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8945b001db
Signed-off-by: Boyuan Feng <boyuan@meta.com> Signed-off-by: Boyuan Feng <fby.1994@gmail.com> Signed-off-by: boyuanfeng <boyuan@meta.com> Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
370 lines
15 KiB
Python
370 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import contextlib
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import inspect
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from typing import Callable, Optional, TypeVar, Union, overload
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from unittest.mock import patch
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import torch
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import torch.nn as nn
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from torch._dynamo.symbolic_convert import InliningInstructionTranslator
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
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from vllm.config import CompilationLevel, VllmConfig
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from vllm.logger import init_logger
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from vllm.sequence import IntermediateTensors
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from vllm.utils import resolve_obj_by_qualname, supports_dynamo
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from .monitor import start_monitoring_torch_compile
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logger = init_logger(__name__)
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IGNORE_COMPILE_KEY = "_ignore_compile_vllm"
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_T = TypeVar("_T", bound=type[nn.Module])
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def ignore_torch_compile(cls: _T) -> _T:
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"""
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A decorator to ignore support_torch_compile decorator
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on the class. This is useful when a parent class has
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a support_torch_compile decorator, but we don't want to
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compile the class `cls` that inherits the parent class.
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This only ignores compiling the forward of the class the
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decorator is applied to.
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If the parent has ignore_torch_compile but the child has
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support_torch_compile, the child will still be compiled.
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If the class has one or more submodules
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that have support_torch_compile decorator applied, compile will
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not be ignored for those submodules.
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"""
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setattr(cls, IGNORE_COMPILE_KEY, True)
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return cls
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def _should_ignore_torch_compile(cls) -> bool:
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"""
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Check if the class should be ignored for torch.compile.
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"""
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return getattr(cls, IGNORE_COMPILE_KEY, False)
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@overload
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def support_torch_compile(
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*,
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enable_if: Optional[Callable[[VllmConfig], bool]] = None,
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) -> Callable[[_T], _T]:
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...
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@overload
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def support_torch_compile(
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*,
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dynamic_arg_dims: Optional[dict[str, Union[int, list[int]]]],
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) -> Callable[[_T], _T]:
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...
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@overload
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def support_torch_compile(cls: _T) -> _T:
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...
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def support_torch_compile(
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cls: Optional[_T] = None,
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*,
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dynamic_arg_dims: Optional[dict[str, Union[int, list[int]]]] = None,
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enable_if: Optional[Callable[[VllmConfig], bool]] = None,
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) -> Union[Callable[[_T], _T], _T]:
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"""
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A decorator to add support for compiling the forward method of a class.
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Usage 1: use directly as a decorator without arguments:
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```python
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@support_torch_compile
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class MyModel(nn.Module):
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def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]):
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...
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```
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Usage 2: use as a decorator with arguments:
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```python
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@support_torch_compile(dynamic_arg_dims={"x": 0, "y": 0})
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class MyModel(nn.Module):
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def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]):
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...
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```
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`dynamic_arg_dims` is a dictionary that maps argument names to the dynamic
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dimensions of the argument. The dynamic dimensions can be either a single
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integer or a list of integers.
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if `dynamic_arg_dims` is `None`, it is inferred from the type annotation
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of the `forward` method, based on the following default rules:
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- if the argument is annotated as `torch.Tensor` or
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`Optional[torch.Tensor]`, the first dimension will be
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marked as dynamic.
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- if the argument is annotated as `IntermediateTensors`, the first
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dimension of all the tensors in the intermediate tensors
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will be marked as dynamic.
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During runtime, when we actually mark dimensions of tensors,
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it depends on the value of arguments:
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- if it is a single integer (can be negative), the corresponding dimension
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of the argument will be marked as dynamic.
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- if it is `None`, ignored.
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- if it is `IntermediateTensors`, all the tensors in the intermediate
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tensors will be marked as dynamic.
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- otherwise, it will raise an error.
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NOTE: if an argument is `None`, it should always be passed as `None` during
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the lifetime of the model, otherwise, it cannot be captured as a single
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computation graph.
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`enable_if` is a function that takes a `VllmConfig` object as input and
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returns a boolean value indicating whether to compile the model or not.
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This is useful if you want to compile the model only when certain
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conditions are met.
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"""
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def cls_decorator_helper(cls: _T) -> _T:
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# helper to pass `dynamic_arg_dims`` to `_support_torch_compile``
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# to avoid too much indentation for `_support_torch_compile``
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if not hasattr(cls, 'forward'):
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raise TypeError("decorated class should have a forward method.")
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sig = inspect.signature(cls.forward)
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inferred_dynamic_arg_dims = dynamic_arg_dims
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if inferred_dynamic_arg_dims is None:
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inferred_dynamic_arg_dims = {}
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for k, v in sig.parameters.items():
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if v.annotation in [
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torch.Tensor, Optional[torch.Tensor],
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IntermediateTensors, Optional[IntermediateTensors]
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]:
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inferred_dynamic_arg_dims[k] = 0
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logger.debug(("Inferred dynamic dimensions for "
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"forward method of %s: %s"), cls,
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list(inferred_dynamic_arg_dims.keys()))
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if len(inferred_dynamic_arg_dims) == 0:
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raise ValueError(
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"No dynamic dimensions found in the forward method of "
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f"{cls}. Please provide dynamic_arg_dims explicitly.")
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for k in inferred_dynamic_arg_dims:
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if k not in sig.parameters:
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raise ValueError(
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f"Argument {k} not found in the forward method of {cls}")
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return _support_torch_compile(cls, inferred_dynamic_arg_dims,
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enable_if)
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if cls is not None:
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# use `support_torch_compile` as a decorator without arguments
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assert isinstance(cls, type)
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return cls_decorator_helper(cls)
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return cls_decorator_helper
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def _support_torch_compile(
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cls: _T,
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dynamic_arg_dims: dict[str, Union[int, list[int]]],
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enable_if: Optional[Callable[[VllmConfig], bool]] = None,
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) -> _T:
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"""
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A decorator to add support for compiling the forward method of a class.
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"""
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if TorchCompileWrapperWithCustomDispatcher in cls.__bases__:
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# support decorating multiple times
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return cls
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# take care of method resolution order
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# make sure super().__init__ is called on the base class
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# other than TorchCompileWrapperWithCustomDispatcher
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cls.__bases__ = cls.__bases__ + (TorchCompileWrapperWithCustomDispatcher, )
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old_init = cls.__init__
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setattr(cls, IGNORE_COMPILE_KEY, False)
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = '', **kwargs):
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old_init(self, vllm_config=vllm_config, prefix=prefix, **kwargs)
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self.vllm_config = vllm_config
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enable_compile = enable_if is None or enable_if(vllm_config)
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# for CompilationLevel.DYNAMO_AS_IS , the upper level model runner
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# will handle the compilation, so we don't need to do anything here.
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self.do_not_compile = \
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vllm_config.compilation_config.level in [
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CompilationLevel.NO_COMPILATION, CompilationLevel.DYNAMO_AS_IS
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] or not supports_dynamo() or _should_ignore_torch_compile(
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self.__class__) or not enable_compile
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if self.do_not_compile:
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return
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compilation_counter.num_models_seen += 1
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TorchCompileWrapperWithCustomDispatcher.__init__(
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self, compilation_level=vllm_config.compilation_config.level)
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cls.__init__ = __init__
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def __call__(self, *args, **kwargs):
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# torch.compiler.is_compiling() means we are inside the compilation
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# e.g. TPU has the compilation logic in model runner, so we don't
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# need to compile the model inside.
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if self.do_not_compile or torch.compiler.is_compiling():
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return self.forward(*args, **kwargs)
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# the first compilation needs to have dynamic shapes marked
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if len(self.compiled_codes) < 1:
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sig = inspect.signature(self.__class__.forward)
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bound_args = sig.bind(self, *args, **kwargs)
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bound_args.apply_defaults()
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for k, dims in dynamic_arg_dims.items():
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arg = bound_args.arguments.get(k)
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if arg is not None:
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dims = [dims] if isinstance(dims, int) else dims
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if isinstance(arg, torch.Tensor):
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# In case dims is specified with negative indexing
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dims = [
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arg.ndim + dim if dim < 0 else dim for dim in dims
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]
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torch._dynamo.mark_dynamic(arg, dims)
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elif isinstance(arg, IntermediateTensors):
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for tensor in arg.tensors.values():
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# In case dims is specified with negative indexing
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dims = [
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tensor.ndim + dim if dim < 0 else dim
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for dim in dims
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]
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torch._dynamo.mark_dynamic(tensor, dims)
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else:
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raise ValueError(
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"Unsupported dynamic dimensions"
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f" {dims} for argument {k} with type {type(arg)}.")
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# here, it is the starting point of the `torch.compile` process
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start_monitoring_torch_compile(self.vllm_config)
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logger.debug("Start compiling function %s",
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self.original_code_object)
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# if we don't use custom dispatcher, we can directly call the
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# compiled function and let torch.compile handle the dispatching,
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# with the overhead of guard evaluation and recompilation.
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if len(self.compiled_codes) < 1 or not self.use_custom_dispatcher:
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# it seems Dynamo reuse the compilation across instances,
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# while we need to make sure the compiled code is not reused.
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# we need to control all the compilation of the model.
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torch._dynamo.eval_frame.remove_from_cache(
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self.original_code_object)
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# collect all relevant files traced by Dynamo,
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# so that the compilation cache can trigger re-compilation
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# properly when any of these files change.
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# 1. the file containing the top-level forward function
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self.vllm_config.compilation_config.traced_files.add(
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self.original_code_object.co_filename)
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# 2. every time Dynamo sees a function call, it will inline
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# the function by calling InliningInstructionTranslator.inline_call
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# we hijack this function to know all the functions called
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# during Dynamo tracing, and their corresponding files
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inline_call = InliningInstructionTranslator.inline_call
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def patched_inline_call(parent, func, args, kwargs):
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code = func.get_code()
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self.vllm_config.compilation_config.traced_files.add(
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code.co_filename)
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return inline_call(parent, func, args, kwargs)
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# Disable the C++ compilation of symbolic shape guards. C++-fication
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# of symbolic shape guards can improve guard overhead. But, since
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# vllm skip guards anyways, setting this flag to False can improve
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# compile time.
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dynamo_config_patches = {}
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try:
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_ = torch._dynamo.config.enable_cpp_symbolic_shape_guards
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dynamo_config_patches[
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"enable_cpp_symbolic_shape_guards"] = False
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except AttributeError:
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# Note: this config is not available in torch 2.6, we can skip
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# if the config doesn't exist
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logger.debug(
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"enable_cpp_symbolic_shape_guards config not available")
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with patch.object(InliningInstructionTranslator, 'inline_call',
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patched_inline_call), torch._dynamo.config.patch(
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**dynamo_config_patches
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), maybe_use_cudagraph_partition_wrapper(
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self.vllm_config):
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output = self.compiled_callable(*args, **kwargs)
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return output
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# usually, capturing the model once is enough, and then we can
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# dispatch to the compiled code directly, without going through
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# the Dynamo guard mechanism.
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with self.dispatch_to_code(0):
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model_output = self.forward(*args, **kwargs)
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return model_output
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cls.__call__ = __call__
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return cls
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@contextlib.contextmanager
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def maybe_use_cudagraph_partition_wrapper(vllm_config: VllmConfig):
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"""
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Context manager to set/unset customized cudagraph partition wrappers.
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If we're using Inductor-based graph partitioning, we currently have the
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whole `fx.Graph` before Inductor lowering and and the piecewise
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splitting happens after all graph passes and fusions. Here, we add
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a custom hook for Inductor to wrap each partition with our static
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graph wrapper class to maintain more control over static graph
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capture and replay.
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"""
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from vllm.config import CUDAGraphMode
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compilation_config = vllm_config.compilation_config
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if (compilation_config.cudagraph_mode != CUDAGraphMode.NONE
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and compilation_config.use_inductor_graph_partition):
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from torch._inductor.utils import CUDAGraphWrapperMetadata
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from vllm.compilation.cuda_graph import CUDAGraphOptions
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from vllm.platforms import current_platform
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static_graph_wrapper_class = resolve_obj_by_qualname(
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current_platform.get_static_graph_wrapper_cls())
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def customized_cudagraph_wrapper(f,
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metadata: CUDAGraphWrapperMetadata):
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partition_id = metadata.partition_index
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num_partitions = metadata.num_partitions
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return static_graph_wrapper_class(
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runnable=f,
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vllm_config=vllm_config,
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runtime_mode=CUDAGraphMode.PIECEWISE,
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cudagraph_options=CUDAGraphOptions(
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debug_log_enable=partition_id == 0,
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gc_disable=partition_id != 0,
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weak_ref_output=partition_id == num_partitions - 1,
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))
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torch._inductor.utils.set_customized_partition_wrappers(
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customized_cudagraph_wrapper)
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yield
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if (compilation_config.cudagraph_mode != CUDAGraphMode.NONE
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and compilation_config.use_inductor_graph_partition):
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torch._inductor.utils.set_customized_partition_wrappers(None)
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