c980e68d40
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
248 lines
9.3 KiB
Python
248 lines
9.3 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/aclgraph_utils.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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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from contextlib import contextmanager
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from typing import Any
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import numpy as np
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import torch
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import torch.nn as nn
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import vllm
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from vllm.config import VllmConfig
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from vllm.config.compilation import CUDAGraphMode
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from vllm.forward_context import get_forward_context, set_forward_context
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from vllm.logger import logger
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.worker.gpu.attn_utils import build_slot_mappings_by_layer
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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.input_batch import InputBuffers
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from vllm.v1.worker.gpu.model_states.interface import ModelState
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from vllm.v1.worker.utils import AttentionGroup
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from vllm_ascend.ascend_forward_context import _EXTRA_CTX
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from vllm_ascend.compilation.acl_graph import set_graph_params, update_full_graph_params
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from vllm_ascend.worker.v2.attn_utils import build_attn_metadata
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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__(
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self,
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vllm_config: VllmConfig,
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use_aux_hidden_state_outputs: bool,
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device: torch.device,
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model_runner: Any, # NPUModelRunner type, in case circular import, so we pass it as Any
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):
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# set model runner attribute, so we can access attributes model runner
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# when call `run_fullgraph` method in CudaGraphManager,
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# then we don't need to # copy `execute_model` method in `NPUModelRunner` class.
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self.model_runner = model_runner
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super().__init__(
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vllm_config,
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use_aux_hidden_state_outputs,
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device,
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)
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# vllm-ascend need to update graph params of attention backend.
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# so we need to set graph params before capture full graph.
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if super().needs_capture():
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set_graph_params(self.cudagraph_sizes)
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def _capture_full_graph(
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self,
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num_tokens: int,
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num_reqs: int,
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model: nn.Module,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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inputs_embeds: torch.Tensor | None,
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num_tokens_across_dp: torch.Tensor,
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attn_metadata: dict[str, Any] | None,
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slot_mappings: dict[str, torch.Tensor] | None,
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has_lora: bool = False,
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) -> None:
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"""Override _capture_full_graph because we need to set capturing=True in forward context."""
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# set capturing=True in before model forward.
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model = ModelWithContext(model)
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return super()._capture_full_graph(
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num_tokens,
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num_reqs,
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model,
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input_ids,
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positions,
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inputs_embeds,
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num_tokens_across_dp,
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attn_metadata,
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slot_mappings,
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has_lora,
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)
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def capture_graph(
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self,
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num_tokens: int,
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capture_cg_mode: CUDAGraphMode,
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model: nn.Module,
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model_state: ModelState,
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input_buffers: InputBuffers,
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block_tables: BlockTables,
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attn_groups: list[list[AttentionGroup]],
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kv_cache_config: KVCacheConfig,
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has_lora: bool = False,
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uniform_decode: bool = False,
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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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capture_cg_mode,
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model,
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model_state,
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input_buffers,
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block_tables,
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attn_groups,
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kv_cache_config,
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has_lora,
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uniform_decode,
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)
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def run_fullgraph(self, num_tokens: int) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
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"""Override run_fullgraph to update full graph params in run_fullgraph."""
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logger.info_once(f"run_fullgraph with num_tokens={num_tokens}")
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ret = super().run_fullgraph(num_tokens)
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assert self.model_runner.cudagraph_and_dp_padding is not None
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positions = self.model_runner.input_buffers.positions[:num_tokens]
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_num_tokens_after_padding, num_tokens_across_dp, synced_cudagraph_mode = (
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self.model_runner.cudagraph_and_dp_padding
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)
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cudagraph_runtime_mode = CUDAGraphMode(synced_cudagraph_mode)
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with set_forward_context(
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self.model_runner.input_batch.attn_metadata,
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self.vllm_config,
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num_tokens=num_tokens,
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cudagraph_runtime_mode=cudagraph_runtime_mode,
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num_tokens_across_dp=num_tokens_across_dp,
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batch_descriptor=None, # Full graph model don't need batch_descriptor
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slot_mapping=self.model_runner.input_batch.slot_mappings,
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):
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forward_context = get_forward_context()
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update_full_graph_params(
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# FIXME(Ronald1995): support hybrid attn backend
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list(self.model_runner.attn_backends.values())[0],
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self.model_runner.update_stream,
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forward_context,
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num_tokens,
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self.vllm_config,
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self.model_runner.speculative_config,
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positions.shape[0],
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)
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return ret
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def is_uniform_decode(
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self,
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num_reqs: int,
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num_tokens: int,
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max_query_len: int,
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):
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return (max_query_len == self.uniform_decode_query_len) and (num_tokens == max_query_len * num_reqs)
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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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ori = vllm.v1.worker.gpu.cudagraph_utils.prepare_inputs_to_capture
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try:
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vllm.v1.worker.gpu.cudagraph_utils.prepare_inputs_to_capture = prepare_inputs_to_capture
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yield
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finally:
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vllm.v1.worker.gpu.cudagraph_utils.prepare_inputs_to_capture = ori
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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_groups: list[list[AttentionGroup]],
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max_model_len: int,
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kv_cache_config: KVCacheConfig,
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uniform_decode_query_len: int = 0,
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) -> tuple[dict[str, Any], dict[str, torch.Tensor]]:
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if uniform_decode_query_len > 0:
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num_tokens_per_req = uniform_decode_query_len
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else:
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num_tokens_per_req = num_tokens // num_reqs
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query_start_loc_np = np.arange(num_reqs + 1, dtype=np.int32) * num_tokens_per_req
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query_start_loc_np[-1] = num_tokens
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query_start_loc_cpu = torch.from_numpy(query_start_loc_np)
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input_buffers.query_start_loc[: num_reqs + 1] = query_start_loc_cpu
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input_buffers.query_start_loc[num_reqs + 1 :] = num_tokens
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query_start_loc = input_buffers.query_start_loc[: num_reqs + 1]
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# HACK(woosuk): For faster warmup, we set seq_lens (GPU) to num_tokens
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# rather than max_model_len.
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input_buffers.seq_lens[:num_reqs] = num_tokens
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input_buffers.seq_lens[num_reqs:] = 0
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input_buffers.seq_lens_cpu[:num_reqs] = num_tokens
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input_buffers.seq_lens_cpu[num_reqs:] = 0
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input_buffers.dcp_local_seq_lens[:num_reqs] = num_tokens
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input_buffers.dcp_local_seq_lens[num_reqs:] = 0
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input_block_tables = [x[:num_reqs] for x in block_tables.input_block_tables]
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slot_mappings = block_tables.slot_mappings[:, :num_tokens]
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slot_mappings_by_layer = build_slot_mappings_by_layer(slot_mappings, kv_cache_config)
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attn_metadata = build_attn_metadata(
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attn_groups=attn_groups,
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num_reqs=num_reqs,
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num_tokens=num_tokens,
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query_start_loc_gpu=query_start_loc,
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query_start_loc_cpu=query_start_loc_cpu,
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max_query_len=num_tokens_per_req,
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seq_lens=input_buffers.seq_lens,
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max_seq_len=max_model_len,
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block_tables=input_block_tables,
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slot_mappings=slot_mappings,
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kv_cache_config=kv_cache_config,
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seq_lens_np=input_buffers.seq_lens_np,
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)
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return attn_metadata, slot_mappings_by_layer
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class ModelWithContext(nn.Module):
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"""Define a wrapper model to inject forward context.
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so we can inherit vllm's CudaGraphManager._capture_full_graph.
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"""
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def __init__(self, original_model):
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super().__init__()
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self.original_model = original_model
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def forward(self, *args, **kwargs):
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# In warmup phase, capturing=False by default.
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# when capturing, we need to set capturing=True in forward context.
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_EXTRA_CTX.capturing = True
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return self.original_model(*args, **kwargs)
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