROOT/wmt20/official/ru/yandex/1mcorpus.zip -# unzipped -CZENG_CORPUS=$WORKDIR_ROOT/wmt20/official/cs/czeng/czeng20-train -CCMT_DIR=$WORKDIR_ROOT/wmt20/official/zh/ccmt/parallel - -download_and_select() { - SUBFOLDER=$1 - URL=$2 - UNCOMPRESS_CMD=$3 - LANG=$4 - INPUT_FILEPATH=$5 - if [[ $# -gt 5 ]]; then - LANG_COL=$6 - EN_COL=$7 - fi - - mkdir -p $SUBFOLDER - cd $SUBFOLDER - wget -nc --content-disposition $URL - $UNCOMPRESS_CMD - - if [[ $# -gt 5 ]]; then - cut -f$LANG_COL $INPUT_FILEPATH > $INPUT_FILEPATH.$LANG - cut -f$EN_COL $INPUT_FILEPATH > $INPUT_FILEPATH.en - fi - cd .. - - ln -sf $SUBFOLDER/$INPUT_FILEPATH.$LANG $SUBFOLDER.$LANG - ln -sf $SUBFOLDER/$INPUT_FILEPATH.en $SUBFOLDER.en -} - -prepare_lid() { - pip install fasttext - - # TODO specify global workdir - MODEL=$WORKDIR/fasttext/lid.176.bin - LID_MULTI=$UTILS/fasttext_multi_filter.py - - if [ ! -f "$MODEL" ]; then - echo "downloading fasttext lid model..." - mkdir -p $WORKDIR/fasttext - wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O $MODEL - fi -} - -prepare_moses() { - pushd $UTILS - echo 'Cloning Moses github repository (for tokenization scripts)...' - git clone https://github.com/moses-smt/mosesdecoder.git - popd -} - -lid_filter() { - # TODO specify global workdir - MODEL=$WORKDIR/fasttext/lid.176.bin - LID_MULTI=$UTILS/fasttext_multi_filter.py - - prepare_lid - - SRC=$1 - SRC_FILE=$2 - SRC_OUTPUT=$3 - TGT=$4 - TGT_FILE=$5 - TGT_OUTPUT=$6 - python $LID_MULTI --model $MODEL --inputs $SRC_FILE $TGT_FILE --langs $SRC $TGT --outputs $SRC_OUTPUT $TGT_OUTPUT -} - -prepare_ja_ted() { - mkdir -p ted - cd ted - - wget -nc https://wit3.fbk.eu/archive/2017-01-trnted//texts/en/ja/en-ja.tgz - tar -zxvf en-ja.tgz - cat en-ja/train.tags.en-ja.en | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.en - cat en-ja/train.tags.en-ja.ja | grep -v -P "^[ ]*\<" | sed 's/^[ \t]*//g' | sed 's/[ \t]*$//g' > en-ja/train.en-ja.ja - - cd .. - ln -sf ted/en-ja/train.en-ja.ja ted.ja - ln -sf ted/en-ja/train.en-ja.en ted.en -} - -prepare_ja() { - OUTPUT_DIR=$TMP_DIR/ja - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select paracrawl "http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/release/2.0/bitext/en-ja.tar.gz" "tar -zxvf en-ja.tar.gz" ja en-ja/en-ja.bicleaner05.txt 4 3 & - download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ja.tsv.gz" "gunzip -f news-commentary-v15.en-ja.tsv.gz" ja news-commentary-v15.en-ja.tsv 2 1 & - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ja-en.tsv.gz" "gunzip -f wikititles-v2.ja-en.tsv.gz" ja wikititles-v2.ja-en.tsv 1 2 & - download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ja.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ja.langid.tsv.gz" ja WikiMatrix.v1.en-ja.langid.tsv 3 2 & - download_and_select subtitle "https://nlp.stanford.edu/projects/jesc/data/split.tar.gz" "tar -zxvf split.tar.gz" ja split/train 2 1 & - download_and_select kftt "http://www.phontron.com/kftt/download/kftt-data-1.0.tar.gz" "tar -zxvf kftt-data-1.0.tar.gz" ja kftt-data-1.0/data/orig/kyoto-train & - - prepare_ja_ted & - - # ted data needs to - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.ja" | sort -V | xargs cat > all.ja - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter ja all.ja $DEST/train.ja_XX-en_XX.ja_XX en all.en $DEST/train.ja_XX-en_XX.en_XX -} - -prepare_ta() { - OUTPUT_DIR=$TMP_DIR/ta - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ta-en.tsv.gz" "gunzip -f wikititles-v2.ta-en.tsv.gz" ta wikititles-v2.ta-en.tsv 1 2 & - download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ta.langid.tsv.gz" "gunzip -f WikiMatrix.v1.en-ta.langid.tsv.gz" ta WikiMatrix.v1.en-ta.langid.tsv 3 2 & - download_and_select pmindia "http://data.statmt.org/pmindia/v1/parallel/pmindia.v1.ta-en.tsv" "" ta pmindia.v1.ta-en.tsv 2 1 & - download_and_select tanzil "https://object.pouta.csc.fi/OPUS-Tanzil/v1/moses/en-ta.txt.zip" "unzip en-ta.txt.zip" ta Tanzil.en-ta & - download_and_select pib "http://preon.iiit.ac.in/~jerin/resources/datasets/pib-v0.tar" "tar -xvf pib-v0.tar" ta pib/en-ta/train & - download_and_select mkb "http://preon.iiit.ac.in/~jerin/resources/datasets/mkb-v0.tar" "tar -xvf mkb-v0.tar" ta mkb/en-ta/mkb & - download_and_select ufal "http://ufal.mff.cuni.cz/~ramasamy/parallel/data/v2/en-ta-parallel-v2.tar.gz" "tar -zxvf en-ta-parallel-v2.tar.gz" ta en-ta-parallel-v2/corpus.bcn.train & - - wait - - # need special handling for nlpc - mkdir -p nlpc - cd nlpc - wget -nc https://raw.githubusercontent.com/nlpc-uom/English-Tamil-Parallel-Corpus/master/En-Ta%20Corpus/En-Ta%20English.txt - wget -nc https://github.com/nlpc-uom/English-Tamil-Parallel-Corpus/raw/master/En-Ta%20Corpus/En-Ta%20Tamil.txt - tail -n +4 "En-Ta English.txt" > en-ta.en - tail -n +4 "En-Ta Tamil.txt" > en-ta.ta - cd .. - ln -sf nlpc/en-ta.en nlpc.en - ln -sf nlpc/en-ta.ta nlpc.ta - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.ta" | sort -V | xargs cat > all.ta - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter ta all.ta $DEST/train.ta_IN-en_XX.ta_IN en all.en $DEST/train.ta_IN-en_XX.en_XX -} - -prepare_iu() { - OUTPUT_DIR=$TMP_DIR/iu - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select nh "https://nrc-digital-repository.canada.ca/eng/view/dataset/?id=c7e34fa7-7629-43c2-bd6d-19b32bf64f60" "tar -zxvf Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0.1.tgz" iu Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/NunavutHansard > /dev/null & - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.iu-en.tsv.gz" "gunzip -f wikititles-v2.iu-en.tsv.gz" iu wikititles-v2.iu-en.tsv 1 2 & - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.iu" | sort -V | xargs cat | nh/Nunavut-Hansard-Inuktitut-English-Parallel-Corpus-3.0/scripts/normalize-iu-spelling.pl > all.iu - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - paste all.iu all.en | awk -F $'\t' '$1!=""&&$2!=""' > all.iuen - cut -f1 all.iuen > $DEST/train.iu_CA-en_XX.iu_CA - cut -f2 all.iuen > $DEST/train.iu_CA-en_XX.en_XX -} - -prepare_km() { - OUTPUT_DIR=$TMP_DIR/km - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-km.xz" "unxz wmt20-sent.en-km.zx" km wmt20-sent.en-km 2 1 & - - # km-parallel has multiple sets, concat all of them together - mkdir -p opus - cd opus - wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/km-parallel.tgz" - tar -zxvf km-parallel.tgz - find ./km-parallel -maxdepth 1 -name "*.km" | sort -V | xargs cat > opus.km - find ./km-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en - cd .. - ln -sf opus/opus.km . - ln -sf opus/opus.en . - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.km" | sort -V | xargs cat > all.km - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter km all.km $DEST/train.km_KH-en_XX.km_KH en all.en $DEST/train.km_KH-en_XX.en_XX -} - -prepare_ps() { - OUTPUT_DIR=$TMP_DIR/ps - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select paracrawl "http://data.statmt.org/wmt20/translation-task/ps-km/wmt20-sent.en-ps.xz" "unxz wmt20-sent.en-ps.xz" ps wmt20-sent.en-ps 2 1 & - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ps-en.tsv.gz" "gunzip -f wikititles-v2.ps-en.tsv.gz" ps wikititles-v2.ps-en.tsv 1 2 & - # ps-parallel has multiple sets, concat all of them together - mkdir -p opus - cd opus - wget -nc "http://data.statmt.org/wmt20/translation-task/ps-km/ps-parallel.tgz" - tar -zxvf ps-parallel.tgz - find ./ps-parallel -maxdepth 1 -name "*.ps" | sort -V | xargs cat > opus.ps - find ./ps-parallel -maxdepth 1 -name "*.en" | sort -V | xargs cat > opus.en - cd .. - ln -sf opus/opus.ps opus.ps - ln -sf opus/opus.en opus.en - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.ps" | sort -V | xargs cat > all.ps - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter ps all.ps $DEST/train.ps_AF-en_XX.ps_AF en all.en $DEST/train.ps_AF-en_XX.en_XX -} - -download_commoncrawl() { - mkdir -p $COMMONCRAWL_DIR - cd $COMMONCRAWL_DIR - - wget -nc "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz" - tar -zxvf training-parallel-commoncrawl.tgz -} -link_commoncrawl() { - LANG=$1 - ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.en commoncrawl.en - ln -sf $COMMONCRAWL_DIR/commoncrawl.$LANG-en.$LANG commoncrawl.$LANG -} - -strip_xlf() { - INPUT_FILE=$1 - SRC=$2 - TGT=$3 - grep '<source xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$SRC - grep '<target xml:lang=' $INPUT_FILE | sed 's/^<[^<>]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$TGT -} - -download_and_process_tilde() { - URL=$1 - UNCOMPRESS_CMD=$2 - FILENAME=$3 - LANG=$4 - PROCESS_CMD=$5 - - mkdir -p tilde - cd tilde - wget -nc $URL - $UNCOMPRESS_CMD - echo "executing cmd" - echo $PROCESS_CMD - $PROCESS_CMD - cd .. - ln -sf tilde/$FILENAME.$LANG tilde.$LANG - ln -sf tilde/$FILENAME.en tilde.en -} - -prepare_cs() { - OUTPUT_DIR=$TMP_DIR/cs - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - #download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.cs-en.tsv.gz" "gunzip europarl-v10.cs-en.tsv.gz" cs europarl-v10.cs-en.tsv 1 2 & - #download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-cs.txt.gz" "gunzip en-cs.txt.gz" cs en-cs.txt 2 1 & - #link_commoncrawl cs - #download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.cs-en.tsv.gz" "gunzip news-commentary-v15.cs-en.tsv.gz" cs news-commentary-v15.cs-en.tsv 1 2 & - #download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.cs-en.tsv.gz" "gunzip wikititles-v2.cs-en.tsv.gz" cs wikititles-v2.cs-en.tsv 1 2 & - #download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.cs-en.xlf.gz" "gunzip RAPID_2019.cs-en.xlf.gz" RAPID_2019.cs-en.xlf cs "strip_xlf RAPID_2019.cs-en.xlf cs en" & - #download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.cs-en.langid.tsv.gz" "gunzip WikiMatrix.v1.cs-en.langid.tsv.gz" cs WikiMatrix.v1.cs-en.langid.tsv 2 3 & - - #wait - - # remove previous results - #rm -f all.?? - #find ./ -maxdepth 1 -name "*.cs" | sort -V | xargs cat > all.cs - #find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - if [ -z $CZENG_CORPUS ] ; - then - echo "Please download CZENG_CORPUS manually and place them at $CZENG_CORPUS. Exitting..." - exit - fi - cat $CZENG_CORPUS | sed '/^$/d' | cut -f5 > all.cs - cat $CZENG_CORPUS | sed '/^$/d' | cut -f6 > all.en - - lid_filter cs all.cs $DEST/train.cs_CZ-en_XX.cs_CZ en all.en $DEST/train.cs_CZ-en_XX.en_XX -} - -prepare_de() { - OUTPUT_DIR=$TMP_DIR/de - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz" "gunzip europarl-v10.de-en.tsv.gz" de europarl-v10.de-en.tsv 1 2 & - download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-de.txt.gz" "gunzip en-de.txt.gz" de en-de.txt 2 1 & - link_commoncrawl de - download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz" "gunzip news-commentary-v15.de-en.tsv.gz" de news-commentary-v15.de-en.tsv 1 2 & - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz" "gunzip wikititles-v2.de-en.tsv.gz" de wikititles-v2.de-en.tsv 1 2 & - download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.de-en.xlf.gz" "gunzip RAPID_2019.de-en.xlf.gz" RAPID_2019.de-en.xlf de "strip_xlf RAPID_2019.de-en.xlf de en" & - download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz" "gunzip WikiMatrix.v1.de-en.langid.tsv.gz" de WikiMatrix.v1.de-en.langid.tsv 2 3 & - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.de" | sort -V | xargs cat > all.de - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter de all.de $DEST/train.de_DE-en_XX.de_DE en all.en $DEST/train.de_DE-en_XX.en_XX -} - -prepare_tmx() { - TMX_FILE=$1 - git clone https://github.com/amake/TMX2Corpus $UTILS/tmx2corpus - pip install tinysegmenter - - python $UTILS/tmx2corpus/tmx2corpus.py $TMX_FILE -} - -prepare_pl() { - OUTPUT_DIR=$TMP_DIR/pl - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - # download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.pl-en.tsv.gz" "gunzip europarl-v10.pl-en.tsv.gz" pl europarl-v10.pl-en.tsv 1 2 & - # download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-pl.txt.gz" "gunzip en-pl.txt.gz" pl en-pl.txt 2 1 & - # download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.pl-en.tsv.gz" "gunzip wikititles-v2.pl-en.tsv.gz" pl wikititles-v2.pl-en.tsv 1 2 & - download_and_select tilde "https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.en-pl.tmx.zip" "gunzip rapid2019.en-pl.tmx.zip" bitext pl "prepare_tmx RAPID_2019.UNIQUE.en-pl.tmx" & - # download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-pl.langid.tsv.gz" "gunzip WikiMatrix.v1.en-pl.langid.tsv.gz" pl WikiMatrix.v1.en-pl.langid.tsv 3 2 & - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.pl" | sort -V | xargs cat > all.pl - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter pl all.pl $DEST/train.pl_PL-en_XX.pl_PL en all.en $DEST/train.pl_PL-en_XX.en_XX -} - -prepare_uncorpus() { - $URLS=$1 - $FILES=$2 - - mkdir -p uncorpus - cd uncorpus - - for URL in $URLS; do - wget -nc $URL - done - cat $FILES > uncorpus.tar.gz - tar -zxvf uncorpus.tar.gz - - cd .. - ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.$LANG uncorpus.$LANG - ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.en uncorpus.en -} - -prepare_yandex() { - mkdir -p yandex - cd yandex - unzip $YANDEX_CORPUS ./ - cd .. - ln -s yandex/corpus.en_ru.1m.en yandex.en - ln -s yandex/corpus.en_ru.1m.ru yandex.ru -} - -prepare_ru() { - OUTPUT_DIR=$TMP_DIR/ru - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" "tar -zxvf paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" ru paracrawl-release1.en-ru.zipporah0-dedup-clean & - link_commoncrawl ru - download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz" "gunzip news-commentary-v15.en-ru.tsv.gz" ru news-commentary-v15.en-ru.tsv 2 1 & - prepare_yandex & - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz" "gunzip wikititles-v2.ru-en.tsv.gz" ru wikititles-v2.ru-en.tsv 1 2 & - prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02" "UNv1.0.en-ru.tar.gz.00 UNv1.0.en-ru.tar.gz.01 UNv1.0.en-ru.tar.gz.02" & - download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz" "gunzip WikiMatrix.v1.en-ru.langid.tsv.gz" ru WikiMatrix.v1.en-ru.langid.tsv 3 2 & - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.ru" | sort -V | xargs cat > all.ru - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter ru all.ru $DEST/train.ru_RU-en_XX.ru_RU en all.en $DEST/train.ru_RU-en_XX.en_XX -} - -prepare_ccmt() { - mkdir -p ccmt - cd ccmt - # assume ccmt data is already unzipped under CCMT_DIR folder - cat $CCMT_DIR/datum2017/Book*_cn.txt | sed 's/ //g' > datum2017.detok.zh - cat $CCMT_DIR/datum2017/Book*_en.txt > datum2017.detok.en - cat $CCMT_DIR/casict2011/casict-A_ch.txt $CCMT_DIR/casict2011/casict-B_ch.txt $CCMT_DIR/casict2015/casict2015_ch.txt $CCMT_DIR/datum2015/datum_ch.txt $CCMT_DIR/neu2017/NEU_cn.txt datum2017.detok.zh > ccmt.zh - cat $CCMT_DIR/casict2011/casict-A_en.txt $CCMT_DIR/casict2011/casict-B_en.txt $CCMT_DIR/casict2015/casict2015_en.txt $CCMT_DIR/datum2015/datum_en.txt $CCMT_DIR/neu2017/NEU_en.txt datum2017.detok.en > ccmt.en - cd .. - ln -sf ccmt/ccmt.zh ccmt.zh - ln -sf ccmt/ccmt.en ccmt.en -} - -prepare_zh() { - OUTPUT_DIR=$TMP_DIR/zh - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - - download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz" "gunzip news-commentary-v15.en-zh.tsv.gz" zh news-commentary-v15.en-zh.tsv 2 1 & - download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz" "gunzip wikititles-v2.zh-en.tsv.gz" zh wikititles-v2.zh-en.tsv 1 2 & - prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01" "UNv1.0.en-zh.tar.gz.00 UNv1.0.en-zh.tar.gz.01" & - prepare_ccmt & - download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz" "gunzip WikiMatrix.v1.en-zh.langid.tsv.gz" zh WikiMatrix.v1.en-zh.langid.tsv 3 2 & - - wait - - # remove previous results - rm -f all.?? - find ./ -maxdepth 1 -name "*.zh" | sort -V | xargs cat > all.zh - find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en - lid_filter zh all.zh $DEST/train.zh_CN-en_XX.zh_CN en all.en $DEST/train.zh_CN-en_XX.en_XX -} - -prepare_tests() { - OUTPUT_DIR=$TMP_DIR - mkdir -p $OUTPUT_DIR - cd $OUTPUT_DIR - wget -nc http://data.statmt.org/wmt20/translation-task/dev.tgz - tar -zxvf dev.tgz - cd dev - - cat newsdev2020-jaen-src.ja.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.ja - cat newsdev2020-jaen-ref.en.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.en - split newsdev2020-jaen.ja -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.ja_XX - split newsdev2020-jaen.en -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.en_XX - split newsdev2020-jaen.ja -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.ja_XX - split newsdev2020-jaen.en -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.en_XX - - cat newsdev2020-iuen-src.iu.sgm | strip_sgm.sh > newsdev2020-iuen.iu - cat newsdev2020-iuen-ref.en.sgm | strip_sgm.sh > newsdev2020-iuen.en - split newsdev2020-iuen.iu -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.iu_CA - split newsdev2020-iuen.en -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.en_XX - split newsdev2020-iuen.iu -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.iu_CA - split newsdev2020-iuen.en -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.en_XX - - cat newsdev2020-taen-src.ta.sgm | strip_sgm.sh > newsdev2020-taen.ta - cat newsdev2020-taen-ref.en.sgm | strip_sgm.sh > newsdev2020-taen.en - split newsdev2020-taen.ta -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.ta_IN - split newsdev2020-taen.en -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.en_XX - split newsdev2020-taen.ta -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.ta_IN - split newsdev2020-taen.en -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.en_XX - - cp wikipedia.dev.km-en.km $DEST/valid.km_KH-en_XX.km_KH - cp wikipedia.dev.km-en.en $DEST/valid.km_KH-en_XX.en_XX - cp wikipedia.devtest.km-en.km $DEST/test.km_KH-en_XX.km_KH - cp wikipedia.devtest.km-en.en $DEST/test.km_KH-en_XX.en_XX - - cp wikipedia.dev.ps-en.ps $DEST/valid.ps_AF-en_XX.ps_AF - cp wikipedia.dev.ps-en.en $DEST/valid.ps_AF-en_XX.en_XX - cp wikipedia.devtest.ps-en.ps $DEST/test.ps_AF-en_XX.ps_AF - cp wikipedia.devtest.ps-en.en $DEST/test.ps_AF-en_XX.en_XX - - cat newsdev2020-plen-src.pl.sgm | strip_sgm.sh > newsdev2020-plen.pl - cat newsdev2020-plen-ref.en.sgm | strip_sgm.sh > newsdev2020-plen.en - split newsdev2020-plen.pl -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.pl_PL - split newsdev2020-plen.en -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.en_XX - split newsdev2020-plen.pl -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.pl_PL - split newsdev2020-plen.en -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.en_XX - - cat newstest2018-encs-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.en_XX - cat newstest2018-encs-ref.cs.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.cs_CZ - cat newstest2019-encs-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.en_XX - cat newstest2019-encs-ref.cs.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.cs_CZ - - cat newstest2018-deen-src.de.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.de_DE - cat newstest2018-deen-ref.en.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.en_XX - cat newstest2018-ende-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.en_XX - cat newstest2018-ende-ref.de.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.de_DE - cat newstest2019-deen-src.de.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.de_DE - cat newstest2019-deen-ref.en.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.en_XX - cat newstest2019-ende-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.en_XX - cat newstest2019-ende-ref.de.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.de_DE - - cat newstest2018-ruen-src.ru.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.ru_RU - cat newstest2018-ruen-ref.en.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.en_XX - cat newstest2018-enru-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.en_XX - cat newstest2018-enru-ref.ru.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.ru_RU - cat newstest2019-ruen-src.ru.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.ru_RU - cat newstest2019-ruen-ref.en.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.en_XX - cat newstest2019-enru-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.en_XX - cat newstest2019-enru-ref.ru.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.ru_RU - - cat newstest2018-zhen-src.zh.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.zh_CN - cat newstest2018-zhen-ref.en.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.en_XX - cat newstest2018-enzh-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.en_XX - cat newstest2018-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.zh_CN - cat newstest2019-zhen-src.zh.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.zh_CN - cat newstest2019-zhen-ref.en.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.en_XX - cat newstest2019-enzh-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.en_XX - cat newstest2019-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.zh_CN -} - -mkdir -p $DEST - -prepare_lid -prepare_moses -download_commoncrawl - -prepare_ja & -prepare_ta & -prepare_km & -prepare_ps & -prepare_iu & -prepare_cs & -prepare_de & -prepare_pl & -prepare_ru & -prepare_zh & - -# prepare valid/test set -prepare_tests & - -# wait - -# TODO remove intermediate files -# rm -rf $TMP_DIR diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/preprocess_ML50_v1.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/preprocess_ML50_v1.sh deleted file mode 100644 index 4655936149cab212b3cfa14f306d71153729f9d7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/preprocess_ML50_v1.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# 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. - -if [ -z $WORKDIR_ROOT ] ; -then - echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..." - exit -fi - -if [ -z $SPM_PATH ] ; -then - echo "Please install sentence piecence from https://github.com/google/sentencepiece and set SPM_PATH pointing to the installed spm_encode.py. Exitting..." - exit -fi - -ML50=${WORKDIR_ROOT}/ML50 - -mkdir -p $ML50/dedup -mkdir -p $ML50/cleaned_dedup - -python ./dedup_all.py --from-folder $ML50/raw --to-folder $ML50/dedup -python ./remove_valid_test_in_train.py --from-folder $ML50/dedup --to-folder $ML50/clean -python ./binarize.py --raw-folder $ML50/clean \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/remove_valid_test_in_train.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/remove_valid_test_in_train.py deleted file mode 100644 index 16fe450aa9b95ee3ac1fbea52f316ef97d583d17..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/remove_valid_test_in_train.py +++ /dev/null @@ -1,293 +0,0 @@ -import glob -import itertools -import os -import sys - -import pandas as pd - -WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None) - -if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip(): - print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."') - sys.exit(-1) - - -def load_langs(path): - with open(path) as fr: - langs = [l.strip() for l in fr] - return langs - - - -def load_sentences(raw_data, split, direction): - src, tgt = direction.split('-') - src_path = f"{raw_data}/{split}.{direction}.{src}" - tgt_path = f"{raw_data}/{split}.{direction}.{tgt}" - if os.path.exists(src_path) and os.path.exists(tgt_path): - return [(src, open(src_path).read().splitlines()), (tgt, open(tgt_path).read().splitlines())] - else: - return [] - -def swap_direction(d): - src, tgt = d.split('-') - return f'{tgt}-{src}' - -def get_all_test_data(raw_data, directions, split='test'): - test_data = [ - x - for dd in directions - for d in [dd, swap_direction(dd)] - for x in load_sentences(raw_data, split, d) - ] - # all_test_data = {s for _, d in test_data for s in d} - all_test_data = {} - for lang, d in test_data: - for s in d: - s = s.strip() - lgs = all_test_data.get(s, set()) - lgs.add(lang) - all_test_data[s] = lgs - return all_test_data, test_data - -def check_train_sentences(raw_data, direction, all_test_data, mess_up_train={}): - src, tgt = direction.split('-') - tgt_path = f"{raw_data}/train.{direction}.{tgt}" - src_path = f"{raw_data}/train.{direction}.{src}" - print(f'check training data in {raw_data}/train.{direction}') - size = 0 - if not os.path.exists(tgt_path) or not os.path.exists(src_path): - return mess_up_train, size - with open(src_path) as f, open(tgt_path) as g: - for src_line, tgt_line in zip(f, g): - s = src_line.strip() - t = tgt_line.strip() - size += 1 - if s in all_test_data: - langs = mess_up_train.get(s, set()) - langs.add(direction) - mess_up_train[s] = langs - if t in all_test_data: - langs = mess_up_train.get(t, set()) - langs.add(direction) - mess_up_train[t] = langs - return mess_up_train, size - -def check_train_all(raw_data, directions, all_test_data): - mess_up_train = {} - data_sizes = {} - for direction in directions: - _, size = check_train_sentences(raw_data, direction, all_test_data, mess_up_train) - data_sizes[direction] = size - return mess_up_train, data_sizes - -def count_train_in_other_set(mess_up_train): - train_in_others = [(direction, s) for s, directions in mess_up_train.items() for direction in directions] - counts = {} - for direction, s in train_in_others: - counts[direction] = counts.get(direction, 0) + 1 - return counts - -def train_size_if_remove_in_otherset(data_sizes, mess_up_train): - counts_in_other = count_train_in_other_set(mess_up_train) - remain_sizes = [] - for direction, count in counts_in_other.items(): - remain_sizes.append((direction, data_sizes[direction] - count, data_sizes[direction], count, 100 * count / data_sizes[direction] )) - return remain_sizes - - -def remove_messed_up_sentences(raw_data, direction, mess_up_train, mess_up_train_pairs, corrected_langs): - split = 'train' - src_lang, tgt_lang = direction.split('-') - - tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" - src = f"{raw_data}/{split}.{direction}.{src_lang}" - print(f'working on {direction}: ', src, tgt) - if not os.path.exists(tgt) or not os.path.exists(src) : - return - - corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" - corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" - line_num = 0 - keep_num = 0 - with open(src, encoding='utf8',) as fsrc, \ - open(tgt, encoding='utf8',) as ftgt, \ - open(corrected_src, 'w', encoding='utf8') as fsrc_corrected, \ - open(corrected_tgt, 'w', encoding='utf8') as ftgt_corrected: - for s, t in zip(fsrc, ftgt): - s = s.strip() - t = t.strip() - if t not in mess_up_train \ - and s not in mess_up_train \ - and (s, t) not in mess_up_train_pairs \ - and (t, s) not in mess_up_train_pairs: - corrected_langs.add(direction) - print(s, file=fsrc_corrected) - print(t, file=ftgt_corrected) - keep_num += 1 - line_num += 1 - if line_num % 1000 == 0: - print(f'completed {line_num} lines', end='\r') - return line_num, keep_num - -########## - - -def merge_valid_test_messup(mess_up_train_valid, mess_up_train_test): - merged_mess = [] - for s in set(list(mess_up_train_valid.keys()) + list(mess_up_train_test.keys())): - if not s: - continue - valid = mess_up_train_valid.get(s, set()) - test = mess_up_train_test.get(s, set()) - merged_mess.append((s, valid | test)) - return dict(merged_mess) - - - -######### -def check_train_pairs(raw_data, direction, all_test_data, mess_up_train={}): - src, tgt = direction.split('-') - #a hack; TODO: check the reversed directions - path1 = f"{raw_data}/train.{src}-{tgt}.{src}" - path2 = f"{raw_data}/train.{src}-{tgt}.{tgt}" - if not os.path.exists(path1) or not os.path.exists(path2) : - return - - with open(path1) as f1, open(path2) as f2: - for src_line, tgt_line in zip(f1, f2): - s = src_line.strip() - t = tgt_line.strip() - if (s, t) in all_test_data or (t, s) in all_test_data: - langs = mess_up_train.get( (s, t), set()) - langs.add(src) - langs.add(tgt) - mess_up_train[(s, t)] = langs - - -def load_pairs(raw_data, split, direction): - src, tgt = direction.split('-') - src_f = f"{raw_data}/{split}.{direction}.{src}" - tgt_f = f"{raw_data}/{split}.{direction}.{tgt}" - if tgt != 'en_XX': - src_f, tgt_f = tgt_f, src_f - if os.path.exists(src_f) and os.path.exists(tgt_f): - return list(zip(open(src_f).read().splitlines(), - open(tgt_f).read().splitlines(), - )) - else: - return [] - -# skip_langs = ['cs_CZ', 'en_XX', 'tl_XX', 'tr_TR'] -def get_messed_up_test_pairs(split, directions): - test_pairs = [ - (d, load_pairs(raw_data, split, d)) - for d in directions - ] - # all_test_data = {s for _, d in test_data for s in d} - all_test_pairs = {} - for direction, d in test_pairs: - src, tgt = direction.split('-') - for s in d: - langs = all_test_pairs.get(s, set()) - langs.add(src) - langs.add(tgt) - all_test_pairs[s] = langs - mess_up_train_pairs = {} - for direction in directions: - check_train_pairs(raw_data, direction, all_test_pairs, mess_up_train_pairs) - return all_test_pairs, mess_up_train_pairs - - - -if __name__ == "__main__": - ####### - import argparse - parser = argparse.ArgumentParser() - parser.add_argument( - '--from-folder', - required=True, - type=str) - parser.add_argument( - '--to-folder', - required=True, - type=str) - parser.add_argument( - '--directions', - default=None, - type=str) - - - args = parser.parse_args() - raw_data = args.from_folder - to_folder = args.to_folder - os.makedirs(to_folder, exist_ok=True) - - if args.directions: - directions = args.directions.split(',') - else: - raw_files = itertools.chain( - glob.glob(f'{raw_data}/train*'), - glob.glob(f'{raw_data}/valid*'), - glob.glob(f'{raw_data}/test*'), - ) - directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files] - print('working on directions: ', directions) - - ########## - - - - all_test_data, test_data = get_all_test_data(raw_data, directions, 'test') - print('==loaded test data==') - all_valid_data, valid_data = get_all_test_data(raw_data, directions, 'valid') - print('==loaded valid data==') - all_valid_test_data = merge_valid_test_messup(all_test_data, all_valid_data) - mess_up_train, data_sizes = check_train_all(raw_data, directions, all_valid_test_data) - print('training messing up with valid, test data:', len(mess_up_train)) - data_situation = train_size_if_remove_in_otherset(data_sizes, mess_up_train) - df = pd.DataFrame(data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) - df.sort_values('remove_percent', ascending=False) - df.to_csv(f'{raw_data}/clean_summary.tsv', sep='\t') - print(f'projected data clean summary in: {raw_data}/clean_summary.tsv') - - # correct the dataset: - all_test_pairs, mess_up_test_train_pairs = get_messed_up_test_pairs('test', directions) - all_valid_pairs, mess_up_valid_train_pairs = get_messed_up_test_pairs('valid', directions) - - all_messed_pairs = set(mess_up_test_train_pairs.keys()).union(set(mess_up_valid_train_pairs.keys())) - corrected_directions = set() - - real_data_situation = [] - for direction in directions: - org_size, new_size = remove_messed_up_sentences(raw_data, direction, mess_up_train, all_messed_pairs, corrected_directions) - if org_size == 0: - print(f"{direction} has size 0") - continue - real_data_situation.append( - (direction, new_size, org_size, org_size - new_size, (org_size - new_size) / org_size * 100) - ) - print('corrected directions: ', corrected_directions) - df = pd.DataFrame(real_data_situation, columns=['direction', 'train_size_after_remove', 'orig_size', 'num_to_remove', 'remove_percent']) - df.sort_values('remove_percent', ascending=False) - df.to_csv(f'{raw_data}/actual_clean_summary.tsv', sep='\t') - print(f'actual data clean summary (which can be different from the projected one because of duplications) in: {raw_data}/actual_clean_summary.tsv') - - import shutil - for direction in directions: - src_lang, tgt_lang = direction.split('-') - for split in ['train', 'valid', 'test']: - # copying valid, test and uncorrected train - if direction in corrected_directions and split == 'train': - continue - tgt = f"{raw_data}/{split}.{direction}.{tgt_lang}" - src = f"{raw_data}/{split}.{direction}.{src_lang}" - if not (os.path.exists(src) and os.path.exists(tgt)): - continue - corrected_tgt = f"{to_folder}/{split}.{direction}.{tgt_lang}" - corrected_src = f"{to_folder}/{split}.{direction}.{src_lang}" - print(f'copying {src} to {corrected_src}') - shutil.copyfile(src, corrected_src) - print(f'copying {tgt} to {corrected_tgt}') - shutil.copyfile(tgt, corrected_tgt) - - print('completed') \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/requirement.txt b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/requirement.txt deleted file mode 100644 index e85d7d540e08a1407f92dfb2311972a1a5a30123..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/requirement.txt +++ /dev/null @@ -1,2 +0,0 @@ -wget -pandas \ No newline at end of file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/dedup.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/dedup.py deleted file mode 100644 index 77d44ae38665a9e025e3aa0693415f8e8e4e869e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/dedup.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 argparse - - -def deup(src_file, tgt_file, src_file_out, tgt_file_out): - seen = set() - dup_count = 0 - with open(src_file, encoding='utf-8') as fsrc, \ - open(tgt_file, encoding='utf-8') as ftgt, \ - open(src_file_out, 'w', encoding='utf-8') as fsrc_out, \ - open(tgt_file_out, 'w', encoding='utf-8') as ftgt_out: - for s, t in zip(fsrc, ftgt): - if (s, t) not in seen: - fsrc_out.write(s) - ftgt_out.write(t) - seen.add((s, t)) - else: - dup_count += 1 - print(f'number of duplication: {dup_count}') - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--src-file", type=str, required=True, - help="src file") - parser.add_argument("--tgt-file", type=str, required=True, - help="tgt file") - parser.add_argument("--src-file-out", type=str, required=True, - help="src ouptut file") - parser.add_argument("--tgt-file-out", type=str, required=True, - help="tgt ouput file") - args = parser.parse_args() - deup(args.src_file, args.tgt_file, args.src_file_out, args.tgt_file_out) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py deleted file mode 100644 index e614bc1f50205369f80afcebadf4564e4d56f7c1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/fasttext_multi_filter.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -#!/bin/python - -import argparse -import contextlib -import io -import sys -from functools import partial -from multiprocessing import Pool - -import fasttext - -model = None -def init(model_path): - global model - model = fasttext.load_model(model_path) - -def pred(lines): - return lines, [model.predict(line.strip())[0][0][9:] for line in lines] - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--model", type=str, required=True, - help="model to load") - parser.add_argument("--inputs", nargs="+", default=['-'], - help="input files to filter") - parser.add_argument("--langs", nargs="+", required=True, - help="lang ids of each input file") - parser.add_argument("--outputs", nargs="+", default=['-'], - help="path to save lid filtered outputs") - parser.add_argument("--num-workers", type=int, metavar="N", default=10, - help="number of processes in parallel") - args = parser.parse_args() - - assert len(args.inputs) == len(args.langs) and len(args.inputs) == len(args.outputs) - - with contextlib.ExitStack() as stack: - inputs = [ - stack.enter_context(open(input, "r", encoding="utf-8", newline="\n", errors="replace")) - if input != "-" else io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8', errors="replace") - for input in args.inputs - ] - outputs = [ - stack.enter_context(open(output, "w", encoding="utf-8", newline="\n")) - if output != "-" else sys.stdout - for output in args.outputs - ] - with Pool(args.num_workers, initializer=partial(init, args.model)) as p: - skip_cnt = 0 - for lines, preds in p.imap(pred, list(zip(*inputs)), chunksize=500): - if not all(a == b for a, b in zip(preds, args.langs)): - skip_cnt += 1 - continue - for line, output_h in zip(lines, outputs): - print(line.strip(), file=output_h) - print(f"Skipped {skip_cnt} lines.") - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/strip_sgm.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/strip_sgm.sh deleted file mode 100644 index 7f4f61d7b1a46f51a1221de6b336cb70b5a0b8b3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/data_scripts/utils/strip_sgm.sh +++ /dev/null @@ -1 +0,0 @@ -grep "seg id" | sed 's/<seg id="[0-9]\+">//g' | sed 's/<\/seg>//g' diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/finetune_multilingual_model.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/finetune_multilingual_model.sh deleted file mode 100644 index 25960c5dc8a02e5580b61837099770a082b4dd83..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/finetune_multilingual_model.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -# 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. - -path_2_data=$1 # <path to data> which contains binarized data for each directions -lang_list=$2 # <path to a file which contains a list of languages separted by new lines> -lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en" -# pretrained can be an mBART pretrained model as well -pretrained_model=$4 #<path to a pretrained model> - - -fairseq-train "$path_2_data" \ - --encoder-normalize-before --decoder-normalize-before \ - --arch transformer --layernorm-embedding \ - --task translation_multi_simple_epoch \ - --finetune-from-model "$pretrained_model" \ - --sampling-method "temperature" \ - --sampling-temperature "1.5" \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" \ - --lang-pairs "$lang_pairs" \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ - --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ - --max-tokens 1024 --update-freq 2 \ - --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ - --seed 222 --log-format simple --log-interval 2 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/multilingual_fairseq_gen.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/multilingual_fairseq_gen.sh deleted file mode 100644 index 65aa322d7daaa428015de98abe4664a6a4164bfd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/multilingual_fairseq_gen.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash -# 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. - -lang_pairs="en-fr,en-cs,fr-en,cs-en" -path_2_data=$1 # <path to data> -lang_list=$2 # <path to a file which contains list of languages separted by new lines> -model=$3 # <path to a trained model> -source_lang=cs -target_lang=en - -fairseq-generate "$path_2_data" \ - --path "$model" \ - --task translation_multi_simple_epoch \ - --gen-subset test \ - --source-lang "$source_lang" \ - --target-lang "$target_lang" \ - --sacrebleu --remove-bpe 'sentencepiece'\ - --batch-size 32 \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" \ - --lang-pairs "$lang_pairs" diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/train_multilingual_model.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/train_multilingual_model.sh deleted file mode 100644 index cc050bd3f02de8a2f303737f187442d2eb80e4ef..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/multilingual/train_multilingual_model.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash -# 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. - -path_2_data=$1 # <path to data> which contains binarized data for each directions -lang_list=$2 # <path to a file which contains a list of languages separted by new lines> -lang_pairs=$3 #a list language pairs to train multilingual models, e.g. "en-fr,en-cs,fr-en,cs-en" - -fairseq-train "$path_2_data" \ - --encoder-normalize-before --decoder-normalize-before \ - --arch transformer --layernorm-embedding \ - --task translation_multi_simple_epoch \ - --sampling-method "temperature" \ - --sampling-temperature 1.5 \ - --encoder-langtok "src" \ - --decoder-langtok \ - --lang-dict "$lang_list" \ - --lang-pairs "$lang_pairs" \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.2 \ - --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \ - --lr-scheduler inverse_sqrt --lr 3e-05 --warmup-updates 2500 --max-update 40000 \ - --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \ - --max-tokens 1024 --update-freq 2 \ - --save-interval 1 --save-interval-updates 5000 --keep-interval-updates 10 --no-epoch-checkpoints \ - --seed 222 --log-format simple --log-interval 2 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/README.md deleted file mode 100644 index 9d101aa874ec36ff3bb5c1166169a4c4f38ffe2b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/README.md +++ /dev/null @@ -1,72 +0,0 @@ -# Simple and Effective Noisy Channel Modeling for Neural Machine Translation (Yee et al., 2019) -This page contains pointers to pre-trained models as well as instructions on how to run the reranking scripts. - -## Citation: -```bibtex -@inproceedings{yee2019simple, - title = {Simple and Effective Noisy Channel Modeling for Neural Machine Translation}, - author = {Kyra Yee and Yann Dauphin and Michael Auli}, - booktitle = {Conference on Empirical Methods in Natural Language Processing}, - year = {2019}, -} -``` - -## Pre-trained Models: - -Model | Description | Download ----|---|--- -`transformer.noisychannel.de-en` | De->En Forward Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/forward_de2en.tar.bz2) -`transformer.noisychannel.en-de` | En->De Channel Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/backward_en2de.tar.bz2) -`transformer_lm.noisychannel.en` | En Language model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/reranking_en_lm.tar.bz2) - -Test Data: [newstest_wmt17](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/wmt17test.tar.bz2) - -## Example usage - -``` -mkdir rerank_example -curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/forward_de2en.tar.bz2 | tar xvjf - -C rerank_example -curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/backward_en2de.tar.bz2 | tar xvjf - -C rerank_example -curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/reranking_en_lm.tar.bz2 | tar xvjf - -C rerank_example -curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/wmt17test.tar.bz2 | tar xvjf - -C rerank_example - -beam=50 -num_trials=1000 -fw_name=fw_model_ex -bw_name=bw_model_ex -lm_name=lm_ex -data_dir=rerank_example/hyphen-splitting-mixed-case-wmt17test-wmt14bpe -data_dir_name=wmt17 -lm=rerank_example/lm/checkpoint_best.pt -lm_bpe_code=rerank_example/lm/bpe32k.code -lm_dict=rerank_example/lm/dict.txt -batch_size=32 -bw=rerank_example/backward_en2de.pt -fw=rerank_example/forward_de2en.pt - -# reranking with P(T|S) P(S|T) and P(T) -python examples/noisychannel/rerank_tune.py $data_dir --tune-param lenpen weight1 weight3 \ - --lower-bound 0 0 0 --upper-bound 3 3 3 --data-dir-name $data_dir_name \ - --num-trials $num_trials --source-lang de --target-lang en --gen-model $fw \ - -n $beam --batch-size $batch_size --score-model2 $fw --score-model1 $bw \ - --backwards1 --weight2 1 \ - -lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ - --model2-name $fw_name --model1-name $bw_name --gen-model-name $fw_name - -# reranking with P(T|S) and P(T) -python examples/noisychannel/rerank_tune.py $data_dir --tune-param lenpen weight3 \ - --lower-bound 0 0 --upper-bound 3 3 --data-dir-name $data_dir_name \ - --num-trials $num_trials --source-lang de --target-lang en --gen-model $fw \ - -n $beam --batch-size $batch_size --score-model1 $fw \ - -lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ - --model1-name $fw_name --gen-model-name $fw_name - -# to run with a preconfigured set of hyperparameters for the lenpen and model weights, using rerank.py instead. -python examples/noisychannel/rerank.py $data_dir \ - --lenpen 0.269 --weight1 1 --weight2 0.929 --weight3 0.831 \ - --data-dir-name $data_dir_name --source-lang de --target-lang en --gen-model $fw \ - -n $beam --batch-size $batch_size --score-model2 $fw --score-model1 $bw --backwards1 \ - -lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ - --model2-name $fw_name --model1-name $bw_name --gen-model-name $fw_name -``` - diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/__init__.py deleted file mode 100644 index 89f1aef4f6328d25425e0bcabb42dfffd2ed35f0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/__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 .rerank_options import * # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank.py deleted file mode 100644 index 76a0b7490a0beff727d5498b6f248e3b3bf87753..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank.py +++ /dev/null @@ -1,427 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 multiprocessing import Pool - -import numpy as np -from examples.noisychannel import ( - rerank_generate, - rerank_options, - rerank_score_bw, - rerank_score_lm, - rerank_utils, -) -from fairseq import options -from fairseq.data import dictionary -from fairseq.scoring import bleu - - -def score_target_hypo( - args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize -): - - print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c) - gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args) - dict = dictionary.Dictionary() - scorer = scorer = bleu.Scorer( - bleu.BleuConfig( - pad=dict.pad(), - eos=dict.eos(), - unk=dict.unk(), - ) - ) - - ordered_hypos = {} - ordered_targets = {} - - for shard_id in range(len(bitext1_lst)): - bitext1 = bitext1_lst[shard_id] - bitext2 = bitext2_lst[shard_id] - gen_output = gen_output_lst[shard_id] - lm_res = lm_res_lst[shard_id] - - total = len(bitext1.rescore_source.keys()) - source_lst = [] - hypo_lst = [] - score_lst = [] - reference_lst = [] - j = 1 - best_score = -math.inf - - for i in range(total): - # length is measured in terms of words, not bpe tokens, since models may not share the same bpe - target_len = len(bitext1.rescore_hypo[i].split()) - - if lm_res is not None: - lm_score = lm_res.score[i] - else: - lm_score = 0 - - if bitext2 is not None: - bitext2_score = bitext2.rescore_score[i] - bitext2_backwards = bitext2.backwards - else: - bitext2_score = None - bitext2_backwards = None - - score = rerank_utils.get_score( - a, - b, - c, - target_len, - bitext1.rescore_score[i], - bitext2_score, - lm_score=lm_score, - lenpen=lenpen, - src_len=bitext1.source_lengths[i], - tgt_len=bitext1.target_lengths[i], - bitext1_backwards=bitext1.backwards, - bitext2_backwards=bitext2_backwards, - normalize=normalize, - ) - - if score > best_score: - best_score = score - best_hypo = bitext1.rescore_hypo[i] - - if j == gen_output.num_hypos[i] or j == args.num_rescore: - j = 1 - hypo_lst.append(best_hypo) - score_lst.append(best_score) - source_lst.append(bitext1.rescore_source[i]) - reference_lst.append(bitext1.rescore_target[i]) - - best_score = -math.inf - best_hypo = "" - else: - j += 1 - - gen_keys = list(sorted(gen_output.no_bpe_target.keys())) - - for key in range(len(gen_keys)): - if args.prefix_len is None: - assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( - "pred and rescore hypo mismatch: i: " - + str(key) - + ", " - + str(hypo_lst[key]) - + str(gen_keys[key]) - + str(gen_output.no_bpe_hypo[key]) - ) - sys_tok = dict.encode_line(hypo_lst[key]) - ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) - scorer.add(ref_tok, sys_tok) - - else: - full_hypo = rerank_utils.get_full_from_prefix( - hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] - ) - sys_tok = dict.encode_line(full_hypo) - ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) - scorer.add(ref_tok, sys_tok) - - # if only one set of hyper parameters is provided, write the predictions to a file - if write_hypos: - # recover the orinal ids from n best list generation - for key in range(len(gen_output.no_bpe_target)): - if args.prefix_len is None: - assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( - "pred and rescore hypo mismatch:" - + "i:" - + str(key) - + str(hypo_lst[key]) - + str(gen_output.no_bpe_hypo[key]) - ) - ordered_hypos[gen_keys[key]] = hypo_lst[key] - ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ - gen_keys[key] - ] - - else: - full_hypo = rerank_utils.get_full_from_prefix( - hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] - ) - ordered_hypos[gen_keys[key]] = full_hypo - ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ - gen_keys[key] - ] - - # write the hypos in the original order from nbest list generation - if args.num_shards == (len(bitext1_lst)): - with open(target_outfile, "w") as t: - with open(hypo_outfile, "w") as h: - for key in range(len(ordered_hypos)): - t.write(ordered_targets[key]) - h.write(ordered_hypos[key]) - - res = scorer.result_string(4) - if write_hypos: - print(res) - score = rerank_utils.parse_bleu_scoring(res) - return score - - -def match_target_hypo(args, target_outfile, hypo_outfile): - """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file""" - if len(args.weight1) == 1: - res = score_target_hypo( - args, - args.weight1[0], - args.weight2[0], - args.weight3[0], - args.lenpen[0], - target_outfile, - hypo_outfile, - True, - args.normalize, - ) - rerank_scores = [res] - else: - print("launching pool") - with Pool(32) as p: - rerank_scores = p.starmap( - score_target_hypo, - [ - ( - args, - args.weight1[i], - args.weight2[i], - args.weight3[i], - args.lenpen[i], - target_outfile, - hypo_outfile, - False, - args.normalize, - ) - for i in range(len(args.weight1)) - ], - ) - - if len(rerank_scores) > 1: - best_index = np.argmax(rerank_scores) - best_score = rerank_scores[best_index] - print("best score", best_score) - print("best lenpen", args.lenpen[best_index]) - print("best weight1", args.weight1[best_index]) - print("best weight2", args.weight2[best_index]) - print("best weight3", args.weight3[best_index]) - return ( - args.lenpen[best_index], - args.weight1[best_index], - args.weight2[best_index], - args.weight3[best_index], - best_score, - ) - - else: - return ( - args.lenpen[0], - args.weight1[0], - args.weight2[0], - args.weight3[0], - rerank_scores[0], - ) - - -def load_score_files(args): - if args.all_shards: - shard_ids = list(range(args.num_shards)) - else: - shard_ids = [args.shard_id] - - gen_output_lst = [] - bitext1_lst = [] - bitext2_lst = [] - lm_res1_lst = [] - - for shard_id in shard_ids: - using_nbest = args.nbest_list is not None - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - rerank1_is_gen = ( - args.gen_model == args.score_model1 and args.source_prefix_frac is None - ) - rerank2_is_gen = ( - args.gen_model == args.score_model2 and args.source_prefix_frac is None - ) - - score1_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1, - ) - if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2, - ) - if args.language_model is not None: - lm_score_file = rerank_utils.rescore_file_name( - pre_gen, args.prefix_len, args.lm_name, lm_file=True - ) - - # get gen output - predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" - if using_nbest: - print("Using predefined n-best list from interactive.py") - predictions_bpe_file = args.nbest_list - gen_output = rerank_utils.BitextOutputFromGen( - predictions_bpe_file, - bpe_symbol=args.post_process, - nbest=using_nbest, - prefix_len=args.prefix_len, - target_prefix_frac=args.target_prefix_frac, - ) - - if rerank1_is_gen: - bitext1 = gen_output - else: - bitext1 = rerank_utils.BitextOutput( - score1_file, - args.backwards1, - args.right_to_left1, - args.post_process, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - if args.score_model2 is not None or args.nbest_list is not None: - if rerank2_is_gen: - bitext2 = gen_output - else: - bitext2 = rerank_utils.BitextOutput( - score2_file, - args.backwards2, - args.right_to_left2, - args.post_process, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - assert ( - bitext2.source_lengths == bitext1.source_lengths - ), "source lengths for rescoring models do not match" - assert ( - bitext2.target_lengths == bitext1.target_lengths - ), "target lengths for rescoring models do not match" - else: - if args.diff_bpe: - assert args.score_model2 is None - bitext2 = gen_output - else: - bitext2 = None - - if args.language_model is not None: - lm_res1 = rerank_utils.LMOutput( - lm_score_file, - args.lm_dict, - args.prefix_len, - args.post_process, - args.target_prefix_frac, - ) - else: - lm_res1 = None - - gen_output_lst.append(gen_output) - bitext1_lst.append(bitext1) - bitext2_lst.append(bitext2) - lm_res1_lst.append(lm_res1) - return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst - - -def rerank(args): - if type(args.lenpen) is not list: - args.lenpen = [args.lenpen] - if type(args.weight1) is not list: - args.weight1 = [args.weight1] - if type(args.weight2) is not list: - args.weight2 = [args.weight2] - if type(args.weight3) is not list: - args.weight3 = [args.weight3] - if args.all_shards: - shard_ids = list(range(args.num_shards)) - else: - shard_ids = [args.shard_id] - - for shard_id in shard_ids: - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - rerank_generate.gen_and_reprocess_nbest(args) - rerank_score_bw.score_bw(args) - rerank_score_lm.score_lm(args) - - if args.write_hypos is None: - write_targets = pre_gen + "/matched_targets" - write_hypos = pre_gen + "/matched_hypos" - else: - write_targets = args.write_hypos + "_targets" + args.gen_subset - write_hypos = args.write_hypos + "_hypos" + args.gen_subset - - if args.all_shards: - write_targets += "_all_shards" - write_hypos += "_all_shards" - - ( - best_lenpen, - best_weight1, - best_weight2, - best_weight3, - best_score, - ) = match_target_hypo(args, write_targets, write_hypos) - - return best_lenpen, best_weight1, best_weight2, best_weight3, best_score - - -def cli_main(): - parser = rerank_options.get_reranking_parser() - args = options.parse_args_and_arch(parser) - rerank(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_generate.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_generate.py deleted file mode 100644 index cb829ff3cc7d23b4dff523d4b90535401d52c9a3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_generate.py +++ /dev/null @@ -1,396 +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. - -""" -Generate n-best translations using a trained model. -""" - -import os -import subprocess -from contextlib import redirect_stdout - -from examples.noisychannel import rerank_options, rerank_utils -from fairseq import options -from fairseq_cli import generate, preprocess - - -def gen_and_reprocess_nbest(args): - if args.score_dict_dir is None: - args.score_dict_dir = args.data - if args.prefix_len is not None: - assert ( - args.right_to_left1 is False - ), "prefix length not compatible with right to left models" - assert ( - args.right_to_left2 is False - ), "prefix length not compatible with right to left models" - - if args.nbest_list is not None: - assert args.score_model2 is None - - if args.backwards1: - scorer1_src = args.target_lang - scorer1_tgt = args.source_lang - else: - scorer1_src = args.source_lang - scorer1_tgt = args.target_lang - - store_data = ( - os.path.join(os.path.dirname(__file__)) + "/rerank_data/" + args.data_dir_name - ) - if not os.path.exists(store_data): - os.makedirs(store_data) - - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - args.shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - assert not ( - args.right_to_left1 and args.backwards1 - ), "backwards right to left not supported" - assert not ( - args.right_to_left2 and args.backwards2 - ), "backwards right to left not supported" - assert not ( - args.prefix_len is not None and args.target_prefix_frac is not None - ), "target prefix frac and target prefix len incompatible" - - # make directory to store generation results - if not os.path.exists(pre_gen): - os.makedirs(pre_gen) - - rerank1_is_gen = ( - args.gen_model == args.score_model1 and args.source_prefix_frac is None - ) - rerank2_is_gen = ( - args.gen_model == args.score_model2 and args.source_prefix_frac is None - ) - - if args.nbest_list is not None: - rerank2_is_gen = True - - # make directories to store preprossed nbest list for reranking - if not os.path.exists(left_to_right_preprocessed_dir): - os.makedirs(left_to_right_preprocessed_dir) - if not os.path.exists(right_to_left_preprocessed_dir): - os.makedirs(right_to_left_preprocessed_dir) - if not os.path.exists(lm_preprocessed_dir): - os.makedirs(lm_preprocessed_dir) - if not os.path.exists(backwards_preprocessed_dir): - os.makedirs(backwards_preprocessed_dir) - - score1_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1, - ) - if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2, - ) - - predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" - - using_nbest = args.nbest_list is not None - - if using_nbest: - print("Using predefined n-best list from interactive.py") - predictions_bpe_file = args.nbest_list - - else: - if not os.path.isfile(predictions_bpe_file): - print("STEP 1: generate predictions using the p(T|S) model with bpe") - print(args.data) - param1 = [ - args.data, - "--path", - args.gen_model, - "--shard-id", - str(args.shard_id), - "--num-shards", - str(args.num_shards), - "--nbest", - str(args.num_rescore), - "--batch-size", - str(args.batch_size), - "--beam", - str(args.num_rescore), - "--batch-size", - str(args.num_rescore), - "--gen-subset", - args.gen_subset, - "--source-lang", - args.source_lang, - "--target-lang", - args.target_lang, - ] - if args.sampling: - param1 += ["--sampling"] - - gen_parser = options.get_generation_parser() - input_args = options.parse_args_and_arch(gen_parser, param1) - - print(input_args) - with open(predictions_bpe_file, "w") as f: - with redirect_stdout(f): - generate.main(input_args) - - gen_output = rerank_utils.BitextOutputFromGen( - predictions_bpe_file, - bpe_symbol=args.post_process, - nbest=using_nbest, - prefix_len=args.prefix_len, - target_prefix_frac=args.target_prefix_frac, - ) - - if args.diff_bpe: - rerank_utils.write_reprocessed( - gen_output.no_bpe_source, - gen_output.no_bpe_hypo, - gen_output.no_bpe_target, - pre_gen + "/source_gen_bpe." + args.source_lang, - pre_gen + "/target_gen_bpe." + args.target_lang, - pre_gen + "/reference_gen_bpe." + args.target_lang, - ) - bitext_bpe = args.rescore_bpe_code - bpe_src_param = [ - "-c", - bitext_bpe, - "--input", - pre_gen + "/source_gen_bpe." + args.source_lang, - "--output", - pre_gen + "/rescore_data." + args.source_lang, - ] - bpe_tgt_param = [ - "-c", - bitext_bpe, - "--input", - pre_gen + "/target_gen_bpe." + args.target_lang, - "--output", - pre_gen + "/rescore_data." + args.target_lang, - ] - - subprocess.call( - [ - "python", - os.path.join( - os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" - ), - ] - + bpe_src_param, - shell=False, - ) - - subprocess.call( - [ - "python", - os.path.join( - os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" - ), - ] - + bpe_tgt_param, - shell=False, - ) - - if (not os.path.isfile(score1_file) and not rerank1_is_gen) or ( - args.score_model2 is not None - and not os.path.isfile(score2_file) - and not rerank2_is_gen - ): - print( - "STEP 2: process the output of generate.py so we have clean text files with the translations" - ) - - rescore_file = "/rescore_data" - if args.prefix_len is not None: - prefix_len_rescore_file = rescore_file + "prefix" + str(args.prefix_len) - if args.target_prefix_frac is not None: - target_prefix_frac_rescore_file = ( - rescore_file + "target_prefix_frac" + str(args.target_prefix_frac) - ) - if args.source_prefix_frac is not None: - source_prefix_frac_rescore_file = ( - rescore_file + "source_prefix_frac" + str(args.source_prefix_frac) - ) - - if not args.right_to_left1 or not args.right_to_left2: - if not args.diff_bpe: - rerank_utils.write_reprocessed( - gen_output.source, - gen_output.hypo, - gen_output.target, - pre_gen + rescore_file + "." + args.source_lang, - pre_gen + rescore_file + "." + args.target_lang, - pre_gen + "/reference_file", - bpe_symbol=args.post_process, - ) - if args.prefix_len is not None: - bw_rescore_file = prefix_len_rescore_file - rerank_utils.write_reprocessed( - gen_output.source, - gen_output.hypo, - gen_output.target, - pre_gen + prefix_len_rescore_file + "." + args.source_lang, - pre_gen + prefix_len_rescore_file + "." + args.target_lang, - pre_gen + "/reference_file", - prefix_len=args.prefix_len, - bpe_symbol=args.post_process, - ) - elif args.target_prefix_frac is not None: - bw_rescore_file = target_prefix_frac_rescore_file - rerank_utils.write_reprocessed( - gen_output.source, - gen_output.hypo, - gen_output.target, - pre_gen - + target_prefix_frac_rescore_file - + "." - + args.source_lang, - pre_gen - + target_prefix_frac_rescore_file - + "." - + args.target_lang, - pre_gen + "/reference_file", - bpe_symbol=args.post_process, - target_prefix_frac=args.target_prefix_frac, - ) - else: - bw_rescore_file = rescore_file - - if args.source_prefix_frac is not None: - fw_rescore_file = source_prefix_frac_rescore_file - rerank_utils.write_reprocessed( - gen_output.source, - gen_output.hypo, - gen_output.target, - pre_gen - + source_prefix_frac_rescore_file - + "." - + args.source_lang, - pre_gen - + source_prefix_frac_rescore_file - + "." - + args.target_lang, - pre_gen + "/reference_file", - bpe_symbol=args.post_process, - source_prefix_frac=args.source_prefix_frac, - ) - else: - fw_rescore_file = rescore_file - - if args.right_to_left1 or args.right_to_left2: - rerank_utils.write_reprocessed( - gen_output.source, - gen_output.hypo, - gen_output.target, - pre_gen + "/right_to_left_rescore_data." + args.source_lang, - pre_gen + "/right_to_left_rescore_data." + args.target_lang, - pre_gen + "/right_to_left_reference_file", - right_to_left=True, - bpe_symbol=args.post_process, - ) - - print("STEP 3: binarize the translations") - if ( - not args.right_to_left1 - or args.score_model2 is not None - and not args.right_to_left2 - or not rerank1_is_gen - ): - - if args.backwards1 or args.backwards2: - if args.backwards_score_dict_dir is not None: - bw_dict = args.backwards_score_dict_dir - else: - bw_dict = args.score_dict_dir - bw_preprocess_param = [ - "--source-lang", - scorer1_src, - "--target-lang", - scorer1_tgt, - "--trainpref", - pre_gen + bw_rescore_file, - "--srcdict", - bw_dict + "/dict." + scorer1_src + ".txt", - "--tgtdict", - bw_dict + "/dict." + scorer1_tgt + ".txt", - "--destdir", - backwards_preprocessed_dir, - ] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(bw_preprocess_param) - preprocess.main(input_args) - - preprocess_param = [ - "--source-lang", - scorer1_src, - "--target-lang", - scorer1_tgt, - "--trainpref", - pre_gen + fw_rescore_file, - "--srcdict", - args.score_dict_dir + "/dict." + scorer1_src + ".txt", - "--tgtdict", - args.score_dict_dir + "/dict." + scorer1_tgt + ".txt", - "--destdir", - left_to_right_preprocessed_dir, - ] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(preprocess_param) - preprocess.main(input_args) - - if args.right_to_left1 or args.right_to_left2: - preprocess_param = [ - "--source-lang", - scorer1_src, - "--target-lang", - scorer1_tgt, - "--trainpref", - pre_gen + "/right_to_left_rescore_data", - "--srcdict", - args.score_dict_dir + "/dict." + scorer1_src + ".txt", - "--tgtdict", - args.score_dict_dir + "/dict." + scorer1_tgt + ".txt", - "--destdir", - right_to_left_preprocessed_dir, - ] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(preprocess_param) - preprocess.main(input_args) - - return gen_output - - -def cli_main(): - parser = rerank_options.get_reranking_parser() - args = options.parse_args_and_arch(parser) - gen_and_reprocess_nbest(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_options.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_options.py deleted file mode 100644 index de91939e6635bdf33c9dc330116be07d9e8be6a2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_options.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. - -from fairseq import options - - -def get_reranking_parser(default_task="translation"): - parser = options.get_parser("Generation and reranking", default_task) - add_reranking_args(parser) - return parser - - -def get_tuning_parser(default_task="translation"): - parser = options.get_parser("Reranking tuning", default_task) - add_reranking_args(parser) - add_tuning_args(parser) - return parser - - -def add_reranking_args(parser): - group = parser.add_argument_group("Reranking") - # fmt: off - group.add_argument('--score-model1', '-s1', type=str, metavar='FILE', required=True, - help='path to first model or ensemble of models for rescoring') - group.add_argument('--score-model2', '-s2', type=str, metavar='FILE', required=False, - help='path to second model or ensemble of models for rescoring') - group.add_argument('--num-rescore', '-n', type=int, metavar='N', default=10, - help='the number of candidate hypothesis to rescore') - group.add_argument('-bz', '--batch-size', type=int, metavar='N', default=128, - help='batch size for generating the nbest list') - group.add_argument('--gen-subset', default='test', metavar='SET', choices=['test', 'train', 'valid'], - help='data subset to generate (train, valid, test)') - group.add_argument('--gen-model', default=None, metavar='FILE', - help='the model to generate translations') - group.add_argument('-b1', '--backwards1', action='store_true', - help='whether or not the first model group is backwards') - group.add_argument('-b2', '--backwards2', action='store_true', - help='whether or not the second model group is backwards') - group.add_argument('-a', '--weight1', default=1, nargs='+', type=float, - help='the weight(s) of the first model') - group.add_argument('-b', '--weight2', default=1, nargs='+', type=float, - help='the weight(s) of the second model, or the gen model if using nbest from interactive.py') - group.add_argument('-c', '--weight3', default=1, nargs='+', type=float, - help='the weight(s) of the third model') - - # lm arguments - group.add_argument('-lm', '--language-model', default=None, metavar='FILE', - help='language model for target language to rescore translations') - group.add_argument('--lm-dict', default=None, metavar='FILE', - help='the dict of the language model for the target language') - group.add_argument('--lm-name', default=None, - help='the name of the language model for the target language') - group.add_argument('--lm-bpe-code', default=None, metavar='FILE', - help='the bpe code for the language model for the target language') - group.add_argument('--data-dir-name', default=None, - help='name of data directory') - group.add_argument('--lenpen', default=1, nargs='+', type=float, - help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences') - group.add_argument('--score-dict-dir', default=None, - help='the directory with dictionaries for the scoring models') - group.add_argument('--right-to-left1', action='store_true', - help='whether the first model group is a right to left model') - group.add_argument('--right-to-left2', action='store_true', - help='whether the second model group is a right to left model') - group.add_argument('--post-process', '--remove-bpe', default='@@ ', - help='the bpe symbol, used for the bitext and LM') - group.add_argument('--prefix-len', default=None, type=int, - help='the length of the target prefix to use in rescoring (in terms of words wo bpe)') - group.add_argument('--sampling', action='store_true', - help='use sampling instead of beam search for generating n best list') - group.add_argument('--diff-bpe', action='store_true', - help='bpe for rescoring and nbest list not the same') - group.add_argument('--rescore-bpe-code', default=None, - help='bpe code for rescoring models') - group.add_argument('--nbest-list', default=None, - help='use predefined nbest list in interactive.py format') - group.add_argument('--write-hypos', default=None, - help='filename prefix to write hypos to') - group.add_argument('--ref-translation', default=None, - help='reference translation to use with nbest list from interactive.py') - group.add_argument('--backwards-score-dict-dir', default=None, - help='the directory with dictionaries for the backwards model,' - 'if None then it is assumed the fw and backwards models share dictionaries') - - # extra scaling args - group.add_argument('--gen-model-name', default=None, - help='the name of the models that generated the nbest list') - group.add_argument('--model1-name', default=None, - help='the name of the set for model1 group ') - group.add_argument('--model2-name', default=None, - help='the name of the set for model2 group') - group.add_argument('--shard-id', default=0, type=int, - help='the id of the shard to generate') - group.add_argument('--num-shards', default=1, type=int, - help='the number of shards to generate across') - group.add_argument('--all-shards', action='store_true', - help='use all shards') - group.add_argument('--target-prefix-frac', default=None, type=float, - help='the fraction of the target prefix to use in rescoring (in terms of words wo bpe)') - group.add_argument('--source-prefix-frac', default=None, type=float, - help='the fraction of the source prefix to use in rescoring (in terms of words wo bpe)') - group.add_argument('--normalize', action='store_true', - help='whether to normalize by src and target len') - # fmt: on - return group - - -def add_tuning_args(parser): - group = parser.add_argument_group("Tuning") - - group.add_argument( - "--lower-bound", - default=[-0.7], - nargs="+", - type=float, - help="lower bound of search space", - ) - group.add_argument( - "--upper-bound", - default=[3], - nargs="+", - type=float, - help="upper bound of search space", - ) - group.add_argument( - "--tune-param", - default=["lenpen"], - nargs="+", - choices=["lenpen", "weight1", "weight2", "weight3"], - help="the parameter(s) to tune", - ) - group.add_argument( - "--tune-subset", - default="valid", - choices=["valid", "test", "train"], - help="the subset to tune on ", - ) - group.add_argument( - "--num-trials", - default=1000, - type=int, - help="number of trials to do for random search", - ) - group.add_argument( - "--share-weights", action="store_true", help="share weight2 and weight 3" - ) - return group diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_score_bw.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_score_bw.py deleted file mode 100644 index ad911128d7094c4147e1fb974b97dd7856a348b7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_score_bw.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 os -from contextlib import redirect_stdout - -from examples.noisychannel import rerank_options, rerank_utils -from fairseq import options -from fairseq_cli import generate - - -def score_bw(args): - if args.backwards1: - scorer1_src = args.target_lang - scorer1_tgt = args.source_lang - else: - scorer1_src = args.source_lang - scorer1_tgt = args.target_lang - - if args.score_model2 is not None: - if args.backwards2: - scorer2_src = args.target_lang - scorer2_tgt = args.source_lang - else: - scorer2_src = args.source_lang - scorer2_tgt = args.target_lang - - rerank1_is_gen = ( - args.gen_model == args.score_model1 and args.source_prefix_frac is None - ) - rerank2_is_gen = ( - args.gen_model == args.score_model2 and args.source_prefix_frac is None - ) - - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - args.shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - score1_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1, - ) - - if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2, - ) - - if args.right_to_left1: - rerank_data1 = right_to_left_preprocessed_dir - elif args.backwards1: - rerank_data1 = backwards_preprocessed_dir - else: - rerank_data1 = left_to_right_preprocessed_dir - - gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"] - if not rerank1_is_gen and not os.path.isfile(score1_file): - print("STEP 4: score the translations for model 1") - - model_param1 = [ - "--path", - args.score_model1, - "--source-lang", - scorer1_src, - "--target-lang", - scorer1_tgt, - ] - gen_model1_param = [rerank_data1] + gen_param + model_param1 - - gen_parser = options.get_generation_parser() - input_args = options.parse_args_and_arch(gen_parser, gen_model1_param) - - with open(score1_file, "w") as f: - with redirect_stdout(f): - generate.main(input_args) - - if ( - args.score_model2 is not None - and not os.path.isfile(score2_file) - and not rerank2_is_gen - ): - print("STEP 4: score the translations for model 2") - - if args.right_to_left2: - rerank_data2 = right_to_left_preprocessed_dir - elif args.backwards2: - rerank_data2 = backwards_preprocessed_dir - else: - rerank_data2 = left_to_right_preprocessed_dir - - model_param2 = [ - "--path", - args.score_model2, - "--source-lang", - scorer2_src, - "--target-lang", - scorer2_tgt, - ] - gen_model2_param = [rerank_data2] + gen_param + model_param2 - - gen_parser = options.get_generation_parser() - input_args = options.parse_args_and_arch(gen_parser, gen_model2_param) - - with open(score2_file, "w") as f: - with redirect_stdout(f): - generate.main(input_args) - - -def cli_main(): - parser = rerank_options.get_reranking_parser() - args = options.parse_args_and_arch(parser) - score_bw(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_score_lm.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_score_lm.py deleted file mode 100644 index f76be16a033dc0d9e9d844d7182c7930807d4526..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_score_lm.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. - -import os - -from examples.noisychannel import rerank_options, rerank_utils -from fairseq import options - - -def score_lm(args): - using_nbest = args.nbest_list is not None - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - args.shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" - if using_nbest: - print("Using predefined n-best list from interactive.py") - predictions_bpe_file = args.nbest_list - - gen_output = rerank_utils.BitextOutputFromGen( - predictions_bpe_file, bpe_symbol=args.post_process, nbest=using_nbest - ) - - if args.language_model is not None: - lm_score_file = rerank_utils.rescore_file_name( - pre_gen, args.prefix_len, args.lm_name, lm_file=True - ) - - if args.language_model is not None and not os.path.isfile(lm_score_file): - print("STEP 4.5: language modeling for P(T)") - if args.lm_bpe_code is None: - bpe_status = "no bpe" - elif args.lm_bpe_code == "shared": - bpe_status = "shared" - else: - bpe_status = "different" - - rerank_utils.lm_scoring( - lm_preprocessed_dir, - bpe_status, - gen_output, - pre_gen, - args.lm_dict, - args.lm_name, - args.language_model, - args.lm_bpe_code, - 128, - lm_score_file, - args.target_lang, - args.source_lang, - prefix_len=args.prefix_len, - ) - - -def cli_main(): - parser = rerank_options.get_reranking_parser() - args = options.parse_args_and_arch(parser) - score_lm(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_tune.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_tune.py deleted file mode 100644 index c4692299203e4aeb4a11c869536e27df418bea02..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_tune.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 random - -import numpy as np -from examples.noisychannel import rerank, rerank_options -from fairseq import options - - -def random_search(args): - param_values = [] - tuneable_parameters = ["lenpen", "weight1", "weight2", "weight3"] - initial_params = [args.lenpen, args.weight1, args.weight2, args.weight3] - for i, elem in enumerate(initial_params): - if type(elem) is not list: - initial_params[i] = [elem] - else: - initial_params[i] = elem - - tune_parameters = args.tune_param.copy() - for i in range(len(args.tune_param)): - assert args.upper_bound[i] >= args.lower_bound[i] - index = tuneable_parameters.index(args.tune_param[i]) - del tuneable_parameters[index] - del initial_params[index] - - tune_parameters += tuneable_parameters - param_values += initial_params - random.seed(args.seed) - - random_params = np.array( - [ - [ - random.uniform(args.lower_bound[i], args.upper_bound[i]) - for i in range(len(args.tune_param)) - ] - for k in range(args.num_trials) - ] - ) - set_params = np.array( - [ - [initial_params[i][0] for i in range(len(tuneable_parameters))] - for k in range(args.num_trials) - ] - ) - random_params = np.concatenate((random_params, set_params), 1) - - rerank_args = vars(args).copy() - if args.nbest_list: - rerank_args["gen_subset"] = "test" - else: - rerank_args["gen_subset"] = args.tune_subset - - for k in range(len(tune_parameters)): - rerank_args[tune_parameters[k]] = list(random_params[:, k]) - - if args.share_weights: - k = tune_parameters.index("weight2") - rerank_args["weight3"] = list(random_params[:, k]) - - rerank_args = argparse.Namespace(**rerank_args) - best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank( - rerank_args - ) - rerank_args = vars(args).copy() - rerank_args["lenpen"] = [best_lenpen] - rerank_args["weight1"] = [best_weight1] - rerank_args["weight2"] = [best_weight2] - rerank_args["weight3"] = [best_weight3] - - # write the hypothesis from the valid set from the best trial - - if args.gen_subset != "valid": - rerank_args["gen_subset"] = "valid" - rerank_args = argparse.Namespace(**rerank_args) - rerank.rerank(rerank_args) - - # test with the best hyperparameters on gen subset - rerank_args = vars(args).copy() - rerank_args["gen_subset"] = args.gen_subset - rerank_args["lenpen"] = [best_lenpen] - rerank_args["weight1"] = [best_weight1] - rerank_args["weight2"] = [best_weight2] - rerank_args["weight3"] = [best_weight3] - rerank_args = argparse.Namespace(**rerank_args) - rerank.rerank(rerank_args) - - -def cli_main(): - parser = rerank_options.get_tuning_parser() - args = options.parse_args_and_arch(parser) - - random_search(args) - - -if __name__ == "__main__": - cli_main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_utils.py b/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_utils.py deleted file mode 100644 index 2c6bf1b1afbb089cf5e84f720eb7a067479fbcbc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/noisychannel/rerank_utils.py +++ /dev/null @@ -1,850 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 os -import re -import subprocess -from contextlib import redirect_stdout - -from fairseq import options -from fairseq_cli import eval_lm, preprocess - - -def reprocess(fle): - # takes in a file of generate.py translation generate_output - # returns a source dict and hypothesis dict, where keys are the ID num (as a string) - # and values and the corresponding source and translation. There may be several translations - # per source, so the values for hypothesis_dict are lists. - # parses output of generate.py - - with open(fle, "r") as f: - txt = f.read() - - """reprocess generate.py output""" - p = re.compile(r"[STHP][-]\d+\s*") - hp = re.compile(r"(\s*[-]?\d+[.]?\d+\s*)|(\s*(-inf)\s*)") - source_dict = {} - hypothesis_dict = {} - score_dict = {} - target_dict = {} - pos_score_dict = {} - lines = txt.split("\n") - - for line in lines: - line += "\n" - prefix = re.search(p, line) - if prefix is not None: - assert len(prefix.group()) > 2, "prefix id not found" - _, j = prefix.span() - id_num = prefix.group()[2:] - id_num = int(id_num) - line_type = prefix.group()[0] - if line_type == "H": - h_txt = line[j:] - hypo = re.search(hp, h_txt) - assert ( - hypo is not None - ), "regular expression failed to find the hypothesis scoring" - _, i = hypo.span() - score = hypo.group() - if id_num in hypothesis_dict: - hypothesis_dict[id_num].append(h_txt[i:]) - score_dict[id_num].append(float(score)) - else: - hypothesis_dict[id_num] = [h_txt[i:]] - score_dict[id_num] = [float(score)] - - elif line_type == "S": - source_dict[id_num] = line[j:] - elif line_type == "T": - target_dict[id_num] = line[j:] - elif line_type == "P": - pos_scores = (line[j:]).split() - pos_scores = [float(x) for x in pos_scores] - if id_num in pos_score_dict: - pos_score_dict[id_num].append(pos_scores) - else: - pos_score_dict[id_num] = [pos_scores] - - return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict - - -def reprocess_nbest(fle): - """reprocess interactive.py output""" - with open(fle, "r") as f: - txt = f.read() - - source_dict = {} - hypothesis_dict = {} - score_dict = {} - target_dict = {} - pos_score_dict = {} - lines = txt.split("\n") - - hp = re.compile(r"[-]?\d+[.]?\d+") - j = -1 - - for _i, line in enumerate(lines): - line += "\n" - line_type = line[0] - - if line_type == "H": - hypo = re.search(hp, line) - _, start_index = hypo.span() - score = hypo.group() - if j in score_dict: - score_dict[j].append(float(score)) - hypothesis_dict[j].append(line[start_index:].strip("\t")) - else: - score_dict[j] = [float(score)] - hypothesis_dict[j] = [line[start_index:].strip("\t")] - elif line_type == "O": - j += 1 - source_dict[j] = line[2:] - # we don't have the targets for interactive.py - target_dict[j] = "filler" - - elif line_type == "P": - pos_scores = [float(pos_score) for pos_score in line.split()[1:]] - if j in pos_score_dict: - pos_score_dict[j].append(pos_scores) - else: - pos_score_dict[j] = [pos_scores] - - assert source_dict.keys() == hypothesis_dict.keys() - assert source_dict.keys() == pos_score_dict.keys() - assert source_dict.keys() == score_dict.keys() - - return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict - - -def write_reprocessed( - sources, - hypos, - targets, - source_outfile, - hypo_outfile, - target_outfile, - right_to_left=False, - prefix_len=None, - bpe_symbol=None, - target_prefix_frac=None, - source_prefix_frac=None, -): - - """writes nbest hypothesis for rescoring""" - assert not ( - prefix_len is not None and target_prefix_frac is not None - ), "in writing reprocessed, only one type of prefix may be used" - assert not ( - prefix_len is not None and source_prefix_frac is not None - ), "in writing reprocessed, only one type of prefix may be used" - assert not ( - target_prefix_frac is not None and source_prefix_frac is not None - ), "in writing reprocessed, only one type of prefix may be used" - - with open(source_outfile, "w") as source_file, open( - hypo_outfile, "w" - ) as hypo_file, open(target_outfile, "w") as target_file: - - assert len(sources) == len(hypos), "sources and hypos list length mismatch" - if right_to_left: - for i in range(len(sources)): - for j in range(len(hypos[i])): - if prefix_len is None: - hypo_file.write(make_right_to_left(hypos[i][j]) + "\n") - else: - raise NotImplementedError() - source_file.write(make_right_to_left(sources[i]) + "\n") - target_file.write(make_right_to_left(targets[i]) + "\n") - else: - for i in sorted(sources.keys()): - for j in range(len(hypos[i])): - if prefix_len is not None: - shortened = ( - get_prefix_no_bpe(hypos[i][j], bpe_symbol, prefix_len) - + "\n" - ) - hypo_file.write(shortened) - source_file.write(sources[i]) - target_file.write(targets[i]) - elif target_prefix_frac is not None: - num_words, shortened, num_bpe_tokens = calc_length_from_frac( - hypos[i][j], target_prefix_frac, bpe_symbol - ) - shortened += "\n" - hypo_file.write(shortened) - source_file.write(sources[i]) - target_file.write(targets[i]) - elif source_prefix_frac is not None: - num_words, shortened, num_bpe_tokensn = calc_length_from_frac( - sources[i], source_prefix_frac, bpe_symbol - ) - shortened += "\n" - hypo_file.write(hypos[i][j]) - source_file.write(shortened) - target_file.write(targets[i]) - else: - hypo_file.write(hypos[i][j]) - source_file.write(sources[i]) - target_file.write(targets[i]) - - -def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol): - # return number of words, (not bpe tokens) that we want - no_bpe_sen = remove_bpe(bpe_sentence, bpe_symbol) - len_sen = len(no_bpe_sen.split()) - - num_words = math.ceil(len_sen * prefix_frac) - prefix = get_prefix_no_bpe(bpe_sentence, bpe_symbol, num_words) - num_bpe_tokens = len(prefix.split()) - return num_words, prefix, num_bpe_tokens - - -def get_prefix(sentence, prefix_len): - """assuming no bpe, gets the prefix of the sentence with prefix_len words""" - tokens = sentence.strip("\n").split() - if prefix_len >= len(tokens): - return sentence.strip("\n") - else: - return " ".join(tokens[:prefix_len]) - - -def get_prefix_no_bpe(sentence, bpe_symbol, prefix_len): - if bpe_symbol is None: - return get_prefix(sentence, prefix_len) - else: - return " ".join(get_prefix_from_len(sentence.split(), bpe_symbol, prefix_len)) - - -def get_prefix_from_len(sentence, bpe_symbol, prefix_len): - """get the prefix of sentence with bpe, with prefix len in terms of words, not bpe tokens""" - bpe_count = sum([bpe_symbol.strip(" ") in t for t in sentence[:prefix_len]]) - if bpe_count == 0: - return sentence[:prefix_len] - else: - return sentence[:prefix_len] + get_prefix_from_len( - sentence[prefix_len:], bpe_symbol, bpe_count - ) - - -def get_num_bpe_tokens_from_len(sentence, bpe_symbol, prefix_len): - """given a prefix length in terms of words, return the number of bpe tokens""" - prefix = get_prefix_no_bpe(sentence, bpe_symbol, prefix_len) - assert len(remove_bpe(prefix, bpe_symbol).split()) <= prefix_len - return len(prefix.split(" ")) - - -def make_right_to_left(line): - tokens = line.split() - tokens.reverse() - new_line = " ".join(tokens) - return new_line - - -def remove_bpe(line, bpe_symbol): - line = line.replace("\n", "") - line = (line + " ").replace(bpe_symbol, "").rstrip() - return line + ("\n") - - -def remove_bpe_dict(pred_dict, bpe_symbol): - new_dict = {} - for i in pred_dict: - if type(pred_dict[i]) == list: - new_list = [remove_bpe(elem, bpe_symbol) for elem in pred_dict[i]] - new_dict[i] = new_list - else: - new_dict[i] = remove_bpe(pred_dict[i], bpe_symbol) - return new_dict - - -def parse_bleu_scoring(line): - p = re.compile(r"(BLEU4 = )\d+[.]\d+") - res = re.search(p, line) - assert res is not None, line - return float(res.group()[8:]) - - -def get_full_from_prefix(hypo_prefix, hypos): - """given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix""" - for hypo in hypos: - hypo_prefix = hypo_prefix.strip("\n") - len_prefix = len(hypo_prefix) - if hypo[:len_prefix] == hypo_prefix: - return hypo - # no match found - raise Exception() - - -def get_score( - a, - b, - c, - target_len, - bitext_score1, - bitext_score2=None, - lm_score=None, - lenpen=None, - src_len=None, - tgt_len=None, - bitext1_backwards=False, - bitext2_backwards=False, - normalize=False, -): - if bitext1_backwards: - bitext1_norm = src_len - else: - bitext1_norm = tgt_len - if bitext_score2 is not None: - if bitext2_backwards: - bitext2_norm = src_len - else: - bitext2_norm = tgt_len - else: - bitext2_norm = 1 - bitext_score2 = 0 - if normalize: - score = ( - a * bitext_score1 / bitext1_norm - + b * bitext_score2 / bitext2_norm - + c * lm_score / src_len - ) - else: - score = a * bitext_score1 + b * bitext_score2 + c * lm_score - - if lenpen is not None: - score /= (target_len) ** float(lenpen) - - return score - - -class BitextOutput(object): - def __init__( - self, - output_file, - backwards, - right_to_left, - bpe_symbol, - prefix_len=None, - target_prefix_frac=None, - source_prefix_frac=None, - ): - """process output from rescoring""" - source, hypo, score, target, pos_score = reprocess(output_file) - if backwards: - self.hypo_fracs = source_prefix_frac - else: - self.hypo_fracs = target_prefix_frac - - # remove length penalty so we can use raw scores - score, num_bpe_tokens = get_score_from_pos( - pos_score, prefix_len, hypo, bpe_symbol, self.hypo_fracs, backwards - ) - source_lengths = {} - target_lengths = {} - - assert hypo.keys() == source.keys(), "key mismatch" - if backwards: - tmp = hypo - hypo = source - source = tmp - for i in source: - # since we are reranking, there should only be one hypo per source sentence - if backwards: - len_src = len(source[i][0].split()) - # record length without <eos> - if len_src == num_bpe_tokens[i][0] - 1: - source_lengths[i] = num_bpe_tokens[i][0] - 1 - else: - source_lengths[i] = num_bpe_tokens[i][0] - - target_lengths[i] = len(hypo[i].split()) - - source[i] = remove_bpe(source[i][0], bpe_symbol) - target[i] = remove_bpe(target[i], bpe_symbol) - hypo[i] = remove_bpe(hypo[i], bpe_symbol) - - score[i] = float(score[i][0]) - pos_score[i] = pos_score[i][0] - - else: - len_tgt = len(hypo[i][0].split()) - # record length without <eos> - if len_tgt == num_bpe_tokens[i][0] - 1: - target_lengths[i] = num_bpe_tokens[i][0] - 1 - else: - target_lengths[i] = num_bpe_tokens[i][0] - - source_lengths[i] = len(source[i].split()) - - if right_to_left: - source[i] = remove_bpe(make_right_to_left(source[i]), bpe_symbol) - target[i] = remove_bpe(make_right_to_left(target[i]), bpe_symbol) - hypo[i] = remove_bpe(make_right_to_left(hypo[i][0]), bpe_symbol) - score[i] = float(score[i][0]) - pos_score[i] = pos_score[i][0] - else: - assert ( - len(hypo[i]) == 1 - ), "expected only one hypothesis per source sentence" - source[i] = remove_bpe(source[i], bpe_symbol) - target[i] = remove_bpe(target[i], bpe_symbol) - hypo[i] = remove_bpe(hypo[i][0], bpe_symbol) - score[i] = float(score[i][0]) - pos_score[i] = pos_score[i][0] - - self.rescore_source = source - self.rescore_hypo = hypo - self.rescore_score = score - self.rescore_target = target - self.rescore_pos_score = pos_score - self.backwards = backwards - self.right_to_left = right_to_left - self.target_lengths = target_lengths - self.source_lengths = source_lengths - - -class BitextOutputFromGen(object): - def __init__( - self, - predictions_bpe_file, - bpe_symbol=None, - nbest=False, - prefix_len=None, - target_prefix_frac=None, - ): - if nbest: - ( - pred_source, - pred_hypo, - pred_score, - pred_target, - pred_pos_score, - ) = reprocess_nbest(predictions_bpe_file) - else: - pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess( - predictions_bpe_file - ) - - assert len(pred_source) == len(pred_hypo) - assert len(pred_source) == len(pred_score) - assert len(pred_source) == len(pred_target) - assert len(pred_source) == len(pred_pos_score) - - # remove length penalty so we can use raw scores - pred_score, num_bpe_tokens = get_score_from_pos( - pred_pos_score, prefix_len, pred_hypo, bpe_symbol, target_prefix_frac, False - ) - - self.source = pred_source - self.target = pred_target - self.score = pred_score - self.pos_score = pred_pos_score - self.hypo = pred_hypo - self.target_lengths = {} - self.source_lengths = {} - - self.no_bpe_source = remove_bpe_dict(pred_source.copy(), bpe_symbol) - self.no_bpe_hypo = remove_bpe_dict(pred_hypo.copy(), bpe_symbol) - self.no_bpe_target = remove_bpe_dict(pred_target.copy(), bpe_symbol) - - # indexes to match those from the rescoring models - self.rescore_source = {} - self.rescore_target = {} - self.rescore_pos_score = {} - self.rescore_hypo = {} - self.rescore_score = {} - self.num_hypos = {} - self.backwards = False - self.right_to_left = False - - index = 0 - - for i in sorted(pred_source.keys()): - for j in range(len(pred_hypo[i])): - - self.target_lengths[index] = len(self.hypo[i][j].split()) - self.source_lengths[index] = len(self.source[i].split()) - - self.rescore_source[index] = self.no_bpe_source[i] - self.rescore_target[index] = self.no_bpe_target[i] - self.rescore_hypo[index] = self.no_bpe_hypo[i][j] - self.rescore_score[index] = float(pred_score[i][j]) - self.rescore_pos_score[index] = pred_pos_score[i][j] - self.num_hypos[index] = len(pred_hypo[i]) - index += 1 - - -def get_score_from_pos( - pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_frac, backwards -): - score_dict = {} - num_bpe_tokens_dict = {} - assert prefix_len is None or hypo_frac is None - for key in pos_score_dict: - score_dict[key] = [] - num_bpe_tokens_dict[key] = [] - for i in range(len(pos_score_dict[key])): - if prefix_len is not None and not backwards: - num_bpe_tokens = get_num_bpe_tokens_from_len( - hypo_dict[key][i], bpe_symbol, prefix_len - ) - score_dict[key].append(sum(pos_score_dict[key][i][:num_bpe_tokens])) - num_bpe_tokens_dict[key].append(num_bpe_tokens) - elif hypo_frac is not None: - num_words, shortened, hypo_prefix_len = calc_length_from_frac( - hypo_dict[key][i], hypo_frac, bpe_symbol - ) - score_dict[key].append(sum(pos_score_dict[key][i][:hypo_prefix_len])) - num_bpe_tokens_dict[key].append(hypo_prefix_len) - else: - score_dict[key].append(sum(pos_score_dict[key][i])) - num_bpe_tokens_dict[key].append(len(pos_score_dict[key][i])) - return score_dict, num_bpe_tokens_dict - - -class LMOutput(object): - def __init__( - self, - lm_score_file, - lm_dict=None, - prefix_len=None, - bpe_symbol=None, - target_prefix_frac=None, - ): - ( - lm_sentences, - lm_sen_scores, - lm_sen_pos_scores, - lm_no_bpe_sentences, - lm_bpe_tokens, - ) = parse_lm( - lm_score_file, - prefix_len=prefix_len, - bpe_symbol=bpe_symbol, - target_prefix_frac=target_prefix_frac, - ) - - self.sentences = lm_sentences - self.score = lm_sen_scores - self.pos_score = lm_sen_pos_scores - self.lm_dict = lm_dict - self.no_bpe_sentences = lm_no_bpe_sentences - self.bpe_tokens = lm_bpe_tokens - - -def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=None): - """parse output of eval_lm""" - with open(input_file, "r") as f: - text = f.readlines() - text = text[7:] - cleaned_text = text[:-2] - - sentences = {} - sen_scores = {} - sen_pos_scores = {} - no_bpe_sentences = {} - num_bpe_tokens_dict = {} - for _i, line in enumerate(cleaned_text): - tokens = line.split() - if tokens[0].isdigit(): - line_id = int(tokens[0]) - scores = [float(x[1:-1]) for x in tokens[2::2]] - sentences[line_id] = " ".join(tokens[1::2][:-1]) + "\n" - if bpe_symbol is not None: - # exclude <eos> symbol to match output from generate.py - bpe_sen = " ".join(tokens[1::2][:-1]) + "\n" - no_bpe_sen = remove_bpe(bpe_sen, bpe_symbol) - no_bpe_sentences[line_id] = no_bpe_sen - - if prefix_len is not None: - num_bpe_tokens = get_num_bpe_tokens_from_len( - bpe_sen, bpe_symbol, prefix_len - ) - sen_scores[line_id] = sum(scores[:num_bpe_tokens]) - num_bpe_tokens_dict[line_id] = num_bpe_tokens - elif target_prefix_frac is not None: - num_words, shortened, target_prefix_len = calc_length_from_frac( - bpe_sen, target_prefix_frac, bpe_symbol - ) - sen_scores[line_id] = sum(scores[:target_prefix_len]) - num_bpe_tokens_dict[line_id] = target_prefix_len - else: - sen_scores[line_id] = sum(scores) - num_bpe_tokens_dict[line_id] = len(scores) - - sen_pos_scores[line_id] = scores - - return sentences, sen_scores, sen_pos_scores, no_bpe_sentences, num_bpe_tokens_dict - - -def get_directories( - data_dir_name, - num_rescore, - gen_subset, - fw_name, - shard_id, - num_shards, - sampling=False, - prefix_len=None, - target_prefix_frac=None, - source_prefix_frac=None, -): - nbest_file_id = ( - "nbest_" - + str(num_rescore) - + "_subset_" - + gen_subset - + "_fw_name_" - + fw_name - + "_shard_" - + str(shard_id) - + "_of_" - + str(num_shards) - ) - - if sampling: - nbest_file_id += "_sampling" - - # the directory containing all information for this nbest list - pre_gen = ( - os.path.join(os.path.dirname(__file__)) - + "/rerank_data/" - + data_dir_name - + "/" - + nbest_file_id - ) - # the directory to store the preprocessed nbest list, for left to right rescoring - left_to_right_preprocessed_dir = pre_gen + "/left_to_right_preprocessed" - if source_prefix_frac is not None: - left_to_right_preprocessed_dir = ( - left_to_right_preprocessed_dir + "/prefix_frac" + str(source_prefix_frac) - ) - # the directory to store the preprocessed nbest list, for right to left rescoring - right_to_left_preprocessed_dir = pre_gen + "/right_to_left_preprocessed" - # the directory to store the preprocessed nbest list, for backwards rescoring - backwards_preprocessed_dir = pre_gen + "/backwards" - if target_prefix_frac is not None: - backwards_preprocessed_dir = ( - backwards_preprocessed_dir + "/prefix_frac" + str(target_prefix_frac) - ) - elif prefix_len is not None: - backwards_preprocessed_dir = ( - backwards_preprocessed_dir + "/prefix_" + str(prefix_len) - ) - - # the directory to store the preprocessed nbest list, for rescoring with P(T) - lm_preprocessed_dir = pre_gen + "/lm_preprocessed" - - return ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) - - -def lm_scoring( - preprocess_directory, - bpe_status, - gen_output, - pre_gen, - cur_lm_dict, - cur_lm_name, - cur_language_model, - cur_lm_bpe_code, - batch_size, - lm_score_file, - target_lang, - source_lang, - prefix_len=None, -): - if prefix_len is not None: - assert ( - bpe_status == "different" - ), "bpe status must be different to use prefix len" - if bpe_status == "no bpe": - # run lm on output without bpe - write_reprocessed( - gen_output.no_bpe_source, - gen_output.no_bpe_hypo, - gen_output.no_bpe_target, - pre_gen + "/rescore_data_no_bpe.de", - pre_gen + "/rescore_data_no_bpe.en", - pre_gen + "/reference_file_no_bpe", - ) - - preprocess_lm_param = [ - "--only-source", - "--trainpref", - pre_gen + "/rescore_data_no_bpe." + target_lang, - "--srcdict", - cur_lm_dict, - "--destdir", - preprocess_directory, - ] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(preprocess_lm_param) - preprocess.main(input_args) - - eval_lm_param = [ - preprocess_directory, - "--path", - cur_language_model, - "--output-word-probs", - "--batch-size", - str(batch_size), - "--max-tokens", - "1024", - "--sample-break-mode", - "eos", - "--gen-subset", - "train", - ] - - eval_lm_parser = options.get_eval_lm_parser() - input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) - - with open(lm_score_file, "w") as f: - with redirect_stdout(f): - eval_lm.main(input_args) - - elif bpe_status == "shared": - preprocess_lm_param = [ - "--only-source", - "--trainpref", - pre_gen + "/rescore_data." + target_lang, - "--srcdict", - cur_lm_dict, - "--destdir", - preprocess_directory, - ] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(preprocess_lm_param) - preprocess.main(input_args) - - eval_lm_param = [ - preprocess_directory, - "--path", - cur_language_model, - "--output-word-probs", - "--batch-size", - str(batch_size), - "--sample-break-mode", - "eos", - "--gen-subset", - "train", - ] - - eval_lm_parser = options.get_eval_lm_parser() - input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) - - with open(lm_score_file, "w") as f: - with redirect_stdout(f): - eval_lm.main(input_args) - - elif bpe_status == "different": - rescore_file = pre_gen + "/rescore_data_no_bpe" - rescore_bpe = pre_gen + "/rescore_data_new_bpe" - - rescore_file += "." - rescore_bpe += "." - - write_reprocessed( - gen_output.no_bpe_source, - gen_output.no_bpe_hypo, - gen_output.no_bpe_target, - rescore_file + source_lang, - rescore_file + target_lang, - pre_gen + "/reference_file_no_bpe", - bpe_symbol=None, - ) - - # apply LM bpe to nbest list - bpe_src_param = [ - "-c", - cur_lm_bpe_code, - "--input", - rescore_file + target_lang, - "--output", - rescore_bpe + target_lang, - ] - subprocess.call( - [ - "python", - os.path.join( - os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py" - ), - ] - + bpe_src_param, - shell=False, - ) - # uncomment to use fastbpe instead of subword-nmt bpe - # bpe_src_param = [rescore_bpe+target_lang, rescore_file+target_lang, cur_lm_bpe_code] - # subprocess.call(["/private/home/edunov/fastBPE/fast", "applybpe"] + bpe_src_param, shell=False) - - preprocess_dir = preprocess_directory - - preprocess_lm_param = [ - "--only-source", - "--trainpref", - rescore_bpe + target_lang, - "--srcdict", - cur_lm_dict, - "--destdir", - preprocess_dir, - ] - preprocess_parser = options.get_preprocessing_parser() - input_args = preprocess_parser.parse_args(preprocess_lm_param) - preprocess.main(input_args) - - eval_lm_param = [ - preprocess_dir, - "--path", - cur_language_model, - "--output-word-probs", - "--batch-size", - str(batch_size), - "--max-tokens", - "1024", - "--sample-break-mode", - "eos", - "--gen-subset", - "train", - ] - - eval_lm_parser = options.get_eval_lm_parser() - input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param) - - with open(lm_score_file, "w") as f: - with redirect_stdout(f): - eval_lm.main(input_args) - - -def rescore_file_name( - nbest_dir, - prefix_len, - scorer_name, - lm_file=False, - target_prefix_frac=None, - source_prefix_frac=None, - backwards=None, -): - if lm_file: - score_file = nbest_dir + "/lm_score_translations_model_" + scorer_name + ".txt" - else: - score_file = nbest_dir + "/" + scorer_name + "_score_translations.txt" - if backwards: - if prefix_len is not None: - score_file += "prefix_len" + str(prefix_len) - elif target_prefix_frac is not None: - score_file += "target_prefix_frac" + str(target_prefix_frac) - else: - if source_prefix_frac is not None: - score_file += "source_prefix_frac" + str(source_prefix_frac) - return score_file diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/nonautoregressive_translation/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/nonautoregressive_translation/README.md deleted file mode 100644 index 8793e225c99732c42c9c19e22075cde37c73341d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/nonautoregressive_translation/README.md +++ /dev/null @@ -1,146 +0,0 @@ -# Non-autoregressive Neural Machine Translation (NAT) - -This page mainly includes instructions for reproducing results from the following papers -* [Levenshtein Transformer (Gu et al., 2019)](https://arxiv.org/abs/1905.11006). -* [Understanding Knowledge Distillation in Non-autoregressive Machine Translation (Zhou et al., 2019)](https://arxiv.org/abs/1911.02727). - -We also provided our own implementations for several popular non-autoregressive-based models as reference:<br> -* [Non-Autoregressive Neural Machine Translation (Gu et al., 2017)](https://arxiv.org/abs/1711.02281)<br> -* [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al., 2018)](https://arxiv.org/abs/1802.06901)<br> -* [Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al., 2019)](https://arxiv.org/abs/1902.03249)<br> -* [Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)](https://arxiv.org/abs/1904.09324v2)<br> -* [Fast Structured Decoding for Sequence Models (Sun et al., 2019)](https://arxiv.org/abs/1910.11555) - -## Dataset - -First, follow the [instructions to download and preprocess the WMT'14 En-De dataset](../translation#wmt14-english-to-german-convolutional). -Make sure to learn a joint vocabulary by passing the `--joined-dictionary` option to `fairseq-preprocess`. - -### Knowledge Distillation -Following [Gu et al. 2019](https://arxiv.org/abs/1905.11006), [knowledge distillation](https://arxiv.org/abs/1606.07947) from an autoregressive model can effectively simplify the training data distribution, which is sometimes essential for NAT-based models to learn good translations. -The easiest way of performing distillation is to follow the [instructions of training a standard transformer model](../translation) on the same data, and then decode the training set to produce a distillation dataset for NAT. - -### Download -We also provided the preprocessed [original](http://dl.fbaipublicfiles.com/nat/original_dataset.zip) and [distillation](http://dl.fbaipublicfiles.com/nat/distill_dataset.zip) datasets. Please build the binarized dataset on your own. - - -## Train a model - -Then we can train a nonautoregressive model using the `translation_lev` task and a new criterion `nat_loss`. -Use the `--noise` flag to specify the input noise used on the target sentences. -In default, we run the task for *Levenshtein Transformer*, with `--noise='random_delete'`. Full scripts to run other models can also be found [here](./scripts.md). - -The following command will train a *Levenshtein Transformer* on the binarized dataset. - -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch levenshtein_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -## Translate - -Once a model is trained, we can generate translations using an `iterative_refinement_generator` which will based on the model's initial output and iteratively read and greedily refine the translation until (1) the model predicts the same translations for two consecutive iterations; or (2) the generator reaches the maximum iterations (`--iter-decode-max-iter`). Use `--print-step` to check the actual # of iteration for each sentence. - -For *Levenshtein Transformer*, it sometimes helps to apply a `--iter-decode-eos-penalty` (typically, 0~3) to penalize the model finishing generation too early and generating too short translations. - -For example, to generate with `--iter-decode-max-iter=9`: -```bash -fairseq-generate \ - data-bin/wmt14_en_de_distill \ - --gen-subset test \ - --task translation_lev \ - --path checkpoints/checkpoint_best.pt \ - --iter-decode-max-iter 9 \ - --iter-decode-eos-penalty 0 \ - --beam 1 --remove-bpe \ - --print-step \ - --batch-size 400 -``` -In the end of the generation, we can see the tokenized BLEU score for the translation. - -## Advanced Decoding Methods -### Ensemble -The NAT models use special implementations of [ensembling](https://github.com/fairinternal/fairseq-py/blob/b98d88da52f2f21f1b169bab8c70c1c4ca19a768/fairseq/sequence_generator.py#L522) to support iterative refinement and a variety of parallel operations in different models, while it shares the same API as standard autoregressive models as follows: -```bash -fairseq-generate \ - data-bin/wmt14_en_de_distill \ - --gen-subset test \ - --task translation_lev \ - --path checkpoint_1.pt:checkpoint_2.pt:checkpoint_3.pt \ - --iter-decode-max-iter 9 \ - --iter-decode-eos-penalty 0 \ - --beam 1 --remove-bpe \ - --print-step \ - --batch-size 400 -``` -We use ``:`` to split multiple models. Note that, not all NAT models support ensembling for now. - - -### Length-beam -For models that predict lengths before decoding (e.g. the vanilla NAT, Mask-Predict, etc), it is possible to improve the translation quality by varying the target lengths around the predicted value, and translating the same example multiple times in parallel. We can select the best translation with the highest scores defined by your model's output. - -Note that, not all models support length beams. For models which dynamically change the lengths (e.g. *Insertion Transformer*, *Levenshtein Transformer*), the same trick does not apply. - -### Re-ranking -If the model generates multiple translations with length beam, we can also introduce an autoregressive model to rerank the translations considering scoring from an autoregressive model is much faster than decoding from that. - -For example, to generate translations with length beam and reranking, -```bash -fairseq-generate \ - data-bin/wmt14_en_de_distill \ - --gen-subset test \ - --task translation_lev \ - --path checkpoints/checkpoint_best.pt:at_checkpoints/checkpoint_best.pt \ - --iter-decode-max-iter 9 \ - --iter-decode-eos-penalty 0 \ - --iter-decode-with-beam 9 \ - --iter-decode-with-external-reranker \ - --beam 1 --remove-bpe \ - --print-step \ - --batch-size 100 -``` -Note that we need to make sure the autoregressive model shares the same vocabulary as our target non-autoregressive model. - - -## Citation - -```bibtex -@incollection{NIPS2019_9297, - title = {Levenshtein Transformer}, - author = {Gu, Jiatao and Wang, Changhan and Zhao, Junbo}, - booktitle = {Advances in Neural Information Processing Systems 32}, - editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, - pages = {11179--11189}, - year = {2019}, - publisher = {Curran Associates, Inc.}, - url = {http://papers.nips.cc/paper/9297-levenshtein-transformer.pdf} -} -``` -```bibtex -@article{zhou2019understanding, - title={Understanding Knowledge Distillation in Non-autoregressive Machine Translation}, - author={Zhou, Chunting and Neubig, Graham and Gu, Jiatao}, - journal={arXiv preprint arXiv:1911.02727}, - year={2019} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/nonautoregressive_translation/scripts.md b/audio/safeear/safeear_repo/fairseq_ours/examples/nonautoregressive_translation/scripts.md deleted file mode 100644 index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/nonautoregressive_translation/scripts.md +++ /dev/null @@ -1,179 +0,0 @@ -# Examples of Training scripts for Non-autoregressive Machine Translation models - -### Non-autoregressive Transformer (NAT, Gu et al., 2017) -Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch nonautoregressive_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019) -Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch nacrf_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --word-ins-loss-factor 0.5 \ - --crf-lowrank-approx 32 \ - --crf-beam-approx 64 \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - -### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018) -Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch iterative_nonautoregressive_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --train-step 4 \ - --dae-ratio 0.5 \ - --stochastic-approx \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -### Insertion Transformer (InsT, Stern et al., 2019) -Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature. - -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch insertion_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - -### Mask Predict (CMLM, Ghazvininejad et al., 2019) -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch cmlm_transformer \ - --noise random_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - - - -### Levenshtein Transformer (LevT, Gu et al., 2019) -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch levenshtein_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/normformer/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/normformer/README.md deleted file mode 100644 index 037b453ff1a4b62521da11e8d6c387d2045280e1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/normformer/README.md +++ /dev/null @@ -1,70 +0,0 @@ -### NormFormer -This is the code for the ["NormFormer: Improved Transformer Pretraining with Extra Normalization"](https://arxiv.org/abs/2110.09456) -- 2021-10-19: Commands for CLM Experiments -- Coming soon: Commands for MLM experiments - -If you have any issues or questions please post a github issue and tag `@sshleifer`. - - -### Data -- To preprocess language modeling data, see [here](https://github.com/pytorch/fairseq/blob/d0fbcb0baef6f6ff3425ded62d8daea0e8b12114/examples/language_model/README.md#1-preprocess-the-data). -- The replication commands below expect `$DATA` to be the path to the binarized data directory. -- Note that NormFormer results in Table 2 use a much larger private dataset, and to get good results you should adapt the pre-processing instructions to your dataset and compare to a baseline on the same data, rather than Table 2. -- The code uses `FSDP`, which requires `pip install fairscale>=0.4.0`. - - -### Modify existing Command -To modify an existing `fairseq-train` command to use NormFormer, simply add the following flags: -```bash -fairseq-train ... \ - --scale-attn --scale-fc --scale-heads -``` -- you probably also want to increase your learning rate -- if your model is small, you may want to add `--scale-resids` - -### Exact Training Commands - -- Note that NormFormer results in Table 2 use a much larger private dataset, and to get good results you should adapt the pre-processing instructions to your dataset. -The full commands are functions defined here, so to run them you must `source examples/normformer/train_lm.sh`. -- We default `--distributed-world-size 8`. You should adjust `--update-freq` and `--batch-size` and such that the effective batch size is (1024x1024x0.5) tokens for 125M and 355M, - and (1024x1024) for 1.3B parameter and above. For small models, `--update-freq`=256/`global_bs`. For large models, `--update-freq`=512/`global_bs`, where `global_bs` = `--batch-size` * `--distributed-world-size` -- The small models will all train on as few as 8 GPUs. - -```bash -train_125M --lr 6e-4 # GPT-3 Replicated -train_125M --lr 1e-3 # stronger high-lr baseline -train_125M --lr 3e-3 --scale-attn --scale-fc --scale-heads # No scale-resids -train_125M --lr 3e-3 --scale-attn --scale-fc --scale-heads --scale-resids # Best command -``` - -```bash -train_355M --lr 6e-4 # GPT-3 Replicated -train_355M --lr 1e-3 # stronger high-lr baseline -train_355M --lr 1e-3 --scale-attn --scale-fc --scale-heads # No scale-resids -train_355M --lr 1e-3 --scale-attn --scale-fc --scale-heads --scale-resids # Slightly better -``` - -```bash -train_1.3B --lr 2e-4 # GPT-3 Replicated -train_1.3B --lr 6e-4 # stronger high-lr baseline -train_1.3B --lr 6e-4 --scale-attn --scale-fc --scale-heads # NormFormer -``` - -```bash -train_2.7B --lr 1.6e-4 # GPT-3 Replicated -train_2.7B --lr 1.6e-4 --activation-fn relu_squared # stronger Relu^2 baseline -train_2.7B --lr 6e-4 --activation-fn relu_squared --scale-attn --scale-fc --scale-heads # NormFormer 2.7B -``` - - -### Citation -```bibtex -@misc{shleifer2021normformer, - title={NormFormer: Improved Transformer Pretraining with Extra Normalization}, - author={Sam Shleifer and Jason Weston and Myle Ott}, - year={2021}, - eprint={2110.09456}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/normformer/train_lm.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/normformer/train_lm.sh deleted file mode 100644 index b081f2ddd3acdb91d87d7fd080257cb2acf5cb1e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/normformer/train_lm.sh +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env bash -train_common () { - fairseq-train "$DATA" \ - --combine-val \ - --train-subset train \ - --num-workers 2 \ - --validate-interval-updates 1000 \ - --save-interval-updates 1000 \ - --no-epoch-checkpoints \ - --ddp-backend fully_sharded \ - --memory-efficient-fp16 \ - --fp16-init-scale 4 \ - --checkpoint-activations \ - --arch transformer_lm_gpt \ - --activation-fn gelu \ - --share-decoder-input-output-embed \ - --task language_modeling \ - --sample-break-mode none \ - --tokens-per-sample 2048 \ - --optimizer adam --adam-betas "(0.9, 0.98)" \ - --adam-eps 1e-08 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay \ - --warmup-updates 750 \ - --dropout 0.1 \ - --attention-dropout 0.1 \ - --weight-decay 0.01 \ - --batch-size 16 \ - --update-freq 2 \ - --required-batch-size-multiple 1 \ - --total-num-update 572204 \ - --max-update 572204 \ - --seed 1 \ - --log-format json --log-interval 1 \ - --distributed-world-size 8 --distributed-port 13177 \ - "$@" -} - -train_125M () { - train_common --decoder-layers 12 \ - --decoder-embed-dim 768 \ - --decoder-ffn-embed-dim 3072 \ - --decoder-attention-heads 12 "$@" -} - -train_355M () { - train_common --decoder-layers 24 \ - --decoder-embed-dim 1024\ - --decoder-ffn-embed-dim 4096 \ - --decoder-attention-heads 16 \ - --dropout 0.0 \ - --attention-dropout 0.0 \ - "$@" -} - -train_1.3B () { - train_common --decoder-layers 24 \ - --decoder-embed-dim 2048 \ - --decoder-ffn-embed-dim 8192 \ - --decoder-attention-heads 32 \ - --batch-size 4 \ - --update-freq 16 \ - --total-num-update 286102 \ - --max-update 286102 \ - "$@" -} - -train_2.7B () { - train_common --decoder-layers 32 \ - --decoder-embed-dim 2560 \ - --decoder-ffn-embed-dim 10240 \ - --decoder-attention-heads 32 \ - --batch-size 4 \ - --update-freq 16 \ - --total-num-update 286102 \ - --max-update 286102 \ - "$@" -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cpu.cpp b/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cpu.cpp deleted file mode 100644 index 13c015308e320d9ce14909fcdcc135300538e705..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cpu.cpp +++ /dev/null @@ -1,166 +0,0 @@ -/** - * Copyright 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. - */ - -#include <torch/extension.h> // @manual=//caffe2:torch_extension -#include <algorithm> - -namespace { - -template <typename T> -void exclusiveCumprod( - const T* p_choose, - T* cumprod_1mp, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len) { - // cumprod_1mp = 1 - p_choose - for (uint32_t b = 0; b < bsz; b++) { - for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { - for (uint32_t src = 0; src < src_len; src++) { - uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; - cumprod_1mp[idx] = 1 - p_choose[idx]; - } - } - } - - // Implementing exclusive cumprod in the innermost dimension - // cumprod_1mp = cumprod(1 - p_choose) - // There is cumprod in pytorch, however there is no exclusive mode. - // cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] - // exclusive means - // cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] - for (uint32_t b = 0; b < bsz; b++) { - for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { - uint32_t idx_offset = b * tgt_len * src_len + tgt * src_len; - T prev = cumprod_1mp[idx_offset]; - // index [b][tgt][0] - cumprod_1mp[idx_offset] = (T)1.0; - T curr; - for (uint32_t src = 1; src < src_len; src++) { - uint32_t idx = idx_offset + src; - curr = cumprod_1mp[idx]; - cumprod_1mp[idx] = cumprod_1mp[idx - 1] * prev; - prev = curr; - } - } - } -} - -template <typename T> -void clamp( - const T* cumprod_1mp, - T* cumprod_1mp_clamp, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len, - T min_val, - T max_val) { - for (uint32_t b = 0; b < bsz; b++) { - for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { - for (uint32_t src = 0; src < src_len; src++) { - uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; - if (cumprod_1mp[idx] < min_val) { - cumprod_1mp_clamp[idx] = min_val; - } else if (cumprod_1mp[idx] > max_val) { - cumprod_1mp_clamp[idx] = max_val; - } else { - cumprod_1mp_clamp[idx] = cumprod_1mp[idx]; - } - } - } - } -} - -template <typename T> -void alignmentTrainCPUImpl( - const T* p_choose, - T* alpha, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len, - float eps) { - // p_choose: bsz , tgt_len, src_len - // cumprod_1mp: bsz , tgt_len, src_len - // cumprod_1mp_clamp : bsz, tgt_len, src_len - // alpha: bsz + 1, tgt_len, src_len - - uint32_t elements = bsz * tgt_len * src_len; - T* cumprod_1mp = new T[elements]; - T* cumprod_1mp_clamp = new T[elements]; - - exclusiveCumprod<T>(p_choose, cumprod_1mp, bsz, tgt_len, src_len); - clamp<T>( - cumprod_1mp, cumprod_1mp_clamp, bsz, tgt_len, src_len, (T)eps, (T)1.0); - - // ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) - - // Initialize alpha [:, 0, 0] - for (uint32_t b = 0; b < bsz; b++) { - alpha[b * tgt_len * src_len] = 1.0; - } - - for (uint32_t tgt = 0; tgt < tgt_len; tgt++) { - for (uint32_t b = 0; b < bsz; b++) { - uint32_t alpha_idx, inout_idx; - T prev_scan = 0, curr_scan, out; - for (uint32_t src = 0; src < src_len; src++) { - // Apply scan/cumsum - if (tgt == 0) { - // alpha index is [b][tgt][src] - alpha_idx = b * tgt_len * src_len + src; - } else { - // alpha index is [b][tgt-1][src] - alpha_idx = b * tgt_len * src_len + (tgt - 1) * src_len + src; - } - // input index is [b][tgt][src] - inout_idx = b * tgt_len * src_len + tgt * src_len + src; - curr_scan = prev_scan + alpha[alpha_idx] / cumprod_1mp_clamp[inout_idx]; - - out = curr_scan * p_choose[inout_idx] * cumprod_1mp[inout_idx]; - alpha[inout_idx] = std::min<T>(std::max<T>(out, 0), 1.0); - prev_scan = curr_scan; - } - } - } - - free(cumprod_1mp); - free(cumprod_1mp_clamp); -} - -void alignmentTrainCPU( - const torch::Tensor& p_choose, - torch::Tensor& alpha, - float eps) { - uint32_t bsz = p_choose.size(0); - uint32_t tgt_len = p_choose.size(1); - uint32_t src_len = p_choose.size(2); - - AT_DISPATCH_FLOATING_TYPES_AND2( - torch::ScalarType::Half, - torch::ScalarType::BFloat16, - p_choose.scalar_type(), - "alignmentCPUImpl", - [&]() { - alignmentTrainCPUImpl<scalar_t>( - p_choose.data_ptr<scalar_t>(), - alpha.data_ptr<scalar_t>(), - bsz, - tgt_len, - src_len, - eps); - }); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def( - "alignment_train_cpu", - &alignmentTrainCPU, - "expected_alignment_from_p_choose (CPU)"); -} - -} // namespace diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cuda.cpp b/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cuda.cpp deleted file mode 100644 index 430e04813923074c5458c11e05dbdab879659bb5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cuda.cpp +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 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. - */ - -#include "alignment_train_cuda.h" -#include "utils.h" - -namespace { - -void alignmentTrainCUDA( - const torch::Tensor& p_choose, - torch::Tensor& alpha, - float eps) { - CHECK_INPUT(p_choose); - CHECK_INPUT(alpha); - - alignmentTrainCUDAWrapper(p_choose, alpha, eps); -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def( - "alignment_train_cuda", - &alignmentTrainCUDA, - "expected_alignment_from_p_choose (CUDA)"); -} - -} // namespace diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cuda.h b/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cuda.h deleted file mode 100644 index 8289d1a69079138de6d73993ff117a59f000df37..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_cuda.h +++ /dev/null @@ -1,16 +0,0 @@ -/** - * Copyright 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. - */ - -#pragma once - -#include <torch/extension.h> // @manual=//caffe2:torch_extension - -void alignmentTrainCUDAWrapper( - const torch::Tensor& p_choose, - torch::Tensor& alpha, - float eps); diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_kernel.cu b/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_kernel.cu deleted file mode 100644 index efae7cc76f1cce8701dbb5bb7b5ff1e402993953..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/alignment_train_kernel.cu +++ /dev/null @@ -1,354 +0,0 @@ -/** - * Copyright 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. - */ - -#include <ATen/ATen.h> -#include <ATen/cuda/CUDAContext.h> // @manual=//caffe2/aten:ATen-cu -#include <cuda_runtime.h> -#include <algorithm> // std::min/max -#include <cub/cub.cuh> - -#include "alignment_train_cuda.h" -#include "utils.h" - -namespace { - -// The thread block length in threads along the X dimension -constexpr int BLOCK_DIM_X = 128; -// The thread block length in threads along the Y dimension -constexpr int BLOCK_DIM_Y = 8; -// The thread block length in threads for scan operation -constexpr int SCAN_BLOCK = 512; - -#define gpuErrchk(ans) \ - { gpuAssert((ans), __FILE__, __LINE__); } - -inline void -gpuAssert(cudaError_t code, const char* file, int line, bool abort = true) { - if (code != cudaSuccess) { - fprintf( - stderr, - "\nGPUassert: %s %s %d\n", - cudaGetErrorString(code), - file, - line); - if (abort) - exit(code); - } -} - -template <typename T> -struct Prod { - /// prod operator, returns <tt>a * b</tt> - __host__ __device__ __forceinline__ T - operator()(const T& a, const T& b) const { - return a * b; - } -}; - -template <typename T> -struct BlockPrefixProdCallbackOp { - // Running prefix - T running_total; - - // Constructor - __device__ BlockPrefixProdCallbackOp(T running_total) - : running_total(running_total) {} - - // Callback operator to be entered by the first warp of threads in the block. - // Thread-0 is responsible for returning a value for seeding the block-wide - // scan. - __device__ T operator()(const T block_aggregate) { - T old_prefix = running_total; - running_total *= block_aggregate; - return old_prefix; - } -}; - -template <typename T> -struct BlockPrefixSumCallbackOp { - // Running prefix - T running_total; - - // Constructor - __device__ BlockPrefixSumCallbackOp(T running_total) - : running_total(running_total) {} - - // Callback operator to be entered by the first warp of threads in the block. - // Thread-0 is responsible for returning a value for seeding the block-wide - // scan. - __device__ T operator()(const T block_aggregate) { - T old_prefix = running_total; - running_total += block_aggregate; - return old_prefix; - } -}; - -template <typename T> -__global__ void oneMinusPKernel( - const T* __restrict__ p_choose, - T* __restrict__ cumprod_1mp, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len) { - for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { - for (uint32_t tgt = threadIdx.y; tgt < tgt_len; tgt += blockDim.y) { - for (uint32_t src = threadIdx.x; src < src_len; src += blockDim.x) { - uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; - cumprod_1mp[idx] = 1 - p_choose[idx]; - } - } - } -} - -template <typename T, int TPB> -__global__ void innermostScanKernel( - T* __restrict__ cumprod_1mp, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len) { - for (uint32_t b = blockIdx.y; b < bsz; b += gridDim.y) { - for (uint32_t tgt = blockIdx.x; tgt < tgt_len; tgt += gridDim.x) { - // Specialize BlockScan for a 1D block of TPB threads on type T - typedef cub::BlockScan<T, TPB> BlockScan; - // Allocate shared memory for BlockScan - __shared__ typename BlockScan::TempStorage temp_storage; - // Initialize running total - BlockPrefixProdCallbackOp<T> prefix_op(1); - - const uint32_t tid = threadIdx.x; - for (uint32_t block_src = 0; block_src < src_len; - block_src += blockDim.x) { - uint32_t src = block_src + tid; - uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; - T thread_data = (src < src_len) ? cumprod_1mp[idx] : (T)0; - - // Collectively compute the block-wide inclusive prefix sum - BlockScan(temp_storage) - .ExclusiveScan(thread_data, thread_data, Prod<T>(), prefix_op); - __syncthreads(); - - // write the scanned value to output - if (src < src_len) { - cumprod_1mp[idx] = thread_data; - } - } - } - } -} - -template <typename T> -__global__ void clampKernel( - const T* __restrict__ cumprod_1mp, - T* __restrict__ cumprod_1mp_clamp, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len, - T min_val, - T max_val) { - for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { - for (uint32_t tgt = threadIdx.y; tgt < tgt_len; tgt += blockDim.y) { - for (uint32_t src = threadIdx.x; src < src_len; src += blockDim.x) { - uint32_t idx = b * tgt_len * src_len + tgt * src_len + src; - if (cumprod_1mp[idx] < min_val) { - cumprod_1mp_clamp[idx] = min_val; - } else if (cumprod_1mp[idx] > max_val) { - cumprod_1mp_clamp[idx] = max_val; - } else { - cumprod_1mp_clamp[idx] = cumprod_1mp[idx]; - } - } - } - } -} - -template <typename T> -__global__ void initAlphaCUDAKernel( - T* alpha, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len) { - // alpha[:, 0, 0] = 1.0 - for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { - alpha[b * tgt_len * src_len] = (T)1.0; - } -} - -template <typename T, int TPB> -__global__ void alignmentTrainCUDAKernel( - const T* __restrict__ p_choose, - const T* __restrict__ cumprod_1mp, - const T* __restrict__ cumprod_1mp_clamp, - T* __restrict__ alpha, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len, - uint32_t tgt) { - for (uint32_t b = blockIdx.x; b < bsz; b += gridDim.x) { - // Specialize BlockScan for a 1D block of TPB threads on type T - typedef cub::BlockScan<T, TPB> BlockScan; - - // Allocate shared memory for BlockScan - __shared__ typename BlockScan::TempStorage temp_storage; - // Initialize running total - BlockPrefixSumCallbackOp<T> prefix_op(0); - - uint32_t b_offset = b * tgt_len * src_len; - const uint32_t tid = threadIdx.x; - for (uint32_t block_src = 0; block_src < src_len; block_src += blockDim.x) { - uint32_t src = block_src + tid; - // Obtain a segment of consecutive items that are blocked across threads - uint32_t inout_idx, alpha_idx; - if (tgt == 0) { - // both alpha and other input index is [b][0][src] - alpha_idx = b_offset + src; - } else { - // alpha index is [b][tgt-1][src] - alpha_idx = b_offset + (tgt - 1) * src_len + src; - } - inout_idx = b_offset + tgt * src_len + src; - T thread_data = (T)0; - if (src < src_len) { - thread_data = alpha[alpha_idx] / cumprod_1mp_clamp[inout_idx]; - } - - // Collectively compute the block-wide inclusive prefix sum - BlockScan(temp_storage).InclusiveSum(thread_data, thread_data, prefix_op); - __syncthreads(); - - if (src < src_len) { - T out = thread_data * p_choose[inout_idx] * cumprod_1mp[inout_idx]; - // Clamps all elements into the range [ 0, 1.0 ] - alpha[inout_idx] = std::min<T>(std::max<T>(out, 0), (T)1.0); - } - } - } -} - -template <typename T> -void exclusiveCumprod( - const T* p_choose, - T* cumprod_1mp, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len, - uint32_t max_grid_x, - uint32_t max_grid_y, - cudaStream_t& stream) { - // cumprod_1mp = 1 - p_choose - dim3 grid(std::min<T>(max_grid_x, bsz), 1, 1); - dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y, 1); - oneMinusPKernel<T><<<grid, block, 0, stream>>>( - p_choose, cumprod_1mp, bsz, tgt_len, src_len); - gpuErrchk(cudaGetLastError()); - - // scan on the innermost dimension of cumprod_1mp - // cumprod_1mp = cumprod(cumprod_1mp) - dim3 grid_scan( - std::min<T>(max_grid_x, tgt_len), std::min<T>(max_grid_y, bsz), 1); - innermostScanKernel<T, SCAN_BLOCK><<<grid_scan, SCAN_BLOCK, 0, stream>>>( - cumprod_1mp, bsz, tgt_len, src_len); - gpuErrchk(cudaGetLastError()); -} - -template <typename T> -void alignmentTrainCUDAImpl( - const T* p_choose, - T* alpha, - uint32_t bsz, - uint32_t tgt_len, - uint32_t src_len, - float eps) { - // p_choose: bsz , tgt_len, src_len - // cumprod_1mp: bsz , tgt_len, src_len - // cumprod_1mp_clamp : bsz, tgt_len, src_len - // alpha: bsz, tgt_len, src_len - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - uint32_t max_grid_x = at::cuda::getCurrentDeviceProperties()->maxGridSize[0]; - uint32_t max_grid_y = at::cuda::getCurrentDeviceProperties()->maxGridSize[1]; - - // Implementing exclusive cumprod. - // cumprod_1mp = cumprod(1 - p_choose) - // There is cumprod in pytorch, however there is no exclusive mode. - // cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] - // exclusive means - // cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] - uint32_t elements = bsz * tgt_len * src_len; - T* cumprod_1mp; - gpuErrchk(cudaMalloc(&cumprod_1mp, elements * sizeof(T))); - exclusiveCumprod<T>( - p_choose, - cumprod_1mp, - bsz, - tgt_len, - src_len, - max_grid_x, - max_grid_y, - stream); - - // clamp cumprod_1mp to the range [eps, 1.0] - T* cumprod_1mp_clamp; - gpuErrchk(cudaMalloc(&cumprod_1mp_clamp, elements * sizeof(T))); - dim3 grid_clamp(std::min<T>(max_grid_x, bsz), 1, 1); - dim3 block_clamp(BLOCK_DIM_X, BLOCK_DIM_Y, 1); - clampKernel<T><<<grid_clamp, block_clamp, 0, stream>>>( - cumprod_1mp, cumprod_1mp_clamp, bsz, tgt_len, src_len, (T)eps, (T)1.0); - gpuErrchk(cudaGetLastError()); - - // ai = p_i * cumprod(1 − pi) * cumsum(a_i / cumprod(1 − pi)) - dim3 grid_init(std::min<int>(max_grid_x, bsz), 1, 1); - initAlphaCUDAKernel<T> - <<<grid_init, 1, 0, stream>>>(alpha, bsz, tgt_len, src_len); - gpuErrchk(cudaGetLastError()); - - const int grid = std::min(bsz, max_grid_x); - - for (uint32_t i = 0; i < tgt_len; i++) { - alignmentTrainCUDAKernel<T, SCAN_BLOCK><<<grid, SCAN_BLOCK, 0, stream>>>( - p_choose, - cumprod_1mp, - cumprod_1mp_clamp, - alpha, - bsz, - tgt_len, - src_len, - i); - gpuErrchk(cudaGetLastError()); - } - - gpuErrchk(cudaFree(cumprod_1mp)); - gpuErrchk(cudaFree(cumprod_1mp_clamp)); -} - -} // namespace - -void alignmentTrainCUDAWrapper( - const torch::Tensor& p_choose, - torch::Tensor& alpha, - float eps) { - // p_choose dimension: bsz, tgt_len, src_len - uint32_t bsz = p_choose.size(0); - uint32_t tgt_len = p_choose.size(1); - uint32_t src_len = p_choose.size(2); - - cudaSetDevice(p_choose.get_device()); - - AT_DISPATCH_FLOATING_TYPES_AND2( - torch::ScalarType::Half, - torch::ScalarType::BFloat16, - p_choose.scalar_type(), - "alignmentTrainCUDAImpl", - [&]() { - alignmentTrainCUDAImpl<scalar_t>( - p_choose.data_ptr<scalar_t>(), - alpha.data_ptr<scalar_t>(), - bsz, - tgt_len, - src_len, - eps); - }); -} diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/utils.h b/audio/safeear/safeear_repo/fairseq_ours/examples/operators/utils.h deleted file mode 100644 index 0ef5b4383f4a8ff05ea7b44d932a3b47e4c7f927..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/operators/utils.h +++ /dev/null @@ -1,19 +0,0 @@ -/** - * Copyright 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. - */ - -#pragma once - -#include <torch/extension.h> // @manual=//caffe2:torch_extension - -#define CHECK_CUDA(x) \ - TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) \ - TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/paraphraser/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/paraphraser/README.md deleted file mode 100644 index 3810311f30f99f0a07fd8e5d3723bffeba9948c3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/paraphraser/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# Paraphrasing with round-trip translation and mixture of experts - -Machine translation models can be used to paraphrase text by translating it to -an intermediate language and back (round-trip translation). - -This example shows how to paraphrase text by first passing it to an -English-French translation model, followed by a French-English [mixture of -experts translation model](/examples/translation_moe). - -##### 0. Setup - -Clone fairseq from source and install necessary dependencies: -```bash -git clone https://github.com/pytorch/fairseq.git -cd fairseq -pip install --editable . -pip install sacremoses sentencepiece -``` - -##### 1. Download models -```bash -wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.en-fr.tar.gz -wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.fr-en.hMoEup.tar.gz -tar -xzvf paraphraser.en-fr.tar.gz -tar -xzvf paraphraser.fr-en.hMoEup.tar.gz -``` - -##### 2. Paraphrase -```bash -python examples/paraphraser/paraphrase.py \ - --en2fr paraphraser.en-fr \ - --fr2en paraphraser.fr-en.hMoEup -# Example input: -# The new date for the Games, postponed for a year in response to the coronavirus pandemic, gives athletes time to recalibrate their training schedules. -# Example outputs: -# Delayed one year in response to the coronavirus pandemic, the new date of the Games gives athletes time to rebalance their training schedule. -# The new date of the Games, which was rescheduled one year in response to the coronavirus (CV) pandemic, gives athletes time to rebalance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, provides athletes with time to rebalance their training schedule. -# The Games' new date, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule. -# The new Games date, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule. -# The new date of the Games, which was postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to re-balance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their schedule of training. -# The new date of the Games, postponed one year in response to the pandemic of coronavirus, gives the athletes time to rebalance their training schedule. -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/paraphraser/paraphrase.py b/audio/safeear/safeear_repo/fairseq_ours/examples/paraphraser/paraphrase.py deleted file mode 100644 index bf80dc811d3027a827439e4f6517dc16420f22a6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/paraphraser/paraphrase.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -u - -import argparse -import fileinput -import logging -import os -import sys - -from fairseq.models.transformer import TransformerModel - -logging.getLogger().setLevel(logging.INFO) - - -def main(): - parser = argparse.ArgumentParser(description="") - parser.add_argument("--en2fr", required=True, help="path to en2fr model") - parser.add_argument( - "--fr2en", required=True, help="path to fr2en mixture of experts model" - ) - parser.add_argument( - "--user-dir", help="path to fairseq examples/translation_moe/src directory" - ) - parser.add_argument( - "--num-experts", - type=int, - default=10, - help="(keep at 10 unless using a different model)", - ) - parser.add_argument( - "files", - nargs="*", - default=["-"], - help='input files to paraphrase; "-" for stdin', - ) - args = parser.parse_args() - - if args.user_dir is None: - args.user_dir = os.path.join( - os.path.dirname(os.path.dirname(os.path.abspath(__file__))), # examples/ - "translation_moe", - "src", - ) - if os.path.exists(args.user_dir): - logging.info("found user_dir:" + args.user_dir) - else: - raise RuntimeError( - "cannot find fairseq examples/translation_moe/src " - "(tried looking here: {})".format(args.user_dir) - ) - - logging.info("loading en2fr model from:" + args.en2fr) - en2fr = TransformerModel.from_pretrained( - model_name_or_path=args.en2fr, - tokenizer="moses", - bpe="sentencepiece", - ).eval() - - logging.info("loading fr2en model from:" + args.fr2en) - fr2en = TransformerModel.from_pretrained( - model_name_or_path=args.fr2en, - tokenizer="moses", - bpe="sentencepiece", - user_dir=args.user_dir, - task="translation_moe", - ).eval() - - def gen_paraphrases(en): - fr = en2fr.translate(en) - return [ - fr2en.translate(fr, inference_step_args={"expert": i}) - for i in range(args.num_experts) - ] - - logging.info("Type the input sentence and press return:") - for line in fileinput.input(args.files): - line = line.strip() - if len(line) == 0: - continue - for paraphrase in gen_paraphrases(line): - print(paraphrase) - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pay_less_attention_paper/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/pay_less_attention_paper/README.md deleted file mode 100644 index 5adab11f4dc3461f9e7126ac391b04e703616e6b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pay_less_attention_paper/README.md +++ /dev/null @@ -1,176 +0,0 @@ -# Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019) - -This page contains pointers to pre-trained models as well as instructions on how to train new models for [our paper](https://arxiv.org/abs/1901.10430). - -## Citation: -```bibtex -@inproceedings{wu2018pay, - title = {Pay Less Attention with Lightweight and Dynamic Convolutions}, - author = {Felix Wu and Angela Fan and Alexei Baevski and Yann Dauphin and Michael Auli}, - booktitle = {International Conference on Learning Representations}, - year = {2019}, - url = {https://arxiv.org/abs/1901.10430}, -} -``` - -## Translation - -### Pre-trained models -For some datasets we release models without GLUs which are faster at inference. - -Model | Description | Dataset | Download ----|---|---|--- -`lightconv.no_glu.iwslt14.de-en` | LightConv (without GLUs) | [IWSLT14 German-English](https://wit3.fbk.eu/archive/2014-01/texts/de/en/de-en.tgz) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.lightconv.tar.gz) <br> IWSLT14 test: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/iwslt14.de-en.test.tar.bz2) -`dynamicconv.no_glu.iwslt14.de-en` | DynamicConv (without GLUs) | [IWSLT14 German-English](https://wit3.fbk.eu/archive/2014-01/texts/de/en/de-en.tgz) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/iwslt14.de-en.dynamicconv.tar.gz) <br> IWSLT14 test: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/iwslt14.de-en.test.tar.bz2) -`lightconv.no_glu.wmt16.en-de` | LightConv (without GLUs) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) -`dynamicconv.no_glu.wmt16.en-de` | DynamicConv (without GLUs) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) -`lightconv.glu.wmt16.en-de` | LightConv | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.lightconv-glu.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) -`dynamicconv.glu.wmt16.en-de` | DynamicConv | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt16.en-de.joined-dict.dynamicconv-glu.tar.gz) <br> newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) -`lightconv.glu.wmt14.en-fr` | LightConv | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.lightconv-glu.tar.gz) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) -`dynamicconv.glu.wmt14.en-fr` | DynamicConv | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt14.en-fr.joined-dict.dynamicconv-glu.tar.gz) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) -`lightconv.glu.wmt17.zh-en` | LightConv | [WMT17 Chinese-English](http://statmt.org/wmt17/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.lightconv-glu.tar.gz) <br> newstest2017: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.zh-en.newstest2017.tar.bz2) -`dynamicconv.glu.wmt17.zh-en` | DynamicConv | [WMT17 Chinese-English](http://statmt.org/wmt17/translation-task.html#Download) | model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/dynamicconv/wmt17.zh-en.dynamicconv-glu.tar.gz) <br> newstest2017: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.zh-en.newstest2017.tar.bz2) - -### Memory-Efficient CUDA Kernels - -Since the PyTorch implementations of Light/Dynamic conv are quite memory intensive, we have developed CUDA kernels that implement the light and dynamic convolution operator in a memory-efficient and performant manner. For large sequence lengths, these kernels save about 50% memory compared to the PyTorch equivalent. - -To install the kernels, use the commands below. Once installed, they will automatically be used in place of the PyTorch implementations whenever a light or dynamic convolution is used. - -```sh -# to install lightconv -cd fairseq/modules/lightconv_layer -python cuda_function_gen.py -python setup.py install - -# to install dynamicconv -cd fairseq/modules/dynamicconv_layer -python cuda_function_gen.py -python setup.py install -``` - -### Example usage (torch.hub) - -We require a few additional Python dependencies for preprocessing: -```bash -pip install sacremoses subword_nmt -``` - -Interactive translation via PyTorch Hub: -```python -import torch - -# List available models -torch.hub.list('pytorch/fairseq') # [..., 'lightconv.glu.wmt17.zh-en', ... ] - -# Load a transformer trained on WMT'16 En-De -zh2en = torch.hub.load('pytorch/fairseq', 'lightconv.glu.wmt17.zh-en', tokenizer='moses', bpe='subword_nmt') - -# The underlying model is available under the *models* attribute -assert isinstance(zh2en.models[0], fairseq.models.lightconv.LightConvModel) - -# Translate a sentence -zh2en.translate('你好 世界') -# 'Hello World' -``` - -Loading custom models: -```python -from fairseq.models.lightconv import LightConvModel -en2fr = LightConvModel.from_pretrained( - '/path/to/checkpoints', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='data-bin/wmt14_en_fr', - bpe='subword_nmt', - bpe_codes='data-bin/wmt14_en_fr/en.code' -) -en2fr.translate('Hello world!') -# 'Bonjour le monde' -``` - -### Preprocessing the training datasets - -Please follow the instructions in [`examples/translation/README.md`](../translation/README.md) to preprocess the data. - -### Training and evaluation options: -To use the model without GLU, please set `--encoder-glu 0 --decoder-glu 0`. -For LightConv, please use `--encoder-conv-type lightweight --decoder-conv-type lightweight`, otherwise the default is DynamicConv. -For best BLEU results, lenpen may need to be manually tuned. - -To use the CUDA kernels, first install the PyTorch modules using the commands -above. Once the CUDA modules are installed, they will automatically be used -instead of the PyTorch modules. - -### IWSLT14 De-En -Training and evaluating DynamicConv (without GLU) on a GPU: -```sh -# Training -SAVE="save/dynamic_conv_iwslt" -mkdir -p $SAVE -CUDA_VISIBLE_DEVICES=0 $(which fairseq-train) data-bin/iwslt14.tokenized.de-en \ - --clip-norm 0 --optimizer adam --lr 0.0005 \ - --source-lang de --target-lang en --max-tokens 4000 --no-progress-bar \ - --log-interval 100 --stop-min-lr '1e-09' --weight-decay 0.0001 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --lr-scheduler inverse_sqrt \ - --ddp-backend=legacy_ddp \ - --max-update 50000 --warmup-updates 4000 --warmup-init-lr '1e-07' \ - --adam-betas '(0.9, 0.98)' --keep-last-epochs 10 \ - -a lightconv_iwslt_de_en --save-dir $SAVE \ - --dropout 0.3 --attention-dropout 0.1 --weight-dropout 0.1 \ - --encoder-glu 0 --decoder-glu 0 -python scripts/average_checkpoints.py --inputs $SAVE \ - --num-epoch-checkpoints 10 --output "${SAVE}/checkpoint_last10_avg.pt" - -# Evaluation -CUDA_VISIBLE_DEVICES=0 fairseq-generate data-bin/iwslt14.tokenized.de-en --path "${SAVE}/checkpoint_last10_avg.pt" --batch-size 128 --beam 4 --remove-bpe --lenpen 1 --gen-subset test --quiet -``` - -### WMT16 En-De -Training and evaluating DynamicConv (with GLU) on WMT16 En-De using cosine scheduler on one machine with 8 V100 GPUs: -```sh -# Training -SAVE="save/dynamic_conv_wmt16en2de" -mkdir -p $SAVE -python -m torch.distributed.launch --nproc_per_node 8 $(which fairseq-train) \ - data-bin/wmt16_en_de_bpe32k --fp16 --log-interval 100 --no-progress-bar \ - --max-update 30000 --share-all-embeddings --optimizer adam \ - --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --stop-min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ - --ddp-backend=legacy_ddp --max-tokens 3584 \ - --lr-scheduler cosine --warmup-init-lr 1e-7 --warmup-updates 10000 \ - --lr-shrink 1 --lr 0.001 --min-lr 1e-7 --warmup-init-lr 1e-07 \ - --t-mult 1 --lr-period-updates 20000 \ - --arch lightconv_wmt_en_de_big --save-dir $SAVE \ - --dropout 0.3 --attention-dropout 0.1 --weight-dropout 0.1 \ - --encoder-glu 1 --decoder-glu 1 - -# Evaluation -CUDA_VISIBLE_DEVICES=0 fairseq-generate data-bin/wmt16.en-de.joined-dict.newstest2014 --path "${SAVE}/checkpoint_best.pt" --batch-size 128 --beam 5 --remove-bpe --lenpen 0.5 --gen-subset test > wmt16_gen.txt -bash scripts/compound_split_bleu.sh wmt16_gen.txt -``` - -### WMT14 En-Fr -Training DynamicConv (with GLU) on WMT14 En-Fr using cosine scheduler on one machine with 8 V100 GPUs: -```sh -# Training -SAVE="save/dynamic_conv_wmt14en2fr" -mkdir -p $SAVE -python -m torch.distributed.launch --nproc_per_node 8 $(which fairseq-train) \ - data-bin/wmt14_en_fr --fp16 --log-interval 100 --no-progress-bar \ - --max-update 30000 --share-all-embeddings --optimizer adam \ - --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --stop-min-lr 1e-09 --update-freq 16 --attention-dropout 0.1 --keep-last-epochs 10 \ - --ddp-backend=legacy_ddp --max-tokens 3584 \ - --lr-scheduler cosine --warmup-init-lr 1e-7 --warmup-updates 10000 \ - --lr-shrink 1 --lr 0.001 --min-lr 1e-7 --warmup-init-lr 1e-07 \ - --t-mult 1 --lr-period-updates 70000 \ - --arch lightconv_wmt_en_fr_big --save-dir $SAVE \ - --dropout 0.1 --attention-dropout 0.1 --weight-dropout 0.1 \ - --encoder-glu 1 --decoder-glu 1 - -# Evaluation -CUDA_VISIBLE_DEVICES=0 fairseq-generate data-bin/wmt14.en-fr.joined-dict.newstest2014 --path "${SAVE}/checkpoint_best.pt" --batch-size 128 --beam 5 --remove-bpe --lenpen 0.9 --gen-subset test -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/README.md deleted file mode 100644 index 60965708254aae2174812ea6686a9807825b7fb6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/README.md +++ /dev/null @@ -1,82 +0,0 @@ -# Transformer with Pointer-Generator Network - -This page describes the `transformer_pointer_generator` model that incorporates -a pointing mechanism in the Transformer model that facilitates copying of input -words to the output. This architecture is described in [Enarvi et al. (2020)](https://www.aclweb.org/anthology/2020.nlpmc-1.4/). - -## Background - -The pointer-generator network was introduced in [See et al. (2017)](https://arxiv.org/abs/1704.04368) -for RNN encoder-decoder attention models. A similar mechanism can be -incorporated in a Transformer model by reusing one of the many attention -distributions for pointing. The attention distribution over the input words is -interpolated with the normal output distribution over the vocabulary words. This -allows the model to generate words that appear in the input, even if they don't -appear in the vocabulary, helping especially with small vocabularies. - -## Implementation - -The mechanism for copying out-of-vocabulary words from the input has been -implemented differently to See et al. In their [implementation](https://github.com/abisee/pointer-generator) -they convey the word identities through the model in order to be able to produce -words that appear in the input sequence but not in the vocabulary. A different -approach was taken in the Fairseq implementation to keep it self-contained in -the model file, avoiding any changes to the rest of the code base. Copying -out-of-vocabulary words is possible by pre-processing the input and -post-processing the output. This is described in detail in the next section. - -## Usage - -The training and evaluation procedure is outlined below. You can also find a -more detailed example for the XSum dataset on [this page](README.xsum.md). - -##### 1. Create a vocabulary and extend it with source position markers - -The pointing mechanism is especially helpful with small vocabularies, if we are -able to recover the identities of any out-of-vocabulary words that are copied -from the input. For this purpose, the model allows extending the vocabulary with -special tokens that can be used in place of `<unk>` tokens to identify different -input positions. For example, the user may add `<unk-0>`, `<unk-1>`, `<unk-2>`, -etc. to the end of the vocabulary, after the normal words. Below is an example -of how to create a vocabulary of 10000 most common words and add 1000 input -position markers. - -```bash -vocab_size=10000 -position_markers=1000 -export LC_ALL=C -cat train.src train.tgt | - tr -s '[:space:]' '\n' | - sort | - uniq -c | - sort -k1,1bnr -k2 | - head -n "$((vocab_size - 4))" | - awk '{ print $2 " " $1 }' >dict.pg.txt -python3 -c "[print('<unk-{}> 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt -``` - -##### 2. Preprocess the text data - -The idea is that any `<unk>` tokens in the text are replaced with `<unk-0>` if -it appears in the first input position, `<unk-1>` if it appears in the second -input position, and so on. This can be achieved using the `preprocess.py` script -that is provided in this directory. - -##### 3. Train a model - -The number of these special tokens is given to the model with the -`--source-position-markers` argument—the model simply maps all of these to the -same word embedding as `<unk>`. - -The attention distribution that is used for pointing is selected using the -`--alignment-heads` and `--alignment-layer` command-line arguments in the same -way as with the `transformer_align` model. - -##### 4. Generate text and postprocess it - -When using the model to generate text, you want to preprocess the input text in -the same way that training data was processed, replacing out-of-vocabulary words -with `<unk-N>` tokens. If any of these tokens are copied to the output, the -actual words can be retrieved from the unprocessed input text. Any `<unk-N>` -token should be replaced with the word at position N in the original input -sequence. This can be achieved using the `postprocess.py` script. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/README.xsum.md b/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/README.xsum.md deleted file mode 100644 index ac3a8c3ddc96cd9810b45d49f6b361e43de1e9fb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/README.xsum.md +++ /dev/null @@ -1,180 +0,0 @@ -## Training a pointer-generator model on the Extreme Summarization dataset - -##### 1. Download the Extreme Summarization data and preprocess it - -Follow the instructions [here](https://github.com/EdinburghNLP/XSum) to obtain -the original Extreme Summarization dataset. You should have six files, -{train,validation,test}.{document,summary}. - -##### 2. Create a vocabulary and extend it with source position markers - -```bash -vocab_size=10000 -position_markers=1000 -export LC_ALL=C -cat train.document train.summary | - tr -s '[:space:]' '\n' | - sort | - uniq -c | - sort -k1,1bnr -k2 | - head -n "$((vocab_size - 4))" | - awk '{ print $2 " " $1 }' >dict.pg.txt -python3 -c "[print('<unk-{}> 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt -``` - -This creates the file dict.pg.txt that contains the 10k most frequent words, -followed by 1k source position markers: - -``` -the 4954867 -. 4157552 -, 3439668 -to 2212159 -a 1916857 -of 1916820 -and 1823350 -... -<unk-0> 0 -<unk-1> 0 -<unk-2> 0 -<unk-3> 0 -<unk-4> 0 -... -``` - -##### 2. Preprocess the text data - -```bash -./preprocess.py --source train.document --target train.summary --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out train.pg.src --target-out train.pg.tgt -./preprocess.py --source validation.document --target validation.summary --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out valid.pg.src --target-out valid.pg.tgt -./preprocess.py --source test.document --vocab <(cut -d' ' -f1 dict.pg.txt) --source-out test.pg.src -``` - -The data should now contain `<unk-N>` tokens in place of out-of-vocabulary words. - -##### 3. Binarize the dataset: - -```bash -fairseq-preprocess \ - --source-lang src \ - --target-lang tgt \ - --trainpref train.pg \ - --validpref valid.pg \ - --destdir bin \ - --workers 60 \ - --srcdict dict.pg.txt \ - --joined-dictionary -``` - -##### 3. Train a model - -```bash -total_updates=20000 -warmup_updates=500 -lr=0.001 -max_tokens=4096 -update_freq=4 -pointer_layer=-2 - -CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fairseq-train bin \ - --user-dir examples/pointer_generator/pointer_generator_src \ - --max-tokens "$max_tokens" \ - --task translation \ - --source-lang src --target-lang tgt \ - --truncate-source \ - --layernorm-embedding \ - --share-all-embeddings \ - --encoder-normalize-before \ - --decoder-normalize-before \ - --required-batch-size-multiple 1 \ - --arch transformer_pointer_generator \ - --alignment-layer "$pointer_layer" \ - --alignment-heads 1 \ - --source-position-markers 1000 \ - --criterion label_smoothed_cross_entropy \ - --label-smoothing 0.1 \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \ - --clip-norm 0.1 \ - --lr-scheduler inverse_sqrt --lr "$lr" --max-update "$total_updates" --warmup-updates "$warmup_updates" \ - --update-freq "$update_freq" \ - --skip-invalid-size-inputs-valid-test -``` - -Above we specify that our dictionary contains 1000 source position markers, and -that we want to use one attention head from the penultimate decoder layer for -pointing. It should run in 5.5 hours on one node with eight 32GB V100 GPUs. The -logged messages confirm that dictionary indices above 10000 will be mapped to -the `<unk>` embedding: - -``` -2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | [src] dictionary: 11000 types -2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | [tgt] dictionary: 11000 types -2020-09-24 20:43:53 | INFO | fairseq.data.data_utils | loaded 11332 examples from: bin/valid.src-tgt.src -2020-09-24 20:43:53 | INFO | fairseq.data.data_utils | loaded 11332 examples from: bin/valid.src-tgt.tgt -2020-09-24 20:43:53 | INFO | fairseq.tasks.translation | bin valid src-tgt 11332 examples -2020-09-24 20:43:53 | INFO | fairseq.models.transformer_pg | dictionary indices from 10000 to 10999 will be mapped to 3 -``` - -##### 4. Summarize the test sequences - -```bash -batch_size=32 -beam_size=6 -max_length=60 -length_penalty=1.0 - -fairseq-interactive bin \ - --user-dir examples/pointer_generator/pointer_generator_src \ - --batch-size "$batch_size" \ - --task translation \ - --source-lang src --target-lang tgt \ - --path checkpoints/checkpoint_last.pt \ - --input test.pg.src \ - --buffer-size 200 \ - --max-len-a 0 \ - --max-len-b "$max_length" \ - --lenpen "$length_penalty" \ - --beam "$beam_size" \ - --skip-invalid-size-inputs-valid-test | - tee generate.out -grep ^H generate.out | cut -f 3- >generate.hyp -``` - -Now you should have the generated sequences in `generate.hyp`. They contain -`<unk-N>` tokens that the model has copied from the source sequence. In order to -retrieve the original words, we need the unprocessed source sequences from -`test.document`. - -##### 5. Process the generated output - -Since we skipped too long inputs when producing `generate.hyp`, we also have to -skip too long sequences now that we read `test.document`. - -```bash -./postprocess.py \ - --source <(awk 'NF<1024' test.document) \ - --target generate.hyp \ - --target-out generate.hyp.processed -``` - -Now you'll find the final sequences from `generate.hyp.processed`, with -`<unk-N>` replaced with the original word from the source sequence. - -##### An example of a summarized sequence - -The original source document in `test.document`: - -> de roon moved to teesside in june 2016 for an initial # 8.8 m fee and played 33 premier league games last term . the netherlands international , 26 , scored five goals in 36 league and cup games during his spell at boro . meanwhile , manager garry monk confirmed the championship club 's interest in signing chelsea midfielder lewis baker . `` he 's a target and one of many that we 've had throughout the summer months , '' said monk . find all the latest football transfers on our dedicated page . - -The preprocessed source document in `test.src.pg`: - -> de \<unk-1> moved to \<unk-4> in june 2016 for an initial # \<unk-12> m fee and played 33 premier league games last term . the netherlands international , 26 , scored five goals in 36 league and cup games during his spell at boro . meanwhile , manager garry monk confirmed the championship club 's interest in signing chelsea midfielder lewis baker . `` he 's a target and one of many that we 've had throughout the summer months , '' said monk . find all the latest football transfers on our dedicated page . - -The generated summary in `generate.hyp`: - -> middlesbrough striker \<unk> de \<unk-1> has joined spanish side \<unk> on a season-long loan . - -The generated summary after postprocessing in `generate.hyp.processed`: - -> middlesbrough striker \<unk> de roon has joined spanish side \<unk> on a season-long loan . diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/pointer_generator_src/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/pointer_generator_src/__init__.py deleted file mode 100644 index c361ff6bd616512fe2521387665de1ad1aff66d0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/pointer_generator_src/__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 . import transformer_pg # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/pointer_generator_src/transformer_pg.py b/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/pointer_generator_src/transformer_pg.py deleted file mode 100644 index d0a8ac84f2d8fb3dc540a875d4dbf0c04cdf07c5..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/pointer_generator_src/transformer_pg.py +++ /dev/null @@ -1,517 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 Any, Dict, List, Optional, Tuple - -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 ( - DEFAULT_MAX_SOURCE_POSITIONS, - DEFAULT_MAX_TARGET_POSITIONS, - TransformerDecoder, - TransformerEncoder, - TransformerModel, - base_architecture, -) -from torch import Tensor - -logger = logging.getLogger(__name__) - - -@register_model("transformer_pointer_generator") -class TransformerPointerGeneratorModel(TransformerModel): - """ - Transformer model from `"Attention Is All You Need" (Vaswani et al, 2017) - <https://arxiv.org/abs/1706.03762>`_, augmented with a pointer-generator - network from `"Get To The Point: Summarization with Pointer-Generator - Networks" (See et al, 2017) <https://arxiv.org/abs/1704.04368>`_. - - Args: - encoder (TransformerPointerGeneratorEncoder): the encoder - decoder (TransformerPointerGeneratorDecoder): the decoder - - The Transformer pointer-generator model provides the following named - architectures and command-line arguments: - - .. argparse:: - :ref: fairseq.models.transformer_pointer_generator_parser - :prog: - """ - - @staticmethod - def add_args(parser): - """Add model-specific arguments to the parser.""" - # fmt: off - TransformerModel.add_args(parser) - parser.add_argument('--alignment-heads', type=int, metavar='N', - help='number of attention heads to be used for ' - 'pointing') - parser.add_argument('--alignment-layer', type=int, metavar='I', - help='layer number to be used for pointing (0 ' - 'corresponding to the bottommost layer)') - parser.add_argument('--source-position-markers', type=int, metavar='N', - help='dictionary includes N additional items that ' - 'represent an OOV token at a particular input ' - 'position') - parser.add_argument('--force-generation', type=float, metavar='P', - default=None, - help='set the vocabulary distribution weight to P, ' - 'instead of predicting it from the input (1.0 ' - 'corresponding to generation, 0.0 to pointing)') - # 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 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 - if getattr(args, "source_position_markers", None) is None: - args.source_position_markers = args.max_source_positions - - src_dict, tgt_dict = task.source_dictionary, task.target_dictionary - if src_dict != tgt_dict: - raise ValueError("Pointer-generator requires a joined dictionary") - - def build_embedding(dictionary, embed_dim, path=None): - # The dictionary may include additional items that can be used in - # place of the normal OOV token and that all map to the same - # embedding. Using a different token for each input position allows - # one to restore the word identities from the original source text. - num_embeddings = len(dictionary) - args.source_position_markers - padding_idx = dictionary.pad() - unk_idx = dictionary.unk() - logger.info( - "dictionary indices from {0} to {1} will be mapped to {2}".format( - num_embeddings, len(dictionary) - 1, unk_idx - ) - ) - emb = Embedding(num_embeddings, embed_dim, padding_idx, unk_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 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" - ) - 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 = cls.build_encoder(args, src_dict, encoder_embed_tokens) - decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens) - return cls(args, encoder, decoder) - - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return TransformerPointerGeneratorEncoder(args, src_dict, embed_tokens) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - return TransformerPointerGeneratorDecoder(args, tgt_dict, embed_tokens) - - -class TransformerPointerGeneratorEncoder(TransformerEncoder): - """ - Transformer encoder consisting of *args.encoder_layers* layers. Each layer - is a :class:`TransformerEncoderLayer`. The pointer-generator variant adds - the source tokens to the encoder output as these are otherwise not passed - to the decoder. - """ - - def forward( - self, - src_tokens, - src_lengths: Optional[Tensor] = None, - return_all_hiddens: bool = False, - token_embeddings: Optional[Tensor] = None - ): - """ - Runs the `forward()` method of the parent Transformer class. Then adds - the source tokens into the encoder output tuple. - - While it might be more elegant that the model would pass the source - tokens to the `forward()` method of the decoder too, this would require - changes to `SequenceGenerator`. - - Args: - src_tokens (torch.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: - namedtuple: - - **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. - - **src_tokens** (Tensor): input token ids of shape - `(batch, src_len)` - """ - encoder_out = self.forward_scriptable(src_tokens, - src_lengths, - return_all_hiddens, - token_embeddings) - - # 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. - return { - "encoder_out": encoder_out["encoder_out"], # T x B x C - "encoder_padding_mask": encoder_out["encoder_padding_mask"], # B x T - "encoder_embedding": encoder_out["encoder_embedding"], # B x T x C - "encoder_states": encoder_out["encoder_states"], # List[T x B x C] - "src_tokens": [src_tokens], # B x T - "src_lengths": [], - } - - -class TransformerPointerGeneratorDecoder(TransformerDecoder): - """ - Transformer decoder consisting of *args.decoder_layers* layers. Each layer - is a :class:`TransformerDecoderLayer`. The pointer-generator variant mixes - the output probabilities with an attention distribution in the output layer. - - Args: - args (argparse.Namespace): parsed command-line arguments - dictionary (~fairseq.data.Dictionary): decoding dictionary - embed_tokens (torch.nn.Embedding): output embedding - """ - - def __init__(self, args, dictionary, embed_tokens): - super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False) - - # In the pointer-generator model these arguments define the decoder - # layer and the number of attention heads that will be averaged to - # create the alignment for pointing. - self.alignment_heads = args.alignment_heads - self.alignment_layer = args.alignment_layer - - input_embed_dim = embed_tokens.embedding_dim - - # Generation probabilities / interpolation coefficients are predicted - # from the current decoder input embedding and the decoder output, which - # is the size of output_embed_dim. - p_gen_input_size = input_embed_dim + self.output_embed_dim - self.project_p_gens = nn.Linear(p_gen_input_size, 1) - nn.init.zeros_(self.project_p_gens.bias) - - # The dictionary may include a separate entry for an OOV token in each - # input position, so that their identity can be restored from the - # original source text. - self.num_types = len(dictionary) - self.num_oov_types = args.source_position_markers - self.num_embeddings = self.num_types - self.num_oov_types - self.force_p_gen = args.force_generation - - 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, - alignment_layer: Optional[int] = 0, - alignment_heads: Optional[int] = 1, - 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 - incremental_state (dict, optional): dictionary used for storing - state during :ref:`Incremental decoding` - features_only (bool, optional): only return features without - applying output layer (default: False) - alignment_layer (int, optional): 0-based index of the layer to be - used for pointing (default: 0) - alignment_heads (int, optional): number of attention heads to be - used for pointing (default: 1) - - Returns: - tuple: - - the decoder's output of shape `(batch, tgt_len, vocab)` - - a dictionary with any model-specific outputs - """ - # The normal Transformer model doesn't pass the alignment_layer and - # alignment_heads parameters correctly. We use our local variables. - x, extra = self.extract_features( - prev_output_tokens, - encoder_out=encoder_out, - incremental_state=incremental_state, - alignment_layer=self.alignment_layer, - alignment_heads=self.alignment_heads, - ) - if not features_only: - # Embedding the tokens again for generation probability prediction, - # so that we don't have to reimplement the whole extract_features() - # method. - if incremental_state is not None: - prev_output_tokens = prev_output_tokens[:, -1:] - prev_output_embed = self.embed_tokens(prev_output_tokens) - prev_output_embed *= self.embed_scale - predictors = torch.cat((prev_output_embed, x), 2) - p_gens = self.project_p_gens(predictors) - p_gens = torch.sigmoid(p_gens.float()) - # Torchscript complains if encoder_out or attn are None because - # `output_layer()` signature expects tensors instead - attn: Optional[Tensor] = extra["attn"][0] - assert encoder_out is not None - assert attn is not None - x = self.output_layer(x, attn, encoder_out["src_tokens"][0], p_gens) - return x, extra - - def output_layer( - self, - features: Tensor, - attn: Tensor, - src_tokens: Tensor, - p_gens: Tensor - ) -> Tensor: - """ - Project features to the vocabulary size and mix with the attention - distributions. - """ - if self.force_p_gen is not None: - p_gens = self.force_p_gen - - # project back to size of vocabulary - if self.adaptive_softmax is None: - logits = self.output_projection(features) - else: - logits = features - - batch_size = logits.shape[0] - output_length = logits.shape[1] - assert logits.shape[2] == self.num_embeddings - assert src_tokens.shape[0] == batch_size - src_length = src_tokens.shape[1] - - # The final output distribution will be a mixture of the normal output - # distribution (softmax of logits) and attention weights. - gen_dists = self.get_normalized_probs_scriptable( - (logits, None), log_probs=False, sample=None - ) - gen_dists = torch.mul(gen_dists, p_gens) - padding_size = (batch_size, output_length, self.num_oov_types) - padding = gen_dists.new_zeros(padding_size) - gen_dists = torch.cat((gen_dists, padding), 2) - assert gen_dists.shape[2] == self.num_types - - # Scatter attention distributions to distributions over the extended - # vocabulary in a tensor of shape [batch_size, output_length, - # vocab_size]. Each attention weight will be written into a location - # that is for other dimensions the same as in the index tensor, but for - # the third dimension it's the value of the index tensor (the token ID). - attn = torch.mul(attn.float(), 1 - p_gens) - index = src_tokens[:, None, :] - index = index.expand(batch_size, output_length, src_length) - attn_dists_size = (batch_size, output_length, self.num_types) - attn_dists = attn.new_zeros(attn_dists_size) - attn_dists.scatter_add_(2, index, attn.float()) - - # Final distributions, [batch_size, output_length, num_types]. - return gen_dists + attn_dists - - 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. - Pointer-generator network output is already normalized. - """ - probs = net_output[0] - # Make sure the probabilities are greater than zero when returning log - # probabilities. - return probs.clamp(1e-10, 1.0).log() if log_probs else probs - - -class Embedding(nn.Embedding): - r"""A simple lookup table that stores embeddings of a fixed dictionary and size. - This module is often used to store word embeddings and retrieve them using indices. - The input to the module is a list of indices, and the output is the corresponding - word embeddings. This subclass differs from the standard PyTorch Embedding class by - allowing additional vocabulary entries that will be mapped to the unknown token - embedding. - Args: - num_embeddings (int): size of the dictionary of embeddings - embedding_dim (int): the size of each embedding vector - padding_idx (int): Pads the output with the embedding vector at :attr:`padding_idx` - (initialized to zeros) whenever it encounters the index. - unk_idx (int): Maps all token indices that are greater than or equal to - num_embeddings to this index. - Attributes: - weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) - initialized from :math:`\mathcal{N}(0, 1)` - Shape: - - Input: :math:`(*)`, LongTensor of arbitrary shape containing the indices to extract - - Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}` - .. note:: - Keep in mind that only a limited number of optimizers support - sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`), - :class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`) - .. note:: - With :attr:`padding_idx` set, the embedding vector at - :attr:`padding_idx` is initialized to all zeros. However, note that this - vector can be modified afterwards, e.g., using a customized - initialization method, and thus changing the vector used to pad the - output. The gradient for this vector from :class:`~torch.nn.Embedding` - is always zero. - """ - __constants__ = ["unk_idx"] - - # Torchscript: Inheriting from Embedding class produces an error when exporting to Torchscript - # -> RuntimeError: Unable to cast Python instance to C++ type (compile in debug mode for details - # It's happening because max_norm attribute from nn.Embedding is None by default and it cannot be - # cast to a C++ type - def __init__( - self, - num_embeddings: int, - embedding_dim: int, - padding_idx: Optional[int], - unk_idx: int, - max_norm: Optional[float] = float("inf"), - ): - super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx, max_norm=max_norm) - self.unk_idx = unk_idx - nn.init.normal_(self.weight, mean=0, std=embedding_dim ** -0.5) - nn.init.constant_(self.weight[padding_idx], 0) - - def forward(self, input): - input = torch.where( - input >= self.num_embeddings, torch.ones_like(input) * self.unk_idx, input - ) - return nn.functional.embedding( - input, self.weight, self.padding_idx, self.max_norm, - self.norm_type, self.scale_grad_by_freq, self.sparse - ) - - -@register_model_architecture( - "transformer_pointer_generator", "transformer_pointer_generator" -) -def transformer_pointer_generator(args): - args.alignment_heads = getattr(args, "alignment_heads", 1) - args.alignment_layer = getattr(args, "alignment_layer", -1) - base_architecture(args) - if args.alignment_layer < 0: - args.alignment_layer = args.decoder_layers + args.alignment_layer - - -@register_model_architecture( - "transformer_pointer_generator", "transformer_pointer_generator_iwslt_de_en" -) -def transformer_pointer_generator_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) - transformer_pointer_generator(args) - - -@register_model_architecture( - "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de" -) -def transformer_pointer_generator_wmt_en_de(args): - transformer_pointer_generator(args) - - -# Transformer pointer-generator with the base Transformer parameters as used in -# the "Attention Is All You Need" paper (Vaswani et al., 2017) -@register_model_architecture( - "transformer_pointer_generator", - "transformer_pointer_generator_vaswani_wmt_en_de_big", -) -def transformer_pointer_generator_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) - transformer_pointer_generator(args) - - -@register_model_architecture( - "transformer_pointer_generator", - "transformer_pointer_generator_vaswani_wmt_en_fr_big", -) -def transformer_pointer_generator_vaswani_wmt_en_fr_big(args): - args.dropout = getattr(args, "dropout", 0.1) - transformer_pointer_generator_vaswani_wmt_en_de_big(args) - - -@register_model_architecture( - "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big" -) -def transformer_pointer_generator_wmt_en_de_big(args): - args.attention_dropout = getattr(args, "attention_dropout", 0.1) - transformer_pointer_generator_vaswani_wmt_en_de_big(args) - - -# default parameters used in tensor2tensor implementation -@register_model_architecture( - "transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big_t2t" -) -def transformer_pointer_generator_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_pointer_generator_vaswani_wmt_en_de_big(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/postprocess.py b/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/postprocess.py deleted file mode 100644 index b213aed80fd1e3d86f975256fcb7d9d4c16ca857..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/postprocess.py +++ /dev/null @@ -1,96 +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 re -import sys - - -class OOVIndexError(IndexError): - def __init__(self, pos, source_seq, target_seq): - super(OOVIndexError, self).__init__( - "A <unk-N> tag in the target sequence refers to a position that is " - "outside the source sequence. Most likely there was a mismatch in " - "provided source and target sequences. Otherwise this would mean that " - "the pointing mechanism somehow attended to a position that is past " - "the actual sequence end." - ) - self.source_pos = pos - self.source_seq = source_seq - self.target_seq = target_seq - - -def replace_oovs(source_in, target_in, target_out): - """Replaces <unk-N> tokens in the target text with the corresponding word in - the source text. - """ - - oov_re = re.compile("^<unk-([0-9]+)>$") - - for source_seq, target_seq in zip(source_in, target_in): - target_seq_out = [] - - pos_to_word = source_seq.strip().split() - for token in target_seq.strip().split(): - m = oov_re.match(token) - if m: - pos = int(m.group(1)) - if pos >= len(pos_to_word): - raise OOVIndexError(pos, source_seq, target_seq) - token_out = pos_to_word[pos] - else: - token_out = token - target_seq_out.append(token_out) - target_out.write(" ".join(target_seq_out) + "\n") - - -def main(): - parser = argparse.ArgumentParser( - description="Replaces <unk-N> tokens in target sequences with words from " - "the corresponding position in the source sequence." - ) - parser.add_argument( - "--source", type=str, help="text file with source sequences", required=True - ) - parser.add_argument( - "--target", type=str, help="text file with target sequences", required=True - ) - parser.add_argument( - "--target-out", - type=str, - help="where to write target sequences without <unk-N> " "entries", - required=True, - ) - args = parser.parse_args() - - target_in = ( - open(args.target, "r", encoding="utf-8") if args.target is not None else None - ) - target_out = ( - open(args.target_out, "w", encoding="utf-8") - if args.target_out is not None - else None - ) - with open(args.source, "r", encoding="utf-8") as source_in, open( - args.target, "r", encoding="utf-8" - ) as target_in, open(args.target_out, "w", encoding="utf-8") as target_out: - replace_oovs(source_in, target_in, target_out) - - -if __name__ == "__main__": - try: - main() - except OOVIndexError as e: - print(e, file=sys.stderr) - print("Source sequence:", e.source_seq.strip(), file=sys.stderr) - print("Target sequence:", e.target_seq.strip(), file=sys.stderr) - print( - "Source sequence length:", - len(e.source_seq.strip().split()), - file=sys.stderr, - ) - print("The offending tag points to:", e.source_pos) - sys.exit(2) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/preprocess.py b/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/preprocess.py deleted file mode 100644 index f72ca7d3d97e12ab7b405dcff314bdb6c0a78755..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/pointer_generator/preprocess.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. - -import argparse -from itertools import zip_longest - - -def replace_oovs(source_in, target_in, vocabulary, source_out, target_out): - """Replaces out-of-vocabulary words in source and target text with <unk-N>, - where N in is the position of the word in the source sequence. - """ - - def format_unk(pos): - return "<unk-{}>".format(pos) - - if target_in is None: - target_in = [] - - for seq_num, (source_seq, target_seq) in enumerate( - zip_longest(source_in, target_in) - ): - source_seq_out = [] - target_seq_out = [] - - word_to_pos = dict() - for position, token in enumerate(source_seq.strip().split()): - if token in vocabulary: - token_out = token - else: - if token in word_to_pos: - oov_pos = word_to_pos[token] - else: - word_to_pos[token] = position - oov_pos = position - token_out = format_unk(oov_pos) - source_seq_out.append(token_out) - source_out.write(" ".join(source_seq_out) + "\n") - - if target_seq is not None: - for token in target_seq.strip().split(): - if token in word_to_pos: - token_out = format_unk(word_to_pos[token]) - else: - token_out = token - target_seq_out.append(token_out) - if target_out is not None: - target_out.write(" ".join(target_seq_out) + "\n") - - -def main(): - parser = argparse.ArgumentParser( - description="Replaces out-of-vocabulary words in both source and target " - "sequences with tokens that indicate the position of the word " - "in the source sequence." - ) - parser.add_argument( - "--source", type=str, help="text file with source sequences", required=True - ) - parser.add_argument( - "--target", type=str, help="text file with target sequences", default=None - ) - parser.add_argument("--vocab", type=str, help="vocabulary file", required=True) - parser.add_argument( - "--source-out", - type=str, - help="where to write source sequences with <unk-N> entries", - required=True, - ) - parser.add_argument( - "--target-out", - type=str, - help="where to write target sequences with <unk-N> entries", - default=None, - ) - args = parser.parse_args() - - with open(args.vocab, encoding="utf-8") as vocab: - vocabulary = vocab.read().splitlines() - - target_in = ( - open(args.target, "r", encoding="utf-8") if args.target is not None else None - ) - target_out = ( - open(args.target_out, "w", encoding="utf-8") - if args.target_out is not None - else None - ) - with open(args.source, "r", encoding="utf-8") as source_in, open( - args.source_out, "w", encoding="utf-8" - ) as source_out: - replace_oovs(source_in, target_in, vocabulary, source_out, target_out) - if target_in is not None: - target_in.close() - if target_out is not None: - target_out.close() - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/quant_noise/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/quant_noise/README.md deleted file mode 100644 index a04d7e4e8a077f11c9f63cfa3d1f20e2b899be8c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/quant_noise/README.md +++ /dev/null @@ -1,298 +0,0 @@ -# Training with Quantization Noise for Extreme Model Compression ({Fan\*, Stock\*} *et al.*, 2020) -This page contains information for how to train and quantize models with Quantization Noise, for both scalar quantization like `int8` and Iterative Product Quantization. -Check out our paper [here](https://arxiv.org/abs/2004.07320). - -Looking for pretrained models? They will be added shortly. -Looking for code to train vision models? We are working on open sourcing our code as part of ClassyVision. Please check back, but note that both the Scalar and Iterative Product Quantization counterparts of the `nn.Conv2d` module are already included in this release. - -**Contents**: -- [Walk through of code](#walk-through-the-code) -- [Reproduce NLP Results](#looking-to-reproduce-the-nlp-results-in-the-paper) -- [Reproduce Vision Results](#looking-to-reproduce-the-vision-results-in-the-paper) - - -## Citation -```bibtex -@article{fan2020training, - title={Training with Quantization Noise for Extreme Model Compression}, - author={Angela Fan* and Pierre Stock* and and Benjamin Graham and Edouard Grave and Remi Gribonval and Herve Jegou and Armand Joulin}, - year={2020}, - eprint={2004.07320}, - archivePrefix={arXiv}, - primaryClass={cs.ML} -} -``` - -## Walk through the code - -Training a model with Quant-Noise improves the performance in subsequent inference-time quantization by training models to be robust to quantization. This technique is useful for both scalar and product quantization methods, as well as multiple domains. We detail below our approach to train, quantize models and integrate our code to quantize your favorite models. - -### Scalar Quantization - -Unlike the section [Iterative Product Quantization](#iterative-product-quantization) which gives state-of-the-art compression, this section showcases the usefulness of our approach for simple scalar quantization baselines such as int8 using on-GPU Fake Quantization. - -#### Training - -Scalar quantization with Quant-Noise consists in randomly quantizing a proportion `p` of the weights during training. Scalar quantization is implemented [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/scalar) under the form of Fake Quantization, meaning that we emulate int8 on GPU by quantizing and de-quantizing both the weights and the activations. We rely on PyTorch's [quantization primitives](https://github.com/pytorch/pytorch/tree/master/torch/quantization). - -To train a model with Quant-Noise, add the following flag: -``` ---quant-noise-scalar 0.5 -``` -Large values of noise make the network easier to quantize but may result in higher non-quantized test and validation perplexities. - -#### Quantization - -When evaluating a network, all quantized modules and activation hooks automatically switch to `p=1` so the validation accuracy reported by Fairseq is actually the quantized one, nothing more to do. - - -#### Integration with your own code - -Looking to quantize your own models with Quant-Noise + Scalar Quantization? -- Use the function `quantize_model_` implemented [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/scalar/utils.py) to (1) replace all your modules by their quantized counterparts and (2) add hooks to those modules to quantize the activations. -- Then, perform your training as usual. Note that in `eval()` mode, the network is always fully quantized (weights and activations) by default (`p=1`). - - - -### Iterative Product Quantization - - -Iterative Product Quantization with Quant-Noise proceeds in two steps. First, a model must be trained uncompressed with Quant-Noise. Second, the model must be quantized with iPQ. Note that we implement here the simplest form of noise, which consists in randomly dropping a proportion `p` of blocks, and that worked as well as assigning those blocks to their current centroid. - -#### Training - -To train a model with Quant-Noise, add the following flags: -``` ---quant-noise-pq 0.1 --quant-noise-pq-block-size 8 -``` -`quant-noise-pq` controls how much dropout is applied to the blocks of the weight matrix. `quant-noise-pq-block-size` controls the size of the weight matrix blocks. -We recommend training with 0.05 to 0.2 Quant-Noise, a value that worked well in our experiments. For the block-size, we recommend training with block-size of 8. Note that the block size must be a multiple of `input_features`, see the size checks [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py). Large block sizes result in higher compression ratio but may induce a loss in accuracy. - -We currently support training Transformer based models, such as sequence-to-sequence, language models, and BERT architectures. The `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py) wraps a module. It splits a weight matrix into blocks and applies random dropout to these blocks. -In the Transformer architectures, quant-noise is applied to the input and output embeddings, the attention, and the FFN. - -Quant-Noise can also be combined with **LayerDrop** (see [here](https://github.com/pytorch/fairseq/tree/main/examples/layerdrop)) to add its pruning effect to the quantized model and make the model even smaller. We recommend training with LayerDrop 0.1 or 0.2. - -#### Quantization - -We implement an improved version of product quantization from Stock et al, **iPQ**, described [here](https://arxiv.org/abs/1907.05686), see code with old API [here](https://github.com/facebookresearch/kill-the-bits). Note that we improved the iPQ API in terms of both compute speed and usability as described below. - -For the particular case of PQ, quantization is made sequentially. We recommend first quantizing the FFNs, then the EMBs, and finally the ATTNs. Quantization is done in two sub-steps: -- First, perform `n` steps of Product Quantization (generally `n=20` is enough). -- Then, finetune the obtained centroids. - -#### Integration with your own code - -Looking to quantize your own models with Quant-Noise + iPQ? -- First wrap your modules with the `quant_noise` function [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quant_noise.py), which is module-agnostic and train your favorite model. -- Then, quantize your trained model using the code [here](https://github.com/pytorch/fairseq/tree/main/fairseq/modules/quantization/pq). This can be done *without any changes to your training loop*. Below is an example code for integration. -Note that we tried our approach only on Transformers and various Convolutional Models such as EfficientNets. - -```python -from fairseq.modules.quantization.pq import quantize_model_, SizeTracker - -# get configuration parameters -n_centroids_config = config["n_centroids"] -block_sizes_config = config["block_sizes"] -layers_to_quantize = config["layers_to_quantize"] - -# size tracker for keeping track of assignments, centroids and non-compressed sizes -size_tracker = SizeTracker(model) - -# Quantize model by stages -for step in range(len(layers_to_quantize)): - - # quantize model in-place - quantized_layers = quantize_model_( - model, - size_tracker, - layers_to_quantize, - block_sizes_config, - n_centroids_config, - step=step, - ) - logger.info(f"Finetuning stage {step}, quantized layers: {quantized_layers}") - logger.info(f"{size_tracker}") - - # Don't forget to re-create/update trainer/optimizer since model parameters have changed - optimizer = ... - - # Finetune the centroids with your usual training loop for a few epochs - trainer.train_epoch() -``` - - -## Looking to reproduce the NLP results in the paper? - -We detail below how to reproduce the state-of-the-art results in reported in the paper for Quant-Noise + Iterative Product Quantization. - -### Training with Quant-Noise - -To **train** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta). -The following command can be used to train a RoBERTa Base + QuantNoise model: - -```bash -TOTAL_UPDATES=125000 -WARMUP_UPDATES=10000 -PEAK_LR=0.0005 -TOKENS_PER_SAMPLE=512 -MAX_POSITIONS=512 -MAX_SENTENCES=16 -UPDATE_FREQ=2 -DATA_DIR=/path/to/data/here - -fairseq-train $DATA_DIR \ - --task masked_lm --criterion masked_lm --arch roberta_base \ - --sample-break-mode complete \ - --tokens-per-sample $TOKENS_PER_SAMPLE --max-positions $MAX_POSITIONS \ - --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-6 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $PEAK_LR \ - --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_UPDATES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.01 \ - --batch-size $MAX_SENTENCES \ - --update-freq $UPDATE_FREQ --max-update $TOTAL_UPDATES \ - --save-dir checkpoint/roberta \ - --ddp-backend legacy_ddp --encoder-layerdrop 0.2 \ - --quant-noise-pq 0.2 --quant-noise-pq-block-size 8 --untie-weights-roberta -``` - -To **finetune** RoBERTa + QuantNoise, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/roberta/README.glue.md). -The following command can be used to finetune a RoBERTa Base + QuantNoise model on the RTE dataset: - -```bash -TOTAL_NUM_UPDATES=2036 -WARMUP_UPDATES=122 -LR=2e-05 -NUM_CLASSES=2 -MAX_SENTENCES=16 -ROBERTA_PATH=/path/to/roberta_quantnoise/model.pt - -fairseq-train /path/to/rte/data/ \ - --restore-file $ROBERTA_PATH \ - --max-positions 512 \ - --batch-size $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --find-unused-parameters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --ddp-backend legacy_ddp \ - --quant-noise-pq 0.2 --quant-noise-pq-block-size 8 -``` - -To **train** Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model). -The following command can be used to train a Transformer + QuantNoise model on Wikitext-103: - -```bash -fairseq-train --task language_modeling /path/to/wikitext-103/data \ - --save-dir checkpoints/transformer_wikitext-103 \ - --adaptive-input --adaptive-input-cutoff 20000,60000 --adaptive-input-factor 4 \ - --adaptive-softmax-cutoff 20000,60000 --adaptive-softmax-dropout 0.2 --adaptive-softmax-factor 4.0 \ - --tie-adaptive-proj --tie-adaptive-weights \ - --arch transformer_lm_gbw \ - --attention-dropout 0.1 --dropout 0.2 --relu-dropout 0.1 \ - --clip-norm 0.1 --criterion adaptive_loss \ - --ddp-backend legacy_ddp \ - --decoder-attention-heads 8 --decoder-embed-dim 1024 --decoder-ffn-embed-dim 4096 --decoder-input-dim 1024 \ - --decoder-layers 16 --decoder-normalize-before --decoder-output-dim 1024 \ - --min-lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --lr 1.0 --t-mult 2.0 \ - --max-tokens 3072 --tokens-per-sample 3072 --momentum 0.99 --optimizer nag \ - --sample-break-mode none --update-freq 3 \ - --warmup-init-lr 1e-07 --warmup-updates 16000 \ - --weight-decay 0 --seed 1 --stop-min-lr 1e-09 \ - --quant-noise-pq 0.05 --quant-noise-pq-block-size 8 -``` - -To **evaluate** this model, note you need to use the `eval.py` script. The following command can be used to evaluate: - -```bash -fairseq-eval-lm /path/to/wikitext-103/data --path /path/to/model/checkpoint \ - --sample-break-mode complete \ - --max-tokens 3072 \ - --context-window 2560 \ - --softmax-batch 1024 \ - --gen-subset valid -``` -and change the `--gen-subset` to `test` if you would like to evaluate on the test set instead. - - -### Iterative Product Quantization - -To quantize the finetuned RoBERTa model, we use this command on 1 GPU. This should run in a day. -```bash -TOTAL_NUM_UPDATES=6108 # 2036 updates for each iteration -WARMUP_UPDATES=122 -LR=2e-05 -NUM_CLASSES=2 -MAX_SENTENCES=16 -fairseq-train --task sentence_prediction /path/to/data/ \ - --restore-file $ROBERTA_PATH \ - --save-dir checkpoints/roberta_finetuned \ - --max-positions 512 \ - --batch-size $MAX_SENTENCES \ - --max-tokens 4400 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 --lr-scheduler polynomial_decay \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --no-progress-bar --skip-invalid-size-inputs-valid-test --ddp-backend legacy_ddp \ - --quantization-config-path /path/to/config/yaml -``` - -To quantize the trained Language Model, we use this command on 8 V100 23GB GPUs. This should run in a couple of hours. -```bash -fairseq-train --task language_modeling /path/to/wikitext-103/data \ - --save-dir checkpoints/transformer_wikitext-103 \ - --adaptive-input --adaptive-input-cutoff 20000,60000 --adaptive-input-factor 4 \ - --adaptive-softmax-cutoff 20000,60000 --adaptive-softmax-dropout 0.2 --adaptive-softmax-factor 4.0 \ - --arch transformer_lm_gbw \ - --attention-dropout 0.1 --dropout 0.2 --relu-dropout 0.1 \ - --bucket-cap-mb 25 --char-embedder-highway-layers 2 --character-embedding-dim 4 \ - --clip-norm 0.1 --criterion adaptive_loss \ - --ddp-backend legacy_ddp \ - --decoder-attention-heads 8 --decoder-embed-dim 1024 --decoder-ffn-embed-dim 4096 --decoder-input-dim 1024 --decoder-layers 16 --decoder-normalize-before --decoder-output-dim 1024 \ - --fp16 --keep-last-epochs -1 \ - --min-lr 0.0001 --lr-period-updates 270000 --lr-scheduler cosine --lr-shrink 0.75 --lr 0.05 --stop-min-lr 1e-09 \ - --max-tokens 2944 --tokens-per-sample 2944\ - --momentum 0.99 --no-epoch-checkpoints --no-progress-bar --optimizer nag --required-batch-size-multiple 8 \ - --sample-break-mode none --t-mult 2.0 --skip-invalid-size-inputs-valid-test \ - --tie-adaptive-proj --tie-adaptive-weights --update-freq 3 --weight-decay 0 --seed 1 \ - --log-interval 100 --no-progress-bar --skip-invalid-size-inputs-valid-test \ - --restore-file path/to/trained/lm/with/quant/noise \ - --max-update 13500 --quantization-config-path /path/to/config/yaml -``` -If you have less capacity or if your distributed training freezes, try reducing `--max-tokens` and `--tokens-per-sample` (this may reduce the quantized accuracy a bit). - -### Remarks - -We try to keep the open-sourced code as readable and as easy-to-plug as possible. Therefore, we did not test it for the following cases: -- Scalar quantization with RoBERTa. -- Quantization with iPQ and `int8` combined. - -If you have trouble adapting it, we will be more than happy to help! - -## Looking to reproduce the Vision results in the paper? - -We are working on open sourcing our code as part of ClassyVision. Please check back. - - -## Having an issue or have a question? - -Please open an issue in this repository with the details of your question. Thanks! diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/quant_noise/transformer_quantization_config.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/quant_noise/transformer_quantization_config.yaml deleted file mode 100644 index d4be14a93a3593f8e6dc66c3b05061bfdde3e0e0..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/quant_noise/transformer_quantization_config.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 - -# Number of Centroids for Product Quantization, by default 256 (byte-aligned) -n_centroids: - Linear: - key: in_features - value: {"*": 256} - Embedding: - key: embedding_dim - value: {"*": 256} - -# Block Sizes for Product Quantization -# We suggest: 8 for FFN, 4 for ATTN, 4 for embedding projections, 8 for embeddings -block_sizes: - Linear: - key: fuzzy_name - value: {fc: 8, attn: 4, emb: 4} - Embedding: - key: fuzzy_name - value: {emb: 8} - -# Layers to Quantize Sequentially -# We suggest: first FFN, then EMB, then ATTN -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/examples/roberta/README.custom_classification.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.custom_classification.md deleted file mode 100644 index 7254bb7d178760ef5b847901bbcac3711af33ca2..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.custom_classification.md +++ /dev/null @@ -1,168 +0,0 @@ -# Finetuning RoBERTa on a custom classification task - -This example shows how to finetune RoBERTa on the IMDB dataset, but should illustrate the process for most classification tasks. - -### 1) Get the data - -```bash -wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz -tar zxvf aclImdb_v1.tar.gz -``` - - -### 2) Format data - -`IMDB` data has one data-sample in each file, below python code-snippet converts it one file for train and valid each for ease of processing. -```python -import argparse -import os -import random -from glob import glob - -random.seed(0) - -def main(args): - for split in ['train', 'test']: - samples = [] - for class_label in ['pos', 'neg']: - fnames = glob(os.path.join(args.datadir, split, class_label) + '/*.txt') - for fname in fnames: - with open(fname) as fin: - line = fin.readline() - samples.append((line, 1 if class_label == 'pos' else 0)) - random.shuffle(samples) - out_fname = 'train' if split == 'train' else 'dev' - f1 = open(os.path.join(args.datadir, out_fname + '.input0'), 'w') - f2 = open(os.path.join(args.datadir, out_fname + '.label'), 'w') - for sample in samples: - f1.write(sample[0] + '\n') - f2.write(str(sample[1]) + '\n') - f1.close() - f2.close() - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--datadir', default='aclImdb') - args = parser.parse_args() - main(args) -``` - - -### 3) BPE encode - -Run `multiprocessing_bpe_encoder`, you can also do this in previous step for each sample but that might be slower. -```bash -# Download encoder.json and vocab.bpe -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' - -for SPLIT in train dev; do - python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json encoder.json \ - --vocab-bpe vocab.bpe \ - --inputs "aclImdb/$SPLIT.input0" \ - --outputs "aclImdb/$SPLIT.input0.bpe" \ - --workers 60 \ - --keep-empty -done -``` - - -### 4) Preprocess data - -```bash -# Download fairseq dictionary. -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' - -fairseq-preprocess \ - --only-source \ - --trainpref "aclImdb/train.input0.bpe" \ - --validpref "aclImdb/dev.input0.bpe" \ - --destdir "IMDB-bin/input0" \ - --workers 60 \ - --srcdict dict.txt - -fairseq-preprocess \ - --only-source \ - --trainpref "aclImdb/train.label" \ - --validpref "aclImdb/dev.label" \ - --destdir "IMDB-bin/label" \ - --workers 60 - -``` - - -### 5) Run training - -```bash -TOTAL_NUM_UPDATES=7812 # 10 epochs through IMDB for bsz 32 -WARMUP_UPDATES=469 # 6 percent of the number of updates -LR=1e-05 # Peak LR for polynomial LR scheduler. -HEAD_NAME=imdb_head # Custom name for the classification head. -NUM_CLASSES=2 # Number of classes for the classification task. -MAX_SENTENCES=8 # Batch size. -ROBERTA_PATH=/path/to/roberta.large/model.pt - -CUDA_VISIBLE_DEVICES=0 fairseq-train IMDB-bin/ \ - --restore-file $ROBERTA_PATH \ - --max-positions 512 \ - --batch-size $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction \ - --classification-head-name $HEAD_NAME \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --shorten-method "truncate" \ - --find-unused-parameters \ - --update-freq 4 -``` - -The above command will finetune RoBERTa-large with an effective batch-size of 32 -sentences (`--batch-size=8 --update-freq=4`). The expected -`best-validation-accuracy` after 10 epochs is ~96.5%. - -If you run out of GPU memory, try decreasing `--batch-size` and increase -`--update-freq` to compensate. - - -### 6) Load model using hub interface - -Now we can load the trained model checkpoint using the RoBERTa hub interface. - -Assuming your checkpoints are stored in `checkpoints/`: -```python -from fairseq.models.roberta import RobertaModel -roberta = RobertaModel.from_pretrained( - 'checkpoints', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='IMDB-bin' -) -roberta.eval() # disable dropout -``` - -Finally you can make predictions using the `imdb_head` (or whatever you set -`--classification-head-name` to during training): -```python -label_fn = lambda label: roberta.task.label_dictionary.string( - [label + roberta.task.label_dictionary.nspecial] -) - -tokens = roberta.encode('Best movie this year') -pred = label_fn(roberta.predict('imdb_head', tokens).argmax().item()) -assert pred == '1' # positive - -tokens = roberta.encode('Worst movie ever') -pred = label_fn(roberta.predict('imdb_head', tokens).argmax().item()) -assert pred == '0' # negative -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.glue.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.glue.md deleted file mode 100644 index 4f596d55af99fba3cdf58b1d5ff3d8f8dbf4383d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.glue.md +++ /dev/null @@ -1,64 +0,0 @@ -# Finetuning RoBERTa on GLUE tasks - -### 1) Download the data from GLUE website (https://gluebenchmark.com/tasks) using following commands: -```bash -wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py -python download_glue_data.py --data_dir glue_data --tasks all -``` - -### 2) Preprocess GLUE task data: -```bash -./examples/roberta/preprocess_GLUE_tasks.sh glue_data <glue_task_name> -``` -`glue_task_name` is one of the following: -`{ALL, QQP, MNLI, QNLI, MRPC, RTE, STS-B, SST-2, CoLA}` -Use `ALL` for preprocessing all the glue tasks. - -### 3) Fine-tuning on GLUE task: -Example fine-tuning cmd for `RTE` task -```bash -ROBERTA_PATH=/path/to/roberta/model.pt - -CUDA_VISIBLE_DEVICES=0 fairseq-hydra-train -config-dir examples/roberta/config/finetuning --config-name rte \ -task.data=RTE-bin checkpoint.restore_file=$ROBERTA_PATH -``` - -There are additional config files for each of the GLUE tasks in the examples/roberta/config/finetuning directory. - -**Note:** - -a) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. - -b) All the settings in above table are suggested settings based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. - -### Inference on GLUE task -After training the model as mentioned in previous step, you can perform inference with checkpoints in `checkpoints/` directory using following python code snippet: - -```python -from fairseq.models.roberta import RobertaModel - -roberta = RobertaModel.from_pretrained( - 'checkpoints/', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='RTE-bin' -) - -label_fn = lambda label: roberta.task.label_dictionary.string( - [label + roberta.task.label_dictionary.nspecial] -) -ncorrect, nsamples = 0, 0 -roberta.cuda() -roberta.eval() -with open('glue_data/RTE/dev.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[1], tokens[2], tokens[3] - tokens = roberta.encode(sent1, sent2) - prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_fn(prediction) - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) - -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.md deleted file mode 100644 index ed4d5df52ccea01216276054a1f253d0d16c0409..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.md +++ /dev/null @@ -1,296 +0,0 @@ -# RoBERTa: A Robustly Optimized BERT Pretraining Approach - -https://arxiv.org/abs/1907.11692 - -## Introduction - -RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details. - -### What's New: - -- December 2020: German model (GottBERT) is available: [GottBERT](https://github.com/pytorch/fairseq/tree/main/examples/gottbert). -- January 2020: Italian model (UmBERTo) is available from Musixmatch Research: [UmBERTo](https://github.com/musixmatchresearch/umberto). -- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/main/examples/camembert). -- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/main/examples/xlmr). -- September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers). -- August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers). -- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/main/examples/roberta/wsc#roberta-training-on-winogrande-dataset). -- August 2019: Added [tutorial for pretraining RoBERTa using your own data](README.pretraining.md). - -## Pre-trained models - -Model | Description | # params | Download ----|---|---|--- -`roberta.base` | RoBERTa using the BERT-base architecture | 125M | [roberta.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz) -`roberta.large` | RoBERTa using the BERT-large architecture | 355M | [roberta.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz) -`roberta.large.mnli` | `roberta.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | 355M | [roberta.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz) -`roberta.large.wsc` | `roberta.large` finetuned on [WSC](wsc/README.md) | 355M | [roberta.large.wsc.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz) - -## Results - -**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)** -_(dev set, single model, single-task finetuning)_ - -Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B ----|---|---|---|---|---|---|---|--- -`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2 -`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4 -`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | - - -**[SuperGLUE (Wang et al., 2019)](https://super.gluebenchmark.com/)** -_(dev set, single model, single-task finetuning)_ - -Model | BoolQ | CB | COPA | MultiRC | RTE | WiC | WSC ----|---|---|---|---|---|---|--- -`roberta.large` | 86.9 | 98.2 | 94.0 | 85.7 | 89.5 | 75.6 | - -`roberta.large.wsc` | - | - | - | - | - | - | 91.3 - -**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)** -_(dev set, no additional data used)_ - -Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1 ----|---|--- -`roberta.large` | 88.9/94.6 | 86.5/89.4 - -**[RACE (Lai et al., 2017)](http://www.qizhexie.com/data/RACE_leaderboard.html)** -_(test set)_ - -Model | Accuracy | Middle | High ----|---|---|--- -`roberta.large` | 83.2 | 86.5 | 81.3 - -**[HellaSwag (Zellers et al., 2019)](https://rowanzellers.com/hellaswag/)** -_(test set)_ - -Model | Overall | In-domain | Zero-shot | ActivityNet | WikiHow ----|---|---|---|---|--- -`roberta.large` | 85.2 | 87.3 | 83.1 | 74.6 | 90.9 - -**[Commonsense QA (Talmor et al., 2019)](https://www.tau-nlp.org/commonsenseqa)** -_(test set)_ - -Model | Accuracy ----|--- -`roberta.large` (single model) | 72.1 -`roberta.large` (ensemble) | 72.5 - -**[Winogrande (Sakaguchi et al., 2019)](https://arxiv.org/abs/1907.10641)** -_(test set)_ - -Model | Accuracy ----|--- -`roberta.large` | 78.1 - -**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)** -_(TRANSLATE-TEST)_ - -Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur ----|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--- -`roberta.large.mnli` | 91.3 | 82.91 | 84.27 | 81.24 | 81.74 | 83.13 | 78.28 | 76.79 | 76.64 | 74.17 | 74.05 | 77.5 | 70.9 | 66.65 | 66.81 - -## Example usage - -##### Load RoBERTa from torch.hub (PyTorch >= 1.1): -```python -import torch -roberta = torch.hub.load('pytorch/fairseq', 'roberta.large') -roberta.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Load RoBERTa (for PyTorch 1.0 or custom models): -```python -# Download roberta.large model -wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz -tar -xzvf roberta.large.tar.gz - -# Load the model in fairseq -from fairseq.models.roberta import RobertaModel -roberta = RobertaModel.from_pretrained('/path/to/roberta.large', checkpoint_file='model.pt') -roberta.eval() # disable dropout (or leave in train mode to finetune) -``` - -##### Apply Byte-Pair Encoding (BPE) to input text: -```python -tokens = roberta.encode('Hello world!') -assert tokens.tolist() == [0, 31414, 232, 328, 2] -roberta.decode(tokens) # 'Hello world!' -``` - -##### Extract features from RoBERTa: -```python -# Extract the last layer's features -last_layer_features = roberta.extract_features(tokens) -assert last_layer_features.size() == torch.Size([1, 5, 1024]) - -# Extract all layer's features (layer 0 is the embedding layer) -all_layers = roberta.extract_features(tokens, return_all_hiddens=True) -assert len(all_layers) == 25 -assert torch.all(all_layers[-1] == last_layer_features) -``` - -##### Use RoBERTa for sentence-pair classification tasks: -```python -# Download RoBERTa already finetuned for MNLI -roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli') -roberta.eval() # disable dropout for evaluation - -# Encode a pair of sentences and make a prediction -tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.') -roberta.predict('mnli', tokens).argmax() # 0: contradiction - -# Encode another pair of sentences -tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.') -roberta.predict('mnli', tokens).argmax() # 2: entailment -``` - -##### Register a new (randomly initialized) classification head: -```python -roberta.register_classification_head('new_task', num_classes=3) -logprobs = roberta.predict('new_task', tokens) # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>) -``` - -##### Batched prediction: -```python -import torch -from fairseq.data.data_utils import collate_tokens - -roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli') -roberta.eval() - -batch_of_pairs = [ - ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'], - ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'], - ['potatoes are awesome.', 'I like to run.'], - ['Mars is very far from earth.', 'Mars is very close.'], -] - -batch = collate_tokens( - [roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1 -) - -logprobs = roberta.predict('mnli', batch) -print(logprobs.argmax(dim=1)) -# tensor([0, 2, 1, 0]) -``` - -##### Using the GPU: -```python -roberta.cuda() -roberta.predict('new_task', tokens) # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>) -``` - -## Advanced usage - -#### Filling masks: - -RoBERTa can be used to fill `<mask>` tokens in the input. Some examples from the -[Natural Questions dataset](https://ai.google.com/research/NaturalQuestions/): -```python -roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3) -# [('The first Star wars movie came out in 1977', 0.9504708051681519, ' 1977'), ('The first Star wars movie came out in 1978', 0.009986862540245056, ' 1978'), ('The first Star wars movie came out in 1979', 0.009574787691235542, ' 1979')] - -roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3) -# [('Vikram samvat calender is official in India', 0.21878819167613983, ' India'), ('Vikram samvat calender is official in Delhi', 0.08547237515449524, ' Delhi'), ('Vikram samvat calender is official in Gujarat', 0.07556215673685074, ' Gujarat')] - -roberta.fill_mask('<mask> is the common currency of the European Union', topk=3) -# [('Euro is the common currency of the European Union', 0.9456493854522705, 'Euro'), ('euro is the common currency of the European Union', 0.025748178362846375, 'euro'), ('€ is the common currency of the European Union', 0.011183084920048714, '€')] -``` - -#### Pronoun disambiguation (Winograd Schema Challenge): - -RoBERTa can be used to disambiguate pronouns. First install spaCy and download the English-language model: -```bash -pip install spacy -python -m spacy download en_core_web_lg -``` - -Next load the `roberta.large.wsc` model and call the `disambiguate_pronoun` -function. The pronoun should be surrounded by square brackets (`[]`) and the -query referent surrounded by underscores (`_`), or left blank to return the -predicted candidate text directly: -```python -roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.wsc', user_dir='examples/roberta/wsc') -roberta.cuda() # use the GPU (optional) - -roberta.disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.') -# True -roberta.disambiguate_pronoun('The trophy would not fit in the brown _suitcase_ because [it] was too big.') -# False - -roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] feared violence.') -# 'The city councilmen' -roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] advocated violence.') -# 'demonstrators' -``` - -See the [RoBERTA Winograd Schema Challenge (WSC) README](wsc/README.md) for more details on how to train this model. - -#### Extract features aligned to words: - -By default RoBERTa outputs one feature vector per BPE token. You can instead -realign the features to match [spaCy's word-level tokenization](https://spacy.io/usage/linguistic-features#tokenization) -with the `extract_features_aligned_to_words` method. This will compute a -weighted average of the BPE-level features for each word and expose them in -spaCy's `Token.vector` attribute: -```python -doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."') -assert len(doc) == 10 -for tok in doc: - print('{:10}{} (...)'.format(str(tok), tok.vector[:5])) -# <s> tensor([-0.1316, -0.0386, -0.0832, -0.0477, 0.1943], grad_fn=<SliceBackward>) (...) -# I tensor([ 0.0559, 0.1541, -0.4832, 0.0880, 0.0120], grad_fn=<SliceBackward>) (...) -# said tensor([-0.1565, -0.0069, -0.8915, 0.0501, -0.0647], grad_fn=<SliceBackward>) (...) -# , tensor([-0.1318, -0.0387, -0.0834, -0.0477, 0.1944], grad_fn=<SliceBackward>) (...) -# " tensor([-0.0486, 0.1818, -0.3946, -0.0553, 0.0981], grad_fn=<SliceBackward>) (...) -# hello tensor([ 0.0079, 0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...) -# RoBERTa tensor([-0.2339, -0.1184, -0.7343, -0.0492, 0.5829], grad_fn=<SliceBackward>) (...) -# . tensor([-0.1341, -0.1203, -0.1012, -0.0621, 0.1892], grad_fn=<SliceBackward>) (...) -# " tensor([-0.1341, -0.1203, -0.1012, -0.0621, 0.1892], grad_fn=<SliceBackward>) (...) -# </s> tensor([-0.0930, -0.0392, -0.0821, 0.0158, 0.0649], grad_fn=<SliceBackward>) (...) -``` - -#### Evaluating the `roberta.large.mnli` model: - -Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set. -```python -label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'} -ncorrect, nsamples = 0, 0 -roberta.cuda() -roberta.eval() -with open('glue_data/MNLI/dev_matched.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[8], tokens[9], tokens[-1] - tokens = roberta.encode(sent1, sent2) - prediction = roberta.predict('mnli', tokens).argmax().item() - prediction_label = label_map[prediction] - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) -# Expected output: 0.9060 -``` - -## Finetuning - -- [Finetuning on GLUE](README.glue.md) -- [Finetuning on custom classification tasks (e.g., IMDB)](README.custom_classification.md) -- [Finetuning on Winograd Schema Challenge (WSC)](wsc/README.md) -- [Finetuning on Commonsense QA (CQA)](commonsense_qa/README.md) - -## Pretraining using your own data - -See the [tutorial for pretraining RoBERTa using your own data](README.pretraining.md). - -## Citation - -```bibtex -@article{liu2019roberta, - title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach}, - author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and - Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and - Luke Zettlemoyer and Veselin Stoyanov}, - journal={arXiv preprint arXiv:1907.11692}, - year = {2019}, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.pretraining.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.pretraining.md deleted file mode 100644 index a4e7453529111fdd198be637d911d1764cb96c0e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.pretraining.md +++ /dev/null @@ -1,84 +0,0 @@ -# Pretraining RoBERTa using your own data - -This tutorial will walk you through pretraining RoBERTa over your own data. - -### 1) Preprocess the data - -Data should be preprocessed following the [language modeling format](/examples/language_model), i.e. each document should be separated by an empty line (only useful with `--sample-break-mode complete_doc`). Lines will be concatenated as a 1D text stream during training. - -We'll use the [WikiText-103 dataset](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/) -to demonstrate how to preprocess raw text data with the GPT-2 BPE. Of course -this dataset is quite small, so the resulting pretrained model will perform -poorly, but it gives the general idea. - -First download the dataset: -```bash -wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip -unzip wikitext-103-raw-v1.zip -``` - -Next encode it with the GPT-2 BPE: -```bash -mkdir -p gpt2_bpe -wget -O gpt2_bpe/encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json -wget -O gpt2_bpe/vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe -for SPLIT in train valid test; do \ - python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json gpt2_bpe/encoder.json \ - --vocab-bpe gpt2_bpe/vocab.bpe \ - --inputs wikitext-103-raw/wiki.${SPLIT}.raw \ - --outputs wikitext-103-raw/wiki.${SPLIT}.bpe \ - --keep-empty \ - --workers 60; \ -done -``` - -Finally preprocess/binarize the data using the GPT-2 fairseq dictionary: -```bash -wget -O gpt2_bpe/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt -fairseq-preprocess \ - --only-source \ - --srcdict gpt2_bpe/dict.txt \ - --trainpref wikitext-103-raw/wiki.train.bpe \ - --validpref wikitext-103-raw/wiki.valid.bpe \ - --testpref wikitext-103-raw/wiki.test.bpe \ - --destdir data-bin/wikitext-103 \ - --workers 60 -``` - -### 2) Train RoBERTa base -```bash -DATA_DIR=data-bin/wikitext-103 - -fairseq-hydra-train -m --config-dir examples/roberta/config/pretraining \ ---config-name base task.data=$DATA_DIR -``` - -**Note:** You can optionally resume training the released RoBERTa base model by -adding `checkpoint.restore_file=/path/to/roberta.base/model.pt`. - -**Note:** The above command assumes training on 8x32GB V100 GPUs. Each GPU uses -a batch size of 16 sequences (`dataset.batch_size`) and accumulates gradients to -further increase the batch size by 16x (`optimization.update_freq`), for a total batch size -of 2048 sequences. If you have fewer GPUs or GPUs with less memory you may need -to reduce `dataset.batch_size` and increase dataset.update_freq to compensate. -Alternatively if you have more GPUs you can decrease `dataset.update_freq` accordingly -to increase training speed. - -**Note:** The learning rate and batch size are tightly connected and need to be -adjusted together. We generally recommend increasing the learning rate as you -increase the batch size according to the following table (although it's also -dataset dependent, so don't rely on the following values too closely): - -batch size | peak learning rate ----|--- -256 | 0.0001 -2048 | 0.0005 -8192 | 0.0007 - -### 3) Load your pretrained model -```python -from fairseq.models.roberta import RobertaModel -roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'path/to/data') -assert isinstance(roberta.model, torch.nn.Module) -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.race.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.race.md deleted file mode 100644 index 13c917e8eca6621e91dce541c7e41436b38cbdc1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/README.race.md +++ /dev/null @@ -1,68 +0,0 @@ -# Finetuning RoBERTa on RACE tasks - -### 1) Download the data from RACE website (http://www.cs.cmu.edu/~glai1/data/race/) - -### 2) Preprocess RACE data: -```bash -python ./examples/roberta/preprocess_RACE.py --input-dir <input-dir> --output-dir <extracted-data-dir> -./examples/roberta/preprocess_RACE.sh <extracted-data-dir> <output-dir> -``` - -### 3) Fine-tuning on RACE: - -```bash -MAX_EPOCH=5 # Number of training epochs. -LR=1e-05 # Peak LR for fixed LR scheduler. -NUM_CLASSES=4 -MAX_SENTENCES=1 # Batch size per GPU. -UPDATE_FREQ=8 # Accumulate gradients to simulate training on 8 GPUs. -DATA_DIR=/path/to/race-output-dir -ROBERTA_PATH=/path/to/roberta/model.pt - -CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=legacy_ddp \ - --restore-file $ROBERTA_PATH \ - --reset-optimizer --reset-dataloader --reset-meters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --task sentence_ranking \ - --num-classes $NUM_CLASSES \ - --init-token 0 --separator-token 2 \ - --max-option-length 128 \ - --max-positions 512 \ - --shorten-method "truncate" \ - --arch roberta_large \ - --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ - --criterion sentence_ranking \ - --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler fixed --lr $LR \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --batch-size $MAX_SENTENCES \ - --required-batch-size-multiple 1 \ - --update-freq $UPDATE_FREQ \ - --max-epoch $MAX_EPOCH -``` - -**Note:** - -a) As contexts in RACE are relatively long, we are using smaller batch size per GPU while increasing update-freq to achieve larger effective batch size. - -b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`. - -c) The setting in above command is based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search. - -### 4) Evaluation: - -``` -DATA_DIR=/path/to/race-output-dir # data directory used during training -MODEL_PATH=/path/to/checkpoint_best.pt # path to the finetuned model checkpoint -PREDS_OUT=preds.tsv # output file path to save prediction -TEST_SPLIT=test # can be test (Middle) or test1 (High) -fairseq-validate \ - $DATA_DIR \ - --valid-subset $TEST_SPLIT \ - --path $MODEL_PATH \ - --batch-size 1 \ - --task sentence_ranking \ - --criterion sentence_ranking \ - --save-predictions $PREDS_OUT -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/README.md deleted file mode 100644 index 7f386decd87d93bf701e2e313c7fea39d982224f..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/README.md +++ /dev/null @@ -1,99 +0,0 @@ -# Finetuning RoBERTa on Commonsense QA - -We follow a similar approach to [finetuning RACE](../README.race.md). Specifically -for each question we construct five inputs, one for each of the five candidate -answer choices. Each input is constructed by concatenating the question and -candidate answer. We then encode each input and pass the resulting "[CLS]" -representations through a fully-connected layer to predict the correct answer. -We train with a standard cross-entropy loss. - -We also found it helpful to prepend a prefix of `Q:` to the question and `A:` to -the answer. The complete input format is: -``` -<s> Q: Where would I not want a fox? </s> A: hen house </s> -``` - -Our final submission is based on a hyperparameter search over the learning rate -(1e-5, 2e-5, 3e-5), batch size (8, 16), number of training steps (2000, 3000, -4000) and random seed. We selected the model with the best performance on the -development set after 100 trials. - -### 1) Download data from the Commonsense QA website (https://www.tau-nlp.org/commonsenseqa) -```bash -bash examples/roberta/commonsense_qa/download_cqa_data.sh -``` - -### 2) Finetune - -```bash -MAX_UPDATES=3000 # Number of training steps. -WARMUP_UPDATES=150 # Linearly increase LR over this many steps. -LR=1e-05 # Peak LR for polynomial LR scheduler. -MAX_SENTENCES=16 # Batch size. -SEED=1 # Random seed. -ROBERTA_PATH=/path/to/roberta/model.pt -DATA_DIR=data/CommonsenseQA - -# we use the --user-dir option to load the task from -# the examples/roberta/commonsense_qa directory: -FAIRSEQ_PATH=/path/to/fairseq -FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/commonsense_qa - -CUDA_VISIBLE_DEVICES=0 fairseq-train --fp16 --ddp-backend=legacy_ddp \ - $DATA_DIR \ - --user-dir $FAIRSEQ_USER_DIR \ - --restore-file $ROBERTA_PATH \ - --reset-optimizer --reset-dataloader --reset-meters \ - --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --task commonsense_qa --init-token 0 --bpe gpt2 \ - --arch roberta_large --max-positions 512 \ - --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ - --criterion sentence_ranking --num-classes 5 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR \ - --warmup-updates $WARMUP_UPDATES --total-num-update $MAX_UPDATES \ - --batch-size $MAX_SENTENCES \ - --max-update $MAX_UPDATES \ - --log-format simple --log-interval 25 \ - --seed $SEED -``` - -The above command assumes training on 1 GPU with 32GB of RAM. For GPUs with -less memory, decrease `--batch-size` and increase `--update-freq` -accordingly to compensate. - -### 3) Evaluate -```python -import json -import torch -from fairseq.models.roberta import RobertaModel -from examples.roberta import commonsense_qa # load the Commonsense QA task -roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'data/CommonsenseQA') -roberta.eval() # disable dropout -roberta.cuda() # use the GPU (optional) -nsamples, ncorrect = 0, 0 -with open('data/CommonsenseQA/valid.jsonl') as h: - for line in h: - example = json.loads(line) - scores = [] - for choice in example['question']['choices']: - input = roberta.encode( - 'Q: ' + example['question']['stem'], - 'A: ' + choice['text'], - no_separator=True - ) - score = roberta.predict('sentence_classification_head', input, return_logits=True) - scores.append(score) - pred = torch.cat(scores).argmax() - answer = ord(example['answerKey']) - ord('A') - nsamples += 1 - if pred == answer: - ncorrect += 1 - -print('Accuracy: ' + str(ncorrect / float(nsamples))) -# Accuracy: 0.7846027846027847 -``` - -The above snippet is not batched, which makes it quite slow. See [instructions -for batched prediction with RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta#batched-prediction). diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/__init__.py deleted file mode 100644 index 42d21f35eb3dd33a053dcf0edd5eadd2dff11294..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/__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 . import commonsense_qa_task # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/commonsense_qa_task.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/commonsense_qa_task.py deleted file mode 100644 index 7d8f8131b38772d0018dbbb3524325bcc915120d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/commonsense_qa_task.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 json -import os - -import numpy as np -import torch -from fairseq.data import ( - Dictionary, - IdDataset, - ListDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - RawLabelDataset, - RightPadDataset, - SortDataset, - data_utils, - encoders, -) -from fairseq.tasks import LegacyFairseqTask, register_task - - -@register_task("commonsense_qa") -class CommonsenseQATask(LegacyFairseqTask): - """Task to finetune RoBERTa for Commonsense QA.""" - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "data", metavar="DIR", help="path to data directory; we load <split>.jsonl" - ) - parser.add_argument( - "--init-token", - type=int, - default=None, - help="add token at the beginning of each batch item", - ) - parser.add_argument("--num-classes", type=int, default=5) - - def __init__(self, args, vocab): - super().__init__(args) - self.vocab = vocab - self.mask = vocab.add_symbol("<mask>") - - self.bpe = encoders.build_bpe(args) - - @classmethod - def load_dictionary(cls, filename): - """Load the dictionary from the filename - - Args: - filename (str): the filename - """ - dictionary = Dictionary.load(filename) - dictionary.add_symbol("<mask>") - return dictionary - - @classmethod - def setup_task(cls, args, **kwargs): - assert ( - args.criterion == "sentence_ranking" - ), "Must set --criterion=sentence_ranking" - - # load data and label dictionaries - vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) - print("| dictionary: {} types".format(len(vocab))) - - return cls(args, vocab) - - def load_dataset( - self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs - ): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - - def binarize(s, append_bos=False): - if self.bpe is not None: - s = self.bpe.encode(s) - tokens = self.vocab.encode_line( - s, - append_eos=True, - add_if_not_exist=False, - ).long() - if append_bos and self.args.init_token is not None: - tokens = torch.cat([tokens.new([self.args.init_token]), tokens]) - return tokens - - if data_path is None: - data_path = os.path.join(self.args.data, split + ".jsonl") - if not os.path.exists(data_path): - raise FileNotFoundError("Cannot find data: {}".format(data_path)) - - src_tokens = [[] for i in range(self.args.num_classes)] - src_lengths = [[] for i in range(self.args.num_classes)] - labels = [] - - with open(data_path) as h: - for line in h: - example = json.loads(line.strip()) - if "answerKey" in example: - label = ord(example["answerKey"]) - ord("A") - labels.append(label) - question = example["question"]["stem"] - assert len(example["question"]["choices"]) == self.args.num_classes - # format: `<s> Q: Where would I not want a fox? </s> A: hen house </s>` - question = "Q: " + question - question_toks = binarize(question, append_bos=True) - for i, choice in enumerate(example["question"]["choices"]): - src = "A: " + choice["text"] - src_bin = torch.cat([question_toks, binarize(src)]) - src_tokens[i].append(src_bin) - src_lengths[i].append(len(src_bin)) - assert all( - len(src_tokens[0]) == len(src_tokens[i]) - for i in range(self.args.num_classes) - ) - assert len(src_tokens[0]) == len(src_lengths[0]) - assert len(labels) == 0 or len(labels) == len(src_tokens[0]) - - for i in range(self.args.num_classes): - src_lengths[i] = np.array(src_lengths[i]) - src_tokens[i] = ListDataset(src_tokens[i], src_lengths[i]) - src_lengths[i] = ListDataset(src_lengths[i]) - - dataset = { - "id": IdDataset(), - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(src_tokens[0], reduce=True), - } - - for i in range(self.args.num_classes): - dataset.update( - { - "net_input{}".format(i + 1): { - "src_tokens": RightPadDataset( - src_tokens[i], - pad_idx=self.source_dictionary.pad(), - ), - "src_lengths": src_lengths[i], - } - } - ) - - if len(labels) > 0: - dataset.update({"target": RawLabelDataset(labels)}) - - dataset = NestedDictionaryDataset( - dataset, - sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])], - ) - - with data_utils.numpy_seed(self.args.seed): - dataset = SortDataset( - dataset, - # shuffle - sort_order=[np.random.permutation(len(dataset))], - ) - - print("| Loaded {} with {} samples".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) - - model.register_classification_head( - "sentence_classification_head", - num_classes=1, - ) - - return model - - @property - def source_dictionary(self): - return self.vocab - - @property - def target_dictionary(self): - return self.vocab diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/download_cqa_data.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/download_cqa_data.sh deleted file mode 100644 index 5f300093fa0a0feb819d8b6aed307b59e3891d01..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/commonsense_qa/download_cqa_data.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -OUTDIR=data/CommonsenseQA - -mkdir -p $OUTDIR - -wget -O $OUTDIR/train.jsonl https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl -wget -O $OUTDIR/valid.jsonl https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl -wget -O $OUTDIR/test.jsonl https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl -wget -O $OUTDIR/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/cola.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/cola.yaml deleted file mode 100644 index ac76611201275fcee6311b625599ea0863c92898..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/cola.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 2 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 16 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 320 - -optimization: - clip_norm: 0.0 - lr: [1e-05] - max_update: 5336 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/mnli.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/mnli.yaml deleted file mode 100644 index 5be10c362fdadae49e5a6018ef74095892903914..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/mnli.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 3 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 32 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 7432 - -optimization: - clip_norm: 0.0 - lr: [1e-05] - max_update: 123873 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/mrpc.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/mrpc.yaml deleted file mode 100644 index aa8b7db393ed00dd9b403ba009de70bf18a75309..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/mrpc.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 2 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 16 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 137 - -optimization: - clip_norm: 0.0 - lr: [1e-05] - max_update: 2296 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/qnli.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/qnli.yaml deleted file mode 100644 index b4595b090ee23b74bb3924c09704702c4208e395..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/qnli.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 2 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 32 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 1986 - -optimization: - clip_norm: 0.0 - lr: [1e-05] - max_update: 33112 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/qqp.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/qqp.yaml deleted file mode 100644 index 5a2b2ed743963af1f558927f226d993c66fbd45c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/qqp.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 2 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 32 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 28318 - -optimization: - clip_norm: 0.0 - lr: [1e-05] - max_update: 113272 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/rte.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/rte.yaml deleted file mode 100644 index 73184650117e5f1ce5ec4542a0076eaf3044c2a3..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/rte.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 2 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 16 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 122 - -optimization: - clip_norm: 0.0 - lr: [2e-05] - max_update: 2036 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/sst_2.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/sst_2.yaml deleted file mode 100644 index a93ad2f22c4c248f043fc18d345d61e9484ed39e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/sst_2.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 2 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - best_checkpoint_metric: accuracy - maximize_best_checkpoint_metric: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - -dataset: - batch_size: 32 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 1256 - -optimization: - clip_norm: 0.0 - lr: [1e-05] - max_update: 20935 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/sts_b.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/sts_b.yaml deleted file mode 100644 index 2d495221ad846162c0b3f15ea6e17d723e7ea754..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/finetuning/sts_b.yaml +++ /dev/null @@ -1,58 +0,0 @@ -# @package _group_ - -common: - fp16: true - fp16_init_scale: 4 - threshold_loss_scale: 1 - fp16_scale_window: 128 - log_format: json - log_interval: 200 - -task: - _name: sentence_prediction - data: ??? - init_token: 0 - separator_token: 2 - num_classes: 1 - max_positions: 512 - -checkpoint: - restore_file: ??? - reset_optimizer: true - reset_dataloader: true - reset_meters: true - no_epoch_checkpoints: true - -distributed_training: - find_unused_parameters: true - distributed_world_size: 1 - -criterion: - _name: sentence_prediction - regression_target: true - -dataset: - batch_size: 16 - required_batch_size_multiple: 1 - max_tokens: 4400 - -optimizer: - _name: adam - weight_decay: 0.1 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 214 - -optimization: - clip_norm: 0.0 - lr: [2e-05] - max_update: 3598 - max_epoch: 10 - -model: - _name: roberta - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/pretraining/base.yaml b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/pretraining/base.yaml deleted file mode 100644 index 97829908f740ba6813c895aa32019cc2760c1eb8..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/config/pretraining/base.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# @package _group_ -common: - fp16: true - log_format: json - log_interval: 200 - -checkpoint: - no_epoch_checkpoints: true - -task: - _name: masked_lm - data: ??? - sample_break_mode: complete - tokens_per_sample: 512 - -criterion: masked_lm - -dataset: - batch_size: 16 - ignore_unused_valid_subsets: true - -optimizer: - _name: adam - weight_decay: 0.01 - adam_betas: (0.9,0.98) - adam_eps: 1e-06 - -lr_scheduler: - _name: polynomial_decay - warmup_updates: 10000 - -optimization: - clip_norm: 0 - lr: [0.0005] - max_update: 125000 - update_freq: [16] - -model: - _name: roberta - max_positions: 512 - dropout: 0.1 - attention_dropout: 0.1 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/multiprocessing_bpe_encoder.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/multiprocessing_bpe_encoder.py deleted file mode 100644 index 43fe0451bf4d5762d734314075b1402c2a8db2bb..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/multiprocessing_bpe_encoder.py +++ /dev/null @@ -1,130 +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. - -import argparse -import contextlib -import sys -from collections import Counter -from multiprocessing import Pool - -from fairseq.data.encoders.gpt2_bpe import get_encoder - - -def main(): - """ - Helper script to encode raw text with the GPT-2 BPE using multiple processes. - - The encoder.json and vocab.bpe files can be obtained here: - - https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json - - https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe - """ - parser = argparse.ArgumentParser() - parser.add_argument( - "--encoder-json", - help="path to encoder.json", - ) - parser.add_argument( - "--vocab-bpe", - type=str, - help="path to vocab.bpe", - ) - 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( - "--keep-empty", - action="store_true", - help="keep empty lines", - ) - parser.add_argument("--workers", type=int, default=20) - args = parser.parse_args() - - assert len(args.inputs) == len( - args.outputs - ), "number of input and output paths should match" - - 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 - ] - - encoder = MultiprocessingEncoder(args) - pool = Pool(args.workers, initializer=encoder.initializer) - encoded_lines = pool.imap(encoder.encode_lines, zip(*inputs), 100) - - stats = Counter() - for i, (filt, enc_lines) in enumerate(encoded_lines, start=1): - if filt == "PASS": - for enc_line, output_h in zip(enc_lines, outputs): - print(enc_line, file=output_h) - else: - stats["num_filtered_" + filt] += 1 - if i % 10000 == 0: - print("processed {} lines".format(i), file=sys.stderr) - - for k, v in stats.most_common(): - print("[{}] filtered {} lines".format(k, v), file=sys.stderr) - - -class MultiprocessingEncoder(object): - def __init__(self, args): - self.args = args - - def initializer(self): - global bpe - bpe = get_encoder(self.args.encoder_json, self.args.vocab_bpe) - - def encode(self, line): - global bpe - ids = bpe.encode(line) - return list(map(str, ids)) - - def decode(self, tokens): - global bpe - return bpe.decode(tokens) - - def encode_lines(self, lines): - """ - Encode a set of lines. All lines will be encoded together. - """ - enc_lines = [] - for line in lines: - line = line.strip() - if len(line) == 0 and not self.args.keep_empty: - return ["EMPTY", None] - tokens = self.encode(line) - enc_lines.append(" ".join(tokens)) - return ["PASS", enc_lines] - - def decode_lines(self, lines): - dec_lines = [] - for line in lines: - tokens = map(int, line.strip().split()) - dec_lines.append(self.decode(tokens)) - return ["PASS", dec_lines] - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_GLUE_tasks.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_GLUE_tasks.sh deleted file mode 100644 index 7f215a3b53e1c4a7b1f0320102915a49d84a5015..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_GLUE_tasks.sh +++ /dev/null @@ -1,185 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -# raw glue data as downloaded by glue download script (https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) -if [[ $# -ne 2 ]]; then - echo "Run as following:" - echo "./examples/roberta/preprocess_GLUE_tasks.sh <glud_data_folder> <task_name>" - exit 1 -fi - -GLUE_DATA_FOLDER=$1 - -# download bpe encoder.json, vocabulary and fairseq dictionary -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' - -TASKS=$2 # QQP - -if [ "$TASKS" = "ALL" ] -then - TASKS="QQP MNLI QNLI MRPC RTE STS-B SST-2 CoLA" -fi - -for TASK in $TASKS -do - echo "Preprocessing $TASK" - - TASK_DATA_FOLDER="$GLUE_DATA_FOLDER/$TASK" - echo "Raw data as downloaded from glue website: $TASK_DATA_FOLDER" - - SPLITS="train dev test" - INPUT_COUNT=2 - if [ "$TASK" = "QQP" ] - then - INPUT_COLUMNS=( 4 5 ) - TEST_INPUT_COLUMNS=( 2 3 ) - LABEL_COLUMN=6 - elif [ "$TASK" = "MNLI" ] - then - SPLITS="train dev_matched dev_mismatched test_matched test_mismatched" - INPUT_COLUMNS=( 9 10 ) - TEST_INPUT_COLUMNS=( 9 10 ) - DEV_LABEL_COLUMN=16 - LABEL_COLUMN=12 - elif [ "$TASK" = "QNLI" ] - then - INPUT_COLUMNS=( 2 3 ) - TEST_INPUT_COLUMNS=( 2 3 ) - LABEL_COLUMN=4 - elif [ "$TASK" = "MRPC" ] - then - INPUT_COLUMNS=( 4 5 ) - TEST_INPUT_COLUMNS=( 4 5 ) - LABEL_COLUMN=1 - elif [ "$TASK" = "RTE" ] - then - INPUT_COLUMNS=( 2 3 ) - TEST_INPUT_COLUMNS=( 2 3 ) - LABEL_COLUMN=4 - elif [ "$TASK" = "STS-B" ] - then - INPUT_COLUMNS=( 8 9 ) - TEST_INPUT_COLUMNS=( 8 9 ) - LABEL_COLUMN=10 - # Following are single sentence tasks. - elif [ "$TASK" = "SST-2" ] - then - INPUT_COLUMNS=( 1 ) - TEST_INPUT_COLUMNS=( 2 ) - LABEL_COLUMN=2 - INPUT_COUNT=1 - elif [ "$TASK" = "CoLA" ] - then - INPUT_COLUMNS=( 4 ) - TEST_INPUT_COLUMNS=( 2 ) - LABEL_COLUMN=2 - INPUT_COUNT=1 - fi - - # Strip out header and filter lines that don't have expected number of fields. - rm -rf "$TASK_DATA_FOLDER/processed" - mkdir -p "$TASK_DATA_FOLDER/processed" - for SPLIT in $SPLITS - do - # CoLA train and dev doesn't have header. - if [[ ( "$TASK" = "CoLA") && ( "$SPLIT" != "test" ) ]] - then - cp "$TASK_DATA_FOLDER/$SPLIT.tsv" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; - else - tail -n +2 "$TASK_DATA_FOLDER/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; - fi - - # Remove unformatted lines from train and dev files for QQP dataset. - if [[ ( "$TASK" = "QQP") && ( "$SPLIT" != "test" ) ]] - then - awk -F '\t' -v NUM_FIELDS=6 'NF==NUM_FIELDS{print}{}' "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp" > "$TASK_DATA_FOLDER/processed/$SPLIT.tsv"; - else - cp "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv"; - fi - rm "$TASK_DATA_FOLDER/processed/$SPLIT.tsv.temp"; - done - - # Split into input0, input1 and label - for SPLIT in $SPLITS - do - for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) - do - if [[ "$SPLIT" != test* ]] - then - COLUMN_NUMBER=${INPUT_COLUMNS[$INPUT_TYPE]} - else - COLUMN_NUMBER=${TEST_INPUT_COLUMNS[$INPUT_TYPE]} - fi - cut -f"$COLUMN_NUMBER" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.raw.input$INPUT_TYPE"; - done - - if [[ "$SPLIT" != test* ]] - then - if [ "$TASK" = "MNLI" ] && [ "$SPLIT" != "train" ] - then - cut -f"$DEV_LABEL_COLUMN" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.label"; - else - cut -f"$LABEL_COLUMN" "$TASK_DATA_FOLDER/processed/$SPLIT.tsv" > "$TASK_DATA_FOLDER/processed/$SPLIT.label"; - fi - fi - - # BPE encode. - for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) - do - LANG="input$INPUT_TYPE" - echo "BPE encoding $SPLIT/$LANG" - python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json encoder.json \ - --vocab-bpe vocab.bpe \ - --inputs "$TASK_DATA_FOLDER/processed/$SPLIT.raw.$LANG" \ - --outputs "$TASK_DATA_FOLDER/processed/$SPLIT.$LANG" \ - --workers 60 \ - --keep-empty; - done - done - - # Remove output directory. - rm -rf "$TASK-bin" - - DEVPREF="$TASK_DATA_FOLDER/processed/dev.LANG" - TESTPREF="$TASK_DATA_FOLDER/processed/test.LANG" - if [ "$TASK" = "MNLI" ] - then - DEVPREF="$TASK_DATA_FOLDER/processed/dev_matched.LANG,$TASK_DATA_FOLDER/processed/dev_mismatched.LANG" - TESTPREF="$TASK_DATA_FOLDER/processed/test_matched.LANG,$TASK_DATA_FOLDER/processed/test_mismatched.LANG" - fi - - # Run fairseq preprocessing: - for INPUT_TYPE in $(seq 0 $((INPUT_COUNT-1))) - do - LANG="input$INPUT_TYPE" - fairseq-preprocess \ - --only-source \ - --trainpref "$TASK_DATA_FOLDER/processed/train.$LANG" \ - --validpref "${DEVPREF//LANG/$LANG}" \ - --testpref "${TESTPREF//LANG/$LANG}" \ - --destdir "$TASK-bin/$LANG" \ - --workers 60 \ - --srcdict dict.txt; - done - if [[ "$TASK" != "STS-B" ]] - then - fairseq-preprocess \ - --only-source \ - --trainpref "$TASK_DATA_FOLDER/processed/train.label" \ - --validpref "${DEVPREF//LANG/label}" \ - --destdir "$TASK-bin/label" \ - --workers 60; - else - # For STS-B output range is converted to be between: [0.0, 1.0] - mkdir -p "$TASK-bin/label" - awk '{print $1 / 5.0 }' "$TASK_DATA_FOLDER/processed/train.label" > "$TASK-bin/label/train.label" - awk '{print $1 / 5.0 }' "$TASK_DATA_FOLDER/processed/dev.label" > "$TASK-bin/label/valid.label" - fi -done diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_RACE.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_RACE.py deleted file mode 100644 index cdd66072718ccb6033304c97926271909a17f9d6..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_RACE.py +++ /dev/null @@ -1,102 +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. - -import argparse -import json -import os -import re - - -class InputExample: - def __init__(self, paragraph, qa_list, label): - self.paragraph = paragraph - self.qa_list = qa_list - self.label = label - - -def get_examples(data_dir, set_type): - """ - Extract paragraph and question-answer list from each json file - """ - examples = [] - - levels = ["middle", "high"] - set_type_c = set_type.split("-") - if len(set_type_c) == 2: - levels = [set_type_c[1]] - set_type = set_type_c[0] - for level in levels: - cur_dir = os.path.join(data_dir, set_type, level) - for filename in os.listdir(cur_dir): - cur_path = os.path.join(cur_dir, filename) - with open(cur_path, "r") as f: - cur_data = json.load(f) - answers = cur_data["answers"] - options = cur_data["options"] - questions = cur_data["questions"] - context = cur_data["article"].replace("\n", " ") - context = re.sub(r"\s+", " ", context) - for i in range(len(answers)): - label = ord(answers[i]) - ord("A") - qa_list = [] - question = questions[i] - for j in range(4): - option = options[i][j] - if "_" in question: - qa_cat = question.replace("_", option) - else: - qa_cat = " ".join([question, option]) - qa_cat = re.sub(r"\s+", " ", qa_cat) - qa_list.append(qa_cat) - examples.append(InputExample(context, qa_list, label)) - - return examples - - -def main(): - """ - Helper script to extract paragraphs questions and answers from RACE datasets. - """ - parser = argparse.ArgumentParser() - parser.add_argument( - "--input-dir", - help="input directory for downloaded RACE dataset", - ) - parser.add_argument( - "--output-dir", - help="output directory for extracted data", - ) - args = parser.parse_args() - - if not os.path.exists(args.output_dir): - os.makedirs(args.output_dir, exist_ok=True) - - for set_type in ["train", "dev", "test-middle", "test-high"]: - examples = get_examples(args.input_dir, set_type) - qa_file_paths = [ - os.path.join(args.output_dir, set_type + ".input" + str(i + 1)) - for i in range(4) - ] - qa_files = [open(qa_file_path, "w") for qa_file_path in qa_file_paths] - outf_context_path = os.path.join(args.output_dir, set_type + ".input0") - outf_label_path = os.path.join(args.output_dir, set_type + ".label") - outf_context = open(outf_context_path, "w") - outf_label = open(outf_label_path, "w") - for example in examples: - outf_context.write(example.paragraph + "\n") - for i in range(4): - qa_files[i].write(example.qa_list[i] + "\n") - outf_label.write(str(example.label) + "\n") - - for f in qa_files: - f.close() - outf_label.close() - outf_context.close() - - -if __name__ == "__main__": - main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_RACE.sh b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_RACE.sh deleted file mode 100644 index 932d2ab6e521fecc7d0297f26a8c43857541ef3b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/preprocess_RACE.sh +++ /dev/null @@ -1,59 +0,0 @@ -#!/bin/bash -# Copyright (c) Facebook, Inc. and 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 should be downloaded and processed with reprocess_RACE.py -if [[ $# -ne 2 ]]; then - echo "Run as following:" - echo "./examples/roberta/preprocess_RACE.sh <race_data_folder> <output_folder>" - exit 1 -fi - -RACE_DATA_FOLDER=$1 -OUT_DATA_FOLDER=$2 - -# download bpe encoder.json, vocabulary and fairseq dictionary -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe' -wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt' - -SPLITS="train dev test-middle test-high" -INPUT_TYPES="input0 input1 input2 input3 input4" -for INPUT_TYPE in $INPUT_TYPES -do - for SPLIT in $SPLITS - do - echo "BPE encoding $SPLIT/$INPUT_TYPE" - python -m examples.roberta.multiprocessing_bpe_encoder \ - --encoder-json encoder.json \ - --vocab-bpe vocab.bpe \ - --inputs "$RACE_DATA_FOLDER/$SPLIT.$INPUT_TYPE" \ - --outputs "$RACE_DATA_FOLDER/$SPLIT.$INPUT_TYPE.bpe" \ - --workers 10 \ - --keep-empty; - - done -done - -for INPUT_TYPE in $INPUT_TYPES - do - LANG="input$INPUT_TYPE" - fairseq-preprocess \ - --only-source \ - --trainpref "$RACE_DATA_FOLDER/train.$INPUT_TYPE.bpe" \ - --validpref "$RACE_DATA_FOLDER/dev.$INPUT_TYPE.bpe" \ - --testpref "$RACE_DATA_FOLDER/test-middle.$INPUT_TYPE.bpe,$RACE_DATA_FOLDER/test-high.$INPUT_TYPE.bpe" \ - --destdir "$OUT_DATA_FOLDER/$INPUT_TYPE" \ - --workers 10 \ - --srcdict dict.txt; -done - -rm -rf "$OUT_DATA_FOLDER/label" -mkdir -p "$OUT_DATA_FOLDER/label" -cp "$RACE_DATA_FOLDER/train.label" "$OUT_DATA_FOLDER/label/" -cp "$RACE_DATA_FOLDER/dev.label" "$OUT_DATA_FOLDER/label/valid.label" -cp "$RACE_DATA_FOLDER/test-middle.label" "$OUT_DATA_FOLDER/label/test.label" -cp "$RACE_DATA_FOLDER/test-high.label" "$OUT_DATA_FOLDER/label/test1.label" diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/README.md deleted file mode 100644 index 21a045d999739836a17574593292e42131315ae9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/README.md +++ /dev/null @@ -1,125 +0,0 @@ -# Finetuning RoBERTa on Winograd Schema Challenge (WSC) data - -The following instructions can be used to finetune RoBERTa on the WSC training -data provided by [SuperGLUE](https://super.gluebenchmark.com/). - -Note that there is high variance in the results. For our GLUE/SuperGLUE -submission we swept over the learning rate (1e-5, 2e-5, 3e-5), batch size (16, -32, 64) and total number of updates (500, 1000, 2000, 3000), as well as the -random seed. Out of ~100 runs we chose the best 7 models and ensembled them. - -**Approach:** The instructions below use a slightly different loss function than -what's described in the original RoBERTa arXiv paper. In particular, -[Kocijan et al. (2019)](https://arxiv.org/abs/1905.06290) introduce a margin -ranking loss between `(query, candidate)` pairs with tunable hyperparameters -alpha and beta. This is supported in our code as well with the `--wsc-alpha` and -`--wsc-beta` arguments. However, we achieved slightly better (and more robust) -results on the development set by instead using a single cross entropy loss term -over the log-probabilities for the query and all mined candidates. **The -candidates are mined using spaCy from each input sentence in isolation, so the -approach remains strictly pointwise.** This reduces the number of -hyperparameters and our best model achieved 92.3% development set accuracy, -compared to ~90% accuracy for the margin loss. Later versions of the RoBERTa -arXiv paper will describe this updated formulation. - -### 1) Download the WSC data from the SuperGLUE website: -```bash -wget https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WSC.zip -unzip WSC.zip - -# we also need to copy the RoBERTa dictionary into the same directory -wget -O WSC/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt -``` - -### 2) Finetune over the provided training data: -```bash -TOTAL_NUM_UPDATES=2000 # Total number of training steps. -WARMUP_UPDATES=250 # Linearly increase LR over this many steps. -LR=2e-05 # Peak LR for polynomial LR scheduler. -MAX_SENTENCES=16 # Batch size per GPU. -SEED=1 # Random seed. -ROBERTA_PATH=/path/to/roberta/model.pt - -# we use the --user-dir option to load the task and criterion -# from the examples/roberta/wsc directory: -FAIRSEQ_PATH=/path/to/fairseq -FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc - -CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train WSC/ \ - --restore-file $ROBERTA_PATH \ - --reset-optimizer --reset-dataloader --reset-meters \ - --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --valid-subset val \ - --fp16 --ddp-backend legacy_ddp \ - --user-dir $FAIRSEQ_USER_DIR \ - --task wsc --criterion wsc --wsc-cross-entropy \ - --arch roberta_large --bpe gpt2 --max-positions 512 \ - --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ - --lr-scheduler polynomial_decay --lr $LR \ - --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \ - --batch-size $MAX_SENTENCES \ - --max-update $TOTAL_NUM_UPDATES \ - --log-format simple --log-interval 100 \ - --seed $SEED -``` - -The above command assumes training on 4 GPUs, but you can achieve the same -results on a single GPU by adding `--update-freq=4`. - -### 3) Evaluate -```python -from fairseq.models.roberta import RobertaModel -from examples.roberta.wsc import wsc_utils # also loads WSC task and criterion -roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'WSC/') -roberta.cuda() -nsamples, ncorrect = 0, 0 -for sentence, label in wsc_utils.jsonl_iterator('WSC/val.jsonl', eval=True): - pred = roberta.disambiguate_pronoun(sentence) - nsamples += 1 - if pred == label: - ncorrect += 1 -print('Accuracy: ' + str(ncorrect / float(nsamples))) -# Accuracy: 0.9230769230769231 -``` - -## RoBERTa training on WinoGrande dataset -We have also provided `winogrande` task and criterion for finetuning on the -[WinoGrande](https://mosaic.allenai.org/projects/winogrande) like datasets -where there are always two candidates and one is correct. -It's more efficient implementation for such subcases. - -```bash -TOTAL_NUM_UPDATES=23750 # Total number of training steps. -WARMUP_UPDATES=2375 # Linearly increase LR over this many steps. -LR=1e-05 # Peak LR for polynomial LR scheduler. -MAX_SENTENCES=32 # Batch size per GPU. -SEED=1 # Random seed. -ROBERTA_PATH=/path/to/roberta/model.pt - -# we use the --user-dir option to load the task and criterion -# from the examples/roberta/wsc directory: -FAIRSEQ_PATH=/path/to/fairseq -FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc - -cd fairseq -CUDA_VISIBLE_DEVICES=0 fairseq-train winogrande_1.0/ \ - --restore-file $ROBERTA_PATH \ - --reset-optimizer --reset-dataloader --reset-meters \ - --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --valid-subset val \ - --fp16 --ddp-backend legacy_ddp \ - --user-dir $FAIRSEQ_USER_DIR \ - --task winogrande --criterion winogrande \ - --wsc-margin-alpha 5.0 --wsc-margin-beta 0.4 \ - --arch roberta_large --bpe gpt2 --max-positions 512 \ - --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \ - --lr-scheduler polynomial_decay --lr $LR \ - --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \ - --batch-size $MAX_SENTENCES \ - --max-update $TOTAL_NUM_UPDATES \ - --log-format simple --log-interval 100 -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/__init__.py deleted file mode 100644 index 78afa4728eeed96142900118f6452730023466c9..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/__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 . import wsc_criterion # noqa -from . import wsc_task # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_criterion.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_criterion.py deleted file mode 100644 index ed0251fdecc3573228ad271f1090aaf914b48cd1..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_criterion.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 math - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.criterions import LegacyFairseqCriterion, register_criterion -from fairseq.data import encoders - - -@register_criterion("wsc") -class WSCCriterion(LegacyFairseqCriterion): - def __init__(self, args, task): - super().__init__(args, task) - if self.args.save_predictions is not None: - self.prediction_h = open(self.args.save_predictions, "w") - else: - self.prediction_h = None - self.bpe = encoders.build_bpe(args.bpe) - self.tokenizer = encoders.build_tokenizer(args.tokenizer) - - def __del__(self): - if self.prediction_h is not None: - self.prediction_h.close() - - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - parser.add_argument("--wsc-margin-alpha", type=float, metavar="A", default=1.0) - parser.add_argument("--wsc-margin-beta", type=float, metavar="B", default=0.0) - parser.add_argument( - "--wsc-cross-entropy", - action="store_true", - help="use cross entropy formulation instead of margin loss", - ) - parser.add_argument( - "--save-predictions", metavar="FILE", help="file to save predictions to" - ) - - def get_masked_input(self, tokens, mask): - masked_tokens = tokens.clone() - masked_tokens[mask] = self.task.mask - return masked_tokens - - def get_lprobs(self, model, tokens, mask): - logits, _ = model(src_tokens=self.get_masked_input(tokens, mask)) - lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float) - scores = lprobs.gather(2, tokens.unsqueeze(-1)).squeeze(-1) - mask = mask.type_as(scores) - scores = (scores * mask).sum(dim=-1) / mask.sum(dim=-1) - return scores - - def get_loss(self, query_lprobs, cand_lprobs): - if self.args.wsc_cross_entropy: - return F.cross_entropy( - torch.cat([query_lprobs, cand_lprobs]).unsqueeze(0), - query_lprobs.new([0]).long(), - ) - else: - return ( - -query_lprobs - + self.args.wsc_margin_alpha - * (cand_lprobs - query_lprobs + self.args.wsc_margin_beta).clamp(min=0) - ).sum() - - def forward(self, model, sample, reduce=True): - # compute loss and accuracy - loss, nloss = 0.0, 0 - ncorrect, nqueries = 0, 0 - - for i, label in enumerate(sample["labels"]): - query_lprobs = self.get_lprobs( - model, - sample["query_tokens"][i].unsqueeze(0), - sample["query_masks"][i].unsqueeze(0), - ) - cand_lprobs = self.get_lprobs( - model, - sample["candidate_tokens"][i], - sample["candidate_masks"][i], - ) - - pred = (query_lprobs >= cand_lprobs).all().item() - - if label is not None: - label = 1 if label else 0 - ncorrect += 1 if pred == label else 0 - nqueries += 1 - - if label: - # only compute a loss for positive instances - nloss += 1 - loss += self.get_loss(query_lprobs, cand_lprobs) - - id = sample["id"][i].item() - if self.prediction_h is not None: - print("{}\t{}\t{}".format(id, pred, label), file=self.prediction_h) - - if nloss == 0: - loss = torch.tensor(0.0, requires_grad=True) - - sample_size = nqueries if nqueries > 0 else 1 - logging_output = { - "loss": utils.item(loss.data) if reduce else loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample["nsentences"], - "sample_size": sample_size, - "ncorrect": ncorrect, - "nqueries": nqueries, - } - return loss, sample_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - - agg_output = { - "loss": loss_sum / sample_size / math.log(2), - "ntokens": ntokens, - "nsentences": nsentences, - "sample_size": sample_size, - } - - ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) - nqueries = sum(log.get("nqueries", 0) for log in logging_outputs) - if nqueries > 0: - agg_output["accuracy"] = ncorrect / float(nqueries) - - return agg_output - - -@register_criterion("winogrande") -class WinograndeCriterion(WSCCriterion): - def forward(self, model, sample, reduce=True): - # compute loss and accuracy - query_lprobs = self.get_lprobs( - model, - sample["query_tokens"], - sample["query_masks"], - ) - cand_lprobs = self.get_lprobs( - model, - sample["candidate_tokens"], - sample["candidate_masks"], - ) - pred = query_lprobs >= cand_lprobs - loss = self.get_loss(query_lprobs, cand_lprobs) - - sample_size = sample["query_tokens"].size(0) - ncorrect = pred.sum().item() - logging_output = { - "loss": utils.item(loss.data) if reduce else loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample["nsentences"], - "sample_size": sample_size, - "ncorrect": ncorrect, - "nqueries": sample_size, - } - return loss, sample_size, logging_output diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_task.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_task.py deleted file mode 100644 index 602ea737ed75a33fddf44dd859e999ecfce2730d..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_task.py +++ /dev/null @@ -1,401 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 numpy as np -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.data import ( - Dictionary, - IdDataset, - ListDataset, - NestedDictionaryDataset, - NumelDataset, - NumSamplesDataset, - PadDataset, - SortDataset, - data_utils, - encoders, -) -from fairseq.tasks import LegacyFairseqTask, register_task - -from . import wsc_utils - - -@register_task("wsc") -class WSCTask(LegacyFairseqTask): - """Task to finetune RoBERTa for Winograd Schemas.""" - - @staticmethod - def add_args(parser): - """Add task-specific arguments to the parser.""" - parser.add_argument( - "data", metavar="DIR", help="path to data directory; we load <split>.jsonl" - ) - parser.add_argument( - "--init-token", - type=int, - default=None, - help="add token at the beginning of each batch item", - ) - - def __init__(self, args, vocab): - super().__init__(args) - self.vocab = vocab - self.mask = vocab.add_symbol("<mask>") - - self.bpe = encoders.build_bpe(args) - self.tokenizer = encoders.build_tokenizer(args) - - # hack to handle GPT-2 BPE, which includes leading spaces - if args.bpe == "gpt2": - self.leading_space = True - self.trailing_space = False - else: - self.leading_space = False - self.trailing_space = True - - @classmethod - def load_dictionary(cls, filename): - """Load the dictionary from the filename - - Args: - filename (str): the filename - """ - dictionary = Dictionary.load(filename) - dictionary.add_symbol("<mask>") - return dictionary - - @classmethod - def setup_task(cls, args, **kwargs): - assert args.criterion == "wsc", "Must set --criterion=wsc" - - # load data and label dictionaries - vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) - print("| dictionary: {} types".format(len(vocab))) - - return cls(args, vocab) - - def binarize(self, s: str, append_eos: bool = False): - if self.tokenizer is not None: - s = self.tokenizer.encode(s) - if self.bpe is not None: - s = self.bpe.encode(s) - tokens = self.vocab.encode_line( - s, - append_eos=append_eos, - add_if_not_exist=False, - ).long() - if self.args.init_token is not None: - tokens = torch.cat([tokens.new([self.args.init_token]), tokens]) - return tokens - - def binarize_with_mask(self, txt, prefix, suffix, leading_space, trailing_space): - toks = self.binarize( - prefix + leading_space + txt + trailing_space + suffix, - append_eos=True, - ) - mask = torch.zeros_like(toks, dtype=torch.bool) - mask_start = len(self.binarize(prefix)) - mask_size = len(self.binarize(leading_space + txt)) - mask[mask_start : mask_start + mask_size] = 1 - return toks, mask - - def load_dataset( - self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs - ): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - if data_path is None: - data_path = os.path.join(self.args.data, split + ".jsonl") - if not os.path.exists(data_path): - raise FileNotFoundError("Cannot find data: {}".format(data_path)) - - query_tokens = [] - query_masks = [] - query_lengths = [] - candidate_tokens = [] - candidate_masks = [] - candidate_lengths = [] - labels = [] - - for sentence, pronoun_span, query, label in wsc_utils.jsonl_iterator(data_path): - prefix = sentence[: pronoun_span.start].text - suffix = sentence[pronoun_span.end :].text_with_ws - - # spaCy spans include trailing spaces, but we need to know about - # leading spaces for the GPT-2 BPE - leading_space = ( - " " if sentence[: pronoun_span.start].text_with_ws.endswith(" ") else "" - ) - trailing_space = " " if pronoun_span.text_with_ws.endswith(" ") else "" - - # get noun phrases, excluding pronouns and anything overlapping with the query - cand_spans = wsc_utils.filter_noun_chunks( - wsc_utils.extended_noun_chunks(sentence), - exclude_pronouns=True, - exclude_query=query, - exact_match=False, - ) - - if query is not None: - query_toks, query_mask = self.binarize_with_mask( - query, prefix, suffix, leading_space, trailing_space - ) - query_len = len(query_toks) - else: - query_toks, query_mask, query_len = None, None, 0 - - query_tokens.append(query_toks) - query_masks.append(query_mask) - query_lengths.append(query_len) - - cand_toks, cand_masks = [], [] - for cand_span in cand_spans: - toks, mask = self.binarize_with_mask( - cand_span.text, - prefix, - suffix, - leading_space, - trailing_space, - ) - cand_toks.append(toks) - cand_masks.append(mask) - - # collate candidates - cand_toks = data_utils.collate_tokens(cand_toks, pad_idx=self.vocab.pad()) - cand_masks = data_utils.collate_tokens(cand_masks, pad_idx=0) - assert cand_toks.size() == cand_masks.size() - - candidate_tokens.append(cand_toks) - candidate_masks.append(cand_masks) - candidate_lengths.append(cand_toks.size(1)) - - labels.append(label) - - query_lengths = np.array(query_lengths) - query_tokens = ListDataset(query_tokens, query_lengths) - query_masks = ListDataset(query_masks, query_lengths) - - candidate_lengths = np.array(candidate_lengths) - candidate_tokens = ListDataset(candidate_tokens, candidate_lengths) - candidate_masks = ListDataset(candidate_masks, candidate_lengths) - - labels = ListDataset(labels, [1] * len(labels)) - - dataset = { - "id": IdDataset(), - "query_tokens": query_tokens, - "query_masks": query_masks, - "candidate_tokens": candidate_tokens, - "candidate_masks": candidate_masks, - "labels": labels, - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(query_tokens, reduce=True), - } - - nested_dataset = NestedDictionaryDataset( - dataset, - sizes=[query_lengths], - ) - - with data_utils.numpy_seed(self.args.seed): - shuffle = np.random.permutation(len(query_tokens)) - dataset = SortDataset( - nested_dataset, - # shuffle - sort_order=[shuffle], - ) - - if return_only: - return dataset - - self.datasets[split] = dataset - return self.datasets[split] - - def build_dataset_for_inference(self, sample_json): - with tempfile.NamedTemporaryFile(buffering=0) as h: - h.write((json.dumps(sample_json) + "\n").encode("utf-8")) - dataset = self.load_dataset( - "disambiguate_pronoun", - data_path=h.name, - return_only=True, - ) - return dataset - - def disambiguate_pronoun(self, model, sentence, use_cuda=False): - sample_json = wsc_utils.convert_sentence_to_json(sentence) - dataset = self.build_dataset_for_inference(sample_json) - sample = dataset.collater([dataset[0]]) - if use_cuda: - sample = utils.move_to_cuda(sample) - - def get_masked_input(tokens, mask): - masked_tokens = tokens.clone() - masked_tokens[mask.bool()] = self.mask - return masked_tokens - - def get_lprobs(tokens, mask): - logits, _ = model(src_tokens=get_masked_input(tokens, mask)) - lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float) - scores = lprobs.gather(2, tokens.unsqueeze(-1)).squeeze(-1) - mask = mask.type_as(scores) - scores = (scores * mask).sum(dim=-1) / mask.sum(dim=-1) - return scores - - cand_lprobs = get_lprobs( - sample["candidate_tokens"][0], - sample["candidate_masks"][0], - ) - if sample["query_tokens"][0] is not None: - query_lprobs = get_lprobs( - sample["query_tokens"][0].unsqueeze(0), - sample["query_masks"][0].unsqueeze(0), - ) - return (query_lprobs >= cand_lprobs).all().item() == 1 - else: - best_idx = cand_lprobs.argmax().item() - full_cand = sample["candidate_tokens"][0][best_idx] - mask = sample["candidate_masks"][0][best_idx] - toks = full_cand[mask.bool()] - return self.bpe.decode(self.source_dictionary.string(toks)).strip() - - @property - def source_dictionary(self): - return self.vocab - - @property - def target_dictionary(self): - return self.vocab - - -@register_task("winogrande") -class WinograndeTask(WSCTask): - """ - Task for WinoGrande dataset. Efficient implementation for Winograd schema - tasks with exactly two candidates, one of which is correct. - """ - - @classmethod - def setup_task(cls, args, **kwargs): - assert args.criterion == "winogrande", "Must set --criterion=winogrande" - - # load data and label dictionaries - vocab = cls.load_dictionary(os.path.join(args.data, "dict.txt")) - print("| dictionary: {} types".format(len(vocab))) - - return cls(args, vocab) - - def load_dataset( - self, split, epoch=1, combine=False, data_path=None, return_only=False, **kwargs - ): - """Load a given dataset split. - - Args: - split (str): name of the split (e.g., train, valid, test) - """ - if data_path is None: - data_path = os.path.join(self.args.data, split + ".jsonl") - if not os.path.exists(data_path): - raise FileNotFoundError("Cannot find data: {}".format(data_path)) - - query_tokens = [] - query_masks = [] - query_lengths = [] - candidate_tokens = [] - candidate_masks = [] - candidate_lengths = [] - - itr = wsc_utils.winogrande_jsonl_iterator(data_path, eval=(split == "test")) - - for sample in itr: - sentence, pronoun_span, query, cand_text = sample - prefix = sentence[: pronoun_span[0]].rstrip() - suffix = sentence[pronoun_span[1] :] - - leading_space = " " if sentence[: pronoun_span[0]].endswith(" ") else "" - trailing_space = "" - - if query is not None: - query_toks, query_mask = self.binarize_with_mask( - query, - prefix, - suffix, - leading_space, - trailing_space, - ) - query_len = len(query_toks) - else: - query_toks, query_mask, query_len = None, None, 0 - - query_tokens.append(query_toks) - query_masks.append(query_mask) - query_lengths.append(query_len) - - cand_toks, cand_mask = self.binarize_with_mask( - cand_text, - prefix, - suffix, - leading_space, - trailing_space, - ) - - candidate_tokens.append(cand_toks) - candidate_masks.append(cand_mask) - candidate_lengths.append(cand_toks.size(0)) - - query_lengths = np.array(query_lengths) - - def get_pad_dataset_fn(tokens, length, pad_idx): - return PadDataset( - ListDataset(tokens, length), - pad_idx=pad_idx, - left_pad=False, - ) - - query_tokens = get_pad_dataset_fn(query_tokens, query_lengths, self.vocab.pad()) - query_masks = get_pad_dataset_fn(query_masks, query_lengths, 0) - - candidate_lengths = np.array(candidate_lengths) - candidate_tokens = get_pad_dataset_fn( - candidate_tokens, candidate_lengths, self.vocab.pad() - ) - candidate_masks = get_pad_dataset_fn(candidate_masks, candidate_lengths, 0) - - dataset = { - "id": IdDataset(), - "query_tokens": query_tokens, - "query_masks": query_masks, - "candidate_tokens": candidate_tokens, - "candidate_masks": candidate_masks, - "nsentences": NumSamplesDataset(), - "ntokens": NumelDataset(query_tokens, reduce=True), - } - - nested_dataset = NestedDictionaryDataset( - dataset, - sizes=[query_lengths], - ) - - with data_utils.numpy_seed(self.args.seed): - shuffle = np.random.permutation(len(query_tokens)) - dataset = SortDataset( - nested_dataset, - # shuffle - sort_order=[shuffle], - ) - - if return_only: - return dataset - - self.datasets[split] = dataset - return self.datasets[split] diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_utils.py b/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_utils.py deleted file mode 100644 index da6ba74383a2490e1108609f315f44ad4b3bf002..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/roberta/wsc/wsc_utils.py +++ /dev/null @@ -1,241 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 -from functools import lru_cache - - -def convert_sentence_to_json(sentence): - if "_" in sentence: - prefix, rest = sentence.split("_", 1) - query, rest = rest.split("_", 1) - query_index = len(prefix.rstrip().split(" ")) - else: - query, query_index = None, None - - prefix, rest = sentence.split("[", 1) - pronoun, rest = rest.split("]", 1) - pronoun_index = len(prefix.rstrip().split(" ")) - - sentence = sentence.replace("_", "").replace("[", "").replace("]", "") - - return { - "idx": 0, - "text": sentence, - "target": { - "span1_index": query_index, - "span1_text": query, - "span2_index": pronoun_index, - "span2_text": pronoun, - }, - } - - -def extended_noun_chunks(sentence): - noun_chunks = {(np.start, np.end) for np in sentence.noun_chunks} - np_start, cur_np = 0, "NONE" - for i, token in enumerate(sentence): - np_type = token.pos_ if token.pos_ in {"NOUN", "PROPN"} else "NONE" - if np_type != cur_np: - if cur_np != "NONE": - noun_chunks.add((np_start, i)) - if np_type != "NONE": - np_start = i - cur_np = np_type - if cur_np != "NONE": - noun_chunks.add((np_start, len(sentence))) - return [sentence[s:e] for (s, e) in sorted(noun_chunks)] - - -def find_token(sentence, start_pos): - found_tok = None - for tok in sentence: - if tok.idx == start_pos: - found_tok = tok - break - return found_tok - - -def find_span(sentence, search_text, start=0): - search_text = search_text.lower() - for tok in sentence[start:]: - remainder = sentence[tok.i :].text.lower() - if remainder.startswith(search_text): - len_to_consume = len(search_text) - start_idx = tok.idx - for next_tok in sentence[tok.i :]: - end_idx = next_tok.idx + len(next_tok.text) - if end_idx - start_idx == len_to_consume: - span = sentence[tok.i : next_tok.i + 1] - return span - return None - - -@lru_cache(maxsize=1) -def get_detokenizer(): - from sacremoses import MosesDetokenizer - - detok = MosesDetokenizer(lang="en") - return detok - - -@lru_cache(maxsize=1) -def get_spacy_nlp(): - import en_core_web_lg - - nlp = en_core_web_lg.load() - return nlp - - -def jsonl_iterator(input_fname, positive_only=False, ngram_order=3, eval=False): - detok = get_detokenizer() - nlp = get_spacy_nlp() - - with open(input_fname) as fin: - for line in fin: - sample = json.loads(line.strip()) - - if positive_only and "label" in sample and not sample["label"]: - # only consider examples where the query is correct - continue - - target = sample["target"] - - # clean up the query - query = target["span1_text"] - if query is not None: - if "\n" in query: - continue - if query.endswith(".") or query.endswith(","): - query = query[:-1] - - # split tokens - tokens = sample["text"].split(" ") - - def strip_pronoun(x): - return x.rstrip('.,"') - - # find the pronoun - pronoun_idx = target["span2_index"] - pronoun = strip_pronoun(target["span2_text"]) - if strip_pronoun(tokens[pronoun_idx]) != pronoun: - # hack: sometimes the index is misaligned - if strip_pronoun(tokens[pronoun_idx + 1]) == pronoun: - pronoun_idx += 1 - else: - raise Exception("Misaligned pronoun!") - assert strip_pronoun(tokens[pronoun_idx]) == pronoun - - # split tokens before and after the pronoun - before = tokens[:pronoun_idx] - after = tokens[pronoun_idx + 1 :] - - # the GPT BPE attaches leading spaces to tokens, so we keep track - # of whether we need spaces before or after the pronoun - leading_space = " " if pronoun_idx > 0 else "" - trailing_space = " " if len(after) > 0 else "" - - # detokenize - before = detok.detokenize(before, return_str=True) - pronoun = detok.detokenize([pronoun], return_str=True) - after = detok.detokenize(after, return_str=True) - - # hack: when the pronoun ends in a period (or comma), move the - # punctuation to the "after" part - if pronoun.endswith(".") or pronoun.endswith(","): - after = pronoun[-1] + trailing_space + after - pronoun = pronoun[:-1] - - # hack: when the "after" part begins with a comma or period, remove - # the trailing space - if after.startswith(".") or after.startswith(","): - trailing_space = "" - - # parse sentence with spacy - sentence = nlp(before + leading_space + pronoun + trailing_space + after) - - # find pronoun span - start = len(before + leading_space) - first_pronoun_tok = find_token(sentence, start_pos=start) - pronoun_span = find_span(sentence, pronoun, start=first_pronoun_tok.i) - assert pronoun_span.text == pronoun - - if eval: - # convert to format where pronoun is surrounded by "[]" and - # query is surrounded by "_" - query_span = find_span(sentence, query) - query_with_ws = "_{}_{}".format( - query_span.text, - (" " if query_span.text_with_ws.endswith(" ") else ""), - ) - pronoun_with_ws = "[{}]{}".format( - pronoun_span.text, - (" " if pronoun_span.text_with_ws.endswith(" ") else ""), - ) - if query_span.start < pronoun_span.start: - first = (query_span, query_with_ws) - second = (pronoun_span, pronoun_with_ws) - else: - first = (pronoun_span, pronoun_with_ws) - second = (query_span, query_with_ws) - sentence = ( - sentence[: first[0].start].text_with_ws - + first[1] - + sentence[first[0].end : second[0].start].text_with_ws - + second[1] - + sentence[second[0].end :].text - ) - yield sentence, sample.get("label", None) - else: - yield sentence, pronoun_span, query, sample.get("label", None) - - -def winogrande_jsonl_iterator(input_fname, eval=False): - with open(input_fname) as fin: - for line in fin: - sample = json.loads(line.strip()) - sentence, option1, option2 = ( - sample["sentence"], - sample["option1"], - sample["option2"], - ) - - pronoun_span = (sentence.index("_"), sentence.index("_") + 1) - - if eval: - query, cand = option1, option2 - else: - query = option1 if sample["answer"] == "1" else option2 - cand = option2 if sample["answer"] == "1" else option1 - yield sentence, pronoun_span, query, cand - - -def filter_noun_chunks( - chunks, exclude_pronouns=False, exclude_query=None, exact_match=False -): - if exclude_pronouns: - chunks = [ - np - for np in chunks - if (np.lemma_ != "-PRON-" and not all(tok.pos_ == "PRON" for tok in np)) - ] - - if exclude_query is not None: - excl_txt = [exclude_query.lower()] - filtered_chunks = [] - for chunk in chunks: - lower_chunk = chunk.text.lower() - found = False - for excl in excl_txt: - if ( - not exact_match and (lower_chunk in excl or excl in lower_chunk) - ) or lower_chunk == excl: - found = True - break - if not found: - filtered_chunks.append(chunk) - chunks = filtered_chunks - - return chunks diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/README.md deleted file mode 100644 index 22a1cc47df23c7e0ebbf0ad805031478d1b4a95e..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/README.md +++ /dev/null @@ -1,52 +0,0 @@ -[Better Fine-Tuning by Reducing Representational Collapse](https://arxiv.org/abs/2008.03156) -===================== -This repo contains the code to replicate all experiments from the _Better Fine-Tuning by Reducing Representational Collapse_ paper excluding the probing results. - -The R3F sentence prediction criterion is registered as `sentence_prediction_r3f` while the label smoothing version of it is implemented as `label_smoothed_cross_entropy_r3f`. The R4F version of the sentence prediction criterion can be achieved by applying spectral norm to the classification head via the `--spectral-norm-classification-head` parameter. - -## Hyper-parameters -Our methods introduce 3 new hyper-parameters; `--eps` which sets the standard deviation or range of the distribution we're sampling from, `--r3f-lambda` which controls the combining of logistic loss and noisy KL loss and `--noise-type` which controls which parametric distribution we use ('normal', 'uniform'). - -For example to run R3F on RTE from GLUE - -``` -TOTAL_NUM_UPDATES=3120 -WARMUP_UPDATES=187 -LR=1e-05 -NUM_CLASSES=2 -MAX_SENTENCES=8 # Batch size. -ROBERTA_PATH=/path/to/roberta/model.pt - -CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin \ - --restore-file $ROBERTA_PATH \ - --max-positions 512 \ - --max-sentences $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction_r3f \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --find-unused-parameters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \ - --noise-type uniform --r3f-lambda 0.7 \ - --user-dir examples/rxf/rxf_src -``` - -## Citation -```bibtex -@article{aghajanyan2020better, - title={Better Fine-Tuning by Reducing Representational Collapse}, - author={Aghajanyan, Armen and Shrivastava, Akshat and Gupta, Anchit and Goyal, Naman and Zettlemoyer, Luke and Gupta, Sonal}, - journal={arXiv preprint arXiv:2008.03156}, - year={2020} -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/__init__.py deleted file mode 100644 index b24cb6b797b4159c9862bab1f882ee6ae95614ab..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/__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 . import rxf_src # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/__init__.py deleted file mode 100644 index 306e232d6f386b26153864601114e162080dcee4..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/__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 . import label_smoothed_cross_entropy_r3f, sentence_prediction_r3f # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py b/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py deleted file mode 100644 index 079db13e61c5ef46d1b1d288012145148eb0be04..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 import metrics, utils -from fairseq.criterions import FairseqCriterion, register_criterion -from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss - - -@register_criterion("label_smoothed_cross_entropy_r3f") -class LabelSmoothedCrossEntropyR3FCriterion(FairseqCriterion): - def __init__( - self, task, sentence_avg, label_smoothing, eps, r3f_lambda, noise_type - ): - super().__init__(task) - self.sentence_avg = sentence_avg - self.label_smoothing = label_smoothing - self.eps = eps - self.r3f_lambda = r3f_lambda - self.noise_type = noise_type - if self.noise_type in {"normal"}: - self.noise_sampler = torch.distributions.normal.Normal( - loc=0.0, scale=self.eps - ) - elif self.noise_type == "uniform": - self.noise_sampler = torch.distributions.uniform.Uniform( - low=-self.eps, high=self.eps - ) - else: - raise Exception(f"unrecognized noise type {self.noise_type}") - - @staticmethod - def add_args(parser): - """Add criterion-specific arguments to the parser.""" - # fmt: off - parser.add_argument('--label-smoothing', default=0., type=float, metavar='D', - help='epsilon for label smoothing, 0 means no label smoothing') - parser.add_argument('--eps', type=float, default=1e-5, - help='noise eps') - parser.add_argument('--r3f-lambda', type=float, default=1.0, - help='lambda for combining logistic loss and noisy KL loss') - parser.add_argument('--noise-type', type=str, default='normal', - choices=['normal', 'uniform'], - help='type of noises') - # fmt: on - - def _get_symm_kl(self, noised_logits, input_logits): - return ( - F.kl_div( - F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), - F.softmax(input_logits, dim=-1, dtype=torch.float32), - None, - None, - "sum", - ) - + F.kl_div( - F.log_softmax(input_logits, dim=-1, dtype=torch.float32), - F.softmax(noised_logits, dim=-1, dtype=torch.float32), - None, - None, - "sum", - ) - ) / noised_logits.size(0) - - def forward(self, model, sample, reduce=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - token_embeddings = model.encoder.embed_tokens(sample["net_input"]["src_tokens"]) - input_logits, extra = model(**sample["net_input"]) - loss, nll_loss = self.compute_loss( - model, (input_logits, extra), sample, reduce=reduce - ) - sample_size = ( - sample["target"].size(0) if self.sentence_avg else sample["ntokens"] - ) - - if model.training: - noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( - token_embeddings - ) - noised_embeddings = token_embeddings.clone() + noise - - noised_logits, _ = model( - **sample["net_input"], token_embeddings=noised_embeddings - ) - symm_kl = self._get_symm_kl(noised_logits, input_logits) - - if model.training: - symm_kl = symm_kl * sample_size - loss = loss + self.r3f_lambda * symm_kl - - logging_output = { - "loss": loss.data, - "nll_loss": nll_loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample["target"].size(0), - "sample_size": sample_size, - } - - if model.training: - logging_output.update( - symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data - ) - - return loss, sample_size, logging_output - - def compute_loss(self, model, net_output, sample, reduce=True): - lprobs = model.get_normalized_probs(net_output, log_probs=True) - lprobs = lprobs.view(-1, lprobs.size(-1)) - target = model.get_targets(sample, net_output).view(-1, 1) - loss, nll_loss = label_smoothed_nll_loss( - lprobs, - target, - self.label_smoothing, - ignore_index=self.padding_idx, - reduce=reduce, - ) - return loss, nll_loss - - @staticmethod - def reduce_metrics(logging_outputs) -> None: - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) - - metrics.log_scalar("symm_kl", symm_kl_sum / sample_size, sample_size, round=3) - metrics.log_scalar( - "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 - ) - metrics.log_scalar( - "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 - ) - metrics.log_derived( - "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) - ) - - @staticmethod - def logging_outputs_can_be_summed() -> bool: - """ - Whether the logging outputs returned by `forward` can be summed - across workers prior to calling `reduce_metrics`. Setting this - to True will improves distributed training speed. - """ - return True diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/sentence_prediction_r3f.py b/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/sentence_prediction_r3f.py deleted file mode 100644 index 6ecffd6b143debb1c67adccd77a6aaed194ec55a..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/rxf/rxf_src/sentence_prediction_r3f.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 math - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.criterions import FairseqCriterion, register_criterion - - -@register_criterion("sentence_prediction_r3f") -class SentencePredictionR3F(FairseqCriterion): - def __init__( - self, - task, - eps, - r3f_lambda, - noise_type, - classification_head_name, - regression_target, - ): - super().__init__(task) - self.eps = eps - self.r3f_lambda = r3f_lambda - self.noise_type = noise_type - self.classification_head_name = classification_head_name - self.regression_target = regression_target - if self.noise_type in {"normal"}: - self.noise_sampler = torch.distributions.normal.Normal( - loc=0.0, scale=self.eps - ) - elif self.noise_type == "uniform": - self.noise_sampler = torch.distributions.uniform.Uniform( - low=-self.eps, high=self.eps - ) - else: - raise Exception(f"unrecognized noise type {self.noise_type}") - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--eps', type=float, default=1e-5, - help='noise eps') - parser.add_argument('--r3f-lambda', type=float, default=1.0, - help='lambda for combining logistic loss and noisy KL loss') - parser.add_argument('--noise-type', type=str, default='uniform', - choices=['normal', 'uniform'], - help='type of noises for RXF methods') - parser.add_argument('--classification-head-name', - default='sentence_classification_head', - help='name of the classification head to use') - parser.add_argument('--regression-target', action='store_true') - # fmt: on - - def _get_symm_kl(self, noised_logits, input_logits): - return ( - F.kl_div( - F.log_softmax(noised_logits, dim=-1, dtype=torch.float32), - F.softmax(input_logits, dim=-1, dtype=torch.float32), - None, - None, - "sum", - ) - + F.kl_div( - F.log_softmax(input_logits, dim=-1, dtype=torch.float32), - F.softmax(noised_logits, dim=-1, dtype=torch.float32), - None, - None, - "sum", - ) - ) / noised_logits.size(0) - - def forward(self, model, sample, reduce=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - assert ( - hasattr(model, "classification_heads") - and self.classification_head_name in model.classification_heads - ), "model must provide sentence classification head for --criterion=sentence_prediction" - - token_embeddings = model.encoder.sentence_encoder.embed_tokens( - sample["net_input"]["src_tokens"] - ) - input_logits, _ = model( - **sample["net_input"], - features_only=True, - classification_head_name=self.classification_head_name, - token_embeddings=token_embeddings, - ) - if model.training and self.noise_sampler: - noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to( - token_embeddings - ) - noised_embeddings = token_embeddings.detach().clone() + noise - - noised_logits, _ = model( - **sample["net_input"], - features_only=True, - classification_head_name=self.classification_head_name, - token_embeddings=noised_embeddings, - ) - symm_kl = self._get_symm_kl(noised_logits, input_logits) - else: - symm_kl = 0 - - targets = model.get_targets(sample, [input_logits]).view(-1) - sample_size = targets.numel() - - if not self.regression_target: - loss = F.nll_loss( - F.log_softmax(input_logits, dim=-1, dtype=torch.float32), - targets, - reduction="sum", - ) - if model.training: - symm_kl = symm_kl * sample_size - loss = loss + self.r3f_lambda * symm_kl - else: - logits = input_logits.squeeze().float() - targets = targets.float() - loss = F.mse_loss(logits, targets, reduction="sum") - - logging_output = { - "loss": utils.item(loss.data) if reduce else loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample_size, - "sample_size": sample_size, - } - - if not self.regression_target: - preds = input_logits.max(dim=1)[1] - logging_output.update(ncorrect=(preds == targets).sum().item()) - - if model.training and self.noise_sampler: - logging_output.update( - symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data - ) - return loss, sample_size, logging_output - - @staticmethod - def aggregate_logging_outputs(logging_outputs): - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - nsentences = sum(log.get("nsentences", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - - agg_output = { - "loss": loss_sum / sample_size / math.log(2), - "symm_kl": symm_kl_sum / sample_size, - "ntokens": ntokens, - "nsentences": nsentences, - "sample_size": sample_size, - } - - if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]: - ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs) - agg_output.update(accuracy=ncorrect / nsentences) - - if sample_size != ntokens: - agg_output["nll_loss"] = loss_sum / ntokens / math.log(2) - return agg_output diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/scaling_nmt/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/scaling_nmt/README.md deleted file mode 100644 index 0cc3360c3bbd58fe35a51591db8f081fc8576877..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/scaling_nmt/README.md +++ /dev/null @@ -1,114 +0,0 @@ -# Scaling Neural Machine Translation (Ott et al., 2018) - -This page includes instructions for reproducing results from the paper [Scaling Neural Machine Translation (Ott et al., 2018)](https://arxiv.org/abs/1806.00187). - -## Pre-trained models - -Model | Description | Dataset | Download ----|---|---|--- -`transformer.wmt14.en-fr` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2) -`transformer.wmt16.en-de` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2) - -## Training a new model on WMT'16 En-De - -First download the [preprocessed WMT'16 En-De data provided by Google](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8). - -Then: - -##### 1. Extract the WMT'16 En-De data -```bash -TEXT=wmt16_en_de_bpe32k -mkdir -p $TEXT -tar -xzvf wmt16_en_de.tar.gz -C $TEXT -``` - -##### 2. Preprocess the dataset with a joined dictionary -```bash -fairseq-preprocess \ - --source-lang en --target-lang de \ - --trainpref $TEXT/train.tok.clean.bpe.32000 \ - --validpref $TEXT/newstest2013.tok.bpe.32000 \ - --testpref $TEXT/newstest2014.tok.bpe.32000 \ - --destdir data-bin/wmt16_en_de_bpe32k \ - --nwordssrc 32768 --nwordstgt 32768 \ - --joined-dictionary \ - --workers 20 -``` - -##### 3. Train a model -```bash -fairseq-train \ - data-bin/wmt16_en_de_bpe32k \ - --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --max-tokens 3584 \ - --fp16 -``` - -Note that the `--fp16` flag requires you have CUDA 9.1 or greater and a Volta GPU or newer. - -***IMPORTANT:*** You will get better performance by training with big batches and -increasing the learning rate. If you want to train the above model with big batches -(assuming your machine has 8 GPUs): -- add `--update-freq 16` to simulate training on 8x16=128 GPUs -- increase the learning rate; 0.001 works well for big batches - -##### 4. Evaluate - -Now we can evaluate our trained model. - -Note that the original [Attention Is All You Need](https://arxiv.org/abs/1706.03762) -paper used a couple tricks to achieve better BLEU scores. We use these same tricks in -the Scaling NMT paper, so it's important to apply them when reproducing our results. - -First, use the [average_checkpoints.py](/scripts/average_checkpoints.py) script to -average the last few checkpoints. Averaging the last 5-10 checkpoints is usually -good, but you may need to adjust this depending on how long you've trained: -```bash -python scripts/average_checkpoints \ - --inputs /path/to/checkpoints \ - --num-epoch-checkpoints 10 \ - --output checkpoint.avg10.pt -``` - -Next, generate translations using a beam width of 4 and length penalty of 0.6: -```bash -fairseq-generate \ - data-bin/wmt16_en_de_bpe32k \ - --path checkpoint.avg10.pt \ - --beam 4 --lenpen 0.6 --remove-bpe > gen.out -``` - -Finally, we apply the ["compound splitting" script](/scripts/compound_split_bleu.sh) to -add spaces around dashes. For example "Café-Liebhaber" would become three tokens: -"Café - Liebhaber". This typically results in larger BLEU scores, but it is not -appropriate to compare these inflated scores to work which does not include this trick. -This trick was used in the [original AIAYN code](https://github.com/tensorflow/tensor2tensor/blob/fc9335c0203685cbbfe2b30c92db4352d8f60779/tensor2tensor/utils/get_ende_bleu.sh), -so we used it in the Scaling NMT paper as well. That said, it's strongly advised to -report [sacrebleu](https://github.com/mjpost/sacrebleu) scores instead. - -To compute "compound split" tokenized BLEU (not recommended!): -```bash -bash scripts/compound_split_bleu.sh gen.out -# BLEU4 = 29.29, 60.3/35.0/22.8/15.3 (BP=1.000, ratio=1.004, syslen=64763, reflen=64496) -``` - -To compute detokenized BLEU with sacrebleu (preferred): -```bash -bash scripts/sacrebleu.sh wmt14/full en de gen.out -# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt14/full+tok.13a+version.1.4.3 = 28.6 59.3/34.3/22.1/14.9 (BP = 1.000 ratio = 1.016 hyp_len = 63666 ref_len = 62688) -``` - -## Citation - -```bibtex -@inproceedings{ott2018scaling, - title = {Scaling Neural Machine Translation}, - author = {Ott, Myle and Edunov, Sergey and Grangier, David and Auli, Michael}, - booktitle = {Proceedings of the Third Conference on Machine Translation (WMT)}, - year = 2018, -} -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/shuffled_word_order/README.finetuning.md b/audio/safeear/safeear_repo/fairseq_ours/examples/shuffled_word_order/README.finetuning.md deleted file mode 100644 index ecbcb65884640c3327a2cbaef8aad4f3cfe812f7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/shuffled_word_order/README.finetuning.md +++ /dev/null @@ -1,135 +0,0 @@ -# Fine-tuning details - -For each task (GLUE and PAWS), we perform hyperparam search for each model, and report the mean and standard deviation across 5 seeds of the best model. First, get the datasets following the instructions in [RoBERTa fine-tuning README](../roberta/README.glue.md). Alternatively, you can use [huggingface datasets](https://huggingface.co/docs/datasets/) to get the task data: - -```python -from datasets import load_dataset -import pandas as pd -from pathlib import Path - -key2file = { -"paws": { - "loc": "paws_data", - "columns": ["id", "sentence1", "sentence2", "label"], - "train": "train.tsv", - "validation": "dev.tsv", - "test": "test.tsv" - } -} - -task_data = load_dataset("paws", "labeled_final") -task_config = key2file["paws"] -save_path = Path(task_config["loc"]) -save_path.mkdir(exist_ok=True, parents=True) -for key, fl in task_config.items(): - if key in ["loc", "columns"]: - continue - print(f"Reading {key}") - columns = task_config["columns"] - df = pd.DataFrame(task_data[key]) - print(df.columns) - df = df[columns] - print(f"Got {len(df)} records") - save_loc = save_path / fl - print(f"Saving to : {save_loc}") - df.to_csv(save_loc, sep="\t", header=None, index=None) - -``` - -- Preprocess using RoBERTa GLUE preprocessing script, while keeping in mind the column numbers for `sentence1`, `sentence2` and `label` (which is 0,1,2 if you save the data according to the above example.) -- Then, fine-tuning is performed similarly to RoBERTa (for example, in case of RTE): - -```bash -TOTAL_NUM_UPDATES=30875 # 10 epochs through RTE for bsz 16 -WARMUP_UPDATES=1852 # 6 percent of the number of updates -LR=2e-05 # Peak LR for polynomial LR scheduler. -NUM_CLASSES=2 -MAX_SENTENCES=16 # Batch size. -SHUFFLED_ROBERTA_PATH=/path/to/shuffled_roberta/model.pt - -CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin/ \ - --restore-file $SHUFFLED_ROBERTA_PATH \ - --max-positions 512 \ - --batch-size $MAX_SENTENCES \ - --max-tokens 4400 \ - --task sentence_prediction \ - --reset-optimizer --reset-dataloader --reset-meters \ - --required-batch-size-multiple 1 \ - --init-token 0 --separator-token 2 \ - --arch roberta_large \ - --criterion sentence_prediction \ - --num-classes $NUM_CLASSES \ - --dropout 0.1 --attention-dropout 0.1 \ - --weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \ - --clip-norm 0.0 \ - --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \ - --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \ - --max-epoch 10 \ - --find-unused-parameters \ - --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric; -``` - -- `TOTAL_NUM_UPDATES` is computed based on the `--batch_size` value and the dataset size. -- `WARMUP_UPDATES` is computed as 6% of `TOTAL_NUM_UPDATES` -- Best hyperparam of `--lr` and `--batch_size` is reported below: - -## `--lr` - -| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | -| --: | :----------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | -| 0 | original | 2e-05 | 2e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | -| 1 | n_1 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | -| 2 | n_2 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | 1e-05 | 3e-05 | -| 3 | n_3 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 3e-05 | 1e-05 | 1e-05 | 2e-05 | -| 4 | n_4 | 3e-05 | 1e-05 | 2e-05 | 2e-05 | 2e-05 | 1e-05 | 1e-05 | 2e-05 | -| 5 | r512 | 1e-05 | 3e-05 | 2e-05 | 2e-05 | 3e-05 | 2e-05 | 3e-05 | 2e-05 | -| 6 | rand_corpus | 2e-05 | 1e-05 | 3e-05 | 1e-05 | 3e-05 | 3e-05 | 3e-05 | 2e-05 | -| 7 | rand_uniform | 2e-05 | 1e-05 | 3e-05 | 2e-05 | 3e-05 | 3e-05 | 3e-05 | 1e-05 | -| 8 | rand_init | 1e-05 | 1e-05 | 3e-05 | 1e-05 | 1e-05 | 1e-05 | 2e-05 | 1e-05 | -| 9 | no_pos | 1e-05 | 3e-05 | 2e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | 1e-05 | - -## `--batch_size` - -| | name | RTE | MRPC | SST-2 | CoLA | QQP | QNLI | MNLI | PAWS | -| --: | :----------- | --: | ---: | ----: | ---: | --: | ---: | ---: | ---: | -| 0 | orig | 16 | 16 | 32 | 16 | 16 | 32 | 32 | 16 | -| 1 | n_1 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 16 | -| 2 | n_2 | 32 | 16 | 32 | 16 | 32 | 32 | 16 | 32 | -| 3 | n_3 | 32 | 32 | 16 | 32 | 32 | 16 | 32 | 32 | -| 4 | n_4 | 32 | 16 | 32 | 16 | 32 | 32 | 32 | 32 | -| 5 | r512 | 32 | 16 | 16 | 32 | 32 | 16 | 16 | 16 | -| 6 | rand_corpus | 16 | 16 | 16 | 16 | 32 | 16 | 16 | 32 | -| 7 | rand_uniform | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | -| 8 | rand_init | 16 | 16 | 32 | 16 | 16 | 16 | 32 | 16 | -| 9 | no_pos | 16 | 32 | 16 | 16 | 32 | 16 | 16 | 16 | - -- Perform inference similar to RoBERTa as well: - -```python -from fairseq.models.roberta import RobertaModel - -roberta = RobertaModel.from_pretrained( - 'checkpoints/', - checkpoint_file='checkpoint_best.pt', - data_name_or_path='PAWS-bin' -) - -label_fn = lambda label: roberta.task.label_dictionary.string( - [label + roberta.task.label_dictionary.nspecial] -) -ncorrect, nsamples = 0, 0 -roberta.cuda() -roberta.eval() -with open('paws_data/dev.tsv') as fin: - fin.readline() - for index, line in enumerate(fin): - tokens = line.strip().split('\t') - sent1, sent2, target = tokens[0], tokens[1], tokens[2] - tokens = roberta.encode(sent1, sent2) - prediction = roberta.predict('sentence_classification_head', tokens).argmax().item() - prediction_label = label_fn(prediction) - ncorrect += int(prediction_label == target) - nsamples += 1 -print('| Accuracy: ', float(ncorrect)/float(nsamples)) - -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/shuffled_word_order/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/shuffled_word_order/README.md deleted file mode 100644 index 6ce0b3927dcec6b375149221d0c0511e9401d70b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/shuffled_word_order/README.md +++ /dev/null @@ -1,94 +0,0 @@ -# Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little - -[https://arxiv.org/abs/2104.06644](https://arxiv.org/abs/2104.06644) - -## Introduction - -In this work, we pre-train [RoBERTa](../roberta) base on various word shuffled variants of BookWiki corpus (16GB). We observe that a word shuffled pre-trained model achieves surprisingly good scores on GLUE, PAWS and several parametric probing tasks. Please read our paper for more details on the experiments. - -## Pre-trained models - -| Model | Description | Download | -| ------------------------------------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | -| `roberta.base.orig` | RoBERTa (base) trained on natural corpus | [roberta.base.orig.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.orig.tar.gz) | -| `roberta.base.shuffle.n1` | RoBERTa (base) trained on n=1 gram sentence word shuffled data | [roberta.base.shuffle.n1.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.tar.gz) | -| `roberta.base.shuffle.n2` | RoBERTa (base) trained on n=2 gram sentence word shuffled data | [roberta.base.shuffle.n2.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n2.tar.gz) | -| `roberta.base.shuffle.n3` | RoBERTa (base) trained on n=3 gram sentence word shuffled data | [roberta.base.shuffle.n3.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n3.tar.gz) | -| `roberta.base.shuffle.n4` | RoBERTa (base) trained on n=4 gram sentence word shuffled data | [roberta.base.shuffle.n4.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n4.tar.gz) | -| `roberta.base.shuffle.512` | RoBERTa (base) trained on unigram 512 word block shuffled data | [roberta.base.shuffle.512.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.512.tar.gz) | -| `roberta.base.shuffle.corpus` | RoBERTa (base) trained on unigram corpus word shuffled data | [roberta.base.shuffle.corpus.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus.tar.gz) | -| `roberta.base.shuffle.corpus_uniform` | RoBERTa (base) trained on unigram corpus word shuffled data, where all words are uniformly sampled | [roberta.base.shuffle.corpus_uniform.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus_uniform.tar.gz) | -| `roberta.base.nopos` | RoBERTa (base) without positional embeddings, trained on natural corpus | [roberta.base.nopos.tar.gz](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.nopos.tar.gz) | - -## Results - -[GLUE (Wang et al, 2019)](https://gluebenchmark.com/) & [PAWS (Zhang et al, 2019)](https://github.com/google-research-datasets/paws) _(dev set, single model, single-task fine-tuning, median of 5 seeds)_ - -| name | CoLA | MNLI | MRPC | PAWS | QNLI | QQP | RTE | SST-2 | -| :----------------------------------- | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | -| `roberta.base.orig` | 61.4 | 86.11 | 89.19 | 94.46 | 92.53 | 91.26 | 74.64 | 93.92 | -| `roberta.base.shuffle.n1` | 35.15 | 82.64 | 86 | 89.97 | 89.02 | 91.01 | 69.02 | 90.47 | -| `roberta.base.shuffle.n2` | 54.37 | 83.43 | 86.24 | 93.46 | 90.44 | 91.36 | 70.83 | 91.79 | -| `roberta.base.shuffle.n3` | 48.72 | 83.85 | 86.36 | 94.05 | 91.69 | 91.24 | 70.65 | 92.02 | -| `roberta.base.shuffle.n4` | 58.64 | 83.77 | 86.98 | 94.32 | 91.69 | 91.4 | 70.83 | 92.48 | -| `roberta.base.shuffle.512` | 12.76 | 77.52 | 79.61 | 84.77 | 85.19 | 90.2 | 56.52 | 86.34 | -| `roberta.base.shuffle.corpus` | 0 | 71.9 | 70.52 | 58.52 | 71.11 | 85.52 | 53.99 | 83.35 | -| `roberta.base.shuffle.corpus_random` | 9.19 | 72.33 | 70.76 | 58.42 | 77.76 | 85.93 | 53.99 | 84.04 | -| `roberta.base.nopos` | 0 | 63.5 | 72.73 | 57.08 | 77.72 | 87.87 | 54.35 | 83.24 | - -For more results on probing tasks, please refer to [our paper](https://arxiv.org/abs/2104.06644). - -## Example Usage - -Follow the same usage as in [RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta) to load and test your models: - -```python -# Download roberta.base.shuffle.n1 model -wget https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.tar.gz -tar -xzvf roberta.base.shuffle.n1.tar.gz -# Copy the dictionary files -cd roberta.base.shuffle.n1.tar.gz -wget -O dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt && wget -O encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json && wget -O vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe -cd .. - -# Load the model in fairseq -from fairseq.models.roberta import RobertaModel -roberta = RobertaModel.from_pretrained('/path/to/roberta.base.shuffle.n1', checkpoint_file='model.pt') -roberta.eval() # disable dropout (or leave in train mode to finetune) -``` - -We have also provided a [Google Colab](https://colab.research.google.com/drive/1IJDVfNVWdvRfLjphQKBGzmob84t-OXpm) notebook to demonstrate the loading of the model. The models were trained on top of Fairseq from the following commit: [62cff008ebeeed855093837507d5e6bf52065ee6](https://github.com/pytorch/fairseq/commit/62cff008ebeeed855093837507d5e6bf52065ee6). - -**Note**: The model trained without positional embeddings (`roberta.base.nopos`) is a modified `RoBERTa` model, where the positional embeddings are not used. Thus, the typical `from_pretrained` method on fairseq version of RoBERTa will not be able to load the above model weights. To do so, construct a new `RoBERTaModel` object by setting the flag `use_positional_embeddings` to `False` (or [in the latest code](https://github.com/pytorch/fairseq/blob/main/fairseq/models/roberta/model.py#L543), set `no_token_positional_embeddings` to `True`), and then load the individual weights. - -## Fine-tuning Evaluation - -We provide the trained fine-tuned models on MNLI here for each model above for quick evaluation (1 seed for each model). Please refer to [finetuning details](README.finetuning.md) for the parameters of these models. Follow [RoBERTa](https://github.com/pytorch/fairseq/tree/main/examples/roberta) instructions to evaluate these models. - -| Model | MNLI M Dev Accuracy | Link | -| :----------------------------------------- | :------------------ | :--------------------------------------------------------------------------------------------------------------- | -| `roberta.base.orig.mnli` | 86.14 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.orig.mnli.tar.gz) | -| `roberta.base.shuffle.n1.mnli` | 82.55 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n1.mnli.tar.gz) | -| `roberta.base.shuffle.n2.mnli` | 83.21 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n2.mnli.tar.gz) | -| `roberta.base.shuffle.n3.mnli` | 83.89 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n3.mnli.tar.gz) | -| `roberta.base.shuffle.n4.mnli` | 84.00 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.n4.mnli.tar.gz) | -| `roberta.base.shuffle.512.mnli` | 77.22 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.512.mnli.tar.gz) | -| `roberta.base.shuffle.corpus.mnli` | 71.88 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus.mnli.tar.gz) | -| `roberta.base.shuffle.corpus_uniform.mnli` | 72.46 | [Download](https://dl.fbaipublicfiles.com/unnatural_pretraining/roberta.base.shuffle.corpus_uniform.mnli.tar.gz) | - -## Citation - -```bibtex -@misc{sinha2021masked, - title={Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little}, - author={Koustuv Sinha and Robin Jia and Dieuwke Hupkes and Joelle Pineau and Adina Williams and Douwe Kiela}, - year={2021}, - eprint={2104.06644}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -``` - -## Contact - -For questions and comments, please reach out to Koustuv Sinha (koustuv.sinha@mail.mcgill.ca). diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/README.md b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/README.md deleted file mode 100644 index 62a005e0ec6f15af9015d335e34b45df6ed89b6c..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Simultaneous Translation -Examples of simultaneous translation in fairseq -- [English-to-Japanese text-to-text wait-k model](docs/enja-waitk.md) -- [English-to-Germen text-to-text monotonic multihead attention model](docs/ende-mma.md) -- [English-to-Germen speech-to-text simultaneous translation model](../speech_to_text/docs/simulst_mustc_example.md) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/__init__.py deleted file mode 100644 index 5835316ba9b23c0d99d1a8f109ee047682211546..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/__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 . import models # noqa diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/docs/ende-mma.md b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/docs/ende-mma.md deleted file mode 100644 index 241d604a3b31a37755da68aad6ff47d46891d3fc..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/docs/ende-mma.md +++ /dev/null @@ -1,74 +0,0 @@ -# Simultaneous Machine Translation - -This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS) - -## Prepare Data - -[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh) - -Another example of training an English to Japanese model can be found [here](docs/enja.md) - -## Training - -- MMA-IL - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type infinite_lookback \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-avg 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- MMA-H - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type hard_aligned \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-var 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- wait-k - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type wait-k \ - --waitk-lagging 3 \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/docs/enja-waitk.md b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/docs/enja-waitk.md deleted file mode 100644 index fb9d82576f80b4405564a99774fc98ac2fe6ad3b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/docs/enja-waitk.md +++ /dev/null @@ -1,106 +0,0 @@ -# An example of English to Japaneses Simultaneous Translation System - -This is an example of training and evaluating a transformer *wait-k* English to Japanese simultaneous text-to-text translation model. - -## Data Preparation -This section introduces the data preparation for training and evaluation. -If you only want to evaluate the model, please jump to [Inference & Evaluation](#inference-&-evaluation) - -For illustration, we only use the following subsets of the available data from [WMT20 news translation task](http://www.statmt.org/wmt20/translation-task.html), which results in 7,815,391 sentence pairs. -- News Commentary v16 -- Wiki Titles v3 -- WikiMatrix V1 -- Japanese-English Subtitle Corpus -- The Kyoto Free Translation Task Corpus - -We use WMT20 development data as development set. Training `transformer_vaswani_wmt_en_de_big` model on such amount of data will result in 17.3 BLEU with greedy search and 19.7 with beam (10) search. Notice that a better performance can be achieved with the full WMT training data. - -We use [sentencepiece](https://github.com/google/sentencepiece) toolkit to tokenize the data with a vocabulary size of 32000. -Additionally, we filtered out the sentences longer than 200 words after tokenization. -Assuming the tokenized text data is saved at `${DATA_DIR}`, -we prepare the data binary with the following command. - -```bash -fairseq-preprocess \ - --source-lang en --target-lang ja \ - --trainpref ${DATA_DIR}/train \ - --validpref ${DATA_DIR}/dev \ - --testpref ${DATA_DIR}/test \ - --destdir ${WMT20_ENJA_DATA_BIN} \ - --nwordstgt 32000 --nwordssrc 32000 \ - --workers 20 -``` - -## Simultaneous Translation Model Training -To train a wait-k `(k=10)` model. -```bash -fairseq-train ${WMT20_ENJA_DATA_BIN} \ - --save-dir ${SAVEDIR} - --simul-type waitk \ - --waitk-lagging 10 \ - --max-epoch 70 \ - --arch transformer_monotonic_vaswani_wmt_en_de_big \ - --optimizer adam \ - --adam-betas '(0.9, 0.98)' \ - --lr-scheduler inverse_sqrt \ - --warmup-init-lr 1e-07 \ - --warmup-updates 4000 \ - --lr 0.0005 \ - --stop-min-lr 1e-09 \ - --clip-norm 10.0 \ - --dropout 0.3 \ - --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy \ - --label-smoothing 0.1 \ - --max-tokens 3584 -``` -This command is for training on 8 GPUs. Equivalently, the model can be trained on one GPU with `--update-freq 8`. - -## Inference & Evaluation -First of all, install [SimulEval](https://github.com/facebookresearch/SimulEval) for evaluation. - -```bash -git clone https://github.com/facebookresearch/SimulEval.git -cd SimulEval -pip install -e . -``` - -The following command is for the evaluation. -Assuming the source and reference files are `${SRC_FILE}` and `${REF_FILE}`, the sentencepiece model file for English is saved at `${SRC_SPM_PATH}` - - -```bash -simuleval \ - --source ${SRC_FILE} \ - --target ${TGT_FILE} \ - --data-bin ${WMT20_ENJA_DATA_BIN} \ - --sacrebleu-tokenizer ja-mecab \ - --eval-latency-unit char \ - --no-space \ - --src-splitter-type sentencepiecemodel \ - --src-splitter-path ${SRC_SPM_PATH} \ - --agent ${FAIRSEQ}/examples/simultaneous_translation/agents/simul_trans_text_agent_enja.py \ - --model-path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \ - --output ${OUTPUT} \ - --scores -``` - -The `--data-bin` should be the same in previous sections if you prepare the data from the scratch. -If only for evaluation, a prepared data directory can be found [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_databin.tgz) and a pretrained checkpoint (wait-k=10 model) can be downloaded from [here](https://dl.fbaipublicfiles.com/simultaneous_translation/wmt20_enja_medium_wait10_ckpt.pt). - -The output should look like this: -```bash -{ - "Quality": { - "BLEU": 11.442253287568398 - }, - "Latency": { - "AL": 8.6587861866951, - "AP": 0.7863304776251316, - "DAL": 9.477850951194764 - } -} -``` -The latency is evaluated by characters (`--eval-latency-unit`) on the target side. The latency is evaluated with `sacrebleu` with `MeCab` tokenizer `--sacrebleu-tokenizer ja-mecab`. `--no-space` indicates that do not add space when merging the predicted words. - -If `--output ${OUTPUT}` option is used, the detailed log and scores will be stored under the `${OUTPUT}` directory. diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py deleted file mode 100644 index 61b8beaa58fe732219a2a6c399c8813a7ca45080..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py +++ /dev/null @@ -1,226 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 sentencepiece as spm -import torch -from fairseq import checkpoint_utils, tasks - -try: - from simuleval import DEFAULT_EOS, READ_ACTION, WRITE_ACTION - from simuleval.agents import TextAgent -except ImportError: - print("Please install simuleval 'pip install simuleval'") - - -BOS_PREFIX = "\u2581" - - -class SimulTransTextAgentJA(TextAgent): - """ - Simultaneous Translation - Text agent for Japanese - """ - def __init__(self, args): - - # Whether use gpu - self.gpu = getattr(args, "gpu", False) - - # Max len - self.max_len = args.max_len - - # Load Model - self.load_model_vocab(args) - - # build word splitter - self.build_word_splitter(args) - - self.eos = DEFAULT_EOS - - def initialize_states(self, states): - states.incremental_states = dict() - states.incremental_states["online"] = dict() - - def to_device(self, tensor): - if self.gpu: - return tensor.cuda() - else: - return tensor.cpu() - - def load_model_vocab(self, args): - - filename = args.model_path - if not os.path.exists(filename): - raise IOError("Model file not found: {}".format(filename)) - - state = checkpoint_utils.load_checkpoint_to_cpu(filename) - - task_args = state["cfg"]["task"] - task_args.data = args.data_bin - - task = tasks.setup_task(task_args) - - # build model for ensemble - state["cfg"]["model"].load_pretrained_encoder_from = None - state["cfg"]["model"].load_pretrained_decoder_from = None - - self.model = task.build_model(state["cfg"]["model"]) - self.model.load_state_dict(state["model"], strict=True) - self.model.eval() - self.model.share_memory() - - if self.gpu: - self.model.cuda() - - # Set dictionary - self.dict = {} - self.dict["tgt"] = task.target_dictionary - self.dict["src"] = task.source_dictionary - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--model-path', type=str, required=True, - help='path to your pretrained model.') - parser.add_argument("--data-bin", type=str, required=True, - help="Path of data binary") - parser.add_argument("--max-len", type=int, default=100, - help="Max length of translation") - parser.add_argument("--tgt-splitter-type", type=str, default="SentencePiece", - help="Subword splitter type for target text.") - parser.add_argument("--tgt-splitter-path", type=str, default=None, - help="Subword splitter model path for target text.") - parser.add_argument("--src-splitter-type", type=str, default="SentencePiece", - help="Subword splitter type for source text.") - parser.add_argument("--src-splitter-path", type=str, default=None, - help="Subword splitter model path for source text.") - # fmt: on - return parser - - def build_word_splitter(self, args): - self.spm = {} - for lang in ['src', 'tgt']: - if getattr(args, f'{lang}_splitter_type', None): - path = getattr(args, f'{lang}_splitter_path', None) - if path: - self.spm[lang] = spm.SentencePieceProcessor() - self.spm[lang].Load(path) - - def segment_to_units(self, segment, states): - # Split a full word (segment) into subwords (units) - return self.spm['src'].EncodeAsPieces(segment) - - def update_model_encoder(self, states): - if len(states.units.source) == 0: - return - - src_indices = [ - self.dict['src'].index(x) - for x in states.units.source.value - ] - - if states.finish_read(): - # Append the eos index when the prediction is over - src_indices += [self.dict["tgt"].eos_index] - - src_indices = self.to_device( - torch.LongTensor(src_indices).unsqueeze(0) - ) - src_lengths = self.to_device( - torch.LongTensor([src_indices.size(1)]) - ) - - states.encoder_states = self.model.encoder(src_indices, src_lengths) - - torch.cuda.empty_cache() - - def update_states_read(self, states): - # Happens after a read action. - self.update_model_encoder(states) - - def units_to_segment(self, units, states): - # Merge sub words (units) to full word (segment). - # For Japanese, we can directly send - # the untokenized token to server except the BOS token - # with following option - # --sacrebleu-tokenizer MeCab - # --eval-latency-unit char - # --no-space - token = units.value.pop() - - if ( - token == self.dict["tgt"].eos_word - or len(states.segments.target) > self.max_len - ): - return DEFAULT_EOS - - if BOS_PREFIX == token: - return None - if token[0] == BOS_PREFIX: - return token[1:] - else: - return token - - def policy(self, states): - - if not getattr(states, "encoder_states", None): - # No encoder states, read a token first - return READ_ACTION - - # encode previous predicted target tokens - tgt_indices = self.to_device( - torch.LongTensor( - [self.model.decoder.dictionary.eos()] - + [ - self.dict['tgt'].index(x) - for x in states.units.target.value - if x is not None - ] - ).unsqueeze(0) - ) - - # Current steps - states.incremental_states["steps"] = { - "src": states.encoder_states["encoder_out"][0].size(0), - "tgt": 1 + len(states.units.target), - } - - # Online only means the reading is not finished - states.incremental_states["online"]["only"] = ( - torch.BoolTensor([not states.finish_read()]) - ) - - x, outputs = self.model.decoder.forward( - prev_output_tokens=tgt_indices, - encoder_out=states.encoder_states, - incremental_state=states.incremental_states, - ) - - states.decoder_out = x - - torch.cuda.empty_cache() - - if outputs.action == 0: - return READ_ACTION - else: - return WRITE_ACTION - - def predict(self, states): - # Predict target token from decoder states - decoder_states = states.decoder_out - - lprobs = self.model.get_normalized_probs( - [decoder_states[:, -1:]], log_probs=True - ) - - index = lprobs.argmax(dim=-1)[0, 0].item() - - if index != self.dict['tgt'].eos_index: - token = self.dict['tgt'].string([index]) - else: - token = self.dict['tgt'].eos_word - - return token diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/__init__.py deleted file mode 100644 index 2407112e8544a000e8e1177b7e10b864c5d046db..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/__init__.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 importlib -import os - -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - model_name = file[: file.find(".py")] - importlib.import_module( - "examples.simultaneous_translation.models." + model_name - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/convtransformer_simul_trans.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/convtransformer_simul_trans.py deleted file mode 100644 index 33502a740aa57554e3da0035681d125096a1fbf7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/convtransformer_simul_trans.py +++ /dev/null @@ -1,207 +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. - -from typing import Dict, List - -from fairseq import checkpoint_utils -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.speech_to_text import ( - ConvTransformerEncoder, - ConvTransformerModel, - convtransformer_espnet, -) -from fairseq.models.speech_to_text.modules.augmented_memory_attention import ( - AugmentedMemoryConvTransformerEncoder, - SequenceEncoder, - augmented_memory, -) -from fairseq.models.speech_to_text.modules.emformer import ( - NoSegAugmentedMemoryTransformerEncoderLayer, -) -from torch import Tensor, nn - - -@register_model("convtransformer_simul_trans") -class SimulConvTransformerModel(ConvTransformerModel): - """ - Implementation of the paper: - - SimulMT to SimulST: Adapting Simultaneous Text Translation to - End-to-End Simultaneous Speech Translation - - https://www.aclweb.org/anthology/2020.aacl-main.58.pdf - """ - - @staticmethod - def add_args(parser): - super(SimulConvTransformerModel, SimulConvTransformerModel).add_args(parser) - parser.add_argument( - "--train-monotonic-only", - action="store_true", - default=False, - help="Only train monotonic attention", - ) - - @classmethod - def build_decoder(cls, args, task, embed_tokens): - tgt_dict = task.tgt_dict - - from examples.simultaneous_translation.models.transformer_monotonic_attention import ( - TransformerMonotonicDecoder, - ) - - decoder = TransformerMonotonicDecoder(args, tgt_dict, 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 - - -@register_model_architecture( - "convtransformer_simul_trans", "convtransformer_simul_trans_espnet" -) -def convtransformer_simul_trans_espnet(args): - convtransformer_espnet(args) - - -@register_model("convtransformer_augmented_memory") -@augmented_memory -class AugmentedMemoryConvTransformerModel(SimulConvTransformerModel): - @classmethod - def build_encoder(cls, args): - encoder = SequenceEncoder(args, AugmentedMemoryConvTransformerEncoder(args)) - - if getattr(args, "load_pretrained_encoder_from", None) is not None: - encoder = checkpoint_utils.load_pretrained_component_from_model( - component=encoder, checkpoint=args.load_pretrained_encoder_from - ) - - return encoder - - -@register_model_architecture( - "convtransformer_augmented_memory", "convtransformer_augmented_memory" -) -def augmented_memory_convtransformer_espnet(args): - convtransformer_espnet(args) - - -# ============================================================================ # -# Convtransformer -# with monotonic attention decoder -# with emformer encoder -# ============================================================================ # - - -class ConvTransformerEmformerEncoder(ConvTransformerEncoder): - def __init__(self, args): - super().__init__(args) - stride = self.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, - ) - ] - ) - self.conv_transformer_encoder = ConvTransformerEncoder(args) - - def forward(self, src_tokens, src_lengths): - encoder_out: Dict[str, List[Tensor]] = self.conv_transformer_encoder(src_tokens, src_lengths.to(src_tokens.device)) - output = encoder_out["encoder_out"][0] - encoder_padding_masks = encoder_out["encoder_padding_mask"] - - return { - "encoder_out": [output], - # This is because that in the original implementation - # the output didn't consider the last segment as right context. - "encoder_padding_mask": [encoder_padding_masks[0][:, : output.size(0)]] if len(encoder_padding_masks) > 0 - else [], - "encoder_embedding": [], - "encoder_states": [], - "src_tokens": [], - "src_lengths": [], - } - - @staticmethod - def conv_layer_stride(args): - # TODO: make it configurable from the args - return 4 - - -@register_model("convtransformer_emformer") -class ConvtransformerEmformer(SimulConvTransformerModel): - @staticmethod - def add_args(parser): - super(ConvtransformerEmformer, ConvtransformerEmformer).add_args(parser) - - parser.add_argument( - "--segment-length", - type=int, - metavar="N", - help="length of each segment (not including left context / right context)", - ) - parser.add_argument( - "--segment-left-context", - type=int, - help="length of left context in a segment", - ) - parser.add_argument( - "--segment-right-context", - type=int, - help="length of right context in a segment", - ) - parser.add_argument( - "--max-memory-size", - type=int, - default=-1, - help="Right context for the segment.", - ) - parser.add_argument( - "--amtrf-tanh-on-mem", - default=False, - action="store_true", - help="whether to use tanh on memory vector", - ) - - @classmethod - def build_encoder(cls, args): - encoder = ConvTransformerEmformerEncoder(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 - - -@register_model_architecture( - "convtransformer_emformer", - "convtransformer_emformer", -) -def convtransformer_emformer_base(args): - convtransformer_espnet(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/transformer_monotonic_attention.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/transformer_monotonic_attention.py deleted file mode 100644 index 2a755bfcc50f94510ae0d5b76b60f68487f43134..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/models/transformer_monotonic_attention.py +++ /dev/null @@ -1,302 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 examples.simultaneous_translation.modules.monotonic_transformer_layer import ( - TransformerMonotonicDecoderLayer, - TransformerMonotonicEncoderLayer, -) -from fairseq.models import ( - register_model, - register_model_architecture, -) -from fairseq.models.transformer import ( - TransformerDecoder, - TransformerEncoder, - TransformerModel, - base_architecture, - tiny_architecture, - transformer_iwslt_de_en, - transformer_vaswani_wmt_en_de_big, -) -from torch import Tensor - -DEFAULT_MAX_SOURCE_POSITIONS = 1024 -DEFAULT_MAX_TARGET_POSITIONS = 1024 -READ_ACTION = 0 -WRITE_ACTION = 1 - -TransformerMonotonicDecoderOut = NamedTuple( - "TransformerMonotonicDecoderOut", - [ - ("action", int), - ("p_choose", Optional[Tensor]), - ("attn_list", Optional[List[Optional[Dict[str, Tensor]]]]), - ("encoder_out", Optional[Dict[str, List[Tensor]]]), - ("encoder_padding_mask", Optional[Tensor]), - ], -) - - -@register_model("transformer_unidirectional") -class TransformerUnidirectionalModel(TransformerModel): - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return TransformerMonotonicEncoder(args, src_dict, embed_tokens) - - -@register_model("transformer_monotonic") -class TransformerModelSimulTrans(TransformerModel): - @classmethod - def build_encoder(cls, args, src_dict, embed_tokens): - return TransformerMonotonicEncoder(args, src_dict, embed_tokens) - - @classmethod - def build_decoder(cls, args, tgt_dict, embed_tokens): - return TransformerMonotonicDecoder(args, tgt_dict, embed_tokens) - - -class TransformerMonotonicEncoder(TransformerEncoder): - def __init__(self, args, dictionary, embed_tokens): - super().__init__(args, dictionary, embed_tokens) - - self.dictionary = dictionary - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - TransformerMonotonicEncoderLayer(args) - for i in range(args.encoder_layers) - ] - ) - - -class TransformerMonotonicDecoder(TransformerDecoder): - """ - 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, args, dictionary, embed_tokens, no_encoder_attn=False): - super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False) - - self.dictionary = dictionary - self.layers = nn.ModuleList([]) - self.layers.extend( - [ - TransformerMonotonicDecoderLayer(args) - for _ in range(args.decoder_layers) - ] - ) - self.policy_criterion = getattr(args, "policy_criterion", "any") - self.num_updates = None - - def set_num_updates(self, num_updates): - self.num_updates = num_updates - - def pre_attention( - self, - prev_output_tokens, - encoder_out_dict: Dict[str, List[Tensor]], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - 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) - - encoder_out = encoder_out_dict["encoder_out"][0] - - if "encoder_padding_mask" in encoder_out_dict: - encoder_padding_mask = ( - encoder_out_dict["encoder_padding_mask"][0] - if encoder_out_dict["encoder_padding_mask"] - and len(encoder_out_dict["encoder_padding_mask"]) > 0 - else None - ) - else: - encoder_padding_mask = None - - return x, encoder_out, encoder_padding_mask - - def post_attention(self, x): - 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 - - def clean_cache( - self, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], - end_id: Optional[int] = None, - ): - """ - Clean cache in the monotonic layers. - The cache is generated because of a forward pass of decoder has run but no prediction, - so that the self attention key value in decoder is written in the incremental state. - end_id is the last idx of the layers - """ - if end_id is None: - end_id = len(self.layers) - - for index, layer in enumerate(self.layers): - if index < end_id: - layer.prune_incremental_state(incremental_state) - - 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, # unused - alignment_layer: Optional[int] = None, # unused - alignment_heads: Optional[int] = None, # unsed - ): - """ - 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 - """ - # incremental_state = None - assert encoder_out is not None - (x, encoder_outs, encoder_padding_mask) = self.pre_attention( - prev_output_tokens, encoder_out, incremental_state - ) - attn = None - inner_states = [x] - attn_list: List[Optional[Dict[str, Tensor]]] = [] - - p_choose = torch.tensor([1.0]) - - for i, layer in enumerate(self.layers): - - x, attn, _ = layer( - x=x, - encoder_out=encoder_outs, - encoder_padding_mask=encoder_padding_mask, - incremental_state=incremental_state, - self_attn_mask=self.buffered_future_mask(x) - if incremental_state is None - else None, - ) - - inner_states.append(x) - attn_list.append(attn) - - if incremental_state is not None: - if_online = incremental_state["online"]["only"] - assert if_online is not None - if if_online.to(torch.bool): - # Online indicates that the encoder states are still changing - assert attn is not None - if self.policy_criterion == "any": - # Any head decide to read than read - head_read = layer.encoder_attn._get_monotonic_buffer(incremental_state)["head_read"] - assert head_read is not None - if head_read.any(): - # We need to prune the last self_attn saved_state - # if model decide not to read - # otherwise there will be duplicated saved_state - self.clean_cache(incremental_state, i + 1) - - return x, TransformerMonotonicDecoderOut( - action=0, - p_choose=p_choose, - attn_list=None, - encoder_out=None, - encoder_padding_mask=None, - ) - - x = self.post_attention(x) - - return x, TransformerMonotonicDecoderOut( - action=1, - p_choose=p_choose, - attn_list=attn_list, - encoder_out=encoder_out, - encoder_padding_mask=encoder_padding_mask, - ) - - -@register_model_architecture("transformer_monotonic", "transformer_monotonic") -def base_monotonic_architecture(args): - base_architecture(args) - args.encoder_unidirectional = getattr(args, "encoder_unidirectional", False) - - -@register_model_architecture( - "transformer_monotonic", "transformer_monotonic_iwslt_de_en" -) -def transformer_monotonic_iwslt_de_en(args): - transformer_iwslt_de_en(args) - base_monotonic_architecture(args) - - -# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017) -@register_model_architecture( - "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_de_big" -) -def transformer_monotonic_vaswani_wmt_en_de_big(args): - transformer_vaswani_wmt_en_de_big(args) - - -@register_model_architecture( - "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_fr_big" -) -def transformer_monotonic_vaswani_wmt_en_fr_big(args): - transformer_monotonic_vaswani_wmt_en_fr_big(args) - - -@register_model_architecture( - "transformer_unidirectional", "transformer_unidirectional_iwslt_de_en" -) -def transformer_unidirectional_iwslt_de_en(args): - transformer_iwslt_de_en(args) - - -@register_model_architecture("transformer_monotonic", "transformer_monotonic_tiny") -def monotonic_tiny_architecture(args): - tiny_architecture(args) - base_monotonic_architecture(args) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/__init__.py deleted file mode 100644 index eb54df8f9c929ba82154c75ce7827e91c06dee99..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/__init__.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 importlib -import os - -from fairseq import registry - -( - build_monotonic_attention, - register_monotonic_attention, - MONOTONIC_ATTENTION_REGISTRY, - _, -) = registry.setup_registry("--simul-type") - -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - model_name = file[: file.find(".py")] - importlib.import_module( - "examples.simultaneous_translation.modules." + model_name - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/fixed_pre_decision.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/fixed_pre_decision.py deleted file mode 100644 index fe98fa8e1bcc474563c9e0120f436a9ffc05a33b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/fixed_pre_decision.py +++ /dev/null @@ -1,190 +0,0 @@ -import math -from functools import partial -from typing import Dict, Optional - -import torch -import torch.nn.functional as F -from torch import Tensor - -from . import register_monotonic_attention -from .monotonic_multihead_attention import ( - MonotonicAttention, - MonotonicInfiniteLookbackAttention, - WaitKAttention, -) - - -def fixed_pooling_monotonic_attention(monotonic_attention): - def create_model(monotonic_attention, klass): - class FixedStrideMonotonicAttention(monotonic_attention): - def __init__(self, args): - self.waitk_lagging = 0 - self.num_heads = 0 - self.noise_mean = 0.0 - self.noise_var = 0.0 - super().__init__(args) - self.pre_decision_type = args.fixed_pre_decision_type - self.pre_decision_ratio = args.fixed_pre_decision_ratio - self.pre_decision_pad_threshold = args.fixed_pre_decision_pad_threshold - assert self.pre_decision_ratio > 1 - - if args.fixed_pre_decision_type == "average": - self.pooling_layer = torch.nn.AvgPool1d( - kernel_size=self.pre_decision_ratio, - stride=self.pre_decision_ratio, - ceil_mode=True, - ) - elif args.fixed_pre_decision_type == "last": - - def last(key): - if key.size(2) < self.pre_decision_ratio: - return key - else: - k = key[ - :, - :, - self.pre_decision_ratio - 1:: self.pre_decision_ratio, - ].contiguous() - if key.size(-1) % self.pre_decision_ratio != 0: - k = torch.cat([k, key[:, :, -1:]], dim=-1).contiguous() - return k - - self.pooling_layer = last - else: - raise NotImplementedError - - @staticmethod - def add_args(parser): - super( - FixedStrideMonotonicAttention, FixedStrideMonotonicAttention - ).add_args(parser) - parser.add_argument( - "--fixed-pre-decision-ratio", - type=int, - required=True, - help=( - "Ratio for the fixed pre-decision," - "indicating how many encoder steps will start" - "simultaneous decision making process." - ), - ) - parser.add_argument( - "--fixed-pre-decision-type", - default="average", - choices=["average", "last"], - help="Pooling type", - ) - parser.add_argument( - "--fixed-pre-decision-pad-threshold", - type=float, - default=0.3, - help="If a part of the sequence has pad" - ",the threshold the pooled part is a pad.", - ) - - def insert_zeros(self, x): - bsz_num_heads, tgt_len, src_len = x.size() - stride = self.pre_decision_ratio - weight = F.pad(torch.ones(1, 1, 1).to(x), (stride - 1, 0)) - x_upsample = F.conv_transpose1d( - x.view(-1, src_len).unsqueeze(1), - weight, - stride=stride, - padding=0, - ) - return x_upsample.squeeze(1).view(bsz_num_heads, tgt_len, -1) - - def p_choose( - self, - query: Optional[Tensor], - key: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - assert key is not None - assert query is not None - src_len = key.size(0) - tgt_len = query.size(0) - batch_size = query.size(1) - - key_pool = self.pooling_layer(key.transpose(0, 2)).transpose(0, 2) - - if key_padding_mask is not None: - key_padding_mask_pool = ( - self.pooling_layer(key_padding_mask.unsqueeze(0).float()) - .squeeze(0) - .gt(self.pre_decision_pad_threshold) - ) - # Make sure at least one element is not pad - key_padding_mask_pool[:, 0] = 0 - else: - key_padding_mask_pool = None - - if incremental_state is not None: - # The floor instead of ceil is used for inference - # But make sure the length key_pool at least 1 - if ( - max(1, math.floor(key.size(0) / self.pre_decision_ratio)) - ) < key_pool.size(0): - key_pool = key_pool[:-1] - if key_padding_mask_pool is not None: - key_padding_mask_pool = key_padding_mask_pool[:-1] - - p_choose_pooled = self.p_choose_from_qk( - query, - key_pool, - key_padding_mask_pool, - incremental_state=incremental_state, - ) - - # Upsample, interpolate zeros - p_choose = self.insert_zeros(p_choose_pooled) - - if p_choose.size(-1) < src_len: - # Append zeros if the upsampled p_choose is shorter than src_len - p_choose = torch.cat( - [ - p_choose, - torch.zeros( - p_choose.size(0), - tgt_len, - src_len - p_choose.size(-1) - ).to(p_choose) - ], - dim=2 - ) - else: - # can be larger than src_len because we used ceil before - p_choose = p_choose[:, :, :src_len] - p_choose[:, :, -1] = p_choose_pooled[:, :, -1] - - assert list(p_choose.size()) == [ - batch_size * self.num_heads, - tgt_len, - src_len, - ] - - return p_choose - - FixedStrideMonotonicAttention.__name__ = klass.__name__ - return FixedStrideMonotonicAttention - - return partial(create_model, monotonic_attention) - - -@register_monotonic_attention("waitk_fixed_pre_decision") -@fixed_pooling_monotonic_attention(WaitKAttention) -class WaitKAttentionFixedStride: - pass - - -@register_monotonic_attention("hard_aligned_fixed_pre_decision") -@fixed_pooling_monotonic_attention(MonotonicAttention) -class MonotonicAttentionFixedStride: - pass - - -@register_monotonic_attention("infinite_lookback_fixed_pre_decision") -@fixed_pooling_monotonic_attention(MonotonicInfiniteLookbackAttention) -class MonotonicInfiniteLookbackAttentionFixedStride: - pass diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/monotonic_multihead_attention.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/monotonic_multihead_attention.py deleted file mode 100644 index ea5fc9b3585e8dec1c3085f94d946509ef944354..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/monotonic_multihead_attention.py +++ /dev/null @@ -1,517 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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, Optional - -import torch -import torch.nn as nn -from examples.simultaneous_translation.utils.monotonic_attention import ( - expected_alignment_from_p_choose, - expected_soft_attention, - mass_preservation, -) -from examples.simultaneous_translation.utils.p_choose_strategy import ( - learnable_p_choose, - waitk_p_choose, -) -from fairseq.modules import MultiheadAttention -from torch import Tensor - -from . import register_monotonic_attention - - -@register_monotonic_attention("hard_aligned") -class MonotonicAttention(MultiheadAttention): - """ - Abstract class of monotonic attentions - """ - k_in_proj: Dict[str, nn.Linear] - q_in_proj: Dict[str, nn.Linear] - - def __init__(self, args): - super().__init__( - embed_dim=args.decoder_embed_dim, - num_heads=args.decoder_attention_heads, - kdim=getattr(args, "encoder_embed_dim", None), - vdim=getattr(args, "encoder_embed_dim", None), - dropout=args.attention_dropout, - encoder_decoder_attention=True, - ) - - self.soft_attention = False - - self.eps = getattr(args, "attention_eps", True) - self.mass_preservation = getattr(args, "mass_preservation", True) - - self.noise_type = args.noise_type - self.noise_mean = args.noise_mean - self.noise_var = args.noise_var - - self.energy_bias_init = args.energy_bias_init - self.energy_bias = ( - nn.Parameter(self.energy_bias_init * torch.ones([1])) - if args.energy_bias is True - else 0 - ) - - self.k_in_proj = {"monotonic": self.k_proj} - self.q_in_proj = {"monotonic": self.q_proj} - self.chunk_size = None - - @staticmethod - def add_args(parser): - # fmt: off - parser.add_argument('--no-mass-preservation', action="store_false", - dest="mass_preservation", - help='Do not stay on the last token when decoding') - parser.add_argument('--mass-preservation', action="store_true", - dest="mass_preservation", - help='Stay on the last token when decoding') - parser.set_defaults(mass_preservation=True) - parser.add_argument('--noise-var', type=float, default=1.0, - help='Variance of discretness noise') - parser.add_argument('--noise-mean', type=float, default=0.0, - help='Mean of discretness noise') - parser.add_argument('--noise-type', type=str, default="flat", - help='Type of discretness noise') - parser.add_argument('--energy-bias', action="store_true", - default=False, - help='Bias for energy') - parser.add_argument('--energy-bias-init', type=float, default=-2.0, - help='Initial value of the bias for energy') - parser.add_argument('--attention-eps', type=float, default=1e-6, - help='Epsilon when calculating expected attention') - - def energy_from_qk( - self, - query: Tensor, - key: Tensor, - energy_type: str, - key_padding_mask: Optional[Tensor] = None, - bias: int = 0 - ): - """ - Compute energy from query and key - q_func_value is a tuple looks like - (q_proj_func, q_tensor) - q_tensor size: bsz, tgt_len, emb_dim - k_tensor size: bsz, src_len, emb_dim - key_padding_mask size: bsz, src_len - attn_mask: bsz, src_len - """ - - length, bsz, _ = query.size() - q = self.q_in_proj[energy_type].forward(query) - q = ( - q.contiguous() - .view(length, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - q = q * self.scaling - length, bsz, _ = key.size() - k = self.k_in_proj[energy_type].forward(key) - k = ( - k.contiguous() - .view(length, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - energy = torch.bmm(q, k.transpose(1, 2)) + bias - - if key_padding_mask is not None: - energy = energy.masked_fill( - key_padding_mask.unsqueeze(1).to(torch.bool), - - float("inf") - ) - - return energy - - def p_choose_from_qk(self, query, key, key_padding_mask, incremental_states=None): - monotonic_energy = self.energy_from_qk( - query, - key, - "monotonic", - key_padding_mask=key_padding_mask, - bias=self.energy_bias, - ) - - p_choose = learnable_p_choose( - monotonic_energy, - self.noise_mean, - self.noise_var, - self.training - ) - return p_choose - - def p_choose(self, query, key, key_padding_mask, incremental_states=None): - return self.p_choose_from_qk(self, query, key, key_padding_mask) - - def monotonic_attention_process_infer( - self, - query: Optional[Tensor], - key: Optional[Tensor], - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], - ): - """ - Monotonic attention at inference time - Notice that this function is designed for simuleval not sequence_generator - """ - assert query is not None - assert key is not None - - if query.size(1) != 1: - raise RuntimeError( - "Simultaneous translation models don't support batch decoding." - ) - # 1. compute stepwise probability - p_choose = self.p_choose( - query, key, None, incremental_state - ).squeeze(1) - - # 2. Compute the alpha - src_len = key.size(0) - # Maximum steps allows in this iteration - max_steps = src_len - 1 if self.mass_preservation else src_len - monotonic_cache = self._get_monotonic_buffer(incremental_state) - # Step for each head - monotonic_step = monotonic_cache.get( - 'head_step', - p_choose.new_zeros(1, self.num_heads).long() - ) - assert monotonic_step is not None - finish_read = monotonic_step.eq(max_steps) - p_choose_i = torch.tensor(1) - - while finish_read.sum().item() < self.num_heads: - # p_choose: self.num_heads, src_len - # only choose the p at monotonic steps - # p_choose_i: 1, self.num_heads - p_choose_i = ( - p_choose.gather( - 1, - monotonic_step - .clamp(0, src_len - 1), - ) - ) - - read_one_step = ( - (p_choose_i < 0.5) - .type_as(monotonic_step) - .masked_fill(finish_read, 0) - ) - # 1 x bsz - # sample actions on unfinished seq - # 0 means stay, finish reading - # 1 means leave, continue reading - - monotonic_step += read_one_step - - finish_read = monotonic_step.eq(max_steps) | (read_one_step == 0) - - # p_choose at last steps - p_choose_i = ( - p_choose.gather( - 1, - monotonic_step - .clamp(0, src_len - 1), - ) - ) - - monotonic_cache["head_step"] = monotonic_step - # Whether a head is looking for new input - monotonic_cache["head_read"] = ( - monotonic_step.eq(max_steps) & (p_choose_i < 0.5) - ) - self._set_monotonic_buffer(incremental_state, monotonic_cache) - - # 2. Update alpha - alpha = ( - p_choose - .new_zeros([self.num_heads, src_len]) - .scatter( - 1, - (monotonic_step) - .view(self.num_heads, 1).clamp(0, src_len - 1), - 1 - ) - ) - - if not self.mass_preservation: - alpha = alpha.masked_fill( - (monotonic_step == max_steps) - .view(self.num_heads, 1), - 0 - ) - - # 4. Compute Beta - if self.soft_attention: - monotonic_step = monotonic_step.t() - beta_mask = torch.arange(src_len).expand_as(alpha).gt(monotonic_step).unsqueeze(1) - # If it's soft attention just do softmax on current context - soft_energy = self.energy_from_qk( - query, - key, - "soft" - ) - beta = torch.nn.functional.softmax( - soft_energy.masked_fill(beta_mask, -float("inf")), dim=-1 - ) - # It could happen that a head doesn't move at all - beta = beta.masked_fill(monotonic_step.eq(0).unsqueeze(1), 0) - else: - # If it's hard attention just select the last state - beta = alpha - - return p_choose, alpha, beta - - def monotonic_attention_process_train( - self, - query: Optional[Tensor], - key: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - ): - """ - Calculating monotonic attention process for training - Including: - stepwise probability: p_choose - expected hard alignment: alpha - expected soft attention: beta - """ - assert query is not None - assert key is not None - - # 1. compute stepwise probability - p_choose = self.p_choose_from_qk(query, key, key_padding_mask) - - # 2. compute expected_alignment - alpha = expected_alignment_from_p_choose( - p_choose, - key_padding_mask, - eps=self.eps, - ) - - if self.mass_preservation: - alpha = mass_preservation( - alpha, key_padding_mask - ) - - # 3. compute expected soft attention (soft aligned model only) - if self.soft_attention: - soft_energy = self.energy_from_qk( - query, - key, - "soft", - key_padding_mask=None, - ) - - beta = expected_soft_attention( - alpha, - soft_energy, - padding_mask=key_padding_mask, - chunk_size=self.chunk_size, - eps=self.eps, - ) - else: - beta = alpha - soft_energy = alpha - - return p_choose, alpha, beta, soft_energy - - def forward( - self, - query: Optional[Tensor], - key: Optional[Tensor], - value: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - attn_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - need_weights: bool = True, static_kv: bool = False, need_head_weights: bool = False, - ): - """ - query: tgt_len, bsz, embed_dim - key: src_len, bsz, embed_dim - value: src_len, bsz, embed_dim - """ - - assert attn_mask is None - assert query is not None - assert key is not None - assert value is not None - - tgt_len, bsz, embed_dim = query.size() - src_len = value.size(0) - - if key_padding_mask is not None: - assert not key_padding_mask[:, 0].any(), ( - "Only right padding is supported." - ) - key_padding_mask = ( - key_padding_mask - .unsqueeze(1) - .expand([bsz, self.num_heads, src_len]) - .contiguous() - .view(-1, src_len) - ) - - if incremental_state is not None: - # Inference - ( - p_choose, alpha, beta - ) = self.monotonic_attention_process_infer( - query, key, incremental_state - ) - soft_energy = beta - else: - # Train - ( - p_choose, alpha, beta, soft_energy - ) = self.monotonic_attention_process_train( - query, key, key_padding_mask - ) - - v = self.v_proj(value) - length, bsz, _ = v.size() - v = ( - v.contiguous() - .view(length, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - attn = torch.bmm(beta.type_as(v), v) - - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - - attn = self.out_proj(attn) - - p_choose = p_choose.view(bsz, self.num_heads, tgt_len, src_len) - alpha = alpha.view(bsz, self.num_heads, tgt_len, src_len) - beta = beta.view(bsz, self.num_heads, tgt_len, src_len) - - return attn, { - "p_choose": p_choose, - "alpha": alpha, - "beta": beta, - } - - def _get_monotonic_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]): - maybe_incremental_state = self.get_incremental_state( - incremental_state, - 'monotonic', - ) - if maybe_incremental_state is None: - typed_empty_dict: Dict[str, Optional[Tensor]] = {} - return typed_empty_dict - else: - return maybe_incremental_state - - def _set_monotonic_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], buffer: Dict[str, Optional[Tensor]]): - self.set_incremental_state( - incremental_state, - 'monotonic', - buffer, - ) - - -@register_monotonic_attention("infinite_lookback") -class MonotonicInfiniteLookbackAttention( - MonotonicAttention -): - def __init__(self, args): - super().__init__(args) - self.soft_attention = True - self.init_soft_attention() - - def init_soft_attention(self): - self.k_proj_soft = nn.Linear(self.kdim, self.embed_dim, bias=True) - self.q_proj_soft = nn.Linear(self.embed_dim, self.embed_dim, bias=True) - self.k_in_proj["soft"] = self.k_proj_soft - self.q_in_proj["soft"] = self.q_proj_soft - - if self.qkv_same_dim: - # Empirically observed the convergence to be much better with - # the scaled initialization - nn.init.xavier_uniform_( - self.k_in_proj["soft"].weight, gain=1 / math.sqrt(2) - ) - nn.init.xavier_uniform_( - self.q_in_proj["soft"].weight, gain=1 / math.sqrt(2) - ) - else: - nn.init.xavier_uniform_(self.k_in_proj["soft"].weight) - nn.init.xavier_uniform_(self.q_in_proj["soft"].weight) - - -@register_monotonic_attention("waitk") -class WaitKAttention( - MonotonicInfiniteLookbackAttention -): - """ - STACL: Simultaneous Translation with Implicit Anticipation and - Controllable Latency using Prefix-to-Prefix Framework - https://www.aclweb.org/anthology/P19-1289/ - """ - def __init__(self, args): - super().__init__(args) - self.q_in_proj["soft"] = self.q_in_proj["monotonic"] - self.k_in_proj["soft"] = self.k_in_proj["monotonic"] - - self.waitk_lagging = args.waitk_lagging - assert self.waitk_lagging > 0, ( - f"Lagging has to been larger than 0, get {self.waitk_lagging}." - ) - - @staticmethod - def add_args(parser): - super( - MonotonicInfiniteLookbackAttention, - MonotonicInfiniteLookbackAttention - ).add_args(parser) - - parser.add_argument( - "--waitk-lagging", type=int, required=True, help="Wait K lagging" - ) - - def p_choose_from_qk( - self, - query: Optional[Tensor], - key: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - ): - assert query is not None - assert key is not None - - p_choose = waitk_p_choose( - tgt_len=query.size(0), - src_len=key.size(0), - bsz=query.size(1) * self.num_heads, - waitk_lagging=self.waitk_lagging, - key_padding_mask=key_padding_mask, - incremental_state=incremental_state, - ) - - return p_choose.to(query) - - -@register_monotonic_attention("chunkwise") -class ChunkwiseAttention( - MonotonicInfiniteLookbackAttention -): - def __init__(self, args): - super().__init__(args) - self.chunk_size = args.mocha_chunk_size - assert self.chunk_size > 1 - - @staticmethod - def add_args(parser): - super( - MonotonicInfiniteLookbackAttention - ).add_args(parser) - - parser.add_argument( - "--mocha-chunk-size", type=int, - required=True, help="Mocha chunk size" - ) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/monotonic_transformer_layer.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/monotonic_transformer_layer.py deleted file mode 100644 index 55ebaa9accc33d980ee8a6ba73c3d55b060c0732..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/modules/monotonic_transformer_layer.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) Facebook, Inc. and 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 -from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer -from torch import Tensor - -from . import build_monotonic_attention - - -class TransformerMonotonicEncoderLayer(TransformerEncoderLayer): - def forward(self, x, encoder_padding_mask): - seq_len, _, _ = x.size() - attn_mask = x.new_ones([seq_len, seq_len]).triu(1) - attn_mask = attn_mask.masked_fill(attn_mask.bool(), float("-inf")) - return super().forward(x, encoder_padding_mask, attn_mask) - - -class TransformerMonotonicDecoderLayer(TransformerDecoderLayer): - def __init__(self, args): - super().__init__(args) - - assert args.simul_type is not None, "A --simul-type is needed." - self.encoder_attn = build_monotonic_attention(args) - - def prune_incremental_state( - self, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] - ): - input_buffer = self.self_attn._get_input_buffer(incremental_state) - for key in ["prev_key", "prev_value"]: - input_buffer_key = input_buffer[key] - assert input_buffer_key is not None - if input_buffer_key.size(2) > 1: - input_buffer[key] = input_buffer_key[:, :, :-1, :] - else: - typed_empty_dict: Dict[str, Optional[Tensor]] = {} - input_buffer = typed_empty_dict - break - assert incremental_state is not None - self.self_attn._set_input_buffer(incremental_state, input_buffer) - - def forward( - self, - x, - encoder_out: Optional[Tensor] = None, - encoder_padding_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - prev_self_attn_state: Optional[List[Tensor]] = None, - prev_attn_state: Optional[List[Tensor]] = None, - self_attn_mask: Optional[Tensor] = None, - self_attn_padding_mask: Optional[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, - ) - x = self.dropout_module(x) - x = self.residual_connection(x, residual) - if not self.normalize_before: - x = self.self_attn_layer_norm(x) - - assert self.encoder_attn 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) - x = self.fc2(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.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 diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/tests/test_alignment_train.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/tests/test_alignment_train.py deleted file mode 100644 index 56fad52b922a707339dd086d1f3d13d1b03ebadd..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/tests/test_alignment_train.py +++ /dev/null @@ -1,90 +0,0 @@ -import unittest - -import hypothesis.strategies as st -import numpy as np -import torch -from examples.simultaneous_translation.utils.functions import exclusive_cumprod -from hypothesis import assume, given, settings -from torch.testing._internal.common_utils import TestCase - -TEST_CUDA = torch.cuda.is_available() - - -class AlignmentTrainTest(TestCase): - def _test_custom_alignment_train_ref(self, p_choose, eps): - cumprod_1mp = exclusive_cumprod(1 - p_choose, dim=2, eps=eps) - cumprod_1mp_clamp = torch.clamp(cumprod_1mp, eps, 1.0) - - bsz = p_choose.size(0) - tgt_len = p_choose.size(1) - src_len = p_choose.size(2) - - alpha_0 = p_choose.new_zeros([bsz, 1, src_len]) - alpha_0[:, :, 0] = 1.0 - - previous_alpha = [alpha_0] - - for i in range(tgt_len): - # p_choose: bsz , tgt_len, src_len - # cumprod_1mp_clamp : bsz, tgt_len, src_len - # previous_alpha[i]: bsz, 1, src_len - # alpha_i: bsz, src_len - alpha_i = ( - p_choose[:, i] - * cumprod_1mp[:, i] - * torch.cumsum( - previous_alpha[i][:, 0] / cumprod_1mp_clamp[:, i], dim=1 - ) - ).clamp(0, 1.0) - - previous_alpha.append(alpha_i.unsqueeze(1)) - - # alpha: bsz * num_heads, tgt_len, src_len - alpha = torch.cat(previous_alpha[1:], dim=1) - return alpha - - def _test_custom_alignment_train_impl(self, p_choose, alpha, eps): - if p_choose.is_cuda: - from alignment_train_cuda_binding import ( - alignment_train_cuda, # @manual=//deeplearning/projects/fairseq-py:alignment_train_cuda_binding - ) - alignment_train_cuda(p_choose, alpha, eps) - else: - from alignment_train_cpu_binding import ( - alignment_train_cpu, # @manual=//deeplearning/projects/fairseq-py:alignment_train_cpu_binding - ) - alignment_train_cpu(p_choose, alpha, eps) - - @settings(deadline=None) - @given( - bsz=st.integers(1, 100), - tgt_len=st.integers(1, 100), - src_len=st.integers(1, 550), - device=st.sampled_from(["cpu", "cuda"]), - ) - def test_alignment_train(self, bsz, tgt_len, src_len, device): - eps = 1e-6 - - assume(device == "cpu" or TEST_CUDA) - p_choose = torch.rand(bsz, tgt_len, src_len, device=device) - - # run the alignment with the custom operator - alpha_act = p_choose.new_zeros([bsz, tgt_len, src_len]) - self._test_custom_alignment_train_impl(p_choose, alpha_act, eps) - - # runu the alignment with the ref implementation - alpha_ref = self._test_custom_alignment_train_ref(p_choose, eps) - - # verify the results - alpha_act = alpha_act.cpu().detach().numpy() - alpha_ref = alpha_ref.cpu().detach().numpy() - np.testing.assert_allclose( - alpha_act, - alpha_ref, - atol=1e-3, - rtol=1e-3, - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/tests/test_text_models.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/tests/test_text_models.py deleted file mode 100644 index 4ca6113c1e87467b055d9f6a5dedbbf05ede7fc7..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/tests/test_text_models.py +++ /dev/null @@ -1,403 +0,0 @@ -import argparse -import unittest -from typing import Any, Dict - -import torch -from examples.simultaneous_translation.models import transformer_monotonic_attention - -from tests.test_roberta import FakeTask - -DEFAULT_CONFIG = { - "attention_eps": 1e-6, - "mass_preservation": True, - "noise_type": "flat", - "noise_mean": 0.0, - "noise_var": 1.0, - "energy_bias_init": -2, - "energy_bias": True -} - - -PAD_INDEX = 1 - - -def generate_config(overrides_kv): - new_dict = {key: value for key, value in DEFAULT_CONFIG.items()} - for key, value in overrides_kv.items(): - new_dict[key] = value - return new_dict - - -def make_sample_with_padding(longer_src=False) -> Dict[str, Any]: - tokens_1 = torch.LongTensor( - [ - [2, 10, 11, 12, 13, 14, 15, 10, 11, 12, 13, 14, 15, 2], - [ - 2, 11, 12, 14, 15, 10, 11, 12, 13, 14, 15, 2, - PAD_INDEX, PAD_INDEX - ], - ] - ) - tokens_2 = torch.LongTensor( - [ - [2, 11, 12, 13, 14, 2, PAD_INDEX, PAD_INDEX], - [2, 11, 22, 33, 2, PAD_INDEX, PAD_INDEX, PAD_INDEX] - ] - ) - if longer_src: - src_tokens = tokens_1[:, 1:] - prev_output_tokens = tokens_2 - else: - src_tokens = tokens_2[:, 1:8] - prev_output_tokens = tokens_1 - - src_lengths = src_tokens.ne(PAD_INDEX).sum(dim=1).long() - - sample = { - "net_input": { - "src_tokens": src_tokens, - "prev_output_tokens": prev_output_tokens, - "src_lengths": src_lengths, - }, - "target": prev_output_tokens[:, 1:], - } - return sample - - -def build_transformer_monotonic_attention(**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_monotonic_attention.monotonic_tiny_architecture(args) - - torch.manual_seed(0) - task = FakeTask(args) - return ( - transformer_monotonic_attention - .TransformerModelSimulTrans - .build_model(args, task) - ) - - -def expected_alignment_formula( - p_choose, - mass_perservation=True, - padding_mask=None -): - # Online and Linear-Time Attention by Enforcing Monotonic Alignments - # https://arxiv.org/pdf/1704.00784.pdf - # Eq 18, 19 - bsz, tgt_len, src_len = p_choose.size() - alpha = torch.zeros_like(p_choose) - - if padding_mask is not None: - bsz_pad = padding_mask.size(0) - num_heads = int(bsz / bsz_pad) - padding_mask = ( - padding_mask - .unsqueeze(1) - .expand([bsz_pad, num_heads, src_len]) - .contiguous() - .view(-1, src_len) - ) - - p_choose = p_choose.masked_fill(padding_mask.unsqueeze(1), 0) - - for bsz_i in range(bsz): - for i in range(tgt_len): - for j in range(src_len): - if i == 0: - if j == 0: - # First source token - alpha[bsz_i, i, j] = p_choose[bsz_i, i, j] - else: - # First target token - alpha[bsz_i, i, j] = ( - p_choose[bsz_i, i, j] - * torch.prod( - 1 - p_choose[bsz_i, i, :j] - ) - ) - else: - alpha[bsz_i, i, j] = alpha[bsz_i, i - 1, j] - for k in range(j): - alpha[bsz_i, i, j] += ( - alpha[bsz_i, i - 1, k] - * torch.prod( - 1 - p_choose[bsz_i, i, k:j] - ) - ) - alpha[bsz_i, i, j] *= p_choose[bsz_i, i, j] - - alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0) - - if mass_perservation: - alpha = mass_perservation_formula(alpha, False, padding_mask) - - return alpha - - -def mass_perservation_formula(alpha, left_padding=False, padding_mask=None): - if padding_mask is None or alpha.size(-1) == 1: - if alpha.size(-1) > 1: - alpha[:, :, -1] = 1 - alpha[:, :, :-1].sum(dim=-1) - return alpha - - src_lens = (padding_mask.logical_not()).sum(dim=1).long() - - bsz, tgt_len, src_len = alpha.size() - - assert ( - not left_padding - or (left_padding and (not padding_mask[:, 0].any())) - ) - - alpha = alpha.masked_fill(padding_mask.unsqueeze(1), 0) - - for bsz_i in range(bsz): - if left_padding: - alpha[bsz_i, :, -1] = ( - 1 - alpha[bsz_i, :, :-1].sum(dim=-1) - ) - else: - alpha[bsz_i, :, src_lens[bsz_i] - 1] = ( - 1 - alpha[bsz_i, :, :src_lens[bsz_i] - 1].sum(dim=-1) - ) - - return alpha - - -def expected_soft_attention_formula( - alpha, - soft_energy, - padding_mask=None, - chunksize=1e10, -): - # Monotonic Infinite Lookback Attention for Simultaneous Machine Translation - # https://arxiv.org/pdf/1906.05218.pdf - # Eq 14 - - # Monotonic Chunkwise Attention - # https://arxiv.org/abs/1712.05382 - # Eq 17 - bsz, tgt_len, src_len = alpha.size() - beta = torch.zeros_like(alpha) - - if padding_mask is not None: - bsz_pad = padding_mask.size(0) - num_heads = int(bsz / bsz_pad) - # Expanding for potential head dimension - padding_mask = ( - padding_mask - .unsqueeze(1) - .expand([bsz_pad, num_heads, src_len]) - .contiguous() - .view(-1, src_len) - ) - soft_energy = soft_energy.masked_fill(padding_mask.unsqueeze(1), float('-inf')) - - for bsz_i in range(bsz): - for i in range(tgt_len): - for j in range(src_len): - for k in range(j, min([src_len, j + chunksize])): - if not padding_mask[bsz_i, j]: - beta[bsz_i, i, j] += ( - alpha[bsz_i, i, k] * torch.exp(soft_energy[bsz_i, i, j]) - / torch.sum(torch.exp(soft_energy[bsz_i, i, max([0, k - chunksize + 1]):k + 1])) - ) - return beta - - -class MonotonicAttentionTestAbstractClass(object): - def test_forward(self): - sample = make_sample_with_padding() - out, _ = self.model.forward(**sample["net_input"]) - loss = out.sum() - loss.backward() - - def test_p_choose(self): - sample = make_sample_with_padding() - _, extra_out = self.model.forward(**sample["net_input"]) - for item in extra_out.attn_list: - p_choose = item["p_choose"] - self.assertTrue(p_choose.le(1.0).all()) - self.assertTrue(p_choose.ge(0.0).all()) - - def test_expected_alignment(self): - for longer_src in [True, False]: - sample = make_sample_with_padding(longer_src) - _, extra_out = self.model.forward(**sample["net_input"]) - for item in extra_out.attn_list: - p_choose = item["p_choose"] - alpha_system = item["alpha"] - self.assertTrue(p_choose.size() == alpha_system.size()) - bsz, num_head, tgt_len, src_len = alpha_system.size() - alpha_system = alpha_system.view(-1, tgt_len, src_len) - p_choose = p_choose.view(-1, tgt_len, src_len) - - alpha_real = expected_alignment_formula( - p_choose, - self.model.decoder.layers[0].encoder_attn.mass_preservation, - sample["net_input"]["src_tokens"].eq(PAD_INDEX) - ) - - self.assertTrue( - torch.abs(alpha_system - alpha_real).le(5e-5).all(), - ) - - -class HardMonotonicAttentionTestCase( - unittest.TestCase, - MonotonicAttentionTestAbstractClass -): - def setUp(self): - self.model = build_transformer_monotonic_attention( - **generate_config({"simul_type": "hard_aligned"}) - ) - - -class InfiniteLookbackTestCase( - unittest.TestCase, - MonotonicAttentionTestAbstractClass -): - def setUp(self): - self.model = build_transformer_monotonic_attention( - **generate_config( - { - "simul_type": "infinite_lookback" - } - ) - ) - self.model.train() - - def test_fp16_for_long_input(self): - sample = { - "net_input": { - "src_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0), - "prev_output_tokens": torch.LongTensor([7] * 1000 + [2]).cuda().unsqueeze(0), - "src_lengths": torch.LongTensor([1000]).cuda(), - }, - "target": torch.LongTensor([2] + [7] * 1000).unsqueeze(0).cuda() - } - self.model.cuda().half() - _, extra_out = self.model.forward(**sample["net_input"]) - for item in extra_out.attn_list: - for key in ["p_choose", "alpha", "beta", "soft_energy"]: - self.assertFalse(torch.isnan(item[key]).any()) - - def test_expected_attention(self): - for longer_src in [True, False]: - sample = make_sample_with_padding(longer_src) - _, extra_out = self.model.forward(**sample["net_input"]) - for item in extra_out.attn_list: - p_choose = item["p_choose"] - alpha_system = item["alpha"] - beta_system = item["beta"] - soft_energy_system = item["soft_energy"] - self.assertTrue(beta_system.size() == alpha_system.size()) - self.assertTrue(p_choose.size() == alpha_system.size()) - - bsz, num_head, tgt_len, src_len = alpha_system.size() - - alpha_system = alpha_system.view(-1, tgt_len, src_len) - beta_system = beta_system.view(-1, tgt_len, src_len) - p_choose = p_choose.view(-1, tgt_len, src_len) - soft_energy_system = soft_energy_system.view(-1, tgt_len, src_len) - - alpha_real = expected_alignment_formula( - p_choose, - self.model.decoder.layers[0].encoder_attn.mass_preservation, - sample["net_input"]["src_tokens"].eq(PAD_INDEX) - ) - - beta_real = expected_soft_attention_formula( - alpha_real, - soft_energy_system, - sample["net_input"]["src_tokens"].eq(PAD_INDEX), - chunksize=getattr( - self.model.decoder.layers[0].encoder_attn, - "chunk_size", - int(1e10) - ) - ) - - self.assertTrue( - torch.abs(beta_system - beta_real).le(1e-5).all(), - ) - - -class ChunkwiswTestCase( - InfiniteLookbackTestCase -): - def setUp(self): - self.model = build_transformer_monotonic_attention( - **generate_config( - { - "simul_type": "chunkwise", - "mocha_chunk_size": 3 - } - ) - ) - - -class WaitkTestCase(InfiniteLookbackTestCase): - def setUp(self): - self.model = build_transformer_monotonic_attention( - **generate_config( - { - "simul_type": "waitk", - "waitk_lagging": 3, - } - ) - ) - - def check_waitk(self, p_choose, lagging, padding_mask): - bsz, tgt_len, src_len = p_choose.size() - for bsz_i in range(bsz): - for i in range(tgt_len): - for j in range(src_len): - if not padding_mask[bsz_i, j]: - if j - i == lagging - 1: - self.assertTrue(p_choose[bsz_i, i, j] == 1) - else: - self.assertTrue(p_choose[bsz_i, i, j] == 0) - - def test_waitk_p_choose(self): - for longer_src in [True, False]: - for k in [1, 3, 10, 20, 100]: - sample = make_sample_with_padding(longer_src) - model = build_transformer_monotonic_attention( - **generate_config( - { - "simul_type": "waitk", - "waitk_lagging": k, - } - ) - ) - model.train() - _, extra_out = model.forward(**sample["net_input"]) - for item in extra_out.attn_list: - p_choose = item["p_choose"] - bsz, num_heads, tgt_len, src_len = p_choose.size() - padding_mask = sample["net_input"]["src_tokens"].eq(PAD_INDEX) - padding_mask = ( - padding_mask - .unsqueeze(1) - .expand([bsz, num_heads, src_len]) - .contiguous() - .view(-1, src_len) - ) - p_choose = p_choose.view(bsz * num_heads, tgt_len, src_len) - self.check_waitk(p_choose, k, padding_mask) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/utils/__init__.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/utils/__init__.py deleted file mode 100644 index d16e9815391d54c608a30db4159707662ab3ca28..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/utils/__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. - -import importlib -import os - -# automatically import any Python files in the criterions/ 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("examples.simultaneous_translation.utils." + module) diff --git a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/utils/functions.py b/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/utils/functions.py deleted file mode 100644 index 590a6c11cea222ac9096b19f0e3dfe1b71b6c10b..0000000000000000000000000000000000000000 --- a/audio/safeear/safeear_repo/fairseq_ours/examples/simultaneous_translation/utils/functions.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 torch - - -def prob_check(tensor, eps=1e-10): - assert not torch.isnan(tensor).any(), ( - "Nan in a probability tensor." - ) - # Add the eps here to prevent errors introduced by precision - assert tensor.le(1.0 + eps).all() and tensor.ge(0.0 - eps).all(), ( - "Incorrect values in a probability tensor" - ", 0.0 <= tensor <= 1.0" - ) - - -def exclusive_cumprod(tensor, dim: int, eps: float = 1e-10): - """ - Implementing exclusive cumprod. - There is cumprod in pytorch, however there is no exclusive mode. - cumprod(x) = [x1, x1x2, x2x3x4, ..., prod_{i=1}^n x_i] - exclusive means - cumprod(x) = [1, x1, x1x2, x1x2x3, ..., prod_{i=1}^{n-1} x_i] - """ - tensor_size = list(tensor.size()) - tensor_size[dim] = 1 - return_tensor = safe_cumprod( - torch.cat([torch.ones(tensor_size).type_as(tensor), tensor], dim=dim), - dim=dim, - eps=eps, - ) - - if dim == 0: - return return_tensor[:-1] - elif dim == 1: - return return_tensor[:, :-1] - elif dim == 2: - return return_tensor[:, :, :-1] - else: - raise RuntimeError( - "Cumprod on dimension 3 and more is not implemented" - ) - - -def safe_cumprod(tensor, dim: int, eps: float = 1e-10): - """ - An implementation of cumprod to prevent precision issue. - cumprod(x) - = [x1, x1x2, x1x2x3, ....] - = [exp(log(x1)), exp(log(x1) + log(x2)), exp(log(x1) + log(x2) + log(x3)), ...] - = exp(cumsum(log(x))) - """ - - if (tensor + eps < 0).any().item(): - raise RuntimeError( - "Safe cumprod can only take non-negative tensors as input." - "Consider use torch.cumprod if you want to calculate negative values." - ) - - log_tensor = torch.log(tensor + eps) - cumsum_log_tensor = torch.cumsum(log_tensor, dim) - exp_cumsum_log_tensor = torch.exp(cumsum_log_tensor) - return exp_cumsum_log_tensor - - -def moving_sum(x, start_idx: int, end_idx: int): - """ - From MONOTONIC CHUNKWISE ATTENTION - https://arxiv.org/pdf/1712.05382.pdf - Equation (18) - - x = [x_1, x_2, ..., x_N] - MovingSum(x, start_idx, end_idx)_n = Sigma_{m=n−(start_idx−1)}^{n+end_idx-1} x_m - for n in {1, 2, 3, ..., N} - - x : src_len, batch_size - start_idx : start idx - end_idx : end idx - - Example - src_len = 5 - batch_size = 3 - x = - [[ 0, 5, 10], - [ 1, 6, 11], - [ 2, 7, 12], - [ 3, 8, 13], - [ 4, 9, 14]] - - MovingSum(x, 3, 1) = - [[ 0, 5, 10], - [ 1, 11, 21], - [ 3, 18, 33], - [ 6, 21, 36], - [ 9, 24, 39]] - - MovingSum(x, 1, 3) = - [[ 3, 18, 33], - [ 6, 21, 36], - [ 9, 24, 39], - [ 7, 17, 27], - [ 4, 9, 14]] - """ - # TODO: Make dimension configurable - assert start_idx > 0 and end_idx > 0 - batch_size, tgt_len, src_len = x.size() - x = x.view(-1, src_len).unsqueeze(1) - # batch_size, 1, src_len - moving_sum_weight = torch.ones([1, 1, end_idx + start_idx - 1]).type_as(x) - - moving_sum = torch.nn.functional.conv1d( - x, moving_sum_weight, padding=start_idx + end_idx - 1 - ).squeeze(1) - - moving_sum = moving_sum[:, end_idx:-start_idx] - - assert src_len == moving_sum.size(1) - assert batch_size * tgt_len == moving_sum.size(0) - - moving_sum = moving_sum.view(batch_size, tgt_len, src_len) - - return moving_sum