[qwen3-omni] Add Qwen3-Omni moe thinker
This commit is contained in:
@@ -1,4 +1,5 @@
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FROM cr.metax-tech.com/public-ai-release/maca/vllm:maca.ai3.0.0.5-torch2.6-py310-ubuntu22.04-amd64
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RUN /opt/conda/bin/pip install --no-cache-dir --upgrade transformers
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COPY vllm/ /opt/conda/lib/python3.10/site-packages/vllm/
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COPY code_generator.py /opt/conda/lib/python3.10/site-packages/triton/compiler/code_generator.py
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@@ -1,6 +1,6 @@
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# metax-c500-vllm
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本项目包含了对于原版 vllm 的升级，使其可以在沐曦 C500芯片上支持运行 gpt-oss
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本项目包含了对于原版 vllm 的升级，使其可以在沐曦 C500芯片上支持运行 gpt-oss, qwen3-omni
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本项目中提供的 Dockerfile 中的主要内容为：
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1. 将 `vllm` 目录覆盖到镜像中的 `/opt/conda/lib/python3.10/site-packages/vllm`。运行`gpt-oss`时需指定`VLLM_ATTENTION_BACKEND=TRITON_ATTN_VLLM_V1`
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@@ -54,6 +54,14 @@ def check_xformers_availability():
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return USE_XFORMERS_OPS
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def check_upstream_fa_availability(dtype: torch.dtype):
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if dtype in (torch.float16, torch.bfloat16) and current_platform.is_cuda(
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) and current_platform.has_device_capability(80):
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from transformers.utils import is_flash_attn_2_available
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return is_flash_attn_2_available()
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return False
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class Attention(nn.Module):
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"""Attention layer.
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@@ -531,7 +531,7 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
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return "<image>"
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if model_type in ("mllama", "llama4"):
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return "<|image|>"
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if model_type in ("qwen2_vl", "qwen2_5_vl"):
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if model_type in ("qwen2_vl", "qwen2_5_vl", "qwen3_omni_moe"):
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return "<|vision_start|><|image_pad|><|vision_end|>"
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if model_type == "qwen2_5_omni":
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return "<|vision_start|><|IMAGE|><|vision_end|>"
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@@ -553,13 +553,15 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
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if model_type in ("qwen2_audio", "qwen2_5_omni"):
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return (f"Audio {current_count}: "
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f"<|audio_bos|><|AUDIO|><|audio_eos|>")
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if model_type == "qwen3_omni_moe":
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return f"<|audio_start|><|audio_pad|><|audio_end|>"
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if model_type == "minicpmo":
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return "(<audio>./</audio>)"
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raise TypeError(f"Unknown model type: {model_type}")
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elif modality == "video":
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if model_type == "internvl_chat":
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return "<video>"
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if model_type in ("qwen2_vl", "qwen2_5_vl"):
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if model_type in ("qwen2_vl", "qwen2_5_vl", "qwen3_omni_moe"):
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return "<|vision_start|><|video_pad|><|vision_end|>"
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if model_type == "qwen2_5_omni":
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return "<|vision_start|><|VIDEO|><|vision_end|>"
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@@ -2,7 +2,7 @@
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import abstractmethod
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from collections.abc import Iterable
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from collections.abc import Iterable, Sequence
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from enum import Enum
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from typing import Callable, Literal, Optional, overload
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@@ -1667,37 +1667,57 @@ class FusedMoE(CustomOp):
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return final_hidden_states
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# @classmethod
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# def make_expert_params_mapping(
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# cls,
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# ckpt_gate_proj_name: str,
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# ckpt_down_proj_name: str,
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# ckpt_up_proj_name: str,
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# num_experts: int,
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# num_redundant_experts: int = 0) -> list[tuple[str, str, int, str]]:
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@classmethod
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def make_expert_params_mapping(
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cls,
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ckpt_gate_proj_name: str,
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ckpt_down_proj_name: str,
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ckpt_up_proj_name: str,
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num_experts: int,
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num_redundant_experts: int = 0) -> list[tuple[str, str, int, str]]:
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# num_physical_experts = num_experts + num_redundant_experts
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num_physical_experts = num_experts + num_redundant_experts
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# # In the returned mapping:
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# # - `expert_id` is the physical expert id
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# # - `weight_name` contains the weight name of the logical expert
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# # So that we should map the expert id to logical in `weight_name`
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# physical_to_logical_map = \
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# EplbState.build_initial_global_physical_to_logical_map(
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# num_experts, num_redundant_experts)
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# return [
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# # (param_name, weight_name, expert_id, shard_id)
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# ("experts.w13_" if weight_name
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# in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
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# f"experts.{physical_to_logical_map[expert_id]}.{weight_name}.",
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# expert_id, shard_id) for expert_id in range(num_physical_experts)
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# for shard_id, weight_name in [
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# ("w1", ckpt_gate_proj_name),
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# ("w2", ckpt_down_proj_name),
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# ("w3", ckpt_up_proj_name),
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# ]
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# ]
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def build_initial_global_physical_to_logical_map(
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num_routed_experts: int,
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num_redundant_experts: int,
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) -> Sequence[int]:
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"""
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Build an initial expert arrangement using the following structure:
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[original routed experts, redundant experts]
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Returns:
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physical_to_logical_map (Sequence[int]): A list of integers,
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where each integer is the index of the logical expert
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that the corresponding physical expert maps to.
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"""
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global_physical_to_logical_map = list(range(num_routed_experts))
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global_physical_to_logical_map += [
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i % num_routed_experts for i in range(num_redundant_experts)
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]
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return global_physical_to_logical_map
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# In the returned mapping:
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# - `expert_id` is the physical expert id
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# - `weight_name` contains the weight name of the logical expert
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# So that we should map the expert id to logical in `weight_name`
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physical_to_logical_map = \
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build_initial_global_physical_to_logical_map(
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num_experts, num_redundant_experts)
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return [
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# (param_name, weight_name, expert_id, shard_id)
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("experts.w13_" if weight_name
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in [ckpt_gate_proj_name, ckpt_up_proj_name] else "experts.w2_",
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f"experts.{physical_to_logical_map[expert_id]}.{weight_name}.",
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expert_id, shard_id) for expert_id in range(num_physical_experts)
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for shard_id, weight_name in [
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("w1", ckpt_gate_proj_name),
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("w2", ckpt_down_proj_name),
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("w3", ckpt_up_proj_name),
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]
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]
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def extra_repr(self) -> str:
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@@ -1227,6 +1227,214 @@ class MRotaryEmbedding(RotaryEmbedding):
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return llm_positions, mrope_position_delta
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@classmethod
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def _omni3_get_input_positions_tensor(
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cls,
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config,
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input_ids: Optional[torch.LongTensor] = None,
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image_grid_thw: Optional[torch.LongTensor] = None,
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video_grid_thw: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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use_audio_in_video: bool = False,
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audio_seqlens: Optional[torch.LongTensor] = None,
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second_per_grids: Optional[torch.Tensor] = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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def _get_feat_extract_output_lengths(input_lengths: torch.LongTensor):
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input_lengths_leave = input_lengths % 100
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feat_lengths = (input_lengths_leave - 1) // 2 + 1
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output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
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return output_lengths
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spatial_merge_size = config.vision_config.spatial_merge_size
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image_token_id = config.image_token_id
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video_token_id = config.video_token_id
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audio_token_id = config.audio_token_id
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vision_start_token_id = config.vision_start_token_id
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audio_start_token_id = config.audio_start_token_id
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position_id_per_seconds = config.position_id_per_seconds
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mrope_position_deltas = []
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if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None):
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total_input_ids = input_ids
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if attention_mask is None:
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attention_mask = torch.ones_like(total_input_ids)
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position_ids = torch.zeros(
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3,
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input_ids.shape[0],
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input_ids.shape[1],
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dtype=input_ids.dtype,
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device=input_ids.device,
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)
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image_idx, video_idx, audio_idx = 0, 0, 0
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attention_mask = attention_mask.to(total_input_ids.device)
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for i, input_ids in enumerate(total_input_ids):
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input_ids = input_ids[attention_mask[i] == 1]
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image_nums, video_nums, audio_nums = 0, 0, 0
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vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
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vision_tokens = input_ids[vision_start_indices + 1]
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audio_nums = torch.sum(input_ids == audio_start_token_id)
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image_nums = (vision_tokens == image_token_id).sum()
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video_nums = (
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(vision_tokens == audio_start_token_id).sum()
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if use_audio_in_video
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else (vision_tokens == video_token_id).sum()
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)
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input_tokens = input_ids.tolist()
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llm_pos_ids_list: list = []
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st = 0
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remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums
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multimodal_nums = (
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image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums
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)
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for _ in range(multimodal_nums):
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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if (image_token_id in input_tokens or video_token_id in input_tokens) and (
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remain_videos > 0 or remain_images > 0
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):
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ed_vision_start = input_tokens.index(vision_start_token_id, st)
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else:
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ed_vision_start = len(input_tokens) + 1
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if audio_token_id in input_tokens and remain_audios > 0:
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ed_audio_start = input_tokens.index(audio_start_token_id, st)
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else:
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ed_audio_start = len(input_tokens) + 1
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min_ed = min(ed_vision_start, ed_audio_start)
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if min_ed == ed_audio_start:
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text_len = min_ed - st
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if text_len != 0:
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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bos_len = 1
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llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
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llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
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llm_pos_ids_list.append(llm_pos_ids)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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eos_len = 1
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llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
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st += text_len + bos_len + audio_len + eos_len
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audio_idx += 1
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remain_audios -= 1
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elif min_ed == ed_vision_start and input_ids[ed_vision_start + 1] == image_token_id:
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text_len = min_ed - st
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if text_len != 0:
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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bos_len = 1
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llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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grid_t = image_grid_thw[image_idx][0]
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grid_hs = image_grid_thw[:, 1]
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grid_ws = image_grid_thw[:, 2]
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t_index = ((torch.arange(grid_t)) * 1 * position_id_per_seconds)
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llm_pos_ids = cls._get_llm_pos_ids_for_vision(
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st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
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)
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image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2)
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llm_pos_ids_list.append(llm_pos_ids)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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eos_len = 1
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llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
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st += text_len + bos_len + image_len + eos_len
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image_idx += 1
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remain_images -= 1
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elif min_ed == ed_vision_start and input_ids[ed_vision_start + 1] == video_token_id and not use_audio_in_video:
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text_len = min_ed - st
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if text_len != 0:
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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bos_len = 1
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llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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grid_t = video_grid_thw[video_idx][0]
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grid_hs = video_grid_thw[:, 1]
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grid_ws = video_grid_thw[:, 2]
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t_index = (
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(torch.arange(grid_t)) * second_per_grids[video_idx].cpu() * position_id_per_seconds
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)
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llm_pos_ids = cls._get_llm_pos_ids_for_vision(
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st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
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)
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video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
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llm_pos_ids_list.append(llm_pos_ids)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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eos_len = 1
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llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
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st += text_len + bos_len + video_len + eos_len
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video_idx += 1
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remain_videos -= 1
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elif min_ed == ed_vision_start and ed_vision_start + 1 == ed_audio_start and use_audio_in_video:
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text_len = min_ed - st
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if text_len != 0:
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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bos_len = 1
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llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
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llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx)
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st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
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audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
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audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
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grid_t = video_grid_thw[video_idx][0]
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grid_hs = video_grid_thw[:, 1]
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grid_ws = video_grid_thw[:, 2]
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t_index = (
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(torch.arange(grid_t)) * second_per_grids[video_idx].cpu() * position_id_per_seconds
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)
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video_llm_pos_ids = cls._get_llm_pos_ids_for_vision(
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st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
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)
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video_data_index, audio_data_index = 0, 0
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while (
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video_data_index < video_llm_pos_ids.shape[-1]
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and audio_data_index < audio_llm_pos_ids.shape[-1]
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):
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if video_llm_pos_ids[0][video_data_index] <= audio_llm_pos_ids[0][audio_data_index]:
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llm_pos_ids_list.append(video_llm_pos_ids[:, video_data_index : video_data_index + 1])
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video_data_index += 1
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else:
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llm_pos_ids_list.append(audio_llm_pos_ids[:, audio_data_index : audio_data_index + 1])
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audio_data_index += 1
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if video_data_index < video_llm_pos_ids.shape[-1]:
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llm_pos_ids_list.append(
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video_llm_pos_ids[:, video_data_index : video_llm_pos_ids.shape[-1]]
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)
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if audio_data_index < audio_llm_pos_ids.shape[-1]:
|
||||
llm_pos_ids_list.append(
|
||||
audio_llm_pos_ids[:, audio_data_index : audio_llm_pos_ids.shape[-1]]
|
||||
)
|
||||
video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
|
||||
st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
|
||||
eos_len = 1
|
||||
llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
|
||||
llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx)
|
||||
st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2
|
||||
audio_idx += 1
|
||||
video_idx += 1
|
||||
remain_videos -= 1
|
||||
remain_audios -= 1
|
||||
if st < len(input_tokens):
|
||||
st_idx = llm_pos_ids_list[-1].long().max() + 1 if len(llm_pos_ids_list) > 0 else 0
|
||||
text_len = len(input_tokens) - st
|
||||
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
|
||||
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
|
||||
|
||||
position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
|
||||
mrope_position_deltas.append(llm_positions.long().max() + 1 - len(input_ids))
|
||||
mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
|
||||
return position_ids, mrope_position_deltas.long()
|
||||
else:
|
||||
position_ids = attention_mask.cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
|
||||
max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
|
||||
mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True)
|
||||
return position_ids, mrope_position_deltas.long()
|
||||
|
||||
|
||||
@classmethod
|
||||
def _omni_get_input_positions_tensor(
|
||||
cls,
|
||||
@@ -1259,7 +1467,29 @@ class MRotaryEmbedding(RotaryEmbedding):
|
||||
# TODO(fyabc): refactor and share more code with
|
||||
# _vl_get_input_positions_tensor.
|
||||
|
||||
model_type = hf_config.model_type
|
||||
thinker_config = hf_config.thinker_config
|
||||
|
||||
if isinstance(image_grid_thw, list):
|
||||
image_grid_thw = torch.tensor(image_grid_thw)
|
||||
if isinstance(video_grid_thw, list):
|
||||
video_grid_thw = torch.tensor(video_grid_thw)
|
||||
|
||||
if "qwen3_omni" in model_type:
|
||||
llm_positions, mrope_position_delta = cls._omni3_get_input_positions_tensor(
|
||||
thinker_config,
|
||||
torch.tensor([input_tokens]),
|
||||
image_grid_thw,
|
||||
video_grid_thw,
|
||||
None,
|
||||
use_audio_in_video,
|
||||
audio_feature_lengths,
|
||||
torch.ones(len(video_grid_thw))
|
||||
)
|
||||
llm_positions = llm_positions.squeeze(1)
|
||||
mrope_position_delta = mrope_position_delta.squeeze()
|
||||
return llm_positions, mrope_position_delta
|
||||
|
||||
audio_token_id = thinker_config.audio_token_index
|
||||
image_token_id = thinker_config.image_token_index
|
||||
video_token_id = thinker_config.video_token_index
|
||||
@@ -1272,11 +1502,6 @@ class MRotaryEmbedding(RotaryEmbedding):
|
||||
tokens_per_second = getattr(thinker_config.vision_config,
|
||||
"tokens_per_second", 25)
|
||||
|
||||
if isinstance(image_grid_thw, list):
|
||||
image_grid_thw = torch.tensor(image_grid_thw)
|
||||
if isinstance(video_grid_thw, list):
|
||||
video_grid_thw = torch.tensor(video_grid_thw)
|
||||
|
||||
src_item = input_tokens
|
||||
audio_seqlens = audio_feature_lengths
|
||||
if not second_per_grid_ts:
|
||||
|
||||
1248
vllm/model_executor/models/qwen3_omni_moe_thinker.py
Normal file
1248
vllm/model_executor/models/qwen3_omni_moe_thinker.py
Normal file
File diff suppressed because it is too large
Load Diff
@@ -214,6 +214,8 @@ _MULTIMODAL_MODELS = {
|
||||
"Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"), # noqa: E501
|
||||
"Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"), # noqa: E501
|
||||
"Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"), # noqa: E501
|
||||
"Qwen3OmniMoeForConditionalGeneration": ("qwen3_omni_moe_thinker", "Qwen3OmniMoeThinkerForConditionalGeneration"),
|
||||
"Qwen3OmniMoeModel": ("qwen3_omni_moe_thinker", "Qwen3OmniMoeThinkerForConditionalGeneration"),
|
||||
"UltravoxModel": ("ultravox", "UltravoxModel"),
|
||||
"Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
|
||||
"TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"), # noqa: E501
|
||||
|
||||
Reference in New Issue
Block a user