mistral_model.py

# coding=utf-8
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 Mistral model."""
import inspect
from dataclasses import dataclass

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

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings, ModelOutput,
)
from .mistral_config import CostWiseMistralConfig

from transformers.models.mistral.modeling_mistral import (
    MistralRMSNorm,
    MistralRotaryEmbedding,
    rotate_half,
    apply_rotary_pos_emb,
    MistralMLP,
    repeat_kv,
    MistralAttention,
    MistralFlashAttention2,
    MistralSdpaAttention,
    MISTRAL_ATTENTION_CLASSES,
    MistralDecoderLayer,
    MISTRAL_START_DOCSTRING,
    MistralPreTrainedModel,
    MISTRAL_INPUTS_DOCSTRING,

)

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "CostWiseMistralConfig"

@dataclass
class CostWiseModelOutputWithPast(ModelOutput):
    last_hidden_state: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    attention_masks: Optional[Tuple[torch.FloatTensor]] = None

@dataclass
class CostWiseCausalLMOutputWithPast(ModelOutput):
    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    attention_masks: Optional[Tuple[torch.FloatTensor]] = None

def token_compress(compress_ratio,
                   hidden_states,
                   attention_mask,
                   query_lengths,
                   prompt_lengths,
                   weights: torch.Tensor = None):
    # hidden_states = hidden_states.to('cpu')
    # attention_mask = attention_mask.to('cpu')
    # query_lengths = query_lengths.to('cpu')
    # prompt_lengths = prompt_lengths.to('cpu')
    # weights = weights.to('cpu')
    # get some specific parameters
    passage_lengths = torch.sum(attention_mask, dim=1, dtype=torch.int) - query_lengths - prompt_lengths # the raw passage lengths
    retain_passage_lengths = (passage_lengths + compress_ratio - 1) // compress_ratio # the passage lengths need to be retained
    final_useful_lengths = query_lengths + prompt_lengths + retain_passage_lengths # the final useful length after compress
    max_passage_length = torch.max(passage_lengths) # the max passage lengths
    max_final_lengths = torch.max(final_useful_lengths) # the max useful lengths after compress
    # make new hidden states and new attention masks
    new_hidden_states = torch.zeros((hidden_states.shape[0], max_final_lengths,
                                     hidden_states.shape[-1]), dtype=hidden_states.dtype).to(hidden_states.device)
    new_attention_mask = torch.ones((hidden_states.shape[0], max_final_lengths), dtype=attention_mask.dtype).to(attention_mask.device)
    # get new attention mask
    mask_attention_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0) >= final_useful_lengths[:, None]
    new_attention_mask[mask_attention_index] = 0
    # get new hidden states
    # add query into new hidden states
    query_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)
    mask_query_index = query_index < query_lengths[:, None]
    new_hidden_states[mask_query_index] = hidden_states[:, : max_final_lengths, :][mask_query_index]
    # add prompt into new hidden states
    # get the index of the prompt in new hidden states
    new_prompt_start_length = query_lengths + retain_passage_lengths
    new_prompt_end_length = new_prompt_start_length + prompt_lengths
    new_prompt_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)
    new_mask_prompt_index_start = new_prompt_index >= new_prompt_start_length[:, None]
    new_mask_prompt_index_end = new_prompt_index < new_prompt_end_length[:, None]
    new_mask_prompt_index = new_mask_prompt_index_start & new_mask_prompt_index_end
    # get the index of the prompt in hidden states
    raw_prompt_start_length = query_lengths + passage_lengths
    raw_prompt_end_length = raw_prompt_start_length + prompt_lengths
    raw_prompt_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
    raw_mask_prompt_index_start = raw_prompt_index >= raw_prompt_start_length[:, None]
    raw_mask_prompt_index_end = raw_prompt_index < raw_prompt_end_length[:, None]
    raw_mask_prompt_index = raw_mask_prompt_index_start & raw_mask_prompt_index_end
    # replace the prompt hidden states
    new_hidden_states[new_mask_prompt_index] = hidden_states[raw_mask_prompt_index]
    # 以上均没问题

    # print(new_hidden_states.view(len(new_hidden_states), -1))
    # print(new_attention_mask)

    # get the index of the passage in new hidden states
    new_passage_start_length = query_lengths
    new_passage_end_length = new_passage_start_length + retain_passage_lengths
    new_passage_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)
    new_mask_passage_index_start = new_passage_index >= new_passage_start_length[:, None]
    new_mask_passage_index_end = new_passage_index < new_passage_end_length[:, None]
    new_mask_passage_index = new_mask_passage_index_start & new_mask_passage_index_end
    # print(query_lengths, prompt_lengths, retain_passage_lengths, final_useful_lengths)
    # add passage into new hidden states
    # get mask hidden states
    psg_start_length = query_lengths
    psg_end_length = query_lengths + passage_lengths
    psg_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
    mask_psg_index_start = psg_index >= psg_start_length[:, None]
    mask_psg_index_end = psg_index < psg_end_length[:, None]
    mask_psg_index = mask_psg_index_start & mask_psg_index_end

    hidden_states = hidden_states * mask_psg_index.unsqueeze(-1)
    passage_hidden_states = torch.zeros((hidden_states.shape[0],
                                         (max_passage_length + compress_ratio - 1) // compress_ratio * compress_ratio,
                                         hidden_states.shape[-1]), dtype=hidden_states.dtype).to(hidden_states.device)
    passage_end_length = passage_lengths
    passage_index = torch.arange(passage_hidden_states.shape[1], device=hidden_states.device).unsqueeze(0) # maybe exceed the max passage length
    mask_passage_index = passage_index < passage_end_length[:, None]

    raw_passage_end_length = query_lengths + passage_lengths
    raw_passage_start_length = query_lengths
    raw_passage_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
    raw_mask_passage_index_start = raw_passage_index >= raw_passage_start_length[:, None]
    raw_mask_passage_index_end = raw_passage_index < raw_passage_end_length[:, None]
    raw_mask_passage_index = raw_mask_passage_index_start & raw_mask_passage_index_end
    passage_hidden_states[mask_passage_index] = hidden_states[raw_mask_passage_index]

    passage_weights = torch.zeros((weights.shape[0],
                                   (max_passage_length + compress_ratio - 1) // compress_ratio * compress_ratio)
                                  , dtype=weights.dtype).to(hidden_states.device)
    weights = torch.sum(weights, dim=1)
    passage_weights[mask_passage_index] = weights[raw_mask_passage_index]
    passage_weights = passage_weights.view(passage_weights.shape[0], -1, compress_ratio)
    passage_weights = passage_weights / torch.sum(passage_weights, dim=-1
                                                  ).view(passage_weights.shape[0], -1, 1)
    passage_weights = passage_weights.view(passage_weights.shape[0], -1)
    # passage_weights = torch.where(passage_weights == torch.nan, 0, passage_weights)
    passage_hidden_states = passage_hidden_states * passage_weights.unsqueeze(-1)
    passage_hidden_states = passage_hidden_states.view(passage_hidden_states.shape[0], -1, compress_ratio,
                                                       passage_hidden_states.shape[-1])
    passage_hidden_states = torch.sum(passage_hidden_states, dim=2)
    passage_end_length = retain_passage_lengths
    passage_index = torch.arange(passage_hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
    mask_passage_index = passage_index < passage_end_length[:, None]
    new_hidden_states[new_mask_passage_index] = passage_hidden_states[mask_passage_index]

    return new_hidden_states, new_attention_mask

@add_start_docstrings(
    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
    MISTRAL_START_DOCSTRING,
)
class CostWiseMistralModel(MistralPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]

    Args:
        config: MistralConfig
    """

    def __init__(self, config: CostWiseMistralConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._attn_implementation = config._attn_implementation
        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        compress_layer: Optional[int] = None,
        compress_ratio: Optional[int] = None,
        cutoff_layers: Optional[List[int]] = None,
        query_lengths: Optional[int] = None,
        prompt_lengths: Optional[int] = None,
    ) -> Union[Tuple, CostWiseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        compress_ratio = None if compress_ratio == 1 else compress_ratio
        if compress_layer is not None and compress_ratio is not None:
            output_attentions = True

        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        if self.config.layer_wise:
            output_hidden_states = True

        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        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

        if compress_layer is not None and compress_ratio is not None:
            logger.warning_once(
                "`use_cache=True` is incompatible with reranker. Setting `use_cache=False`."
            )
            use_cache = False

        past_key_values_length = 0

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

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

        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                raise ValueError(
                    "You are attempting to perform batched generation with padding_side='right'"
                    " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                )

        if self._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            input_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self._attn_implementation == "sdpa" and not output_attentions:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            input_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window=self.config.sliding_window,
            )
        else:
            # 4d mask is passed through the layers
            input_attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window=self.config.sliding_window,
            )

        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_attention_masks = ()
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) and (
                torch.sum(attention_mask) != attention_mask.shape[0] * attention_mask.shape[1])
        query_lengths = [0] * hidden_states.shape[0] if query_lengths is None else query_lengths
        prompt_lengths = [0] * hidden_states.shape[0] if prompt_lengths is None else prompt_lengths
        if not isinstance(query_lengths, torch.Tensor):
            query_lengths = torch.tensor(query_lengths, device=hidden_states.device)
        if not isinstance(prompt_lengths, torch.Tensor):
            prompt_lengths = torch.tensor(prompt_lengths, device=hidden_states.device)

        if cutoff_layers is None:
            max_layer = self.config.num_hidden_layers
            cutoff_layers = [max_layer]
        if isinstance(cutoff_layers, int):
            max_layer = cutoff_layers
            cutoff_layers = [cutoff_layers]
        else:
            max_layer = max(cutoff_layers)

        for idx, decoder_layer in enumerate(self.layers):
            if self.config.layer_wise:
                if idx in cutoff_layers and output_hidden_states:
                    all_hidden_states += (self.norm(hidden_states),)
                    all_attention_masks += (attention_mask,)
                if idx == max_layer:
                    break
            elif output_hidden_states:
                all_hidden_states += (hidden_states,)

            if compress_layer is not None and compress_ratio is not None and idx in compress_layer and idx != 0:
                # if all_self_attns is not None:
                #     # weights = all_self_attns[-1][:, :, -1, :]
                #     weights = all_self_attns
                # else:
                #     weights = None

                if left_padding:
                    raise ValueError('You must use right padding...')
                hidden_states, attention_mask = token_compress(compress_ratio, hidden_states, attention_mask,
                                                               query_lengths, prompt_lengths, all_self_attns)
                torch.cuda.empty_cache()
                device = input_ids.device if input_ids is not None else inputs_embeds.device
                seq_length = hidden_states.shape[1]
                position_ids = torch.arange(
                    past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
                )
                position_ids = position_ids.unsqueeze(0)
                if self._attn_implementation == "flash_attention_2":
                    # 2d mask is passed through the layers
                    input_attention_mask = attention_mask if (
                                attention_mask is not None and 0 in attention_mask) else None
                elif self._attn_implementation == "sdpa" and not output_attentions:
                    # output_attentions=True can not be supported when using SDPA, and we fall back on
                    # the manual implementation that requires a 4D causal mask in all cases.
                    input_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                        attention_mask,
                        (batch_size, seq_length),
                        inputs_embeds,
                        past_key_values_length,
                    )
                else:
                    # 4d mask is passed through the layers
                    input_attention_mask = _prepare_4d_causal_attention_mask(
                        attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
                    )

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    input_attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=input_attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                # all_self_attns += (layer_outputs[1],)
                all_self_attns = layer_outputs[1][:, :, -1, :]

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if not self.config.layer_wise:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
                all_attention_masks += (attention_mask,)
        else:
            if output_hidden_states and self.config.num_hidden_layers == max_layer:
                all_hidden_states += (hidden_states,)
                all_attention_masks += (attention_mask,)

        next_cache = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache

        torch.cuda.empty_cache()

        if not return_dict:
            return tuple(
                v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_attention_masks] if
                v is not None)
        return CostWiseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            attention_masks=all_attention_masks
        )

class CostWiseHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, input_size, output_size):
        super().__init__()
        self.linear_head = nn.Linear(input_size, output_size, bias=False)

    def forward(self, **kwargs):
        return self.linear_head(**kwargs)

class CostWiseMistralForCausalLM(MistralPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = CostWiseMistralModel(config)
        self.vocab_size = config.vocab_size
        if not config.layer_wise:
            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        else:
            self.lm_head = nn.ModuleList(
                [CostWiseHead(config.hidden_size, 1) for _ in range(
                    config.start_layer, config.num_hidden_layers + 1, config.layer_sep
                )]
            )

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

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

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

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[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,
        compress_layer: Optional[int] = None,
        compress_ratio: Optional[int] = None,
        cutoff_layers: Optional[List[int]] = None,
        query_lengths: Optional[int] = None,
        prompt_lengths: Optional[int] = None,
    ) -> Union[Tuple, CostWiseCausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, MistralForCausalLM

        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""

        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 compress_ratio is not None and compress_ratio == 1:
            compress_ratio = None

        if self.config.layer_wise:
            if cutoff_layers is None:
                cutoff_layers = [self.config.num_hidden_layers]
            elif isinstance(cutoff_layers, int):
                cutoff_layers = [cutoff_layers]
            can_use_layers = list(range(self.config.start_layer, self.config.num_hidden_layers + 1, self.config.layer_sep))
            remove_layers = [i for i in cutoff_layers if i not in can_use_layers]
            if len(remove_layers) > 0:
                logger.warning_once(
                    f"layers {remove_layers} are incompatible with the setting. They will be removed..."
                )
            cutoff_layers = [i for i in cutoff_layers if i not in remove_layers]
            if len(cutoff_layers) == 0:
                raise ValueError(f"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]")

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            compress_layer=compress_layer,
            compress_ratio=compress_ratio,
            query_lengths=query_lengths,
            prompt_lengths=prompt_lengths,
            cutoff_layers=cutoff_layers
        )

        if not self.config.layer_wise:
            hidden_states = outputs[0]
            logits = self.lm_head(hidden_states)
            logits = logits.float()
            loss = None
            if labels is not None:
                # Shift so that tokens < n predict n
                shift_logits = logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
                # Flatten the tokens
                loss_fct = CrossEntropyLoss()
                shift_logits = shift_logits.view(-1, self.config.vocab_size)
                shift_labels = shift_labels.view(-1)
                # Enable model parallelism
                shift_labels = shift_labels.to(shift_logits.device)
                loss = loss_fct(shift_logits, shift_labels)
        else:
            hidden_states = outputs.hidden_states
            logits = ()
            for i in range(len(hidden_states)):
                tmp_logits = self.lm_head[i].linear_head(hidden_states[i])
                tmp_logits = tmp_logits.float()
                tmp_logits = tmp_logits.reshape(hidden_states[i].shape[0], -1)
                logits = logits + (tmp_logits,)
            loss = None

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

        return CostWiseCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            attention_masks=outputs[-1] if self.model.config.layer_wise else outputs[-1][-1]
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        # Omit tokens covered by past_key_values
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

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

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past