Always return .int() conversion from unwrap_kjt #1939
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Summary: Moves quantize and shard methods from aps_predict_factory_input_trec_sharding_format.py inside torchrec/inference. This resolves dependency issues from multiple places depending on this API for eager model processing (AIMP FX, AIMP PT2, unit tests, etc.) Along with this change, compute device is now CUDA to correctly capture device propagation for eventually placing remote and merge on CUDA device Differential Revision: D56728862 Reviewed By: ZhengkaiZ
Summary: Introduce BoundsCheckMode fused_param for TBE BoundsCheckMode. There is no reason really to run bounds_check_indices during inference use case (AIMP has it off by default: https://fburl.com/code/q8zhundg), and it causes issues with the PT2 IR (bounds_check_indices is a mutating op) Differential Revision: D56743992
Summary: With _unwrap_kjt_for_cpu, and recording the runtime_device in QuantEmbeddingBag, the conditional for the input device in _unwrap_kjt is no longer needed, as this path should always record the cuda device impl (as features will either be on cuda or meta) Reviewed By: IvanKobzarev Differential Revision: D56765511
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Summary: With _unwrap_kjt_for_cpu, and recording the runtime_device in QuantEmbeddingBag, the conditional for the input device in _unwrap_kjt is no longer needed, as this path should always record the cuda device impl (as features will either be on cuda or meta)
Differential Revision: D56765511