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538 lines
22 KiB
Python
538 lines
22 KiB
Python
# 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 Any, List, Optional, Union
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import torch
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import vllm.envs as envs
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionMetadata
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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 (QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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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.rotary_embedding import get_rope
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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_npu.ascend_config import get_ascend_config
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from vllm_npu.attention.attention_v1 import AscendAttentionState
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from vllm_npu.torchair.ops.sequence_parallel import (MetadataForPadding,
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init_metadata_for_sp)
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from vllm_npu.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE
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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 = TorchairAscendFusedMoE(
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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 CustomQwen3MoeAttention(Qwen3MoeAttention):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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head_dim: Optional[int] = None,
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rms_norm_eps: float = 1e-06,
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qkv_bias: bool = False,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = 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 = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim or (hidden_size // self.total_num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=qkv_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj")
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self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj")
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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ascend_config = get_ascend_config()
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self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
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@staticmethod
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def normalize_qkv(qkv: torch.Tensor, q_size: int, kv_size: int,
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head_dim: int, q_norm, k_norm):
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q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)
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q_by_head = q.view(*q.shape[:-1], q.shape[-1] // head_dim, head_dim)
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q_by_head = q_norm(q_by_head)
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q = q_by_head.view(q.shape)
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k_by_head = k.view(*k.shape[:-1], k.shape[-1] // head_dim, head_dim)
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k_by_head = k_norm(k_by_head)
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k = k_by_head.view(k.shape)
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return q, k, v
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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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kv_cache: Optional[torch.Tensor] = None,
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attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = self.normalize_qkv(qkv, self.q_size, self.kv_size,
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self.head_dim, self.q_norm, self.k_norm)
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if (self.torchair_graph_enabled and attn_metadata is not None and
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attn_metadata.attn_state == AscendAttentionState.DecodeOnly):
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q, k = self.rotary_emb(positions,
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q,
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k,
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is_prefill=False,
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is_qwen_torchair=True)
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forward_kwargs = {}
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if envs.VLLM_USE_V1:
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output_shape = q.shape
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output = torch.empty(output_shape,
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dtype=q.dtype,
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device=q.device)
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forward_kwargs['output'] = output
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attn_output = self.attn.impl.forward(self.attn,
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q,
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k,
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v,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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trace_flag=False,
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**forward_kwargs)
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output, _ = self.o_proj(attn_output)
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return output
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else:
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q, k = self.rotary_emb(positions, q, k, is_qwen_torchair=True)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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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 = CustomQwen3MoeAttention(
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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(vllm_config=vllm_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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kv_cache: Optional[torch.Tensor] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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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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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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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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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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kv_caches: Optional[List[torch.Tensor]] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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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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kv_caches[i -
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self.start_layer] if kv_caches is not None else None,
|
|
attn_metadata,
|
|
_metadata_for_padding=_metadata_for_padding)
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill:
|
|
hidden_states = _metadata_for_padding.allgather_unpadding_aligned(
|
|
hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
"experts":
|
|
["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
nn.Module.__init__(self)
|
|
SupportsPP.__init__(self)
|
|
SupportsLoRA.__init__(self)
|
|
MixtureOfExperts.__init__(self)
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = CustomQwen3MoeModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
self.lm_head = ParallelLMHead(config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=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(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
self.enable_sequence_parallelism = vllm_config.compilation_config.pass_config.enable_sequence_parallelism
|
|
# Set MoE hyperparameters
|
|
self.expert_weights: list[torch.Tensor] = []
|
|
|
|
self.moe_layers: list[FusedMoE] = []
|
|
example_layer = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, Qwen3MoeDecoderLayer)
|
|
if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock):
|
|
example_layer = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_layer is None:
|
|
raise RuntimeError("No Qwen3MoE layer found in the model.layers.")
|
|
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
self.num_expert_groups = 1
|
|
self.num_shared_experts = 0
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: Optional[List[torch.Tensor]] = None,
|
|
attn_metadata: Optional[AttentionMetadata] = None,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
_metadata_for_padding = init_metadata_for_sp(
|
|
input_ids, self.enable_sequence_parallelism)
|
|
hidden_states = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, intermediate_tensors,
|
|
inputs_embeds, _metadata_for_padding)
|
|
return hidden_states
|