mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
e20736bf1d
TL;DR: Cuts vLLM cudagraph collection from 80s -> 24s
Stop garbage collecting by default on every cudagraph recording. The old behavior can be re-enabled by setting `TORCH_CUDAGRAPH_GC=1` or the config `force_cudagraph_gc`.
We were previously garbage collecting at the beginning of each cudagraph
capture. vLLM collects 5427 graphs and most of those garbage collections weren't
actually collecting any memory (CPU or GPU). This changes it to not collect more
than every 10s so if we're capturing in a loop we don't burn all our cycles
looking for garbage.
(These number have a lot of variance from run to run but give the correct
general scale)
```
| calls | total | synchronize | gcs | collect | empty cache | sys freed | cuda freed |
-------+-------+-------+-------------+------+---------+-------------+-----------+------------+
before | 5427 | 78s | 1.48s | 5427 | 53.22s | 1.21s | 145855 | 1539309568 |
-------+-------+-------+-------------+------+---------+-------------+-----------+------------+
after | 5427 | 24s | 0s | 3 | 1.53s | 0.84s | 592 | 1539309568 |
-------+-------+-------+-------------+------+---------+-------------+-----------+------------+
```
total - this is the total time reported by vLLM's "Graph capturing finished" log.
The rest of these are measured in torch.cuda.graphs.graph.__enter__():
calls - number of times torch.cuda.graphs.graph.__enter__ was called
synchronize - this is the duration taken by the cuda.synchronize call
gcs - number of times gc.collect was called
collect - this is the duration taken by the gc.collect call
empty cache - this is the duration taken by the torch.cuda.empty_cache call
sys freed - the number of bytes reported freed by gc.collect
cuda freed - the number of bytes reported freed by torch.cuda.memory_reserved
So it seems like the heavy lifting is done by torch.cuda.empty_cache() which is
fairly quick.
Cudagraph results from the TorchInductor Performance DashBoard (this is from the original version using the GC clock so the real results will be slightly better than this):
<img width="1494" height="382" alt="image" src="https://github.com/user-attachments/assets/69b705ef-47ce-4b6e-9733-1ec941cad93d" />
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158193
Approved by: https://github.com/ngimel
600 lines
26 KiB
Python
600 lines
26 KiB
Python
from __future__ import annotations
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import gc
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import typing
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from typing import Callable, Optional, overload, TYPE_CHECKING, Union
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from typing_extensions import ParamSpec, Self, TypeAlias, TypeVar
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import torch
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from torch import Tensor
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if TYPE_CHECKING:
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# importing _POOL_HANDLE at runtime toplevel causes an import cycle
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from torch.cuda import _POOL_HANDLE
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from .._utils import _dummy_type
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__all__ = [
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"is_current_stream_capturing",
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"graph_pool_handle",
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"CUDAGraph",
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"graph",
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"make_graphed_callables",
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]
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_R = TypeVar("_R")
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_P = ParamSpec("_P")
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if not hasattr(torch._C, "_CudaStreamBase"):
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# Define dummy base classes
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torch._C.__dict__["_CUDAGraph"] = _dummy_type("_CUDAGraph")
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torch._C.__dict__["_graph_pool_handle"] = _dummy_type("_graph_pool_handle")
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torch._C.__dict__["_cuda_isCurrentStreamCapturing"] = _dummy_type(
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"_cuda_isCurrentStreamCapturing"
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)
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from torch._C import ( # noqa: F401
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_cuda_isCurrentStreamCapturing,
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_CUDAGraph,
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_graph_pool_handle,
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)
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def is_current_stream_capturing() -> bool:
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r"""Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.
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If a CUDA context does not exist on the current device, returns False without initializing the context.
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"""
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return _cuda_isCurrentStreamCapturing()
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# Python shim helps Sphinx process docstrings more reliably.
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def graph_pool_handle() -> _POOL_HANDLE:
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r"""Return an opaque token representing the id of a graph memory pool.
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See :ref:`Graph memory management<graph-memory-management>`.
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.. warning::
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This API is in beta and may change in future releases.
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"""
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return torch.cuda._POOL_HANDLE(_graph_pool_handle())
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# Python shim helps Sphinx process docstrings more reliably.
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class CUDAGraph(torch._C._CUDAGraph):
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r"""Wrapper around a CUDA graph.
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Arguments:
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keep_graph (bool, optional): If ``keep_graph=False``, the
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cudaGraphExec_t will be instantiated on GPU at the end of
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``capture_end`` and the underlying cudaGraph_t will be
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destroyed. Users who want to query or otherwise modify the
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underlying cudaGraph_t before instantiation can set
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``keep_graph=True`` and access it via ``raw_cuda_graph`` after
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``capture_end``. Note that the cudaGraphExec_t will not be
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instantiated at the end of ``capture_end`` in this
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case. Instead, it will be instantiated via an explicit called
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to ``instantiate`` or automatically on the first call to
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``replay`` if ``instantiate`` was not already called. Calling
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``instantiate`` manually before ``replay`` is recommended to
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prevent increased latency on the first call to ``replay``. It
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is allowed to modify the raw cudaGraph_t after first calling
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``instantiate``, but the user must call ``instantiate`` again
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manually to make sure the instantiated graph has these
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changes. Pytorch has no means of tracking these changes.
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.. warning::
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This API is in beta and may change in future releases.
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"""
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def __new__(cls, keep_graph: bool = False) -> Self:
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return super().__new__(cls, keep_graph)
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def capture_begin(
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self, pool: Optional[_POOL_HANDLE] = None, capture_error_mode: str = "global"
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) -> None:
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r"""Begin capturing CUDA work on the current stream.
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Typically, you shouldn't call ``capture_begin`` yourself.
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Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
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which call ``capture_begin`` internally.
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Arguments:
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pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
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:meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) that hints this graph may share memory
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with the indicated pool. See :ref:`Graph memory management<graph-memory-management>`.
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capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream.
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Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc,
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may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for
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actions in the current thread, and "relaxed" will not error on these actions. Do NOT change this setting
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unless you're familiar with `cudaStreamCaptureMode <https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85>`_
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""" # noqa: B950
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super().capture_begin(pool=pool, capture_error_mode=capture_error_mode)
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def capture_end(self) -> None:
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r"""End CUDA graph capture on the current stream.
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After ``capture_end``, ``replay`` may be called on this instance.
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Typically, you shouldn't call ``capture_end`` yourself.
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Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
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which call ``capture_end`` internally.
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"""
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super().capture_end()
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def instantiate(self) -> None:
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r"""Instantiate the CUDA graph. Will be called by
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``capture_end`` if ``keep_graph=False``, or by ``replay`` if
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``keep_graph=True`` and ``instantiate`` has not already been
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explicitly called. Does not destroy the cudaGraph_t returned
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by ``raw_cuda_graph``.
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"""
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super().instantiate()
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def replay(self) -> None:
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r"""Replay the CUDA work captured by this graph."""
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super().replay()
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def reset(self) -> None:
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r"""Delete the graph currently held by this instance."""
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super().reset()
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def pool(self) -> _POOL_HANDLE:
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r"""Return an opaque token representing the id of this graph's memory pool.
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This id can optionally be passed to another graph's ``capture_begin``,
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which hints the other graph may share the same memory pool.
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"""
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return super().pool()
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def enable_debug_mode(self) -> None:
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r"""Enable debugging mode for CUDAGraph.debug_dump."""
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return super().enable_debug_mode()
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def debug_dump(self, debug_path: str) -> None:
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r"""
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Arguments:
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debug_path (required): Path to dump the graph to.
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Calls a debugging function to dump the graph if the debugging is
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enabled via CUDAGraph.enable_debug_mode()
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"""
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return super().debug_dump(debug_path)
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def raw_cuda_graph(self) -> int:
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r"""Returns the underlying cudaGraph_t. ``keep_graph`` must be True.
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See the following for APIs for how to manipulate this object: `Graph Managmement <https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__GRAPH.html>`_ and `cuda-python Graph Management bindings <https://nvidia.github.io/cuda-python/cuda-bindings/latest/module/runtime.html#graph-management>`_
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""" # noqa: B950
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return super().raw_cuda_graph()
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class graph:
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r"""Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph` object for later replay.
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See :ref:`CUDA Graphs <cuda-graph-semantics>` for a general introduction,
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detailed use, and constraints.
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Arguments:
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cuda_graph (torch.cuda.CUDAGraph): Graph object used for capture.
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pool (optional): Opaque token (returned by a call to :func:`~torch.cuda.graph_pool_handle()` or
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:meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) hinting this graph's capture
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may share memory from the specified pool. See :ref:`Graph memory management<graph-memory-management>`.
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stream (torch.cuda.Stream, optional): If supplied, will be set as the current stream in the context.
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If not supplied, ``graph`` sets its own internal side stream as the current stream in the context.
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capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream.
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Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc,
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may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for
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actions in the current thread, and "relaxed" will not error on actions. Do NOT change this setting
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unless you're familiar with `cudaStreamCaptureMode <https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85>`_
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.. note::
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For effective memory sharing, if you pass a ``pool`` used by a previous capture and the previous capture
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used an explicit ``stream`` argument, you should pass the same ``stream`` argument to this capture.
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.. warning::
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This API is in beta and may change in future releases.
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.. _cudaStreamCaptureMode:
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https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85
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""" # noqa: B950
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default_capture_stream: Optional[torch.cuda.Stream] = None
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def __init__(
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self,
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cuda_graph: CUDAGraph,
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pool: Optional[_POOL_HANDLE] = None,
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stream: Optional[torch.cuda.Stream] = None,
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capture_error_mode: str = "global",
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):
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# Lazy-init of default_capture_stream helps avoid circular-import errors.
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# Not thread safe, but graphs already have the general (explicitly documented)
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# restriction that only one capture may be underway at a time in the process.
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if self.__class__.default_capture_stream is None:
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self.__class__.default_capture_stream = torch.cuda.Stream()
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self.pool: Union[tuple[()], tuple[_POOL_HANDLE]] = (
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() if pool is None else (pool,)
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)
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self.capture_stream = (
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stream if stream is not None else self.__class__.default_capture_stream
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)
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assert self.capture_stream is not None
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self.stream_ctx = torch.cuda.stream(self.capture_stream)
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self.cuda_graph = cuda_graph
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self.capture_error_mode = capture_error_mode
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def __enter__(self) -> None:
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# Free as much memory as we can for the graph
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torch.cuda.synchronize()
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if torch.compiler.config.force_cudagraph_gc:
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# Originally we unconditionally garbage collected here. On one hand
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# that's nice because we have a chance to collect more memory, but
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# on the other hand it is REALLY expensive, especially for doing
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# multiple cudagraph captures in a row. In theory it will only help
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# when a dead python cycle is holding onto CUDA memory.
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gc.collect()
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torch.cuda.empty_cache()
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# Stackoverflow seems comfortable with this pattern
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# https://stackoverflow.com/questions/26635684/calling-enter-and-exit-manually#39172487
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self.stream_ctx.__enter__()
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self.cuda_graph.capture_begin(
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# type: ignore[misc]
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*self.pool,
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capture_error_mode=self.capture_error_mode,
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)
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def __exit__(self, *args: object) -> None:
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self.cuda_graph.capture_end()
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self.stream_ctx.__exit__(*args)
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# returning None should propagate exceptions from either capture_end or stream_ctx.__exit__()
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_ModuleOrCallable: TypeAlias = Union["torch.nn.Module", Callable[..., object]]
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@overload
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def make_graphed_callables(
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callables: _ModuleOrCallable,
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sample_args: tuple[Tensor, ...],
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num_warmup_iters: int = 3,
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allow_unused_input: bool = False,
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pool: Optional[_POOL_HANDLE] = None,
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) -> _ModuleOrCallable: ...
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@overload
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def make_graphed_callables(
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callables: tuple[_ModuleOrCallable, ...],
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sample_args: tuple[tuple[Tensor, ...], ...],
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num_warmup_iters: int = 3,
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allow_unused_input: bool = False,
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pool: Optional[_POOL_HANDLE] = None,
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) -> tuple[_ModuleOrCallable, ...]: ...
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def make_graphed_callables(
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callables: Union[_ModuleOrCallable, tuple[_ModuleOrCallable, ...]],
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sample_args: Union[tuple[Tensor, ...], tuple[tuple[Tensor, ...], ...]],
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num_warmup_iters: int = 3,
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allow_unused_input: bool = False,
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pool: Optional[_POOL_HANDLE] = None,
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) -> Union[_ModuleOrCallable, tuple[_ModuleOrCallable, ...]]:
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r"""Accept callables (functions or :class:`nn.Module<torch.nn.Module>`\ s) and returns graphed versions.
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Each graphed callable's forward pass runs its source callable's
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forward CUDA work as a CUDA graph inside a single autograd node.
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The graphed callable's forward pass also appends
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a backward node to the autograd graph. During backward, this node runs the
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callable's backward work as a CUDA graph.
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Therefore, each graphed callable should be a drop-in replacement for its source callable
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in an autograd-enabled training loop.
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See :ref:`Partial-network capture<partial-network-capture>` for detailed use and constraints.
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If you pass a tuple of several callables, their captures will use the same memory pool.
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See :ref:`Graph memory management<graph-memory-management>` for when this is appropriate.
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Arguments:
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callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph.
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See :ref:`Graph memory management<graph-memory-management>` for when passing a tuple of callables
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is appropriate. If you pass a tuple of callables, their order in the tuple must be the same order
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they'll run in the live workload.
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sample_args (tuple of Tensors, or tuple of tuples of Tensors): Samples args for each callable.
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If a single callable was passed, ``sample_args`` must be a single tuple of argument Tensors.
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If a tuple of callables was passed, ``sample_args`` must be tuple of tuples of argument Tensors.
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num_warmup_iters (int): The number of warmup iterations. Currently, ``DataDistributedParallel`` needs
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11 iterations for warm up. Default: ``3``.
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allow_unused_input (bool): If False, specifying inputs that were not used when computing outputs
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(and therefore their grad is always zero) is an error. Defaults to False.
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pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
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:meth:`other_Graph_instance.pool()<torch.cuda.CUDAGraph.pool>`) that hints this graph may share memory
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with the indicated pool. See :ref:`Graph memory management<graph-memory-management>`.
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.. note::
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The ``requires_grad`` state of each Tensor in ``sample_args`` must match the state
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that's expected for the corresponding real input in the training loop.
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.. warning::
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This API is in beta and may change in future releases.
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.. warning::
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``sample_args`` for each callable must contain only Tensors. Other types are not allowed.
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.. warning::
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Returned callables do not support higher order differentiation (e.g., double backward).
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.. warning::
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In any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters
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may be trainable. Buffers must have ``requires_grad=False``.
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.. warning::
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After you pass a :class:`torch.nn.Module` through :func:`~make_graphed_callables`,
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you may not add or remove any of that Module's parameters or buffers.
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.. warning::
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:class:`torch.nn.Module`\s passed to :func:`~torch.cuda.make_graphed_callables` must not have module hooks
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registered on them at the time they are passed. However, registering hooks on modules *after* passing them
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through :func:`~torch.cuda.make_graphed_callables` is allowed.
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.. warning::
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When running a graphed callable, you must pass its arguments in the same order and format
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they appeared in that callable's ``sample_args``.
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.. warning::
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The automatic mixed precision is supported in :func:`~torch.cuda.make_graphed_callables` only with disabled
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caching. The context manager `torch.cuda.amp.autocast()` must have `cache_enabled=False`.
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"""
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if torch.is_autocast_enabled() and torch.is_autocast_cache_enabled():
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raise RuntimeError(
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"make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`."
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)
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just_one_callable = False
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_sample_args: tuple[tuple[Tensor, ...], ...]
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if not isinstance(callables, tuple):
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just_one_callable = True
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callables = (callables,)
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_sample_args = (typing.cast(tuple[Tensor, ...], sample_args),)
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else:
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_sample_args = typing.cast(tuple[tuple[Tensor, ...], ...], sample_args)
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flatten_sample_args = []
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for c, args in zip(callables, _sample_args):
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if isinstance(c, torch.nn.Module):
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assert (
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len(c._backward_hooks) == 0
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and len(c._forward_hooks) == 0
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and len(c._forward_pre_hooks) == 0
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), (
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"Modules must not have hooks registered at the time they are passed. However, registering hooks "
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+ "on modules after passing them through make_graphed_callables is allowed."
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)
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assert all(b.requires_grad is False for b in c.buffers()), (
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"In any :class:`~torch.nn.Module` passed to "
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+ ":func:`~make_graphed_callables`, only parameters may be trainable. All buffers must have "
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+ "``requires_grad=False``."
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)
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flatten_arg = torch.utils._pytree.arg_tree_leaves(*args)
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flatten_sample_args.append(tuple(flatten_arg))
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assert all(isinstance(arg, torch.Tensor) for arg in flatten_arg), (
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"In the beta API, sample_args "
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+ "for each callable must contain only Tensors. Other types are not allowed."
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)
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# If a callable is an nn.Module, its graph's full input surface is the args the user explicitly
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# passes to forward (ie, its sample_args) AND the module's parameter attributes.
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per_callable_len_user_args = [len(args) for args in flatten_sample_args]
|
|
per_callable_module_params = [
|
|
tuple(c.parameters()) if isinstance(c, torch.nn.Module) else ()
|
|
for c in callables
|
|
]
|
|
per_callable_static_input_surfaces = [
|
|
flatten_sample_args[i] + per_callable_module_params[i]
|
|
for i in range(len(callables))
|
|
]
|
|
|
|
fwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
|
|
bwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
|
|
|
|
mempool = graph_pool_handle() if pool is None else pool
|
|
|
|
# Warmup
|
|
# Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work
|
|
# from ending up in any captures.
|
|
torch.cuda.synchronize()
|
|
with torch.cuda.stream(torch.cuda.Stream()):
|
|
for func, args, static_input_surface in zip(
|
|
callables, _sample_args, per_callable_static_input_surfaces
|
|
):
|
|
grad_inputs, outputs, outputs_grad = None, None, None
|
|
for _ in range(num_warmup_iters):
|
|
outputs = torch.utils._pytree.tree_leaves(func(*args))
|
|
outputs_grad = tuple(o for o in outputs if o.requires_grad)
|
|
if len(outputs_grad) > 0:
|
|
grad_inputs = torch.autograd.grad(
|
|
outputs=outputs_grad,
|
|
inputs=tuple(
|
|
i for i in static_input_surface if i.requires_grad
|
|
),
|
|
grad_outputs=tuple(
|
|
torch.empty_like(o) for o in outputs if o.requires_grad
|
|
),
|
|
only_inputs=True,
|
|
allow_unused=allow_unused_input,
|
|
)
|
|
for v in [outputs, outputs_grad, grad_inputs]:
|
|
del v
|
|
|
|
torch.cuda.synchronize()
|
|
|
|
# All captures here share a mempool. To avoid replays corrupting each other's memory,
|
|
# the safest approach is to capture all passes in the same order they'll run:
|
|
# fwd 1, fwd 2, ... fwd N, then bwd N, bwd N-1, ... bwd 1.
|
|
|
|
# Capture forward graphs
|
|
per_callable_static_outputs = []
|
|
per_callable_output_unflatten_spec = []
|
|
for func, args, fwd_graph in zip(callables, _sample_args, fwd_graphs):
|
|
with torch.cuda.graph(fwd_graph, pool=mempool):
|
|
func_outputs = func(*args)
|
|
|
|
flatten_outputs, spec = torch.utils._pytree.tree_flatten(func_outputs)
|
|
per_callable_static_outputs.append(tuple(flatten_outputs))
|
|
per_callable_output_unflatten_spec.append(spec)
|
|
|
|
# Capture backward graphs in reverse order
|
|
per_callable_static_grad_outputs = []
|
|
per_callable_static_grad_inputs = []
|
|
for static_input_surface, static_outputs, bwd_graph in zip(
|
|
reversed(per_callable_static_input_surfaces),
|
|
reversed(per_callable_static_outputs),
|
|
reversed(bwd_graphs),
|
|
):
|
|
# For now, assumes all static_outputs require grad
|
|
# assert all(o.requires_grad for o in static_outputs), "Outputs of graphed callables must require grad."
|
|
static_grad_outputs = tuple(
|
|
torch.empty_like(o) if o.requires_grad else None for o in static_outputs
|
|
)
|
|
|
|
outputs_grad = tuple(o for o in static_outputs if o.requires_grad)
|
|
grad_inputs = None
|
|
if len(outputs_grad) > 0:
|
|
with torch.cuda.graph(bwd_graph, pool=mempool):
|
|
grad_inputs = torch.autograd.grad(
|
|
outputs=outputs_grad,
|
|
inputs=tuple(i for i in static_input_surface if i.requires_grad),
|
|
grad_outputs=tuple(o for o in static_grad_outputs if o is not None),
|
|
only_inputs=True,
|
|
allow_unused=allow_unused_input,
|
|
)
|
|
|
|
# Constructs a tuple suitable for returning from Graphed.backward:
|
|
# Pads out the actually-needed grads with Nones in gradient slots for inputs that don't require grad.
|
|
# I couldn't think of a slick one-liner for this pattern.
|
|
static_grad_inputs = []
|
|
grad_idx = 0
|
|
for arg in static_input_surface:
|
|
if arg.requires_grad and grad_inputs is not None:
|
|
static_grad_inputs.append(grad_inputs[grad_idx])
|
|
grad_idx += 1
|
|
else:
|
|
static_grad_inputs.append(None) # type: ignore[arg-type]
|
|
static_grad_inputs = tuple(static_grad_inputs) # type: ignore[assignment]
|
|
|
|
per_callable_static_grad_outputs.append(static_grad_outputs)
|
|
per_callable_static_grad_inputs.append(static_grad_inputs)
|
|
|
|
# Reverses the most recent two lists
|
|
per_callable_static_grad_outputs.reverse()
|
|
per_callable_static_grad_inputs.reverse()
|
|
# Now for every per_callable list, per_callable_*[i] holds the stuff for the ith callable.
|
|
|
|
def make_graphed_autograd_function(
|
|
fwd_graph: CUDAGraph,
|
|
bwd_graph: CUDAGraph,
|
|
module_params: tuple[torch.nn.Parameter, ...],
|
|
len_user_args: int,
|
|
output_unflatten_spec: torch.utils._pytree.TreeSpec,
|
|
static_input_surface: tuple[Tensor, ...],
|
|
static_outputs: tuple[Tensor, ...],
|
|
static_grad_outputs: tuple[Optional[Tensor], ...],
|
|
static_grad_inputs: tuple[Tensor, ...],
|
|
) -> Callable[..., object]:
|
|
class Graphed(torch.autograd.Function):
|
|
@staticmethod
|
|
def forward(ctx: object, *inputs: Tensor) -> tuple[Tensor, ...]:
|
|
# At this stage, only the user args may (potentially) be new tensors.
|
|
for i in range(len_user_args):
|
|
if static_input_surface[i].data_ptr() != inputs[i].data_ptr():
|
|
static_input_surface[i].copy_(inputs[i])
|
|
fwd_graph.replay()
|
|
assert isinstance(static_outputs, tuple)
|
|
return tuple(o.detach() for o in static_outputs)
|
|
|
|
@staticmethod
|
|
@torch.autograd.function.once_differentiable
|
|
def backward(ctx: object, *grads: Tensor) -> tuple[Tensor, ...]:
|
|
assert len(grads) == len(static_grad_outputs)
|
|
for g, grad in zip(static_grad_outputs, grads):
|
|
if g is not None:
|
|
# don't copy if autograd gods have been kind and the
|
|
# incoming grad is already in the right place
|
|
if g.data_ptr() != grad.data_ptr():
|
|
g.copy_(grad)
|
|
bwd_graph.replay()
|
|
|
|
# Input args that didn't require grad expect a None gradient.
|
|
assert isinstance(static_grad_inputs, tuple)
|
|
return tuple(
|
|
b.detach() if b is not None else b for b in static_grad_inputs
|
|
)
|
|
|
|
def functionalized(*user_args: object) -> object:
|
|
# Runs the autograd function with inputs == all inputs to the graph that might require grad
|
|
# (explicit user args + module parameters)
|
|
# Assumes module params didn't change since capture.
|
|
flatten_user_args = torch.utils._pytree.arg_tree_leaves(*user_args)
|
|
out = Graphed.apply(*(tuple(flatten_user_args) + module_params))
|
|
return torch.utils._pytree.tree_unflatten(out, output_unflatten_spec)
|
|
|
|
return functionalized
|
|
|
|
# Put together the final graphed callables
|
|
ret: list[_ModuleOrCallable] = []
|
|
for i, func in enumerate(callables):
|
|
graphed = make_graphed_autograd_function(
|
|
fwd_graphs[i],
|
|
bwd_graphs[i],
|
|
per_callable_module_params[i],
|
|
per_callable_len_user_args[i],
|
|
per_callable_output_unflatten_spec[i],
|
|
per_callable_static_input_surfaces[i],
|
|
per_callable_static_outputs[i],
|
|
per_callable_static_grad_outputs[i],
|
|
per_callable_static_grad_inputs[i],
|
|
)
|
|
|
|
if isinstance(func, torch.nn.Module):
|
|
|
|
def make_graphed_forward(
|
|
func: torch.nn.Module,
|
|
graph_training_state: bool,
|
|
graphed: Callable[_P, _R],
|
|
orig_fwd: Callable[_P, _R],
|
|
) -> Callable[_P, _R]:
|
|
def new_fwd(*user_args: _P.args, **user_kwargs: _P.kwargs) -> _R:
|
|
# If the module's training-or-eval state matches what we graphed,
|
|
# run the graph, otherwise run the original forward method
|
|
if func.training == graph_training_state:
|
|
return graphed(*user_args, **user_kwargs)
|
|
else:
|
|
return orig_fwd(*user_args, **user_kwargs)
|
|
|
|
return new_fwd
|
|
|
|
func.forward = make_graphed_forward(
|
|
func, func.training, graphed, func.forward
|
|
)
|
|
ret.append(func)
|
|
else:
|
|
ret.append(graphed)
|
|
|
|
if just_one_callable:
|
|
return ret[0]
|
|
|
|
return tuple(ret)
|