mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 21:53:54 +08:00
c611291661
### What this PR does / why we need it?
Qwen3 MoE supports SP. In scenarios like AlltoAll, AlltoAllv, and MC2,
replacing AllReduce with Reduce-Scatter and AllGather achieves
computational benefits in norm operations while saving one AllGather
communication. This feature is enabled during the P-phase and delivers
notable gains in long-sequence scenarios (e.g., 16k–25k), with
performance improvements reaching 5%–10%.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
```
compilation_config={
"pass_config":{
"enable_sequence_parallelism": True
}
},
enable_expert_parallel=True,
```
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: libaokui <libaokui@huawei.com>
Co-authored-by: libaokui <libaokui@huawei.com>
289 lines
11 KiB
Python
289 lines
11 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# This file is a part of the vllm-ascend project.
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#
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import gc
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from datetime import timedelta
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from typing import TYPE_CHECKING, Optional, Tuple
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import torch
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import vllm.envs as envs
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from torch.distributed import ProcessGroup
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from torch.distributed.distributed_c10d import PrefixStore
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from vllm.logger import logger
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from vllm.platforms import Platform, PlatformEnum
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from vllm_ascend.ascend_config import (check_ascend_config, get_ascend_config,
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init_ascend_config)
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from vllm_ascend.utils import (ASCEND_QUATIZATION_METHOD, is_310p,
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register_ascend_customop, update_aclgraph_sizes)
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if TYPE_CHECKING:
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from vllm.config import ModelConfig, VllmConfig
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from vllm.utils import FlexibleArgumentParser
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else:
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ModelConfig = None
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VllmConfig = None
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FlexibleArgumentParser = None
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class NPUPlatform(Platform):
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_enum = PlatformEnum.OOT
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device_name: str = "npu"
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device_type: str = "npu"
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simple_compile_backend: str = "eager" # Disable torch.compile()
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ray_device_key: str = "NPU"
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device_control_env_var: str = "ASCEND_RT_VISIBLE_DEVICES"
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dispatch_key: str = "PrivateUse1"
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supported_quantization: list[str] = [ASCEND_QUATIZATION_METHOD]
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def is_sleep_mode_available(self) -> bool:
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return True
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@classmethod
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def pre_register_and_update(cls,
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parser: Optional[FlexibleArgumentParser] = None
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) -> None:
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# Adapt the global patch here.
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from vllm_ascend.utils import adapt_patch
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adapt_patch(is_global_patch=True)
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# For online serving, "ascend" quantization method is not a choice natively,
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# so we need to add "ascend" quantization method to quantization methods list
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# and the user can enable quantization using "vllm serve --quantization ascend".
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if parser is not None:
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quant_action = parser._option_string_actions.get('--quantization')
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if quant_action and hasattr(quant_action,
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'choices') and quant_action.choices:
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if ASCEND_QUATIZATION_METHOD not in quant_action.choices:
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quant_action.choices.append(ASCEND_QUATIZATION_METHOD)
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from vllm_ascend.quantization.quant_config import \
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AscendQuantConfig # noqa: F401
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@classmethod
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def get_device_capability(cls, device_id: int = 0):
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return None
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@classmethod
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def get_device_name(cls, device_id: int = 0) -> str:
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return torch.npu.get_device_name(device_id)
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@classmethod
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def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
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return True
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@classmethod
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def inference_mode(cls):
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return torch.inference_mode()
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@classmethod
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def set_device(cls, device: torch.device):
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torch.npu.set_device(device)
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@classmethod
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def empty_cache(cls):
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torch.npu.empty_cache()
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@classmethod
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def synchronize(cls):
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torch.npu.synchronize()
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@classmethod
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def mem_get_info(cls) -> Tuple[int, int]:
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return torch.npu.mem_get_info()
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@classmethod
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def clear_npu_memory(cls):
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gc.collect()
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torch.npu.empty_cache()
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torch.npu.reset_peak_memory_stats()
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@classmethod
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def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
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if not envs.VLLM_USE_V1:
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raise ValueError("vLLM Ascend does not support V0 engine.")
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# initialize ascend config from vllm additional_config
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ascend_config = init_ascend_config(vllm_config)
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from vllm.config import CompilationLevel # noqa: E402
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compilation_config = vllm_config.compilation_config
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model_config = vllm_config.model_config
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parallel_config = vllm_config.parallel_config
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cache_config = vllm_config.cache_config
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kv_cache_dtype = vllm_config.additional_config.get(
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"kv_cache_dtype", None)
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if kv_cache_dtype is not None:
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vllm_config.cache_config.cache_dtype = kv_cache_dtype
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if model_config is None:
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logger.warning("Model config is missing. This may indicate "
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"that we are running a test case")
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enforce_eager = False
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else:
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enforce_eager = getattr(model_config, "enforce_eager", False)
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check_ascend_config(vllm_config, enforce_eager)
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if enforce_eager or compilation_config.level == CompilationLevel.NO_COMPILATION:
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logger.info("Compilation disabled, using eager mode by default")
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compilation_config.level = CompilationLevel.NO_COMPILATION
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elif compilation_config.level != CompilationLevel.PIECEWISE:
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logger.warning(
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"NPU does not support %s compilation level. Setting level to NO_COMPILATION",
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compilation_config.level)
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compilation_config.level = CompilationLevel.NO_COMPILATION
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elif ascend_config.torchair_graph_config.enabled:
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logger.info(
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"Torchair compilation enabled on NPU. Setting level to NO_COMPILATION"
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)
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compilation_config.level = CompilationLevel.NO_COMPILATION
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elif parallel_config.distributed_executor_backend == "ray":
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logger.warning(
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"Ray distributed executor backend is not compatible with ACL Graph mode "
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"right now. Setting level to NO_COMPILATION")
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compilation_config.level = CompilationLevel.NO_COMPILATION
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else:
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logger.info(
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"PIECEWISE compilation enabled on NPU. use_inductor not supported - "
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"using only ACL Graph mode")
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compilation_config.use_inductor = False
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compilation_config.splitting_ops.extend(
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["vllm.unified_ascend_attention_with_output"])
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update_aclgraph_sizes(vllm_config)
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if parallel_config and parallel_config.worker_cls == "auto":
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if ascend_config.torchair_graph_config.enabled:
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parallel_config.worker_cls = "vllm_ascend.torchair.torchair_worker.NPUTorchairWorker"
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else:
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parallel_config.worker_cls = "vllm_ascend.worker.worker_v1.NPUWorker"
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if cache_config:
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if cache_config.block_size is None:
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cache_config.block_size = 128
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if cache_config.enable_prefix_caching and cache_config.block_size != 128:
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logger.warning(
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"If prefix caching is enabled, block size must be set to 128."
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)
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cache_config.block_size = 128
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# Activate custom ops for v1, except on 310P
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if not is_310p():
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compilation_config.custom_ops = ["all"]
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# If ascend_scheduler_config is enabled,
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# extents original scheduler_config to use AscendScheduler.
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if ascend_config.ascend_scheduler_config.enabled:
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from vllm_ascend.core.schedule_config import AscendSchedulerConfig
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ascend_scheduler_config = AscendSchedulerConfig.initialize_from_config(
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vllm_config.scheduler_config,
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ascend_config.ascend_scheduler_config)
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vllm_config.scheduler_config = ascend_scheduler_config
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if compilation_config.pass_config.enable_sequence_parallelism:
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if not parallel_config.enable_expert_parallel or vllm_config.model_config.hf_config.model_type != "qwen3_moe":
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raise NotImplementedError(
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"For better performance in Qwen3 MoE, SP only works exclusively with MC2, AllToAll, and AllToAllV."
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)
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# register Ascend CustomOp
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register_ascend_customop()
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@classmethod
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def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
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kv_cache_dtype, block_size, use_v1, use_mla):
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if not use_v1:
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raise ValueError("vLLM Ascend does not support V0 engine.")
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use_torchair = get_ascend_config().torchair_graph_config.enabled
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if use_mla:
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return "vllm_ascend.attention.mla_v1.AscendMLABackend"
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elif use_torchair:
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return "vllm_ascend.attention.attention_v1_torchair.AscendAttentionTorchairBackend"
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else:
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return "vllm_ascend.attention.attention_v1.AscendAttentionBackend"
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@classmethod
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def get_punica_wrapper(cls) -> str:
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return "vllm_ascend.lora.punica_wrapper.punica_npu.PunicaWrapperNPU"
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@classmethod
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def get_current_memory_usage(cls,
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device: Optional[torch.types.Device] = None
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) -> float:
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torch.npu.reset_peak_memory_stats(device)
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return torch.npu.max_memory_allocated(device)
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@classmethod
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def get_device_communicator_cls(cls) -> str:
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return "vllm_ascend.distributed.communicator.NPUCommunicator"
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@classmethod
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def is_pin_memory_available(cls):
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return True
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@classmethod
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def supports_v1(cls, model_config: ModelConfig) -> bool:
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"""Returns whether the current platform can support v1 for the supplied
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model configuration.
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"""
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return True
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@classmethod
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def get_piecewise_backend_cls(cls) -> str:
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"""
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Get piecewise backend class for piecewise graph.
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"""
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return "vllm_ascend.compilation.piecewise_backend.NPUPiecewiseBackend" # noqa
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@classmethod
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def stateless_init_device_torch_dist_pg(
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cls,
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backend: str,
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prefix_store: PrefixStore,
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group_rank: int,
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group_size: int,
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timeout: timedelta,
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) -> ProcessGroup:
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from torch.distributed import is_hccl_available
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from torch_npu._C._distributed_c10d import ProcessGroupHCCL
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assert is_hccl_available()
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pg: ProcessGroup = ProcessGroup(
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prefix_store,
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group_rank,
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group_size,
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)
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backend_options = ProcessGroupHCCL.Options()
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backend_options._timeout = timeout
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backend_class = ProcessGroupHCCL(prefix_store, group_rank, group_size,
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backend_options)
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device = torch.device("npu")
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# TODO(Yizhou): Like we mentioned above, _set_default_backend is not
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# implemented in the 2.5.1 version of PyTorch. But we need to set it
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# after the latest version is released.
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# pg._set_default_backend(backend_type)
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backend_class._set_sequence_number_for_group()
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backend_type = ProcessGroup.BackendType.CUSTOM
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pg._register_backend(device, backend_type, backend_class)
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return pg
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