Initial commit for vLLM-Kunlun Plugin
This commit is contained in:
dongxinyu03
836
vllm_kunlun/models/qwen3_moe.py
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836
vllm_kunlun/models/qwen3_moe.py
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#
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# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
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# Adapted from vllm/model_executor/models/qwen3_moe.py
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# Copyright 2023 The vLLM team.
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#
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# This file is a part of the vllm-kunlun project.
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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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"""Inference-only Qwen3MoE model compatible with HuggingFace weights."""
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import os
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from collections.abc import Iterable
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from typing import Any, Optional, Union, Tuple, Set
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import torch
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import os
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from torch import nn
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from transformers import PretrainedConfig
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from vllm_kunlun.ops.attention.layer import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.logger import init_logger
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from vllm_kunlun.ops.activation import SiluAndMul
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from vllm_kunlun.ops.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 (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm_kunlun.ops.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.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm_kunlun.ops.vocab_parallel_embedding import VocabParallelEmbedding
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.model_executor.models.interfaces import SupportsPP
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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from vllm_kunlun.ops.rotary_embedding import Split_Norm_Rope
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logger = init_logger(__name__)
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class Qwen3MoeMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class Qwen3MoeSparseMoeBlock(nn.Module):
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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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super().__init__()
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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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)
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self.experts = FusedMoE(
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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.quant_config = quant_config
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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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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_dim = hidden_states.shape[-1]
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.quant_config is None:
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kunlun_linear_weights = self.gate.get_weights()
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final_hidden_states = self.experts(
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hidden_states=hidden_states, linear_weights=kunlun_linear_weights
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)
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else:
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kunlun_linear_weights = self.gate.get_weights()
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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linear_weights=kunlun_linear_weights,
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)
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if self.tp_size > 1:
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final_hidden_states = (
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self.experts.maybe_all_reduce_tensor_model_parallel( # noqa E501
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final_hidden_states
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)
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)
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return final_hidden_states.view(orig_shape)
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class Qwen3MoeAttention(nn.Module):
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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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super().__init__()
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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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if rope_scaling is not None:
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scaling_factor = rope_scaling["factor"]
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self.max_position_embeddings = int(
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self.max_position_embeddings * scaling_factor
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)
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self.qkv_proj = QKVParallelLinear(
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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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)
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self.o_proj = RowParallelLinear(
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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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)
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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=self.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(
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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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)
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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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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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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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if os.getenv("FUSED_QK_ROPE_OP") == "1":
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# Rope fusion operators
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q, k, v = Split_Norm_Rope(
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qkv,
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self.rotary_emb.cos_sin_cache,
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self.q_norm.weight,
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self.k_norm.weight,
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positions,
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self.max_position_embeddings,
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self.num_heads,
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self.num_kv_heads,
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self.head_dim,
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)
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else:
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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# Add qk-norm
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q_by_head = q.view(
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*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim
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)
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q_by_head = self.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(
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*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim
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)
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k_by_head = self.k_norm(k_by_head)
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k = k_by_head.view(k.shape)
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q, k = self.rotary_emb(positions, q, k)
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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 Qwen3MoeDecoderLayer(nn.Module):
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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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prefix: str = "",
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) -> None:
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super().__init__()
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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", 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 = (
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[] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
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)
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if (layer_idx not in mlp_only_layers) and (
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config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
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):
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self.mlp = Qwen3MoeSparseMoeBlock(
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config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
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)
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else:
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self.mlp = Qwen3MoeMLP(
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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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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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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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) -> tuple[torch.Tensor, torch.Tensor]:
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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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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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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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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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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 Qwen3MoeModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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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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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, config.hidden_size, prefix=f"{prefix}.embed_tokens"
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)
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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: Qwen3MoeDecoderLayer(
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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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prefix=prefix,
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),
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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 = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
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||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
hidden_states = self.get_input_embeddings(input_ids)
|
||||
residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
for i in range(self.start_layer, self.end_layer):
|
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layer = self.layers[i]
|
||||
hidden_states, residual = layer(positions, hidden_states, residual)
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="gate_proj",
|
||||
ckpt_down_proj_name="down_proj",
|
||||
ckpt_up_proj_name="up_proj",
|
||||
num_experts=self.config.num_experts,
|
||||
)
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: Set[str] = set()
|
||||
weights_to_quantize = {}
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
# Skip non-stacked layers and experts (experts handled below).
|
||||
if weight_name not in name:
|
||||
continue
|
||||
# We have mlp.experts[0].gate_proj in the checkpoint.
|
||||
# Since we handle the experts below in expert_params_mapping,
|
||||
# we need to skip here BEFORE we update the name, otherwise
|
||||
# name will be updated to mlp.experts[0].gate_up_proj, which
|
||||
# will then be updated below in expert_params_mapping
|
||||
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
||||
if "mlp.experts" in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if (
|
||||
name.endswith(".bias") or name.endswith("_bias")
|
||||
) and name not in params_dict:
|
||||
continue
|
||||
# Skip layers on other devices.
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
if name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
loaded_params.add(name)
|
||||
break
|
||||
else:
|
||||
for mapping in expert_params_mapping:
|
||||
param_name, weight_name, expert_id, shard_id = mapping
|
||||
if weight_name not in name:
|
||||
continue
|
||||
# Map to the parameter name in the model
|
||||
name_mapped = name.replace(weight_name, param_name)
|
||||
|
||||
# Layer/PP skip judgment
|
||||
if is_pp_missing_parameter(name_mapped, self):
|
||||
continue
|
||||
if (
|
||||
name_mapped.endswith(".bias") or name_mapped.endswith("_bias")
|
||||
) and name_mapped not in params_dict:
|
||||
continue
|
||||
|
||||
# Get the param and target module
|
||||
param = params_dict.get(name_mapped, None)
|
||||
if param is None:
|
||||
continue
|
||||
|
||||
# === Only when the target MoE layer has int8 weights and scales, and the name matches, the "streaming quantization" is performed ===
|
||||
if self._should_stream_quantize(name_mapped):
|
||||
# Note: Pass the mapped name_mapped instead of the original name
|
||||
self._stream_quantize_moe_weight(
|
||||
name_mapped,
|
||||
param,
|
||||
loaded_weight,
|
||||
expert_id=expert_id,
|
||||
shard_id=shard_id,
|
||||
)
|
||||
loaded_params.add(name_mapped)
|
||||
else:
|
||||
# Fallback: Normal weight loading (non-quantized)
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name_mapped,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
loaded_params.add(name_mapped)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if (
|
||||
name.endswith(".bias") or name.endswith("_bias")
|
||||
) and name not in params_dict:
|
||||
continue
|
||||
# Skip layers on other devices.
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
if name.endswith("kv_scale"):
|
||||
remapped_kv_scale_name = name.replace(
|
||||
".kv_scale", ".attn.kv_scale"
|
||||
)
|
||||
if remapped_kv_scale_name not in params_dict:
|
||||
logger.warning_once(
|
||||
"Found kv scale in the checkpoint "
|
||||
f"(e.g. {name}), but not found the expected "
|
||||
f"name in the model "
|
||||
f"(e.g. {remapped_kv_scale_name}). "
|
||||
"kv-scale is not loaded."
|
||||
)
|
||||
continue
|
||||
else:
|
||||
name = remapped_kv_scale_name
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
# loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
def _is_moe_weight(self, name: str) -> bool:
|
||||
"""Check if the weight is MoE weight"""
|
||||
return name.endswith("w13_weight") or name.endswith("w2_weight")
|
||||
|
||||
def _is_expert_complete(self, cache_key):
|
||||
cache = self._moe_weight_cache.get(cache_key)
|
||||
if cache is None:
|
||||
return False
|
||||
w13_ok = (0 in cache["w13_shards"]) and (1 in cache["w13_shards"])
|
||||
w2_ok = cache["w2_weight"] is not None
|
||||
return w13_ok and w2_ok
|
||||
|
||||
@torch.no_grad()
|
||||
def _stream_quantize_moe_weight(
|
||||
self,
|
||||
param_name: str,
|
||||
param: nn.Parameter,
|
||||
loaded_weight: torch.Tensor,
|
||||
*,
|
||||
expert_id,
|
||||
shard_id,
|
||||
):
|
||||
|
||||
rank = os.environ.get("RANK", "0")
|
||||
|
||||
# Ensure expert_id is an integer
|
||||
try:
|
||||
expert_id = int(expert_id)
|
||||
except (ValueError, TypeError):
|
||||
if isinstance(expert_id, str):
|
||||
expert_id = int(expert_id)
|
||||
|
||||
# Process shard_id
|
||||
if isinstance(shard_id, str):
|
||||
if shard_id in ("gate", "w1"):
|
||||
shard_id = 0
|
||||
elif shard_id in ("up", "w3"):
|
||||
shard_id = 1
|
||||
elif shard_id == "w2":
|
||||
shard_id = 0
|
||||
else:
|
||||
try:
|
||||
shard_id = int(shard_id)
|
||||
except ValueError:
|
||||
shard_id = 0
|
||||
else:
|
||||
shard_id = int(shard_id)
|
||||
|
||||
# Initialize cache
|
||||
if not hasattr(self, "_moe_weight_cache"):
|
||||
self._moe_weight_cache = {}
|
||||
self._expert_batch_count = 0 # Batch counter
|
||||
|
||||
module_path = ".".join(param_name.split(".")[:-1])
|
||||
cache_key = (module_path, expert_id)
|
||||
|
||||
cache = self._moe_weight_cache.get(cache_key)
|
||||
if cache is None:
|
||||
cache = {
|
||||
"w13_shards": {},
|
||||
"w2_weight": None,
|
||||
"target_module": self.get_submodule(module_path),
|
||||
"done": False,
|
||||
}
|
||||
self._moe_weight_cache[cache_key] = cache
|
||||
|
||||
if cache.get("done", False):
|
||||
return
|
||||
|
||||
# Cache weights (keep original precision)
|
||||
if "w13_weight" in param_name:
|
||||
cache["w13_shards"][shard_id] = loaded_weight.clone()
|
||||
elif "w2_weight" in param_name:
|
||||
cache["w2_weight"] = loaded_weight.clone()
|
||||
|
||||
# Check if complete
|
||||
if self._is_expert_complete(cache_key):
|
||||
# Quantize this expert
|
||||
self._quantize_expert_weights(cache_key)
|
||||
cache["done"] = True
|
||||
self._moe_weight_cache.pop(cache_key, None)
|
||||
|
||||
# Force synchronization every 4 experts
|
||||
self._expert_batch_count += 1
|
||||
if self._expert_batch_count % 4 == 0:
|
||||
torch.cuda.synchronize() # Force synchronization
|
||||
# print(f"[Rank {rank}] Completed batch of {self._expert_batch_count} experts")
|
||||
|
||||
def _quantize_expert_weights(self, cache_key):
|
||||
"""Quantize the complete weights of an expert (supports TP sharding)"""
|
||||
module_path, expert_id = cache_key
|
||||
cache = self._moe_weight_cache[cache_key]
|
||||
target_module = cache["target_module"]
|
||||
|
||||
# Get TP config
|
||||
from vllm.distributed import (
|
||||
get_tensor_model_parallel_rank,
|
||||
get_tensor_model_parallel_world_size,
|
||||
)
|
||||
|
||||
tp_rank = get_tensor_model_parallel_rank()
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
|
||||
# Get actual shapes
|
||||
E, twoN, H = target_module.w13_weight.shape
|
||||
_, H2, N = target_module.w2_weight.shape
|
||||
|
||||
qmax = 127.0
|
||||
|
||||
# Process w13_weight: concatenate gate and up
|
||||
gate_weight = cache["w13_shards"][0] # [768, 2048]
|
||||
up_weight = cache["w13_shards"][1] # [768, 2048]
|
||||
|
||||
# TP sharding
|
||||
if tp_size > 1:
|
||||
# Calculate shard for each TP rank
|
||||
gate_per_rank = gate_weight.shape[0] // tp_size
|
||||
up_per_rank = up_weight.shape[0] // tp_size
|
||||
|
||||
gate_start = tp_rank * gate_per_rank
|
||||
gate_end = (tp_rank + 1) * gate_per_rank
|
||||
up_start = tp_rank * up_per_rank
|
||||
up_end = (tp_rank + 1) * up_per_rank
|
||||
|
||||
gate_weight = gate_weight[gate_start:gate_end, :] # [192, 2048]
|
||||
up_weight = up_weight[up_start:up_end, :] # [192, 2048]
|
||||
|
||||
w13_complete = torch.cat([gate_weight, up_weight], dim=0) # [384, 2048]
|
||||
|
||||
# Quantize w13_weight
|
||||
w13_f = w13_complete.float()
|
||||
w13_abs_max = torch.amax(torch.abs(w13_f), dim=-1) # [384]
|
||||
w13_scale_2d = torch.clamp(w13_abs_max, min=1e-6) / qmax # [384]
|
||||
w13_scale_3d = w13_scale_2d.unsqueeze(-1) # [384, 1]
|
||||
w13_q = torch.round(w13_f / w13_scale_3d).clamp_(-128, 127).to(torch.int8)
|
||||
|
||||
# Write w13_weight
|
||||
target_module.w13_weight.data[expert_id, :, :].copy_(
|
||||
w13_q.to(target_module.w13_weight.device)
|
||||
)
|
||||
|
||||
# Update w13_scale - pre-multiply 127
|
||||
s = getattr(target_module, "w13_weight_scale")
|
||||
s.data[expert_id, :].copy_((w13_scale_2d * 127.0).to(s.device))
|
||||
|
||||
# Process w2_weight
|
||||
w2_weight = cache["w2_weight"] # [2048, 768]
|
||||
|
||||
# TP sharding for w2 weight
|
||||
if tp_size > 1:
|
||||
w2_per_rank = w2_weight.shape[1] // tp_size
|
||||
w2_start = tp_rank * w2_per_rank
|
||||
w2_end = (tp_rank + 1) * w2_per_rank
|
||||
w2_weight = w2_weight[:, w2_start:w2_end] # [2048, 192]
|
||||
|
||||
w2_f = w2_weight.float() # [2048, 192]
|
||||
w2_abs_max = torch.amax(torch.abs(w2_f), dim=-1) # [2048]
|
||||
w2_scale_2d = torch.clamp(w2_abs_max, min=1e-6) / qmax # [2048]
|
||||
w2_scale_3d = w2_scale_2d.unsqueeze(-1) # [2048, 1]
|
||||
w2_q = torch.round(w2_f / w2_scale_3d).clamp_(-128, 127).to(torch.int8)
|
||||
|
||||
# Write w2_weight
|
||||
w2_param = getattr(target_module, "w2_weight")
|
||||
w2_param.data[expert_id, :, :].copy_(w2_q.to(w2_param.device))
|
||||
|
||||
# Update w2_scale - pre-multiply 127
|
||||
w2_s = getattr(target_module, "w2_weight_scale")
|
||||
w2_s.data[expert_id, :].copy_((w2_scale_2d * 127.0).to(w2_s.device))
|
||||
|
||||
# Clear cache
|
||||
cache["w13_shards"].clear()
|
||||
cache["w2_weight"] = None
|
||||
|
||||
def _is_int8_moe_target_module(self, module_path: str) -> bool:
|
||||
"""Check if a module_path is a FusedMoE target using INT8(W8A8).
|
||||
Determine by the actual existing parameters and dtype, not relying on quant_config names.
|
||||
"""
|
||||
try:
|
||||
mod = self.get_submodule(module_path)
|
||||
except Exception:
|
||||
return False
|
||||
# Need to have both int8 weights and float32 scales, and dimensions come from CompressedTensorsW8A8 path
|
||||
if not (
|
||||
hasattr(mod, "w13_weight")
|
||||
and hasattr(mod, "w2_weight")
|
||||
and hasattr(mod, "w13_weight_scale")
|
||||
and hasattr(mod, "w2_weight_scale")
|
||||
):
|
||||
return False
|
||||
try:
|
||||
return (
|
||||
mod.w13_weight.dtype == torch.int8
|
||||
and mod.w2_weight.dtype == torch.int8
|
||||
and mod.w13_weight_scale.dtype == torch.float32
|
||||
and mod.w2_weight_scale.dtype == torch.float32
|
||||
)
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _should_stream_quantize(self, param_name: str) -> bool:
|
||||
"""Only when (1) the parameter name corresponds to the MoE weights we defined; and
|
||||
(2) the MoE layer is indeed the INT8 path (exists int8 weights + scales)
|
||||
Stream quantization is enabled; otherwise, it falls back to the default loading.
|
||||
"""
|
||||
# First, determine if it is the MoE weight name we want to process (w13_weight / w2_weight)
|
||||
if not self._is_moe_weight(param_name):
|
||||
return False
|
||||
# Then, check if the module containing this param is the INT8 path
|
||||
module_path = ".".join(param_name.split(".")[:-1])
|
||||
return self._is_int8_moe_target_module(module_path)
|
||||
|
||||
|
||||
class Qwen3MoeForCausalLM(nn.Module, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
fall_back_to_pt_during_load = False
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
self.model = Qwen3MoeModel(
|
||||
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
||||
)
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size, config.hidden_size, quant_config=quant_config
|
||||
)
|
||||
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
|
||||
)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.get_input_embeddings(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
kv_caches: list[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
hidden_states = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[torch.Tensor]:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata)
|
||||
return logits
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(weights)
|
||||
Reference in New Issue
Block a user