999 lines
35 KiB
Python
999 lines
35 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 The vLLM team.
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# Copyright 2025 The Qwen Team.
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# Copyright 2025 The HuggingFace Inc. team.
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# All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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 Qwen3.5 Series compatible with HuggingFace weights."""
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import typing
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from collections.abc import Callable, Iterable
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import torch
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from einops import rearrange
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from torch import nn
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from transformers.activations import ACT2FN
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (
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CacheConfig,
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ModelConfig,
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SpeculativeConfig,
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VllmConfig,
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get_current_vllm_config,
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)
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from vllm.distributed import (
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divide,
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.layernorm import (
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GemmaRMSNorm as Qwen3_5RMSNorm,
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)
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from vllm.model_executor.layers.layernorm import RMSNormGated
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.mamba_mixer2 import (
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mamba_v2_sharded_weight_loader,
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)
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateCopyFunc,
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MambaStateCopyFuncCalculator,
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MambaStateDtypeCalculator,
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MambaStateShapeCalculator,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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sharded_weight_loader,
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)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.qwen3_5 import (
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Qwen3_5Config,
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Qwen3_5TextConfig,
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)
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from vllm.transformers_utils.configs.qwen3_5_moe import (
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Qwen3_5MoeConfig,
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Qwen3_5MoeTextConfig,
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)
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from .interfaces import (
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HasInnerState,
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IsHybrid,
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MixtureOfExperts,
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MultiModalEmbeddings,
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SupportsLoRA,
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SupportsPP,
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_require_is_multimodal,
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)
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from .qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
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from .qwen3_next import (
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ChunkGatedDeltaRule,
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Qwen3NextAttention,
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Qwen3NextDecoderLayer,
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Qwen3NextGatedDeltaNet,
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Qwen3NextModel,
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Qwen3NextSparseMoeBlock,
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QwenNextMixtureOfExperts,
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)
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from .qwen3_vl import (
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Qwen3_VisionTransformer,
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Qwen3VLDummyInputsBuilder,
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Qwen3VLForConditionalGeneration,
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Qwen3VLMultiModalProcessor,
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Qwen3VLProcessingInfo,
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)
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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_merge_multimodal_embeddings,
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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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logger = init_logger(__name__)
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class Qwen3_5ProcessingInfo(Qwen3VLProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(Qwen3_5Config)
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class Qwen3_5MoeProcessingInfo(Qwen3VLProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(Qwen3_5MoeConfig)
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class Qwen3_5GatedDeltaNet(Qwen3NextGatedDeltaNet):
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def __init__(
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self,
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config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig,
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model_config: ModelConfig | None = None,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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speculative_config: SpeculativeConfig | None = None,
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prefix: str = "",
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) -> None:
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super(Qwen3NextGatedDeltaNet, self).__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.tp_rank = get_tensor_model_parallel_rank()
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self.hidden_size = config.hidden_size
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self.num_v_heads = config.linear_num_value_heads
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self.num_k_heads = config.linear_num_key_heads
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self.head_k_dim = config.linear_key_head_dim
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self.head_v_dim = config.linear_value_head_dim
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self.key_dim = self.head_k_dim * self.num_k_heads
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self.value_dim = self.head_v_dim * self.num_v_heads
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self.conv_kernel_size = config.linear_conv_kernel_dim
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self.layer_idx = extract_layer_index(prefix)
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self.activation = config.hidden_act
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self.act = ACT2FN[config.hidden_act]
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self.layer_norm_epsilon = config.rms_norm_eps
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self.prefix = prefix
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self.config = config
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self.model_config = model_config
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self.cache_config = cache_config
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self.quant_config = quant_config
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self.speculative_config = speculative_config
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self.num_spec = (
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self.speculative_config.num_speculative_tokens
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if self.speculative_config
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else 0
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)
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# QKV
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self.conv_dim = self.key_dim * 2 + self.value_dim
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self.conv1d = ColumnParallelLinear(
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input_size=self.conv_kernel_size,
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output_size=self.conv_dim,
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bias=False,
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prefix=f"{prefix}.conv1d",
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)
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self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
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self.in_proj_qkv = MergedColumnParallelLinear(
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input_size=self.hidden_size,
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output_sizes=[self.key_dim, self.key_dim, self.value_dim],
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.in_proj_qkv",
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)
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self.in_proj_z = ColumnParallelLinear(
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input_size=self.hidden_size,
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output_size=self.value_dim,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.in_proj_z",
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)
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self.in_proj_b = ColumnParallelLinear(
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input_size=self.hidden_size,
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output_size=self.num_v_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.in_proj_b",
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)
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self.in_proj_a = ColumnParallelLinear(
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input_size=self.hidden_size,
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output_size=self.num_v_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.in_proj_a",
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)
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query_key_settings = (self.key_dim, 0, False)
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value_settings = (self.value_dim, 0, False)
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delattr(self.conv1d.weight, "weight_loader")
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set_weight_attrs(
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self.conv1d.weight,
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{
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"weight_loader": mamba_v2_sharded_weight_loader(
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[
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query_key_settings,
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query_key_settings,
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value_settings,
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],
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self.tp_size,
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self.tp_rank,
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)
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},
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)
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# selective projection used to make dt, B and C input dependant
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# time step projection (discretization)
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# instantiate once and copy inv_dt in init_weights of PretrainedModel
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self.dt_bias = nn.Parameter(
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torch.ones(self.num_v_heads // self.tp_size),
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)
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self.A_log = nn.Parameter(
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torch.empty(
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divide(self.num_v_heads, self.tp_size),
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)
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)
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set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
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set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
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self.norm = RMSNormGated(
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self.head_v_dim,
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eps=self.layer_norm_epsilon,
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group_size=None,
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norm_before_gate=True,
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device=current_platform.current_device(),
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dtype=config.dtype,
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)
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self.out_proj = RowParallelLinear(
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self.value_dim,
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self.hidden_size,
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bias=False,
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input_is_parallel=True,
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quant_config=quant_config,
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prefix=f"{prefix}.out_proj",
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)
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self.chunk_gated_delta_rule = ChunkGatedDeltaRule()
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compilation_config = get_current_vllm_config().compilation_config
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if prefix in compilation_config.static_forward_context:
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raise ValueError(f"Duplicate layer name: {prefix}")
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compilation_config.static_forward_context[prefix] = self
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def fix_query_key_value_ordering(
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self,
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mixed_qkv,
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z,
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b,
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a,
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):
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raise NotImplementedError(
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"Qwen3.5 Series dont need to fix query key value ordering"
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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output: torch.Tensor,
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):
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"""
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Forward pass with three parts:
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1. Input projection
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2. Core attention (custom op)
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3. Output projection
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"""
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num_tokens = hidden_states.size(0)
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# ============================================================
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# Part 1: Input Projection
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# ============================================================
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mixed_qkv, _ = self.in_proj_qkv(hidden_states)
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z, _ = self.in_proj_z(hidden_states)
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z = z.reshape(z.size(0), -1, self.head_v_dim)
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b, _ = self.in_proj_b(hidden_states)
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a, _ = self.in_proj_a(hidden_states)
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b = b.contiguous()
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a = a.contiguous()
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# ============================================================
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# Part 2: Core Attention (Custom Op)
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# ============================================================
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# Note: we should not use torch.empty here like other attention backends,
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# see discussions in https://github.com/vllm-project/vllm/pull/28182
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core_attn_out = torch.zeros(
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(num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
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dtype=hidden_states.dtype,
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device=hidden_states.device,
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)
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torch.ops.vllm.gdn_attention_core(
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mixed_qkv,
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b,
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a,
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core_attn_out,
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self.prefix,
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)
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# ============================================================
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# Part 3: Output Projection
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# ============================================================
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z_shape_og = z.shape
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# Reshape input data into 2D tensor
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core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
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z = z.reshape(-1, z.shape[-1])
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core_attn_out = self.norm(core_attn_out, z)
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core_attn_out = core_attn_out.reshape(z_shape_og)
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core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
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output[:num_tokens], _ = self.out_proj(core_attn_out)
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class Qwen3_5DecoderLayer(Qwen3NextDecoderLayer):
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def __init__(
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self,
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vllm_config: VllmConfig,
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layer_type: str,
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prefix: str = "",
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) -> None:
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super(Qwen3NextDecoderLayer, self).__init__()
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config = vllm_config.model_config.hf_text_config
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model_config = vllm_config.model_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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speculative_config = vllm_config.speculative_config
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self.layer_type = layer_type
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self.layer_idx = extract_layer_index(prefix)
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if self.layer_type == "linear_attention":
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self.linear_attn = Qwen3_5GatedDeltaNet(
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config,
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model_config=model_config,
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cache_config=cache_config,
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quant_config=quant_config,
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speculative_config=speculative_config,
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prefix=f"{prefix}.linear_attn",
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)
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elif self.layer_type == "full_attention":
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self.self_attn = Qwen3NextAttention(
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config,
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model_config=model_config,
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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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else:
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raise ValueError(f"Invalid layer_type {self.layer_type}")
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# NOTE: Determine the MLP type based on the model type
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# Qwen3.5 use all layers for MLP / Qwen3.5-MoE use sparse MoE blocks
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if config.model_type == "qwen3_5_moe_text":
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self.mlp = Qwen3NextSparseMoeBlock(
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vllm_config=vllm_config,
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prefix=f"{prefix}.mlp",
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)
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elif config.model_type == "qwen3_5_text":
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self.mlp = Qwen3NextMLP(
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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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else:
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raise ValueError(f"Invalid model_type {config.model_type}")
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self.input_layernorm = Qwen3_5RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.post_attention_layernorm = Qwen3_5RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.layer_scale = getattr(config, "layer_scale", False)
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if self.layer_scale:
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self.attn_layer_scale = torch.nn.Parameter(
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torch.zeros(
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1,
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1,
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config.hidden_size,
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dtype=config.dtype,
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),
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)
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self.ffn_layer_scale = torch.nn.Parameter(
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torch.zeros(
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1,
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1,
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config.hidden_size,
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dtype=config.dtype,
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),
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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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)
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class Qwen3_5Model(Qwen3NextModel):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super(Qwen3NextModel, self).__init__()
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config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig = (
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vllm_config.model_config.hf_text_config
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)
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parallel_config = vllm_config.parallel_config
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eplb_config = parallel_config.eplb_config
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self.num_redundant_experts = eplb_config.num_redundant_experts
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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)
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def get_layer(prefix: str):
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return Qwen3_5DecoderLayer(
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vllm_config,
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layer_type=config.layer_types[extract_layer_index(prefix)],
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prefix=prefix,
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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, get_layer, prefix=f"{prefix}.layers"
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)
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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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if get_pp_group().is_last_rank:
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self.norm = Qwen3_5RMSNorm(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 load_fused_expert_weights(
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self,
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name: str,
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params_dict: dict,
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loaded_weight: torch.Tensor,
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shard_id: str,
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num_experts: int,
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) -> bool:
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param = params_dict[name]
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weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
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loaded_local_expert = False
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for expert_id in range(num_experts):
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curr_expert_weight = loaded_weight[expert_id]
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success = weight_loader(
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param,
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curr_expert_weight,
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name,
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shard_id,
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expert_id,
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return_success=True,
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)
|
|
if success:
|
|
loaded_local_expert = True
|
|
|
|
return loaded_local_expert
|
|
|
|
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_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
is_fused_expert = False
|
|
fused_expert_params_mapping = [
|
|
("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
|
|
("experts.w2_weight", "experts.down_proj", 0, "w2"),
|
|
]
|
|
num_experts = (
|
|
self.config.num_experts if hasattr(self.config, "num_experts") else 0
|
|
)
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
if name.startswith("mtp."):
|
|
continue
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if "experts.gate_up_proj" in name or "experts.down_proj" in name:
|
|
is_fused_expert = True
|
|
expert_params_mapping = fused_expert_params_mapping
|
|
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
if "mlp.experts" in name:
|
|
continue
|
|
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# name = apply_attn_prefix(name, params_dict)
|
|
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:
|
|
is_expert_weight = False
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
is_expert_weight = True
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
if is_fused_expert:
|
|
# qwen3.5 no need to transpose
|
|
# loaded_weight = loaded_weight.transpose(-1, -2)
|
|
if "experts.gate_up_proj" in name:
|
|
loaded_weight = loaded_weight.chunk(2, dim=-2)
|
|
success_w1 = self.load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight[0],
|
|
"w1",
|
|
num_experts,
|
|
)
|
|
success_w3 = self.load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight[1],
|
|
"w3",
|
|
num_experts,
|
|
)
|
|
success = success_w1 and success_w3
|
|
else:
|
|
# down_proj
|
|
success = self.load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight,
|
|
shard_id,
|
|
num_experts,
|
|
)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if (
|
|
name_mapped.endswith(".bias")
|
|
or name_mapped.endswith("_bias")
|
|
) and name_mapped not in params_dict:
|
|
continue
|
|
param = params_dict[name_mapped]
|
|
weight_loader = param.weight_loader
|
|
success = weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True,
|
|
)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if name not in params_dict:
|
|
logger.warning_once(
|
|
f"Parameter {name} not found in params_dict, skip loading"
|
|
)
|
|
continue
|
|
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
|
|
|
|
|
|
class Qwen3_5ForCausalLMBase(
|
|
nn.Module,
|
|
HasInnerState,
|
|
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 = ""):
|
|
config = vllm_config.model_config.hf_text_config
|
|
self.vllm_config = vllm_config
|
|
self.model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
|
|
scheduler_config = vllm_config.scheduler_config
|
|
if cache_config.mamba_cache_mode == "all":
|
|
raise NotImplementedError(
|
|
"Qwen3.5 currently does not support 'all' prefix caching, "
|
|
"please use '--mamba-cache-mode=align' instead"
|
|
)
|
|
self.quant_config = vllm_config.quant_config
|
|
|
|
super().__init__()
|
|
self.config = config
|
|
self.scheduler_config = scheduler_config
|
|
self.model = Qwen3_5Model(
|
|
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,
|
|
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 embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.embed_input_ids(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs: object,
|
|
):
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
return self.logits_processor(self.lm_head, hidden_states)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=["mtp."],
|
|
)
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
class Qwen3_5ForCausalLM(Qwen3_5ForCausalLMBase):
|
|
pass
|
|
|
|
|
|
class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLMBase, QwenNextMixtureOfExperts):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
# set MoE hyperparameters
|
|
self.set_moe_parameters()
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|
|
|
|
|
|
########################################################
|
|
# Qwen3_5-Dense
|
|
########################################################
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(
|
|
Qwen3VLMultiModalProcessor,
|
|
info=Qwen3_5ProcessingInfo,
|
|
dummy_inputs=Qwen3VLDummyInputsBuilder,
|
|
)
|
|
class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration, IsHybrid):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
# protocols have not __init__ method, so we need to use nn.Module.__init__
|
|
nn.Module.__init__(self)
|
|
config: Qwen3_5Config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
|
|
self.config = config
|
|
self.multimodal_config = multimodal_config
|
|
self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
|
|
self.video_pruning_rate = multimodal_config.video_pruning_rate
|
|
self.is_multimodal_pruning_enabled = (
|
|
multimodal_config.is_multimodal_pruning_enabled()
|
|
)
|
|
|
|
with self._mark_tower_model(vllm_config, {"image", "video"}):
|
|
self.visual = Qwen3_VisionTransformer(
|
|
config.vision_config,
|
|
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "visual"),
|
|
)
|
|
|
|
with self._mark_language_model(vllm_config):
|
|
self.language_model = Qwen3_5ForCausalLM(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
def embed_input_ids(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: MultiModalEmbeddings | None = None,
|
|
*,
|
|
is_multimodal: torch.Tensor | None = None,
|
|
handle_oov_mm_token: bool = False,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self._embed_text_input_ids(
|
|
input_ids,
|
|
self.language_model.embed_input_ids,
|
|
is_multimodal=is_multimodal,
|
|
handle_oov_mm_token=handle_oov_mm_token,
|
|
)
|
|
|
|
if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
|
|
return inputs_embeds
|
|
|
|
is_multimodal = _require_is_multimodal(is_multimodal)
|
|
|
|
inputs_embeds = _merge_multimodal_embeddings(
|
|
inputs_embeds=inputs_embeds,
|
|
multimodal_embeddings=multimodal_embeddings,
|
|
is_multimodal=is_multimodal,
|
|
)
|
|
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs: object,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
"""Run forward pass for Qwen3.5.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
positions: Flattened (concatenated) position ids corresponding to a
|
|
batch.
|
|
**NOTE**: If mrope is enabled (default setting for Qwen3VL
|
|
opensource models), the shape will be `(3, seq_len)`,
|
|
otherwise it will be `(seq_len,).
|
|
intermediate_tensors: Intermediate tensors from previous pipeline
|
|
stages.
|
|
inputs_embeds: Pre-computed input embeddings.
|
|
**kwargs: Additional keyword arguments including:
|
|
- pixel_values: Pixel values to be fed to a model.
|
|
`None` if no images are passed.
|
|
- image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in
|
|
LLM. `None` if no images are passed.
|
|
- pixel_values_videos: Pixel values of videos to be fed to a
|
|
model. `None` if no videos are passed.
|
|
- video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in
|
|
LLM. `None` if no videos are passed.
|
|
"""
|
|
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.language_model.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=["mtp."],
|
|
)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
) -> tuple[torch.dtype, torch.dtype]:
|
|
return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
|
|
vllm_config.model_config.dtype,
|
|
vllm_config.cache_config.mamba_cache_dtype,
|
|
vllm_config.cache_config.mamba_ssm_cache_dtype,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(
|
|
cls, vllm_config: "VllmConfig"
|
|
) -> tuple[tuple[int, int], tuple[int, int]]:
|
|
parallel_config = vllm_config.parallel_config
|
|
hf_config = vllm_config.model_config.hf_text_config
|
|
tp_size = parallel_config.tensor_parallel_size
|
|
num_spec = (
|
|
vllm_config.speculative_config.num_speculative_tokens
|
|
if vllm_config.speculative_config
|
|
else 0
|
|
)
|
|
return MambaStateShapeCalculator.gated_delta_net_state_shape(
|
|
tp_size,
|
|
hf_config.linear_num_key_heads,
|
|
hf_config.linear_num_value_heads,
|
|
hf_config.linear_key_head_dim,
|
|
hf_config.linear_value_head_dim,
|
|
hf_config.linear_conv_kernel_dim,
|
|
num_spec,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
|
|
return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()
|
|
|
|
|
|
########################################################
|
|
# Qwen3_5-MoE
|
|
########################################################
|
|
|
|
|
|
class Qwen3_5_MoeMixtureOfExperts(MixtureOfExperts):
|
|
def update_physical_experts_metadata(
|
|
self,
|
|
num_physical_experts: int,
|
|
num_local_physical_experts: int,
|
|
) -> None:
|
|
assert self.num_local_physical_experts == num_local_physical_experts
|
|
self.num_physical_experts = num_physical_experts
|
|
self.num_local_physical_experts = num_local_physical_experts
|
|
self.num_redundant_experts = num_physical_experts - self.num_logical_experts
|
|
for layer in self.language_model.model.layers:
|
|
if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
|
|
moe = layer.mlp
|
|
moe.n_local_physical_experts = num_local_physical_experts
|
|
moe.n_physical_experts = num_physical_experts
|
|
moe.n_redundant_experts = self.num_redundant_experts
|
|
moe.experts.update_expert_map()
|
|
|
|
def set_moe_parameters(self):
|
|
self.expert_weights = []
|
|
|
|
self.moe_layers = []
|
|
example_moe = None
|
|
for layer in self.language_model.model.layers:
|
|
if isinstance(layer, Qwen3_5DecoderLayer) and isinstance(
|
|
layer.mlp, Qwen3NextSparseMoeBlock
|
|
):
|
|
example_moe = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
if example_moe is None:
|
|
raise RuntimeError(
|
|
"No Qwen3_5 layer found in the language_model.model.layers."
|
|
)
|
|
|
|
# Set MoE hyperparameters
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
self.num_expert_groups = 1
|
|
self.num_shared_experts = 0
|
|
self.num_logical_experts = example_moe.n_logical_experts
|
|
self.num_physical_experts = example_moe.n_physical_experts
|
|
self.num_local_physical_experts = example_moe.n_local_physical_experts
|
|
self.num_routed_experts = example_moe.n_routed_experts
|
|
self.num_redundant_experts = example_moe.n_redundant_experts
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(
|
|
Qwen3VLMultiModalProcessor,
|
|
info=Qwen3_5MoeProcessingInfo,
|
|
dummy_inputs=Qwen3VLDummyInputsBuilder,
|
|
)
|
|
class Qwen3_5MoeForConditionalGeneration(
|
|
Qwen3_5ForConditionalGeneration, Qwen3_5_MoeMixtureOfExperts
|
|
):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
# protocols have not __init__ method, so we need to use nn.Module.__init__
|
|
nn.Module.__init__(self)
|
|
config: Qwen3_5MoeConfig = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
|
|
self.config = config
|
|
self.multimodal_config = multimodal_config
|
|
self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
|
|
self.video_pruning_rate = multimodal_config.video_pruning_rate
|
|
self.is_multimodal_pruning_enabled = (
|
|
multimodal_config.is_multimodal_pruning_enabled()
|
|
)
|
|
|
|
with self._mark_tower_model(vllm_config, {"image", "video"}):
|
|
self.visual = Qwen3_VisionTransformer(
|
|
config.vision_config,
|
|
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "visual"),
|
|
)
|
|
|
|
with self._mark_language_model(vllm_config):
|
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self.language_model = Qwen3_5MoeForCausalLM(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")
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)
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors
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)
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# set MoE hyperparameters
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self.set_moe_parameters()
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