840 lines
31 KiB
Python
840 lines
31 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 ast
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import dataclasses
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import hashlib
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import json
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import operator
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import os
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import pprint
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import time
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from collections.abc import Callable, Sequence
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from contextlib import contextmanager
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from copy import deepcopy
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from functools import partial
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from typing import Any
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import torch
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import torch.fx as fx
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from torch._dispatch.python import enable_python_dispatcher
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import vllm.envs as envs
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from vllm.compilation.inductor_pass import pass_context
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from vllm.compilation.partition_rules import (
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inductor_partition_rule_context,
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should_split,
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)
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from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig
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from vllm.config.compilation import DynamicShapesType
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from vllm.config.utils import Range, hash_factors
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from vllm.logger import init_logger
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from vllm.logging_utils import lazy
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from vllm.platforms import current_platform
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from vllm.utils.import_utils import resolve_obj_by_qualname
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from vllm.utils.torch_utils import is_torch_equal_or_newer
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from .caching import VllmSerializableFunction
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from .compiler_interface import (
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CompilerInterface,
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EagerAdaptor,
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InductorAdaptor,
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InductorStandaloneAdaptor,
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is_compile_cache_enabled,
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)
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from .counter import compilation_counter
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from .inductor_pass import InductorPass
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from .pass_manager import PostGradPassManager
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logger = init_logger(__name__)
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def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface:
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if compilation_config.backend == "inductor":
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# Use standalone compile only if requested, version is new enough,
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# and the symbol actually exists in this PyTorch build.
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if (
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envs.VLLM_USE_STANDALONE_COMPILE
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and is_torch_equal_or_newer("2.8.0.dev")
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and hasattr(torch._inductor, "standalone_compile")
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):
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logger.debug("Using InductorStandaloneAdaptor")
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return InductorStandaloneAdaptor(
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compilation_config.compile_cache_save_format
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)
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else:
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logger.debug("Using InductorAdaptor")
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return InductorAdaptor()
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elif compilation_config.backend == "eager":
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logger.debug("Using EagerAdaptor")
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return EagerAdaptor()
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else:
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logger.debug("Using custom backend: %s", compilation_config.backend)
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compiler = resolve_obj_by_qualname(current_platform.get_compile_backend())()
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assert isinstance(compiler, CompilerInterface)
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return compiler
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class CompilerManager:
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"""
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A manager to manage the compilation process, including
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caching the compiled graph, loading the compiled graph,
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and compiling the graph.
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The cache is a dict mapping
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`(runtime_shape, graph_index, backend_name)`
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to `any_data` returned from the compiler.
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When serializing the cache, we save it to a Python file
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for readability. We don't use json here because json doesn't
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support int as key.
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"""
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def __init__(self, compilation_config: CompilationConfig):
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self.cache: dict[tuple[Range, int, str], Any] = dict()
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self.is_cache_updated = False
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self.compilation_config = compilation_config
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self.compiler = make_compiler(compilation_config)
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def compute_hash(self, vllm_config: VllmConfig) -> str:
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return self.compiler.compute_hash(vllm_config)
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@contextmanager
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def compile_context(self, compile_range: Range):
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"""Provide compilation context for the duration of compilation to set
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any torch global properties we want to scope to a single Inductor
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compilation (e.g. partition rules, pass context)."""
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with pass_context(compile_range):
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if self.compilation_config.use_inductor_graph_partition:
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with inductor_partition_rule_context(
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self.compilation_config.splitting_ops
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):
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yield
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else:
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yield
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def initialize_cache(
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self, cache_dir: str, disable_cache: bool = False, prefix: str = ""
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):
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"""
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Initialize the cache directory for the compiler.
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The organization of the cache directory is as follows:
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cache_dir=/path/to/hash_str/rank_i_j/prefix/
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inside cache_dir, there will be:
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- vllm_compile_cache.py
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- computation_graph.py
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- transformed_code.py
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for multiple prefixes, they can share the same
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base cache dir of /path/to/hash_str/rank_i_j/ ,
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to store some common compilation artifacts.
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"""
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self.disable_cache = disable_cache
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self.cache_dir = cache_dir
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self.cache_file_path = os.path.join(cache_dir, "vllm_compile_cache.py")
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if not disable_cache and os.path.exists(self.cache_file_path):
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# load the cache from the file
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with open(self.cache_file_path) as f:
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# we use ast.literal_eval to parse the data
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# because it is a safe way to parse Python literals.
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# do not use eval(), it is unsafe.
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cache = ast.literal_eval(f.read())
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def check_type(value, ty):
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if not isinstance(value, ty):
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raise TypeError(f"Expected {ty} but got {type(value)} for {value}")
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def parse_key(key: Any) -> tuple[Range, int, str]:
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range_tuple, graph_index, compiler_name = key
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check_type(graph_index, int)
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check_type(compiler_name, str)
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if isinstance(range_tuple, tuple):
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start, end = range_tuple
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check_type(start, int)
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check_type(end, int)
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range_tuple = Range(start=start, end=end)
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check_type(range_tuple, Range)
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return range_tuple, graph_index, compiler_name
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self.cache = {parse_key(key): value for key, value in cache.items()}
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self.compiler.initialize_cache(
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cache_dir=cache_dir, disable_cache=disable_cache, prefix=prefix
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)
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def save_to_file(self):
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if self.disable_cache or not self.is_cache_updated:
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return
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printer = pprint.PrettyPrinter(indent=4)
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data = printer.pformat(self.cache)
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with open(self.cache_file_path, "w") as f:
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f.write(data)
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def load(
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self,
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graph: fx.GraphModule,
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example_inputs: list[Any],
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graph_index: int,
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compile_range: Range,
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) -> Callable | None:
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if (compile_range, graph_index, self.compiler.name) not in self.cache:
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return None
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handle = self.cache[(compile_range, graph_index, self.compiler.name)]
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compiled_graph = self.compiler.load(
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handle, graph, example_inputs, graph_index, compile_range
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)
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logger.debug(
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"Directly load the %s-th graph for compile range %sfrom %s via handle %s",
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graph_index,
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str(compile_range),
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self.compiler.name,
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handle,
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)
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return compiled_graph
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def compile(
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self,
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graph: fx.GraphModule,
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example_inputs,
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additional_inductor_config,
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compilation_config: CompilationConfig,
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compile_range: Range,
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graph_index: int = 0,
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num_graphs: int = 1,
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) -> Any:
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if graph_index == 0:
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# before compiling the first graph, record the start time
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global compilation_start_time
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compilation_start_time = time.time()
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compilation_counter.num_backend_compilations += 1
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compiled_graph = None
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# try to load from the cache
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compiled_graph = self.load(graph, example_inputs, graph_index, compile_range)
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if compiled_graph is not None:
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if graph_index == num_graphs - 1:
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# after loading the last graph for this shape, record the time.
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# there can be multiple graphs due to piecewise compilation.
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now = time.time()
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elapsed = now - compilation_start_time
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compilation_config.compilation_time += elapsed
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logger.info(
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"Directly load the compiled graph(s) for compile range %s "
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"from the cache, took %.3f s",
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str(compile_range),
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elapsed,
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)
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return compiled_graph
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# no compiler cached the graph, or the cache is disabled,
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# we need to compile it
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if isinstance(self.compiler, InductorAdaptor):
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# Let compile_fx generate a key for us
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maybe_key = None
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else:
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maybe_key = "artifact_compile_range_"
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maybe_key += f"{compile_range.start}_{compile_range.end}"
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maybe_key += f"_subgraph_{graph_index}"
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with self.compile_context(compile_range):
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compiled_graph, handle = self.compiler.compile(
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graph,
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example_inputs,
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additional_inductor_config,
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compile_range,
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maybe_key,
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)
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assert compiled_graph is not None, "Failed to compile the graph"
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# store the artifact in the cache
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if is_compile_cache_enabled(additional_inductor_config) and handle is not None:
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self.cache[(compile_range, graph_index, self.compiler.name)] = handle
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compilation_counter.num_cache_entries_updated += 1
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self.is_cache_updated = True
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if graph_index == 0:
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# adds some info logging for the first graph
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logger.info_once(
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"Cache the graph of compile range %s for later use",
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str(compile_range),
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)
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logger.debug(
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"Store the %s-th graph for compile range%s from %s via handle %s",
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graph_index,
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str(compile_range),
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self.compiler.name,
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handle,
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)
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# after compiling the last graph, record the end time
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if graph_index == num_graphs - 1:
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now = time.time()
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elapsed = now - compilation_start_time
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compilation_config.compilation_time += elapsed
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logger.info_once(
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"Compiling a graph for compile range %s takes %.2f s",
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str(compile_range),
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elapsed,
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scope="local",
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)
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return compiled_graph
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@dataclasses.dataclass
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class SplitItem:
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submod_name: str
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graph_id: int
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is_splitting_graph: bool
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graph: fx.GraphModule
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def split_graph(
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graph: fx.GraphModule, splitting_ops: list[str]
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) -> tuple[fx.GraphModule, list[SplitItem]]:
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# split graph by ops
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subgraph_id = 0
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node_to_subgraph_id: dict[fx.Node, int] = {}
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split_op_graphs: list[int] = []
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for node in graph.graph.nodes:
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if node.op in ("output", "placeholder"):
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continue
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# Check if this is a getitem operation on a node from an earlier subgraph.
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# If so, assign it to the same subgraph as its input to avoid passing entire
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# tuple as input to submodules, which is against standalone_compile and
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# AoTAutograd input requirement.
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if node.op == "call_function" and node.target == operator.getitem:
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# Assign this getitem to the same subgraph as its input
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input_node = node.args[0]
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if input_node.op != "placeholder":
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assert input_node in node_to_subgraph_id
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node_to_subgraph_id[node] = node_to_subgraph_id[input_node]
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continue
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if should_split(node, splitting_ops):
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subgraph_id += 1
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node_to_subgraph_id[node] = subgraph_id
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split_op_graphs.append(subgraph_id)
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subgraph_id += 1
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else:
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node_to_subgraph_id[node] = subgraph_id
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# `keep_original_order` is important!
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# otherwise pytorch might reorder the nodes and
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# the semantics of the graph will change when we
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# have mutations in the graph
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split_gm = torch.fx.passes.split_module.split_module(
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graph, None, lambda node: node_to_subgraph_id[node], keep_original_order=True
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)
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outputs = []
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names = [name for (name, module) in split_gm.named_modules()]
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for name in names:
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if "." in name or name == "":
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# recursive child module or the root module
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continue
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module = getattr(split_gm, name)
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graph_id = int(name.replace("submod_", ""))
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outputs.append(SplitItem(name, graph_id, (graph_id in split_op_graphs), module))
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# sort by integer graph_id, rather than string name
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outputs.sort(key=lambda x: x.graph_id)
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return split_gm, outputs
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compilation_start_time = 0.0
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class PiecewiseCompileInterpreter(torch.fx.Interpreter):
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"""Code adapted from `torch.fx.passes.shape_prop.ShapeProp`.
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It runs the given graph with fake inputs, and compile some
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submodules specified by `compile_submod_names` with the given
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compilation configs.
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NOTE: the order in `compile_submod_names` matters, because
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it will be used to determine the order of the compiled piecewise
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graphs. The first graph will handle logging, and the last graph
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has some special cudagraph output handling.
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"""
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def __init__(
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self,
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module: torch.fx.GraphModule,
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compile_submod_names: list[str],
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vllm_config: VllmConfig,
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vllm_backend: "VllmBackend",
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):
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super().__init__(module)
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from torch._guards import detect_fake_mode
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self.fake_mode = detect_fake_mode()
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self.compile_submod_names = compile_submod_names
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self.compilation_config = vllm_config.compilation_config
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self.vllm_config = vllm_config
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self.vllm_backend = vllm_backend
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# When True, it annoyingly dumps the torch.fx.Graph on errors.
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self.extra_traceback = False
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def run(self, *args):
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# maybe instead just assert inputs are fake?
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fake_args = [
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self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t
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for t in args
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]
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with self.fake_mode, enable_python_dispatcher():
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return super().run(*fake_args)
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def call_module(
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self,
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target: torch.fx.node.Target,
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args: tuple[torch.fx.node.Argument, ...],
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kwargs: dict[str, Any],
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) -> Any:
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assert isinstance(target, str)
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output = super().call_module(target, args, kwargs)
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if target in self.compile_submod_names:
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index = self.compile_submod_names.index(target)
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submod = self.fetch_attr(target)
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sym_shape_indices = [
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i for i, x in enumerate(args) if isinstance(x, torch.SymInt)
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]
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# Lazy import here to avoid circular import
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from .piecewise_backend import PiecewiseBackend
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piecewise_backend = PiecewiseBackend(
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submod,
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self.vllm_config,
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index,
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len(self.compile_submod_names),
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sym_shape_indices,
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self.vllm_backend,
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)
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if (
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self.compilation_config.cudagraph_mode.has_piecewise_cudagraphs()
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and not self.compilation_config.use_inductor_graph_partition
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):
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# We're using Dynamo-based piecewise splitting, so we wrap
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# the whole subgraph with a static graph wrapper.
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from .cuda_graph import CUDAGraphOptions
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# resolve the static graph wrapper class (e.g. CUDAGraphWrapper
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# class) as platform dependent.
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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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)
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# Always assign PIECEWISE runtime mode to the
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# CUDAGraphWrapper for piecewise_backend, to distinguish
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# it from the FULL cudagraph runtime mode, no matter it
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# is wrapped on a full or piecewise fx graph.
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self.module.__dict__[target] = static_graph_wrapper_class(
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runnable=piecewise_backend,
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vllm_config=self.vllm_config,
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runtime_mode=CUDAGraphMode.PIECEWISE,
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cudagraph_options=CUDAGraphOptions(
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debug_log_enable=piecewise_backend.is_first_graph,
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gc_disable=not piecewise_backend.is_first_graph,
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weak_ref_output=piecewise_backend.is_last_graph,
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),
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)
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else:
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self.module.__dict__[target] = piecewise_backend
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compilation_counter.num_piecewise_capturable_graphs_seen += 1
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return output
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# the tag for the part of model being compiled,
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# e.g. backbone/eagle_head
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model_tag: str = "backbone"
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model_is_encoder: bool = False
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@contextmanager
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def set_model_tag(tag: str, is_encoder: bool = False):
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"""Context manager to set the model tag."""
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global model_tag
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global model_is_encoder
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assert tag != model_tag, (
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f"Model tag {tag} is the same as the current tag {model_tag}."
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)
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old_tag = model_tag
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old_is_encoder = model_is_encoder
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model_tag = tag
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model_is_encoder = is_encoder
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try:
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yield
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finally:
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model_tag = old_tag
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model_is_encoder = old_is_encoder
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class VllmBackend:
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"""The compilation backend for `torch.compile` with vLLM.
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It is used for compilation mode of `CompilationMode.VLLM_COMPILE`,
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where we customize the compilation.
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The major work of this backend is to split the graph into
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piecewise graphs, and pass them to the piecewise backend.
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This backend also adds the PostGradPassManager to Inductor config,
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which handles the post-grad passes.
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"""
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vllm_config: VllmConfig
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compilation_config: CompilationConfig
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_called: bool = False
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# the graph we compiled
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graph: fx.GraphModule
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# the stiching graph module for all the piecewise graphs
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split_gm: fx.GraphModule
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piecewise_graphs: list[SplitItem]
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returned_callable: Callable
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# Inductor passes to run on the graph pre-defunctionalization
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post_grad_passes: Sequence[Callable]
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sym_tensor_indices: list[int]
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input_buffers: list[torch.Tensor]
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compiler_manager: CompilerManager
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# Copy of CompilationConfig.inductor_compile_config +
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|
# an entry for PostGradPassManager
|
|
inductor_config: dict[str, Any]
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
prefix: str = "",
|
|
):
|
|
# if the model is initialized with a non-empty prefix,
|
|
# then usually it's enough to use that prefix,
|
|
# e.g. language_model, vision_model, etc.
|
|
# when multiple parts are initialized as independent
|
|
# models, we need to use the model_tag to distinguish
|
|
# them, e.g. backbone (default), eagle_head, etc.
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|
self.prefix = prefix or model_tag
|
|
|
|
# Mark compilation for encoder.
|
|
self.is_encoder = model_is_encoder
|
|
|
|
# Passes to run on the graph post-grad.
|
|
self.pass_manager = resolve_obj_by_qualname(
|
|
current_platform.get_pass_manager_cls()
|
|
)()
|
|
self.pass_key = current_platform.pass_key
|
|
|
|
self.sym_tensor_indices = []
|
|
self.input_buffers = []
|
|
|
|
self.vllm_config = vllm_config
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|
self.compilation_config = vllm_config.compilation_config
|
|
|
|
self.compiler_manager: CompilerManager = CompilerManager(
|
|
self.compilation_config
|
|
)
|
|
|
|
# Deepcopy the inductor config to detach the post-grad custom pass
|
|
# from CompilationConfig.
|
|
# We want to avoid PostGradPassManager in CompilationConfig because
|
|
# in future we need PostGradPassManager.uuid() to be executed
|
|
# only at compile time.
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|
self.inductor_config = deepcopy(self.compilation_config.inductor_compile_config)
|
|
# `torch.compile` is JIT compiled, so we don't need to
|
|
# do anything here
|
|
|
|
def configure_post_pass(self):
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|
self.pass_manager.configure(self.vllm_config)
|
|
|
|
# Post-grad custom passes are run using the post_grad_custom_post_pass
|
|
# hook. If a pass for that hook exists, add it to the pass manager.
|
|
if self.pass_key in self.inductor_config:
|
|
if isinstance(self.inductor_config[self.pass_key], PostGradPassManager):
|
|
raise ValueError(
|
|
"PostGradPassManager can not be kept in CompilationConfig."
|
|
)
|
|
else:
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|
# Config should automatically wrap all inductor passes
|
|
assert isinstance(
|
|
self.compilation_config.inductor_compile_config[self.pass_key],
|
|
InductorPass,
|
|
)
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|
self.pass_manager.add(
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|
self.compilation_config.inductor_compile_config[self.pass_key]
|
|
)
|
|
self.inductor_config[self.pass_key] = self.pass_manager
|
|
|
|
def __call__(
|
|
self, graph: fx.GraphModule, example_inputs
|
|
) -> VllmSerializableFunction:
|
|
vllm_config = self.vllm_config
|
|
# Minimal hashing here with existing utilities, reused below.
|
|
|
|
env_factors = envs.compile_factors()
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|
env_hash = hash_factors(env_factors)
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|
# Compute config/compiler/code hashes once and reuse
|
|
config_hash = vllm_config.compute_hash()
|
|
compiler_hash = self.compiler_manager.compute_hash(vllm_config)
|
|
forward_code_files = list(sorted(self.compilation_config.traced_files))
|
|
|
|
logger.debug(
|
|
"Traced files (to be considered for compilation cache):\n%s",
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|
lazy(lambda: "\n".join(forward_code_files)),
|
|
)
|
|
hash_content = []
|
|
for filepath in forward_code_files:
|
|
hash_content.append(filepath)
|
|
if filepath == "<string>":
|
|
# This means the function was dynamically generated, with
|
|
# e.g. exec(). We can't actually check these.
|
|
continue
|
|
try:
|
|
with open(filepath) as f:
|
|
hash_content.append(f.read())
|
|
except Exception:
|
|
logger.warning("Failed to read file %s", filepath)
|
|
continue
|
|
code_hash = hashlib.sha256("\n".join(hash_content).encode()).hexdigest()
|
|
# Clear after consumption
|
|
self.compilation_config.traced_files.clear()
|
|
if not self.compilation_config.cache_dir:
|
|
# no provided cache dir, generate one based on the known factors
|
|
# that affects the compilation. if none of the factors change,
|
|
# the cache dir will be the same so that we can reuse the compiled
|
|
# graph.
|
|
factors = [env_hash, config_hash, code_hash, compiler_hash]
|
|
# Use SHA-256 for cache key hashing to be consistent across
|
|
# compute_hash functions. Truncate for a short cache dir name.
|
|
hash_key = hashlib.sha256(str(factors).encode()).hexdigest()[:10]
|
|
cache_dir = os.path.join(
|
|
envs.VLLM_CACHE_ROOT, "torch_compile_cache", hash_key
|
|
)
|
|
self.compilation_config.cache_dir = cache_dir
|
|
|
|
cache_dir = self.compilation_config.cache_dir
|
|
os.makedirs(cache_dir, exist_ok=True)
|
|
self.compilation_config.cache_dir = cache_dir
|
|
rank = vllm_config.parallel_config.rank
|
|
dp_rank = vllm_config.parallel_config.data_parallel_rank
|
|
local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}", self.prefix)
|
|
os.makedirs(local_cache_dir, exist_ok=True)
|
|
self.compilation_config.local_cache_dir = local_cache_dir
|
|
|
|
# Honors opt-outs such as CompilationMode.NONE or VLLM_DISABLE_COMPILE_CACHE.
|
|
disable_cache = not is_compile_cache_enabled(self.inductor_config)
|
|
|
|
if disable_cache:
|
|
logger.info_once("vLLM's torch.compile cache is disabled.", scope="local")
|
|
else:
|
|
logger.info_once(
|
|
"Using cache directory: %s for vLLM's torch.compile",
|
|
local_cache_dir,
|
|
scope="local",
|
|
)
|
|
|
|
self.compiler_manager.initialize_cache(
|
|
local_cache_dir, disable_cache, self.prefix
|
|
)
|
|
|
|
# Reuses existing cache key
|
|
|
|
logger.debug(
|
|
"torch.compile cache factors: env=%s cfg=%s comp=%s code=%s dir=%s",
|
|
env_hash,
|
|
config_hash,
|
|
compiler_hash,
|
|
code_hash,
|
|
local_cache_dir,
|
|
)
|
|
|
|
# Persist and log only hash-relevant factors together.
|
|
try:
|
|
logger.debug(
|
|
"Compile env factors (raw):\n%s\nVllm config hash: %s",
|
|
lazy(partial(pprint.pformat, env_factors, width=120)),
|
|
config_hash,
|
|
)
|
|
meta_path = os.path.join(local_cache_dir, "cache_key_factors.json")
|
|
if not os.path.exists(meta_path):
|
|
with open(meta_path, "w") as f:
|
|
json.dump(
|
|
{
|
|
"env": env_factors, # raw factors used for env_hash
|
|
"config_hash": config_hash,
|
|
"code_hash": code_hash,
|
|
"compiler_hash": compiler_hash,
|
|
},
|
|
f,
|
|
indent=2,
|
|
sort_keys=True,
|
|
)
|
|
except Exception:
|
|
# Best-effort only; metadata write failures are non-fatal.
|
|
logger.warning(
|
|
(
|
|
"Could not write compile cache metadata at %s; continuing without "
|
|
"metadata. Compiled cache remains valid; diagnostics may be "
|
|
"limited."
|
|
),
|
|
local_cache_dir,
|
|
exc_info=True,
|
|
)
|
|
|
|
# when dynamo calls the backend, it means the bytecode
|
|
# transform and analysis are done
|
|
compilation_counter.num_graphs_seen += 1
|
|
from .monitor import torch_compile_start_time
|
|
|
|
dynamo_time = time.time() - torch_compile_start_time
|
|
logger.info_once(
|
|
"Dynamo bytecode transform time: %.2f s", dynamo_time, scope="local"
|
|
)
|
|
self.compilation_config.compilation_time += dynamo_time
|
|
|
|
# we control the compilation process, each instance can only be
|
|
# called once
|
|
assert not self._called, "VllmBackend can only be called once"
|
|
|
|
self.graph = graph
|
|
self.configure_post_pass()
|
|
|
|
if self.compilation_config.use_inductor_graph_partition:
|
|
# Let Inductor decide partitioning; avoid FX-level pre-splitting.
|
|
fx_split_ops: list[str] = []
|
|
else:
|
|
fx_split_ops = self.compilation_config.splitting_ops or []
|
|
|
|
self.split_gm, self.piecewise_graphs = split_graph(graph, fx_split_ops)
|
|
|
|
from torch._dynamo.utils import lazy_format_graph_code
|
|
|
|
# depyf will hook lazy_format_graph_code and dump the graph
|
|
# for debugging, no need to print the graph here
|
|
lazy_format_graph_code("before split", self.graph)
|
|
lazy_format_graph_code("after split", self.split_gm)
|
|
|
|
compilation_counter.num_piecewise_graphs_seen += len(self.piecewise_graphs)
|
|
submod_names_to_compile = [
|
|
item.submod_name
|
|
for item in self.piecewise_graphs
|
|
if not item.is_splitting_graph
|
|
]
|
|
|
|
# Extract fake values from the graph to use them when needed.
|
|
all_fake_values = []
|
|
for i in graph.graph.find_nodes(op="placeholder"):
|
|
all_fake_values.append(i.meta["example_value"])
|
|
|
|
fake_args = [
|
|
all_fake_values[i] if isinstance(t, torch.Tensor) else t
|
|
for i, t in enumerate(example_inputs)
|
|
]
|
|
|
|
# propagate the split graph to the piecewise backend,
|
|
# compile submodules with symbolic shapes
|
|
PiecewiseCompileInterpreter(
|
|
self.split_gm, submod_names_to_compile, self.vllm_config, self
|
|
).run(*fake_args)
|
|
|
|
from torch._guards import detect_fake_mode
|
|
|
|
fake_mode = detect_fake_mode()
|
|
|
|
if (
|
|
self.compilation_config.dynamic_shapes_config.evaluate_guards
|
|
and self.compilation_config.dynamic_shapes_config.type
|
|
== DynamicShapesType.BACKED
|
|
):
|
|
from torch.utils._sympy.value_ranges import ValueRanges
|
|
|
|
# Drop counter-0/1 specializations guards; for backed dynamic shapes,
|
|
# torch.compile will specialize for 0/1 inputs or otherwise guards that
|
|
# shape is >= 2. This is because it's really hard not to hit a check
|
|
# against 0/1. When we evaluate shape guards, we exclude checking those
|
|
# guards (We would fail always otherwise).
|
|
|
|
# We avoid that by updating the ranges of backed sizes when the min is
|
|
# 2 for any, we assume it's 0.
|
|
for s, r in fake_mode.shape_env.var_to_range.items():
|
|
if r.lower == 2:
|
|
fake_mode.shape_env.var_to_range[s] = ValueRanges(0, r.upper)
|
|
|
|
graph_path = os.path.join(local_cache_dir, "computation_graph.py")
|
|
if not os.path.exists(graph_path):
|
|
# code adapted from
|
|
# https://github.com/thuml/depyf/blob/dab831108a752d1facc00acdd6d4243891845c37/depyf/explain/patched_lazy_format_graph_code.py#L30
|
|
# use `print_readable` because it can include submodules
|
|
src = (
|
|
"from __future__ import annotations\nimport torch\n"
|
|
+ self.split_gm.print_readable(print_output=False)
|
|
)
|
|
src = src.replace("<lambda>", "GraphModule")
|
|
with open(graph_path, "w") as f:
|
|
f.write(src)
|
|
|
|
logger.debug_once(
|
|
"Computation graph saved to %s", graph_path, scope="local"
|
|
)
|
|
|
|
self._called = True
|
|
|
|
if (
|
|
self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE
|
|
or not self.compilation_config.cudagraph_copy_inputs
|
|
):
|
|
return VllmSerializableFunction(
|
|
graph, example_inputs, self.prefix, self.split_gm
|
|
)
|
|
|
|
# index of tensors that have symbolic shapes (batch size)
|
|
# for weights and static buffers, they will have concrete shapes.
|
|
# symbolic shape only happens for input tensors.
|
|
from torch.fx.experimental.symbolic_shapes import is_symbolic
|
|
|
|
self.sym_tensor_indices = [
|
|
i
|
|
for i, x in enumerate(fake_args)
|
|
if isinstance(x, torch._subclasses.fake_tensor.FakeTensor)
|
|
and any(is_symbolic(d) for d in x.size())
|
|
]
|
|
|
|
# compiler managed cudagraph input buffers
|
|
# we assume the first run with symbolic shapes
|
|
# has the maximum size among all the tensors
|
|
self.input_buffers = [
|
|
example_inputs[x].clone() for x in self.sym_tensor_indices
|
|
]
|
|
|
|
# this is the callable we return to Dynamo to run
|
|
def copy_and_call(*args):
|
|
list_args = list(args)
|
|
for i, index in enumerate(self.sym_tensor_indices):
|
|
runtime_tensor = list_args[index]
|
|
runtime_shape = runtime_tensor.shape[0]
|
|
static_tensor = self.input_buffers[i][:runtime_shape]
|
|
|
|
# copy the tensor to the static buffer
|
|
static_tensor.copy_(runtime_tensor)
|
|
|
|
# replace the tensor in the list_args to the static buffer
|
|
list_args[index] = static_tensor
|
|
return self.split_gm(*list_args)
|
|
|
|
return VllmSerializableFunction(
|
|
graph, example_inputs, self.prefix, copy_and_call
|
|
)
|