Jamba-v0.1 / modeling_jamba.py

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	# coding=utf-8
	# Copyright 2024 AI21 Labs Ltd. 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 Jamba model."""
	import inspect
	import math
	from typing import Any, Dict, List, Optional, Tuple, Union

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

	from transformers.activations import ACT2FN
	from transformers.cache_utils import DynamicCache # we need __iter__ and __len__ of pkv
	from transformers.modeling_attn_mask_utils import (
	AttentionMaskConverter,
	)
	from transformers.modeling_outputs import (
	MoeCausalLMOutputWithPast,
	MoeModelOutputWithPast,
	SequenceClassifierOutputWithPast,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	is_flash_attn_greater_or_equal_2_10,
	logging,
	replace_return_docstrings,
	)
	from transformers.utils.import_utils import (
	is_causal_conv1d_available,
	is_flash_attn_2_available,
	is_mamba_ssm_available,
	)
	from .configuration_jamba import JambaConfig


	# try except block so it'll work with trust_remote_code.
	try:
	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)
	except ImportError:
	pass


	# try except block so it'll work with trust_remote_code.
	try:
	from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn
	from mamba_ssm.ops.triton.selective_state_update import selective_state_update
	except ImportError:
	selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None

	# try except block so it'll work with trust_remote_code.
	try:
	from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
	except ImportError:
	causal_conv1d_update, causal_conv1d_fn = None, None

	is_fast_path_available = all(
	(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
	)


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "JambaConfig"


	# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func with gate->router
	def load_balancing_loss_func(
	router_logits: torch.Tensor,
	num_experts: torch.Tensor = None,
	top_k=2,
	attention_mask: Optional[torch.Tensor] = None,
	) -> 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:
	router_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
	Logits from the `router`, should be a tuple of model.config.num_hidden_layers tensors of
	shape [batch_size X sequence_length, num_experts].
	attention_mask (`torch.Tensor`, None):
	The attention_mask used in forward function
	shape [batch_size X sequence_length] if not None.
	num_experts (`int`, optional):
	Number of experts

	Returns:
	The auxiliary loss.
	"""
	if router_logits is None or not isinstance(router_logits, tuple):
	return 0

	if isinstance(router_logits, tuple):
	compute_device = router_logits[0].device
	concatenated_router_logits = torch.cat(
	[layer_router.to(compute_device) for layer_router in router_logits], dim=0
	)

	routing_weights = torch.nn.functional.softmax(concatenated_router_logits, dim=-1)

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

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

	if attention_mask is None:
	# Compute the percentage of tokens routed to each experts
	tokens_per_expert = torch.mean(expert_mask.float(), dim=0)

	# Compute the average probability of routing to these experts
	router_prob_per_expert = torch.mean(routing_weights, dim=0)
	else:
	batch_size, sequence_length = attention_mask.shape
	num_hidden_layers = concatenated_router_logits.shape[0] // (batch_size * sequence_length)

	# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
	expert_attention_mask = (
	attention_mask[None, :, :, None, None]
	.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
	.reshape(-1, top_k, num_experts)
	.to(compute_device)
	)

	# Compute the percentage of tokens routed to each experts
	tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
	expert_attention_mask, dim=0
	)

	# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
	router_per_expert_attention_mask = (
	attention_mask[None, :, :, None]
	.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
	.reshape(-1, num_experts)
	.to(compute_device)
	)

	# Compute the average probability of routing to these experts
	router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
	router_per_expert_attention_mask, dim=0
	)

	overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
	return overall_loss * num_experts


	# 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.int32), (1, 0))
	return (
	indices,
	cu_seqlens,
	max_seqlen_in_batch,
	)


	# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Jamba
	class JambaRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	JambaRMSNorm 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.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 HybridMambaAttentionDynamicCache(DynamicCache):
	"""
	A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
	(which has a constant shape regardless of seq_len).

	This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
	and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
	For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
	while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
	For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
	while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
	and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
	"""

	def __init__(self, config, batch_size, dtype=torch.float16, device=None):
	self.dtype = dtype
	self.layers_block_type = config.layers_block_type
	self.has_previous_state = False # only used by mamba
	intermediate_size = config.mamba_expand * config.hidden_size
	ssm_state_size = config.mamba_d_state
	conv_kernel_size = config.mamba_d_conv
	self.conv_states = []
	self.ssm_states = []
	for i in range(config.num_hidden_layers):
	if self.layers_block_type[i] == "mamba":
	self.conv_states += [
	torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype)
	]
	self.ssm_states += [
	torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype)
	]
	else:
	self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
	self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]

	self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
	self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]

	def update(
	self,
	key_states: torch.Tensor,
	value_states: torch.Tensor,
	layer_idx: int,
	cache_kwargs: Optional[Dict[str, Any]] = None,
	) -> Tuple[torch.Tensor, torch.Tensor]:
	# Update the cache
	if self.key_cache[layer_idx].shape[-1] == 0:
	self.key_cache[layer_idx] = key_states
	self.value_cache[layer_idx] = value_states
	else:
	self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
	self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)

	return self.key_cache[layer_idx], self.value_cache[layer_idx]

	def reorder_cache(self, beam_idx: torch.LongTensor):
	"""Reorders the cache for beam search, given the selected beam indices."""
	for layer_idx in range(len(self.key_cache)):
	device = self.key_cache[layer_idx].device
	self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
	device = self.value_cache[layer_idx].device
	self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))

	device = self.conv_states[layer_idx].device
	self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
	device = self.ssm_states[layer_idx].device
	self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))

	def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
	raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.")

	@classmethod
	def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None) -> "DynamicCache":
	raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.")


	# Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Jamba
	class JambaAttention(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: JambaConfig, 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.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)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	cache_position: Optional[torch.LongTensor] = None,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	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)

	if past_key_value is not None:
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)

	# 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 attention_mask is not None: # no matter the length, we just slice it
	causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
	attn_weights = attn_weights + causal_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


	# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Jamba
	class JambaFlashAttention2(JambaAttention):
	"""
	Jamba flash attention module. This module inherits from `JambaAttention` 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[HybridMambaAttentionDynamicCache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	cache_position: Optional[torch.LongTensor] = None,
	**kwargs,
	):
	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)

	# Flash attention requires the input to have the shape
	# batch_size x seq_length x head_dim x hidden_dim
	# therefore we just need to keep the original shape
	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 = cache_position[-1]

	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 = cache_position[0] > 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)

	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)

	# 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 (`float`, 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

	# Copied from transformers.models.mixtral.modeling_mixtral.MixtralFlashAttention2._upad_input
	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),
	)


	# Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Jamba
	class JambaSdpaAttention(JambaAttention):
	"""
	Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
	`JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
	SDPA API.
	"""

	# Adapted from JambaAttention.forward
	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	cache_position: Optional[torch.LongTensor] = None,
	) -> 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(
	"JambaModel is using JambaSdpaAttention, 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)

	if past_key_value is not None:
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)

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

	causal_mask = attention_mask
	if attention_mask is not None:
	causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]

	# 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=causal_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.view(bsz, q_len, self.hidden_size)

	attn_output = self.o_proj(attn_output)

	return attn_output, None, past_key_value


	JAMBA_ATTENTION_CLASSES = {
	"eager": JambaAttention,
	"flash_attention_2": JambaFlashAttention2,
	"sdpa": JambaSdpaAttention,
	}


	# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
	class JambaMambaMixer(nn.Module):
	"""
	Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
	A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
	∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
	and is why Mamba is called selective state spaces)
	"""

	def __init__(self, config: JambaConfig, layer_idx):
	super().__init__()
	self.config = config
	self.layer_idx = layer_idx
	self.hidden_size = config.hidden_size
	self.ssm_state_size = config.mamba_d_state
	self.conv_kernel_size = config.mamba_d_conv
	self.intermediate_size = config.mamba_expand * config.hidden_size
	self.time_step_rank = config.mamba_dt_rank
	self.use_conv_bias = config.mamba_conv_bias
	self.use_bias = config.mamba_proj_bias
	self.conv1d = nn.Conv1d(
	in_channels=self.intermediate_size,
	out_channels=self.intermediate_size,
	bias=self.use_conv_bias,
	kernel_size=self.conv_kernel_size,
	groups=self.intermediate_size,
	padding=self.conv_kernel_size - 1,
	)

	self.activation = config.hidden_act
	self.act = ACT2FN[config.hidden_act]

	self.use_fast_kernels = config.use_mamba_kernels

	# projection of the input hidden states
	self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=self.use_bias)
	# selective projection used to make dt, B and C input dependant
	self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
	# time step projection (discretization)
	self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)

	# S4D real initialization. These are not discretized!
	# The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
	A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :]
	A = A.expand(self.intermediate_size, -1).contiguous()

	self.A_log = nn.Parameter(torch.log(A))
	self.D = nn.Parameter(torch.ones(self.intermediate_size))
	self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)

	self.dt_layernorm = JambaRMSNorm(self.time_step_rank, eps=config.rms_norm_eps)
	self.b_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)
	self.c_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)

	if not is_fast_path_available:
	logger.warning_once(
	"The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
	" is None. To install follow https://github.com/state-spaces/mamba/#installation and"
	" https://github.com/Dao-AILab/causal-conv1d. If you want to use the naive implementation, set `use_mamba_kernels=False` in the model config"
	)

	def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: HybridMambaAttentionDynamicCache = None):
	batch_size, seq_len, _ = hidden_states.shape
	use_precomputed_states = (
	cache_params is not None
	and cache_params.has_previous_state
	and seq_len == 1
	and cache_params.conv_states[self.layer_idx].shape[0]
	== cache_params.ssm_states[self.layer_idx].shape[0]
	== batch_size
	)
	# 1. Gated MLP's linear projection
	projected_states = self.in_proj(hidden_states).transpose(1, 2)

	# We can't use `mamba_inner_fn` even if in training and without cache params because we have the
	# inner layernorms which isn't supported by this fused kernel
	hidden_states, gate = projected_states.chunk(2, dim=1)

	# 2. Convolution sequence transformation
	conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
	if use_precomputed_states:
	hidden_states = causal_conv1d_update(
	hidden_states.squeeze(-1),
	cache_params.conv_states[self.layer_idx],
	conv_weights,
	self.conv1d.bias,
	self.activation,
	)
	hidden_states = hidden_states.unsqueeze(-1)
	else:
	if cache_params is not None:
	conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
	cache_params.conv_states[self.layer_idx].copy_(conv_states)
	hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)

	# 3. State Space Model sequence transformation
	# 3.a. input varying initialization of time_step, B and C
	ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
	time_step, B, C = torch.split(
	ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
	)

	time_step = self.dt_layernorm(time_step)
	B = self.b_layernorm(B)
	C = self.c_layernorm(C)

	# Here we need to apply dt_proj without the bias, as the bias is added in the selective scan kernel.
	# This is a hack to apply dt_proj while still using the forward pass of `torch.nn.Linear`, which is needed
	# in order to make quantization work. Quantization code replaces `torch.nn.Linear` layers with quantized
	# linear layers, and requires to call the forward pass directly.
	# The original code here was: ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)```
	time_proj_bias = self.dt_proj.bias
	self.dt_proj.bias = None
	discrete_time_step = self.dt_proj(time_step).transpose(1, 2)
	self.dt_proj.bias = time_proj_bias

	A = -torch.exp(self.A_log.float())
	# 3.c perform the recurrence y ← SSM(A, B, C)(x)
	time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
	if use_precomputed_states:
	scan_outputs = selective_state_update(
	cache_params.ssm_states[self.layer_idx],
	hidden_states[..., 0],
	discrete_time_step[..., 0],
	A,
	B[:, 0],
	C[:, 0],
	self.D,
	gate[..., 0],
	time_proj_bias,
	dt_softplus=True,
	).unsqueeze(-1)
	else:
	scan_outputs, ssm_state = selective_scan_fn(
	hidden_states,
	discrete_time_step,
	A,
	B.transpose(1, 2),
	C.transpose(1, 2),
	self.D.float(),
	gate,
	time_proj_bias,
	delta_softplus=True,
	return_last_state=True,
	)
	if ssm_state is not None and cache_params is not None:
	cache_params.ssm_states[self.layer_idx].copy_(ssm_state)

	# 4. Final linear projection
	contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))

	return contextualized_states

	# fmt: off
	def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCache = None):
	batch_size, seq_len, _ = input_states.shape
	dtype = input_states.dtype
	# 1. Gated MLP's linear projection
	projected_states = self.in_proj(input_states).transpose(1, 2) # [batch, 2 * intermediate_size, seq_len]
	hidden_states, gate = projected_states.chunk(2, dim=1)

	use_cache = isinstance(cache_params,HybridMambaAttentionDynamicCache)
	# 2. Convolution sequence transformation
	if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size:
	if self.training:
	# In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
	ssm_state = cache_params.ssm_states[self.layer_idx].clone()
	else:
	ssm_state = cache_params.ssm_states[self.layer_idx]

	if cache_params.has_previous_state and seq_len == 1 and \
	cache_params.conv_states[self.layer_idx].shape[0] == batch_size:
	conv_state = cache_params.conv_states[self.layer_idx] # [batch, intermediate_size, conv_kernel_size]
	conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
	conv_state[:, :, -1] = hidden_states[:, :, 0]
	cache_params.conv_states[self.layer_idx] = conv_state
	hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
	if self.use_conv_bias:
	hidden_states += self.conv1d.bias
	hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) # [batch, intermediate_size, 1] : decoding
	else:
	conv_state = nn.functional.pad(
	hidden_states,
	(self.conv_kernel_size - hidden_states.shape[-1], 0)
	)
	cache_params.conv_states[self.layer_idx] = conv_state
	hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
	else:
	ssm_state = torch.zeros(
	(batch_size, self.intermediate_size, self.ssm_state_size),
	device=hidden_states.device, dtype=dtype
	)
	hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]

	# 3. State Space Model sequence transformation
	# 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
	ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
	time_step, B, C = torch.split(
	ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
	)

	time_step = self.dt_layernorm(time_step)
	B = self.b_layernorm(B)
	C = self.c_layernorm(C)

	discrete_time_step = self.dt_proj(time_step) # [batch, seq_len, intermediate_size]
	discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) # [batch, intermediate_size, seq_len]

	# 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
	A = -torch.exp(self.A_log.float()) # [intermediate_size, ssm_state_size]
	discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) # [batch, intermediate_size, seq_len, ssm_state_size]
	discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() # [batch, intermediade_size, seq_len, ssm_state_size]
	deltaB_u = discrete_B * hidden_states[:, :, :, None].float()

	# 3.c perform the recurrence y ← SSM(A, B, C)(x)
	scan_outputs = []
	for i in range(seq_len):
	ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] # [batch, intermediade_size, ssm_state]
	scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) # [batch, intermediade_size, 1]
	scan_outputs.append(scan_output[:, :, 0])
	scan_output = torch.stack(scan_outputs, dim=-1) # [batch, intermediade_size, seq_len]
	scan_output = scan_output + (hidden_states * self.D[None, :, None])
	scan_output = (scan_output * self.act(gate))

	if use_cache:
	cache_params.ssm_states[self.layer_idx] = ssm_state

	# 4. Final linear projection
	contextualized_states = self.out_proj(scan_output.transpose(1, 2)) # [batch, seq_len, hidden_size]
	return contextualized_states
	# fmt: on

	def forward(self, hidden_states, cache_params: HybridMambaAttentionDynamicCache = None):
	if self.use_fast_kernels:
	if not is_fast_path_available or "cuda" not in self.x_proj.weight.device.type:
	raise ValueError(
	"Fast Mamba kernels are not available. Make sure to they are installed and that the mamba module is on a CUDA device"
	)
	return self.cuda_kernels_forward(hidden_states, cache_params)
	return self.slow_forward(hidden_states, cache_params)


	# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Jamba
	class JambaMLP(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]

	def forward(self, x):
	return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


	# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock with Mistral->Jamba
	class JambaSparseMoeBlock(nn.Module):
	"""
	This implementation is
	strictly equivalent to standard MoE with full capacity (no
	dropped tokens). It's faster since it formulates MoE operations
	in terms of block-sparse operations to accomodate imbalanced
	assignments of tokens to experts, whereas standard MoE either
	(1) drop tokens at the cost of reduced performance or (2) set
	capacity factor to number of experts and thus waste computation
	and memory on padding.
	"""

	def __init__(self, config: JambaConfig):
	super().__init__()
	self.hidden_dim = config.hidden_size
	self.ffn_dim = config.intermediate_size
	self.num_experts = config.num_experts
	self.top_k = config.num_experts_per_tok

	self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
	self.experts = nn.ModuleList([JambaMLP(config) for _ in range(self.num_experts)])

	def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
	""" """
	batch_size, sequence_length, hidden_dim = hidden_states.shape

	hidden_states = hidden_states.view(-1, hidden_dim)
	# router_logits: (batch * sequence_length, n_experts)
	router_logits = self.router(hidden_states)
	routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
	routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
	# we cast back to the input dtype
	routing_weights = routing_weights.to(hidden_states.dtype)

	final_hidden_states = torch.zeros(
	(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
	)

	# One hot encode the selected experts to create an expert mask
	# this will be used to easily index which expert is going to be sollicitated
	expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)

	# Loop over all available experts in the model and perform the computation on each expert
	for expert_idx in range(self.num_experts):
	expert_layer = self.experts[expert_idx]
	idx, top_x = torch.where(expert_mask[expert_idx])

	if top_x.shape[0] == 0:
	continue

	# Index the correct hidden states and compute the expert hidden state for
	# the current expert. We need to make sure to multiply the output hidden
	# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
	current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
	current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]

	# However `index_add_` only support torch tensors for indexing so we'll use
	# the `top_x` tensor here.
	final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
	final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
	return final_hidden_states, router_logits


	class JambaAttentionDecoderLayer(nn.Module):
	def __init__(self, config: JambaConfig, layer_idx: int):
	super().__init__()
	num_experts = config.layers_num_experts[layer_idx]
	self.self_attn = JAMBA_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)

	ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP
	self.feed_forward = ffn_layer_class(config)
	self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.pre_ff_layernorm = JambaRMSNorm(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[HybridMambaAttentionDynamicCache] = None,
	output_attentions: Optional[bool] = False,
	output_router_logits: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	cache_position: Optional[torch.LongTensor] = None,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	"""
	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.
	past_key_value (`HybridMambaAttentionDynamicCache`, optional): cached past key and value projection states
	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_router_logits (`bool`, optional):
	Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
	should not be returned during inference.
	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`).
	cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional):
	Indices depicting the position of the input sequence tokens in the sequence.
	"""

	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)

	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,
	cache_position=cache_position,
	)

	# residual connection after attention
	hidden_states = residual + hidden_states

	# feed-forward (experts/MLP)
	residual = hidden_states
	hidden_states = self.pre_ff_layernorm(hidden_states)
	ff_outputs = self.feed_forward(hidden_states)
	if isinstance(ff_outputs, tuple):
	hidden_states, router_logits = ff_outputs
	else:
	hidden_states, router_logits = ff_outputs, None
	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


	class JambaMambaDecoderLayer(nn.Module):
	def __init__(self, config: JambaConfig, layer_idx: int):
	super().__init__()
	num_experts = config.layers_num_experts[layer_idx]
	self.mamba = JambaMambaMixer(config=config, layer_idx=layer_idx)

	ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP
	self.feed_forward = ffn_layer_class(config)
	self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.pre_ff_layernorm = JambaRMSNorm(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[HybridMambaAttentionDynamicCache] = None,
	output_attentions: Optional[bool] = False,
	output_router_logits: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	cache_position: Optional[torch.LongTensor] = None,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	"""
	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.
	past_key_value (`HybridMambaAttentionDynamicCache`, optional): cached past key and value projection states
	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_router_logits (`bool`, optional):
	Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
	should not be returned during inference.
	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`).
	cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional):
	Indices depicting the position of the input sequence tokens in the sequence.
	"""

	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)

	hidden_states = self.mamba(
	hidden_states=hidden_states,
	cache_params=past_key_value,
	)
	self_attn_weights = None

	# residual connection after mamba
	hidden_states = residual + hidden_states

	# feed-forward (experts/MLP)
	residual = hidden_states
	hidden_states = self.pre_ff_layernorm(hidden_states)
	ff_outputs = self.feed_forward(hidden_states)
	if isinstance(ff_outputs, tuple):
	hidden_states, router_logits = ff_outputs
	else:
	hidden_states, router_logits = ff_outputs, None
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (past_key_value,)

	if output_router_logits:
	outputs += (router_logits,)

	return outputs


	JAMBA_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 ([`JambaConfig`]):
	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 Jamba Model outputting raw hidden-states without any specific head on top.",
	JAMBA_START_DOCSTRING,
	)
	class JambaPreTrainedModel(PreTrainedModel):
	config_class = JambaConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["JambaAttentionDecoderLayer", "JambaMambaDecoderLayer"]
	_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, nn.Conv1d)):
	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_()


	JAMBA_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 `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 (`HybridMambaAttentionDynamicCache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	A HybridMambaAttentionDynamicCache object containing pre-computed hidden-states (keys and values in the
	self-attention blocks and convolution and ssm states in the mamba blocks) that can be used (see
	`past_key_values` input) to speed up sequential decoding.
	Key and value cache tensors have shape `(batch_size, num_heads, seq_len, head_dim)`.
	Convolution and ssm states tensors have shape `(batch_size, d_inner, d_conv)` and
	`(batch_size, d_inner, d_state)` respectively.
	See the `HybridMambaAttentionDynamicCache` class for more details.

	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.
	output_router_logits (`bool`, optional):
	Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
	should not be returned during inference.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional):
	Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
	this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
	the complete sequence length.
	"""

	ALL_DECODER_LAYER_TYPES = {"attention": JambaAttentionDecoderLayer, "mamba": JambaMambaDecoderLayer}


	@add_start_docstrings(
	"The bare Jamba Model outputting raw hidden-states without any specific head on top.",
	JAMBA_START_DOCSTRING,
	)
	# Adapted from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->JAMBA, Mistral->Jamba
	class JambaModel(JambaPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`JambaDecoderLayer`]

	Args:
	config: JambaConfig
	"""

	def __init__(self, config: JambaConfig):
	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)
	decoder_layers = []
	for i in range(config.num_hidden_layers):
	layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
	decoder_layers.append(layer_class(config, layer_idx=i))
	self.layers = nn.ModuleList(decoder_layers)

	self._attn_implementation = config._attn_implementation
	self.final_layernorm = JambaRMSNorm(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(JAMBA_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[HybridMambaAttentionDynamicCache] = 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,
	cache_position: Optional[torch.LongTensor] = None,
	) -> Union[Tuple, MoeModelOutputWithPast]:
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_router_logits = (
	output_router_logits if output_router_logits is not None else self.config.output_router_logits
	)
	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

	if (input_ids is None) ^ (inputs_embeds is not None):
	raise ValueError(
	"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
	)

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

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

	if use_cache and past_key_values is None:
	logger.warning_once(
	"Jamba requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was "
	"provided, so no cache will be returned."
	)

	if cache_position is None:
	cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device)
	if position_ids is None:
	position_ids = cache_position.unsqueeze(0)

	causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)

	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

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

	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	decoder_layer.__call__,
	hidden_states,
	causal_mask,
	position_ids,
	past_key_values,
	output_attentions,
	output_router_logits,
	use_cache,
	cache_position,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=causal_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,
	cache_position=cache_position,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	if layer_outputs[1] is not None:
	# append attentions only of attention layers. Mamba layers return `None` as the attention weights
	all_self_attns += (layer_outputs[1],)

	if output_router_logits:
	if layer_outputs[-1] is not None:
	# append router logits only of expert layers. Regular MLP layers return `None` as the router logits
	all_router_logits += (layer_outputs[-1],)

	hidden_states = self.final_layernorm(hidden_states)

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

	if past_key_values and not past_key_values.has_previous_state:
	past_key_values.has_previous_state = True

	next_cache = None if not use_cache else past_key_values

	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,
	)

	def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
	if self.config._attn_implementation == "flash_attention_2":
	if attention_mask is not None and 0.0 in attention_mask:
	return attention_mask
	return None

	dtype, device = input_tensor.dtype, input_tensor.device
	min_dtype = torch.finfo(dtype).min
	sequence_length = input_tensor.shape[1]
	target_length = cache_position[-1] + 1

	causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
	if sequence_length != 1:
	causal_mask = torch.triu(causal_mask, diagonal=1)
	causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
	causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
	if attention_mask is not None:
	causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
	if attention_mask.dim() == 2:
	mask_length = attention_mask.shape[-1]
	padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
	causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)

	if (
	self.config._attn_implementation == "sdpa"
	and attention_mask is not None
	and attention_mask.device.type == "cuda"
	):
	# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
	# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
	# Details: https://github.com/pytorch/pytorch/issues/110213
	causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

	return causal_mask


	# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->Jamba
	class JambaForCausalLM(JambaPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config: JambaConfig):
	super().__init__(config)
	self.model = JambaModel(config)
	self.vocab_size = config.vocab_size
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
	self.router_aux_loss_coef = config.router_aux_loss_coef
	self.num_experts = config.num_experts
	self.num_experts_per_tok = config.num_experts_per_tok
	# 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(JAMBA_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	# Ignore copy
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[HybridMambaAttentionDynamicCache] = 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,
	cache_position: Optional[torch.LongTensor] = None,
	num_logits_to_keep: Optional[Union[int, None]] = None,
	) -> Union[Tuple, MoeCausalLMOutputWithPast]:
	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]`.

	num_logits_to_keep (`int` or `None`, optional):
	Calculate logits for the last `num_logits_to_keep` tokens. If `None`, calculate logits for all
	`input_ids`. Only last token logits are needed for generation, and calculating them only for that token
	can save memory, which becomes pretty significant for long sequences.

	Returns:

	Example:

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

	>>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
	>>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-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_router_logits = (
	output_router_logits if output_router_logits is not None else self.config.output_router_logits
	)

	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,
	output_router_logits=output_router_logits,
	cache_position=cache_position,
	return_dict=return_dict,
	)

	hidden_states = outputs[0]
	if num_logits_to_keep is None:
	logits = self.lm_head(hidden_states)
	else:
	logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
	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.num_experts,
	self.num_experts_per_tok,
	attention_mask,
	)
	if labels is not None:
	loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device

	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,
	output_router_logits=False,
	cache_position=None,
	**kwargs,
	):
	empty_past_kv = past_key_values is None

	# Omit tokens covered by past_key_values
	if not empty_past_kv:
	past_length = cache_position[0] if cache_position is not None else attention_mask.shape[1]
	max_cache_length = self.config.sliding_window
	# 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 past_length + input_ids.shape[1] > max_cache_length
	):
	attention_mask = attention_mask[:, -max_cache_length:]
	else:
	past_key_values = HybridMambaAttentionDynamicCache(
	self.config, input_ids.shape[0], self.dtype, device=self.device
	)

	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 not empty_past_kv:
	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 empty_past_kv:
	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,
	"output_router_logits": output_router_logits,
	"num_logits_to_keep": self.config.num_logits_to_keep,
	"cache_position": cache_position,
	}
	)
	return model_inputs


	@add_start_docstrings(
	"""
	The Jamba Model with a sequence classification head on top (linear layer).

	[`JambaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
	(e.g. GPT-2) do.

	Since it does classification on the last token, it requires to know the position of the last token. If a
	`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
	no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
	padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
	each row of the batch).
	""",
	JAMBA_START_DOCSTRING,
	)
	# Copied from transformers.models.mixtral.modeling_mixtral.MixtralForSequenceClassification with Mixtral->Jamba, MIXTRAL->JAMBA
	class JambaForSequenceClassification(JambaPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = JambaModel(config)
	self.score = nn.Linear(config.hidden_size, self.num_labels, 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

	@add_start_docstrings_to_model_forward(JAMBA_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,
	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, SequenceClassifierOutputWithPast]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.model(
	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 = transformer_outputs[0]
	logits = self.score(hidden_states)

	if input_ids is not None:
	batch_size = input_ids.shape[0]
	else:
	batch_size = inputs_embeds.shape[0]

	if self.config.pad_token_id is None and batch_size != 1:
	raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
	sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
	sequence_lengths = sequence_lengths % input_ids.shape[-1]
	sequence_lengths = sequence_lengths.to(logits.device)
	else:
	sequence_lengths = -1

	pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

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

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

	return SequenceClassifierOutputWithPast(
	loss=loss,
	logits=pooled_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)