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e7eeee473c
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129765 Approved by: https://github.com/ezyang
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
# mypy: allow-untyped-defs
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from typing import Callable, Optional
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from torch._prims.context import TorchRefsMode
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from torch.fx import GraphModule
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from torch.fx.experimental.proxy_tensor import make_fx, wrapper_and_args_for_make_fx
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def execute(
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gm: GraphModule,
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*args,
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executor: str = "aten",
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executor_parameters: Optional[dict] = None,
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):
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"""
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Prototype ATen executor.
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Just executes the context's graph.
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"""
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if executor == "aten":
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return gm.forward(*args)
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msg = f"Received unexpected value for 'executor': {executor}. Allowed values are: aten."
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raise ValueError(msg)
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def make_traced(fn: Callable):
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"""
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Returns a function that, when called, will
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trace its torch operations to prims and then
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execute those prims on the requested trace executor
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(possibly lowering them to that trace executor first).
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Only supports the torch operations defined in _torch_to_reference_map
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in context.py and operations with positional args. All args must
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be tensors.
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In the near future all these restrictions will be lifted.
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Example usage:
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def foo(a, b):
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return torch.add(a, b)
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traced_foo = make_traced(foo)
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a = torch.randn((1, 2, 3, 4, 5), device='cuda')
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b = torch.randn((1, 2, 3, 4, 5), device='cuda')
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result = traced_foo(a, b, executor='aten')
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"""
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def _traced(*args, executor="aten", **kwargs):
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# TODO: caching
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wrapped, all_args = wrapper_and_args_for_make_fx(fn, args, kwargs)
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with TorchRefsMode():
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gm = make_fx(wrapped)(all_args)
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return execute(gm, all_args, executor=executor)
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return _traced
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