mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-23 20:25:01 +08:00
fac9b430ec
Signed-off-by: whx-sjtu <2952154980@qq.com> Signed-off-by: yewentao256 <zhyanwentao@126.com>
360 lines
17 KiB
Python
360 lines
17 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
# Copyright 2025 The vLLM team.
|
|
# Copyright 2025 The Qwen Team.
|
|
# Copyright 2025 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-VL-MoE model compatible with HuggingFace weights."""
|
|
import typing
|
|
from collections.abc import Iterable
|
|
from typing import Callable, Optional, Union
|
|
|
|
import torch
|
|
from transformers.models.qwen3_vl_moe.configuration_qwen3_vl_moe import (
|
|
Qwen3VLMoeConfig)
|
|
|
|
from vllm.compilation.decorators import support_torch_compile
|
|
from vllm.config import VllmConfig
|
|
from vllm.distributed import get_pp_group
|
|
from vllm.logger import init_logger
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
|
|
from vllm.model_executor.model_loader.weight_utils import (
|
|
default_weight_loader, maybe_remap_kv_scale_name)
|
|
from vllm.multimodal import MULTIMODAL_REGISTRY
|
|
from vllm.sequence import IntermediateTensors
|
|
|
|
from .qwen3_moe import Qwen3MoeForCausalLM, Qwen3MoeModel
|
|
from .qwen3_vl import (Qwen3_VisionTransformer, Qwen3VLDummyInputsBuilder,
|
|
Qwen3VLForConditionalGeneration,
|
|
Qwen3VLMultiModalProcessor, Qwen3VLProcessingInfo)
|
|
from .utils import is_pp_missing_parameter, maybe_prefix
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
class Qwen3VLMoeProcessingInfo(Qwen3VLProcessingInfo):
|
|
|
|
def get_hf_config(self):
|
|
return self.ctx.get_hf_config(Qwen3VLMoeConfig)
|
|
|
|
|
|
@support_torch_compile(
|
|
dynamic_arg_dims={
|
|
"input_ids": 0,
|
|
# positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
|
|
# otherwise (seq_len, ).
|
|
"positions": -1,
|
|
"intermediate_tensors": 0,
|
|
"inputs_embeds": 0,
|
|
# the same shape as input_embeds
|
|
"deepstack_input_embeds": 0
|
|
})
|
|
class Qwen3MoeLLMModel(Qwen3MoeModel):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
|
if not get_pp_group().is_first_rank:
|
|
assert self.start_layer >= len(
|
|
vllm_config.model_config.hf_config.vision_config.
|
|
deepstack_visual_indexes), (
|
|
"start_layer should be greater than or equal to "
|
|
"len(deepstack_visual_indexes)")
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
deepstack_input_embeds: Optional[IntermediateTensors] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.get_input_embeddings(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
for layer_idx, layer in enumerate(
|
|
self.layers[self.start_layer:self.end_layer]):
|
|
layer_idx = layer_idx + self.start_layer
|
|
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
residual,
|
|
)
|
|
|
|
if deepstack_input_embeds is not None and \
|
|
layer_idx in range(0, len(deepstack_input_embeds)):
|
|
hidden_states = hidden_states + deepstack_input_embeds[
|
|
f"deepstack_input_embeds_{layer_idx}"]
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
def load_fused_expert_weights(self, name: str, params_dict: dict,
|
|
loaded_weight: torch.Tensor, shard_id: str,
|
|
num_experts: int) -> bool:
|
|
param = params_dict[name]
|
|
weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
|
|
loaded_local_expert = False
|
|
for expert_id in range(num_experts):
|
|
curr_expert_weight = loaded_weight[expert_id]
|
|
success = weight_loader(param,
|
|
curr_expert_weight,
|
|
name,
|
|
shard_id,
|
|
expert_id,
|
|
return_success=True)
|
|
if success:
|
|
loaded_local_expert = True
|
|
|
|
return loaded_local_expert
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
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),
|
|
]
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
ignore_suffixes = (".bias", "_bias", ".k_scale", "_k_scale",
|
|
".v_scale", "_v_scale", ".weight_scale",
|
|
"_weight_scale", ".input_scale", "_input_scale")
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
is_fused_expert = False
|
|
fused_expert_params_mapping = [
|
|
("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
|
|
("experts.w2_weight", "experts.down_proj", 0, "w2"),
|
|
]
|
|
num_experts = self.config.num_experts
|
|
for name, loaded_weight in weights:
|
|
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
|
if ("experts.gate_up_proj" in name
|
|
or "experts.down_proj" in name):
|
|
is_fused_expert = True
|
|
expert_params_mapping = fused_expert_params_mapping
|
|
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if name.endswith("scale"):
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
if weight_loader == default_weight_loader:
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
is_expert_weight = False
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
# Anyway, this is an expert weight and should not be
|
|
# attempted to load as other weights later
|
|
is_expert_weight = True
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
if is_fused_expert:
|
|
loaded_weight = loaded_weight.transpose(-1,
|
|
-2) # no bias
|
|
if "experts.gate_up_proj" in name:
|
|
loaded_weight = loaded_weight.chunk(2, dim=-2)
|
|
success_w1 = self.load_fused_expert_weights(
|
|
name_mapped, params_dict, loaded_weight[0],
|
|
"w1", num_experts)
|
|
success_w3 = self.load_fused_expert_weights(
|
|
name_mapped, params_dict, loaded_weight[1],
|
|
"w3", num_experts)
|
|
success = success_w1 and success_w3
|
|
else:
|
|
# down_proj
|
|
success = self.load_fused_expert_weights(
|
|
name_mapped, params_dict, loaded_weight,
|
|
shard_id, num_experts)
|
|
else:
|
|
# Skip loading extra parameters for GPTQ/modelopt models
|
|
if name_mapped.endswith(
|
|
ignore_suffixes
|
|
) and name_mapped not in params_dict:
|
|
continue
|
|
param = params_dict[name_mapped]
|
|
# We should ask the weight loader to return success or
|
|
# not here since otherwise we may skip experts with
|
|
# other available replicas.
|
|
weight_loader = typing.cast(Callable[..., bool],
|
|
param.weight_loader)
|
|
success = weight_loader(param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(
|
|
ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
if name.endswith("kv_scale"):
|
|
remapped_kv_scale_name = name.replace(
|
|
".kv_scale", ".attn.kv_scale")
|
|
if remapped_kv_scale_name not in params_dict:
|
|
logger.warning_once(
|
|
"Found kv scale in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). kv-scale is not loaded.", # noqa: E501
|
|
name,
|
|
remapped_kv_scale_name,
|
|
)
|
|
continue
|
|
else:
|
|
name = remapped_kv_scale_name
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class Qwen3MoeLLMForCausalLM(Qwen3MoeForCausalLM):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super(Qwen3MoeForCausalLM, self).__init__()
|
|
self.config = vllm_config.model_config.hf_config.text_config
|
|
self.quant_config = vllm_config.quant_config
|
|
self.model = Qwen3MoeLLMModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
self.lm_head = ParallelLMHead(self.config.vocab_size,
|
|
self.config.hidden_size,
|
|
quant_config=self.quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"))
|
|
if self.config.tie_word_embeddings:
|
|
self.lm_head.weight = self.model.embed_tokens.weight
|
|
self.logits_processor = LogitsProcessor(self.config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(Qwen3VLMultiModalProcessor,
|
|
info=Qwen3VLMoeProcessingInfo,
|
|
dummy_inputs=Qwen3VLDummyInputsBuilder)
|
|
class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super(Qwen3VLForConditionalGeneration, self).__init__()
|
|
config: Qwen3VLMoeConfig = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
|
|
self.config = config
|
|
self.multimodal_config = multimodal_config
|
|
self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
|
|
|
|
if not multimodal_config.get_limit_per_prompt("image") and \
|
|
not multimodal_config.get_limit_per_prompt("video"):
|
|
self.visual = None
|
|
else:
|
|
self.visual = Qwen3_VisionTransformer(
|
|
config.vision_config,
|
|
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "visual"),
|
|
use_data_parallel=self.use_data_parallel,
|
|
)
|
|
|
|
self.language_model = Qwen3MoeLLMForCausalLM(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(
|
|
prefix,
|
|
"language_model"))
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors)
|
|
|
|
self.use_deepstack = hasattr(config.vision_config,
|
|
'deepstack_visual_indexes')
|
|
self.deepstack_num_level = len(
|
|
config.vision_config.deepstack_visual_indexes
|
|
) if self.use_deepstack else 0
|
|
# register buffer for deepstack
|
|
if self.use_deepstack and self.visual is not None:
|
|
self.deepstack_input_embeds = [
|
|
torch.zeros(
|
|
vllm_config.scheduler_config.max_num_batched_tokens,
|
|
config.text_config.hidden_size)
|
|
for _ in range(self.deepstack_num_level)
|
|
]
|
|
else:
|
|
self.deepstack_input_embeds = None
|
|
self.visual_dim = config.vision_config.out_hidden_size
|
|
self.multiscale_dim = self.visual_dim * self.deepstack_num_level
|