enable feature score auto collection in EBC #3475
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Summary: X-link: meta-pytorch/torchrec#3475 X-link: facebookresearch/FBGEMM#2044 Enable feature score auto collection for EBC in the similar way of EC. The configuration has no difference in embedding table config: virtual_table_eviction_policy=FeatureScoreBasedEvictionPolicy( training_id_eviction_trigger_count=260_000_000, # 260M training_id_keep_count=160_000_000, # 160M enable_auto_feature_score_collection=True, feature_score_mapping={ "sparse_public_original_content_creator": 1.0, }, feature_score_default_value=0.5, ), Differential Revision: D85017179
Summary: X-link: pytorch/FBGEMM#5030 X-link: facebookresearch/FBGEMM#2043 Enable feature score auto collection in ShardedEmbeddingCollection based on static feature to score mapping. If user needs custom score for specific id, they can disable auto collection and then change model code explicitly to collect score for each id. Here is the sample eviction policy config in embedding_table config to enable auto score collection: virtual_table_eviction_policy=FeatureScoreBasedEvictionPolicy( training_id_eviction_trigger_count=260_000_000, # 260M training_id_keep_count=160_000_000, # 160M enable_auto_feature_score_collection=True, feature_score_mapping={ "sparse_public_original_content_creator": 1.0, }, feature_score_default_value=0.5, ), Additionally the counter collected previously during EC dedup is not used by kvzch backend, so this diff removed that counter and allow KJT to transfer a single float32 weight tensor to backend. This allows feature score collection for EBC since there could have another float weight for EBC pooling already. Reviewed By: EddyLXJ Differential Revision: D83945722
Summary: X-link: pytorch/FBGEMM#5031 X-link: facebookresearch/FBGEMM#2044 Enable feature score auto collection for EBC in the similar way of EC. The configuration has no difference in embedding table config: virtual_table_eviction_policy=FeatureScoreBasedEvictionPolicy( training_id_eviction_trigger_count=260_000_000, # 260M training_id_keep_count=160_000_000, # 160M enable_auto_feature_score_collection=True, feature_score_mapping={ "sparse_public_original_content_creator": 1.0, }, feature_score_default_value=0.5, ), Differential Revision: D85017179
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Summary: X-link: meta-pytorch/torchrec#3475 X-link: facebookresearch/FBGEMM#2044 Enable feature score auto collection for EBC in the similar way of EC. The configuration has no difference in embedding table config: virtual_table_eviction_policy=FeatureScoreBasedEvictionPolicy( training_id_eviction_trigger_count=260_000_000, # 260M training_id_keep_count=160_000_000, # 160M enable_auto_feature_score_collection=True, feature_score_mapping={ "sparse_public_original_content_creator": 1.0, }, feature_score_default_value=0.5, ), Differential Revision: D85017179
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Summary:
X-link: https://github.com/facebookresearch/FBGEMM/pull/2044
Enable feature score auto collection for EBC in the similar way of EC. The configuration has no difference in embedding table config:
Differential Revision: D85017179