add qwen3
This commit is contained in:
@@ -45,6 +45,7 @@ _MODELS = {
|
||||
"PhiForCausalLM": ("phi", "PhiForCausalLM"),
|
||||
"QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
|
||||
"Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
|
||||
"Qwen3ForCausalLM": ("qwen3", "Qwen3ForCausalLM"),
|
||||
"RWForCausalLM": ("falcon", "FalconForCausalLM"),
|
||||
"StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
|
||||
"StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
|
||||
|
||||
332
vllm/model_executor/models/qwen3.py
Normal file
332
vllm/model_executor/models/qwen3.py
Normal file
@@ -0,0 +1,332 @@
|
||||
# coding=utf-8
|
||||
# Adapted from
|
||||
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
|
||||
# Copyright 2024 The Qwen team.
|
||||
# Copyright 2023 The vLLM team.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Inference-only Qwen3 model compatible with HuggingFace weights."""
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import Qwen3Config
|
||||
|
||||
from vllm.model_executor.input_metadata import InputMetadata
|
||||
from vllm.model_executor.layers.activation import SiluAndMul
|
||||
from vllm.model_executor.layers.attention import PagedAttention
|
||||
from vllm.model_executor.layers.layernorm import RMSNorm
|
||||
from vllm.model_executor.layers.linear import (LinearMethodBase,
|
||||
MergedColumnParallelLinear,
|
||||
QKVParallelLinear,
|
||||
RowParallelLinear)
|
||||
from vllm.model_executor.layers.rotary_embedding import get_rope
|
||||
from vllm.model_executor.layers.sampler import Sampler
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
||||
VocabParallelEmbedding, ParallelLMHead)
|
||||
from vllm.model_executor.parallel_utils.parallel_state import (
|
||||
get_tensor_model_parallel_world_size)
|
||||
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
||||
from vllm.model_executor.weight_utils import (default_weight_loader,
|
||||
hf_model_weights_iterator)
|
||||
from vllm.sequence import SamplerOutput
|
||||
from .qwen2 import Qwen2MLP as Qwen3MLP
|
||||
# from .qwen2 import Qwen2Model
|
||||
|
||||
KVCache = Tuple[torch.Tensor, torch.Tensor]
|
||||
|
||||
|
||||
class Qwen3Attention(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
hidden_size: int,
|
||||
num_heads: int,
|
||||
num_kv_heads: int,
|
||||
max_position: int = 4096 * 32,
|
||||
head_dim: Optional[int] = None,
|
||||
rms_norm_eps: float = 1e-06,
|
||||
qkv_bias: bool = False,
|
||||
rope_theta: float = 10000,
|
||||
use_sliding_window: bool = False,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
sliding_window: Optional[int] = None) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = hidden_size
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
self.total_num_heads = num_heads
|
||||
assert self.total_num_heads % tp_size == 0
|
||||
self.num_heads = self.total_num_heads // tp_size
|
||||
self.total_num_kv_heads = num_kv_heads
|
||||
if self.total_num_kv_heads >= tp_size:
|
||||
# Number of KV heads is greater than TP size, so we partition
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert self.total_num_kv_heads % tp_size == 0
|
||||
else:
|
||||
# Number of KV heads is less than TP size, so we replicate
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert tp_size % self.total_num_kv_heads == 0
|
||||
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
||||
self.head_dim = head_dim or hidden_size // self.total_num_heads
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.rope_theta = rope_theta
|
||||
self.sliding_window = sliding_window if use_sliding_window else None
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=qkv_bias,
|
||||
linear_method=linear_method,
|
||||
)
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.total_num_heads * self.head_dim,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
linear_method=linear_method,
|
||||
)
|
||||
|
||||
self.rotary_emb = get_rope(
|
||||
self.head_dim,
|
||||
rotary_dim=self.head_dim,
|
||||
max_position=max_position,
|
||||
base=self.rope_theta,
|
||||
)
|
||||
self.attn = PagedAttention(self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
sliding_window=self.sliding_window)
|
||||
self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
|
||||
self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
kv_cache: KVCache,
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
# Add qk-norm
|
||||
q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim,
|
||||
self.head_dim)
|
||||
q_by_head = self.q_norm(q_by_head.contiguous())
|
||||
q = q_by_head.view(q.shape)
|
||||
k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim,
|
||||
self.head_dim)
|
||||
k_by_head = self.k_norm(k_by_head.contiguous())
|
||||
k = k_by_head.view(k.shape)
|
||||
q, k = self.rotary_emb(positions, q, k)
|
||||
k_cache, v_cache = kv_cache
|
||||
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
|
||||
class Qwen3DecoderLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Qwen3Config,
|
||||
layer_idx: int,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
# Requires transformers > 4.32.0
|
||||
rope_theta = getattr(config, "rope_theta", 1000000)
|
||||
use_sliding_window = config.use_sliding_window and layer_idx < config.max_window_layers
|
||||
self.self_attn = Qwen3Attention(
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
max_position=config.max_position_embeddings,
|
||||
num_kv_heads=config.num_key_value_heads,
|
||||
rope_theta=rope_theta,
|
||||
rms_norm_eps=config.rms_norm_eps,
|
||||
qkv_bias=getattr(config, 'attention_bias', False),
|
||||
head_dim=getattr(config, 'head_dim', None),
|
||||
use_sliding_window=use_sliding_window,
|
||||
linear_method=linear_method,
|
||||
sliding_window=config.sliding_window)
|
||||
self.mlp = Qwen3MLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
linear_method=linear_method,
|
||||
)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
kv_cache: KVCache,
|
||||
input_metadata: InputMetadata,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
# Self Attention
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
kv_cache=kv_cache,
|
||||
input_metadata=input_metadata,
|
||||
)
|
||||
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.post_attention_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class Qwen3Model(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Qwen3Config,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
self.layers = nn.ModuleList([
|
||||
Qwen3DecoderLayer(config, layer_idx, linear_method)
|
||||
for layer_idx in range(config.num_hidden_layers)
|
||||
])
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[KVCache],
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
residual = None
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(
|
||||
positions,
|
||||
hidden_states,
|
||||
kv_caches[i],
|
||||
input_metadata,
|
||||
residual,
|
||||
)
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class Qwen3ForCausalLM(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Qwen3Config,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.linear_method = linear_method
|
||||
self.model = Qwen3Model(config, linear_method)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.model.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
|
||||
self.sampler = Sampler(config.vocab_size)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[KVCache],
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, kv_caches,
|
||||
input_metadata)
|
||||
return hidden_states
|
||||
|
||||
def sample(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[SamplerOutput]:
|
||||
next_tokens = self.sampler(self.lm_head.weight, hidden_states,
|
||||
sampling_metadata)
|
||||
return next_tokens
|
||||
|
||||
def load_weights(self,
|
||||
model_name_or_path: str,
|
||||
cache_dir: Optional[str] = None,
|
||||
load_format: str = "auto",
|
||||
revision: Optional[str] = None):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in hf_model_weights_iterator(
|
||||
model_name_or_path, cache_dir, load_format, revision):
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
try:
|
||||
param = params_dict[name]
|
||||
except:
|
||||
assert name=="lm_head.weight" # for qwen1.5 0.5b,skip this
|
||||
continue
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
Reference in New Issue
Block a user