f863994084
Signed-off-by: zhxchen17 <zhxchen17@fb.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
344 lines
14 KiB
Python
344 lines
14 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import dataclasses
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import io
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import json
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import pickle
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import time
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from collections.abc import Callable
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from pickle import Pickler
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from typing import Any
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import torch._functorch.config
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import torch.fx as fx
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from torch._inductor.runtime.triton_heuristics import CachingAutotuner
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from torch._logging._internal import trace_structured
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from vllm.compilation.backends import VllmBackend
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from vllm.compilation.monitor import end_monitoring_torch_compile
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from vllm.config import VllmConfig
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from vllm.config.utils import Range
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from vllm.logger import init_logger
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logger = init_logger(__name__)
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@dataclasses.dataclass
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class RangeEntry:
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compile_range: Range
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compiled: bool = False
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runnable: Callable[..., Any] = None # type: ignore
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class PiecewiseBackend:
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def __init__(
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self,
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graph: fx.GraphModule | None,
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vllm_config: VllmConfig,
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piecewise_compile_index: int,
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total_piecewise_compiles: int,
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sym_shape_indices: list[int],
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vllm_backend: VllmBackend,
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returns_tuple: bool,
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compiled_runnables: dict[str, Callable[..., Any]] | None = None,
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submod_name: str = "",
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):
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"""
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The backend for piecewise compilation.
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It mainly handles the compilation of static shapes and
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dispatching based on runtime shape.
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We will compile `self.graph` once for the general shape,
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and then compile for different shapes specified in
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`compilation_config.compile_sizes`.
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This class supports two mutually exclusive modes:
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1. Compilation (graph is set, compiled_runnables is None):
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Used during initial compilation when we have the FX graph
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and need to compile it for each shape range.
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2. Precompilation (graph is None, compiled_runnables is set):
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Used when loading from cache/AOT artifacts where we already
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have pre-compiled callables and don't need the original graph.
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Exactly one of graph or compiled_runnables must be provided.
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"""
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assert bool(graph is not None) ^ bool(compiled_runnables is not None), (
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"exactly one of graph and compiled_runnables should be set."
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)
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self.graph = graph
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self.vllm_config = vllm_config
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self.compilation_config = vllm_config.compilation_config
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self.piecewise_compile_index = piecewise_compile_index
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self.total_piecewise_compiles = total_piecewise_compiles
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self.vllm_backend = vllm_backend
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self.compiled_runnables = compiled_runnables
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self.submod_name = submod_name
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self.is_first_graph = piecewise_compile_index == 0
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self.is_last_graph = piecewise_compile_index == total_piecewise_compiles - 1
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self.is_full_graph = total_piecewise_compiles == 1
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self.is_encoder_compilation = vllm_backend.is_encoder
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self.compile_ranges = self.compilation_config.get_compile_ranges()
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if self.is_encoder_compilation:
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# For encoder compilation we use the max int32 value
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# to set the upper bound of the compile ranges
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max_int32 = 2**31 - 1
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last_compile_range = self.compile_ranges[-1]
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assert (
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last_compile_range.end
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== vllm_config.scheduler_config.max_num_batched_tokens
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)
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self.compile_ranges[-1] = Range(
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start=last_compile_range.start, end=max_int32
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)
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log_string = f"PiecewiseBackend: compile_ranges: {self.compile_ranges}"
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logger.debug_once(log_string)
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self.compile_sizes = self.compilation_config.compile_sizes
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log_string = f"PiecewiseBackend: compile_sizes: {self.compile_sizes}"
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logger.debug_once(log_string)
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self.sym_shape_indices = sym_shape_indices
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self.returns_tuple = returns_tuple
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# the entries for ranges that we need to either
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self.range_entries: dict[Range, RangeEntry] = {}
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# to_be_compiled_ranges tracks the remaining ranges to compile,
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# and updates during the compilation process, so we need to copy it
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self.to_be_compiled_ranges: set[Range] = set(self.compile_ranges)
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# We only keep compilation management inside this class directly.
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if self.compile_sizes is not None:
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for size in self.compile_sizes:
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if isinstance(size, str):
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assert size == "cudagraph_capture_sizes"
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raise NotImplementedError(
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"cudagraph_capture_sizes not supported in compile_sizes."
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"This should be handled in `post_init_cudagraph_sizes`."
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)
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else:
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assert isinstance(size, int)
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range = Range(start=size, end=size)
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if range not in self.compile_ranges:
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self.range_entries[range] = RangeEntry(
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compile_range=range,
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)
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self.to_be_compiled_ranges.add(range)
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for range in self.compile_ranges:
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self.range_entries[range] = RangeEntry(
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compile_range=range,
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)
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# Track whether we've logged the graph for this subgraph (only log once)
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self._graph_logged = False
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# get the on_compilation_complete callback from context...
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# PiecewiseBackend is created during the first call,
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# which is when the context is set (see compilation/decorators.py)
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from vllm.compilation.backends import _on_compilation_complete_callback
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self.on_compilation_complete = _on_compilation_complete_callback.get()
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def get_compiled_graph_wrapper(
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self, compiled_graph: Callable[..., Any]
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) -> Callable[..., Any]:
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def compiled_graph_wrapper(*args: Any) -> Any:
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graph_output = compiled_graph(*args)
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# unpack the tuple if needed
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# TODO(rzou): the implication is that we're not
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# reading the python bytecode correctly in vLLM?
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if self.returns_tuple or not isinstance(graph_output, (tuple, list)):
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return graph_output
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else:
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return graph_output[0]
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return compiled_graph_wrapper
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def check_for_ending_compilation(self) -> None:
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if self.is_last_graph and not self.to_be_compiled_ranges:
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# no specific sizes to compile
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# save the hash of the inductor graph for the next run
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time_before_saving = time.perf_counter()
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self.vllm_backend.compiler_manager.save_to_file()
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elapsed = time.perf_counter() - time_before_saving
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if elapsed > 1:
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logger.info_once(
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"Saved compiler manager cache in %.2f seconds.",
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elapsed,
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scope="local",
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)
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end_monitoring_torch_compile(self.vllm_config)
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# Call the completion callback (e.g., to save AOT compiled function)
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if self.on_compilation_complete is not None:
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self.on_compilation_complete()
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def to_bytes(self) -> dict[str, bytes]:
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class StandaloneCompiledArtifactsPickler(Pickler):
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def reducer_override(self, obj: object) -> Any:
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if isinstance(obj, CachingAutotuner):
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obj.prepare_for_pickle()
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return pickle.loads, (
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pickle.dumps(
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obj,
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),
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)
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return NotImplemented
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def serialize(fn: Callable[..., Any]) -> bytes:
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assert hasattr(fn, "serialize"), "fn must have serialize method"
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with torch._functorch.config.patch("bundled_autograd_cache", True):
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entry = fn.serialize()
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f = io.BytesIO()
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StandaloneCompiledArtifactsPickler(f).dump(entry)
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result = f.getvalue()
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return result
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out = {}
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for range_key, entry in self.range_entries.items():
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if not entry.compiled:
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logger.debug(
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"entry with range %s not compiled, so cannot get its bytes",
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range_key,
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)
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continue
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if hasattr(entry.runnable, "serialize"):
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out[str(range_key)] = serialize(entry.runnable)
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return out
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def _fakify_args(self, args: tuple[Any, ...]) -> list[Any]:
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# We need to pass fake example_inputs, otherwise torch.compile
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# will fakify the example_inputs potentially causing some non dynamic
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# dimension to be be duck shaped to other existing shapes that have hints
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# matching their values.
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# This is problem because it can lead to unintended specializations!
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# if the new wrongly dynamic dim is specialized
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# it will force specializing the whole shape
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# torch.compile probably should not accept
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# non fake tensors as example inputs!
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# See issue https://github.com/vllm-project/vllm/issues/27899
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fake_example_inputs = []
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assert self.graph is not None
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for node in self.graph.graph.nodes:
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# All place holders come first
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if node.op == "placeholder":
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fake_example_inputs.append(node.meta["example_value"])
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else:
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break
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assert len(fake_example_inputs) == len(args)
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return fake_example_inputs
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def _log_compile_start(self, compile_range: Range):
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"""Log compilation event for TORCH_TRACE/tlparse."""
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is_cudagraph_size = (
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self.compile_sizes is not None and compile_range.start in self.compile_sizes
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)
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subgraph_index = self.piecewise_compile_index
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submod_name = self.submod_name
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trace_structured(
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"artifact",
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metadata_fn=lambda: {
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"name": "vllm_piecewise_compile_start",
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"encoding": "json",
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},
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payload_fn=lambda: json.dumps(
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{
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"piecewise_index": subgraph_index,
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"submod_name": submod_name,
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"total_piecewise_compiles": self.total_piecewise_compiles,
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"compile_range_start": compile_range.start,
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"compile_range_end": compile_range.end,
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"is_single_size": compile_range.is_single_size(),
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"is_cudagraph_capture_size": is_cudagraph_size,
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}
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),
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)
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# Log the subgraph graph dump only once per subgraph (not per size)
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# to reduce log file size. The graph code is the same for all sizes.
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if not self._graph_logged:
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self._graph_logged = True
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assert self.graph is not None
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trace_structured(
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"graph_dump",
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metadata_fn=lambda: {
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"name": f"vllm_{submod_name}",
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},
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payload_fn=lambda: self.graph.print_readable(print_output=False),
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)
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def _maybe_compile_for_range_entry(
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self, range_entry: RangeEntry, args: tuple[Any, ...]
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) -> Any:
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if not range_entry.compiled:
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if self.compiled_runnables is not None:
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range_entry.runnable = self.get_compiled_graph_wrapper(
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self.compiled_runnables[str(range_entry.compile_range)]
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)
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else:
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self._log_compile_start(range_entry.compile_range)
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# args are real arguments
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# fakify for range, real args for concrete size.
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# For concrete size, we clear the shape env in
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# compiler_manager.compile() so no need to fakify.
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args_list = (
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self._fakify_args(args)
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if not range_entry.compile_range.is_single_size()
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else list(args)
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)
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with (
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torch._functorch.config.patch("bundled_autograd_cache", True),
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):
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range_entry.runnable = self.vllm_backend.compiler_manager.compile(
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self.graph,
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args_list,
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self.vllm_backend.inductor_config,
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self.compilation_config,
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compile_range=range_entry.compile_range,
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graph_index=self.piecewise_compile_index,
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num_graphs=self.total_piecewise_compiles,
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)
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range_entry.compiled = True
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self.to_be_compiled_ranges.remove(range_entry.compile_range)
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self.check_for_ending_compilation()
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def _find_range_for_shape(self, runtime_shape: int) -> RangeEntry | None:
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# First we try to find the range entry for the concrete compile size
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# If not found, we search for the range entry
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# that contains the runtime shape.
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if self.compile_sizes is None:
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return None
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if runtime_shape in self.compile_sizes:
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return self.range_entries[Range(start=runtime_shape, end=runtime_shape)]
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else:
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for range in self.compile_ranges:
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if runtime_shape in range:
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return self.range_entries[range]
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return None
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def __call__(self, *args: Any) -> Any:
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runtime_shape = args[self.sym_shape_indices[0]]
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range_entry = self._find_range_for_shape(runtime_shape)
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assert range_entry is not None, (
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f"Shape: {runtime_shape} out of considered ranges: {self.compile_ranges}"
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)
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self._maybe_compile_for_range_entry(range_entry, args)
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return range_entry.runnable(*args)
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