Enable quantization as default for XNNPack for previous failing models #6242
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/6242
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This pull request was exported from Phabricator. Differential Revision: D64081319 |
digantdesai
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pytorch#6242) Summary: Since master has migrated aot_compiler to use to_edge_transform_and_lower in a previous change pytorch#6026, quantization XNNPack options can be enabled by default for the following models: - Quantized ViT - Quantized Mobilebert - Quantized Emformer Predict - Quantized Emformer Transcribe Reviewed By: digantdesai Differential Revision: D64081319
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This pull request was exported from Phabricator. Differential Revision: D64081319 |
qma
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Oct 16, 2024
pytorch#6242) Summary: Since master has migrated aot_compiler to use to_edge_transform_and_lower in a previous change pytorch#6026, quantization XNNPack options can be enabled by default for the following models: - Quantized ViT - Quantized Mobilebert - Quantized Emformer Predict - Quantized Emformer Transcribe Reviewed By: digantdesai Differential Revision: D64081319
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This pull request was exported from Phabricator. Differential Revision: D64081319 |
pytorch#6242) Summary: Since master has migrated aot_compiler to use to_edge_transform_and_lower in a previous change pytorch#6026, quantization XNNPack options can be enabled by default for the following models: - Quantized ViT - Quantized Mobilebert - Quantized Emformer Predict - Quantized Emformer Transcribe Reviewed By: digantdesai Differential Revision: D64081319
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This pull request was exported from Phabricator. Differential Revision: D64081319 |
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This pull request has been merged in 5f12f28. |
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Summary:
Since master has migrated aot_compiler to use to_edge_transform_and_lower in a previous change #6026, quantization XNNPack options can be enabled by default for the following models:
Differential Revision: D64081319