# 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 copy
import importlib
import math
import pathlib
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import warnings
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
SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2
from .configuration_qwen import QWenConfig
from .qwen_generation_utils import (
HistoryType,
make_context,
decode_tokens,
get_stop_words_ids,
StopWordsLogitsProcessor,
)
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
flash_attn_func = None
def _import_flash_attn():
global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_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
_flash_attn_func = None
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:
if int(flash_attn.__version__.split(".")[1]) >= 1:
from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func
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
flash_attn_func = _flash_attn_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):
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)))
batch_size, seqlen_q = q.shape[0], q.shape[1]
seqlen_k = k.shape[1]
seqlen_out = seqlen_q
if flash_attn_func is not None and batch_size == 1:
dropout_p = self.dropout_p if self.training else 0
output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal)
return output
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 batch_size > 1 and attention_mask is not None:
k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask)
if q.size(0) == v.size(0):
q = q[indices_k]
cu_seqlens_q = cu_seqlens_k
seqlen_q = seqlen_k
v = v[indices_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 batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k:
output = self.pad_input(output, indices_k, batch_size, seqlen_out)
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.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False
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)
if config.use_cache_quantization and config.use_cache_kernel:
# pre check if the support files existing
module_root = pathlib.Path(__file__).parent
src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu")
if any(not (module_root/src).is_file() for src in src_files):
warnings.warn("KV cache kernel source files (.cpp and .cu) not found.")
self.cache_kernels = None
else:
try:
from .cpp_kernels import cache_autogptq_cuda_256
self.cache_kernels = cache_autogptq_cuda_256
except ImportError:
warnings.warn("Failed to import KV cache kernels.")
self.cache_kernels = None
def _attn(self, query, key, value, causal_mask=None, 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 self.cache_kernels is not None:
shape = query.shape[:-1] + (qk.shape[-2],)
attn_weights = torch.zeros(shape, dtype=torch.float16, device=device)
self.cache_kernels.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 / (size_temp ** 0.5)
mask_value = torch.finfo(attn_weights.dtype).min
if causal_mask is not None:
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
if self.softmax_in_fp32:
attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1)
else:
attn_weights = nn.functional.softmax(attn_weights, 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 self.cache_kernels is not None:
shape = attn_weights.shape[:-1] + (query.shape[-1],)
attn_output = torch.zeros(shape, dtype=torch.float16, device=device)
self.cache_kernels.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 _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[List[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
key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
if key_size > self.seq_length and 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, :, :].type_as(query)
query = query * logn_tensor.expand_as(query)
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
attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask)
else:
key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
if query.size(1) == key_size:
causal_mask = torch.tril(
torch.ones((key_size, key_size), dtype=torch.bool, device=query.device)
).view(1, 1, key_size, key_size)
else:
causal_mask = None
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 (
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)
if not self.use_cache_quantization and SUPPORT_TORCH2:
if attention_mask is not None:
attention_mask = attention_mask.expand(-1, -1, query.size(2), -1)
if causal_mask is not None:
attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
else:
attention_mask = causal_mask
attn_output = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask
).transpose(1, 2)
attn_weight = None
else:
attn_output, attn_weight = self._attn(
query, key, value, 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:
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")
elif not self.use_cache_quantization and SUPPORT_TORCH2:
raise ValueError("Cannot output attentions while using scaled_dot_product_attention")
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[List[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,
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"]
_skip_keys_device_placement = "past_key_values"
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 = 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.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):
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)
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:
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]):
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 = [
self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list
]
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,
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,
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\".")
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()
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
):
if past_key_values:
input_ids = input_ids[:, -1].unsqueeze(-1)
if input_ids.size(0) == 1:
attention_mask = None
else:
attention_mask = kwargs.get("attention_mask", 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"),
"attention_mask": attention_mask,
}
)
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.",
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 = []
else:
# make a copy of the user's input such that is is left untouched
history = copy.deepcopy(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'
)
# as history is a copy of the user inputs,
# we can always return the new turn to the user.
# separating input history and output history also enables the user
# to implement more complex history management
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, seqlen, ntk_alpha=1.0):
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, ntk_alpha=1.0):
self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha)
cos, sin = self._rotary_pos_emb_cache
return [cos[:, :max_seq_len], sin[:, :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):
""" Apply rotary embedding to the first rotary_dim of the iput
Arguments:
t (tensor(batch_size, seq_len, n_head, head_dim)):
the input embedding/hidden states
freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]):
the cached cos/sin position embeddings
"""
rot_dim = freqs[0].shape[-1]
cos, sin = freqs
t_float = t.float()
if apply_rotary_emb_func is not None and t.is_cuda:
# apply_rotary_emb in flash_attn requires cos/sin to be of
# shape (seqlen, rotary_dim / 2) and apply rotary embedding
# to the first rotary_dim of the input
cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2]
sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2]
return apply_rotary_emb_func(t_float, cos, sin).type_as(t)
else:
t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:]
t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin)
return torch.cat((t_rot, 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