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Fix some ci issue and refactor modelrunner (#2445)

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### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with existing test.

- vLLM version: v0.10.0
- vLLM main:
4d9c61993a

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
This commit is contained in:
Mengqing Cao
2025-08-20 09:01:04 +08:00
committed by GitHub
parent 955411611c
commit 1327f9be1c
28 changed files with 1612 additions and 1020 deletions
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186
vllm_ascend/compilation/acl_graph.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import dataclasses
from contextlib import ExitStack
from typing import Any, Callable, Optional
from unittest.mock import patch
import torch
import vllm.envs as envs
from vllm.compilation.counter import compilation_counter
from vllm.compilation.cuda_graph import CUDAGraphOptions
from vllm.compilation.monitor import validate_cudagraph_capturing_enabled
from vllm.config import CUDAGraphMode, VllmConfig
from vllm.forward_context import BatchDescriptor, get_forward_context
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.utils import weak_ref_tensors
logger = init_logger(__name__)
@dataclasses.dataclass
class ACLGraphEntry:
batch_descriptor: BatchDescriptor
aclgraph: Optional[torch.npu.NPUGraph] = None
output: Optional[Any] = None
# for aclgraph debugging, track the input addresses
# during capture, and check if they are the same during replay
input_addresses: Optional[list[int]] = None
class ACLGraphWrapper:
"""Wraps a runnable to add acl graph capturing and replaying ability. And
provide attribute access to the underlying `runnable` via `__getattr__`.
The workflow of this wrapper in the aclgraph dispatching is as follows:
1. At initialization, a runtime mode is assigned to the wrapper (FULL or
PIECEWISE).
2. At runtime, the wrapper receives a runtime_mode and a
batch_descriptor(key) from the forward context and blindly trust them
for aclgraph dispatching.
3. If runtime_mode is NONE or runtime_mode does not match the mode of the
wrapper, just call the runnable directly.
4. Otherwise, i.e., the runtime_mode matches the mode of the wrapper,
the wrapper will perform aclgraph capture(if key does not exist, create
a new entry and cache it) or replay (if key exists in the cache).
Note: ACLGraphWrapper does not store persistent buffers or copy any
runtime inputs into that buffers for replay. We assume implementing them
is done outside of the wrapper. That is because we do not make any
assumption on the dynamic shape (batch size) of the runtime inputs, as a
trade-off for staying orthogonal to compilation logic. Nevertheless,
tracing and checking the input addresses to be consistent during replay is
guaranteed when VLLM_LOGGING_LEVEL == "DEBUG".
"""
def __init__(self,
runnable: Callable,
vllm_config: VllmConfig,
runtime_mode: CUDAGraphMode,
graph_pool: Any = None,
cudagraph_options: Optional[CUDAGraphOptions] = None):
self.runnable = runnable
self.vllm_config = vllm_config
self.graph_pool = graph_pool
self.runtime_mode = runtime_mode
self.compilation_config = vllm_config.compilation_config
self.first_run_finished = False
self.is_debugging_mode = envs.VLLM_LOGGING_LEVEL == "DEBUG"
# assert runtime_mode is not NONE(no aclgraph), otherwise, we don't
# need to initialize a ACLGraphWrapper.
assert self.runtime_mode != CUDAGraphMode.NONE
if self.graph_pool is None:
self.graph_pool = current_platform.get_global_graph_pool()
if cudagraph_options is None:
cudagraph_options = CUDAGraphOptions()
self.aclgraph_options = cudagraph_options
# the entries for different batch descriptors that we need to capture
# aclgraphs for.
self.concrete_aclgraph_entries: dict[BatchDescriptor, ACLGraphEntry]\
= {}
def __getattr__(self, key: str):
# allow accessing the attributes of the runnable.
if hasattr(self.runnable, key):
return getattr(self.runnable, key)
raise AttributeError(f"Attribute {key} not exists in the runnable of "
f"aclgraph wrapper: {self.runnable}")
def unwrap(self) -> Callable:
# in case we need to access the original runnable.
return self.runnable
def __call__(self, *args, **kwargs):
forward_context = get_forward_context()
batch_descriptor = forward_context.batch_descriptor
aclgraph_runtime_mode = forward_context.cudagraph_runtime_mode
if aclgraph_runtime_mode == CUDAGraphMode.NONE or \
aclgraph_runtime_mode != self.runtime_mode:
# CUDAGraphMode.NONE could mean the profile run, a warmup run, or
# running without aclgraphs.
# We do not trigger capture/replay if the runtime mode is not
# matches. This enables properly dispatching to the correct
# CUDAGraphWrapper when nesting multiple instances with different
# runtime modes.
return self.runnable(*args, **kwargs)
if batch_descriptor not in self.concrete_aclgraph_entries:
# create a new entry for this batch descriptor
self.concrete_aclgraph_entries[batch_descriptor] = \
ACLGraphEntry(batch_descriptor=batch_descriptor)
entry = self.concrete_aclgraph_entries[batch_descriptor]
if entry.aclgraph is None:
if self.aclgraph_options.debug_log_enable:
# Since we capture aclgraph for many different shapes and
# capturing is fast, we don't need to log it for every
# shape. E.g. we only log it for the first subgraph in
# piecewise mode.
logger.debug("Capturing a aclgraph on (%s,%s)",
self.runtime_mode.name, entry.batch_descriptor)
# validate that aclgraph capturing is legal at this point.
validate_cudagraph_capturing_enabled()
input_addresses = [
x.data_ptr() for x in args if isinstance(x, torch.Tensor)
]
entry.input_addresses = input_addresses
aclgraph = torch.npu.NPUGraph()
with ExitStack() as stack:
if self.aclgraph_options.gc_disable:
# during every model forward for piecewise aclgraph
# mode, we will capture many pieces of aclgraphs
# (roughly one per layer). running gc again and again
# across layers will make the aclgraph capture very slow.
# therefore, we only run gc for the first graph,
# and disable gc for the rest of the graphs.
stack.enter_context(patch("gc.collect", lambda: None))
stack.enter_context(
patch("torch.npu.empty_cache", lambda: None))
# mind-exploding: carefully manage the reference and memory.
with torch.npu.graph(aclgraph, pool=self.graph_pool):
# `output` is managed by pytorch's aclgraph pool
output = self.runnable(*args, **kwargs)
if self.aclgraph_options.weak_ref_output:
# by converting it to weak ref,
# the original `output` will immediately be released
# to save memory. It is only safe to do this for
# the last graph in piecewise aclgraph mode, because
# the output of the last graph will not be used by
# any other acl graph.
output = weak_ref_tensors(output)
# here we always use weak ref for the output
# to save memory
entry.output = weak_ref_tensors(output)
entry.aclgraph = aclgraph
compilation_counter.num_cudagraph_captured += 1
# important: we need to return the output, rather than
# the weak ref of the output, so that pytorch can correctly
# manage the memory during acl graph capture
return output
if self.is_debugging_mode:
# check if the input addresses are the same
new_input_addresses = [
x.data_ptr() for x in args if isinstance(x, torch.Tensor)
]
assert new_input_addresses == entry.input_addresses, (
f"Input addresses for aclgraphs are different "
f"during replay. Expected {entry.input_addresses}, "
f"got {new_input_addresses}")
entry.aclgraph.replay()
return entry.output