nanoLLaVA

Runtime error

App Files Files Community

nanoLLaVA / modeling_llava_qwen2.py

qnguyen3

Update modeling_llava_qwen2.py

0872fb5 verified about 2 months ago

raw history blame contribute delete

No virus

103 kB

	from transformers import AutoConfig, AutoModelForCausalLM
	from abc import ABC, abstractmethod

	'''
	# Adapted from https://huggingface.co/MILVLG/imp-v1-3b/blob/main/vision_encoder.py
	'''

	from typing import Optional, Tuple, Union, Dict
	from dataclasses import dataclass
	from functools import partial, reduce
	from PIL import Image
	import torch.utils.checkpoint
	from torch import nn
	import torch
	import spaces
	from transformers.image_processing_utils import BatchFeature, get_size_dict
	from transformers.image_transforms import (convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format, )
	from transformers.image_utils import (ChannelDimension, PILImageResampling, to_numpy_array, )
	from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import ModelOutput
	import subprocess

	torch.set_default_device('cuda')
	subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

	class SigLipImageProcessor:
	def __init__(self,
	image_mean=(0.5, 0.5, 0.5),
	image_std=(0.5, 0.5, 0.5),
	size=(384, 384),
	crop_size: Dict[str, int] = None,
	resample=PILImageResampling.BICUBIC,
	rescale_factor=1 / 255,
	data_format=ChannelDimension.FIRST):
	crop_size = crop_size if crop_size is not None else {"height": 384, "width": 384}
	crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")

	self.image_mean = image_mean
	self.image_std = image_std
	self.size = size
	self.resample = resample
	self.rescale_factor = rescale_factor
	self.data_format = data_format
	self.crop_size = crop_size

	def preprocess(self, images, return_tensors):
	if isinstance(images, Image.Image):
	images = [images]
	else:
	assert isinstance(images, list)

	transforms = [
	convert_to_rgb,
	to_numpy_array,
	partial(resize, size=self.size, resample=self.resample, data_format=self.data_format),
	partial(rescale, scale=self.rescale_factor, data_format=self.data_format),
	partial(normalize, mean=self.image_mean, std=self.image_std, data_format=self.data_format),
	partial(to_channel_dimension_format, channel_dim=self.data_format, input_channel_dim=self.data_format),
	]

	images = reduce(lambda x, f: [*map(f, x)], transforms, images)
	data = {"pixel_values": images}

	return BatchFeature(data=data, tensor_type=return_tensors)


	from configuration_llava_qwen2 import SigLipVisionConfig


	@dataclass
	# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->SigLip
	class SigLipVisionModelOutput(ModelOutput):
	"""
	Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.

	Args:
	image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`):
	The image embeddings obtained by applying the projection layer to the pooler_output.
	last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the model.
	hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
	Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

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

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

	image_embeds: Optional[torch.FloatTensor] = None
	last_hidden_state: torch.FloatTensor = None
	hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	attentions: Optional[Tuple[torch.FloatTensor]] = None


	class SigLipVisionEmbeddings(nn.Module):
	def __init__(self, config: SigLipVisionConfig):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.image_size = config.image_size
	self.patch_size = config.patch_size

	self.patch_embedding = nn.Conv2d(
	in_channels=config.num_channels,
	out_channels=self.embed_dim,
	kernel_size=self.patch_size,
	stride=self.patch_size,
	padding="valid",
	)

	self.num_patches = (self.image_size // self.patch_size) ** 2
	self.num_positions = self.num_patches
	self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
	self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)

	def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
	patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
	embeddings = patch_embeds.flatten(2).transpose(1, 2)

	embeddings = embeddings + self.position_embedding(self.position_ids)
	return embeddings


	class SigLipAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_heads
	if self.head_dim * self.num_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
	f" {self.num_heads})."
	)
	self.scale = self.head_dim ** -0.5
	self.dropout = config.attention_dropout

	self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
	self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
	self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
	self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	batch_size, 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(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)

	k_v_seq_len = key_states.shape[-2]
	attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale

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

	if attention_mask is not None:
	if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
	raise ValueError(
	f"Attention mask should be of size {(batch_size, 1, q_len, k_v_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.dropout, training=self.training)
	attn_output = torch.matmul(attn_weights, value_states)

	if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(batch_size, 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(batch_size, q_len, self.embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights


	# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->SigLip
	class SigLipMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.activation_fn = ACT2FN[config.hidden_act]
	self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
	self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.fc1(hidden_states)
	hidden_states = self.activation_fn(hidden_states)
	hidden_states = self.fc2(hidden_states)
	return hidden_states


	# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->SigLip
	class SigLipEncoderLayer(nn.Module):
	def __init__(self, config: SigLipVisionConfig):
	super().__init__()
	self.embed_dim = config.hidden_size
	self.self_attn = SigLipAttention(config)
	self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
	self.mlp = SigLipMLP(config)
	self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

	# Ignore copy
	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`):
	Input to the layer of shape `(batch, seq_len, embed_dim)`.
	attention_mask (`torch.FloatTensor`):
	Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
	output_attentions (`bool`, optional, defaults to `False`):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states

	hidden_states = self.layer_norm1(hidden_states)
	hidden_states, attn_weights = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	output_attentions=output_attentions,
	)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.layer_norm2(hidden_states)
	hidden_states = self.mlp(hidden_states)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


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

	config_class = SigLipVisionConfig
	base_model_prefix = "siglip"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	"""Initialize the weights"""
	pass


	# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->SigLip
	class SigLipEncoder(nn.Module):
	"""
	Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
	[`SigLipEncoderLayer`].

	Args:
	config: SigLipVisionConfig
	"""

	def __init__(self, config: SigLipVisionConfig):
	super().__init__()
	self.config = config
	self.layers = nn.ModuleList([SigLipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = False

	# Ignore copy
	def forward(
	self,
	inputs_embeds,
	attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	r"""
	Args:
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	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.
	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)
	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.
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	hidden_states = inputs_embeds
	for encoder_layer in self.layers:
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	encoder_layer.__call__,
	hidden_states,
	attention_mask,
	output_attentions,
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

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


	class SigLipVisionTransformer(nn.Module):
	def __init__(self, config: SigLipVisionConfig):
	super().__init__()
	self.config = config
	embed_dim = config.hidden_size

	self.embeddings = SigLipVisionEmbeddings(config)
	self.encoder = SigLipEncoder(config)
	self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
	self.head = SigLipMultiheadAttentionPoolingHead(config)

	def forward(
	self,
	pixel_values,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	hidden_states = self.embeddings(pixel_values)

	encoder_outputs = self.encoder(
	inputs_embeds=hidden_states,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	last_hidden_state = encoder_outputs[0]
	last_hidden_state = self.post_layernorm(last_hidden_state)

	pooled_output = self.head(last_hidden_state)

	if not return_dict:
	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPooling(
	last_hidden_state=last_hidden_state,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	)


	class SigLipMultiheadAttentionPoolingHead(nn.Module):
	"""Multihead Attention Pooling."""

	def __init__(self, config: SigLipVisionConfig):
	super().__init__()

	self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
	self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
	self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.mlp = SigLipMLP(config)

	def forward(self, hidden_state):
	batch_size = hidden_state.shape[0]
	probe = self.probe.repeat(batch_size, 1, 1)

	hidden_state = self.attention(probe, hidden_state, hidden_state)[0]

	residual = hidden_state
	hidden_state = self.layernorm(hidden_state)
	hidden_state = residual + self.mlp(hidden_state)

	return hidden_state[:, 0]


	class SigLipVisionModel(SigLipPreTrainedModel):
	config_class = SigLipVisionConfig
	main_input_name = "pixel_values"
	_no_split_modules = ["SigLipEncoderLayer"]

	def __init__(self, config: SigLipVisionConfig):
	super().__init__(config)

	self.vision_model = SigLipVisionTransformer(config)

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

	def get_input_embeddings(self) -> nn.Module:
	return self.vision_model.embeddings.patch_embedding

	def forward(
	self,
	pixel_values,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, SigLipVisionModel

	>>> model = SigLipVisionModel.from_pretrained("google/siglip-base-patch16-224")
	>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)

	>>> inputs = processor(images=image, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> last_hidden_state = outputs.last_hidden_state
	>>> pooled_output = outputs.pooler_output # pooled features
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	return self.vision_model(
	pixel_values=pixel_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)


	class SigLipVisionTower(nn.Module):
	def __init__(self, vision_tower, vision_tower_cfg, delay_load=False):
	super().__init__()

	self.is_loaded = False

	self.config = SigLipVisionConfig()

	self.vision_tower_name = vision_tower

	self.image_processor = SigLipImageProcessor()

	if not delay_load:
	self.load_model()
	else:
	self.cfg_only = self.config

	def load_model(self):
	if self.is_loaded:
	return

	self.vision_tower = SigLipVisionModel.from_pretrained(self.vision_tower_name)

	del self.vision_tower.vision_model.encoder.layers[-1:]
	self.vision_tower.vision_model.head = nn.Identity()
	self.vision_tower.requires_grad_(False)
	self.vision_tower.eval()

	self.is_loaded = True

	@torch.no_grad()
	@spaces.GPU
	def forward(self, images):
	if type(images) is list:
	image_features = []
	for image in images:
	image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
	output_hidden_states=True)
	image_feature = image_forward_out.hidden_states[-1].to(image.dtype)
	assert image_features.shape[-2] == 729
	image_features.append(image_feature)
	else:
	image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
	output_hidden_states=True)
	image_features = image_forward_outs.hidden_states[-1].to(images.dtype)
	assert image_features.shape[-2] == 729

	return image_features

	@property
	def dummy_feature(self):
	return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)

	@property
	def dtype(self):
	for p in self.vision_tower.parameters():
	return p.dtype

	@property
	def device(self):
	for p in self.vision_tower.parameters():
	return p.device

	@property
	def hidden_size(self):
	return self.config.hidden_size

	@property
	def num_patches(self):
	return (self.config.image_size // self.config.patch_size) ** 2


	def build_vision_tower(vision_tower_cfg, **kwargs):
	vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))

	return SigLipVisionTower(vision_tower, vision_tower_cfg=vision_tower_cfg, **kwargs)


	import re


	def build_vision_projector(config, delay_load=False, **kwargs):
	projector_type = getattr(config, 'mm_projector_type', 'mlp2x_gelu')

	mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
	if mlp_gelu_match:
	mlp_depth = int(mlp_gelu_match.group(1))
	modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
	for _ in range(1, mlp_depth):
	modules.append(nn.GELU())
	modules.append(nn.Linear(config.hidden_size, config.hidden_size))
	return nn.Sequential(*modules)


	# Model Constants
	IGNORE_INDEX = -100
	IMAGE_TOKEN_INDEX = -200


	class LlavaMetaModel:

	def __init__(self, config):
	super(LlavaMetaModel, self).__init__(config)

	if hasattr(config, "mm_vision_tower"):
	self.vision_tower = build_vision_tower(config, delay_load=True)
	self.mm_projector = build_vision_projector(config)

	def get_vision_tower(self):
	vision_tower = getattr(self, 'vision_tower', None)
	if type(vision_tower) is list:
	vision_tower = vision_tower[0]
	return vision_tower

	def initialize_vision_modules(self, model_args):
	vision_tower = model_args.vision_tower

	pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter

	self.config.mm_vision_tower = vision_tower

	if self.get_vision_tower() is None:
	vision_tower = build_vision_tower(model_args)
	self.vision_tower = vision_tower
	else:
	vision_tower = self.vision_tower
	vision_tower.load_model()

	self.config.use_mm_proj = True
	self.config.mm_projector_type = getattr(model_args, 'mm_projector_type')
	self.config.mm_hidden_size = vision_tower.hidden_size

	if getattr(self, 'mm_projector', None) is None:
	self.mm_projector = build_vision_projector(self.config)
	else:
	# In case it is frozen by LoRA
	for p in self.mm_projector.parameters():
	p.requires_grad = True

	if pretrain_mm_mlp_adapter is not None:
	mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')

	def get_w(weights, keyword):
	return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}

	self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))


	class LlavaMetaForCausalLM(ABC):

	@abstractmethod
	def get_model(self):
	pass

	def get_vision_tower(self):
	return self.get_model().get_vision_tower()

	def encode_images(self, images):
	image_features = self.get_model().get_vision_tower().cuda()(images)
	image_features = self.get_model().mm_projector(image_features)
	return image_features

	def prepare_inputs_labels_for_multimodal(
	self, input_ids, position_ids, attention_mask, past_key_values, labels, images
	):
	vision_tower = self.get_vision_tower().cuda()
	if vision_tower is None or images is None or input_ids.shape[1] == 1:
	if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[
	1] == 1:
	target_shape = past_key_values[-1][-1].shape[-2] + 1
	attention_mask = torch.cat((attention_mask, torch.ones(
	(attention_mask.shape[0], target_shape - attention_mask.shape[1]),
	dtype=attention_mask.dtype,
	device=attention_mask.device
	)), dim=1)
	position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
	return input_ids, position_ids, attention_mask, past_key_values, None, labels

	if type(images) is list or images.ndim == 5:
	concat_images = torch.cat([image for image in images], dim=0)
	image_features = self.encode_images(concat_images)
	split_sizes = [image.shape[0] for image in images]
	image_features = torch.split(image_features, split_sizes, dim=0)
	image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
	else:
	image_features = self.encode_images(images).to(self.device)

	# Let's just add dummy tensors if they do not exist,
	# it is a headache to deal with None all the time.
	# But it is not ideal, and if you have a better idea,
	# please open an issue / submit a PR, thanks.
	_labels = labels
	_position_ids = position_ids
	_attention_mask = attention_mask
	if attention_mask is None:
	attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
	else:
	attention_mask = attention_mask.bool()
	if position_ids is None:
	position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
	if labels is None:
	labels = torch.full_like(input_ids, IGNORE_INDEX)

	# remove the padding using attention_mask -- TODO: double check
	input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in
	zip(input_ids, attention_mask)]
	labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]

	new_input_embeds = []
	new_labels = []
	cur_image_idx = 0
	for batch_idx, cur_input_ids in enumerate(input_ids):
	num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
	if num_images == 0:
	cur_image_features = image_features[cur_image_idx]
	cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
	cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
	new_input_embeds.append(cur_input_embeds)
	new_labels.append(labels[batch_idx])
	cur_image_idx += 1
	continue

	image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [
	cur_input_ids.shape[0]]
	cur_input_ids_noim = []
	cur_labels = labels[batch_idx]
	cur_labels_noim = []
	for i in range(len(image_token_indices) - 1):
	cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1:image_token_indices[i + 1]])
	cur_labels_noim.append(cur_labels[image_token_indices[i] + 1:image_token_indices[i + 1]])
	split_sizes = [x.shape[0] for x in cur_labels_noim]
	cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
	cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
	cur_new_input_embeds = []
	cur_new_labels = []

	for i in range(num_images + 1):
	cur_new_input_embeds.append(cur_input_embeds_no_im[i])
	cur_new_labels.append(cur_labels_noim[i])
	if i < num_images:
	cur_image_features = image_features[cur_image_idx]
	cur_image_idx += 1
	cur_new_input_embeds.append(cur_image_features)
	cur_new_labels.append(
	torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device,
	dtype=cur_labels.dtype))

	cur_new_input_embeds = torch.cat(cur_new_input_embeds)
	cur_new_labels = torch.cat(cur_new_labels)

	new_input_embeds.append(cur_new_input_embeds)
	new_labels.append(cur_new_labels)

	# Truncate sequences to max length as image embeddings can make the sequence longer
	tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
	if tokenizer_model_max_length is not None:
	new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
	new_labels = [x[:tokenizer_model_max_length] for x in new_labels]

	# Combine them
	max_len = max(x.shape[0] for x in new_input_embeds)
	batch_size = len(new_input_embeds)

	new_input_embeds_padded = []
	new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype,
	device=new_labels[0].device)
	attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
	position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)

	for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
	cur_len = cur_new_embed.shape[0]
	if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
	new_input_embeds_padded.append(torch.cat((
	torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype,
	device=cur_new_embed.device),
	cur_new_embed
	), dim=0))
	if cur_len > 0:
	new_labels_padded[i, -cur_len:] = cur_new_labels
	attention_mask[i, -cur_len:] = True
	position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype,
	device=position_ids.device)
	else:
	new_input_embeds_padded.append(torch.cat((
	cur_new_embed,
	torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype,
	device=cur_new_embed.device)
	), dim=0))
	if cur_len > 0:
	new_labels_padded[i, :cur_len] = cur_new_labels
	attention_mask[i, :cur_len] = True
	position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype,
	device=position_ids.device)

	new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)

	if _labels is None:
	new_labels = None
	else:
	new_labels = new_labels_padded

	if _attention_mask is None:
	attention_mask = None
	else:
	attention_mask = attention_mask.to(dtype=_attention_mask.dtype)

	if _position_ids is None:
	position_ids = None

	return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels


	# coding=utf-8
	# Copyright 2024 The Qwen team, Alibaba Group 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 Qwen2 model."""
	import inspect
	import math
	import warnings
	from typing import List, Optional, Tuple, Union

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

	from 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,
	)
	from configuration_llava_qwen2 import Qwen2Config



	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__)


	_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
	_CONFIG_FOR_DOC = "Qwen2Config"

	QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"Qwen/Qwen2-7B-beta",
	# See all Qwen2 models at https://huggingface.co/models?filter=qwen2
	]


	# 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->Qwen2
	class Qwen2RMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	Qwen2RMSNorm 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.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Qwen2
	class Qwen2RotaryEmbedding(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, dtype=torch.int64).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=torch.int64).type_as(self.inv_freq)

	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.mistral.modeling_mistral.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


	# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2
	class Qwen2MLP(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))


	# 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 Qwen2Attention(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: Qwen2Config, 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 `layer_idx` is not recommended and will "
	"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=True)
	self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
	self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

	self.rotary_emb = Qwen2RotaryEmbedding(
	self.head_dim,
	max_position_embeddings=self.max_position_embeddings,
	base=self.rope_theta,
	)

	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 Qwen2FlashAttention2(Qwen2Attention):
	"""
	Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention`
	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. Additionally, for sliding window attention, we apply SWA only to the bottom
	config.max_window_layers layers.
	"""

	# 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
	and self.config.use_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 (`float`):
	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

	# Decide whether to use SWA or not by layer index.
	if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
	use_sliding_windows = False

	# 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.mistral.modeling_mistral.MistralFlashAttention2._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),
	)


	# Copied from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Qwen2
	class Qwen2SdpaAttention(Qwen2Attention):
	"""
	Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
	`Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
	SDPA API.
	"""

	# Adapted from Qwen2Attention.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(
	"Qwen2Model is using Qwen2SdpaAttention, 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.view(bsz, q_len, self.hidden_size)

	attn_output = self.o_proj(attn_output)

	return attn_output, None, past_key_value


	QWEN2_ATTENTION_CLASSES = {
	"eager": Qwen2Attention,
	"flash_attention_2": Qwen2FlashAttention2,
	"sdpa": Qwen2SdpaAttention,
	}


	class Qwen2DecoderLayer(nn.Module):
	def __init__(self, config: Qwen2Config, layer_idx: int):
	super().__init__()
	self.hidden_size = config.hidden_size

	if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
	logger.warning_once(
	f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
	"unexpected results may be encountered."
	)
	self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)

	self.mlp = Qwen2MLP(config)
	self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)

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

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

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	QWEN2_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 ([`Qwen2Config`]):
	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 Qwen2 Model outputting raw hidden-states without any specific head on top.",
	QWEN2_START_DOCSTRING,
	)
	class Qwen2PreTrainedModel(PreTrainedModel):
	config_class = Qwen2Config
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["Qwen2DecoderLayer"]
	_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_()


	QWEN2_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 Qwen2 Model outputting raw hidden-states without any specific head on top.",
	QWEN2_START_DOCSTRING,
	)
	class Qwen2Model(Qwen2PreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`Qwen2DecoderLayer`]

	Args:
	config: Qwen2Config
	"""

	def __init__(self, config: Qwen2Config):
	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(
	[Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
	)
	self._attn_implementation = config._attn_implementation
	self.norm = Qwen2RMSNorm(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(QWEN2_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

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

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

	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

	past_key_values_length = 0

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

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

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

	if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
	is_padding_right = attention_mask[:, -1].sum().item() != batch_size
	if is_padding_right:
	raise ValueError(
	"You are attempting to perform batched generation with padding_side='right'"
	" this may lead to unexpected behaviour for Flash Attention version of Qwen2. 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
	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:
	layer_outputs = self._gradient_checkpointing_func(
	decoder_layer.__call__,
	hidden_states,
	attention_mask,
	position_ids,
	past_key_values,
	output_attentions,
	use_cache,
	)
	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,
	use_cache=use_cache,
	)

	hidden_states = layer_outputs[0]

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

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

	hidden_states = self.norm(hidden_states)

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

	next_cache = 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] if v is not None)
	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	)


	class Qwen2ForCausalLM(Qwen2PreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.model = Qwen2Model(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(QWEN2_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	Args:
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, Qwen2ForCausalLM

	>>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
	>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

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

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

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

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

	hidden_states = outputs[0]
	logits = self.lm_head(hidden_states)
	logits = logits.float()

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

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

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

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

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

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

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

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


	@add_start_docstrings(
	"""
	The Qwen2 Model transformer with a sequence classification head on top (linear layer).

	[`Qwen2ForSequenceClassification`] 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).
	""",
	QWEN2_START_DOCSTRING,
	)
	class Qwen2ForSequenceClassification(Qwen2PreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = Qwen2Model(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(QWEN2_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,
	)


	from configuration_llava_qwen2 import LlavaQwen2Config


	class LlavaQwen2Model(LlavaMetaModel, Qwen2Model):
	config_class = LlavaQwen2Config

	def __init__(self, config: Qwen2Config):
	super(LlavaQwen2Model, self).__init__(config)


	class LlavaQwen2ForCausalLM(Qwen2ForCausalLM, LlavaMetaForCausalLM):
	config_class = LlavaQwen2Config

	def __init__(self, config):
	super(Qwen2ForCausalLM, self).__init__(config)
	self.model = LlavaQwen2Model(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_model(self):
	return self.model

	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,
	images: Optional[torch.FloatTensor] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:

	if inputs_embeds is None:
	(
	input_ids,
	position_ids,
	attention_mask,
	past_key_values,
	inputs_embeds,
	labels
	) = self.prepare_inputs_labels_for_multimodal(
	input_ids,
	position_ids,
	attention_mask,
	past_key_values,
	labels,
	images
	)

	return super().forward(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	labels=labels,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict
	)

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
	**kwargs):
	images = kwargs.pop("images", None)

	_inputs = super().prepare_inputs_for_generation(
	input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
	**kwargs
	)

	if images is not None:
	_inputs['images'] = images
	return _inputs

	def expand2square(self, pil_img, background_color):
	width, height = pil_img.size
	if width == height:
	return pil_img
	elif width > height:
	result = Image.new(pil_img.mode, (width, width), background_color)
	result.paste(pil_img, (0, (width - height) // 2))
	return result
	else:
	result = Image.new(pil_img.mode, (height, height), background_color)
	result.paste(pil_img, ((height - width) // 2, 0))
	return result

	def process_images(self, images, model_cfg):
	vision_tower = self.get_vision_tower()
	if not vision_tower.is_loaded:
	vision_tower.load_model()
	image_processor = vision_tower.image_processor
	image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
	new_images = []
	if image_aspect_ratio == 'pad':
	for image in images:
	image = self.expand2square(image, tuple(int(x * 255) for x in image_processor.image_mean))
	image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
	new_images.append(image)
	else:
	return image_processor(images, return_tensors='pt')['pixel_values']
	if all(x.shape == new_images[0].shape for x in new_images):
	new_images = torch.stack(new_images, dim=0)
	return new_images


	AutoConfig.register("llava-qwen2", LlavaQwen2Config)
	AutoModelForCausalLM.register(LlavaQwen2Config, LlavaQwen2ForCausalLM)