# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import os import sys from abc import abstractmethod from contextlib import contextmanager from types import CodeType from typing import Callable, Optional import torch import vllm.envs as envs from vllm.config import CompilationLevel, get_current_vllm_config from vllm.logger import init_logger logger = init_logger(__name__) class TorchCompileWrapperWithCustomDispatcher: """ A wrapper class for torch.compile, with a custom dispatch logic. Subclasses should: 1. Implement the forward method 2. Implement the dispatch logic in the __call__ method It can use `self.compiled_codes` to access the compiled bytecode, and `with self.dispatch_to_code(index):` to dispatch to the compiled code. 3. Implement the `__init__` method to determine how to call `torch.compile` over the forward method. """ def __init__(self, compiled_callable: Optional[Callable] = None, compilation_level: int = 0): vllm_config = get_current_vllm_config() self.vllm_config = vllm_config if compiled_callable is None: # default compilation settings # compiling the forward method backend = vllm_config.compilation_config.init_backend(vllm_config) options = None if isinstance(backend, str) and backend == "inductor": options = get_current_vllm_config( ).compilation_config.inductor_compile_config compiled_callable = torch.compile( self.forward, fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE, backend=backend, options=options) self.compiled_callable = compiled_callable self.original_code_object = self.__class__.forward.__code__ self.compiled_codes: list[CodeType] = [] torch._dynamo.convert_frame.register_bytecode_hook(self.bytecode_hook) # read the env var to determine whether to use the custom dispatcher # subclasses can use this to switch between the custom dispatcher # and the default Dynamo guard mechanism. self.use_custom_dispatcher: bool = \ compilation_level >= CompilationLevel.DYNAMO_ONCE def __call__(self, *args, **kwargs): """Implement the dispatch logic here, beyond the torch.compile level. NOTE: this function can have additional arguments beyond the forward method, for directly dispatching to the compiled code. """ return self.compiled_callable(*args, **kwargs) @abstractmethod def forward(self, *args, **kwargs): ... def bytecode_hook(self, old_code: CodeType, new_code: CodeType): """Hook to save the compiled bytecode for direct execution.""" if old_code is not self.original_code_object: return # code borrowed from https://github.com/thuml/depyf/blob/f4ad79fadee27ea113b4c75202db1eb1a11c0dbc/depyf/explain/enable_debugging.py#L25 frame = sys._getframe() while frame and frame.f_back: frame = frame.f_back code_name = frame.f_code.co_name file_name = frame.f_code.co_filename.split(os.path.sep)[-1] if code_name == "_compile" and file_name == "convert_frame.py": break frame = frame.f_locals["frame"] assert frame.f_code == old_code if frame.f_locals["self"] is not self: return self.compiled_codes.append(new_code) local_cache_dir = self.vllm_config.compilation_config.local_cache_dir if isinstance(local_cache_dir, str): decompiled_file_name = ("transformed_code.py" if envs.VLLM_COMPILE_DEPYF else "transformed_code_README.txt") decompiled_file = os.path.join(local_cache_dir, decompiled_file_name) if not os.path.exists(decompiled_file): try: # usually the decompilation will succeed for most models, # as we guarantee a full-graph compilation in Dynamo. # but there's no 100% guarantee, since decompliation is # not a reversible process. if envs.VLLM_COMPILE_DEPYF: import depyf src = depyf.decompile(new_code) else: src = ( "To get a transformed_code.py file, re-run with " "VLLM_COMPILE_DEPYF=1") with open(decompiled_file, "w") as f: f.write(src) logger.debug("Dynamo transformed code saved to %s", decompiled_file) except Exception: pass if self.vllm_config.compilation_config.use_cudagraph and \ "update" in new_code.co_names: import depyf src = depyf.decompile(new_code) msg = "Assigning / modifying buffers of nn.Module during forward pass is not allowed when using cudagraph inside the compiler because it will cause silent errors. Please use eager mode or fix the code. The following code contains clues about which buffer is being modified (please search for the usage of the function `update`):\n" + src # noqa raise RuntimeError(msg) @contextmanager def dispatch_to_code(self, index: int): """Context manager to dispatch to the compiled code. Why does this work? Because Dynamo guarantees that the compiled bytecode has exactly the same arguments, cell variables, and free variables as the original code. Therefore we can directly switch the code object in the function and call it. See https://dev-discuss.pytorch.org/t/what-is-the-relationship-requirement-among-original-bytecode-transformed-bytecode-and-bytecode-returned-by-hooks-in-dynamo/1693/7 for more details. """ # noqa self.__class__.forward.__code__ = self.compiled_codes[index] yield self.__class__.forward.__code__ = self.original_code_object