sparsetral-16x7B-v2-8.0bpw-h8-exl2 / modeling_sparsetral.py

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	# 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 dataclasses import dataclass
	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 CrossEntropyLoss

	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 .configuration_sparsetral import SparsetralConfig


	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
	)


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "SparsetralConfig"


	@dataclass
	class MoEModelOutputWithPast(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
	router_logits: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	class MoECausalLMOutputWithPast(ModelOutput):
	loss: Optional[torch.FloatTensor] = None
	aux_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
	router_logits: Optional[Tuple[torch.FloatTensor]] = None


	# Copied from transformers.models.llama.modeling_llama._get_unpad_data
	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,
	)


	# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
	class MistralRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	MistralRMSNorm is equivalent to T5LayerNorm
	"""
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

	def forward(self, hidden_states):
	input_dtype = hidden_states.dtype
	hidden_states = hidden_states.to(torch.float32)
	variance = hidden_states.pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
	return self.weight * hidden_states.to(input_dtype)


	# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral
	class MistralRotaryEmbedding(nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	super().__init__()

	self.dim = dim
	self.max_position_embeddings = max_position_embeddings
	self.base = base
	inv_freq = 1.0 / (
	self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
	)
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	# Build here to make `torch.jit.trace` work.
	self._set_cos_sin_cache(
	seq_len=max_position_embeddings,
	device=self.inv_freq.device,
	dtype=torch.get_default_dtype(),
	)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(
	self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
	)

	freqs = torch.outer(t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	if seq_len > self.max_seq_len_cached:
	self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

	return (
	self.cos_cached[:seq_len].to(dtype=x.dtype),
	self.sin_cached[:seq_len].to(dtype=x.dtype),
	)


	# Copied from transformers.models.llama.modeling_llama.rotate_half
	def rotate_half(x):
	"""Rotates half the hidden dims of the input."""
	x1 = x[..., : x.shape[-1] // 2]
	x2 = x[..., x.shape[-1] // 2 :]
	return torch.cat((-x2, x1), dim=-1)


	# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
	def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
	"""Applies Rotary Position Embedding to the query and key tensors.

	Args:
	q (`torch.Tensor`): The query tensor.
	k (`torch.Tensor`): The key tensor.
	cos (`torch.Tensor`): The cosine part of the rotary embedding.
	sin (`torch.Tensor`): The sine part of the rotary embedding.
	position_ids (`torch.Tensor`):
	The position indices of the tokens corresponding to the query and key tensors. For example, this can be
	used to pass offsetted position ids when working with a KV-cache.
	unsqueeze_dim (`int`, optional, defaults to 1):
	The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
	sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
	that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
	k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
	cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
	the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
	Returns:
	`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
	"""
	cos = cos[position_ids].unsqueeze(unsqueeze_dim)
	sin = sin[position_ids].unsqueeze(unsqueeze_dim)
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	# Mistral MoE
	def load_balancing_loss_func(
	gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2
	) -> float:
	r"""
	Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

	See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
	function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
	experts is too unbalanced.

	Args:
	gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
	Logits from the `gate`, should be a tuple of tensors. Shape: [batch_size, seqeunce_length, num_experts].
	num_experts (`int`, optional):
	Number of experts

	Returns:
	The auxiliary loss.
	"""
	if gate_logits is None:
	return 0

	if isinstance(gate_logits, tuple):
	# cat along the layers?
	compute_device = gate_logits[0].device
	gate_logits = torch.cat(
	[gate.to(compute_device) for gate in gate_logits], dim=0
	)

	routing_weights, selected_experts = torch.topk(gate_logits, top_k, dim=-1)
	routing_weights = routing_weights.softmax(dim=-1)

	# cast the expert indices to int64, otherwise one-hot encoding will fail
	if selected_experts.dtype != torch.int64:
	selected_experts = selected_experts.to(torch.int64)

	if len(selected_experts.shape) == 2:
	selected_experts = selected_experts.unsqueeze(2)

	expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)

	# For a given token, determine if it was routed to a given expert.
	expert_mask = torch.max(expert_mask, axis=-2).values

	# cast to float32 otherwise mean will fail
	expert_mask = expert_mask.to(torch.float32)
	tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)

	router_prob_per_group_and_expert = torch.mean(routing_weights, axis=-1)
	return torch.mean(
	tokens_per_group_and_expert * router_prob_per_group_and_expert.unsqueeze(-1)
	) * (num_experts**2)


	class ParallelAdapterMLP(nn.Module):
	def __init__(self, config, adapter_dim, adapter_scaling):
	super().__init__()
	self.config = config
	self.intermediate_size = config.intermediate_size
	self.hidden_size = config.hidden_size
	self.adapter_down = nn.Linear(self.hidden_size, adapter_dim, bias=False)
	self.adapter_up = nn.Linear(adapter_dim, self.hidden_size, bias=False)
	self.adapter_act = nn.GELU()

	self.adapter_dropout = nn.Dropout(p=config.adapter_dropout)
	self.adapter_scaling = adapter_scaling

	def forward(self, x):
	x = self.adapter_dropout(x)
	x = self.adapter_scaling * self.adapter_up(
	self.adapter_act(self.adapter_down(x))
	)
	return x


	class SparsetralGateAdapter(nn.Module):
	def __init__(self, config: SparsetralConfig):
	super().__init__()

	self.intermediate_size = config.intermediate_size
	self.hidden_size = config.hidden_size

	# Step 1: Router
	self.num_experts = config.num_experts
	self.topk = config.topk
	self.router = nn.Linear(config.hidden_size, self.num_experts, bias=False)
	self.dtype = getattr(torch, config.moe_dtype)

	# Step 2: Get the experts
	self.expert_indicies = list(range(self.num_experts))
	self.experts = nn.ModuleList(
	[
	ParallelAdapterMLP(config, config.adapter_dim, config.moe_scaling)
	for _ in self.expert_indicies
	]
	)

	def forward(self, input_hidden_states, output_hidden_states, router_hidden_states):
	orig_shape = output_hidden_states.shape
	input_hidden_states = input_hidden_states.view(
	-1, input_hidden_states.shape[-1]
	)
	output_hidden_states = output_hidden_states.view(
	-1, output_hidden_states.shape[-1]
	)
	router_hidden_states = router_hidden_states.view(
	-1, router_hidden_states.shape[-1]
	)

	router_logits = self.router(router_hidden_states)

	routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
	routing_weights, selected_experts = torch.topk(
	routing_weights, self.topk, dim=-1
	)
	routing_weights /= routing_weights.sum(dim=-1, keepdim=True)

	final_hidden_states = None
	for expert_idx in self.expert_indicies:
	expert_layer = self.experts[expert_idx]
	expert_mask = selected_experts == expert_idx
	expert_weights = (routing_weights * expert_mask).sum(dim=-1, keepdim=True)

	current_hidden_states = (
	expert_layer(input_hidden_states)
	.add_(output_hidden_states)
	.mul_(expert_weights)
	)
	if final_hidden_states is None:
	final_hidden_states = current_hidden_states
	else:
	final_hidden_states.add_(current_hidden_states)

	return final_hidden_states.view(*orig_shape), router_logits


	class MistralMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.intermediate_size = config.intermediate_size
	self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
	self.act_fn = ACT2FN[config.hidden_act]

	self.moe_adapter = SparsetralGateAdapter(config)

	def forward(self, x):
	router_hidden_states = x
	up_proj = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
	down_proj = self.down_proj(up_proj)
	down_proj, router_logits = self.moe_adapter(
	down_proj, down_proj, router_hidden_states
	)

	return down_proj, router_logits


	# Copied from transformers.models.llama.modeling_llama.repeat_kv
	def repeat_kv(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(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: SparsetralConfig, layer_idx: Optional[int] = None):
	super().__init__()
	self.config = config
	self.layer_idx = layer_idx
	if layer_idx is None:
	logger.warning_once(
	f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
	"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
	"when creating this class."
	)

	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.attention_dropout = config.attention_dropout

	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,
	)

	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 forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Cache] = 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:
	if self.layer_idx is None:
	raise ValueError(
	f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
	"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
	"with a layer index."
	)
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	query_states, key_states = apply_rotary_pos_emb(
	query_states, key_states, cos, sin, position_ids
	)

	if past_key_value is not None:
	cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	key_states, value_states = past_key_value.update(
	key_states, value_states, self.layer_idx, cache_kwargs
	)

	# repeat k/v heads if n_kv_heads < n_heads
	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

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(
	attn_weights, dim=-1, dtype=torch.float32
	).to(query_states.dtype)
	attn_weights = nn.functional.dropout(
	attn_weights, p=self.attention_dropout, training=self.training
	)
	attn_output = torch.matmul(attn_weights, value_states)

	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 MistralFlashAttention2(MistralAttention):
	"""
	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.
	"""

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)

	# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
	# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
	# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
	self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Cache] = 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()

	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:
	if self.layer_idx is None:
	raise ValueError(
	f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
	"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
	"with a layer index."
	)
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

	# 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, seq_len=rotary_seq_len)

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

	use_sliding_windows = (
	_flash_supports_window_size
	and getattr(self.config, "sliding_window", None) 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:
	# Activate slicing cache only if the config has a value `sliding_windows` attribute
	cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
	if (
	getattr(self.config, "sliding_window", None) is not None
	and kv_seq_len > self.config.sliding_window
	and cache_has_contents
	):
	slicing_tokens = 1 - self.config.sliding_window

	past_key = past_key_value[self.layer_idx][0]
	past_value = past_key_value[self.layer_idx][1]

	past_key = past_key[:, :, slicing_tokens:, :].contiguous()
	past_value = past_value[:, :, slicing_tokens:, :].contiguous()

	if past_key.shape[-2] != self.config.sliding_window - 1:
	raise ValueError(
	f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
	f" {past_key.shape}"
	)

	if attention_mask is not None:
	attention_mask = attention_mask[:, slicing_tokens:]
	attention_mask = torch.cat(
	[attention_mask, torch.ones_like(attention_mask[:, -1:])],
	dim=-1,
	)

	cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	key_states, value_states = past_key_value.update(
	key_states, value_states, self.layer_idx, cache_kwargs
	)

	# repeat k/v heads if n_kv_heads < n_heads
	key_states = repeat_kv(key_states, self.num_key_value_groups)
	value_states = repeat_kv(value_states, self.num_key_value_groups)
	dropout_rate = 0.0 if not self.training else self.attention_dropout

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in float16 just to be sure everything works as expected.
	input_dtype = query_states.dtype
	if input_dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(self.config, "_pre_quantization_dtype"):
	target_dtype = self.config._pre_quantization_dtype
	else:
	target_dtype = self.q_proj.weight.dtype

	logger.warning_once(
	f"The input hidden states seems to be silently casted in float32, this might be related to"
	f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
	f" {target_dtype}."
	)

	query_states = query_states.to(target_dtype)
	key_states = key_states.to(target_dtype)
	value_states = value_states.to(target_dtype)

	# Reashape to the expected shape for Flash Attention
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)

	attn_output = self._flash_attention_forward(
	query_states,
	key_states,
	value_states,
	attention_mask,
	q_len,
	dropout=dropout_rate,
	use_sliding_windows=use_sliding_windows,
	)

	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

	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.
	"""
	if not self._flash_attn_uses_top_left_mask:
	causal = self.is_causal
	else:
	# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
	causal = self.is_causal and query_length != 1

	# 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=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=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=causal,
	)
	else:
	attn_output = flash_attn_func(
	query_states,
	key_states,
	value_states,
	dropout,
	softmax_scale=softmax_scale,
	causal=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),
	)


	# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral
	class MistralSdpaAttention(MistralAttention):
	"""
	Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
	`MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
	SDPA API.
	"""

	# Adapted from MistralAttention.forward
	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	if output_attentions:
	# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
	logger.warning_once(
	"MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
	'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
	)
	return super().forward(
	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,
	)

	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.get_usable_length(kv_seq_len, self.layer_idx)
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

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

	if past_key_value is not None:
	cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	key_states, value_states = past_key_value.update(
	key_states, value_states, self.layer_idx, cache_kwargs
	)

	key_states = repeat_kv(key_states, self.num_key_value_groups)
	value_states = repeat_kv(value_states, self.num_key_value_groups)

	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()}"
	)

	# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
	# Reference: https://github.com/pytorch/pytorch/issues/112577.
	if query_states.device.type == "cuda" and attention_mask is not None:
	query_states = query_states.contiguous()
	key_states = key_states.contiguous()
	value_states = value_states.contiguous()

	attn_output = torch.nn.functional.scaled_dot_product_attention(
	query_states,
	key_states,
	value_states,
	attn_mask=attention_mask,
	dropout_p=self.attention_dropout if self.training else 0.0,
	# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
	is_causal=self.is_causal and attention_mask is None and q_len > 1,
	)

	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)

	return attn_output, None, past_key_value


	MISTRAL_ATTENTION_CLASSES = {
	"eager": MistralAttention,
	"flash_attention_2": MistralFlashAttention2,
	"sdpa": MistralSdpaAttention,
	}


	class MistralDecoderLayer(nn.Module):
	def __init__(self, config: SparsetralConfig, layer_idx: int):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size

	self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](
	config, layer_idx
	)

	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,
	output_router_logits: 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, router_logits = 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,)

	if output_router_logits:
	outputs += (router_logits,)

	return outputs


	MISTRAL_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 ([`SparsetralConfig`]):
	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.
	"""


	@add_start_docstrings(
	"The bare Mistral Model outputting raw hidden-states without any specific head on top.",
	MISTRAL_START_DOCSTRING,
	)
	class MistralPreTrainedModel(PreTrainedModel):
	config_class = SparsetralConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["MistralDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_supports_flash_attn_2 = True
	_supports_sdpa = True
	_supports_cache_class = True

	def _init_weights(self, module):
	std = self.config.initializer_range
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, MistralModel):
	module.gradient_checkpointing = value


	MISTRAL_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_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.

	[What are attention masks?](../glossary#attention-mask)

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

	If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
	`past_key_values`).

	If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
	and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
	information on the default strategy.

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.
	position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, optional):
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
	returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

	Two formats are allowed:
	- a [`~cache_utils.Cache`] instance;
	- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
	shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
	cache format.

	The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
	legacy cache format will be returned.

	If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
	have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
	of shape `(batch_size, sequence_length)`.
	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.
	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`).
	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 Mistral Model outputting raw hidden-states without any specific head on top.",
	MISTRAL_START_DOCSTRING,
	)
	class MistralModel(MistralPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`MistralDecoderLayer`]

	Args:
	config: MistralConfig
	"""

	def __init__(self, config: SparsetralConfig):
	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,
	output_router_logits: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, MoEModelOutputWithPast]:
	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
	)
	output_router_logits = (
	output_router_logits
	if output_router_logits is not None
	else self.config.output_router_logits
	)
	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

	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]
	seq_length_with_past = seq_length_with_past + past_key_values_length

	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
	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.
	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
	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_self_attns = () if output_attentions else None
	all_router_logits = () if output_router_logits else None
	next_decoder_cache = None

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

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(
	*inputs, output_attentions, output_router_logits, None
	)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	None,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_values,
	output_attentions=output_attentions,
	output_router_logits=output_router_logits,
	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],)

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

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


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

	def __init__(self, config):
	super().__init__(config)
	self.config = config
	self.model = MistralModel(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,
	output_router_logits: 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("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
	)
	output_router_logits = (
	output_router_logits
	if output_router_logits is not None
	else self.config.output_router_logits
	)
	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,
	output_router_logits=output_router_logits,
	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)

	aux_loss = None
	if output_router_logits:
	aux_loss = load_balancing_loss_func(
	outputs.router_logits if return_dict else outputs[-1],
	self.config.num_experts,
	self.config.topk,
	)
	if labels is not None:
	loss += self.config.router_aux_loss_coef * aux_loss

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

	return MoECausalLMOutputWithPast(
	loss=loss,
	aux_loss=aux_loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	router_logits=outputs.router_logits,
	)

	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[:, -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 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