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caed485873
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26060 This PR enables BUILD_NAMEDTENSOR by default. This is done via including a header, `c10/core/EnableNamedTensor`, that sets `BUILD_NAMEDTENSOR`. In the future, the plan is to get rid of the flag entirely: we can incrementally delete usages after this PR goes in. This PR also maintains the namedtensor ci vs regular ci distinction. `test/test_namedtensor.py` only runs if TEST_NAMEDTENSOR=1 is specified. TEST_NAMEDTENSOR=1 is set on the namedtensor ci. I'll remove this distinction later and send out an announcement about it; devs will be responsible for named tensor failures after that. The initial reason why we had the BUILD_NAMEDTENSOR flag was so that we could quickly prototype named tensor features without worrying about adding overhead to the framework. The overheads can be categorized as memory overhead and performance overhead. Memory overhead: named tensors adds 1 additional word per Tensor. This is because TensorImpl stores a `unique_ptr<NamedTensorMetaInterface>` field. This is not a lot of overhead. Performance overhead: At all entry points to name inference, we check if inputs to an op are named. If inputs are not named, we short-circuit and don't do name inference. These calls should therefore be as efficient as error-checking code and not take up a lot of time. My plan is to benchmark a few functions and then post the results in a comment to this PR. Test Plan: - [namedtensor ci] Differential Revision: D17331635 Pulled By: zou3519 fbshipit-source-id: deed901347448ae2c26066c1fa432e3dc0cadb92
12 lines
288 B
C
12 lines
288 B
C
#pragma once
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#include <torch/csrc/python_headers.h>
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#include <ATen/Dimname.h>
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#include <ATen/core/EnableNamedTensor.h>
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#ifdef BUILD_NAMEDTENSOR
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at::Dimname THPDimname_parse(PyObject* obj);
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bool THPUtils_checkDimname(PyObject* obj);
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bool THPUtils_checkDimnameList(PyObject* obj);
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#endif
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