cpu memory optimization rebased to main #3868
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narendasan
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narendasan
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| @needs_refit # type: ignore[misc] | ||
| def _insert_engine_to_cache(self, hash_val: str, serialized_engine: bytes) -> None: | ||
| def _insert_engine_to_cache(self, hash_val: str, engine: trt.ICudaEngine) -> None: |
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@zewenli98 when do these calls run? will this conflict with the goal of keeping mem usage under 3x?
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should we do caching in a post processing step?
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Like we can give the cache entry as one of the Interpreter Result fields
py/torch_tensorrt/dynamo/partitioning/_adjacency_partitioner.py
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| ((6, 7, 5, 4, 5),), | ||
| ] | ||
| ) | ||
| @unittest.skip("Skipping prod dim int default test for now") |
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Can you do xfail and provide a reason for the skip?
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| # PLATFORM_SUPPORTS_CUDNN_ATTENTION, | ||
| # "Platform doesn't support cuDNN attention", | ||
| # ) | ||
| @unittest.skip("Skipping cuDNN attention test for now") |
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Provide reasons for the skip
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