b69b04d3a9
### What this PR does / why we need it?
This PR aim to implement model runner v2 basic framework in vllm-ascend,
the e2e function is not guaranteed by this pr.
### Does this PR introduce _any_ user-facing change?
use envs.VLLM_USE_V2_MODEL_RUNNER to decide if choose model_runenr_v2.
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from contextlib import contextmanager
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from typing import Any
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.v1.attention.backends.utils import AttentionMetadataBuilder
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.worker.gpu.block_table import BlockTables
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from vllm.v1.worker.gpu.cudagraph_utils import CudaGraphManager
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from vllm.v1.worker.gpu.cudagraph_utils import \
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prepare_inputs_to_capture as prepare_inputs_to_capture_gpu
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from vllm.v1.worker.gpu.input_batch import InputBuffers
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from vllm_ascend.worker.v2.utils import torch_cuda_wrapper
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class AclGraphManager(CudaGraphManager):
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"""ACL Graph Manager for Ascend NPUs."""
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def __init__(self, vllm_config: VllmConfig, device: torch.device):
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with torch_cuda_wrapper():
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super().__init__(vllm_config, device)
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def capture_graph(
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self,
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num_tokens: int,
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model: nn.Module,
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input_buffers: InputBuffers,
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block_tables: BlockTables,
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attn_metadata_builders: list[AttentionMetadataBuilder],
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kv_cache_config: KVCacheConfig,
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) -> None:
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with (torch_cuda_wrapper(), prepare_capture_inputs_wrapper()):
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super().capture_graph(
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num_tokens,
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model,
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input_buffers,
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block_tables,
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attn_metadata_builders,
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kv_cache_config,
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)
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@contextmanager
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def prepare_capture_inputs_wrapper():
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"""Context manager to override input preparation for NPU graph capture."""
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# TODO(Ronald1995): make prepare_inputs_to_capture as static method
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# in CudaGraphManager.
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global prepare_inputs_to_capture_gpu
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try:
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ori_func = prepare_inputs_to_capture_gpu
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prepare_inputs_to_capture_gpu = prepare_inputs_to_capture
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yield
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finally:
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prepare_inputs_to_capture_gpu = ori_func
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def prepare_inputs_to_capture(
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num_reqs: int,
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num_tokens: int,
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input_buffers: InputBuffers,
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block_tables: BlockTables,
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attn_metadata_builders: list[AttentionMetadataBuilder],
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max_model_len: int,
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kv_cache_config: KVCacheConfig,
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) -> dict[str, Any]:
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# TODO(Ronald1995): Implement NPU specific input preparation.
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return {}
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