vllm.compilation.backends ¶

class CompilerManager:
    """
    A manager to manage the compilation process, including
    caching the compiled graph, loading the compiled graph,
    and compiling the graph.

    The cache is a dict mapping
    `(runtime_shape, graph_index, backend_name)`
    to `any_data` returned from the compiler.

    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.
    """

    def __init__(self, compilation_config: CompilationConfig) -> None:
        self.cache: dict[tuple[Range, int, str], Any] = dict()
        self.is_cache_updated = False
        self.compilation_config = compilation_config
        self.compiler = make_compiler(compilation_config)

    def compute_hash(self, vllm_config: VllmConfig) -> str:
        return self.compiler.compute_hash(vllm_config)

    @contextmanager
    def compile_context(self, compile_range: Range) -> Generator[None, None, None]:
        """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)."""
        with pass_context(compile_range):
            if self.compilation_config.use_inductor_graph_partition:
                with inductor_partition_rule_context(
                    self.compilation_config.splitting_ops
                ):
                    yield
            else:
                yield

    def initialize_cache(
        self, cache_dir: str, disable_cache: bool = False, prefix: str = ""
    ) -> None:
        """
        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.
        """

        self.disable_cache = disable_cache
        self.cache_dir = cache_dir
        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
            with open(self.cache_file_path) as f:
                # 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.
                cache = ast.literal_eval(f.read())

            def check_type(value: Any, ty: type) -> None:
                if not isinstance(value, ty):
                    raise TypeError(f"Expected {ty} but got {type(value)} for {value}")

            def parse_key(key: Any) -> tuple[Range, int, str]:
                range_tuple, graph_index, compiler_name = key
                check_type(graph_index, int)
                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)
                return range_tuple, graph_index, compiler_name

            self.cache = {parse_key(key): value for key, value in cache.items()}

        self.compiler.initialize_cache(
            cache_dir=cache_dir, disable_cache=disable_cache, prefix=prefix
        )

    def save_to_file(self) -> None:
        if self.disable_cache or not self.is_cache_updated:
            return
        printer = pprint.PrettyPrinter(indent=4)
        data = printer.pformat(self.cache)
        with open(self.cache_file_path, "w") as f:
            f.write(data)

    def load(
        self,
        graph: fx.GraphModule,
        example_inputs: list[Any],
        graph_index: int,
        compile_range: Range,
    ) -> Callable[..., Any] | None:
        if (compile_range, graph_index, self.compiler.name) not in self.cache:
            return None
        handle = self.cache[(compile_range, graph_index, self.compiler.name)]
        compiled_graph = self.compiler.load(
            handle, graph, example_inputs, graph_index, compile_range
        )
        logger.debug(
            "Directly load the %s-th graph for compile range %sfrom %s via handle %s",
            graph_index,
            str(compile_range),
            self.compiler.name,
            handle,
        )
        return compiled_graph

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

        compilation_counter.num_backend_compilations += 1

        compiled_graph = None

        # try to load from the cache
        compiled_graph = self.load(graph, example_inputs, graph_index, compile_range)
        if compiled_graph is not None:
            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.
                now = time.time()
                elapsed = now - compilation_start_time
                compilation_config.compilation_time += elapsed
                logger.info(
                    "Directly load the compiled graph(s) for compile range %s "
                    "from the cache, took %.3f s",
                    str(compile_range),
                    elapsed,
                )
            return compiled_graph

        # no compiler cached the graph, or the cache is disabled,
        # we need to compile it
        if isinstance(self.compiler, InductorAdaptor):
            # Let compile_fx generate a key for us
            maybe_key = None
        else:
            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):
            compiled_graph, handle = self.compiler.compile(
                graph,
                example_inputs,
                additional_inductor_config,
                compile_range,
                maybe_key,
            )

        assert compiled_graph is not None, "Failed to compile the graph"

        # store the artifact in the cache
        if is_compile_cache_enabled(additional_inductor_config) and handle is not None:
            self.cache[(compile_range, graph_index, self.compiler.name)] = handle
            compilation_counter.num_cache_entries_updated += 1
            self.is_cache_updated = True
            if graph_index == 0:
                # adds some info logging for the first graph
                logger.info_once(
                    "Cache the graph of compile range %s for later use",
                    str(compile_range),
                )
            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,
            )

        # after compiling the last graph, record the end time
        if graph_index == num_graphs - 1:
            now = time.time()
            elapsed = now - compilation_start_time
            compilation_config.compilation_time += elapsed
            logger.info_once(
                "Compiling a graph for compile range %s takes %.2f s",
                str(compile_range),
                elapsed,
                scope="local",
            )

        return compiled_graph

class PiecewiseCompileInterpreter(torch.fx.Interpreter):  # type: ignore[misc]
    """Code adapted from `torch.fx.passes.shape_prop.ShapeProp`.
    It runs the given graph with fake inputs, and compile some
    submodules specified by `compile_submod_names` with the given
    compilation configs.

    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.

    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.
    """

    def __init__(
        self,
        module: torch.fx.GraphModule,
        compile_submod_names: list[str],
        vllm_config: VllmConfig,
        vllm_backend: "VllmBackend",
    ) -> None:
        super().__init__(module)
        from torch._guards import detect_fake_mode

        self.fake_mode = detect_fake_mode()
        self.compile_submod_names = compile_submod_names
        self.compilation_config = vllm_config.compilation_config
        self.vllm_config = vllm_config
        self.vllm_backend = vllm_backend
        # When True, it annoyingly dumps the torch.fx.Graph on errors.
        self.extra_traceback = False

    def run(self, *args: Any) -> Any:
        # maybe instead just assert inputs are fake?
        fake_args = [
            self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t
            for t in args
        ]
        with self.fake_mode, enable_python_dispatcher():
            return super().run(*fake_args)

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

        output = super().call_module(target, args, kwargs)

        if target in self.compile_submod_names:
            index = self.compile_submod_names.index(target)
            submod = self.fetch_attr(target)

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

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

            from .piecewise_backend import PiecewiseBackend

            piecewise_backend = PiecewiseBackend(
                submod,
                self.vllm_config,
                index,
                len(self.compile_submod_names),
                sym_shape_indices,
                self.vllm_backend,
                graph_returns_tuple(submod),
            )

            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,
            )

            compilation_counter.num_piecewise_capturable_graphs_seen += 1

        return output

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

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

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

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

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

    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
        is_encoder: bool = False,
    ) -> None:
        # if the model is initialized with a non-empty prefix,
        # then usually it's enough to use that prefix,
        # e.g. language_model, vision_model, etc.
        # when multiple parts are initialized as independent
        # models, we need to use the model_tag to distinguish
        # them, e.g. backbone (default), eagle_head, etc.
        self.prefix = prefix or model_tag

        # Mark compilation for encoder.
        self.is_encoder = is_encoder or model_is_encoder

        # Passes to run on the graph post-grad.
        self.pass_manager = resolve_obj_by_qualname(
            current_platform.get_pass_manager_cls()
        )()
        self.pass_key = current_platform.pass_key

        self.vllm_config = vllm_config
        self.compilation_config = vllm_config.compilation_config

        self.compiler_manager: CompilerManager = CompilerManager(
            self.compilation_config
        )

        # Deepcopy the inductor config to detach the post-grad custom pass
        # from CompilationConfig.
        # We want to avoid PostGradPassManager in CompilationConfig because
        # in future we need PostGradPassManager.uuid() to be executed
        # only at compile time.
        self.inductor_config = deepcopy(self.compilation_config.inductor_compile_config)
        # `torch.compile` is JIT compiled, so we don't need to
        # do anything here

    def 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

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

        # Post-grad custom passes are run using the post_grad_custom_post_pass
        # hook. If a pass for that hook exists, add it to the pass manager.
        if self.pass_key in self.inductor_config:
            if isinstance(self.inductor_config[self.pass_key], PostGradPassManager):
                raise ValueError(
                    "PostGradPassManager can not be kept in CompilationConfig."
                )
            else:
                # Config should automatically wrap all inductor passes
                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]
                )
        self.inductor_config[self.pass_key] = self.pass_manager

    def __call__(self, graph: fx.GraphModule, example_inputs: Sequence[Any]) -> Any:
        from .caching import (
            VllmSerializableFunction,
        )

        vllm_config = self.vllm_config
        # 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())
            except (OSError, UnicodeDecodeError):
                logger.warning("Failed to read file %s", filepath)
                continue
        code_hash = hashlib.sha256("\n".join(hash_content).encode()).hexdigest()
        # Clear after consumption
        self.compilation_config.traced_files.clear()
        if not self.compilation_config.cache_dir:
            # no provided cache dir, generate one based on the known factors
            # that affects the compilation. if none of the factors change,
            # the cache dir will be the same so that we can reuse the compiled
            # graph.
            factors = [env_hash, config_hash, code_hash, compiler_hash]
            # Use SHA-256 for cache key hashing to be consistent across
            # compute_hash functions. Truncate for a short cache dir name.
            hash_key = hashlib.sha256(str(factors).encode()).hexdigest()[:10]
            cache_dir = os.path.join(
                envs.VLLM_CACHE_ROOT, "torch_compile_cache", hash_key
            )
            self.compilation_config.cache_dir = cache_dir

        cache_dir = self.compilation_config.cache_dir
        os.makedirs(cache_dir, exist_ok=True)
        self.compilation_config.cache_dir = cache_dir
        rank = vllm_config.parallel_config.rank
        dp_rank = vllm_config.parallel_config.data_parallel_index
        local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}", self.prefix)
        os.makedirs(local_cache_dir, exist_ok=True)
        self.compilation_config.local_cache_dir = local_cache_dir

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

        if disable_cache:
            logger.info_once("vLLM's torch.compile cache is disabled.", scope="local")
        else:
            logger.info_once(
                "Using cache directory: %s for vLLM's torch.compile",
                local_cache_dir,
                scope="local",
            )

        self.compiler_manager.initialize_cache(
            local_cache_dir, disable_cache, self.prefix
        )

        # Reuses existing cache key

        logger.debug(
            "torch.compile cache factors: env=%s cfg=%s comp=%s code=%s dir=%s",
            env_hash,
            config_hash,
            compiler_hash,
            code_hash,
            local_cache_dir,
        )

        # Persist and log only hash-relevant factors together.
        try:
            logger.debug(
                "Compile env factors (raw):\n%s\nVllm config hash: %s",
                lazy(partial(pprint.pformat, env_factors, width=120)),
                config_hash,
            )
            meta_path = os.path.join(local_cache_dir, "cache_key_factors.json")
            if not os.path.exists(meta_path):
                with open(meta_path, "w") as f:
                    json.dump(
                        {
                            "env": env_factors,  # raw factors used for env_hash
                            "config_hash": config_hash,
                            "code_hash": code_hash,
                            "compiler_hash": compiler_hash,
                        },
                        f,
                        indent=2,
                        sort_keys=True,
                    )
        except Exception:
            # Best-effort only; metadata write failures are non-fatal.
            logger.warning(
                (
                    "Could not write compile cache metadata at %s; continuing without "
                    "metadata. Compiled cache remains valid; diagnostics may be "
                    "limited."
                ),
                local_cache_dir,
                exc_info=True,
            )

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

        dynamo_time = time.time() - torch_compile_start_time
        logger.info_once(
            "Dynamo bytecode transform time: %.2f s", dynamo_time, scope="local"
        )
        self.compilation_config.compilation_time += dynamo_time

        # we control the compilation process, each instance can only be
        # called once
        assert not self._called, "VllmBackend can only be called once"

        self.graph = graph
        self.configure_post_pass()

        if self.compilation_config.use_inductor_graph_partition:
            # Let Inductor decide partitioning; avoid FX-level pre-splitting.
            fx_split_ops: list[str] = []
        else:
            fx_split_ops = self.compilation_config.splitting_ops or []

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

        # 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)

        from torch._dynamo.utils import lazy_format_graph_code

        # depyf will hook lazy_format_graph_code and dump the graph
        # for debugging, no need to print the graph here
        lazy_format_graph_code("before split", self.graph)
        lazy_format_graph_code("after split", self.split_gm)

        compilation_counter.num_piecewise_graphs_seen += len(self.piecewise_graphs)
        submod_names_to_compile = [
            item.submod_name
            for item in self.piecewise_graphs
            if not item.is_splitting_graph
        ]

        # Extract fake values from the graph to use them when needed.
        all_fake_values = []
        for i in graph.graph.find_nodes(op="placeholder"):
            all_fake_values.append(i.meta["example_value"])

        fake_args = [
            all_fake_values[i] if isinstance(t, torch.Tensor) else t
            for i, t in enumerate(example_inputs)
        ]

        # propagate the split graph to the piecewise backend,
        # compile submodules with symbolic shapes
        PiecewiseCompileInterpreter(
            self.split_gm, submod_names_to_compile, self.vllm_config, self
        ).run(*fake_args)

        from torch._guards import detect_fake_mode

        fake_mode = detect_fake_mode()

        if (
            self.compilation_config.dynamic_shapes_config.evaluate_guards
            and self.compilation_config.dynamic_shapes_config.type
            == DynamicShapesType.BACKED
        ):
            from torch.utils._sympy.value_ranges import ValueRanges

            # Drop counter-0/1 specializations guards; for backed dynamic shapes,
            # torch.compile will specialize for 0/1 inputs or otherwise guards that
            # shape is >= 2. This is because it's really hard not to hit a check
            # against 0/1. When we evaluate shape guards, we exclude checking those
            # guards (We would fail always otherwise).

            # We avoid that by updating the ranges of backed sizes when the min is
            # 2 for any, we assume it's 0.
            for s, r in fake_mode.shape_env.var_to_range.items():
                if r.lower == 2:
                    fake_mode.shape_env.var_to_range[s] = ValueRanges(0, r.upper)

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

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

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

        if (
            self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE
            or not self.compilation_config.cudagraph_copy_inputs
        ):
            return VllmSerializableFunction(
                graph_to_serialize,
                example_inputs,
                self.prefix,
                self.split_gm,
                is_encoder=self.is_encoder,
                vllm_backend=self,
            )

        # 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

        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
        copy_and_call = make_copy_and_call(
            sym_tensor_indices,
            [example_inputs[x].clone() for x in sym_tensor_indices],
            self.split_gm,
        )

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