178ca1607e
### What this PR does / why we need it?
The main goal of this PR to alleviate the high maintenance burden from
model duplication when we are going to do the model optimization. Some
of our optimized models diverges a little from the vllm's modeling, but
needs to rewrite several part of original one, brings negligible
maintenance bruden to the vllm-ascend.In order to solve that, we propose
to leverage `torch.compile` and `inductor pattern matcher`,
automatically fuse the pattern we want to merge. For more details can
refer to the RFC https://github.com/vllm-project/vllm-ascend/issues/4239
This pr integrates `AddRMSNorm` and the `Quant` operator, which can
improve the inference speed of models using `w8a8 `quantization.
### Does this PR introduce _any_ user-facing change?
Yes, add new additional_config
### How was this patch tested?
```python
def main():
prompts = [
"The president of the United States is Mr.",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95)
# Create an LLM.
llm = LLM(
model="/root/.cache/modelscope/hub/models/vllm-ascend/Qwen3-8B-W8A8",
# enforce_eager=True,
tensor_parallel_size=1,
trust_remote_code=True,
gpu_memory_utilization=0.7,
quantization="ascend",
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
```text
Prompt: 'The president of the United States is Mr.', Generated text: ' Trump. The president of the United States is Mr. Biden. Which of the following statements is correct? \n\nA. Mr. Trump is Mr. Biden. \nB. Mr. Trump is not Mr. Biden. \nC. The president of the United States is not Mr. Trump. \nD. The president of the United States is not Mr. Biden.\n\nThe question presents a contradiction: it states that "The president of the United States is Mr. Trump" and "The president of'
```
- vLLM version: 86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24
- vLLM main:
86e178f7c4
---------
Signed-off-by: Icey <1790571317@qq.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
474 lines
22 KiB
Python
474 lines
22 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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import os
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from typing import TYPE_CHECKING, Optional, Tuple
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import torch
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from vllm.logger import logger
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from vllm.platforms import Platform, PlatformEnum
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# todo: please remove it when solve cuda hard code in vllm
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os.environ["VLLM_DISABLE_SHARED_EXPERTS_STREAM"] = "1"
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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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# isort: off
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from vllm_ascend.utils import (
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ASCEND_QUANTIZATION_METHOD, COMPRESSED_TENSORS_METHOD, AscendDeviceType,
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enable_sp, get_ascend_device_type, is_vl_model,
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prefill_context_parallel_enable, update_aclgraph_sizes,
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update_cudagraph_capture_sizes, update_default_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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CUSTOM_OP_REGISTERED = False
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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] = [
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ASCEND_QUANTIZATION_METHOD, COMPRESSED_TENSORS_METHOD
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]
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def is_sleep_mode_available(self) -> bool:
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return True
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@property
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def pass_key(self) -> str:
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"""
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Inductor config key for the PassManager custom pass, for example 'post_grad_custom_post_pass'.
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It is a parameter of inductor_config used to register custom passes.
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Currently, we only use Inductor's 'pattern matcher' functionality, so we define our own pass_key.
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"""
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return "graph_fusion_manager"
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@classmethod
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def get_pass_manager_cls(cls) -> str:
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"""
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Get the pass manager class for this platform.
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It will be registered as a custom pass under the current_platform.pass_key.
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"""
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return "vllm_ascend.compilation.graph_fusion_pass_manager.GraphFusionPassManager"
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@classmethod
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def get_compile_backend(self) -> str:
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"""
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Get the custom compile backend. Previously, we used EagerAdaptor by default.
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To use graph fusion operations, we defined our own backend compiler.
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"""
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from vllm_ascend.compilation.compiler_interface import AscendCompiler
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return AscendCompiler.__module__ + "." + AscendCompiler.__name__
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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.compressed_tensors.compressed_tensors import \
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AscendCompressedTensorsConfig # noqa: F401
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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 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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# 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 CompilationMode # 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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ascend_scheduler_config = ascend_config.ascend_scheduler_config
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ascend_compilation_config = ascend_config.ascend_compilation_config
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if ascend_compilation_config:
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vllm_config.additional_config.setdefault(
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"ascend_compilation_config", {}).update(
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vars(ascend_compilation_config
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) if not isinstance(ascend_compilation_config, dict)
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else ascend_compilation_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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elif model_config and hasattr(model_config.hf_config, "index_topk"):
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vllm_config.cache_config.cache_dtype = str(
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model_config.dtype).replace("torch.", "")
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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.mode = CompilationMode.NONE
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if compilation_config.splitting_ops is None:
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compilation_config.splitting_ops = []
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compilation_config.cudagraph_num_of_warmups = 1
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compilation_config.pass_config.fuse_norm_quant = False
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compilation_config.pass_config.fuse_act_quant = False
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if compilation_config.mode not in [
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CompilationMode.NONE, CompilationMode.VLLM_COMPILE
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]:
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logger.warning(
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"NPU does not support %s compilation mode. Setting CUDAGraphMode to NONE",
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compilation_config.mode)
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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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# set cudaprah sizes before extending `compilation_config.splitting_ops`
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vllm_config._set_cudagraph_sizes()
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# There are cases where default cudagraph_capture_sizes are not friendly
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# to ascend ops && hardwares. We update these sizes here to improve
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# default performance.
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update_default_aclgraph_sizes(vllm_config)
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# TODO delete graph size update here when compilation_config.pass_config.enable_sp
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# is supported by vllm-ascend.
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if vllm_config.parallel_config.tensor_parallel_size > 1 and not vllm_config.model_config.enforce_eager and \
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enable_sp(vllm_config):
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original_sizes = compilation_config.cudagraph_capture_sizes
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sp_aclgraph_sizes = \
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vllm_config.update_sizes_for_sequence_parallelism(original_sizes)
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assert sp_aclgraph_sizes, (
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f"cudagraph_capture_sizes {original_sizes} does not contain"
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f"values that are multiples of tp_size "
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f"{vllm_config.parallel_config.tensor_parallel_size}")
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if len(sp_aclgraph_sizes) != len(original_sizes):
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compilation_config.cudagraph_capture_sizes = sp_aclgraph_sizes
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update_cudagraph_capture_sizes(vllm_config, sp_aclgraph_sizes)
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# TODO: Full graph is fully supported later, and the default value will be set to full graph.
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if compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
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compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
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from vllm_ascend.compilation.compiler_interface import AscendCompiler
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compilation_config.oot_compiler = AscendCompiler.__module__ + "." + AscendCompiler.__name__
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if compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
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compilation_config.mode = CompilationMode.NONE
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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.mode == CompilationMode.VLLM_COMPILE, \
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"When enabling VLLM_COMPILE aclgraph, please make sure compilation_config.mode == CompilationMode.VLLM_COMPILE and compilation_config.cudagraph_mode == CUDAGraphMode.VLLM_COMPILE"
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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(["vllm::mla_forward"])
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update_aclgraph_sizes(vllm_config)
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elif compilation_config.cudagraph_mode == CUDAGraphMode.FULL_DECODE_ONLY or\
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compilation_config.cudagraph_mode == CUDAGraphMode.FULL:
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logger.info(
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"FULL_DECODE_ONLY 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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warning_message = """\033[91m
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**********************************************************************************
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* WARNING: You have enabled the *full graph* feature.
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* This is an early experimental stage and may involve various unknown issues.
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* A known problem is that capturing too many batch sizes can lead to OOM
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* (Out of Memory) errors or inference hangs. If you encounter such issues,
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* consider reducing `gpu_memory_utilization` or manually specifying a smaller
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* batch size for graph capture.
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* For more details, please refer to:
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* https://docs.vllm.ai/en/stable/configuration/conserving_memory.html#reduce-cuda-graphs
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**********************************************************************************\033[0m
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"""
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logger.warning(warning_message)
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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.mode = CompilationMode.NONE
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# TODO: Remove this check when ACL Graph supports ASCEND_LAUNCH_BLOCKING=1
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# Then, we will have to discuss the error handling strategy and user experience
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if compilation_config.cudagraph_mode != CUDAGraphMode.NONE and \
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os.environ.get("ASCEND_LAUNCH_BLOCKING", "0") == "1":
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raise ValueError(
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"ACL graph is incompatible with ASCEND_LAUNCH_BLOCKING=1. "
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"Please unset ASCEND_LAUNCH_BLOCKING or set it to 0. If you "
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"need ASCEND_LAUNCH_BLOCKING for debugging, consider other methods — "
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"for example, check the plog files (default: $HOME/ascend/log/debug) "
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"for more information about runtime errors.")
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if parallel_config and parallel_config.worker_cls == "auto":
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# TODO: this is a tricky way to disable `use_sequence_parallel_moe` in vllm.
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parallel_config.all2all_backend = "flashinfer_all2allv"
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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 or \
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not ascend_scheduler_config.enabled or \
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getattr(ascend_scheduler_config, "enable_chunked_prefill", False):
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logger.warning(
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"If chunked prefill or prefix caching is enabled, block size must be set to 128."
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)
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origin_block_size = cache_config.block_size
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cache_config.block_size = 128
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# TODO(MengqingCao): Remove the model_type check, after resolving the hidden error in get_kv_cache_groups.
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if model_config and model_config.hf_config.model_type == "qwen3_next":
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logger.warning(
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"When running qwen3-next model, block_size needs to be restored to its original value."
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)
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cache_config.block_size = origin_block_size
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# Activate custom ops for v1, except on 310P
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if get_ascend_device_type() != AscendDeviceType._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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elif ascend_config.recompute_scheduler_enable:
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from vllm_ascend.core.recompute_schedule_config import \
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RecomputeSchedulerConfig
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recompute_scheduler_config = RecomputeSchedulerConfig.initialize_from_config(
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vllm_config.scheduler_config)
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vllm_config.scheduler_config = recompute_scheduler_config
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# Extend original scheduler_config to use SchedulerDynamicBatch.
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if ascend_config.SLO_limits_for_dynamic_batch != -1:
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vllm_config.scheduler_config.scheduler_cls = (
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"vllm_ascend.core.scheduler_dynamic_batch.SchedulerDynamicBatch"
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)
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vllm_config.scheduler_config.enable_chunked_prefill = True
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vllm_config.scheduler_config.SLO_limits_for_dynamic_batch = ascend_config.SLO_limits_for_dynamic_batch
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if vllm_config.kv_transfer_config is not None and \
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prefill_context_parallel_enable() and \
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cache_config.block_size != parallel_config.cp_kv_cache_interleave_size and \
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parallel_config.decode_context_parallel_size * parallel_config.prefill_context_parallel_size > 1:
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raise AssertionError(
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f"cp_kv_cache_interleave_size({parallel_config.cp_kv_cache_interleave_size}) "
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f"and block_size({cache_config.block_size}) "
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"needs to be equal if use cp or dcp > 1 in P/D disaggregate scenario."
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)
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if is_vl_model(vllm_config):
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if bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM", '0'))) or \
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bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM1", '0'))):
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raise ValueError(
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"Currently, VL models doesn't support "
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"FLASHCOMM in vllm-ascend. We will fix this in the future. "
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"Please set VLLM_ASCEND_ENABLE_FLASHCOMM1=0.")
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@classmethod
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def import_kernels(cls) -> None:
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# Directly importing vllm_ascend_C prevents ASCEND_RT_VISIBLE_DEVICES
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# from being applied during runtime initialization, which causes bugs
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# in the RL module. Therefore, we currently use lazy initialization
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# to avoid this issue. See https://github.com/vllm-project/vllm-ascend/pull/884.
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# TODO: when the above issue is fixed, we can uncomment the following lines.
|
|
# from vllm_ascend.utils import enable_custom_op
|
|
# enable_custom_op()
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|
# set custom ops path
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|
global CUSTOM_OP_REGISTERED
|
|
if CUSTOM_OP_REGISTERED:
|
|
return
|
|
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
|
|
CUSTOM_OPP_PATH = os.path.join(CUR_DIR, "_cann_ops_custom", "vendors",
|
|
"vllm-ascend")
|
|
if os.path.exists(CUSTOM_OPP_PATH):
|
|
current_cust_opp_path = os.environ.get("ASCEND_CUSTOM_OPP_PATH",
|
|
"")
|
|
if current_cust_opp_path:
|
|
os.environ[
|
|
"ASCEND_CUSTOM_OPP_PATH"] = f"{CUSTOM_OPP_PATH}:{current_cust_opp_path}"
|
|
else:
|
|
os.environ["ASCEND_CUSTOM_OPP_PATH"] = CUSTOM_OPP_PATH
|
|
CUSTOM_OP_REGISTERED = True
|
|
|
|
@classmethod
|
|
def get_attn_backend_cls(
|
|
cls,
|
|
selected_backend,
|
|
head_size,
|
|
dtype,
|
|
kv_cache_dtype,
|
|
block_size,
|
|
use_mla,
|
|
has_sink=False,
|
|
use_sparse=False,
|
|
attn_type: str | None = None,
|
|
):
|
|
ascend_config = get_ascend_config()
|
|
|
|
if use_mla and ascend_config.enable_shared_expert_dp:
|
|
if use_mla and use_sparse:
|
|
return "vllm_ascend.torchair.torchair_sfa.AscendSFATorchairBackend"
|
|
|
|
use_torchair = ascend_config.torchair_graph_config.enabled
|
|
# choose attention backend based on use_mla and use_torchair
|
|
backend_map = {
|
|
(True, False, True):
|
|
"vllm_ascend.torchair.torchair_mla.AscendMLATorchairBackend",
|
|
(True, False, False):
|
|
"vllm_ascend.attention.mla_v1.AscendMLABackend",
|
|
(False, False, True):
|
|
"vllm_ascend.torchair.torchair_attention.AscendAttentionTorchairBackend",
|
|
(False, False, False):
|
|
"vllm_ascend.attention.attention_v1.AscendAttentionBackend",
|
|
(True, True, False):
|
|
"vllm_ascend.attention.sfa_v1.AscendSFABackend",
|
|
(True, True, True):
|
|
"vllm_ascend.torchair.torchair_sfa.AscendSFATorchairBackend",
|
|
}
|
|
return backend_map[(use_mla, use_sparse, use_torchair)]
|
|
|
|
@classmethod
|
|
def get_punica_wrapper(cls) -> str:
|
|
return "vllm_ascend.lora.punica_npu.PunicaWrapperNPU"
|
|
|
|
@classmethod
|
|
def get_current_memory_usage(cls,
|
|
device: Optional[torch.types.Device] = None
|
|
) -> float:
|
|
torch.npu.reset_peak_memory_stats(device)
|
|
return torch.npu.max_memory_allocated(device)
|
|
|
|
@classmethod
|
|
def get_device_communicator_cls(cls) -> str:
|
|
return "vllm_ascend.distributed.communicator.NPUCommunicator"
|
|
|
|
@classmethod
|
|
def is_pin_memory_available(cls):
|
|
return True
|
|
|
|
@classmethod
|
|
def opaque_attention_op(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def get_static_graph_wrapper_cls(cls) -> str:
|
|
"""
|
|
Get piecewise backend class for piecewise graph.
|
|
"""
|
|
return "vllm_ascend.compilation.acl_graph.ACLGraphWrapper" # noqa
|
|
|
|
@classmethod
|
|
def support_hybrid_kv_cache(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def support_static_graph_mode(cls) -> bool:
|
|
return True
|