Qwen-VL / modeling_qwen.py

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	# Copyright (c) Alibaba Cloud.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.

	import importlib
	import math
	from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator

	import torch
	import torch.nn.functional as F
	import torch.utils.checkpoint
	from torch.cuda.amp import autocast

	from torch.nn import CrossEntropyLoss
	from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
	from transformers.generation.logits_process import LogitsProcessorList

	if TYPE_CHECKING:
	from transformers.generation.streamers import BaseStreamer
	from transformers.generation.utils import GenerateOutput
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging

	try:
	from einops import rearrange
	except ImportError:
	rearrange = None
	from torch import nn

	SUPPORT_CUDA = torch.cuda.is_available()
	SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
	SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7

	from .configuration_qwen import QWenConfig
	from .qwen_generation_utils import (
	HistoryType,
	make_context,
	decode_tokens,
	get_stop_words_ids,
	StopWordsLogitsProcessor,
	)
	from .visual import VisionTransformer


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "qwen"
	_CONFIG_FOR_DOC = "QWenConfig"

	QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]

	_ERROR_BAD_CHAT_FORMAT = """\
	We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
	If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
	我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。
	如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。
	"""

	_SENTINEL = object()
	_ERROR_STREAM_IN_CHAT = """\
	Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
	向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
	"""

	apply_rotary_emb_func = None
	rms_norm = None


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	def _make_causal_mask(
	input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
	):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
	mask_cond = torch.arange(mask.size(-1), device=device)
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

	if past_key_values_length > 0:
	mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
	return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


	# Copied from transformers.models.bart.modeling_bart._expand_mask
	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


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

	self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
	self.seq_length = config.seq_length

	self.hidden_size = config.hidden_size
	self.split_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads

	self.scale_attn_weights = True

	self.projection_size = config.kv_channels * config.num_attention_heads

	assert self.projection_size % config.num_attention_heads == 0
	self.hidden_size_per_attention_head = (
	self.projection_size // config.num_attention_heads
	)

	self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)

	self.c_proj = nn.Linear(
	config.hidden_size, self.projection_size, bias=not config.no_bias
	)

	self.is_fp32 = not (config.bf16 or config.fp16)
	self.bf16 = config.bf16

	self.use_dynamic_ntk = config.use_dynamic_ntk
	self.use_logn_attn = config.use_logn_attn

	logn_list = [
	math.log(i, self.seq_length) if i > self.seq_length else 1
	for i in range(1, 32768)
	]
	self.logn_tensor = torch.tensor(logn_list)[None, :, None, None]

	self.attn_dropout = nn.Dropout(config.attn_dropout_prob)

	def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None):
	attn_weights = torch.matmul(query, key.transpose(-1, -2))

	if self.scale_attn_weights:
	attn_weights = attn_weights / torch.full(
	[],
	value.size(-1) ** 0.5,
	dtype=attn_weights.dtype,
	device=attn_weights.device,
	)

	query_length, key_length = query.size(-2), key.size(-2)
	# causal_mask = self.bias[
	# :, :, key_length - query_length : key_length, :key_length
	# ]
	# mask_value = torch.finfo(attn_weights.dtype).min
	# mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(
	# attn_weights.device
	# )
	# attn_weights = torch.where(
	# causal_mask, attn_weights.to(attn_weights.dtype), mask_value
	# )
	attn_weights = attn_weights + attention_mask

	attn_weights = nn.functional.softmax(attn_weights, dim=-1)

	attn_weights = attn_weights.type(value.dtype)
	attn_weights = self.attn_dropout(attn_weights)

	if head_mask is not None:
	attn_weights = attn_weights * head_mask

	attn_output = torch.matmul(attn_weights, value)
	attn_output = attn_output.transpose(1, 2)

	return attn_output, attn_weights

	def _upcast_and_reordered_attn(
	self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None
	):
	bsz, num_heads, q_seq_len, dk = query.size()
	_, _, k_seq_len, _ = key.size()

	attn_weights = torch.empty(
	bsz * num_heads,
	q_seq_len,
	k_seq_len,
	dtype=torch.float32,
	device=query.device,
	)

	scale_factor = 1.0
	if self.scale_attn_weights:
	scale_factor /= float(value.size(-1)) ** 0.5

	with autocast(enabled=False):
	q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(
	-1, dk, k_seq_len
	)
	attn_weights = torch.baddbmm(
	attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor
	)
	attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)

	query_length, key_length = query.size(-2), key.size(-2)
	causal_mask = registered_causal_mask[
	:, :, key_length - query_length : key_length, :key_length
	]
	mask_value = torch.finfo(attn_weights.dtype).min
	mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(
	attn_weights.device
	)
	attn_weights = torch.where(causal_mask, attn_weights, mask_value)

	if attention_mask is not None:
	attn_weights = attn_weights + attention_mask

	attn_weights = nn.functional.softmax(attn_weights, dim=-1)

	if attn_weights.dtype != torch.float32:
	raise RuntimeError(
	"Error with upcasting, attn_weights does not have dtype torch.float32"
	)
	attn_weights = attn_weights.type(value.dtype)
	attn_weights = self.attn_dropout(attn_weights)

	if head_mask is not None:
	attn_weights = attn_weights * head_mask

	attn_output = torch.matmul(attn_weights, value)

	return attn_output, attn_weights

	def _split_heads(self, tensor, num_heads, attn_head_size):
	new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
	tensor = tensor.view(new_shape)
	return tensor

	def _merge_heads(self, tensor, num_heads, attn_head_size):
	tensor = tensor.contiguous()
	new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
	return tensor.view(new_shape)

	def forward(
	self,
	hidden_states: Optional[Tuple[torch.FloatTensor]],
	rotary_pos_emb: Optional[List[torch.Tensor]] = None,
	registered_causal_mask: Optional[torch.Tensor] = None,
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	):

	mixed_x_layer = self.c_attn(hidden_states)

	query, key, value = mixed_x_layer.split(self.split_size, dim=2)

	query = self._split_heads(query, self.num_heads, self.head_dim)
	key = self._split_heads(key, self.num_heads, self.head_dim)
	value = self._split_heads(value, self.num_heads, self.head_dim)

	if rotary_pos_emb is not None:
	cur_len = query.shape[1]
	rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
	rotary_pos_emb = (rotary_pos_emb,) * 2
	q_pos_emb, k_pos_emb = rotary_pos_emb
	# Slice the pos emb for current inference
	query = apply_rotary_pos_emb(query, q_pos_emb)
	key = apply_rotary_pos_emb(key, k_pos_emb)

	if layer_past is not None:
	past_key, past_value = layer_past[0], layer_past[1]
	key = torch.cat((past_key, key), dim=1)
	value = torch.cat((past_value, value), dim=1)

	if use_cache:
	present = (key, value)
	else:
	present = None

	if self.use_logn_attn and not self.training:
	if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
	self.logn_tensor = self.logn_tensor.to(query.device).type_as(query)
	seq_start = key.size(1) - query.size(1)
	seq_end = key.size(1)
	logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :]
	query = query * logn_tensor.expand_as(query)

	query = query.permute(0, 2, 1, 3)
	key = key.permute(0, 2, 1, 3)
	value = value.permute(0, 2, 1, 3)
	attn_output, attn_weight = self._attn(
	query, key, value, registered_causal_mask, attention_mask, head_mask
	)
	context_layer = self._merge_heads(
	attn_output, self.num_heads, self.head_dim
	)

	attn_output = self.c_proj(context_layer)

	outputs = (attn_output, present)
	if output_attentions:
	outputs += (attn_weight,)

	return outputs


	class QWenMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.w1 = nn.Linear(
	config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
	)
	self.w2 = nn.Linear(
	config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
	)
	ff_dim_in = config.intermediate_size // 2
	self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)

	def forward(self, hidden_states):
	a1 = self.w1(hidden_states)
	a2 = self.w2(hidden_states)
	intermediate_parallel = a1 * F.silu(a2)
	output = self.c_proj(intermediate_parallel)
	return output

	class QWenBlock(nn.Module):
	def __init__(self, config):
	super().__init__()
	hidden_size = config.hidden_size
	self.bf16 = config.bf16

	self.ln_1 = RMSNorm(
	hidden_size,
	eps=config.layer_norm_epsilon,
	)
	self.attn = QWenAttention(config)
	self.ln_2 = RMSNorm(
	hidden_size,
	eps=config.layer_norm_epsilon,
	)

	self.mlp = QWenMLP(config)

	def forward(
	self,
	hidden_states: Optional[Tuple[torch.FloatTensor]],
	rotary_pos_emb: Optional[List[torch.Tensor]] = None,
	registered_causal_mask: Optional[torch.Tensor] = None,
	layer_past: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	):
	layernorm_output = self.ln_1(hidden_states)

	attn_outputs = self.attn(
	layernorm_output,
	rotary_pos_emb,
	registered_causal_mask=registered_causal_mask,
	layer_past=layer_past,
	attention_mask=attention_mask,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0]

	outputs = attn_outputs[1:]

	residual = hidden_states
	layernorm_input = attn_output + residual

	layernorm_output = self.ln_2(layernorm_input)

	residual = layernorm_input
	mlp_output = self.mlp(layernorm_output)
	hidden_states = residual + mlp_output

	if use_cache:
	outputs = (hidden_states,) + outputs
	else:
	outputs = (hidden_states,) + outputs[1:]

	return outputs


	class QWenPreTrainedModel(PreTrainedModel):
	config_class = QWenConfig
	base_model_prefix = "transformer"
	is_parallelizable = False
	supports_gradient_checkpointing = True
	_no_split_modules = ["QWenBlock"]

	def __init__(self, inputs, *kwargs):
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, RMSNorm):
	module.weight.data.fill_(1.0)

	for name, p in module.named_parameters():
	if name == "c_proj.weight":
	p.data.normal_(
	mean=0.0,
	std=(
	self.config.initializer_range
	/ math.sqrt(2 * self.config.num_hidden_layers)
	),
	)

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


	class QWenModel(QWenPreTrainedModel):
	_keys_to_ignore_on_load_missing = ["attn.masked_bias"]

	def __init__(self, config):
	super().__init__(config)
	self.vocab_size = config.vocab_size
	self.num_hidden_layers = config.num_hidden_layers
	self.embed_dim = config.hidden_size

	self.gradient_checkpointing = False
	self.use_dynamic_ntk = config.use_dynamic_ntk
	self.seq_length = config.seq_length

	self.wte = nn.Embedding(self.vocab_size, self.embed_dim)

	self.drop = nn.Dropout(config.emb_dropout_prob)

	if config.rotary_pct == 1.0:
	self.rotary_ndims = None
	else:
	assert config.rotary_pct < 1
	self.rotary_ndims = int(
	config.kv_channels * config.rotary_pct
	)
	dim = (
	self.rotary_ndims
	if self.rotary_ndims is not None
	else config.kv_channels
	)
	self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)

	self.use_flash_attn = config.use_flash_attn
	self.is_fp32 = not (config.bf16 or config.fp16)
	self.registered_causal_mask = None
	# if (
	# self.use_flash_attn
	# and flash_attn_unpadded_func is not None
	# and not self.is_fp32
	# ):
	# self.registered_causal_mask = None
	# else:
	# max_positions = config.max_position_embeddings
	# self.register_buffer(
	# "registered_causal_mask",
	# torch.tril(
	# torch.ones((max_positions, max_positions), dtype=torch.bool)
	# ).view(1, 1, max_positions, max_positions),
	# persistent=False,
	# )

	self.h = nn.ModuleList(
	[
	QWenBlock(
	config
	)
	for i in range(config.num_hidden_layers)
	]
	)
	self.ln_f = RMSNorm(
	self.embed_dim,
	eps=config.layer_norm_epsilon,
	)

	self.visual = VisionTransformer(**config.visual)

	self.post_init()

	def get_input_embeddings(self):
	return self.wte

	def set_input_embeddings(self, new_embeddings):
	self.wte = new_embeddings

	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(
	input_shape,
	inputs_embeds.dtype,
	device=inputs_embeds.device,
	past_key_values_length=past_key_values_length,
	)

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
	inputs_embeds.device
	)
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask


	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: 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,
	):
	if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']):
	bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
	eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
	assert (bos_pos[0] == eos_pos[0]).all()
	img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
	images = []
	for i, a, b in img_pos:
	image = input_ids[i][a + 1 : b - 1].tolist()
	image = image[ : image.index(self.config.visual['image_start_id'] + 2)]
	images.append(bytes(image).decode('utf-8'))

	images = self.visual.encode(images)
	assert images.shape[0] == len(images)
	fake_images = None
	elif self.training:
	fake_images=torch.zeros(1,3,224,224).to(
	dtype=self.visual.conv1.weight.dtype, device=self.visual.conv1.weight.device)
	images = self.visual(fake_images)
	else:
	fake_images = None
	images = None

	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
	)

	if input_ids is not None and inputs_embeds is not None:
	raise ValueError(
	"You cannot specify both input_ids and inputs_embeds at the same time"
	)
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	batch_size = input_ids.shape[0]
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	batch_size = inputs_embeds.shape[0]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	device = input_ids.device if input_ids is not None else inputs_embeds.device

	if token_type_ids is not None:
	token_type_ids = token_type_ids.view(-1, input_shape[-1])
	if position_ids is not None:
	position_ids = position_ids.view(-1, input_shape[-1])

	if past_key_values is None:
	past_length = 0
	past_key_values = tuple([None] * len(self.h))
	else:
	past_length = past_key_values[0][0].size(-2)

	if position_ids is None:
	position_ids = torch.arange(
	past_length,
	input_shape[-1] + past_length,
	dtype=torch.long,
	device=device,
	)
	position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

	encoder_attention_mask = None
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

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

	if batch_size <= 0:
	raise ValueError("batch_size has to be defined and > 0")
	attention_mask = self._prepare_decoder_attention_mask(
	attention_mask, input_shape, inputs_embeds, past_length
	)

	hidden_states = inputs_embeds

	kv_seq_len = hidden_states.size()[1]
	if past_key_values[0] is not None:
	# past key values[0][0] shape: bs * seq_len * head_num * dim
	kv_seq_len += past_key_values[0][0].shape[1]
	if (
	self.use_dynamic_ntk
	and kv_seq_len == hidden_states.size()[1]
	and not self.training
	):
	context_value = math.log(kv_seq_len / self.seq_length, 2) + 1
	ntk_alpha = 2 ** math.ceil(context_value) - 1
	ntk_alpha = max(ntk_alpha, 1)
	else:
	ntk_alpha = self.rotary_emb._ntk_alpha_cached

	rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha)
	for idx in range(len(rotary_pos_emb)):
	rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device)

	hidden_states = self.drop(hidden_states).clone()
	if fake_images is not None:
	hidden_states = hidden_states + images.mean()*0
	elif images is not None:
	for idx, (i, a, b) in enumerate(img_pos):
	hidden_states[i][a + 1 : b] = images[idx]
	output_shape = input_shape + (hidden_states.size(-1),)

	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

	presents = () if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if self.gradient_checkpointing and self.training:

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

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states,
	rotary_pos_emb,
	self.registered_causal_mask,
	None,
	attention_mask,
	head_mask[i],
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	rotary_pos_emb=rotary_pos_emb,
	registered_causal_mask=self.registered_causal_mask,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states = outputs[0]
	if use_cache is True:
	presents = presents + (outputs[1],)

	if output_attentions:
	all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

	hidden_states = self.ln_f(hidden_states)
	hidden_states = hidden_states.view(output_shape)
	# Add last hidden state
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v for v in [hidden_states, presents, all_hidden_states] if v is not None
	)

	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	)


	class QWenLMHeadModel(QWenPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
	_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]

	def __init__(self, config):
	super().__init__(config)
	assert (
	config.bf16 + config.fp16 + config.fp32 <= 1
	), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"

	autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0

	if autoset_precision:
	if SUPPORT_BF16:
	logger.warn(
	"The model is automatically converting to bf16 for faster inference. "
	"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
	)
	config.bf16 = True
	elif SUPPORT_FP16:
	logger.warn(
	"The model is automatically converting to fp16 for faster inference. "
	"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
	)
	config.fp16 = True
	else:
	config.fp32 = True

	if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
	logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
	if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
	logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
	if config.fp32:
	if SUPPORT_BF16:
	logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
	elif SUPPORT_FP16:
	logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")

	self.transformer = QWenModel(config)
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	if config.bf16:
	self.transformer.bfloat16()
	self.lm_head.bfloat16()
	if config.fp16:
	self.transformer.half()
	self.lm_head.half()
	self.post_init()

	def get_output_embeddings(self):
	return self.lm_head

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

	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
	):
	token_type_ids = kwargs.get("token_type_ids", None)
	if past_key_values:
	input_ids = input_ids[:, -1].unsqueeze(-1)
	if token_type_ids is not None:
	token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

	attention_mask = kwargs.get("attention_mask", None)
	position_ids = kwargs.get("position_ids", None)

	if attention_mask is not None and position_ids is None:
	position_ids = attention_mask.long().cumsum(-1) - 1
	position_ids.masked_fill_(attention_mask == 0, 1)
	if past_key_values:
	position_ids = position_ids[:, -1].unsqueeze(-1)
	else:
	position_ids = None

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

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: 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]:

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

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]

	lm_logits = self.lm_head(hidden_states)

	loss = None
	if labels is not None:
	labels = labels.to(lm_logits.device)
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(
	shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
	)

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

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

	@staticmethod
	def _reorder_cache(
	past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
	) -> Tuple[Tuple[torch.Tensor]]:

	return tuple(
	tuple(
	past_state.index_select(0, beam_idx.to(past_state.device))
	for past_state in layer_past
	)
	for layer_past in past_key_values
	)

	def chat(
	self,
	tokenizer: PreTrainedTokenizer,
	query: str,
	history: Optional[HistoryType],
	system: str = "You are a helpful assistant.",
	append_history: bool = True,
	stream: Optional[bool] = _SENTINEL,
	stop_words_ids: Optional[List[List[int]]] = None,
	generation_config: Optional[GenerationConfig] = None,
	**kwargs,
	) -> Tuple[str, HistoryType]:
	generation_config = generation_config if generation_config is not None else self.generation_config

	assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
	assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
	if history is None:
	history = []
	if stop_words_ids is None:
	stop_words_ids = []

	max_window_size = kwargs.get('max_window_size', None)
	if max_window_size is None:
	max_window_size = generation_config.max_window_size
	raw_text, context_tokens = make_context(
	tokenizer,
	query,
	history=history,
	system=system,
	max_window_size=max_window_size,
	chat_format=generation_config.chat_format,
	)

	stop_words_ids.extend(get_stop_words_ids(
	generation_config.chat_format, tokenizer
	))
	input_ids = torch.tensor([context_tokens]).to(self.device)
	outputs = self.generate(
	input_ids,
	stop_words_ids=stop_words_ids,
	return_dict_in_generate=False,
	generation_config=generation_config,
	**kwargs,
	)

	response = decode_tokens(
	outputs[0],
	tokenizer,
	raw_text_len=len(raw_text),
	context_length=len(context_tokens),
	chat_format=generation_config.chat_format,
	verbose=False,
	errors='replace'
	)

	if append_history:
	history.append((query, response))

	return response, history

	def chat_stream(
	self,
	tokenizer: PreTrainedTokenizer,
	query: str,
	history: Optional[HistoryType],
	system: str = "You are a helpful assistant.",
	stop_words_ids: Optional[List[List[int]]] = None,
	logits_processor: Optional[LogitsProcessorList] = None,
	generation_config: Optional[GenerationConfig] = None,
	**kwargs,
	) -> Generator[str, Any, None]:
	generation_config = generation_config if generation_config is not None else self.generation_config
	assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
	if history is None:
	history = []
	if stop_words_ids is None:
	stop_words_ids = []

	max_window_size = kwargs.get('max_window_size', None)
	if max_window_size is None:
	max_window_size = generation_config.max_window_size
	raw_text, context_tokens = make_context(
	tokenizer,
	query,
	history=history,
	system=system,
	max_window_size=max_window_size,
	chat_format=generation_config.chat_format,
	)

	stop_words_ids.extend(get_stop_words_ids(
	generation_config.chat_format, tokenizer
	))
	if stop_words_ids is not None:
	stop_words_logits_processor = StopWordsLogitsProcessor(
	stop_words_ids=stop_words_ids,
	eos_token_id=generation_config.eos_token_id,
	)
	if logits_processor is None:
	logits_processor = LogitsProcessorList([stop_words_logits_processor])
	else:
	logits_processor.append(stop_words_logits_processor)
	input_ids = torch.tensor([context_tokens]).to(self.device)

	from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
	self.__class__.generate_stream = NewGenerationMixin.generate
	self.__class__.sample_stream = NewGenerationMixin.sample_stream
	stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)

	def stream_generator():
	outputs = []
	for token in self.generate_stream(
	input_ids,
	return_dict_in_generate=False,
	generation_config=stream_config,
	logits_processor=logits_processor,
	seed=-1,
	**kwargs):
	outputs.append(token.item())
	yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore', keep_image_special=True)

	return stream_generator()

	def generate(
	self,
	inputs: Optional[torch.Tensor] = None,
	generation_config: Optional[GenerationConfig] = None,
	logits_processor: Optional[LogitsProcessorList] = None,
	stopping_criteria: Optional[StoppingCriteriaList] = None,
	prefix_allowed_tokens_fn: Optional[
	Callable[[int, torch.Tensor], List[int]]
	] = None,
	synced_gpus: Optional[bool] = None,
	assistant_model: Optional["PreTrainedModel"] = None,
	streamer: Optional["BaseStreamer"] = None,
	**kwargs,
	) -> Union[GenerateOutput, torch.LongTensor]:
	generation_config = generation_config if generation_config is not None else self.generation_config

	# Process stop_words_ids.
	stop_words_ids = kwargs.pop("stop_words_ids", None)
	if stop_words_ids is None and generation_config is not None:
	stop_words_ids = getattr(generation_config, "stop_words_ids", None)
	if stop_words_ids is None:
	stop_words_ids = getattr(generation_config, "stop_words_ids", None)

	if stop_words_ids is not None:
	stop_words_logits_processor = StopWordsLogitsProcessor(
	stop_words_ids=stop_words_ids,
	eos_token_id=generation_config.eos_token_id,
	)
	if logits_processor is None:
	logits_processor = LogitsProcessorList([stop_words_logits_processor])
	else:
	logits_processor.append(stop_words_logits_processor)

	return super().generate(
	inputs,
	generation_config=generation_config,
	logits_processor=logits_processor,
	stopping_criteria=stopping_criteria,
	prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
	synced_gpus=synced_gpus,
	assistant_model=assistant_model,
	streamer=streamer,
	**kwargs,
	)


	class RotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, base=10000):
	super().__init__()
	self.dim = dim
	self.base = base
	self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
	if importlib.util.find_spec("einops") is None:
	raise RuntimeError("einops is required for Rotary Embedding")

	self._rotary_pos_emb_cache = None
	self._seq_len_cached = 0
	self._ntk_alpha_cached = 1.0

	def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0):
	seqlen = max_seq_len + offset
	if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
	base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
	self.inv_freq = 1.0 / (
	base
	** (
	torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
	/ self.dim
	)
	)
	self._seq_len_cached = max(2 * seqlen, 16)
	self._ntk_alpha_cached = ntk_alpha
	seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
	freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)

	emb = torch.cat((freqs, freqs), dim=-1)
	from einops import rearrange

	emb = rearrange(emb, "n d -> 1 n 1 d")

	cos, sin = emb.cos(), emb.sin()
	self._rotary_pos_emb_cache = [cos, sin]

	def forward(self, max_seq_len, offset=0, ntk_alpha=1.0):
	self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha)
	cos, sin = self._rotary_pos_emb_cache
	return [cos[:, offset : offset + max_seq_len], sin[:, offset : offset + max_seq_len]]


	def _rotate_half(x):
	from einops import rearrange

	x = rearrange(x, "... (j d) -> ... j d", j=2)
	x1, x2 = x.unbind(dim=-2)
	return torch.cat((-x2, x1), dim=-1)


	def apply_rotary_pos_emb(t, freqs):
	cos, sin = freqs
	if apply_rotary_emb_func is not None and t.is_cuda:
	t_ = t.float()
	cos = cos.squeeze(0).squeeze(1)[:, : cos.shape[-1] // 2]
	sin = sin.squeeze(0).squeeze(1)[:, : sin.shape[-1] // 2]
	output = apply_rotary_emb_func(t_, cos, sin).type_as(t)
	return output
	else:
	rot_dim = freqs[0].shape[-1]
	cos, sin = freqs
	t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
	t_ = t_.float()
	t_pass_ = t_pass_.float()
	t_ = (t_ * cos) + (_rotate_half(t_) * sin)
	return torch.cat((t_, t_pass_), dim=-1).type_as(t)


	class RMSNorm(torch.nn.Module):
	def __init__(self, dim: int, eps: float = 1e-6):
	super().__init__()
	self.eps = eps
	self.weight = nn.Parameter(torch.ones(dim))

	def _norm(self, x):
	return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

	def forward(self, x):
	if rms_norm is not None and x.is_cuda:
	return rms_norm(x, self.weight, self.eps)
	else:
	output = self._norm(x.float()).type_as(x)
	return output * self.weight