backends.py 45.9 KB
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# 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
import pprint
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import time
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from collections import defaultdict
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from collections.abc import Callable, Generator, 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
import torch.fx as fx
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from torch._dynamo.utils import dynamo_timed
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from torch._logging._internal import trace_structured
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import vllm.envs as envs
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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.tracing import instrument, instrument_manual
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from vllm.utils.import_utils import resolve_obj_by_qualname
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from .compiler_interface import (
    CompilerInterface,
    EagerAdaptor,
    InductorAdaptor,
    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 .partition_rules import (
    inductor_partition_rule_context,
    should_split,
)
from .passes.inductor_pass import InductorPass, pass_context
from .passes.pass_manager import PostGradPassManager
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logger = init_logger(__name__)

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def make_copy_and_call(
    sym_tensor_indices: list[int],
    input_buffers: list[torch.Tensor | None],
    callable_fn: Callable[..., Any],
) -> Callable[..., Any]:
    """Create a wrapper that copies inputs to static buffers before calling.

    This is used for cudagraph input copying where we need to copy dynamic
    tensors to static buffers before invoking the compiled graph.

    Args:
        sym_tensor_indices: Indices of tensors with symbolic shapes
        input_buffers: List of static buffers (can contain None for lazy init)
        callable_fn: The compiled function to call

    Returns:
        A wrapper function that copies inputs and calls the compiled function
    """

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    def copy_and_call(*args: Any) -> Any:
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        list_args = list(args)
        for i, index in enumerate(sym_tensor_indices):
            runtime_tensor = list_args[index]
            runtime_shape = runtime_tensor.shape[0]

            # lazy initialization of buffer on first call
            if input_buffers[i] is None:
                input_buffers[i] = runtime_tensor.clone()

            static_tensor = input_buffers[i][:runtime_shape]  # type: ignore[index]
            static_tensor.copy_(runtime_tensor)
            list_args[index] = static_tensor
        return callable_fn(*list_args)

    return copy_and_call


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def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface:
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    assert not envs.VLLM_USE_MEGA_AOT_ARTIFACT or envs.VLLM_USE_STANDALONE_COMPILE, (
        "VLLM_USE_MEGA_AOT_ARTIFACT=1 requires VLLM_USE_STANDALONE_COMPILE=1"
    )

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    if compilation_config.backend == "inductor":
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        # Use standalone compile only if requested, version is new enough,
        # and the symbol actually exists in this PyTorch build.
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        if envs.VLLM_USE_STANDALONE_COMPILE and hasattr(
            torch._inductor, "standalone_compile"
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        ):
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            logger.debug("Using InductorStandaloneAdaptor")
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            return InductorStandaloneAdaptor(
                compilation_config.compile_cache_save_format
            )
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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:
        logger.debug("Using custom backend: %s", compilation_config.backend)
        compiler = resolve_obj_by_qualname(current_platform.get_compile_backend())()
        assert isinstance(compiler, CompilerInterface)
        return compiler
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class CompilerManager:
    """
    A manager to manage the compilation process, including
    caching the compiled graph, loading the compiled graph,
    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
    for readability. We don't use json here because json doesn't
    support int as key.
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    """

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    def __init__(self, compilation_config: CompilationConfig) -> None:
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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
        self.compiler = make_compiler(compilation_config)
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        self.loaded_artifacts: dict[str, Any] = {}
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    def compute_hash(self, vllm_config: VllmConfig) -> str:
        return self.compiler.compute_hash(vllm_config)
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    @contextmanager
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    def compile_context(self, compile_range: Range) -> Generator[None, None, None]:
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        """Provide compilation context for the duration of compilation to set
        any torch global properties we want to scope to a single Inductor
        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
            else:
                yield

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    def initialize_cache(
        self, cache_dir: str, disable_cache: bool = False, prefix: str = ""
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    ) -> None:
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        """
        Initialize the cache directory for the compiler.

        The organization of the cache directory is as follows:
        cache_dir=/path/to/hash_str/rank_i_j/prefix/
        inside cache_dir, there will be:
        - vllm_compile_cache.py
        - computation_graph.py
        - transformed_code.py

        for multiple prefixes, they can share the same
        base cache dir of /path/to/hash_str/rank_i_j/ ,
        to store some common compilation artifacts.
        """

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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")

        if not disable_cache and os.path.exists(self.cache_file_path):
            # 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
                # because it is a safe way to parse Python literals.
                # 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: Any, ty: type) -> None:
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                if not isinstance(value, ty):
                    raise TypeError(f"Expected {ty} but got {type(value)} for {value}")

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            def parse_key(key: Any) -> tuple[Range, int, str]:
                range_tuple, graph_index, compiler_name = key
                check_type(graph_index, int)
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                check_type(compiler_name, str)
                if isinstance(range_tuple, tuple):
                    start, end = range_tuple
                    check_type(start, int)
                    check_type(end, int)
                    range_tuple = Range(start=start, end=end)
                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(
            cache_dir=cache_dir, disable_cache=disable_cache, prefix=prefix
        )
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    def save_to_file(self) -> None:
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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)
        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,
        graph: fx.GraphModule,
        example_inputs: list[Any],
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        graph_index: int,
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        compile_range: Range,
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    ) -> Callable[..., Any] | 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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        def parse_value(value: Any) -> tuple[tuple[str, str], str]:
            assert isinstance(value, dict)
            handle = value["graph_handle"]
            assert isinstance(handle[0], str)
            assert isinstance(handle[1], str)
            cache_key = value["cache_key"]
            return handle, cache_key

        try:
            handle, cache_key = parse_value(
                self.cache[(compile_range, graph_index, self.compiler.name)]
            )
        except Exception:
            # When the cache is outdated, we should ignore the existing file.
            # This should cause the correct cache to be generated again.
            return None

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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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        self.loaded_artifacts[cache_key] = compiled_graph
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        logger.debug(
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            "Directly load the %s-th graph for compile range %sfrom %s via handle %s",
            graph_index,
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            str(compile_range),
            self.compiler.name,
            handle,
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        )
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        return compiled_graph

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    @instrument(span_name="Compile graph")
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    def compile(
        self,
        graph: fx.GraphModule,
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        example_inputs: list[Any],
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        additional_inductor_config: dict[str, Any],
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        compilation_config: CompilationConfig,
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        compile_range: Range,
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        graph_index: int = 0,
        num_graphs: int = 1,
    ) -> Any:
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        if graph_index == 0:
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            # before compiling the first graph, record the start time
            global compilation_start_time
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            compilation_start_time = time.perf_counter()
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        compilation_counter.num_backend_compilations += 1

        compiled_graph = None

        # 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:
                # after loading the last graph for this shape, record the time.
                # there can be multiple graphs due to piecewise compilation.
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                elapsed = time.perf_counter() - compilation_start_time
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                compilation_config.compilation_time += elapsed
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                logger.info_once(
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                    "Directly load the compiled graph(s) for compile range %s "
                    "from the cache, took %.3f s",
                    str(compile_range),
                    elapsed,
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                    scope="local",
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                )
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            return compiled_graph

        # no compiler cached the graph, or the cache is disabled,
        # we need to compile it
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        if isinstance(self.compiler, InductorAdaptor):
            # Let compile_fx generate a key for us
            maybe_key = None
        else:
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            maybe_key = "artifact_compile_range_"
            maybe_key += f"{compile_range.start}_{compile_range.end}"
            maybe_key += f"_subgraph_{graph_index}"
        with self.compile_context(compile_range):
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            # There is a compilation time optimization here.
            #
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            # If the (input metadata, graph, compiler config) are the same, then
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            # we want to avoid compiling the same artifact again. If we didn't
            # do this optimization, the backend compilation (InductorAdaptor or
            # InductorStandaloneAdaptor)
            # is able to cache hit and produce an artifact faster if it was
            # already created, but it is still a duplicate artifact that
            # requires unnecessary things e.g. disk IO.
            #
            # The optimization is: If the backend compilation cache hits,
            # then do an early return from the backend compilation and look up
            # which of the previous in-memory artifacts we created to reuse.
            #
            # We implemented this by monkey-patching torch (torch does not
            # easily expose the cache_key function), but in the future torch
            # should expose the cache_key function that we can just call
            # directly before invoking backend compilation.
            cache_key = None
            orig = torch._functorch._aot_autograd.autograd_cache.autograd_cache_key

            def autograd_cache_key(*args, **kwargs):
                result = orig(*args, **kwargs)
                if result is None:
                    return None
                nonlocal cache_key
                cache_key = result[0]
                if cache_key in self.loaded_artifacts:
                    raise StopCompiling()
                return result

            from unittest.mock import patch

            with (
                # Graphs that are isometric (different node names but same
                # structure) should be treated as the same.
                torch._functorch.config.patch(autograd_cache_normalize_inputs=True),
                patch(
                    "torch._functorch._aot_autograd.autograd_cache.autograd_cache_key",
                    autograd_cache_key,
                ),
            ):
                try:
                    compiled_graph, handle = self.compiler.compile(
                        graph,
                        example_inputs,
                        additional_inductor_config,
                        compile_range,
                        maybe_key,
                    )
                except StopCompiling:
                    assert cache_key is not None
                    return self.loaded_artifacts[cache_key]
            if cache_key is not None and compiled_graph is not None:
                self.loaded_artifacts[cache_key] = compiled_graph
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        assert compiled_graph is not None, "Failed to compile the graph"

        # 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)] = {
                "graph_handle": handle,
                "cache_key": cache_key,
            }
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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:
                # adds some info logging for the first graph
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                logger.info_once(
                    "Cache the graph of compile range %s for later use",
                    str(compile_range),
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                )
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            logger.debug(
                "Store the %s-th graph for compile range%s from %s via handle %s",
                graph_index,
                str(compile_range),
                self.compiler.name,
                handle,
            )
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        # after compiling the last graph, record the end time
        if graph_index == num_graphs - 1:
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            elapsed = time.perf_counter() - compilation_start_time
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            compilation_config.compilation_time += elapsed
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            logger.info_once(
                "Compiling a graph for compile range %s takes %.2f s",
                str(compile_range),
                elapsed,
                scope="local",
            )
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        return compiled_graph
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class StopCompiling(BaseException):
    pass


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@dataclasses.dataclass
class SplitItem:
    submod_name: str
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    graph_id: int
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    is_splitting_graph: bool
    graph: fx.GraphModule


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def _is_empty_allocation_node(node: fx.Node) -> bool:
    if node.op == "call_method":
        return node.target == "new_empty"

    if node.op != "call_function":
        return False

    target = node.target
    if target in (torch.empty, torch.empty_like, torch.empty_strided):
        return True

    if isinstance(target, torch._ops.OpOverloadPacket):
        packet_name = target._qualified_op_name
    elif isinstance(target, torch._ops.OpOverload):
        packet_name = target.name()
    else:
        return False

    return packet_name.startswith("aten::empty") or packet_name.startswith(
        "aten::new_empty"
    )


def _merge_empty_only_subgraphs(
    node_to_subgraph_id: dict[fx.Node, int],
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    split_op_graphs: list[int],
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) -> None:
    """
    Merge a partition that only contains an empty allocation op into the
    previous partition. This avoids generating standalone empty submodules,
    which can lead to empty cudagraph captures.
    """

    nodes_by_subgraph_id: dict[int, list[fx.Node]] = defaultdict(list)
    for node, subgraph_id in node_to_subgraph_id.items():
        nodes_by_subgraph_id[subgraph_id].append(node)

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    splitting_subgraphs = set(split_op_graphs)
    prev_non_splitting_subgraph_id: int | None = None

    max_subgraph_id = max(node_to_subgraph_id.values(), default=-1)
    for subgraph_id in range(max_subgraph_id + 1):
        nodes = nodes_by_subgraph_id.get(subgraph_id, [])
        if not nodes:
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            continue
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        is_non_splitting_subgraph = subgraph_id not in splitting_subgraphs
        is_empty_only_subgraph = len(nodes) == 1 and _is_empty_allocation_node(nodes[0])
        merged = False

        if is_empty_only_subgraph and prev_non_splitting_subgraph_id is not None:
            # Safety check: don't move allocation before any input producer.
            empty_node = nodes[0]
            if all(
                input_node.op == "placeholder"
                or node_to_subgraph_id[input_node] <= prev_non_splitting_subgraph_id
                for input_node in empty_node.all_input_nodes
            ):
                node_to_subgraph_id[empty_node] = prev_non_splitting_subgraph_id
                merged = True

        if not merged and is_non_splitting_subgraph:
            prev_non_splitting_subgraph_id = subgraph_id
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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
    subgraph_id = 0
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    node_to_subgraph_id: dict[fx.Node, int] = {}
    split_op_graphs: list[int] = []
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    for node in graph.graph.nodes:
        if node.op in ("output", "placeholder"):
            continue
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        # Check if this is a getitem operation on a node from an earlier subgraph.
        # If so, assign it to the same subgraph as its input to avoid passing entire
        # tuple as input to submodules, which is against standalone_compile and
        # AoTAutograd input requirement.
        if node.op == "call_function" and node.target == operator.getitem:
            # Assign this getitem to the same subgraph as its input
            input_node = node.args[0]
            if input_node.op != "placeholder":
                assert input_node in node_to_subgraph_id
                node_to_subgraph_id[node] = node_to_subgraph_id[input_node]
                continue

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        if should_split(node, splitting_ops):
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            subgraph_id += 1
            node_to_subgraph_id[node] = subgraph_id
            split_op_graphs.append(subgraph_id)
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            # keep consecutive splitting ops together
            # (we know node.next exists because node isn't the last (output) node)
            if should_split(node.next, splitting_ops):
                # this will get incremented by the next node
                subgraph_id -= 1
            else:
                subgraph_id += 1
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        else:
            node_to_subgraph_id[node] = subgraph_id

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    _merge_empty_only_subgraphs(node_to_subgraph_id, split_op_graphs)
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    # `keep_original_order` is important!
    # otherwise pytorch might reorder the nodes and
    # the semantics of the graph will change when we
    # have mutations in the graph
    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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    outputs = []
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    names = [name for (name, module) in split_gm.named_modules()]
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    for name in names:
        if "." in name or name == "":
            # recursive child module or the root module
            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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def wrap_with_cudagraph_if_needed(
    piecewise_backend: Any,
    vllm_config: VllmConfig,
    compilation_config: CompilationConfig,
    is_first_graph: bool,
    is_last_graph: bool,
) -> Any:
    """
    Wrap a piecewise backend with CUDA graph wrapper if needed.
    This function is shared between VllmBackend and
    construct_serializable_fn_from_inductor_cache.

    Args:
        piecewise_backend: The backend to wrap
        vllm_config: The vLLM configuration
        compilation_config: The compilation configuration
        is_first_graph: Whether this is the first graph in the sequence
        is_last_graph: Whether this is the last graph in the sequence

    Returns:
        The wrapped backend if CUDA graphs are enabled, otherwise the original backend
    """
    if (
        not compilation_config.cudagraph_mode.has_piecewise_cudagraphs()
        or compilation_config.use_inductor_graph_partition
    ):
        return piecewise_backend

    # We're using Dynamo-based piecewise splitting, so we wrap
    # the whole subgraph with a static graph wrapper.
    from .cuda_graph import CUDAGraphOptions

    # resolve the static graph wrapper class (e.g. CUDAGraphWrapper
    # class) as platform dependent.
    static_graph_wrapper_class = resolve_obj_by_qualname(
        current_platform.get_static_graph_wrapper_cls()
    )

    # Always assign PIECEWISE runtime mode to the
    # CUDAGraphWrapper for piecewise_backend, to distinguish
    # it from the FULL cudagraph runtime mode, no matter it
    # is wrapped on a full or piecewise fx graph.
    return static_graph_wrapper_class(
        runnable=piecewise_backend,
        vllm_config=vllm_config,
        runtime_mode=CUDAGraphMode.PIECEWISE,
        cudagraph_options=CUDAGraphOptions(
            debug_log_enable=is_first_graph,
            gc_disable=not is_first_graph,
            weak_ref_output=is_last_graph,
        ),
    )


599
class PiecewiseCompileInterpreter(torch.fx.Interpreter):  # type: ignore[misc]
600
    """Code adapted from `torch.fx.passes.shape_prop.ShapeProp`.
601
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    It runs the given split graph interpreter, and for each submodule in
    `compile_submod_names`, creates a PiecewiseBackend and compiles all
    ranges up front.
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    NOTE: the order in `compile_submod_names` matters, because
    it will be used to determine the order of the compiled piecewise
    graphs. The first graph will handle logging, and the last graph
    has some special cudagraph output handling.
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    Note: This class shares similar logic with
    reconstruct_serializable_fn_from_mega_artifact in caching.py.
    Both create PiecewiseBackend instances and wrap them with cudagraph.
    The key difference is:
    - reconstruct_serializable_fn_from_mega_artifact: PiecewiseBackend receives
      pre-compiled runnables (compiled_runnables is set, graph is None)
    - this class: PiecewiseBackend receives the FX graph to compile
      (graph is set, compiled_runnables is None)


    If modifying the backend creation/wrapping logic, consider updating both.
621
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    """

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    def __init__(
        self,
        module: torch.fx.GraphModule,
        compile_submod_names: list[str],
        vllm_config: VllmConfig,
        vllm_backend: "VllmBackend",
629
    ) -> None:
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        super().__init__(module)
        self.compile_submod_names = compile_submod_names
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        self.compilation_config = vllm_config.compilation_config
        self.vllm_config = vllm_config
634
        self.vllm_backend = vllm_backend
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        # When True, it annoyingly dumps the torch.fx.Graph on errors.
        self.extra_traceback = False
637

638
    @instrument(span_name="Inductor compilation")
639
    def run(self, *args: Any) -> Any:
640
        return super().run(*args)
641

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    def call_module(
        self,
        target: torch.fx.node.Target,
        args: tuple[torch.fx.node.Argument, ...],
        kwargs: dict[str, Any],
    ) -> Any:
648
        assert isinstance(target, str)
649

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        gm = getattr(self.module, target)
        outputs = gm.graph.output_node().args[0]
        output = fx.map_arg(outputs, lambda node: node.meta["example_value"])
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        if target in self.compile_submod_names:
655
            index = self.compile_submod_names.index(target)
656
            submod = self.fetch_attr(target)
657

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660
            sym_shape_indices = [
                i for i, x in enumerate(args) if isinstance(x, torch.SymInt)
            ]
661

662
            # Lazy import here to avoid circular import
663
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            from torch._inductor.compile_fx import graph_returns_tuple

665
            from .piecewise_backend import PiecewiseBackend
666

667
            piecewise_backend = PiecewiseBackend(
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                submod,
                self.vllm_config,
                index,
                len(self.compile_submod_names),
                sym_shape_indices,
                self.vllm_backend,
674
                graph_returns_tuple(submod),
675
                submod_name=target,
676
            )
677

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            self.module.__dict__[target] = wrap_with_cudagraph_if_needed(
                piecewise_backend,
                self.vllm_config,
                self.compilation_config,
                piecewise_backend.is_first_graph,
                piecewise_backend.is_last_graph,
            )
685

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            compilation_counter.num_piecewise_capturable_graphs_seen += 1

        return output


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# the tag for the part of model being compiled,
# e.g. backbone/eagle_head
model_tag: str = "backbone"
694
model_is_encoder: bool = False
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697


@contextmanager
698
def set_model_tag(tag: str, is_encoder: bool = False) -> Generator[None, None, None]:
699
700
    """Context manager to set the model tag."""
    global model_tag
701
    global model_is_encoder
702
    assert tag != model_tag, (
703
        f"Model tag {tag} is the same as the current tag {model_tag}."
704
    )
705
    old_tag = model_tag
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    old_is_encoder = model_is_encoder

708
    model_tag = tag
709
    model_is_encoder = is_encoder
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    try:
        yield
    finally:
        model_tag = old_tag
714
        model_is_encoder = old_is_encoder
715
716


717
class VllmBackend:
718
    """The compilation backend for `torch.compile` with vLLM.
719
    It is used for compilation mode of `CompilationMode.VLLM_COMPILE`,
720
    where we customize the compilation.
721

722
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    The major work of this backend is to split the graph into
    piecewise graphs, and pass them to the piecewise backend.
724

725
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    This backend also adds the PostGradPassManager to Inductor config,
    which handles the post-grad passes.
727
    """
728

729
730
    vllm_config: VllmConfig
    compilation_config: CompilationConfig
731
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    _called: bool = False
    # the graph we compiled
    graph: fx.GraphModule
    # the stiching graph module for all the piecewise graphs
    split_gm: fx.GraphModule
736
    piecewise_graphs: list[SplitItem]
737
    returned_callable: Callable[..., Any]
738
    # Inductor passes to run on the graph pre-defunctionalization
739
    post_grad_passes: Sequence[Callable[..., Any]]
740
    compiler_manager: CompilerManager
741
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743
    # Copy of CompilationConfig.inductor_compile_config +
    # an entry for PostGradPassManager
    inductor_config: dict[str, Any]
744

745
746
    def __init__(
        self,
747
        vllm_config: VllmConfig,
748
        prefix: str = "",
749
        is_encoder: bool = False,
750
    ) -> None:
751
752
        # if the model is initialized with a non-empty prefix,
        # then usually it's enough to use that prefix,
753
        # e.g. language_model, vision_model, etc.
754
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756
757
758
        # 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.
        self.prefix = prefix or model_tag

759
        # Mark compilation for encoder.
760
        self.is_encoder = is_encoder or model_is_encoder
761

762
        # Passes to run on the graph post-grad.
763
764
765
766
        self.pass_manager = resolve_obj_by_qualname(
            current_platform.get_pass_manager_cls()
        )()
        self.pass_key = current_platform.pass_key
767

768
769
        self.vllm_config = vllm_config
        self.compilation_config = vllm_config.compilation_config
770

771
        self.compiler_manager: CompilerManager = CompilerManager(
772
773
            self.compilation_config
        )
774

775
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778
779
780
        # 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.
        self.inductor_config = deepcopy(self.compilation_config.inductor_compile_config)
781
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783
        # `torch.compile` is JIT compiled, so we don't need to
        # do anything here

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845
    def collect_standalone_compile_artifacts(
        self,
    ) -> tuple[Any, dict[str, list[int]] | None, dict[str, bool] | None]:
        """Collect inductor cache artifacts from all piecewise backends.

        Returns:
            tuple: (standalone_compile_artifacts, sym_shape_indices_map,
                    returns_tuple_map)
                - standalone_compile_artifacts: StandaloneCompiledArtifacts
                  with compiled artifacts
                - sym_shape_indices_map: dict mapping submod_name to
                  sym_shape_indices
                - returns_tuple_map: dict mapping submod_name to
                  returns_tuple
        """

        if not envs.VLLM_USE_MEGA_AOT_ARTIFACT:
            return None, None, None

        from .caching import StandaloneCompiledArtifacts
        from .piecewise_backend import PiecewiseBackend

        standalone_compile_artifacts = StandaloneCompiledArtifacts()
        sym_shape_indices_map = {}
        returns_tuple_map = {}

        for name, _ in self.split_gm.named_children():
            # get the actual attribute (shadowed by PiecewiseBackend in __dict__)
            child = getattr(self.split_gm, name)
            # unwrap the static graph wrapper class if applicable
            piecewise_backend = child.runnable if hasattr(child, "runnable") else child

            if not isinstance(piecewise_backend, PiecewiseBackend):
                continue

            submod_name = name
            sym_shape_indices_map[submod_name] = piecewise_backend.sym_shape_indices
            returns_tuple_map[submod_name] = piecewise_backend.returns_tuple

            for shape_str, bytes_data in piecewise_backend.to_bytes().items():
                standalone_compile_artifacts.insert(submod_name, shape_str, bytes_data)
                logger.debug(
                    "collected artifact for %s shape %s (%d bytes)",
                    submod_name,
                    shape_str,
                    len(bytes_data),
                )

        logger.info(
            "collected artifacts: %d entries, %d artifacts, %d bytes total",
            standalone_compile_artifacts.num_entries(),
            standalone_compile_artifacts.num_artifacts(),
            standalone_compile_artifacts.size_bytes(),
        )

        logger.debug(
            "standalone compile artifact keys: %s",
            list(standalone_compile_artifacts.submodule_bytes.keys()),
        )

        return standalone_compile_artifacts, sym_shape_indices_map, returns_tuple_map

846
    def configure_post_pass(self) -> None:
847
        self.pass_manager.configure(self.vllm_config)
848

849
850
        # 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.
851
852
853
854
855
        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."
                )
856
            else:
857
                # Config should automatically wrap all inductor passes
858
859
860
861
862
863
864
                assert isinstance(
                    self.compilation_config.inductor_compile_config[self.pass_key],
                    InductorPass,
                )
                self.pass_manager.add(
                    self.compilation_config.inductor_compile_config[self.pass_key]
                )
865
        self.inductor_config[self.pass_key] = self.pass_manager
866

867
868
869
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895
896
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898
899
900
    def _log_compilation_config(self):
        """Log vLLM compilation config for TORCH_TRACE/tlparse."""
        cc = self.compilation_config
        pass_cfg = cc.pass_config

        # Helper to convert lists to comma-separated strings for tlparse display
        def list_to_str(lst: list | None) -> str:
            if lst is None:
                return ""
            return ", ".join(str(x) for x in lst)

        # Get enabled passes by introspecting dataclass fields
        enabled_passes = [
            f.name
            for f in dataclasses.fields(pass_cfg)
            if isinstance(getattr(pass_cfg, f.name), bool) and getattr(pass_cfg, f.name)
        ]

        trace_structured(
            "artifact",
            metadata_fn=lambda: {
                "name": "vllm_compilation_config",
                "encoding": "json",
            },
            payload_fn=lambda: json.dumps(
                {
                    "model": self.vllm_config.model_config.model,
                    "prefix": self.prefix,
                    "mode": str(cc.mode),
                    "backend": cc.backend,
                    "custom_ops": list_to_str(cc.custom_ops),
                    "splitting_ops": list_to_str(cc.splitting_ops),
                    "cudagraph_mode": str(cc.cudagraph_mode),
                    "compile_sizes": list_to_str(cc.compile_sizes),
901
902
                    "compile_ranges_endpoints": list_to_str(
                        cc.compile_ranges_endpoints
903
904
905
906
907
908
909
910
911
912
                    ),
                    "use_inductor_graph_partition": cc.use_inductor_graph_partition,
                    "inductor_passes": list_to_str(list(cc.inductor_passes.keys())),
                    "enabled_passes": list_to_str(enabled_passes),
                    "dynamic_shapes_type": str(cc.dynamic_shapes_config.type),
                    "dynamic_shapes_evaluate_guards": cc.dynamic_shapes_config.evaluate_guards,  # noqa: E501
                }
            ),
        )

913
    @dynamo_timed("vllm_backend")
914
915
916
917
918
    def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
        from .caching import (
            VllmSerializableFunction,
        )

919
        vllm_config = self.vllm_config
920
921
922

        self._log_compilation_config()

923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
        # Minimal hashing here with existing utilities, reused below.

        env_factors = envs.compile_factors()
        env_hash = hash_factors(env_factors)
        # 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",
            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())
946
            except (OSError, UnicodeDecodeError):
947
948
949
950
951
                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()
952
953
954
955
956
        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.
957
958
959
960
            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]
961
            cache_dir = os.path.join(
962
                envs.VLLM_CACHE_ROOT, "torch_compile_cache", hash_key
963
964
965
            )
            self.compilation_config.cache_dir = cache_dir

966
        cache_dir = self.compilation_config.cache_dir
967
        os.makedirs(cache_dir, exist_ok=True)
968
        self.compilation_config.cache_dir = cache_dir
969
        rank = vllm_config.parallel_config.rank
970
        dp_rank = vllm_config.parallel_config.data_parallel_index
971
        local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}", self.prefix)
972
        os.makedirs(local_cache_dir, exist_ok=True)
973
        self.compilation_config.local_cache_dir = local_cache_dir
974

975
        # Honors opt-outs such as CompilationMode.NONE or VLLM_DISABLE_COMPILE_CACHE.
976
        disable_cache = not is_compile_cache_enabled(self.inductor_config)
977

978
979
980
981
982
983
984
        # TODO(patchy): ngram gpu kernel will cause vllm torch compile cache errors.
        is_ngram_gpu_enabled = (
            vllm_config.speculative_config is not None
            and vllm_config.speculative_config.use_ngram_gpu()
        )
        disable_cache = disable_cache or is_ngram_gpu_enabled

985
        if disable_cache:
986
            logger.info_once("vLLM's torch.compile cache is disabled.", scope="local")
987
        else:
988
989
990
991
            logger.info_once(
                "Using cache directory: %s for vLLM's torch.compile",
                local_cache_dir,
                scope="local",
992
            )
993

994
995
996
        self.compiler_manager.initialize_cache(
            local_cache_dir, disable_cache, self.prefix
        )
997

998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
        # 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,
            )

1042
1043
        # when dynamo calls the backend, it means the bytecode
        # transform and analysis are done
1044
        compilation_counter.num_graphs_seen += 1
1045
        from .monitor import torch_compile_start_time
1046

1047
        dynamo_time = time.perf_counter() - torch_compile_start_time
1048
1049
1050
        logger.info_once(
            "Dynamo bytecode transform time: %.2f s", dynamo_time, scope="local"
        )
1051
        self.compilation_config.compilation_time += dynamo_time
1052

1053
1054
1055
1056
1057
        # Record Dynamo time in tracing if available
        start_time = int(torch_compile_start_time * 1e9)
        attributes = {"dynamo.time_seconds": dynamo_time}
        instrument_manual("Dynamo bytecode transform", start_time, None, attributes)

1058
1059
1060
1061
1062
        # 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
1063
        self.configure_post_pass()
1064

1065
1066
1067
1068
1069
1070
        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 []

1071
        self.split_gm, self.piecewise_graphs = split_graph(graph, fx_split_ops)
1072

1073
1074
1075
1076
1077
1078
        # keep a split_gm copy from BEFORE the interpreter replaces
        # submodules with PiecewiseBackend -- used for serialization
        original_split_gm = None
        if envs.VLLM_USE_MEGA_AOT_ARTIFACT:
            original_split_gm = deepcopy(self.split_gm)

1079
        from torch._dynamo.utils import lazy_format_graph_code
1080
1081
1082
1083
1084

        # 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)
1085

1086
1087
1088
1089
1090
1091
1092
        # Log the piecewise split graph for TORCH_TRACE/tlparse
        trace_structured(
            "graph_dump",
            metadata_fn=lambda: {"name": "vllm_piecewise_split_graph"},
            payload_fn=lambda: self.split_gm.print_readable(print_output=False),
        )

1093
        compilation_counter.num_piecewise_graphs_seen += len(self.piecewise_graphs)
1094
        submod_names_to_compile = [
1095
1096
            item.submod_name
            for item in self.piecewise_graphs
1097
1098
1099
            if not item.is_splitting_graph
        ]

1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
        # 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)
        ]

1110
        # propagate the split graph to the piecewise backend,
1111
1112
1113
        # compile submodules with symbolic shapes, and compile all ranges
        # up front so that compilation is complete before the callable
        # is returned.
1114
1115
        PiecewiseCompileInterpreter(
            self.split_gm, submod_names_to_compile, self.vllm_config, self
1116
        ).run(*fake_args)
1117

1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
        # All compilation is done. Save the cache.
        time_before_saving = time.perf_counter()
        self.compiler_manager.save_to_file()
        elapsed = time.perf_counter() - time_before_saving
        if elapsed > 1:
            logger.info_once(
                "Saved compiler manager cache in %.2f seconds.",
                elapsed,
                scope="local",
            )

1129
1130
1131
1132
1133
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        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)

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        graph_path = os.path.join(local_cache_dir, "computation_graph.py")
        if not os.path.exists(graph_path):
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            # code adapted from
            # https://github.com/thuml/depyf/blob/dab831108a752d1facc00acdd6d4243891845c37/depyf/explain/patched_lazy_format_graph_code.py#L30
1156
            # use `print_readable` because it can include submodules
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            src = (
                "from __future__ import annotations\nimport torch\n"
                + self.split_gm.print_readable(print_output=False)
            )
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            src = src.replace("<lambda>", "GraphModule")
            with open(graph_path, "w") as f:
                f.write(src)

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            logger.debug_once(
                "Computation graph saved to %s", graph_path, scope="local"
            )
1168

1169
        self._called = True
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        graph_to_serialize = (
            original_split_gm if envs.VLLM_USE_MEGA_AOT_ARTIFACT else self.graph
        )
1173

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        if (
            self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE
            or not self.compilation_config.cudagraph_copy_inputs
        ):
1178
            return VllmSerializableFunction(
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                graph_to_serialize,
                example_inputs,
                self.prefix,
                self.split_gm,
                is_encoder=self.is_encoder,
                vllm_backend=self,
1185
            )
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        # index of tensors that have symbolic shapes (batch size)
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        # 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
1191

1192
        sym_tensor_indices = [
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            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())
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        ]

        # compiler managed cudagraph input buffers
        # we assume the first run with symbolic shapes
        # has the maximum size among all the tensors
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        copy_and_call = make_copy_and_call(
            sym_tensor_indices,
            [example_inputs[x].clone() for x in sym_tensor_indices],
            self.split_gm,
        )
1207

1208
        return VllmSerializableFunction(
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            graph_to_serialize,
            example_inputs,
            self.prefix,
            copy_and_call,
            is_encoder=self.is_encoder,
            vllm_backend=self,
            sym_tensor_indices=sym_tensor_indices,
1216
        )