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https://github.com/zebrajr/pytorch.git
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47330ca133
Fixes https://github.com/pytorch/pytorch/issues/124499 (I also changed the warn to an info to avoid noise) That'll take some investigation, but rather than reverting I'm gating the view-replay behind a config that I default to False. To get the behavior back for XLA, can you have `import torch_xla` set this config? Pull Request resolved: https://github.com/pytorch/pytorch/pull/124488 Approved by: https://github.com/ezyang, https://github.com/Microve
108 lines
4.5 KiB
Python
108 lines
4.5 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 TYPE_CHECKING
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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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# Today, if you are in a situation where there is "false aliasing"
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# (e.g. you have a bunch of model parameters that all alias the same underlying buffer),
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# our checks for this situation are very slow if these inputs have dynamic shapes.
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# This config is set to ensure that there aren't too many aliased inputs in this situation,
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# so that we error loudly instead of compiling forever.
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# Eventually, we should make these checks faster.
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# For now, however, you can simply turn off dynamic shapes by marking your inputs static
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# when you run into this situation.
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_max_aliased_inputs_with_dynamic_shapes_enabled = 5
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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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# When AOTAutograd regenerates aliased graph outputs,
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# attempte to use functionalization's view-replay logic
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# before falling back to the autograd engine's view replay or as_strided.
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# This can have some perf implications
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# (although for many models this will not matter).
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# (1) If you have many view ops chained together, replaying all of them
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# at runtime can have more overhead compared to a single as_strided call
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# (2) If you are doing training, AsStridedBackward is quite slow,
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# and the individual view op backward formulas will likely be faster.
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# (3) Some backends like XLA do not support as_strided
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view_replay_for_aliased_outputs = False
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# Restricts the amount of computation AOTAutograd can do.
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# NB: We have essentially disabled this heuristic now. However, this is kept
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# here for now in case it's useful. Setting it low can artificially reduce the
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# amount of recomputation AOTAutograd performs, although not in any kind of
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# principled way.
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max_dist_from_bw = 1000
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# Bans recomputation of nodes that are reading from nodes that is far before
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# the current node
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ban_recompute_used_far_apart = True
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# Breaks up long chain of fusible ops, as otherwise we can have an arbitrarily
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# long chain of recomputation in the backwards pass.
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ban_recompute_long_fusible_chains = True
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# Bans recomputation of nodes that must be materialized in the backwards pass
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# (used by a non-fusible node)
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ban_recompute_materialized_backward = True
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# Chooses to ban recomputation of nodes based off an allowlist. Setting it to
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# False changes it to use a denylist. Main change is on operators like
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# sort/pool/stuff that isn't cheap enough to be fusible for free but also isn't
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# that expensive
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ban_recompute_not_in_allowlist = True
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# Chooses to ban recomputation of reductions. This is generally a good idea, as
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# the result of reductions is generally very small but recomputing reductions in
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# a fusion can be expensive.
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ban_recompute_reductions = True
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# Sets all of the ban_recompute heuristics to False except ban_recompute_reductions
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# Generally, this will probably result in some memory improvement, but at the
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# cost of some performance
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aggressive_recomputation = False
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# If FakeTensor.data_ptr() should error.
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# This option is independent of AOTAutograd and torch.compile, but our policy
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# is to turn it off during torch.compile.
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fake_tensor_allow_unsafe_data_ptr_access = True
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# Unlifts effect tokens from the inputs/outputs in the traced graph and instead
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# inserts make_token/sink_token calls in the graph to create tokens and then
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# sink them at the end. Note that this means the graph is no longer functional
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# which may lead to silent errors unless the backend knows how to handle the
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# tokens.
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unlift_effect_tokens = False
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if TYPE_CHECKING:
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from torch.utils._config_typing import * # noqa: F401, F403
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from torch.utils._config_module 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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