# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import dataclasses import io import pickle from collections.abc import Callable from pickle import Pickler from typing import Any import torch._functorch.config import torch.fx as fx from torch._inductor.runtime.triton_heuristics import CachingAutotuner from vllm.compilation.backends import VllmBackend from vllm.compilation.monitor import end_monitoring_torch_compile from vllm.config import VllmConfig from vllm.config.utils import Range from vllm.logger import init_logger logger = init_logger(__name__) @dataclasses.dataclass class RangeEntry: compile_range: Range compiled: bool = False runnable: Callable[..., Any] = None # type: ignore class PiecewiseBackend: def __init__( self, graph: fx.GraphModule | None, vllm_config: VllmConfig, piecewise_compile_index: int, total_piecewise_compiles: int, sym_shape_indices: list[int], vllm_backend: VllmBackend, returns_tuple: bool, compiled_runnables: dict[str, Callable[..., Any]] | None = None, ): """ The backend for piecewise compilation. It mainly handles the compilation of static shapes and dispatching based on runtime shape. We will compile `self.graph` once for the general shape, and then compile for different shapes specified in `compilation_config.compile_sizes`. This class supports two mutually exclusive modes: 1. Compilation (graph is set, compiled_runnables is None): Used during initial compilation when we have the FX graph and need to compile it for each shape range. 2. Precompilation (graph is None, compiled_runnables is set): Used when loading from cache/AOT artifacts where we already have pre-compiled callables and don't need the original graph. Exactly one of graph or compiled_runnables must be provided. """ assert bool(graph is not None) ^ bool(compiled_runnables is not None), ( "exactly one of graph and compiled_runnables should be set." ) self.graph = graph self.vllm_config = vllm_config self.compilation_config = vllm_config.compilation_config self.piecewise_compile_index = piecewise_compile_index self.total_piecewise_compiles = total_piecewise_compiles self.vllm_backend = vllm_backend self.compiled_runnables = compiled_runnables self.is_first_graph = piecewise_compile_index == 0 self.is_last_graph = piecewise_compile_index == total_piecewise_compiles - 1 self.is_full_graph = total_piecewise_compiles == 1 self.is_encoder_compilation = vllm_backend.is_encoder self.compile_ranges = self.compilation_config.get_compile_ranges() if self.is_encoder_compilation: # For encoder compilation we use the max int32 value # to set the upper bound of the compile ranges max_int32 = 2**31 - 1 last_compile_range = self.compile_ranges[-1] assert ( last_compile_range.end == vllm_config.scheduler_config.max_num_batched_tokens ) self.compile_ranges[-1] = Range( start=last_compile_range.start, end=max_int32 ) log_string = f"PiecewiseBackend: compile_ranges: {self.compile_ranges}" logger.debug_once(log_string) self.compile_sizes = self.compilation_config.compile_sizes log_string = f"PiecewiseBackend: compile_sizes: {self.compile_sizes}" logger.debug_once(log_string) self.sym_shape_indices = sym_shape_indices self.returns_tuple = returns_tuple # the entries for ranges that we need to either self.range_entries: dict[Range, RangeEntry] = {} # to_be_compiled_ranges tracks the remaining ranges to compile, # and updates during the compilation process, so we need to copy it self.to_be_compiled_ranges: set[Range] = set(self.compile_ranges) # We only keep compilation management inside this class directly. if self.compile_sizes is not None: for size in self.compile_sizes: if isinstance(size, str): assert size == "cudagraph_capture_sizes" raise NotImplementedError( "cudagraph_capture_sizes not supported in compile_sizes." "This should be handled in `post_init_cudagraph_sizes`." ) else: assert isinstance(size, int) range = Range(start=size, end=size) if range not in self.compile_ranges: self.range_entries[range] = RangeEntry( compile_range=range, ) self.to_be_compiled_ranges.add(range) for range in self.compile_ranges: self.range_entries[range] = RangeEntry( compile_range=range, ) # get the on_compilation_complete callback from context... # PiecewiseBackend is created during the first call, # which is when the context is set (see compilation/decorators.py) from vllm.compilation.backends import _on_compilation_complete_callback self.on_compilation_complete = _on_compilation_complete_callback.get() def get_compiled_graph_wrapper( self, compiled_graph: Callable[..., Any] ) -> Callable[..., Any]: def compiled_graph_wrapper(*args: Any) -> Any: graph_output = compiled_graph(*args) # unpack the tuple if needed # TODO(rzou): the implication is that we're not # reading the python bytecode correctly in vLLM? if self.returns_tuple or not isinstance(graph_output, (tuple, list)): return graph_output else: return graph_output[0] return compiled_graph_wrapper def check_for_ending_compilation(self) -> None: if self.is_last_graph and not self.to_be_compiled_ranges: # no specific sizes to compile # save the hash of the inductor graph for the next run self.vllm_backend.compiler_manager.save_to_file() end_monitoring_torch_compile(self.vllm_config) # Call the completion callback (e.g., to save AOT compiled function) if self.on_compilation_complete is not None: self.on_compilation_complete() def to_bytes(self) -> dict[str, bytes]: class StandaloneCompiledArtifactsPickler(Pickler): def reducer_override(self, obj: object) -> Any: if isinstance(obj, CachingAutotuner): obj.prepare_for_pickle() return pickle.loads, ( pickle.dumps( obj, ), ) return NotImplemented def serialize(fn: Callable[..., Any]) -> bytes: assert hasattr(fn, "serialize"), "fn must have serialize method" with torch._functorch.config.patch("bundled_autograd_cache", True): entry = fn.serialize() f = io.BytesIO() StandaloneCompiledArtifactsPickler(f).dump(entry) result = f.getvalue() return result out = {} for range_key, entry in self.range_entries.items(): if not entry.compiled: logger.debug( "entry with range %s not compiled, so cannot get its bytes", range_key, ) continue if hasattr(entry.runnable, "serialize"): out[str(range_key)] = serialize(entry.runnable) return out def _fakify_args(self, args: tuple[Any, ...]) -> list[Any]: # We need to pass fake example_inputs, otherwise torch.compile # will fakify the example_inputs potentially causing some non dynamic # dimension to be be duck shaped to other existing shapes that have hints # matching their values. # This is problem because it can lead to unintended specializations! # if the new wrongly dynamic dim is specialized # it will force specializing the whole shape # torch.compile probably should not accept # non fake tensors as example inputs! # See issue https://github.com/vllm-project/vllm/issues/27899 fake_example_inputs = [] assert self.graph is not None for node in self.graph.graph.nodes: # All place holders come first if node.op == "placeholder": fake_example_inputs.append(node.meta["example_value"]) else: break assert len(fake_example_inputs) == len(args) return fake_example_inputs def _maybe_compile_for_range_entry( self, range_entry: RangeEntry, args: tuple[Any, ...] ) -> Any: if not range_entry.compiled: if self.compiled_runnables is not None: range_entry.runnable = self.get_compiled_graph_wrapper( self.compiled_runnables[str(range_entry.compile_range)] ) else: # args are real arguments # fakify for range, real args for concrete size. # For concrete size, we clear the shape env in # compiler_manager.compile() so no need to fakify. args_list = ( self._fakify_args(args) if not range_entry.compile_range.is_single_size() else list(args) ) with ( torch._functorch.config.patch("bundled_autograd_cache", True), ): range_entry.runnable = self.vllm_backend.compiler_manager.compile( self.graph, args_list, self.vllm_backend.inductor_config, self.compilation_config, compile_range=range_entry.compile_range, graph_index=self.piecewise_compile_index, num_graphs=self.total_piecewise_compiles, ) range_entry.compiled = True self.to_be_compiled_ranges.remove(range_entry.compile_range) self.check_for_ending_compilation() def _find_range_for_shape(self, runtime_shape: int) -> RangeEntry | None: # First we try to find the range entry for the concrete compile size # If not found, we search for the range entry # that contains the runtime shape. if self.compile_sizes is None: return None if runtime_shape in self.compile_sizes: return self.range_entries[Range(start=runtime_shape, end=runtime_shape)] else: for range in self.compile_ranges: if runtime_shape in range: return self.range_entries[range] return None def __call__(self, *args: Any) -> Any: runtime_shape = args[self.sym_shape_indices[0]] range_entry = self._find_range_for_shape(runtime_shape) assert range_entry is not None, ( f"Shape: {runtime_shape} out of considered ranges: {self.compile_ranges}" ) self._maybe_compile_for_range_entry(range_entry, args) return range_entry.runnable(*args)