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This PR introduces the ability to whitelist sources as dynamic. This is particularly useful for large models with graph breaks, as you can keep the dynamism across graph breaks since source names stay consistent. Additionally you can use this to mark ints as dynamic. NB: I intentionally didn't complicate the interface by supporting specification of per dimension dynamism. There is virtue in keeping true to the standard way of representing sources (eg. L['x']). If we find in practice that we need more more fine grained control, we can explore further affordances at that time. Pull Request resolved: https://github.com/pytorch/pytorch/pull/147979 Approved by: https://github.com/Mingming-Ding
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
"""
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This is the top-level configuration module for the compiler, containing
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cross-cutting configuration options that affect all parts of the compiler
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stack.
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You may also be interested in the per-component configuration modules, which
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contain configuration options that affect only a specific part of the compiler:
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* :mod:`torch._dynamo.config`
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* :mod:`torch._inductor.config`
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* :mod:`torch._functorch.config`
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* :mod:`torch.fx.experimental.config`
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"""
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import sys
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from typing import Optional
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from torch.utils._config_module import Config, install_config_module
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__all__ = [
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"job_id",
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]
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# NB: Docblocks go UNDER variable definitions! Use spacing to make the
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# grouping clear.
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# FB-internal note: you do NOT have to specify this explicitly specify this if
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# you run on MAST, we will automatically default this to
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# mast:MAST_JOB_NAME:MAST_JOB_VERSION.
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job_id: Optional[str] = Config(env_name_default="TORCH_COMPILE_JOB_ID", default=None)
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"""
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Semantically, this should be an identifier that uniquely identifies, e.g., a
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training job. You might have multiple attempts of the same job, e.g., if it was
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preempted or needed to be restarted, but each attempt should be running
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substantially the same workload with the same distributed topology. You can
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set this by environment variable with :envvar:`TORCH_COMPILE_JOB_ID`.
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Operationally, this controls the effect of profile-guided optimization related
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persistent state. PGO state can affect how we perform compilation across
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multiple invocations of PyTorch, e.g., the first time you run your program we
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may compile twice as we discover what inputs are dynamic, and then PGO will
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save this state so subsequent invocations only need to compile once, because
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they remember it is dynamic. This profile information, however, is sensitive
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to what workload you are running, so we require you to tell us that two jobs
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are *related* (i.e., are the same workload) before we are willing to reuse
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this information. Notably, PGO does nothing (even if explicitly enabled)
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unless a valid ``job_id`` is available. In some situations, PyTorch can
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configured to automatically compute a ``job_id`` based on the environment it
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is running in.
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Profiles are always collected on a per rank basis, so different ranks may have
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different profiles. If you know your workload is truly SPMD, you can run with
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:data:`torch._dynamo.config.enable_compiler_collectives` to ensure nodes get
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consistent profiles across all ranks.
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"""
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cache_key_tag: str = Config(env_name_default="TORCH_COMPILE_CACHE_KEY_TAG", default="")
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"""
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Tag to be included in the cache key generation for all torch compile caching.
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A common use case for such a tag is to break caches.
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"""
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dynamic_sources: str = Config(
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env_name_default="TORCH_COMPILE_DYNAMIC_SOURCES", default=""
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)
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"""
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Comma delimited list of sources that should be marked as dynamic. Primarily useful for large
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models with graph breaks where you need intermediate tensors and ints to be marked dynamic.
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This whitelist is dominant over all other flags dynamic=False, force_nn_module_property_static_shapes
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and force_parameter_static_shapes.
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"""
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install_config_module(sys.modules[__name__])
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