# 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
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 quant.new_pack import triton_quantize_and_pack_along_last_dim
from quant.matmul import cuda_bmm_fA_qB_outer

from transformers.models.mistral.configuration_mistral import *
from transformers.models.mistral.modeling_mistral import *
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask

_CONFIG_FOR_DOC = "MistralConfig"

DEBUG = False

if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)

def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )
'''
def repeat_kv_quant(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
'''
class MistralAttention_MUSTAFAR(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
    and "Generating Long Sequences with Sparse Transformers".
    """

    def __init__(self, config: MistralConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True
        self.k_sparsity = config.k_sparsity
        self.v_sparsity = config.v_sparsity 
        #self.k_bits = config.k_bits
        #self.v_bits = config.v_bits
        self.group_size = config.group_size
        self.residual_length = config.residual_length

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

        self.rotary_emb = MistralRotaryEmbedding(
            self.head_dim,
            max_position_embeddings=self.max_position_embeddings,
            base=self.rope_theta,
        )
        #self.key_score_accumulator = None
        #self.value_score_accumulator = None
        #self.generation_count = None

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def dh_prune_key(self, key_states: torch.Tensor, target_sparsity=None):
        if target_sparsity is None:
            target_sparsity = self.k_sparsity
        """
        Performs magnitude-based pruning along the hidden dimension.
        
        Args:
            key_states (torch.Tensor): Tensor of shape [batch_size, num_heads, tokens, hidden_dim].
            target_sparsity (float): Fraction of elements to prune per vector (between 0 and 1).
            
        Returns:
            torch.Tensor: Pruned tensor with the same shape, with values pruned to zero.
        """
        assert 0 <= target_sparsity < 1, "Target sparsity must be between 0 and 1"

        # Get the shape of key_states
        B, H, T, D = key_states.shape  # Batch size, number of heads, tokens, hidden dimension

        # Compute the number of elements to keep per vector (hidden dimension)
        #num_to_keep = max(1, int((1 - target_sparsity) * D))
        num_to_keep = max(1, int((target_sparsity) * D))
        if DEBUG: print("NUM TO KEEP for Key: ", num_to_keep)

        # Flatten along batch, head, and tokens, keeping only the hidden_dim axis separate
        key_states_flat = key_states.reshape(-1, D)  # Shape: [(B * H * T), D]

        # Compute pruning threshold per vector
        threshold_values, _ = torch.kthvalue(torch.abs(key_states_flat), num_to_keep, dim=-1, keepdim=True)

        # Create a mask: Keep only values larger than or equal to the threshold
        mask = torch.abs(key_states_flat) >= threshold_values

        # Apply the mask (zero out pruned elements)
        pruned_key_states = key_states_flat * mask

        if DEBUG: 
                print("Debug: -- sparsity of just pruned key: ", self.calculate_sparsity(pruned_key_states.view(B, H, T, D)))

        # Reshape back to original dimensions
        return pruned_key_states.view(B, H, T, D)
        
        #return key[:, :, -self.residual_length:, :].contiguous()
        
    
    #def dh_prune_value(self, value_states: torch.Tensor, target_sparsity = 0.9, group_size = 128):
    #def dh_prune_value(self, value_states: torch.Tensor, target_sparsity = 0.3, group_size = 128):
    #pass in: iteration number, window of attention score computed, value_state, score accumulator, 
        #note attention_score is [B, H, 1, seq_len]
    #output: pruned_flag, pruned_value_states
    def dh_prune_value(self, key_states: torch.Tensor, target_sparsity=None):
        if target_sparsity is None:
            target_sparsity = self.v_sparsity
        """
        Performs magnitude-based pruning along the hidden dimension.
        
        Args:
            key_states (torch.Tensor): Tensor of shape [batch_size, num_heads, tokens, hidden_dim].
            target_sparsity (float): Fraction of elements to prune per vector (between 0 and 1).
            
        Returns:
            torch.Tensor: Pruned tensor with the same shape, with values pruned to zero.
        """
        assert 0 <= target_sparsity < 1, "Target sparsity must be between 0 and 1"

        # Get the shape of key_states
        B, H, T, D = key_states.shape  # Batch size, number of heads, tokens, hidden dimension

        # Compute the number of elements to keep per vector (hidden dimension)
        #num_to_keep = max(1, int((1 - target_sparsity) * D))
        num_to_keep = max(1, int((target_sparsity) * D))
        if DEBUG: print("NUM TO KEEP for Key: ", num_to_keep)

        # Flatten along batch, head, and tokens, keeping only the hidden_dim axis separate
        key_states_flat = key_states.reshape(-1, D)  # Shape: [(B * H * T), D]

        # Compute pruning threshold per vector
        threshold_values, _ = torch.kthvalue(torch.abs(key_states_flat), num_to_keep, dim=-1, keepdim=True)

        # Create a mask: Keep only values larger than or equal to the threshold
        mask = torch.abs(key_states_flat) >= threshold_values

        # Apply the mask (zero out pruned elements)
        pruned_key_states = key_states_flat * mask

        if DEBUG: print("Debug: -- sparsity of just pruned value: ", self.calculate_sparsity(pruned_key_states.view(B, H, T, D)), "Sized: ", pruned_key_states.view(B, H, T, D).shape)

        # Reshape back to original dimensions
        return pruned_key_states.view(B, H, T, D)

    def calculate_sparsity(self, tensor: torch.Tensor) -> float:
        """
        Calculates the sparsity of a 4D PyTorch tensor.
        
        Sparsity is defined as the fraction of elements that are zero.
        
        Args:
            tensor (torch.Tensor): A 4D tensor (batch, channels, height, width)
        
        Returns:
            float: The sparsity ratio (between 0 and 1)
        """
        if tensor.dim() != 4:
            raise ValueError("Input tensor must be 4D (batch, channels, height, width)")
        
        total_elements = tensor.numel()  # Total number of elements
        zero_elements = torch.sum(tensor == 0).item()  # Count of zero elements
        
        if DEBUG: print(f"Matrix size: {tensor.shape}, Zero elements: {zero_elements}, Total elements: {total_elements}")
        sparsity = zero_elements / total_elements  # Compute sparsity ratio
        return sparsity

    #note than this forward method is not used for MUSTAFAR.
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[-1]
        #print("Debug: kv_seq_len: ", kv_seq_len)
        #cos, sin = self.rotary_emb(value_states, kv_seq_len)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            key_states_quant_trans = past_key_value[0]
            key_states_full = past_key_value[1]
            key_scale_trans = past_key_value[2]
            key_mn_trans = past_key_value[3]
            value_states_quant = past_key_value[4]
            value_states_full = past_key_value[5]
            value_scale = past_key_value[6]
            value_mn = past_key_value[7]

            if key_states_quant_trans is not None:
                # import ipdb; ipdb.set_trace()
                key_states_quant_trans_repeat = repeat_kv_quant(key_states_quant_trans, self.num_key_value_groups)
                key_scale_trans_repeat = repeat_kv_quant(key_scale_trans, self.num_key_value_groups)
                key_mn_trans_repeat = repeat_kv_quant(key_mn_trans, self.num_key_value_groups)
                att_qkquant = cuda_bmm_fA_qB_outer(self.group_size, query_states, key_states_quant_trans_repeat, 
                                key_scale_trans_repeat, key_mn_trans_repeat, self.k_bits) # key_states_quant_trans, key_scale_trans, key_mn_trans need to be repeated
            else:
                att_qkquant = None

            if key_states_full is not None:
                key_states_full = torch.cat([key_states_full, key_states], dim=2)
            else:
                key_states_full = key_states
            
            key_states_full_repeat = repeat_kv(key_states_full, self.num_key_value_groups)
            att_qkfull = torch.matmul(query_states, key_states_full_repeat.transpose(2, 3)) # key_states_full need to be repeated
            if att_qkquant is not None:
                attn_weights = torch.cat([att_qkquant, att_qkfull], dim=-1) / math.sqrt(self.head_dim)
            else:
                attn_weights = att_qkfull / math.sqrt(self.head_dim)

            if key_states_full.shape[-2] == self.residual_length:
                key_states_quant_trans_new, key_scale_trans_new, key_mn_trans_new = triton_quantize_and_pack_along_last_dim(key_states_full.transpose(2, 3).contiguous(), 
                                                                                                                            self.group_size, 
                                                                                                                            self.k_bits)
                key_states_full = None
                if key_states_quant_trans is not None:
                    key_states_quant_trans = torch.cat([key_states_quant_trans, key_states_quant_trans_new], dim=3)
                    key_scale_trans = torch.cat([key_scale_trans, key_scale_trans_new], dim=3)
                    key_mn_trans = torch.cat([key_mn_trans, key_mn_trans_new], dim=3)
                else:
                    key_states_quant_trans = key_states_quant_trans_new
                    key_scale_trans = key_scale_trans_new
                    key_mn_trans = key_mn_trans_new

            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                    f" {attn_weights.size()}"
                )

            if attention_mask is not None:
                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                    raise ValueError(
                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                    )
                attn_weights = attn_weights + attention_mask
                attn_weights = torch.max(
                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
                )

            # upcast attention to fp32
            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)

            value_states_full = torch.cat([value_states_full, value_states], dim=2)
            value_full_length = value_states_full.shape[-2]
            if value_states_quant is None:
                value_states_full_repeat = repeat_kv(value_states_full, self.num_key_value_groups)
                attn_output = torch.matmul(attn_weights, value_states_full_repeat) # value_states_full need to be repeated
            else:
                value_states_quant_repeat = repeat_kv_quant(value_states_quant, self.num_key_value_groups)
                value_scale_repeat = repeat_kv_quant(value_scale, self.num_key_value_groups)
                value_mn_repeat = repeat_kv_quant(value_mn, self.num_key_value_groups)
                attn_output = cuda_bmm_fA_qB_outer(self.group_size, attn_weights[:, :, :, :-value_full_length], value_states_quant_repeat, 
                                                value_scale_repeat, value_mn_repeat, self.v_bits) # value_states_quant, value_scale, value_mn need to be repeated
                
                value_states_full_repeat = repeat_kv(value_states_full, self.num_key_value_groups)
                attn_output += torch.matmul(attn_weights[:, :, :, -value_full_length:], value_states_full_repeat) # value_states_full need to be repeated

            if value_states_full.shape[-2] > self.residual_length:
                assert value_states_full.shape[-2] == self.residual_length + 1
                value_states_quant_new, scale, mn = triton_quantize_and_pack_along_last_dim(value_states_full[:, :, :1, :].contiguous(), 
                                                                                                self.group_size, 
                                                                                                self.v_bits)
                value_states_full = value_states_full[:, :, 1:, :].contiguous()
                if value_states_quant is not None:
                    value_states_quant = torch.cat([value_states_quant, value_states_quant_new], dim=2)
                    value_scale = torch.cat([value_scale, scale], dim=2)
                    value_mn = torch.cat([value_mn, mn], dim=2)
                else:
                    value_states_quant = value_states_quant_new
                    value_scale = scale
                    value_mn = mn
        
        else:

            # quantize
            if key_states.shape[-2] % self.residual_length != 0:
                if key_states.shape[-2] < self.residual_length:
                    key_states_quant = None
                    key_states_full = key_states
                else:
                    key_states_quant = key_states[:, :, :-(key_states.shape[-2] % self.residual_length), :].contiguous()
                    key_states_full = key_states[:, :, -(key_states.shape[-2] % self.residual_length):, :].contiguous()
            else:
                key_states_quant = key_states
                key_states_full = None
            if key_states_quant is not None:
                key_states_quant_trans, key_scale_trans, key_mn_trans = triton_quantize_and_pack_along_last_dim(key_states_quant.transpose(2, 3).contiguous(), self.group_size, self.k_bits)
            else:
                key_states_quant_trans = None
                key_scale_trans = None
                key_mn_trans = None
            
            if value_states.shape[-2] <= self.residual_length:
                value_states_quant = None
                value_states_full = value_states
                value_scale = None
                value_mn = None
            else:
                value_states_quant = value_states[:, :, :-self.residual_length, :].contiguous()
                value_states_full = value_states[:, :, -self.residual_length:, :].contiguous()
                value_states_quant, value_scale, value_mn = triton_quantize_and_pack_along_last_dim(value_states_quant, 
                                                                                                self.group_size, 
                                                                                                self.v_bits)
        
            key_states = repeat_kv(key_states, self.num_key_value_groups)
            value_states = repeat_kv(value_states, self.num_key_value_groups)
            attn_weights = torch.matmul(query_states, 
                                        key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                    f" {attn_weights.size()}"
                )

            if attention_mask is not None:
                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                    raise ValueError(
                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                    )
                attn_weights = attn_weights + attention_mask
                attn_weights = torch.max(
                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
                )

            # upcast attention to fp32
            attn_weights = nn.functional.softmax(
                attn_weights, dim=-1, dtype=torch.float32
            ).to(query_states.dtype)

            attn_output = torch.matmul(attn_weights, value_states) 
        past_key_value = (key_states_quant_trans, key_states_full, key_scale_trans, key_mn_trans, value_states_quant, value_states_full, value_scale, value_mn, kv_seq_len) if use_cache else None
        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class MistralFlashAttention_MUSTAFAR(MistralAttention_MUSTAFAR):
    """
    Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ):
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

            # overwrite attention_mask with padding_mask
            attention_mask = kwargs.pop("padding_mask")
        bsz, q_len, _ = hidden_states.size()

        #'''
        #key_score_accumulator = self.key_score_accumulator
        #value_score_accumulator = self.value_score_accumulator
        #if key_score_accumulator is None:
        #    #key_score_accumulator = torch.zeros([bsz, self.num_heads, self.group_size, self.head_dim], dtype=torch.float32, device='cuda') #[B, H, self.group_size, D]
        #    key_score_accumulator = torch.zeros([bsz, self.num_heads // self.num_key_value_groups, self.group_size, self.head_dim], dtype=torch.float32, device='cuda') #[B, G, self.group_size, D]
        #if value_score_accumulator is None:
        #    #value_score_accumulator = torch.zeros([bsz, self.num_heads, 2 * self.group_size, self.head_dim], dtype=torch.float32, device='cuda') #[B, H, self.group_size, D]
        #    value_score_accumulator = torch.zeros([bsz, self.num_heads // self.num_key_value_groups, 2 * self.group_size, self.head_dim], dtype=torch.float32, device='cuda') #[B, G, self.group_size, D]
        #'''

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[-1]

        # Because the input can be padded, the absolute sequence length depends on the max position id.
        #rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
        #cos, sin = self.rotary_emb(value_states, rotary_seq_len)
        cos, sin = self.rotary_emb(value_states, position_ids)

        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        # use_sliding_windows = (
        #     _flash_supports_window_size
        #     and hasattr(self.config, "sliding_window") is not None
        #     and kv_seq_len > self.config.sliding_window
        # )

        # if not _flash_supports_window_size:
        #     logger.warning_once(
        #         "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
        #         " make sure to upgrade flash-attn library."
        #     )

        if past_key_value is not None:
            key_states_full = past_key_value[0]
            assert key_states_full is not None
            value_states_full = past_key_value[1]
            assert value_states_full is not None
            self.generation_count += 1

            # import ipdb; ipdb.set_trace()
            if DEBUG: print("Debug: self.num_key_value_groups: ", self.num_key_value_groups)
            if key_states_full is not None:
                key_states_full = torch.cat([key_states_full, key_states], dim=2)
            else:
                key_states_full = key_states

            att_qkfull = torch.matmul(query_states, repeat_kv(key_states_full, self.num_key_value_groups).transpose(2, 3))
            attn_weights = att_qkfull / math.sqrt(self.head_dim)

            if (key_states_full.shape[-2] / self.residual_length) == 0:
                pass
            else:
                #take the first token of 'residual group' and prune it. 
                #so compressed_key + newly_pruned + 127 residual group ones + new cache. 
                
                # Get all states except the one we want to modify
                prefix = key_states_full[:, :, :-(self.residual_length+1), :]
                suffix = key_states_full[:, :, -(self.residual_length):, :]
                
                # Reshape pruned state to match original dimensions
                #key_states_new = self.dh_prune_key(key_states_full[:, :, -(self.residual_length+1), :]).contiguous()
                key_states_new = self.dh_prune_key(key_states_full[:, :, -(self.residual_length+1):(-(self.residual_length+1)+1), :]).contiguous()
                
                
                # Concatenate back together with pruned state in the middle
                key_states_full = torch.cat([prefix, key_states_new, suffix], dim=2)
                
                if DEBUG: print("Debug:---- key_states_after modification: ", key_states_full.shape)

            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                    f" {attn_weights.size()}"
                )

            if attention_mask is not None:
                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                    raise ValueError(
                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                    )
                attn_weights = attn_weights + attention_mask
                attn_weights = torch.max(
                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
                )

            # upcast attention to fp32
            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)

            value_states_full = torch.cat([value_states_full, value_states], dim=2)
            
            attn_output = torch.matmul(attn_weights, repeat_kv(value_states_full, self.num_key_value_groups)) #[B, H, 1, seq_len]

            '''
            pruned_flag, value_states_pruned = self.dh_prune_value(self.generation_count, attn_weights, value_states_full, value_score_accumulator)#.contiguous() #pass in the last residual_length number of vectors. 
            if pruned_flag:
                if DEBUG: print("Debug:---- During Decode, Value Cache pruned!: ")
                value_states_full = torch.cat((value_states_full[:,:,:-2*self.group_size,:], value_states_pruned, value_states_full[:,:,-self.group_size:,:]), dim=2)
            else: 
                if DEBUG: print("Debug:---- During Decode, Value Cache not pruned!: ")
                pass #do nothing. 
            '''
            if (value_states_full.shape[-2] / self.residual_length) == 0:
                pass
            else:
                #take the first token of 'residual group' and prune it. 
                #so compressed_key + newly_pruned + 127 residual group ones + new cache. 
                
                # Get all states except the one we want to modify
                prefix = value_states_full[:, :, :-(self.residual_length+1), :]
                suffix = value_states_full[:, :, -(self.residual_length):, :]
                
                # Reshape pruned state to match original dimensions
                #key_states_new = self.dh_prune_key(key_states_full[:, :, -(self.residual_length+1), :]).contiguous()
                value_states_new = self.dh_prune_value(value_states_full[:, :, -(self.residual_length+1):(-(self.residual_length+1)+1), :]).contiguous()
                
                
                # Concatenate back together with pruned state in the middle
                value_states_full = torch.cat([prefix, value_states_new, suffix], dim=2)
                
                if DEBUG: print("Debug:---- value_states_after modification: ", value_states_full.shape)


        else:
            if DEBUG: print(f"Prefill, Using Flash Attention!")
            input_dtype = query_states.dtype
            self.generation_count = 0
            
            #key_states_repeat = repeat_kv(key_states, self.num_key_value_groups)
            #value_states_repeat = repeat_kv(value_states, self.num_key_value_groups)
        
            attn_output = self._flash_attention_forward(
                query_states.transpose(1, 2), key_states.transpose(1, 2), 
                value_states.transpose(1, 2), None, q_len, dropout=0.0
            )

            #attn_output = self._flash_attention_forward(
            #    query_states.transpose(1, 2), key_states.transpose(1, 2), 
            #    value_states.transpose(1, 2), None, q_len, dropout=0.0
            #)

            if (key_states.shape[-2] / self.residual_length) == 0:
                key_states_full = self.dh_prune_key(key_states)
            else:
                prefix = key_states[:, :, :-(self.residual_length), :]
                suffix = key_states[:, :, -(self.residual_length):, :]
                prefix_pruned = self.dh_prune_key(prefix).contiguous()
                key_states_full = torch.cat([prefix_pruned, suffix], dim=2) 

            #[05/01] last 32 attention score for value cache pruning during decode.
            
            
            #query_states: [B, n_heads, q_len, d]
            #key_states_full: [B, n_kv_heads, k_len, d]
            #num_key_value_groups = n_heads // n_kv_heads
            #print("Debug: num_key_value_groups: ", self.num_key_value_groups)

            #for Now, let's just use the last attention score.
            #attn_weights = torch.matmul(query_states[:, :, -1:, :], repeat_kv(key_states_full, self.num_key_value_groups).transpose(-2, -1)) / math.sqrt(self.head_dim)  # [B, H, 32, kv_len]
            attn_weights = torch.matmul(query_states[:, :, -self.group_size:, :], repeat_kv(key_states_full, self.num_key_value_groups).transpose(-2, -1)) / math.sqrt(self.head_dim)  # [B, H, 32, kv_len]
            if DEBUG: print("Debug: attention_mask shape: ", attention_mask.shape) #[1, 1, T, T]
            if DEBUG: print("attention_mask: ", attention_mask)
            assert attention_mask is not None
            attn_mask = attention_mask[:, :, -self.group_size:, :]
            attn_mask = attn_mask.expand(-1, self.num_heads, -1, -1)
            if DEBUG: print("Debug: attn_mask: ", attn_mask.shape)
            if DEBUG: print("Debug: attn_mask: ", attn_mask[0, 0, :, :])
            attn_weights = attn_weights + attn_mask
            attn_weights = torch.max(
                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
                )
            if DEBUG: print("Debug: attn_weights: ", attn_weights.shape)
            if DEBUG: print("Debug: attn_weights: ", attn_weights[0, 0, :, :])
            #the above code contains room for memory footprint optimization. 

            #softmax is needed here. 
            attn_weights = nn.functional.softmax(
                    attn_weights, dim=-1, dtype=torch.float32
                ).to(query_states.dtype)
            if DEBUG: print("Debug: attn_weights: ", attn_weights.shape)
            if DEBUG: print("Debug: attn_weights: ", attn_weights[0, 0, :, :])
            
            #_, value_states_full = self.dh_prune_value(self.generation_count, attn_weights, value_states, value_score_accumulator)
            if (value_states.shape[-2] / self.residual_length) == 0:
                value_states_full = self.dh_prune_value(value_states)
            else:
                prefix = value_states[:, :, :-(self.residual_length), :]
                suffix = value_states[:, :, -(self.residual_length):, :]
                prefix_pruned = self.dh_prune_value(prefix).contiguous()
                value_states_full = torch.cat([prefix_pruned, suffix], dim=2)
            
        past_key_value = (key_states_full, value_states_full, kv_seq_len)

        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
        attn_output = self.o_proj(attn_output)

        # if not output_attentions:
        attn_weights = None
        if DEBUG: print(f"Iteration: {self.generation_count}","KV sparsity: ",  self.calculate_sparsity(key_states_full),  self.calculate_sparsity(value_states_full),  "   ________________________________")


        return attn_output, attn_weights, past_key_value

    def _flash_attention_forward(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        query_length,
        dropout=0.0,
        softmax_scale=None,
        use_sliding_windows=False,
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`int`, *optional*):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
            use_sliding_windows (`bool`, *optional*):
                Whether to activate sliding window attention.
        """
        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            if not use_sliding_windows:
                attn_output_unpad = flash_attn_varlen_func(
                    query_states,
                    key_states,
                    value_states,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_k=cu_seqlens_k,
                    max_seqlen_q=max_seqlen_in_batch_q,
                    max_seqlen_k=max_seqlen_in_batch_k,
                    dropout_p=dropout,
                    softmax_scale=softmax_scale,
                    causal=self.is_causal,
                )
            else:
                attn_output_unpad = flash_attn_varlen_func(
                    query_states,
                    key_states,
                    value_states,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_k=cu_seqlens_k,
                    max_seqlen_q=max_seqlen_in_batch_q,
                    max_seqlen_k=max_seqlen_in_batch_k,
                    dropout_p=dropout,
                    softmax_scale=softmax_scale,
                    causal=self.is_causal,
                    window_size=(self.config.sliding_window, self.config.sliding_window),
                )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            if not use_sliding_windows:
                attn_output = flash_attn_func(
                    query_states,
                    key_states,
                    value_states,
                    dropout,
                    softmax_scale=softmax_scale,
                    causal=self.is_causal,
                )
            else:
                attn_output = flash_attn_func(
                    query_states,
                    key_states,
                    value_states,
                    dropout,
                    softmax_scale=softmax_scale,
                    causal=self.is_causal,
                    window_size=(self.config.sliding_window, self.config.sliding_window),
                )

        return attn_output

    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape

        # On the first iteration we need to properly re-create the padding mask
        # by slicing it on the proper place
        if kv_seq_len != attention_mask.shape[-1]:
            attention_mask_num_tokens = attention_mask.shape[-1]
            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]

        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)

        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)

        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


class MistralDecoderLayer_MUSTAFAR(nn.Module):
    def __init__(self, config: MistralConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = (
            MistralAttention_MUSTAFAR(config=config)
            if not getattr(config, "use_flash", False)
            else MistralFlashAttention_MUSTAFAR(config)
        )
        self.mlp = MistralMLP(config)
        self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, sequence_length)` where padding elements are indicated by 0.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


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

    Args:
        config: MistralConfig
    """

    def __init__(self, config: MistralConfig):
        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_MUSTAFAR(config) for _ in range(config.num_hidden_layers)])
        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,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        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

        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")

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            # past_key_values_length = past_key_values[0][0].shape[2]
            past_key_values_length = past_key_values[0][-1]

            seq_length_with_past = seq_length_with_past + past_key_values_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 hasattr(self.config, "_flash_attn_2_enabled")
            and self.config._flash_attn_2_enabled
            and past_key_values is not None
        ):
            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 getattr(self.config, "_flash_attn_2_enabled", False):
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        else:
            # 4d mask is passed through the layers
            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

        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

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

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = past_key_values[idx] if past_key_values is not None else None

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

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


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

    def __init__(self, config):
        super().__init__(config)
        self.model = MistralModel_MUSTAFAR(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # 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,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        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(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> 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

        # 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,
        )

        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)

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

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if isinstance(past_key_values, DynamicCache):
            past_key_values = past_key_values.to_legacy_cache()
            if len(past_key_values) == 0:
                past_key_values = None
        
        # Omit tokens covered by past_key_values
        if past_key_values:
            # past_length = past_key_values[0][0].shape[2]
            past_length = past_key_values[0][-1]

            # Some generation methods already pass only the last input ID
            if input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = input_ids.shape[1] - 1

            input_ids = input_ids[:, remove_prefix_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