90a75a90a9
### What this PR does / why we need it?
This PR ports #2312 #2506 #2531 to main branch.
Original implementation of torchair caching forces users to make
everything prepared, fix all the configuration and enable
`use_cached_npu_graph`, and it might cause some problems confusing to
understand and tackle for users. It is better to compile the graph twice
instead of reusing the old kvcaches and cached torchair graph. And the
extra duration time is acceptable. Additionally, this pr fixes a
recompilation problem of torchair graph mode caused by
`running_in_graph` variable in `AscendMLATorchairImpl`.
### Does this PR introduce _any_ user-facing change?
If users want to enabling torchair.cache_compile with high compilation
speed, it is recommended to enable both `use_cached_kv_cache_bytes` and
`use_cached_graph` in `torchair_graph_config`. Without
`use_cached_kv_cache_bytes`, we'll compile torchair computation graph
twice to avoid runtime error caused by configuration mismtaches (the
second compilation will be much faster). Additionally, we've made a
change to how the TORCHAIR_CACHE_HOME enviroment variable is utilized to
enhance safety and prevent accidental file deletion by adding a suffix
directory.
### How was this patch tested?
CI and e2e vllm serving pass.
- vLLM version: v0.10.1.1
- vLLM main:
70549c1245
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
346 lines
14 KiB
Python
346 lines
14 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_vllm
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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.torchair.utils import (check_torchair_cache_exist,
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delete_torchair_cache_file)
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from vllm_ascend.utils import (ASCEND_QUANTIZATION_METHOD, is_310p,
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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_QUANTIZATION_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_QUANTIZATION_METHOD not in quant_action.choices:
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quant_action.choices.append(ASCEND_QUANTIZATION_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.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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from vllm.config.compilation import CUDAGraphMode
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if enforce_eager:
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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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compilation_config.cudagraph_num_of_warmups = 1
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# TODO: make vllm support oot platform to set `compilation_config.cudagraph_mode`
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# if cudagraph_mode is not explicitly set by users, set default value
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if compilation_config.level == CompilationLevel.PIECEWISE:
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compilation_config.cudagraph_mode = \
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CUDAGraphMode.PIECEWISE
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elif compilation_config.level not in [
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CompilationLevel.NO_COMPILATION, CompilationLevel.PIECEWISE
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]:
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logger.warning(
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"NPU does not support %s compilation level. Setting CUDAGraphMode to NONE",
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compilation_config.level)
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compilation_config.cudagraph_mode = CUDAGraphMode.NONE
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else:
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logger.warning(
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"compilation_config.level = CompilationLevel.NO_COMPILATION is set, Setting CUDAGraphMode to NONE"
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)
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compilation_config.cudagraph_mode = CUDAGraphMode.NONE
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# set CUDAGraphMode to None when torchair is enabled, no mather what compilation_config.level is.
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if ascend_config.torchair_graph_config.enabled:
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logger.info(
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"Torchair compilation enabled on NPU. Setting CUDAGraphMode to NONE"
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)
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compilation_config.cudagraph_mode = CUDAGraphMode.NONE
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# Note: We delete the torchair cache folder here to prevent runtime issues caused by dimension
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# mismatches or configuration inconsistencies when users reuse cached computation graphs. Though
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# this will increase graph compilation duration, it significantly enhances robustness and decreases
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# graph launching time during inference.
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if check_torchair_cache_exist(
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) and not ascend_config.torchair_graph_config.use_cached_kv_cache_bytes:
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logger.warning(
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"Torchair cache folder is deleted here to prevent runtime issues caused by dimension "
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"mismatches or configuration inconsistencies when users reuse cached computation graphs. "
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"In order to decrease torchair graph compilation time, users can enable both use_cached_graph "
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"and use_cached_kv_cache_bytes in torchair_graph_config.")
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delete_torchair_cache_file()
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if 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 CUDAGraphMode to NONE")
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compilation_config.cudagraph_mode = CUDAGraphMode.NONE
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# set cudaprah sizes before extending `compilation_config.splitting_ops`
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vllm_config._set_cudagraph_sizes()
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if compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
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compilation_config.level = CompilationLevel.NO_COMPILATION
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elif compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE:
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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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assert compilation_config.level == CompilationLevel.PIECEWISE, \
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"When enabling piecewise aclgraph, please make sure compilation_config.level == CompilationLevel.PIECEWISE and compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE"
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compilation_config.set_splitting_ops_for_v1()
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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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else:
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logger.info(
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"%s cudagraph_mode is not support on NPU. falling back to NONE",
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compilation_config.cudagraph_mode)
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compilation_config.cudagraph_mode = CUDAGraphMode.NONE
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compilation_config.level = CompilationLevel.NO_COMPILATION
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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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@classmethod
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def get_attn_backend_cls(cls,
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selected_backend,
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head_size,
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dtype,
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kv_cache_dtype,
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block_size,
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use_v1,
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use_mla,
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has_sink=False):
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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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# choose attention backend based on use_mla and use_torchair
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backend_map = {
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(True, True):
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"vllm_ascend.torchair.torchair_mla.AscendMLATorchairBackend",
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(True, False):
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"vllm_ascend.attention.mla_v1.AscendMLABackend",
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(False, True):
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"vllm_ascend.torchair.torchair_attention.AscendAttentionTorchairBackend",
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(False, False):
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"vllm_ascend.attention.attention_v1.AscendAttentionBackend"
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}
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return backend_map[(use_mla, use_torchair)]
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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_static_graph_wrapper_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.acl_graph.ACLGraphWrapper" # 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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