9acb21ae27
Co-authored-by: liusy58 <liusy58@linux.alibaba.com> Co-authored-by: ZhengWG <zwg0606@gmail.com> Co-authored-by: Nicholas <45984215+liusy58@users.noreply.github.com> Co-authored-by: Shangming Cai <csmthu@gmail.com> Co-authored-by: Yuhao Yang <47235274+yhyang201@users.noreply.github.com>
346 lines
14 KiB
Python
346 lines
14 KiB
Python
# Copyright 2025 Qwen Team
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# Copyright 2025 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Inference-only Qwen3-VL model compatible with HuggingFace weights."""
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import logging
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import re
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from functools import lru_cache
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from typing import Iterable, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from sglang.srt.configs.qwen3_vl import Qwen3VLMoeConfig, Qwen3VLMoeTextConfig
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from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.qwen3_moe import Qwen3MoeModel
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from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration
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from sglang.srt.utils.hf_transformers_utils import get_processor
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logger = logging.getLogger(__name__)
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cached_get_processor = lru_cache(get_processor)
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class Qwen3MoeLLMModel(Qwen3MoeModel):
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def __init__(
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self,
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*,
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config: Qwen3VLMoeTextConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__(config=config, quant_config=quant_config, prefix=prefix)
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self.hidden_size = config.hidden_size
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def get_input_embeddings(self) -> nn.Embedding:
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return self.embed_tokens
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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input_deepstack_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, PPProxyTensors]:
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if self.pp_group.is_first_rank:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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residual = None
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else:
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assert pp_proxy_tensors is not None
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hidden_states = pp_proxy_tensors["hidden_states"]
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residual = pp_proxy_tensors["residual"]
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aux_hidden_states = []
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for layer_idx, layer in enumerate(
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self.layers[self.start_layer : self.end_layer]
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):
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layer_idx += self.start_layer
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if layer_idx in self.layers_to_capture:
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aux_hidden_states.append(
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hidden_states + residual if residual is not None else hidden_states
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)
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hidden_states, residual = layer(
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positions,
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hidden_states,
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forward_batch,
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residual,
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)
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# process deepstack
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if input_deepstack_embeds is not None and layer_idx < 3:
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sep = self.hidden_size * layer_idx
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hidden_states.add_(
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input_deepstack_embeds[:, sep : sep + self.hidden_size]
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)
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if not self.pp_group.is_last_rank:
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return PPProxyTensors(
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{
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"hidden_states": hidden_states,
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"residual": residual,
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}
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)
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else:
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if hidden_states.shape[0] != 0:
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if residual is None:
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, _ = self.norm(hidden_states, residual)
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if len(aux_hidden_states) == 0:
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return hidden_states
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return hidden_states, aux_hidden_states
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def load_fused_expert_weights(
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name: str,
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params_dict: dict,
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loaded_weight: torch.Tensor,
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shard_id: str,
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num_experts: int,
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):
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param = params_dict[name]
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# weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
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weight_loader = param.weight_loader
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# let ep moe layer to gracefully handle expert_ids that do not belong to local moe rank
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for expert_id in range(num_experts):
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curr_expert_weight = loaded_weight[expert_id]
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weight_loader(
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param,
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curr_expert_weight,
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name,
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shard_id,
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expert_id,
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)
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return True
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class Qwen3VLMoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
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def __init__(
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self,
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config: Qwen3VLMoeConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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language_model_cls=Qwen3MoeLLMModel,
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):
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super().__init__(config, quant_config, prefix, language_model_cls)
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# Only allow LoRA on attention projections within text layers for MoE.
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_lora_pattern_moe = re.compile(
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r"^model\.layers\.(\d+)\.self_attn\.(?:qkv_proj|o_proj)$"
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)
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def should_apply_lora(self, module_name: str) -> bool:
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return bool(self._lora_pattern_moe.match(module_name))
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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("gate_up_proj", "up_proj", 1),
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("gate_up_proj", "gate_proj", 0),
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]
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.num_experts,
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)
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# Skip loading extra parameters for GPTQ/modelopt models.
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ignore_suffixes = (
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".bias",
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"_bias",
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".k_scale",
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"_k_scale",
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".v_scale",
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"_v_scale",
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".weight_scale",
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"_weight_scale",
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".input_scale",
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"_input_scale",
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)
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is_fused_expert = False
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fused_expert_params_mapping = [
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("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
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("experts.w2_weight", "experts.down_proj", 0, "w2"),
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]
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num_experts = self.config.num_experts
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# Cache params_dict to avoid repeated expensive traversal of model parameters
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if not hasattr(self, "_cached_params_dict"):
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self._cached_params_dict = dict(self.named_parameters())
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params_dict = self._cached_params_dict
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for name, loaded_weight in weights:
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name = name.replace(r"model.language_model.", r"model.")
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if "experts.gate_up_proj" in name or "experts.down_proj" in name:
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is_fused_expert = True
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expert_params_mapping = fused_expert_params_mapping
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# Skip non-stacked layers and experts (experts handled below).
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if weight_name not in name:
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continue
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if "visual" in name:
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continue
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# We have mlp.experts[0].gate_proj in the checkpoint.
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# Since we handle the experts below in expert_params_mapping,
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# we need to skip here BEFORE we update the name, otherwise
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# name will be updated to mlp.experts[0].gate_up_proj, which
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# will then be updated below in expert_params_mapping
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# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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if "mlp.experts" in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra parameters for GPTQ/modelopt models.
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if name.endswith(ignore_suffixes) and name not in params_dict:
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continue
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# [TODO] Skip layers that are on other devices (check if sglang has a similar function)
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# if is_pp_missing_parameter(name, self):
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# continue
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if name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Track if this is an expert weight to enable early skipping
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is_expert_weight = False
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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if "visual" in name or self.config.encoder_only:
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continue
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# Anyway, this is an expert weight and should not be
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# attempted to load as other weights later
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is_expert_weight = True
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name_mapped = name.replace(weight_name, param_name)
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if is_fused_expert:
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loaded_weight = loaded_weight.transpose(-1, -2) # no bias
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if "experts.gate_up_proj" in name:
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loaded_weight = loaded_weight.chunk(2, dim=-2)
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load_fused_expert_weights(
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name_mapped,
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params_dict,
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loaded_weight[0],
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"w1",
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num_experts,
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)
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load_fused_expert_weights(
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name_mapped,
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params_dict,
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loaded_weight[1],
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"w3",
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num_experts,
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)
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else:
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load_fused_expert_weights(
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name_mapped,
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params_dict,
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loaded_weight,
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shard_id,
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num_experts,
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)
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else:
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# Skip loading extra parameters for GPTQ/modelopt models.
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if (
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name_mapped.endswith(ignore_suffixes)
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and name_mapped not in params_dict
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):
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continue
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param = params_dict[name_mapped]
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# We should ask the weight loader to return success or
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# not here since otherwise we may skip experts with
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# # other available replicas.
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weight_loader = param.weight_loader
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weight_loader(
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param,
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loaded_weight,
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name_mapped,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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name = name_mapped
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break
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else:
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if is_expert_weight:
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# This is an expert weight but not mapped to this rank, skip all remaining processing
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continue
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if "visual" in name:
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# adapt to VisionAttention
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name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
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name = name.replace(r"model.visual.", r"visual.")
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# Skip loading extra parameters for GPTQ/modelopt models.
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if name.endswith(ignore_suffixes) and name not in params_dict:
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continue
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# Skip loading mm/language parameters
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if (
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self.config.encoder_only or self.config.language_only
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) and name not in params_dict:
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continue
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if name in params_dict.keys():
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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else:
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logger.warning(f"Parameter {name} not found in params_dict")
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# TODO mimic deepseek
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# Lazy initialization of expert weights cache to avoid slowing down load_weights
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# if not hasattr(self, "routed_experts_weights_of_layer"):
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# self.routed_experts_weights_of_layer = {
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# layer_id: self.model.layers[layer_id].mlp.get_moe_weights()
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# for layer_id in range(self.start_layer, self.end_layer)
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# if isinstance(self.model.layers[layer_id].mlp, Qwen3MoeSparseMoeBlock)
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# }
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@classmethod
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def get_model_config_for_expert_location(cls, config):
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return ModelConfigForExpertLocation(
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num_layers=config.text_config.num_hidden_layers,
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num_logical_experts=config.text_config.num_experts,
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num_groups=None,
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)
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EntryClass = Qwen3VLMoeForConditionalGeneration
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