diff --git a/docs/source/models/supported_models.md b/docs/source/models/supported_models.md index 74b4eab92043..958fd6eafdce 100644 --- a/docs/source/models/supported_models.md +++ b/docs/source/models/supported_models.md @@ -478,6 +478,16 @@ See [this page](#generative-models) for more information on how to use generativ * `Qwen/Qwen1.5-MoE-A2.7B`, `Qwen/Qwen1.5-MoE-A2.7B-Chat`, etc. * * ✅︎ +- * `Qwen3ForCausalLM` + * Qwen3 + * `Qwen/Qwen3-8B`, etc. + * ✅︎ + * ✅︎ +- * `Qwen3MoeForCausalLM` + * Qwen3MoE + * `Qwen/Qwen3-MoE-15B-A2B`, etc. + * ✅︎ + * ✅︎ - * `StableLmForCausalLM` * StableLM * `stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc. diff --git a/tests/models/registry.py b/tests/models/registry.py index 39e104a11ab1..8bfd89e9c99a 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -202,6 +202,16 @@ def check_available_online( "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-7B-Instruct", extras={"2.5": "Qwen/Qwen2.5-7B-Instruct"}), # noqa: E501 "Qwen2MoeForCausalLM": _HfExamplesInfo("Qwen/Qwen1.5-MoE-A2.7B-Chat"), + "Qwen3ForCausalLM": _HfExamplesInfo( + "Qwen/Qwen3-8B", + is_available_online=False, + min_transformers_version="4.51" + ), + "Qwen3MoeForCausalLM": _HfExamplesInfo( + "Qwen/Qwen3-MoE-15B-A2B", + is_available_online=False, + min_transformers_version="4.51" + ), "RWForCausalLM": _HfExamplesInfo("tiiuae/falcon-40b", is_available_online=False), "StableLMEpochForCausalLM": _HfExamplesInfo("stabilityai/stablelm-zephyr-3b", # noqa: E501 diff --git a/vllm/model_executor/models/qwen2.py b/vllm/model_executor/models/qwen2.py index c4d02e5ddeb1..2831a5a12330 100644 --- a/vllm/model_executor/models/qwen2.py +++ b/vllm/model_executor/models/qwen2.py @@ -263,7 +263,11 @@ def forward( }) class Qwen2Model(nn.Module): - def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + def __init__(self, + *, + vllm_config: VllmConfig, + prefix: str = "", + decoder_layer_type: type[nn.Module] = Qwen2DecoderLayer): super().__init__() config = vllm_config.model_config.hf_config @@ -297,12 +301,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): else: self.embed_tokens = PPMissingLayer() + # Use the provided decoder layer type or default to Qwen2DecoderLayer + decoder_layer_type = decoder_layer_type or Qwen2DecoderLayer self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, - lambda prefix: Qwen2DecoderLayer(config=config, - cache_config=cache_config, - quant_config=quant_config, - prefix=prefix), + lambda prefix: decoder_layer_type(config=config, + cache_config=cache_config, + quant_config=quant_config, + prefix=prefix), prefix=f"{prefix}.layers", ) diff --git a/vllm/model_executor/models/qwen3.py b/vllm/model_executor/models/qwen3.py new file mode 100644 index 000000000000..9c14038e6113 --- /dev/null +++ b/vllm/model_executor/models/qwen3.py @@ -0,0 +1,329 @@ +# SPDX-License-Identifier: Apache-2.0 + +# 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.""" +from typing import Iterable, Optional, Set, Tuple, Union + +import torch +from torch import nn +from transformers import Qwen3Config + +from vllm.attention import Attention, AttentionType +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 +from vllm.model_executor.layers.linear import (QKVParallelLinear, + RowParallelLinear) +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.sampler import SamplerOutput, get_sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors + +from .interfaces import SupportsLoRA, SupportsPP +from .qwen2 import Qwen2MLP as Qwen3MLP +from .qwen2 import Qwen2Model +from .utils import AutoWeightsLoader, PPMissingLayer, maybe_prefix + +logger = init_logger(__name__) + + +class Qwen3Attention(nn.Module): + + def __init__(self, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + max_position: int = 4096 * 32, + head_dim: Optional[int] = None, + rms_norm_eps: float = 1e-06, + qkv_bias: bool = False, + rope_theta: float = 10000, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + rope_scaling: Optional[Tuple] = None, + prefix: str = "", + attn_type: str = AttentionType.DECODER) -> None: + 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 + self.rope_theta = rope_theta + + 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, + rotary_dim=self.head_dim, + max_position=max_position, + base=self.rope_theta, + rope_scaling=rope_scaling, + ) + 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) + 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) + # Add qk-norm + q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, + self.head_dim) + q_by_head = self.q_norm.forward_native(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) + k_by_head = self.k_norm.forward_native(k_by_head) + k = k_by_head.view(k.shape) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v) + output, _ = self.o_proj(attn_output) + return output + + +class Qwen3DecoderLayer(nn.Module): + + def __init__( + self, + config: Qwen3Config, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + # Requires transformers > 4.32.0 + rope_theta = getattr(config, "rope_theta", 1000000) + rope_scaling = getattr(config, "rope_scaling", None) + + # 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, + rope_theta=rope_theta, + rms_norm_eps=config.rms_norm_eps, + qkv_bias=getattr(config, 'attention_bias', False), + head_dim=getattr(config, 'head_dim', None), + cache_config=cache_config, + quant_config=quant_config, + rope_scaling=rope_scaling, + prefix=f"{prefix}.self_attn", + attn_type=attn_type, + ) + 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", + ) + 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) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + residual: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm( + hidden_states, residual) + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + ) + + # Fully Connected + hidden_states, residual = self.post_attention_layernorm( + hidden_states, residual) + 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, + }) +class Qwen3Model(Qwen2Model): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, + prefix=prefix, + decoder_layer_type=Qwen3DecoderLayer) + + +class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP): + 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 + 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.sampler = get_sampler() + + 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, + ) -> 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 sample( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + 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) diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py new file mode 100644 index 000000000000..390bb7adf255 --- /dev/null +++ b/vllm/model_executor/models/qwen3_moe.py @@ -0,0 +1,531 @@ +# SPDX-License-Identifier: Apache-2.0 + +# 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 Qwen3MoE model compatible with HuggingFace weights.""" +from typing import Any, Dict, Iterable, Optional, Set, Tuple, Union + +import torch +from torch import nn +from transformers import PretrainedConfig + +from vllm.attention import Attention +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, + tensor_model_parallel_all_reduce) +from vllm.logger import init_logger +from vllm.model_executor.layers.activation import SiluAndMul +from vllm.model_executor.layers.fused_moe import FusedMoE +from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, + QKVParallelLinear, + ReplicatedLinear, + RowParallelLinear) +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.sampler import SamplerOutput, get_sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, VocabParallelEmbedding) +from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors + +from .interfaces import SupportsPP +from .utils import (extract_layer_index, is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, make_layers, + maybe_prefix) + +logger = init_logger(__name__) + + +class Qwen3MoeMLP(nn.Module): + + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + quant_config: Optional[QuantizationConfig] = None, + reduce_results: bool = True, + prefix: str = "", + ) -> None: + super().__init__() + self.gate_up_proj = MergedColumnParallelLinear( + hidden_size, [intermediate_size] * 2, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.gate_up_proj") + self.down_proj = RowParallelLinear(intermediate_size, + hidden_size, + bias=False, + quant_config=quant_config, + reduce_results=reduce_results, + prefix=f"{prefix}.down_proj") + if hidden_act != "silu": + raise ValueError(f"Unsupported activation: {hidden_act}. " + "Only silu is supported for now.") + self.act_fn = SiluAndMul() + + def forward(self, x): + gate_up, _ = self.gate_up_proj(x) + x = self.act_fn(gate_up) + x, _ = self.down_proj(x) + return x + + +class Qwen3MoeSparseMoeBlock(nn.Module): + + def __init__( + self, + config: PretrainedConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ): + super().__init__() + self.tp_size = get_tensor_model_parallel_world_size() + + if self.tp_size > config.num_experts: + raise ValueError( + f"Tensor parallel size {self.tp_size} is greater than " + f"the number of experts {config.num_experts}.") + + self.experts = FusedMoE(num_experts=config.num_experts, + top_k=config.num_experts_per_tok, + hidden_size=config.hidden_size, + intermediate_size=config.moe_intermediate_size, + reduce_results=False, + renormalize=config.norm_topk_prob, + quant_config=quant_config, + prefix=f"{prefix}.experts") + + self.gate = ReplicatedLinear(config.hidden_size, + config.num_experts, + bias=False, + quant_config=None, + prefix=f"{prefix}.gate") + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # NOTE: hidden_states can have either 1D or 2D shape. + orig_shape = hidden_states.shape + hidden_dim = hidden_states.shape[-1] + hidden_states = hidden_states.view(-1, hidden_dim) + + # router_logits: (num_tokens, n_experts) + router_logits, _ = self.gate(hidden_states) + final_hidden_states = self.experts(hidden_states=hidden_states, + router_logits=router_logits) + final_hidden_states = final_hidden_states + if self.tp_size > 1: + final_hidden_states = tensor_model_parallel_all_reduce( + final_hidden_states) + + return final_hidden_states.view(orig_shape) + + +class Qwen3MoeAttention(nn.Module): + + def __init__( + self, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + rope_theta: float = 10000, + rope_scaling: Optional[Dict[str, Any]] = None, + max_position_embeddings: int = 8192, + head_dim: Optional[int] = None, + rms_norm_eps: float = 1e-06, + qkv_bias: bool = False, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + 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 + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + + 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, + rotary_dim=self.head_dim, + max_position=max_position_embeddings, + base=rope_theta, + rope_scaling=rope_scaling, + ) + 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") + + 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) + # Add qk-norm + q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, + self.head_dim) + q_by_head = self.q_norm.forward_native(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) + k_by_head = self.k_norm.forward_native(k_by_head) + k = k_by_head.view(k.shape) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v) + output, _ = self.o_proj(attn_output) + return output + + +class Qwen3MoeDecoderLayer(nn.Module): + + def __init__( + self, + config: PretrainedConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + rope_theta = getattr(config, "rope_theta", 10000) + rope_scaling = getattr(config, "rope_scaling", None) + max_position_embeddings = getattr(config, "max_position_embeddings", + 8192) + self.self_attn = Qwen3MoeAttention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + num_kv_heads=config.num_key_value_heads, + rope_theta=rope_theta, + rope_scaling=rope_scaling, + max_position_embeddings=max_position_embeddings, + rms_norm_eps=config.rms_norm_eps, + qkv_bias=getattr(config, 'attention_bias', False), + head_dim=getattr(config, 'head_dim', None), + cache_config=cache_config, + quant_config=quant_config, + prefix=f"{prefix}.self_attn", + ) + + # `mlp_only_layers` in the config. + layer_idx = extract_layer_index(prefix) + mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else + config.mlp_only_layers) + if (layer_idx not in mlp_only_layers) and ( + config.num_experts > 0 and + (layer_idx + 1) % config.decoder_sparse_step == 0): + self.mlp = Qwen3MoeSparseMoeBlock(config=config, + quant_config=quant_config, + prefix=f"{prefix}.mlp") + else: + self.mlp = Qwen3MoeMLP(hidden_size=config.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + quant_config=quant_config, + prefix=f"{prefix}.mlp") + 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) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + residual: Optional[torch.Tensor], + ) -> torch.Tensor: + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm( + hidden_states, residual) + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + ) + + # Fully Connected + hidden_states, residual = self.post_attention_layernorm( + hidden_states, residual) + hidden_states = self.mlp(hidden_states) + return hidden_states, residual + + +@support_torch_compile +class Qwen3MoeModel(nn.Module): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + + config = vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = VocabParallelEmbedding( + config.vocab_size, + config.hidden_size, + prefix=f"{prefix}.embed_tokens") + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: Qwen3MoeDecoderLayer(config=config, + cache_config=cache_config, + quant_config=quant_config, + prefix=prefix), + prefix=f"{prefix}.layers", + ) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.make_empty_intermediate_tensors = ( + make_empty_intermediate_tensors_factory( + ["hidden_states", "residual"], config.hidden_size)) + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + + 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: + 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): + 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 + + +class Qwen3MoeForCausalLM(nn.Module, SupportsPP): + + 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.sampler = get_sampler() + 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, + ) -> 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 sample( + self, + logits: Optional[torch.Tensor], + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + 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() + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + 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) + break + else: + for mapping in expert_params_mapping: + param_name, weight_name, expert_id, shard_id = mapping + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + # Skip layers on other devices. + if is_pp_missing_parameter(name, self): + continue + # Skip loading extra bias for GPTQ models. + if ((name.endswith(".bias") or name.endswith("_bias")) + and name not in params_dict): + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, + loaded_weight, + name, + shard_id=shard_id, + expert_id=expert_id) + 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) + return loaded_params diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 6ead6509bfe8..080aef8982d5 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -100,6 +100,8 @@ "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"), "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"), "Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"), + "Qwen3ForCausalLM": ("qwen3", "Qwen3ForCausalLM"), + "Qwen3MoeForCausalLM": ("qwen3_moe", "Qwen3MoeForCausalLM"), "RWForCausalLM": ("falcon", "FalconForCausalLM"), "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"), "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),