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17d7be68ee
Using fake tensor with AOTAutograd is now mandatory, simplifying our logic. Unfortunately, this means debug_fake_cross_ref must go, but I don't think anyone has used it recently. Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/99314 Approved by: https://github.com/eellison, https://github.com/zou3519
145 lines
4.3 KiB
Python
145 lines
4.3 KiB
Python
import contextlib
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import functools
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import logging
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from unittest.mock import patch
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import torch
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from torch._dynamo import eval_frame
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from torch._dynamo.utils import counters
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from torch._functorch.aot_autograd import aot_module_simplified
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from torch._subclasses import FakeTensor
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from torch.utils._python_dispatch import _disable_current_modes
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log = logging.getLogger(__name__)
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def aot_autograd(**kwargs):
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def compiler_fn(gm: torch.fx.GraphModule, example_inputs):
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import functorch.compile
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# Hack to get around circular import problems with aot_eager_decomp_partition
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if callable(kwargs.get("decompositions")):
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kwargs["decompositions"] = kwargs["decompositions"]()
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# TODO: stop monkeypatching here (without even cleaning up, UGH!)
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functorch.compile.config.use_functionalize = True
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counters["aot_autograd"]["total"] += 1
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use_fallback = False
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if use_fallback:
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log.debug("Unable to use AOT Autograd because graph has mutation")
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counters["aot_autograd"]["not_ok"] += 1
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return gm
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# OK attempt to compile
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def _wrapped_bw_compiler(*args, **kwargs):
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# stop TorchDynamo from trying to compile our generated backwards pass
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return eval_frame.disable(eval_frame.disable(bw_compiler)(*args, **kwargs))
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bw_compiler = kwargs.get("bw_compiler") or kwargs["fw_compiler"]
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kwargs["bw_compiler"] = _wrapped_bw_compiler
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kwargs["inference_compiler"] = (
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kwargs.get("inference_compiler") or kwargs["fw_compiler"]
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)
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from functorch.compile import nop
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from torch._inductor.debug import enable_aot_logging
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# debug asserts slow down compile time noticeably,
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# So only default them on when the aot_eager backend is used.
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if kwargs.get("fw_compiler", None) == nop:
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patch_config = patch("functorch.compile.config.debug_assert", True)
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else:
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patch_config = contextlib.nullcontext()
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try:
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# NB: NOT cloned!
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with enable_aot_logging(), patch_config:
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cg = aot_module_simplified(gm, example_inputs, **kwargs)
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counters["aot_autograd"]["ok"] += 1
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return eval_frame.disable(cg)
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except Exception:
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counters["aot_autograd"]["not_ok"] += 1
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raise
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return compiler_fn
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def mem_efficient_fusion_kwargs(use_decomps):
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from functorch.compile import (
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default_decompositions,
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min_cut_rematerialization_partition,
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ts_compile,
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)
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kwargs = {
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# these are taken from memory_efficient_fusion()
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"fw_compiler": ts_compile,
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"bw_compiler": ts_compile,
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"partition_fn": min_cut_rematerialization_partition,
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}
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if use_decomps:
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kwargs["decompositions"] = default_decompositions
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return kwargs
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def fake_tensor_unsupported(fn):
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"""
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Decorator for backends that need real inputs. We swap out fake
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tensors for zero tensors.
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"""
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def defake(x):
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if not isinstance(x, FakeTensor):
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return x
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if x._has_symbolic_sizes_strides:
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size = [
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s.node.shape_env.size_hint(s.node.expr)
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if isinstance(s, torch.SymInt)
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else s
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for s in x.size()
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]
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stride = [
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s.node.shape_env.size_hint(s.node.expr)
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if isinstance(s, torch.SymInt)
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else s
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for s in x.stride()
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]
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else:
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size = x.size()
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stride = x.stride()
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y = torch.empty_strided(
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size,
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stride,
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dtype=x.dtype,
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device=x.device,
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requires_grad=x.requires_grad,
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)
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y.zero_()
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return y
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@functools.wraps(fn)
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def wrapper(model, inputs, **kwargs):
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with _disable_current_modes():
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inputs = list(map(defake, inputs))
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return fn(model, inputs, **kwargs)
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return wrapper
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def device_from_inputs(example_inputs) -> torch.device:
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for x in example_inputs:
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if hasattr(x, "device"):
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return x.device
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def dtype_from_inputs(example_inputs) -> torch.dtype:
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for x in example_inputs:
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if hasattr(x, "dtype"):
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return x.dtype
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