Input:
# coding=utf-8
# Copyright 2023 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang The HuggingFace Inc. team. 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.
""" PyTorch ErnieM model."""

import math
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn, tensor
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_ernie_m import ErnieMConfig

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "susnato/ernie-m-base_pytorch"
_CONFIG_FOR_DOC = "ErnieMConfig"
_TOKENIZER_FOR_DOC = "ErnieMTokenizer"

ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "susnato/ernie-m-base_pytorch",
    "susnato/ernie-m-large_pytorch",
    # See all ErnieM models at https://huggingface.co/models?filter=ernie_m
]

# Adapted from paddlenlp.transformers.ernie_m.modeling.ErnieEmbeddings
class ErnieMEmbeddings(nn.Module):
    """Construct the embeddings from word and position embeddings."""

    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size, padding_idx=config.pad_token_id
        )
        self.layer_norm = nn.LayerNorm(normalized_shape=config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(p=config.hidden_dropout_prob)
        self.padding_idx = config.pad_token_id

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
        past_key_values_length: int = 0,
    ) -> torch.Tensor:
        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
        if position_ids is None:
            input_shape = inputs_embeds.size()[:-1]
            ones = torch.ones(input_shape, dtype=torch.int64, device=inputs_embeds.device)
            seq_length = torch.cumsum(ones, dim=1)
            position_ids = seq_length - ones

            if past_key_values_length > 0:
                position_ids = position_ids + past_key_values_length
        # to mimic paddlenlp implementation
        position_ids += 2
        position_embeddings = self.position_embeddings(position_ids)
        embeddings = inputs_embeds + position_embeddings
        embeddings = self.layer_norm(embeddings)
        embeddings = self.dropout(embeddings)

        return embeddings

# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->ErnieM,self.value->self.v_proj,self.key->self.k_proj,self.query->self.q_proj
class ErnieMSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.q_proj = nn.Linear(config.hidden_size, self.all_head_size)
        self.k_proj = nn.Linear(config.hidden_size, self.all_head_size)
        self.v_proj = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(
            config, "position_embedding_type", "absolute"
        )
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

        self.is_decoder = config.is_decoder

    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.q_proj(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(self.k_proj(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.v_proj(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.k_proj(hidden_states))
            value_layer = self.transpose_for_scores(self.v_proj(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.k_proj(hidden_states))
            value_layer = self.transpose_for_scores(self.v_proj(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        use_cache = past_key_value is not None
        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
            if use_cache:
                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
                    -1, 1
                )
            else:
                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r

            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in ErnieMModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        if self.is_decoder:
            outputs = outputs + (past_key_value,)
        return outputs

class ErnieMAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self_attn = ErnieMSelfAttention(config, position_embedding_type=position_embedding_type)
        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self_attn.num_attention_heads, self.self_attn.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.self_attn.q_proj = prune_linear_layer(self.self_attn.q_proj, index)
        self.self_attn.k_proj = prune_linear_layer(self.self_attn.k_proj, index)
        self.self_attn.v_proj = prune_linear_layer(self.self_attn.v_proj, index)
        self.out_proj = prune_linear_layer(self.out_proj, index, dim=1)

        # Update hyper params and store pruned heads
        self.self_attn.num_attention_heads = self.self_attn.num_attention_heads - len(heads)
        self.self_attn.all_head_size = self.self_attn.attention_head_size * self.self_attn.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        self_outputs = self.self_attn(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )
        attention_output = self.out_proj(self_outputs[0])
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs

class ErnieMEncoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        # to mimic paddlenlp implementation
        dropout = 0.1 if config.hidden_dropout_prob is None else config.hidden_dropout_prob
        act_dropout = config.hidden_dropout_prob if config.act_dropout is None else config.act_dropout

        self.self_attn = ErnieMAttention(config)
        self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.dropout = nn.Dropout(act_dropout)
        self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size)
        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        if isinstance(config.hidden_act, str):
            self.activation = ACT2FN[config.hidden_act]
        else:
            self.activation = config.hidden_act

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = True,
    ):
        residual = hidden_states
        if output_attentions:
            hidden_states, attention_opt_weights = self.self_attn(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                head_mask=head_mask,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
            )

        else:
            hidden_states = self.self_attn(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                head_mask=head_mask,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
            )
        hidden_states = residual + self.dropout1(hidden_states)
        hidden_states = self.norm1(hidden_states)
        residual = hidden_states

        hidden_states = self.linear1(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.linear2(hidden_states)
        hidden_states = residual + self.dropout2(hidden_states)
        hidden_states = self.norm2(hidden_states)

        if output_attentions:
            return hidden_states, attention_opt_weights
        else:
            return hidden_states

class ErnieMEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList([ErnieMEncoderLayer(config) for _ in range(config.num_hidden_layers)])

    def forward(
        self,
        input_embeds: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
        hidden_states = () if output_hidden_states else None
        attentions = () if output_attentions else None

        output = input_embeds
        if output_hidden_states:
            hidden_states = hidden_states + (output,)
        for i, layer in enumerate(self.layers):
            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            output, opt_attn_weights = layer(
                hidden_states=output,
                attention_mask=attention_mask,
                head_mask=layer_head_mask,
                past_key_value=past_key_value,
            )

            if output_hidden_states:
                hidden_states = hidden_states + (output,)
            if output_attentions:
                attentions = attentions + (opt_attn_weights,)

        last_hidden_state = output
        if not return_dict:
            return tuple(v for v in [last_hidden_state, hidden_states, attentions] if v is not None)

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=attentions
        )

# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->ErnieM
class ErnieMPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output

class ErnieMPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = ErnieMConfig
    base_model_prefix = "ernie_m"

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

ERNIE_M_START_DOCSTRING = r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ErnieMConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

ERNIE_M_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`ErnieMTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare ErnieM Model transformer outputting raw hidden-states without any specific head on top.",
    ERNIE_M_START_DOCSTRING,
)
class ErnieMModel(ErnieMPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super(ErnieMModel, self).__init__(config)
        self.initializer_range = config.initializer_range
        self.embeddings = ErnieMEmbeddings(config)
        self.encoder = ErnieMEncoder(config)
        self.pooler = ErnieMPooler(config) if add_pooling_layer else None
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layers[layer].self_attn.prune_heads(heads)

    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[tensor] = None,
        position_ids: Optional[tensor] = None,
        attention_mask: Optional[tensor] = None,
        head_mask: Optional[tensor] = None,
        inputs_embeds: Optional[tensor] = None,
        past_key_values: Optional[Tuple[Tuple[tensor]]] = None,
        use_cache: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        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.")

        # init the default bool value
        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.return_dict

        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        past_key_values_length = 0
        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]

        # Adapted from paddlenlp.transformers.ernie_m.ErnieMModel
        if attention_mask is None:
            attention_mask = (input_ids == self.config.pad_token_id).to(torch.float32)
            attention_mask *= torch.finfo(attention_mask.dtype).min
            if past_key_values is not None:
                batch_size = past_key_values[0][0].shape[0]
                past_mask = torch.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
                attention_mask = torch.concat([past_mask, attention_mask], dim=-1)
        # For 2D attention_mask from tokenizer
        elif attention_mask.ndim == 2:
            attention_mask = attention_mask.to(torch.float32)
            attention_mask = 1.0 - attention_mask
            attention_mask *= torch.finfo(attention_mask.dtype).min

        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            past_key_values=past_key_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            sequence_output = encoder_outputs[0]
            pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
            return (sequence_output, pooler_output) + encoder_outputs[1:]

        sequence_output = encoder_outputs["last_hidden_state"]
        pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
        hidden_states = None if not output_hidden_states else encoder_outputs["hidden_states"]
        attentions = None if not output_attentions else encoder_outputs["attentions"]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooler_output,
            hidden_states=hidden_states,
            attentions=attentions,
        )

@add_start_docstrings(
    """ErnieM Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks.""",
    ERNIE_M_START_DOCSTRING,
)
class ErnieMForSequenceClassification(ErnieMPreTrainedModel):
    # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->ErnieM,bert->ernie_m
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.ernie_m = ErnieMModel(config)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.Tensor]] = None,
        use_cache: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = True,
        labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple[torch.FloatTensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.ernie_m(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            past_key_values=past_key_values,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """ErnieM Model with a multiple choice classification head on top (a linear layer on top of
    the pooled output and a softmax) e.g. for RocStories/SWAG tasks.""",
    ERNIE_M_START_DOCSTRING,
)
class ErnieMForMultipleChoice(ErnieMPreTrainedModel):
    # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->ErnieM,bert->ernie_m
    def __init__(self, config):
        super().__init__(config)

        self.ernie_m = ErnieMModel(config)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, 1)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple[torch.FloatTensor], MultipleChoiceModelOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.ernie_m(
            input_ids,
            attention_mask=attention_mask,
            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,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """ErnieM Model with a token classification head on top (a linear layer on top of
    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.""",
    ERNIE_M_START_DOCSTRING,
)
class ErnieMForTokenClassification(ErnieMPreTrainedModel):
    # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->ErnieM,bert->ernie_m
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.Tensor]] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = True,
        labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple[torch.FloatTensor], TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.ernie_m(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            past_key_values=past_key_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """ErnieM Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).""",
    ERNIE_M_START_DOCSTRING,
)
class ErnieMForQuestionAnswering(ErnieMPreTrainedModel):
    # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->ErnieM,bert->ernie_m
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple[torch.FloatTensor], QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.ernie_m(
            input_ids,
            attention_mask=attention_mask,
            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]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """ErnieMForInformationExtraction is a Ernie-M Model with two linear layer on top of the hidden-states output to
    compute `start_prob` and `end_prob`, designed for Universal Information Extraction.""",
    ERNIE_M_START_DOCSTRING,
)
# Copied from paddlenlp.transformers.ernie_m.modeling.UIEM
class ErnieMForInformationExtraction(ErnieMPreTrainedModel):
    def __init__(self, config):
        super(ErnieMForInformationExtraction, self).__init__(config)
        self.ernie_m = ErnieMModel(config)
        self.linear_start = nn.Linear(config.hidden_size, 1)
        self.linear_end = nn.Linear(config.hidden_size, 1)
        self.sigmoid = nn.Sigmoid()
        self.post_init()

    @add_start_docstrings_to_model_forward(ERNIE_M_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple[torch.FloatTensor], QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for position (index) for computing the start_positions loss. Position outside of the sequence are
            not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) for computing the end_positions loss. Position outside of the sequence are not
            taken into account for computing the loss.
        """

        result = self.ernie_m(
            input_ids,
            attention_mask=attention_mask,
            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,
        )
        if return_dict:
            sequence_output = result.last_hidden_state
        elif not return_dict:
            sequence_output = result[0]

        start_logits = self.linear_start(sequence_output)
        start_logits = start_logits.squeeze(-1)
        end_logits = self.linear_end(sequence_output)
        end_logits = end_logits.squeeze(-1)

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = BCEWithLogitsLoss()
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            return tuple(
                i
                for i in [total_loss, start_logits, end_logits, result.hidden_states, result.attentions]
                if i is not None
            )

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=result.hidden_states,
            attentions=result.attentions,
        )

Output: ErnieMEmbeddings

Output: ErnieMModel

Output: ErnieMEncoderLayer

Output: ErnieMPreTrainedModel

Output: ErnieMForSequenceClassification

Output: ErnieMForMultipleChoice

Output: ErnieMForInformationExtraction

Output: ErnieMAttention

Output: ErnieMSelfAttention

Output: ErnieMEncoder

Output: ErnieMForTokenClassification

Output: ErnieMPooler

Output: ErnieMForQuestionAnswering


Input:
# coding=utf-8
# Copyright 2023 Bo Peng and 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.
"""PyTorch RWKV model."""

import math
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

from ...modeling_utils import PreTrainedModel
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_bitsandbytes_available,
    is_ninja_available,
    is_torch_cuda_available,
    logging,
)
from .configuration_rwkv import RwkvConfig

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "RWKV/rwkv-4-169m-pile"
_CONFIG_FOR_DOC = "RwkvConfig"

RWKV_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "RWKV/rwkv-4-169m-pile",
    "RWKV/rwkv-4-430m-pile",
    "RWKV/rwkv-4-1b5-pile",
    "RWKV/rwkv-4-3b-pile",
    "RWKV/rwkv-4-7b-pile",
    "RWKV/rwkv-4-14b-pile",
    "RWKV/rwkv-raven-1b5",
    "RWKV/rwkv-raven-3b",
    "RWKV/rwkv-raven-7b",
    "RWKV/rwkv-raven-14b",
    # See all RWKV models at https://huggingface.co/models?filter=rwkv
]

rwkv_cuda_kernel = None

def load_wkv_cuda_kernel(context_length):
    from torch.utils.cpp_extension import load as load_kernel

    global rwkv_cuda_kernel

    kernel_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "rwkv"
    cuda_kernel_files = [kernel_folder / f for f in ["wkv_op.cpp", "wkv_cuda.cu", "wkv_cuda_bf16.cu"]]

    # Only load the kernel if it's not been loaded yet or if we changed the context length
    if rwkv_cuda_kernel is not None and rwkv_cuda_kernel.max_seq_length == context_length:
        return

    logger.info(f"Loading CUDA kernel for RWKV at context length of {context_length}.")

    flags = [
        "-res-usage",
        "--maxrregcount 60",
        "--use_fast_math",
        "-O3",
        "-Xptxas -O3",
        "--extra-device-vectorization",
        f"-DTmax={context_length}",
    ]
    rwkv_cuda_kernel = load_kernel(
        name=f"wkv_{context_length}",
        sources=cuda_kernel_files,
        verbose=(logging.get_verbosity() == logging.DEBUG),
        extra_cuda_cflags=flags,
    )
    rwkv_cuda_kernel.max_seq_length = context_length

class RwkvLinearAttention(torch.autograd.Function):
    @staticmethod
    def forward(ctx, time_decay, time_first, key, value, state=None, return_state=False):
        batch_size, seq_len, hidden_size = key.size()
        if seq_len > rwkv_cuda_kernel.max_seq_length:
            raise ValueError(
                f"Cannot process a batch with {seq_len} tokens at the same time, use a maximum of "
                f"{rwkv_cuda_kernel.max_seq_length} with this model."
            )
        if batch_size * hidden_size % min(hidden_size, 32) != 0:
            raise ValueError(
                f"The product of batch size ({batch_size}) and hidden size ({hidden_size}) needs to be a round "
                f"multiple of {min(hidden_size, 32)}."
            )

        ctx.input_dtype = key.dtype

        if (
            time_decay.device.type != "cuda"
            or time_first.device.type != "cuda"
            or key.device.type != "cuda"
            or value.device.type != "cuda"
        ):
            raise ValueError("Calling the CUDA kernel for wkv attention requires all tensors to be on CUDA devices.")

        time_decay = -torch.exp(time_decay.float().contiguous())
        if key.dtype == torch.float16:
            time_first = time_first.float()
            key = key.float()
            value = value.float()
        time_first = time_first.contiguous()
        key = key.contiguous()
        value = value.contiguous()
        # The CUDA kernel will fill this tensor.
        output = torch.empty_like(key, memory_format=torch.contiguous_format)
        if return_state or state is not None:
            if state is None:
                state = torch.zeros(
                    batch_size,
                    hidden_size,
                    3,
                    dtype=torch.float32,
                    device=key.device,
                    memory_format=torch.contiguous_format,
                )
                state[:, :, 2] -= 1e38
            else:
                state = torch.cat([s.unsqueeze(2) for s in state], dim=2).contiguous()
            if key.dtype == torch.bfloat16:
                forward_func = rwkv_cuda_kernel.forward_with_state_bf16
            else:
                forward_func = rwkv_cuda_kernel.forward_with_state
            forward_func(time_decay, time_first, key, value, output, state)
        else:
            forward_func = rwkv_cuda_kernel.forward_bf16 if key.dtype == torch.bfloat16 else rwkv_cuda_kernel.forward
            forward_func(time_decay, time_first, key, value, output)

        ctx.save_for_backward(time_decay, time_first, key, value, output)

        if state is not None:
            state = [s.squeeze(2) for s in torch.chunk(state, 3, dim=2)]

        return output.to(ctx.input_dtype), state

    @staticmethod
    # g stands for grad
    def backward(ctx, g_output, g_state=None):
        input_dtype = ctx.input_dtype

        time_decay, time_first, key, value, output = ctx.saved_tensors
        # The CUDA kernel will fill those tensors.
        g_time_decay = torch.empty_like(
            time_decay,
            memory_format=torch.contiguous_format,
            dtype=torch.bfloat16 if input_dtype == torch.bfloat16 else torch.float32,
        )
        g_time_first = torch.empty_like(time_first, memory_format=torch.contiguous_format)
        g_key = torch.empty_like(key, memory_format=torch.contiguous_format)
        g_value = torch.empty_like(value, memory_format=torch.contiguous_format)

        if input_dtype == torch.float16:
            g_output = g_output.float()
        backward_func = rwkv_cuda_kernel.backward_bf16 if input_dtype == torch.bfloat16 else rwkv_cuda_kernel.backward
        backward_func(
            time_decay,
            time_first,
            key,
            value,
            output,
            g_output.contiguous(),
            g_time_decay,
            g_time_first,
            g_key,
            g_value,
        )

        return (
            g_time_decay.to(input_dtype),
            g_time_first.to(input_dtype),
            g_key.to(input_dtype),
            g_value.to(input_dtype),
            None,
            None,
        )

def rwkv_linear_attention_cpu(time_decay, time_first, key, value, state=None, return_state=False):
    # For CPU fallback. Will be slower and probably take more memory than the custom CUDA kernel if not executed
    # within a torch.no_grad.
    _, seq_length, _ = key.size()
    output = torch.zeros_like(key)

    if state is None:
        num_state = torch.zeros_like(key[:, 0], dtype=torch.float32)
        den_state = torch.zeros_like(key[:, 0], dtype=torch.float32)
        max_state = torch.zeros_like(key[:, 0], dtype=torch.float32) - 1e38
    else:
        num_state, den_state, max_state = state
    # For numerical stability
    #    real_numerator_state = num_state * torch.exp(max_state)
    #    real_denominator_state = den_state * torch.exp(max_state)

    time_decay = -torch.exp(time_decay)

    for current_index in range(seq_length):
        current_key = key[:, current_index].float()
        current_value = value[:, current_index]

        # wkv computation at time t
        max_for_output = torch.maximum(max_state, current_key + time_first)
        e1 = torch.exp(max_state - max_for_output)
        e2 = torch.exp(current_key + time_first - max_for_output)
        numerator = e1 * num_state + e2 * current_value
        denominator = e1 * den_state + e2
        output[:, current_index] = (numerator / denominator).to(output.dtype)

        # Update state for next iteration
        max_for_state = torch.maximum(max_state + time_decay, current_key)
        e1 = torch.exp(max_state + time_decay - max_for_state)
        e2 = torch.exp(current_key - max_for_state)
        num_state = e1 * num_state + e2 * current_value
        den_state = e1 * den_state + e2
        max_state = max_for_state

    if return_state or state is not None:
        state = [num_state, den_state, max_state]

    return output, state

def rwkv_linear_attention(time_decay, time_first, key, value, state=None, return_state=False):
    no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, key, value])
    # Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version
    # in this case).
    one_token = key.size(1) == 1
    if rwkv_cuda_kernel is None or no_cuda or one_token:
        return rwkv_linear_attention_cpu(time_decay, time_first, key, value, state=state, return_state=return_state)
    else:
        return RwkvLinearAttention.apply(time_decay, time_first, key, value, state, return_state)

class RwkvSelfAttention(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.config = config
        kernel_loaded = rwkv_cuda_kernel is not None and rwkv_cuda_kernel.max_seq_length == config.context_length
        if is_ninja_available() and is_torch_cuda_available() and not kernel_loaded:
            try:
                load_wkv_cuda_kernel(config.context_length)
            except Exception:
                logger.info("Could not load the custom CUDA kernel for RWKV attention.")
        self.layer_id = layer_id
        hidden_size = config.hidden_size
        attention_hidden_size = (
            config.attention_hidden_size if config.attention_hidden_size is not None else hidden_size
        )
        self.attention_hidden_size = attention_hidden_size

        self.time_decay = nn.Parameter(torch.empty(attention_hidden_size))
        self.time_first = nn.Parameter(torch.empty(attention_hidden_size))

        self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
        self.time_mix_value = nn.Parameter(torch.empty(1, 1, hidden_size))
        self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False)
        self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False)
        self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False)
        self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False)

    # TODO: maybe jit, otherwise move inside forward
    def extract_key_value(self, hidden, state=None):
        # Mix hidden with the previous timestep to produce key, value, receptance
        if hidden.size(1) == 1 and state is not None:
            shifted = state[1][:, :, self.layer_id]
        else:
            shifted = self.time_shift(hidden)
            if state is not None:
                shifted[:, 0] = state[1][:, :, self.layer_id]
        key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
        value = hidden * self.time_mix_value + shifted * (1 - self.time_mix_value)
        receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)

        key = self.key(key)
        value = self.value(value)
        receptance = torch.sigmoid(self.receptance(receptance))
        if state is not None:
            state[1][:, :, self.layer_id] = hidden[:, -1]
        return receptance, key, value, state

    def forward(self, hidden, state=None, use_cache=False):
        receptance, key, value, state = self.extract_key_value(hidden, state=state)
        layer_state = tuple(s[:, :, self.layer_id] for s in state[2:]) if state is not None else None
        rwkv, layer_state = rwkv_linear_attention(
            self.time_decay,
            self.time_first,
            key,
            value,
            state=layer_state,
            return_state=use_cache,
        )

        if layer_state is not None:
            state[2][:, :, self.layer_id] = layer_state[0]
            state[3][:, :, self.layer_id] = layer_state[1]
            state[4][:, :, self.layer_id] = layer_state[2]

        return self.output(receptance * rwkv), state

class RwkvFeedForward(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.config = config
        self.layer_id = layer_id
        hidden_size = config.hidden_size
        intermediate_size = (
            config.intermediate_size if config.intermediate_size is not None else 4 * config.hidden_size
        )

        self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
        self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
        self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size))

        self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
        self.value = nn.Linear(intermediate_size, hidden_size, bias=False)

    def forward(self, hidden, state=None):
        if hidden.size(1) == 1 and state is not None:
            shifted = state[0][:, :, self.layer_id]
        else:
            shifted = self.time_shift(hidden)
            if state is not None:
                shifted[:, 0] = state[0][:, :, self.layer_id]
        key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key)
        receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance)

        key = torch.square(torch.relu(self.key(key)))
        value = self.value(key)
        receptance = torch.sigmoid(self.receptance(receptance))

        if state is not None:
            state[0][:, :, self.layer_id] = hidden[:, -1]

        return receptance * value, state

class RwkvBlock(nn.Module):
    def __init__(self, config, layer_id):
        super().__init__()
        self.config = config
        self.layer_id = layer_id

        if layer_id == 0:
            self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)

        self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)

        self.attention = RwkvSelfAttention(config, layer_id)
        self.feed_forward = RwkvFeedForward(config, layer_id)

    def forward(self, hidden, state=None, use_cache=False, output_attentions=False):
        if self.layer_id == 0:
            hidden = self.pre_ln(hidden)

        attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache)
        hidden = hidden + attention

        feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
        hidden = hidden + feed_forward

        outputs = (hidden, state)
        if output_attentions:
            outputs += (attention,)
        else:
            outputs += (None,)

        return outputs

class RwkvPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = RwkvConfig
    base_model_prefix = "rwkv"
    _no_split_modules = ["RwkvBlock"]
    _keep_in_fp32_modules = ["time_decay", "time_first"]
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, RwkvSelfAttention):
            layer_id = module.layer_id
            num_hidden_layers = module.config.num_hidden_layers
            hidden_size = module.config.hidden_size
            attention_hidden_size = module.attention_hidden_size

            ratio_0_to_1 = layer_id / (num_hidden_layers - 1)  # 0 to 1
            ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers)  # 1 to ~0

            time_weight = torch.tensor(
                [i / hidden_size for i in range(hidden_size)],
                dtype=module.time_mix_key.dtype,
                device=module.time_mix_key.device,
            )
            time_weight = time_weight[None, None, :]

            decay_speed = [
                -5 + 8 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
                for h in range(attention_hidden_size)
            ]
            decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device)
            zigzag = (
                torch.tensor(
                    [(i + 1) % 3 - 1 for i in range(attention_hidden_size)],
                    dtype=module.time_first.dtype,
                    device=module.time_first.device,
                )
                * 0.5
            )

            with torch.no_grad():
                module.time_decay.data = decay_speed
                module.time_first.data = torch.ones_like(module.time_first * math.log(0.3) + zigzag)

                module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
                module.time_mix_value.data = torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
                module.time_mix_receptance.data = torch.pow(time_weight, 0.5 * ratio_1_to_almost0)
        elif isinstance(module, RwkvFeedForward):
            layer_id = module.layer_id
            num_hidden_layers = module.config.num_hidden_layers
            hidden_size = module.config.hidden_size

            ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers)  # 1 to ~0

            time_weight = torch.tensor(
                [i / hidden_size for i in range(hidden_size)],
                dtype=module.time_mix_key.dtype,
                device=module.time_mix_key.device,
            )
            time_weight = time_weight[None, None, :]

            with torch.no_grad():
                module.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
                module.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0)

@dataclass
class RwkvOutput(ModelOutput):
    """
    Class for the RWKV model outputs.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
            avoid providing the old `input_ids`.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    last_hidden_state: torch.FloatTensor = None
    state: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None

@dataclass
class RwkvCausalLMOutput(ModelOutput):
    """
    Base class for causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
            avoid providing the old `input_ids`.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    state: Optional[List[torch.FloatTensor]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None

RWKV_START_DOCSTRING = r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RwkvConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

RWKV_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
            `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
            sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            This is currently not used by `RwkvModel`, but will be supported in the future.

            [What are attention masks?](../glossary#attention-mask)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*):
            If passed along, the model uses the previous state in all the blocks (which will give the output for the
            `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
        use_cache (`bool`, *optional*):
            If set to `True`, the last state is returned and can be used to quickly generate the next logits.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare RWKV Model transformer outputting raw hidden-states without any specific head on top.",
    RWKV_START_DOCSTRING,
)
class RwkvModel(RwkvPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
        self.blocks = nn.ModuleList([RwkvBlock(config, layer_id=idx) for idx in range(config.num_hidden_layers)])
        self.ln_out = nn.LayerNorm(config.hidden_size)

        self.layers_are_rescaled = False

        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings

    def set_input_embeddings(self, new_embeddings):
        self.embeddings = new_embeddings

    @add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=RwkvOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,  # noqa
        inputs_embeds: Optional[torch.FloatTensor] = None,
        state: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, RwkvOutput]:
        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
        )
        use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if self.training == self.layers_are_rescaled:
            self._rescale_layers()

        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 None and inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embeddings(input_ids)

        if use_cache and state is None:
            shape = (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers)
            state = [
                torch.zeros(
                    *shape, dtype=inputs_embeds.dtype if i <= 1 else torch.float32, device=inputs_embeds.device
                )
                for i in range(5)
            ]
            state[4] -= 1e30

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        hidden_states = inputs_embeds

        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for idx, block in enumerate(self.blocks):
            if self.gradient_checkpointing and self.training:
                hidden_states, state, attentions = self._gradient_checkpointing_func(
                    block.__call__, hidden_states, state, use_cache, output_attentions
                )
            else:
                hidden_states, state, attentions = block(
                    hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions
                )

            if (
                self.layers_are_rescaled
                and self.config.rescale_every > 0
                and (idx + 1) % self.config.rescale_every == 0
            ):
                hidden_states = hidden_states / 2

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if output_attentions:
                all_self_attentions = all_self_attentions + (attentions,)

        hidden_states = self.ln_out(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(x for x in [hidden_states, state, all_hidden_states, all_self_attentions] if x is not None)

        return RwkvOutput(
            last_hidden_state=hidden_states,
            state=state,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )

    def _rescale_layers(self):
        # Layers should be rescaled for inference only.
        if self.layers_are_rescaled == (not self.training):
            return
        if self.config.rescale_every > 0:
            with torch.no_grad():
                for block_id, block in enumerate(self.blocks):
                    if self.training:
                        block.attention.output.weight.mul_(2 ** int(block_id // self.config.rescale_every))
                        block.feed_forward.value.weight.mul_(2 ** int(block_id // self.config.rescale_every))
                    else:
                        # Deal with quantization statistics
                        if hasattr(block.attention.output.weight, "SCB"):
                            block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
                            block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
                        elif hasattr(block.attention.output.weight, "quant_state"):
                            self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id)
                            self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id)
                        else:
                            block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
                            block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))

        self.layers_are_rescaled = not self.training

    def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id):
        r"""
        Perform the dequantization and rescaling of the weights of a given layer. After that operation the layer will
        be quantized again.
        """
        if not is_bitsandbytes_available():
            raise ImportError("Please install bitsandbytes to use this method.")
        import bitsandbytes as bnb

        dequant_weights = bnb.functional.dequantize_4bit(target_layer.weight.data, target_layer.weight.quant_state)

        dequant_weights.div_(2 ** int(block_id // self.config.rescale_every))

        # re-quantize the model:
        # we need to put it first on CPU then back to the device
        # this will create an overhead :/
        # We set requires_grad=False as we cannot compute gradients on top of 4bit parameters anyway and to avoid
        # bugs with bnb
        quant_weight = bnb.nn.Params4bit(dequant_weights.to("cpu"), requires_grad=False).to(dequant_weights.device)
        setattr(target_layer, "weight", quant_weight)

@add_start_docstrings(
    """
    The RWKV Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """,
    RWKV_START_DOCSTRING,
)
class RwkvForCausalLM(RwkvPreTrainedModel):
    _tied_weights_keys = ["head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.rwkv = RwkvModel(config)
        self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_output_embeddings(self):
        return self.head

    def set_output_embeddings(self, new_embeddings):
        self.head = new_embeddings

    def generate(self, *args, **kwargs):
        # Thin wrapper to raise exceptions when trying to generate with methods that manipulate `past_key_values`.
        # RWKV is one of the few models that don't have it (it has `state` instead, which has different properties and
        # usage).
        try:
            gen_output = super().generate(*args, **kwargs)
        except AttributeError as exc:
            # Expected exception: "AttributeError: '(object name)' object has no attribute 'past_key_values'"
            if "past_key_values" in str(exc):
                raise AttributeError(
                    "You tried to call `generate` with a decoding strategy that manipulates `past_key_values`. RWKV "
                    "doesn't have that attribute, try another generation strategy instead. For the available "
                    "generation strategies, check this doc: https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
                )
            else:
                raise exc
        return gen_output

    def prepare_inputs_for_generation(self, input_ids, state=None, inputs_embeds=None, **kwargs):
        # only last token for inputs_ids if the state is passed along.
        if state is not None:
            input_ids = input_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and state is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs["state"] = state
        return model_inputs

    @add_start_docstrings_to_model_forward(RWKV_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=RwkvCausalLMOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,  # noqa
        inputs_embeds: Optional[torch.FloatTensor] = None,
        state: Optional[List[torch.FloatTensor]] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, RwkvCausalLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        rwkv_outputs = self.rwkv(
            input_ids,
            inputs_embeds=inputs_embeds,
            state=state,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = rwkv_outputs[0]

        logits = self.head(hidden_states)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

        if not return_dict:
            output = (logits,) + rwkv_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return RwkvCausalLMOutput(
            loss=loss,
            logits=logits,
            state=rwkv_outputs.state,
            hidden_states=rwkv_outputs.hidden_states,
            attentions=rwkv_outputs.attentions,
        )

Output: rwkv_linear_attention

Output: rwkv_linear_attention_cpu

Output: load_wkv_cuda_kernel

Output: RwkvSelfAttention

Output: RwkvLinearAttention

Output: RwkvFeedForward

Output: RwkvPreTrainedModel

Output: RwkvCausalLMOutput

Output: RwkvForCausalLM

Output: RwkvModel

Output: RwkvBlock

Output: RwkvOutput