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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
43 lines
1.2 KiB
Python
43 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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use_functionalize = True
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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 = 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 = 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 = False
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debug_partitioner = os.environ.get("AOT_PARTITIONER_DEBUG", False)
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static_weight_shapes = True
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# Applies CSE to the graph before partitioning
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cse = True
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# Restricts the amount of computation AOTAutograd can do.
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max_dist_from_bw = 3
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from .._dynamo.config_utils import install_config_module
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# adds patch, save_config, invalid config checks, etc
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install_config_module(sys.modules[__name__])
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