531 lines
22 KiB
Python
531 lines
22 KiB
Python
#
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# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
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# Adapted from vllm/model_executor/models/qwen3.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 Qwen3 model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from typing import Optional, Union
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import xtorch_ops
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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 Qwen3Config
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from vllm.attention import AttentionType, AttentionMetadata
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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, get_current_vllm_config
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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.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (QKVParallelLinear,
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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 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 (
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default_weight_loader, maybe_remap_kv_scale_name)
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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 import envs
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from vllm.model_executor.models.adapters import as_seq_cls_model
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from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
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from .qwen2 import Qwen2MLP as Qwen3MLP
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from vllm.model_executor.models.utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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from vllm.forward_context import ForwardContext, get_forward_context
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from vllm.platforms import current_platform
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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 Qwen3Attention(nn.Module):
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def __init__(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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max_position: int = 4096 * 32,
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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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rope_theta: float = 10000,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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rope_scaling: Optional[tuple] = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER) -> 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 = max_position
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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 = int(self.max_position * scaling_factor)
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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,
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base=self.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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attn_type=attn_type)
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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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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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# TODO: Supports both original Rope and Kunlun Rope fusion operators
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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(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,
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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(*q.shape[:-1], q.shape[-1] // self.head_dim,
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self.head_dim)
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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(*k.shape[:-1], k.shape[-1] // self.head_dim,
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self.head_dim)
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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 Qwen3DecoderLayer(nn.Module):
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def __init__(
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self,
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config: Qwen3Config,
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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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# Requires transformers > 4.32.0
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rope_theta = getattr(config, "rope_theta", 1000000)
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rope_scaling = getattr(config, "rope_scaling", None)
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# By default, Qwen3 uses causal attention as it is a decoder-only model.
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# You can override the HF config with `is_causal=False` to enable
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# bidirectional attention, which is used in some embedding models
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# (e.g. Alibaba-NLP/gte-Qwen3-7B-instruct)
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if getattr(config, "is_causal", True):
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attn_type = AttentionType.DECODER
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else:
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attn_type = AttentionType.ENCODER_ONLY
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self.self_attn = Qwen3Attention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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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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rope_scaling=rope_scaling,
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prefix=f"{prefix}.self_attn",
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attn_type=attn_type,
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)
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self.mlp = Qwen3MLP(
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hidden_size=self.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,
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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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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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attn_metadata: AttentionMetadata,
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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(
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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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attn_metadata=attn_metadata,
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residual=residual,
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)
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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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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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ALL_DECODER_LAYER_TYPES = {
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"attention": Qwen3DecoderLayer,
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}
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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# positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
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# otherwise (seq_len, ).
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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})
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class Qwen3Model(nn.Module):
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"""Qwen3Model"""
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def __init__(self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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decoder_layer_type: type[nn.Module] = Qwen3DecoderLayer):
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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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# TODO (@robertgshaw2): see if this can be moved out
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if (cache_config.sliding_window is not None
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and hasattr(config, "max_window_layers")):
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assert config.max_window_layers == config.num_hidden_layers, (
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"Sliding window for some but all layers is not supported. "
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"This model uses sliding window but `max_window_layers` = {} "
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"is less than `num_hidden_layers` = {}. Please open an issue "
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"to discuss this feature.".format(
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config.max_window_layers,
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config.num_hidden_layers,
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))
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self.config = config
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self.quant_config = quant_config
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self.vocab_size = config.vocab_size
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if get_pp_group().is_first_rank or (config.tie_word_embeddings
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and get_pp_group().is_last_rank):
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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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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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)
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else:
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self.embed_tokens = PPMissingLayer()
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# Use the provided decoder layer type or default to Qwen2DecoderLayer
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decoder_layer_type = decoder_layer_type or Qwen3DecoderLayer
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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: decoder_layer_type(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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prefix=f"{prefix}.layers",
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)
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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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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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"""get_input_embeddings"""
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return self.embed_tokens(input_ids)
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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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"""
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Args:
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input_ids (torch.Tensor): Input sequence of shape `(batch, seq_len)`.
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Indices are expected to be in the range `[0, config.vocab_size]`.
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positions (torch.Tensor): Positional tensor of shape `(batch, seq_len)`.
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intermediate_tensors (Optional[IntermediateTensors], optional):
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Intermediate tensors from previous forward pass. Defaults to `None`.
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inputs_embeds (Optional[torch.Tensor], optional):
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Optionally, instead of positional embeddings, you can choose to
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provide your own embedding lookup matrix of shape `(batch, seq_len, emb_dim)`.
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If None, the model will create one on its own using the input ids.
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Defaults to `None`.
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Returns:
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Union[torch.Tensor, IntermediateTensors]:
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If `intermediate_tensors` is not None, returns a IntermediateTensors object.
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Otherwise, returns a tensor of shape `(batch, seq_len, hidden_size)` representing
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the output of the last transformer encoder layer.
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"""
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forward_context: ForwardContext = get_forward_context()
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attn_metadata = forward_context.attn_metadata
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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, layer in enumerate(self.layers[self.start_layer:self.end_layer], start=self.start_layer):
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hidden_states, residual = layer(
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positions,
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hidden_states,
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attn_metadata,
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residual,
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)
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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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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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"""Load model weights.
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Args:
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weights (Iterable[tuple[str, torch.Tensor]]): An iterator containing weight names and their corresponding values.
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Returns (set[str]):
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A set of already loaded weight names.
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Exceptions:
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None.
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"""
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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|
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class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
|
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": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
lora_config = vllm_config.lora_config
|
|
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
|
|
self.quant_config = quant_config
|
|
self.model = Qwen3Model(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
|
|
if get_pp_group().is_last_rank:
|
|
if config.tie_word_embeddings:
|
|
self.lm_head = self.model.embed_tokens
|
|
else:
|
|
self.lm_head = ParallelLMHead(config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(
|
|
prefix, "lm_head"))
|
|
else:
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
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,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
Qwen3ForSequenceClassification = as_seq_cls_model(Qwen3ForCausalLM)
|