[gpt-oss] Add gpt-oss bf16 support
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vllm/compilation/cuda_piecewise_backend.py
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218
vllm/compilation/cuda_piecewise_backend.py
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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 dataclasses
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from contextlib import ExitStack
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from typing import Any, Callable, Optional
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from unittest.mock import patch
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import torch
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import torch.fx as fx
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import vllm.envs as envs
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from vllm.compilation.backends import VllmBackend
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from vllm.compilation.counter import compilation_counter
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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.forward_context import get_forward_context
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from vllm.logger import init_logger
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from vllm.utils import weak_ref_tensors
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logger = init_logger(__name__)
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@dataclasses.dataclass
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class ConcreteSizeEntry:
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runtime_shape: int
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need_to_compile: bool # the size is in compile_sizes
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use_cudagraph: bool # the size is in cudagraph_capture_sizes
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compiled: bool = False
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runnable: Callable = None # type: ignore
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num_finished_warmup: int = 0
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cudagraph: Optional[torch.cuda.CUDAGraph] = None
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output: Optional[Any] = None
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# for cudagraph debugging, track the input addresses
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# during capture, and check if they are the same during replay
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input_addresses: Optional[list[int]] = None
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class CUDAPiecewiseBackend:
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def __init__(self, graph: fx.GraphModule, vllm_config: VllmConfig,
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graph_pool: Any, piecewise_compile_index: int,
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total_piecewise_compiles: int, sym_shape_indices: list[int],
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compiled_graph_for_general_shape: Callable,
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vllm_backend: VllmBackend):
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"""
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The backend for piecewise compilation.
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It mainly handles the compilation and cudagraph capturing.
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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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Independently, we will capture cudagraph for different shapes.
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If a shape needs both compilation and cudagraph, we will
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compile it first, and then capture cudagraph.
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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.graph_pool = graph_pool
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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.is_first_graph = piecewise_compile_index == 0
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self.is_last_graph = (
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piecewise_compile_index == total_piecewise_compiles - 1)
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self.compile_sizes: set[int] = set(
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self.compilation_config.compile_sizes)
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self.cudagraph_capture_sizes: set[int] = set(
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self.compilation_config.cudagraph_capture_sizes
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) if self.compilation_config.use_cudagraph else set()
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self.first_run_finished = False
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self.compiled_graph_for_general_shape = compiled_graph_for_general_shape # noqa
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self.sym_shape_indices = sym_shape_indices
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self.is_debugging_mode = envs.VLLM_LOGGING_LEVEL == "DEBUG"
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# the entries for different shapes that we need to either
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# compile or capture cudagraph
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self.concrete_size_entries: dict[int, ConcreteSizeEntry] = {}
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# to_be_compiled_sizes tracks the remaining sizes 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_sizes: set[int] = self.compile_sizes.copy()
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for shape in self.compile_sizes.union(self.cudagraph_capture_sizes):
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self.concrete_size_entries[shape] = ConcreteSizeEntry(
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runtime_shape=shape,
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need_to_compile=shape in self.compile_sizes,
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use_cudagraph=shape in self.cudagraph_capture_sizes,
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)
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def check_for_ending_compilation(self):
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if self.is_last_graph and not self.to_be_compiled_sizes:
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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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self.vllm_backend.compiler_manager.save_to_file()
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end_monitoring_torch_compile(self.vllm_config)
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def __call__(self, *args) -> Any:
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if not self.first_run_finished:
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self.first_run_finished = True
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self.check_for_ending_compilation()
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return self.compiled_graph_for_general_shape(*args)
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runtime_shape = args[self.sym_shape_indices[0]]
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if runtime_shape not in self.concrete_size_entries:
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# we don't need to do anything for this shape
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return self.compiled_graph_for_general_shape(*args)
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entry = self.concrete_size_entries[runtime_shape]
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if entry.runnable is None:
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entry.runnable = self.compiled_graph_for_general_shape
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if entry.need_to_compile and not entry.compiled:
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entry.compiled = True
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self.to_be_compiled_sizes.remove(runtime_shape)
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# args are real arguments
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entry.runnable = self.vllm_backend.compiler_manager.compile(
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self.graph,
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args,
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self.compilation_config.inductor_compile_config,
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self.compilation_config,
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graph_index=self.piecewise_compile_index,
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num_graphs=self.total_piecewise_compiles,
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runtime_shape=runtime_shape)
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# finished compilations for all required shapes
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if self.is_last_graph and not self.to_be_compiled_sizes:
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self.check_for_ending_compilation()
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# Skip CUDA graphs if this entry doesn't use them OR
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# if we're supposed to skip them globally
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skip_cuda_graphs = get_forward_context().skip_cuda_graphs
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if not entry.use_cudagraph or skip_cuda_graphs:
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return entry.runnable(*args)
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if entry.cudagraph is None:
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if entry.num_finished_warmup < self.compilation_config.cudagraph_num_of_warmups: # noqa
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entry.num_finished_warmup += 1
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if self.is_first_graph:
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logger.debug(
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"Warming up %s/%s for shape %s",
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entry.num_finished_warmup,
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self.compilation_config.cudagraph_num_of_warmups,
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runtime_shape)
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return entry.runnable(*args)
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if self.is_first_graph:
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# Since we capture cudagraph for many different shapes and
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# capturing is fast, we don't need to log it for every shape.
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# We only log it in the debug mode.
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logger.debug("Capturing a cudagraph for shape %s",
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runtime_shape)
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input_addresses = [
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x.data_ptr() for x in args if isinstance(x, torch.Tensor)
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]
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entry.input_addresses = input_addresses
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cudagraph = torch.cuda.CUDAGraph()
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with ExitStack() as stack:
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if not self.is_first_graph:
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# during every model forward, we will capture
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# many pieces of cudagraphs (roughly one per layer).
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# running gc again and again across layers will
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# make the cudagraph capture very slow.
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# therefore, we only run gc for the first graph,
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# and disable gc for the rest of the graphs.
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stack.enter_context(patch("gc.collect", lambda: None))
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stack.enter_context(
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patch("torch.cuda.empty_cache", lambda: None))
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# mind-exploding: carefully manage the reference and memory.
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with torch.cuda.graph(cudagraph, pool=self.graph_pool):
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# `output` is managed by pytorch's cudagraph pool
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output = entry.runnable(*args)
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if self.is_last_graph:
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# by converting it to weak ref,
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# the original `output` will immediately be released
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# to save memory. It is only safe to do this for
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# the last graph, because the output of the last graph
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# will not be used by any other cuda graph.
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output = weak_ref_tensors(output)
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# here we always use weak ref for the output
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# to save memory
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entry.output = weak_ref_tensors(output)
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entry.cudagraph = cudagraph
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compilation_counter.num_cudagraph_captured += 1
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# important: we need to return the output, rather than
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# the weak ref of the output, so that pytorch can correctly
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# manage the memory during cuda graph capture
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return output
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if self.is_debugging_mode:
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# check if the input addresses are the same
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new_input_addresses = [
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x.data_ptr() for x in args if isinstance(x, torch.Tensor)
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]
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assert new_input_addresses == entry.input_addresses, (
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"Input addresses for cudagraphs are different during replay."
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f" Expected {entry.input_addresses}, got {new_input_addresses}"
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)
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entry.cudagraph.replay()
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return entry.output
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