qwen3.py 16.1 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen3 model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from typing import Any, Optional
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import torch
from torch import nn
from transformers import Qwen3Config

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from vllm.attention.layer import Attention
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from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.config import set_default_rope_theta
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from vllm.v1.attention.backend import AttentionType
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from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
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from .qwen2 import Qwen2MLP as Qwen3MLP
from .qwen2 import Qwen2Model
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from .utils import AutoWeightsLoader, PPMissingLayer, extract_layer_index, maybe_prefix
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import vllm.envs as envs
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from vllm import _custom_ops as ops
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logger = init_logger(__name__)


class Qwen3Attention(nn.Module):
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    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
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        rope_parameters: dict,
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        max_position: int = 4096 * 32,
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        head_dim: int | None = None,
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        rms_norm_eps: float = 1e-06,
        qkv_bias: bool = False,
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        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
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        dual_chunk_attention_config: dict[str, Any] | None = None,
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    ) -> None:
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        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = head_dim or hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
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        self.dual_chunk_attention_config = dual_chunk_attention_config
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        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position,
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            rope_parameters=rope_parameters,
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            dual_chunk_attention_config=dual_chunk_attention_config,
        )
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
            attn_type=attn_type,
            **{
                "layer_idx": extract_layer_index(prefix),
                "dual_chunk_attention_config": dual_chunk_attention_config,
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            }
            if dual_chunk_attention_config
            else {},
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        )
        self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
        self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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        used_fused = False
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        if envs.VLLM_USE_FUSED_RMS_ROPE and positions.ndim == 1:
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            if hasattr(torch.ops.vllm, "rms_rotary_embedding_fuse"):
                # Fused RMSNorm + RoPE path through custom op.
                cos_sin_cache = self.rotary_emb.cos_sin_cache
                if (cos_sin_cache.device != q.device
                        or cos_sin_cache.dtype != q.dtype):
                    cos_sin_cache = cos_sin_cache.to(q.device,
                                                     dtype=q.dtype,
                                                     non_blocking=True)
                    # Persist the converted cache so we don't re-copy/re-allocate
                    # on every forward when the original buffer starts on CPU.
                    self.rotary_emb.cos_sin_cache = cos_sin_cache
                q = q.contiguous()
                k = k.contiguous()
                torch.ops.vllm.rms_rotary_embedding_fuse(
                    positions,
                    q,
                    k,
                    self.head_dim,
                    cos_sin_cache,
                    self.rotary_emb.is_neox_style,
                    self.q_norm.weight,
                    self.k_norm.weight,
                    None,
                    None,
                    self.q_norm.variance_epsilon,
                )
                used_fused = True
            else:
                logger.warning_once(
                    "VLLM_USE_FUSED_RMS_ROPE is enabled and positions.ndim == 1, "
                    "but the RoPE fused op is unavailable; falling back to the "
                    "default RMSNorm + RoPE path."
                )
        elif envs.VLLM_USE_FUSED_RMS_ROPE and positions.ndim == 2:
            mrope_section = getattr(self.rotary_emb, "mrope_section", None)
            if mrope_section is not None and hasattr(torch.ops.vllm,
                                                     "rms_mrope_fuse"):
                # Fused RMSNorm + M-RoPE path through custom op.
                cos_sin_cache = self.rotary_emb.cos_sin_cache
                if (cos_sin_cache.device != q.device
                        or cos_sin_cache.dtype != q.dtype):
                    cos_sin_cache = cos_sin_cache.to(q.device,
                                                     dtype=q.dtype,
                                                     non_blocking=True)
                    self.rotary_emb.cos_sin_cache = cos_sin_cache

                cos_sin = cos_sin_cache[positions]
                cos, sin = cos_sin.chunk(2, dim=-1)

                q = q.contiguous()
                k = k.contiguous()
                cos = cos.contiguous()
                sin = sin.contiguous()
                assert len(mrope_section) == 3
                torch.ops.vllm.rms_mrope_fuse(
                    q,
                    k,
                    cos,
                    sin,
                    self.head_dim,
                    self.rotary_emb.rotary_dim,
                    mrope_section[0],
                    mrope_section[1],
                    mrope_section[2],
                    self.rotary_emb.mrope_interleaved,
                    self.q_norm.weight,
                    self.k_norm.weight,
                    self.q_norm.variance_epsilon,
                    None,
                    None,
                )
                used_fused = True
            else:
                logger.warning_once(
                    "VLLM_USE_FUSED_RMS_ROPE is enabled and positions.ndim == 2, "
                    "but the M-RoPE fused op is unavailable; falling back to the "
                    "default RMSNorm + RoPE path."
                )
        if not used_fused:
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            # Add qk-norm
            q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
            if envs.VLLM_USE_APEX_RN:
                q_by_head = self.q_norm.forward_apex(q_by_head)
            else:
                q_by_head = self.q_norm.forward_cuda(q_by_head)
            q = q_by_head.view(q.shape)
            k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
            if envs.VLLM_USE_APEX_RN:
                k_by_head = self.k_norm.forward_apex(k_by_head)
            else:
                k_by_head = self.k_norm.forward_cuda(k_by_head)
            k = k_by_head.view(k.shape)
            q, k = self.rotary_emb(positions, q, k)
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        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output


class Qwen3DecoderLayer(nn.Module):
    def __init__(
        self,
        config: Qwen3Config,
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        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
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        set_default_rope_theta(config, default_theta=1000000)
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        dual_chunk_attention_config = getattr(
            config, "dual_chunk_attention_config", None
        )
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        # By default, Qwen3 uses causal attention as it is a decoder-only model.
        # You can override the HF config with `is_causal=False` to enable
        # bidirectional attention, which is used in some embedding models
        # (e.g. Alibaba-NLP/gte-Qwen3-7B-instruct)
        if getattr(config, "is_causal", True):
            attn_type = AttentionType.DECODER
        else:
            attn_type = AttentionType.ENCODER_ONLY

        self.self_attn = Qwen3Attention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            max_position=config.max_position_embeddings,
            num_kv_heads=config.num_key_value_heads,
            rms_norm_eps=config.rms_norm_eps,
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            qkv_bias=getattr(config, "attention_bias", False),
            head_dim=getattr(config, "head_dim", None),
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            cache_config=cache_config,
            quant_config=quant_config,
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            rope_parameters=config.rope_parameters,
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            prefix=f"{prefix}.self_attn",
            attn_type=attn_type,
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            dual_chunk_attention_config=dual_chunk_attention_config,
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        )
        self.mlp = Qwen3MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
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        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
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    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
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        residual: torch.Tensor | None,
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    ) -> tuple[torch.Tensor, torch.Tensor]:
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        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
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            hidden_states, residual = self.input_layernorm(hidden_states, residual)
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        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )

        # 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)
        return hidden_states, residual


ALL_DECODER_LAYER_TYPES = {
    "attention": Qwen3DecoderLayer,
}


@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
        # otherwise (seq_len, ).
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
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    }
)
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class Qwen3Model(Qwen2Model):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__(
            vllm_config=vllm_config, prefix=prefix, decoder_layer_type=Qwen3DecoderLayer
        )
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class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
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    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "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

        self.config = config

        self.quant_config = quant_config
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        self.model = Qwen3Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
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        if get_pp_group().is_last_rank:
            if config.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
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                self.lm_head = ParallelLMHead(
                    config.vocab_size,
                    config.hidden_size,
                    quant_config=quant_config,
                    prefix=maybe_prefix(prefix, "lm_head"),
                )
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        else:
            self.lm_head = PPMissingLayer()

        self.logits_processor = LogitsProcessor(config.vocab_size)

        self.make_empty_intermediate_tensors = (
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            self.model.make_empty_intermediate_tensors
        )
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    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
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        self.model.aux_hidden_state_layers = layers

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    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
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        num_layers = len(self.model.layers)
        return (2, num_layers // 2, num_layers - 3)

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    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)
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    def forward(
        self,
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        input_ids: torch.Tensor,
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        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
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        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
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        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
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    ) -> torch.Tensor | None:
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        logits = self.logits_processor(self.lm_head, hidden_states)
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        return logits

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    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        loader = AutoWeightsLoader(
            self,
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            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
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        )
        return loader.load_weights(weights)