Qwen-14b-chat-yarn-32k / modeling_qwen_yarn.py

Update modeling_qwen_yarn.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

	try:
	from kernels.cpp_kernels import cache_autogptq_cuda_256
	except ImportError:
	cache_autogptq_cuda_256 = None

	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 flash_attn.bert_padding import unpad_input, pad_input

	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)。
	"""

	_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\
	We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained).
	检测到您的模型已激活了flash attention支持，但正在执行CPU运算任务。如使用flash attention，请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算，请您在载入模型（调用AutoModelForCausalLM.from_pretrained）时，按照readme说法，指定device_map="cpu"以禁用flash attention。
	"""

	apply_rotary_emb_func = None
	rms_norm = None
	flash_attn_unpadded_func = None

	def _import_flash_attn():
	global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func
	try:
	from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func
	apply_rotary_emb_func = __apply_rotary_emb_func
	except ImportError:
	logger.warn(
	"Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency "
	"https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary"
	)

	try:
	from flash_attn.ops.rms_norm import rms_norm as __rms_norm
	rms_norm = __rms_norm
	except ImportError:
	logger.warn(
	"Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency "
	"https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm"
	)

	try:
	import flash_attn
	if not hasattr(flash_attn, '__version__'):
	from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
	else:
	if int(flash_attn.__version__.split(".")[0]) >= 2:
	from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func
	else:
	from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
	flash_attn_unpadded_func = __flash_attn_unpadded_func
	except ImportError:
	logger.warn(
	"Warning: import flash_attn fail, please install FlashAttention to get higher efficiency "
	"https://github.com/Dao-AILab/flash-attention"
	)

	def quantize_cache_v(fdata, bits, qmax, qmin):
	# b, s, head, h-dim->b, head, s, h-dim
	qtype = torch.uint8
	device = fdata.device
	shape = fdata.shape

	fdata_cal = torch.flatten(fdata, 2)
	fmax = torch.amax(fdata_cal, dim=-1, keepdim=True)
	fmin = torch.amin(fdata_cal, dim=-1, keepdim=True)
	# Compute params
	if qmax.device != fmax.device:
	qmax = qmax.to(device)
	qmin = qmin.to(device)
	scale = (fmax - fmin) / (qmax - qmin)
	zero = qmin - fmin / scale
	scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
	zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
	# Quantize
	res_data = fdata / scale + zero
	qdata = torch.clamp(res_data, qmin, qmax).to(qtype)
	return qdata.contiguous(), scale, zero

	def dequantize_cache_torch(qdata, scale, zero):
	data = scale * (qdata - zero)
	return data

	class FlashSelfAttention(torch.nn.Module):
	def __init__(
	self,
	causal=False,
	softmax_scale=None,
	attention_dropout=0.0,
	):
	super().__init__()
	assert flash_attn_unpadded_func is not None, (
	"Please install FlashAttention first, " "e.g., with pip install flash-attn"
	)
	assert (
	rearrange is not None
	), "Please install einops first, e.g., with pip install einops"
	self.causal = causal
	self.softmax_scale = softmax_scale
	self.dropout_p = attention_dropout

	def unpad_input(self, hidden_states, attention_mask):
	valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0)
	seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32)
	indices = torch.nonzero(valid_mask.flatten(), as_tuple=False).flatten()
	max_seqlen_in_batch = seqlens_in_batch.max().item()
	cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
	hidden_states = hidden_states[indices]
	return hidden_states, indices, cu_seqlens, max_seqlen_in_batch

	def pad_input(self, hidden_states, indices, batch, seqlen):
	#torch.cuda.empty_cache()
	output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device,
	dtype=hidden_states.dtype)
	output[indices] = hidden_states
	return rearrange(output, '(b s) ... -> b s ...', b=batch)

	def forward(self, q, k, v, attention_mask=None):
	assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
	assert all((i.is_cuda for i in (q, k, v)))
	q_len_origin=q.shape[1]
	batch_size, seqlen_q = q.shape[0], q.shape[1]
	seqlen_k = k.shape[1]

	q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
	cu_seqlens_q = torch.arange(
	0,
	(batch_size + 1) * seqlen_q,
	step=seqlen_q,
	dtype=torch.int32,
	device=q.device,
	)

	if attention_mask is not None:
	k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask)
	v = v[indices_k]
	if self.training or q.size(0) == k.size(0):
	q = q[indices_k]
	cu_seqlens_q = cu_seqlens_k
	seqlen_q = seqlen_k
	else:
	cu_seqlens_k = torch.arange(
	0,
	(batch_size + 1) * seqlen_k,
	step=seqlen_k,
	dtype=torch.int32,
	device=q.device,
	)

	if self.training:
	assert seqlen_k == seqlen_q
	is_causal = self.causal
	dropout_p = self.dropout_p
	else:
	is_causal = seqlen_q == seqlen_k
	dropout_p = 0

	output = flash_attn_unpadded_func(
	q,
	k,
	v,
	cu_seqlens_q,
	cu_seqlens_k,
	seqlen_q,
	seqlen_k,
	dropout_p,
	softmax_scale=self.softmax_scale,
	causal=is_causal,
	)
	if attention_mask is not None and seqlen_q == seqlen_k:
	output = self.pad_input(output, indices_k, batch_size, seqlen_q)


	else:
	new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:]
	output = output.view(new_shape)
	return output


	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.use_flash_attn = config.use_flash_attn
	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)
	if (
	self.use_flash_attn
	and flash_attn_unpadded_func is not None
	and not self.is_fp32
	):
	self.core_attention_flash = FlashSelfAttention(
	causal=True, attention_dropout=config.attn_dropout_prob
	)
	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)
	]
	logn_tensor = torch.tensor(logn_list)[None, :, None, None]
	self.register_buffer("logn_tensor", logn_tensor, persistent=False)

	self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
	self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False
	self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False
	cache_dtype = torch.float
	if self.bf16:
	cache_dtype=torch.bfloat16
	elif config.fp16:
	cache_dtype = torch.float16
	self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype)
	self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype)

	def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None):
	device = query.device
	if self.use_cache_quantization:
	qk, qk_scale, qk_zero = key
	if self.use_cache_kernel and cache_autogptq_cuda_256 is not None:
	shape = query.shape[:-1] + (qk.shape[-2],)
	attn_weights = torch.zeros(shape, dtype=torch.float16, device=device)
	cache_autogptq_cuda_256.vecquant8matmul_batched_faster_old(
	query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(),
	qk.transpose(-1, -2).contiguous(),
	attn_weights,
	qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(),
	qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous())
	# attn_weights = attn_weights.to(query.dtype).contiguous()
	else:
	key = dequantize_cache_torch(qk, qk_scale, qk_zero)
	attn_weights = torch.matmul(query, key.transpose(-1, -2))
	else:
	attn_weights = torch.matmul(query, key.transpose(-1, -2))

	if self.scale_attn_weights:
	if self.use_cache_quantization:
	size_temp = value[0].size(-1)
	else:
	size_temp = value.size(-1)
	attn_weights = attn_weights / torch.full(
	[],
	size_temp ** 0.5,
	dtype=attn_weights.dtype,
	device=attn_weights.device,
	)
	if self.use_cache_quantization:
	query_length, key_length = query.size(-2), key[0].size(-2)
	else:
	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.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
	)

	if attention_mask is not None:
	attn_weights = attn_weights + attention_mask

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

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

	if head_mask is not None:
	attn_weights = attn_weights * head_mask

	if self.use_cache_quantization:
	qv, qv_scale, qv_zero = value
	if self.use_cache_kernel and cache_autogptq_cuda_256 is not None:
	shape = attn_weights.shape[:-1] + (query.shape[-1],)
	attn_output = torch.zeros(shape, dtype=torch.float16, device=device)
	cache_autogptq_cuda_256.vecquant8matmul_batched_column_compression_faster_old(
	attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(),
	qv.contiguous(), # dtype: int32
	attn_output,
	qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(),
	qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous())
	if attn_output.dtype != query.dtype:
	attn_output = attn_output.to(query.dtype)
	attn_weights = attn_weights.to(query.dtype)
	else:
	value = dequantize_cache_torch(qv, qv_scale, qv_zero)
	attn_output = torch.matmul(attn_weights, value)
	else:
	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
	] #registered_causal_mask是shape为(1, 1, max_positions, max_positions)的二值张量，其中max_positions为8192
	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) #causal_mask中为1的位置，attn_weights中保留，否则用mask_value填充

	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_list: 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_list is not None:
	cur_len = query.shape[1]
	if len(rotary_pos_emb_list) == 1:
	rotary_pos_emb = rotary_pos_emb_list[0]
	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)
	else:
	query_list = []
	key_list = []
	for i, rotary_pos_emb in enumerate(rotary_pos_emb_list):
	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_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)]
	key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)]
	query = torch.cat(query_list, dim=0)
	key = torch.cat(key_list, dim=0)

	if self.use_cache_quantization:
	key = quantize_cache_v(key.permute(0, 2, 1, 3),
	bits=8,
	qmin=self.cache_qmin,
	qmax=self.cache_qmax)
	value = quantize_cache_v(value.permute(0, 2, 1, 3),
	bits=8,
	qmin=self.cache_qmin,
	qmax=self.cache_qmax)


	if layer_past is not None:
	past_key, past_value = layer_past[0], layer_past[1]
	if self.use_cache_quantization:
	# use_cache_quantization:
	# present=((q_key,key_scale,key_zero_point),
	# (q_value,value_scale,value_zero_point))
	key = (torch.cat((past_key[0], key[0]), dim=2),
	torch.cat((past_key[1], key[1]), dim=2),
	torch.cat((past_key[2], key[2]), dim=2))
	value = (torch.cat((past_value[0], value[0]), dim=2),
	torch.cat((past_value[1], value[1]), dim=2),
	torch.cat((past_value[2], value[2]), dim=2))
	else:
	# not use_cache_quantization:
	# present=(key,value)
	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.use_cache_quantization:
	seq_start = key[0].size(2) - query.size(1)
	seq_end = key[0].size(2)
	else:
	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) #使用logn_attn时，query乘以logn_tensor，避免长文本时注意力不稳定

	if (
	self.use_flash_attn
	and flash_attn_unpadded_func is not None
	and not self.is_fp32
	and query.is_cuda
	):
	q, k, v = query, key, value
	context_layer = self.core_attention_flash(q, k, v, attention_mask=attention_mask)

	# b s h d -> b s (h d)
	context_layer = context_layer.flatten(2,3).contiguous()

	else:
	query = query.permute(0, 2, 1, 3)
	if not self.use_cache_quantization:
	key = key.permute(0, 2, 1, 3)
	value = value.permute(0, 2, 1, 3)
	if (
	registered_causal_mask is None
	and self.use_flash_attn
	and flash_attn_unpadded_func is not None
	and not self.is_fp32
	and not query.is_cuda
	):
	raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED)
	attn_output, attn_weight = self._attn(
	query, key, value, registered_causal_mask, attention_mask, head_mask
	) #如果没有使用flash attention，才会使用registered_causal_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:
	if (
	self.use_flash_attn
	and flash_attn_unpadded_func is not None
	and not self.is_fp32
	):
	raise ValueError("Cannot output attentions while using flash-attn")
	else:
	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_list: 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_list,
	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.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False

	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 = YaRNRotaryEmbedding(dim, base=config.rotary_emb_base,original_max_position_embeddings=config.seq_length)
	#self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)
	import warnings
	warnings.warn("使用YarnRotaryEmbedding，强制设置config.use_logn_attn = False，config.use_dynamic_ntk = True")
	config.use_logn_attn = False
	config.use_dynamic_ntk = True

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

	def get_input_embeddings(self):
	return self.wte

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

	def get_ntk_alpha(self, true_seq_len):
	ntk_alpha = true_seq_len / self.seq_length + 512/self.seq_length
	ntk_alpha = max(ntk_alpha, 1)
	# context_value = math.log(true_seq_len / self.seq_length, 2) + 1
	# ntk_alpha = 2 ** math.ceil(context_value) - 1
	# ntk_alpha = max(ntk_alpha, 1)

	#假设seq_length=2k，true_seq_len=8k，context_value=3，ntk_alpha=7，相当于扩展到14k长度
	return ntk_alpha

	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,
	):
	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:
	if self.use_cache_quantization:
	past_length = past_key_values[0][0][0].size(2)
	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])

	if attention_mask is not None:
	if batch_size <= 0:
	raise ValueError("batch_size has to be defined and > 0")
	attention_mask = attention_mask.view(batch_size, -1)
	attention_mask = attention_mask[:, None, None, :]
	attention_mask = attention_mask.to(dtype=self.dtype)
	attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

	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)
	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
	if self.use_cache_quantization:
	kv_seq_len += past_key_values[0][0][0].shape[2]
	else:
	kv_seq_len += past_key_values[0][0].shape[1]

	#if self.training or not self.use_dynamic_ntk:
	if not self.use_dynamic_ntk:
	ntk_alpha_list = [1.0]
	elif kv_seq_len != hidden_states.size()[1]:
	ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
	else:
	ntk_alpha_list = []
	if attention_mask is not None and kv_seq_len > self.seq_length:
	true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32)
	for i in range(hidden_states.size()[0]):
	#给batch中的每个样本计算ntk_alpha，计算方法是 true_seq_len / self.seq_length。qwen-7b中，self.seq_length=8192，qwen-14b中，self.seq_length=2048
	true_seq_len = true_seq_lens[i].item()
	ntk_alpha = self.get_ntk_alpha(true_seq_len)
	ntk_alpha_list.append(ntk_alpha)
	else:
	ntk_alpha = self.get_ntk_alpha(kv_seq_len)
	ntk_alpha_list.append(ntk_alpha)
	self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list

	rotary_pos_emb_list = []
	for ntk_alpha in ntk_alpha_list:
	rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) #训练时，ntk_alpha=1.0，rotary_emb根据kv_seq_len生成
	rotary_pos_emb_list.append(rotary_pos_emb)

	hidden_states = self.drop(hidden_states)
	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_list,
	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_list=rotary_pos_emb_list,
	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"
	logger.warn(
	"Warning: please make sure that you are using the latest codes and checkpoints, "
	"especially if you used Qwen-7B before 09.25.2023."
	"请使用最新模型和代码，尤其如果你在9月25日前已经开始使用Qwen-7B，千万注意不要使用错误代码和模型。"
	)

	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\".")

	if config.use_flash_attn == "auto":
	if config.bf16 or config.fp16:
	logger.warn("Try importing flash-attention for faster inference...")
	config.use_flash_attn = True
	else:
	config.use_flash_attn = False
	if config.use_flash_attn and config.fp32:
	logger.warn("Flash attention will be disabled because it does NOT support fp32.")

	if config.use_flash_attn:
	_import_flash_attn()


	if hasattr(config, 'use_cache_quantization') and config.use_cache_quantization:
	config.use_flash_attn = False
	if hasattr(config, 'use_cache_kernel') and config.use_cache_kernel:
	try:
	from kernels.cpp_kernels import cache_autogptq_cuda_256
	except ImportError:
	cache_autogptq_cuda_256 = None

	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

	#训练时节约显存
	# if self.training:
	# lm_logits=None

	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')

	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
	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
	self.register_buffer("inv_freq", inv_freq, persistent=False)
	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
	self._ntk_alpha_cached_list = [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:
	#计算新的base。ntk_alpha=1时，base不变；ntk_alpha>1时，base变大
	base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
	#波长=2pibase^(2d/D),即频率的倒数。此处算出了每个维度对应的频率。由于每两个维度对应一个频率，所以频率的个数为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(seqlen+512, 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) #做外积，获得seq_len*(dim/2)的矩阵

	emb = torch.cat((freqs, freqs), dim=-1) #获得(seq_len2)dim的矩阵
	from einops import rearrange

	emb = rearrange(emb, "n d -> 1 n 1 d") #获得1(seq_len2)1dim的矩阵

	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]]


	class YaRNRotaryEmbedding(RotaryEmbedding):
	def __init__(self, dim, base=10000,interleaved=False,extrapolation_factor=1,
	attn_factor=1, beta_fast=32, beta_slow=1,original_max_position_embeddings=2048):
	"""
	interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
	of 1st half and 2nd half (GPT-NeoX style).
	pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
	otherwise they might be in lower precision.
	This option was added because previously (before 2023-07-02), when we construct
	the position indices, we use the dtype of self.inv_freq. In most cases this would
	be fp32, but if the model is trained in pure bf16 (not mixed precision), then
	self.inv_freq would be bf16, and the position indices are also in bf16.
	Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
	embeddings for some positions will coincide.
	To maintain compatibility with models previously trained in pure bf16,
	we add this option.
	scaling_factor: RotaryEmbedding extended with YaRN scaling.
	"""
	super().__init__(dim, base)
	self.interleaved = interleaved
	self.extrapolation_factor = extrapolation_factor
	self.attn_factor = attn_factor
	self.beta_fast = beta_fast
	self.beta_slow = beta_slow
	self.original_max_position_embeddings = original_max_position_embeddings

	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:
	#波长=2pibase^(2d/D),即频率的倒数。此处算出了每个维度对应的频率。由于每两个维度对应一个频率，所以频率的个数为dim/2
	self._compute_inv_freq(ntk_alpha, device=self.inv_freq.device)
	self.mscale = float(_yarn_get_mscale(ntk_alpha) * self.attn_factor)

	self._seq_len_cached = max(seqlen+512, 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) #seq和逆频率做外积，获得seq_len*(dim/2)的矩阵

	emb = torch.cat((freqs, freqs), dim=-1) #获得(seq_len2)dim的矩阵
	from einops import rearrange

	emb = rearrange(emb, "n d -> 1 n 1 d") #获得1(seq_len2)1dim的矩阵

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

	def _compute_inv_freq(self, scaling_factor, device=None):
	pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
	inv_freq_extrapolation = 1.0 / pos_freqs
	inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)

	low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
	inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
	inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
	inv_freq=inv_freq.float()
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	# Inverse dim formula to find dim based on number of rotations
	def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
	return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))

	# Find dim range bounds based on rotations
	def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
	low = math.floor(_yarn_find_correction_dim(
	low_rot, dim, base, max_position_embeddings))
	high = math.ceil(_yarn_find_correction_dim(
	high_rot, dim, base, max_position_embeddings))
	return max(low, 0), min(high, dim-1) # Clamp values just in case

	def _yarn_linear_ramp_mask(min, max, dim):
	if min == max:
	max += 0.001 # Prevent singularity

	linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
	ramp_func = torch.clamp(linear_func, 0, 1)
	return ramp_func

	def _yarn_get_mscale(scale=1):
	if scale <= 1:
	return 1.0
	return 0.1 * math.log(scale) + 1.0



	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