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Closed the perf gap of resnet and enabled refit #3629

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Description

Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.

Fixes # (issue)

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  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
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@github-actions github-actions bot added component: conversion Issues re: Conversion stage component: converters Issues re: Specific op converters component: api [Python] Issues re: Python API component: dynamo Issues relating to the `torch.compile` or `torch._dynamo.export` paths labels Jun 27, 2025
@github-actions github-actions bot requested a review from apbose June 27, 2025 01:01
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minor comment. LGTM

@@ -90,7 +91,18 @@ def construct_refit_mapping_from_weight_name_map(
) -> dict[Any, Any]:
engine_weight_map = {}
for engine_weight_name, (sd_weight_name, np_weight_type) in weight_name_map.items():
if sd_weight_name not in state_dict:
if engine_weight_name.split(" ")[-1] in ["SCALE", "SHIFT"]:
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We should abstract this imo. Like if there are any weight types that require constant folding in converter this should be associated with the converter. Then the refit system will just iterate through all these constant fold operations. Ideally the converter can use the same implementation

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Currently BN is the only one. Do you think we should have a constant_fold function and have refit and conversion call that function?

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cla signed component: api [Python] Issues re: Python API component: conversion Issues re: Conversion stage component: converters Issues re: Specific op converters component: dynamo Issues relating to the `torch.compile` or `torch._dynamo.export` paths
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🐛 [Bug] current BN implementation results in slower performance
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