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https://git.datalinker.icu/vllm-project/vllm.git
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611 lines
23 KiB
Python
611 lines
23 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 os
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import pprint
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import time
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from collections.abc import Sequence
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from contextlib import contextmanager
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from typing import Any, Callable, Optional
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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.config import CompilationConfig, VllmConfig
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from vllm.utils import is_torch_equal_or_newer, resolve_obj_by_qualname
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from .compiler_interface import (CompilerInterface, EagerAdaptor,
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InductorAdaptor, InductorStandaloneAdaptor)
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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.use_inductor:
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if envs.VLLM_USE_STANDALONE_COMPILE and is_torch_equal_or_newer(
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"2.8.0.dev"):
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logger.debug("Using InductorStandaloneAdaptor")
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return InductorStandaloneAdaptor()
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else:
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logger.debug("Using InductorAdaptor")
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return InductorAdaptor()
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else:
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logger.debug("Using EagerAdaptor")
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return EagerAdaptor()
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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[Optional[int], 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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def initialize_cache(self,
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cache_dir: str,
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disable_cache: bool = False,
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prefix: str = ""):
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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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self.cache = ast.literal_eval(f.read())
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self.compiler.initialize_cache(cache_dir=cache_dir,
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disable_cache=disable_cache,
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prefix=prefix)
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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(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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runtime_shape: Optional[int] = None) -> Optional[Callable]:
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if (runtime_shape, graph_index, self.compiler.name) not in self.cache:
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return None
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handle = self.cache[(runtime_shape, graph_index, self.compiler.name)]
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compiled_graph = self.compiler.load(handle, graph, example_inputs,
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graph_index, runtime_shape)
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logger.debug(
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"Directly load the %s-th graph for shape %s from %s via "
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"handle %s", graph_index, str(runtime_shape), self.compiler.name,
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handle)
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return compiled_graph
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def compile(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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graph_index: int = 0,
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num_graphs: int = 1,
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runtime_shape: Optional[int] = None) -> 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,
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runtime_shape)
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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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logger.info(
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"Directly load the compiled graph(s) for shape %s "
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"from the cache, took %.3f s", str(runtime_shape), elapsed)
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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 = \
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f"artifact_shape_{runtime_shape}_subgraph_{graph_index}"
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compiled_graph, handle = self.compiler.compile(
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graph, example_inputs, additional_inductor_config, runtime_shape,
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maybe_key)
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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 handle is not None:
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self.cache[(runtime_shape, graph_index,
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self.compiler.name)] = handle
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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("Cache the graph of shape %s for later use",
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str(runtime_shape))
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logger.debug(
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"store the %s-th graph for shape %s from %s via handle %s",
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graph_index, str(runtime_shape), self.compiler.name, handle)
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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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if runtime_shape is None:
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logger.info("Compiling a graph for general shape takes %.2f s",
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elapsed)
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else:
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logger.info("Compiling a graph for shape %s takes %.2f s",
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runtime_shape, elapsed)
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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(graph: fx.GraphModule,
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ops: list[str]) -> 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 = {}
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split_op_graphs = []
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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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if node.op == 'call_function' and str(node.target) in 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,
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None,
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lambda node: node_to_subgraph_id[node],
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keep_original_order=True)
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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(
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SplitItem(name, graph_id, (graph_id in split_op_graphs), module))
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# sort by intetger 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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# we share the global graph pool among all the backends
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global_graph_pool = None
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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__(self, module: torch.fx.GraphModule,
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compile_submod_names: list[str], vllm_config: VllmConfig,
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graph_pool, vllm_backend: "VllmBackend"):
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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.graph_pool = graph_pool
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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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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(self, target: torch.fx.node.Target,
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args: tuple[torch.fx.node.Argument,
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...], kwargs: dict[str, Any]) -> 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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global compilation_start_time
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compiled_graph_for_general_shape = self.vllm_backend.\
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compiler_manager.compile(
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submod,
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args,
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self.compilation_config.inductor_compile_config,
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self.compilation_config,
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graph_index=index,
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num_graphs=len(self.compile_submod_names),
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runtime_shape=None)
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piecewise_backend = resolve_obj_by_qualname(
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current_platform.get_piecewise_backend_cls())
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self.module.__dict__[target] = piecewise_backend(
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submod, self.vllm_config, self.graph_pool, index,
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len(self.compile_submod_names), sym_shape_indices,
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compiled_graph_for_general_shape, self.vllm_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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@contextmanager
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def set_model_tag(tag: str):
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"""Context manager to set the model tag."""
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global model_tag
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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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old_tag = model_tag
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model_tag = tag
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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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class VllmBackend:
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"""The compilation backend for `torch.compile` with vLLM.
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It is used for compilation level of `CompilationLevel.PIECEWISE`,
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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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graph_pool: Any
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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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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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):
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# if the model is initialized with a non-empty prefix,
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# then usually it's enough to use that prefix,
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# e.g. launguage_model, vision_model, etc.
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# when multiple parts are initialized as independent
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# models, we need to use the model_tag to distinguish
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# them, e.g. backbone (default), eagle_head, etc.
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self.prefix = prefix or model_tag
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global global_graph_pool
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if global_graph_pool is None:
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global_graph_pool = current_platform.graph_pool_handle()
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# TODO: in the future, if we want to use multiple
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# streams, it might not be safe to share a global pool.
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# only investigate this when we use multiple streams
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self.graph_pool = global_graph_pool
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# Passes to run on the graph post-grad.
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self.post_grad_pass_manager = PostGradPassManager()
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self.sym_tensor_indices = []
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self.input_buffers = []
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self.vllm_config = vllm_config
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self.compilation_config = vllm_config.compilation_config
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self.compiler_manager: CompilerManager = CompilerManager(
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self.compilation_config)
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# `torch.compile` is JIT compiled, so we don't need to
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# do anything here
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def configure_post_pass(self):
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config = self.compilation_config
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self.post_grad_pass_manager.configure(self.vllm_config)
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# Post-grad custom passes are run using the post_grad_custom_post_pass
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# hook. If a pass for that hook exists, add it to the pass manager.
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inductor_config = config.inductor_compile_config
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PASS_KEY = "post_grad_custom_post_pass"
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if PASS_KEY in inductor_config:
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# Config should automatically wrap all inductor passes
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if isinstance(inductor_config[PASS_KEY], PostGradPassManager):
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assert (inductor_config[PASS_KEY].uuid() ==
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self.post_grad_pass_manager.uuid())
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else:
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assert isinstance(inductor_config[PASS_KEY], InductorPass)
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self.post_grad_pass_manager.add(inductor_config[PASS_KEY])
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inductor_config[PASS_KEY] = self.post_grad_pass_manager
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def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable:
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vllm_config = self.vllm_config
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if not self.compilation_config.cache_dir:
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# no provided cache dir, generate one based on the known factors
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# that affects the compilation. if none of the factors change,
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# the cache dir will be the same so that we can reuse the compiled
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# graph.
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factors = []
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# 0. factors come from the env, for example, The values of
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# VLLM_PP_LAYER_PARTITION will affects the computation graph.
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env_hash = envs.compute_hash()
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factors.append(env_hash)
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# 1. factors come from the vllm_config (it mainly summarizes how the
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# model is created)
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config_hash = vllm_config.compute_hash()
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factors.append(config_hash)
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# 2. factors come from the code files that are traced by Dynamo (
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# it mainly summarizes how the model is used in forward pass)
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forward_code_files = list(
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sorted(self.compilation_config.traced_files))
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self.compilation_config.traced_files.clear()
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logger.debug(
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"Traced files (to be considered for compilation cache):\n%s",
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"\n".join(forward_code_files))
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hash_content = []
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for filepath in forward_code_files:
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hash_content.append(filepath)
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if filepath == "<string>":
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# This means the function was dynamically generated, with
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# e.g. exec(). We can't actually check these.
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continue
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with open(filepath) as f:
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hash_content.append(f.read())
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import hashlib
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code_hash = hashlib.md5("\n".join(hash_content).encode(),
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usedforsecurity=False).hexdigest()
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factors.append(code_hash)
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# 3. compiler hash
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compiler_hash = self.compiler_manager.compute_hash(vllm_config)
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factors.append(compiler_hash)
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# combine all factors to generate the cache dir
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hash_key = hashlib.md5(str(factors).encode(),
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usedforsecurity=False).hexdigest()[:10]
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cache_dir = os.path.join(
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envs.VLLM_CACHE_ROOT,
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"torch_compile_cache",
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hash_key,
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)
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self.compilation_config.cache_dir = cache_dir
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cache_dir = self.compilation_config.cache_dir
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os.makedirs(cache_dir, exist_ok=True)
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self.compilation_config.cache_dir = cache_dir
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rank = vllm_config.parallel_config.rank
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dp_rank = vllm_config.parallel_config.data_parallel_rank
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local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}",
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self.prefix)
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os.makedirs(local_cache_dir, exist_ok=True)
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self.compilation_config.local_cache_dir = local_cache_dir
|
|
|
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disable_cache = envs.VLLM_DISABLE_COMPILE_CACHE
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|
|
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if disable_cache:
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logger.info("vLLM's torch.compile cache is disabled.")
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else:
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|
logger.info("Using cache directory: %s for vLLM's torch.compile",
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|
local_cache_dir)
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|
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self.compiler_manager.initialize_cache(local_cache_dir, disable_cache,
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|
self.prefix)
|
|
|
|
# when dynamo calls the backend, it means the bytecode
|
|
# transform and analysis are done
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|
compilation_counter.num_graphs_seen += 1
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from .monitor import torch_compile_start_time
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dynamo_time = time.time() - torch_compile_start_time
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|
logger.info("Dynamo bytecode transform time: %.2f s", dynamo_time)
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self.compilation_config.compilation_time += dynamo_time
|
|
|
|
# we control the compilation process, each instance can only be
|
|
# called once
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|
assert not self._called, "VllmBackend can only be called once"
|
|
|
|
self.graph = graph
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|
self.configure_post_pass()
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|
|
|
self.split_gm, self.piecewise_graphs = split_graph(
|
|
graph, self.compilation_config.splitting_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)
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|
lazy_format_graph_code("after split", self.split_gm)
|
|
|
|
compilation_counter.num_piecewise_graphs_seen += len(
|
|
self.piecewise_graphs)
|
|
submod_names_to_compile = [
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|
item.submod_name for item in self.piecewise_graphs
|
|
if not item.is_splitting_graph
|
|
]
|
|
|
|
# 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.graph_pool,
|
|
self).run(*example_inputs)
|
|
|
|
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 # noqa
|
|
# 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("Computation graph saved to %s", graph_path)
|
|
|
|
self._called = True
|
|
|
|
if not self.compilation_config.use_cudagraph or \
|
|
not self.compilation_config.cudagraph_copy_inputs:
|
|
return self.split_gm
|
|
|
|
# if we need to copy input buffers for cudagraph
|
|
from torch._guards import detect_fake_mode
|
|
fake_mode = detect_fake_mode()
|
|
fake_args = [
|
|
fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t
|
|
for t in example_inputs
|
|
]
|
|
|
|
# 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 copy_and_call
|