enable feature score auto collection in EBC #5031
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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
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: 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: 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