mirror of
https://github.com/zebrajr/pytorch.git
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3864207c2a
Summary:
Replace _dynamo.config with an object instead of module
Current usage patterns of setting and reading fields on config will work
unchanged.
Only changes needed going forward:
1. import torch._dynamo.config will not work. However, just doing
import torch._dynamo is sufficient to access dynamo config
as torch._dynamo.config.
2. Files inside of _dynamo folder need to access config via
from torch._dynamo.config_util import config instead of
from torch._dynamo import config. Because _dynamo/__init__.py
imports some of the files so it would be circular import.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96455
Approved by: https://github.com/jansel
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Global flags for aot autograd
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"""
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import os
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import sys
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from typing import Union
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# Converts torch rng ops to their functional philox rng equivalents. Note that
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# we functionalize only CUDA rng ops today.
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functionalize_rng_ops: bool = False
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# can be useful for debugging if we are incorrectly creating meta fake tensors
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fake_tensor_allow_meta: Union[str, bool] = os.environ.get("FAKE_ALLOW_META", True)
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# Enables optional asserts in hotpath code to check for errors. If
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# you are seeing weird accuracy problems, try turning this on.
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# This is currently off by default as it will harm tracing time,
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# but it is on by default for aot_eager.
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debug_assert: bool = False
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debug_partitioner: Union[str, bool] = os.environ.get("AOT_PARTITIONER_DEBUG", False)
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static_weight_shapes: bool = True
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# Applies CSE to the graph before partitioning
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cse: bool = True
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# Restricts the amount of computation AOTAutograd can do.
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max_dist_from_bw: int = 3
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from torch._config_utils import install_config_module
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install_config_module('FunctorchConfig', sys.modules[__name__])
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