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19747cbbe6
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/75046 Merge the code needed for dynamic+ltc integration from the staging branch to the master branch. Test Plan: Unit test ``` pytest test_extract_compiled_graph ``` test thru dynamo ``` LTC_TS_CUDA=1 time python torchbench.py --speedup-ltc -dcuda --nvfuser --randomize-input --only <model name> ``` Reviewed By: alanwaketan Differential Revision: D35300646 Pulled By: shunting314 fbshipit-source-id: 09ed20d3bb8ef80e4b93ba87ea3356a07d2dccdb (cherry picked from commit 2b56771cdfd2cfa825c65ee9fd42338fb372fb32)
21 lines
722 B
Python
21 lines
722 B
Python
import torch._C._lazy
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def render_ir_graph(tensors):
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"""Return a text dump of the LTC IR graph in dot format for the tensors.
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The text can be processed by tools like dot to be rendered in pdf,png etc."""
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return torch._C._lazy._get_tensors_dot(tensors)
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def dump_ir(tensors, ir_format):
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"""Return a dump of the tensors in the specified format.
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Valid format are
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- text: for LTC IR
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- backend: for the activate backend IR
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"""
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if ir_format == "text":
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return torch._C._lazy._get_tensors_text(tensors)
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elif ir_format == "backend":
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return torch._C._lazy._get_tensors_backend(tensors)
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else:
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raise RuntimeError(f"Unrecognized IR format: {ir_format}")
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