v0.10.1rc1
This commit is contained in:
393
vllm_ascend/models/qwen3_moe.py
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393
vllm_ascend/models/qwen3_moe.py
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2024 The Qwen team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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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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# Adapted from vllm/model_executor/models/qwen3_moe.py
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# This file is a part of the vllm-ascend project.
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from typing import Optional, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, CompilationLevel, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.distributed.parallel_state import (get_dp_group, get_ep_group,
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get_tp_group)
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.fused_moe.layer import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import ReplicatedLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.models.interfaces import (MixtureOfExperts,
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SupportsLoRA, SupportsPP)
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from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention,
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Qwen3MoeDecoderLayer,
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Qwen3MoeForCausalLM,
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Qwen3MoeMLP, Qwen3MoeModel,
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Qwen3MoeSparseMoeBlock)
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from vllm.model_executor.models.utils import (
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PPMissingLayer, extract_layer_index,
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make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
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from vllm.sequence import IntermediateTensors
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from vllm_ascend.ops.fused_moe import AscendFusedMoE
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from vllm_ascend.ops.sequence_parallel import (MetadataForPadding,
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init_metadata_for_sp)
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from vllm_ascend.utils import vllm_version_is
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class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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nn.Module.__init__(self)
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self.tp_size = get_tensor_model_parallel_world_size()
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if self.tp_size > config.num_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_experts}.")
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_experts,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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self.experts = AscendFusedMoE(
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num_experts=config.num_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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)
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self.top_k = config.num_experts_per_tok
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self.dp_size = get_dp_group().world_size
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self.tp_group = get_tp_group().device_group
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self.tp_rank = get_tp_group().rank_in_group
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self.ep_group = get_ep_group()
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self.params_dtype = torch.get_default_dtype()
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def forward(
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self,
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hidden_states,
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attn_metadata=None,
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_metadata_for_padding: Optional[MetadataForPadding] = None,
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):
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if attn_metadata is None:
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attn_metadata = get_forward_context().attn_metadata
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# when profile runs, force experts to load balanced tokens
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# to avoid high memory consumption on a single rank.
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enable_force_load_balance = get_forward_context().in_profile_run
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is_prefill = get_forward_context().with_prefill
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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hidden_states = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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is_prefill=is_prefill,
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top_k=self.top_k,
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enable_force_load_balance=enable_force_load_balance,
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shared_experts=None,
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_metadata_for_padding=_metadata_for_padding,
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)
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return hidden_states
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class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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vllm_config: Optional[VllmConfig] = None,
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prefix: str = "",
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) -> None:
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nn.Module.__init__(self)
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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self.self_attn = Qwen3MoeAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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rms_norm_eps=config.rms_norm_eps,
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qkv_bias=getattr(config, 'attention_bias', False),
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head_dim=getattr(config, 'head_dim', None),
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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# `mlp_only_layers` in the config.
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layer_idx = extract_layer_index(prefix)
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mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
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config.mlp_only_layers)
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self.use_aclgraph = (vllm_config is not None
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and vllm_config.compilation_config.level
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== CompilationLevel.PIECEWISE
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and not vllm_config.model_config.enforce_eager)
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if (layer_idx not in mlp_only_layers) and (
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config.num_experts > 0 and
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(layer_idx + 1) % config.decoder_sparse_step == 0):
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if not self.use_aclgraph:
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# FIXME: custom sparse moe block doesn't work with aclgraph.
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self.mlp = CustomSparseMoeBlock(config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp")
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else:
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self.mlp = Qwen3MoeSparseMoeBlock(config=config,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp")
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else:
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self.mlp = Qwen3MoeMLP(hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp")
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.enable_sequence_parallelism = (
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vllm_config.compilation_config.pass_config.
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enable_sequence_parallelism if vllm_config is not None else False)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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_metadata_for_padding: Optional[MetadataForPadding] = None,
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) -> torch.Tensor:
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# To prevent precision issues during the decoder phase when only prefilling enables SP
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if not self.enable_sequence_parallelism:
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self.self_attn.o_proj.reduce_results = True
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else:
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self.self_attn.o_proj.reduce_results = not _metadata_for_padding.not_dummy_and_is_prefill if _metadata_for_padding is not None else True
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# Self Attention
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if residual is None:
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residual = hidden_states
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if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
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residual = _metadata_for_padding.padding_slice(residual)
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
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hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
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hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
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hidden_states = _metadata_for_padding.padding_aligned_reduce_scatter(
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hidden_states)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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if not self.use_aclgraph:
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hidden_states = self.mlp(
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hidden_states, _metadata_for_padding=_metadata_for_padding)
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else:
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class CustomQwen3MoeModel(Qwen3MoeModel):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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parallel_config = vllm_config.parallel_config
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if vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1"):
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self.num_redundant_experts = parallel_config.num_redundant_experts
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else:
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eplb_config = parallel_config.eplb_config
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self.num_redundant_experts = eplb_config.num_redundant_experts
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.config = config
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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prefix=f"{prefix}.embed_tokens")
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: CustomQwen3MoeDecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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vllm_config=vllm_config,
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prefix=prefix),
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prefix=f"{prefix}.layers",
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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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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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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_metadata_for_padding: Optional[MetadataForPadding] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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_metadata_for_padding=_metadata_for_padding)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm(hidden_states, residual)
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if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
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hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
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hidden_states)
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return hidden_states
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class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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"experts":
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["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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SupportsPP.__init__(self)
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SupportsLoRA.__init__(self)
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MixtureOfExperts.__init__(self)
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.model = CustomQwen3MoeModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"))
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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self.enable_sequence_parallelism = vllm_config.compilation_config.pass_config.enable_sequence_parallelism
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# Set MoE hyperparameters
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self.expert_weights: list[torch.Tensor] = []
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self.moe_layers: list[FusedMoE] = []
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example_layer = None
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for layer in self.model.layers:
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if isinstance(layer, PPMissingLayer):
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continue
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assert isinstance(layer, Qwen3MoeDecoderLayer)
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if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
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example_layer = layer.mlp
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self.moe_layers.append(layer.mlp.experts)
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if example_layer is None:
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raise RuntimeError("No Qwen3MoE layer found in the model.layers.")
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self.num_moe_layers = len(self.moe_layers)
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self.num_expert_groups = 1
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self.num_shared_experts = 0
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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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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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_metadata_for_padding = init_metadata_for_sp(
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input_ids, self.enable_sequence_parallelism)
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hidden_states = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds, _metadata_for_padding)
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return hidden_states
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