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Refactor generate_vbe_metadata (pytorch#3087)
Summary: X-link: facebookresearch/FBGEMM#178 Pull Request resolved: pytorch#3087 Moves `generate_vbe_metadata` into the `fbgemm_gpu.split_table_batched_embeddings_ops_training_common`. This is a preparation for VBE enablement in SSD-TBE Reviewed By: q10 Differential Revision: D62215222 fbshipit-source-id: 4db9d0c097f3f9b7aaf25c49ef777cf33a5c718d
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fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_training_common.py
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| #!/usr/bin/env python3 | ||
| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
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| from typing import List, Optional | ||
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| # @manual=//deeplearning/fbgemm/fbgemm_gpu/codegen:split_embedding_codegen_lookup_invokers | ||
| import fbgemm_gpu.split_embedding_codegen_lookup_invokers as invokers | ||
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| import torch | ||
| from fbgemm_gpu.split_embedding_configs import EmbOptimType as OptimType | ||
| from fbgemm_gpu.split_table_batched_embeddings_ops_common import PoolingMode | ||
| from torch import Tensor | ||
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||
| try: | ||
| try: | ||
| from torch.compiler import is_compiling | ||
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| def is_torchdynamo_compiling() -> bool: # type: ignore[misc] | ||
| # at least one test fails if we import is_compiling as a different name | ||
| return is_compiling() | ||
|
|
||
| except Exception: | ||
| # torch.compiler.is_compiling is not available in torch 1.10 | ||
| from torch._dynamo import is_compiling as is_torchdynamo_compiling | ||
| except Exception: | ||
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| def is_torchdynamo_compiling() -> bool: # type: ignore[misc] | ||
| return False | ||
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| def generate_vbe_metadata( | ||
| offsets: Tensor, | ||
| batch_size_per_feature_per_rank: Optional[List[List[int]]], | ||
| optimizer: OptimType, | ||
| pooling_mode: PoolingMode, | ||
| feature_dims_cpu: Tensor, | ||
| device: torch.device, | ||
| ) -> invokers.lookup_args.VBEMetadata: | ||
| """ | ||
| Generate VBE metadata based on batch_size_per_feature_per_rank. | ||
| Metadata includes: | ||
| 1) B_offsets - A tensor that contains batch size offsets for each | ||
| feature | ||
| 2) output_offsets_feature_rank - A tensor that contains output | ||
| offsets for each feature | ||
| 3) B_offsets_per_rank_per_feature - A tensor that contains batch | ||
| size offsets for each feature | ||
| and rank | ||
| 4) max_B - The maximum batch size for all features | ||
| 5) max_B_feature_rank - The maximum batch size for all ranks and | ||
| features | ||
| 6) output_size - The output size (number of elements) | ||
| """ | ||
| if batch_size_per_feature_per_rank is not None: | ||
| assert ( | ||
| pooling_mode != PoolingMode.NONE | ||
| ), "Variable batch size TBE support is not enabled for PoolingMode.NONE" | ||
| # TODO: Add input check | ||
| zero_tensor = torch.zeros(1, device="cpu", dtype=torch.int32) | ||
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| # Create B offsets | ||
| total_batch_size_per_feature = torch.tensor( | ||
| batch_size_per_feature_per_rank, dtype=torch.int32, device="cpu" | ||
| ).sum(dim=1) | ||
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| max_B = total_batch_size_per_feature.max().item() | ||
| if not torch.jit.is_scripting() and is_torchdynamo_compiling(): | ||
| torch._check_is_size(max_B) | ||
| torch._check(max_B < offsets.numel()) | ||
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| Bs = torch.concat([zero_tensor, total_batch_size_per_feature]) | ||
| B_offsets = Bs.cumsum(dim=0).to(torch.int) | ||
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| # Create output offsets | ||
| B_feature_rank = torch.tensor( | ||
| batch_size_per_feature_per_rank, | ||
| device="cpu", | ||
| dtype=torch.int64, | ||
| ) | ||
| max_B_feature_rank = B_feature_rank.max().item() | ||
| if not torch.jit.is_scripting() and is_torchdynamo_compiling(): | ||
| torch._check_is_size(max_B_feature_rank) | ||
| torch._check(max_B_feature_rank <= offsets.size(0)) | ||
| output_sizes_feature_rank = B_feature_rank.transpose( | ||
| 0, 1 | ||
| ) * feature_dims_cpu.view(1, -1) | ||
| output_offsets_feature_rank = torch.concat( | ||
| [ | ||
| zero_tensor.to(torch.int64), | ||
| output_sizes_feature_rank.flatten().cumsum(dim=0), | ||
| ] | ||
| ) | ||
| output_size = output_offsets_feature_rank[-1].item() | ||
| if not torch.jit.is_scripting() and is_torchdynamo_compiling(): | ||
| torch._check_is_size(output_size) | ||
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||
| # TODO: Support INT8 output | ||
| # B_offsets_rank_per_feature is for rank and (b, t) mapping | ||
| B_offsets_rank_per_feature = ( | ||
| torch.tensor( | ||
| [ | ||
| [0] + batch_size_per_feature | ||
| for batch_size_per_feature in batch_size_per_feature_per_rank | ||
| ], | ||
| device="cpu", | ||
| dtype=torch.int32, | ||
| ) | ||
| .cumsum(dim=1) | ||
| .to(torch.int) | ||
| ) | ||
|
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| B_offsets = B_offsets.to(device, non_blocking=True) | ||
| output_offsets_feature_rank = output_offsets_feature_rank.to( | ||
| device, non_blocking=True | ||
| ) | ||
| B_offsets_rank_per_feature = B_offsets_rank_per_feature.to( | ||
| device, non_blocking=True | ||
| ) | ||
|
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| # TODO: Use int32 for B_offsets and int64 for output_offsets_feature_rank | ||
| vbe_metadata = invokers.lookup_args.VBEMetadata( | ||
| B_offsets=B_offsets, | ||
| output_offsets_feature_rank=output_offsets_feature_rank, | ||
| B_offsets_rank_per_feature=B_offsets_rank_per_feature, | ||
| # pyre-ignore | ||
| max_B=max_B, | ||
| # pyre-ignore | ||
| max_B_feature_rank=max_B_feature_rank, | ||
| # pyre-ignore | ||
| output_size=output_size, | ||
| ) | ||
| else: | ||
| vbe_metadata = invokers.lookup_args.VBEMetadata( | ||
| B_offsets=None, | ||
| output_offsets_feature_rank=None, | ||
| B_offsets_rank_per_feature=None, | ||
| max_B=-1, | ||
| max_B_feature_rank=-1, | ||
| output_size=-1, | ||
| ) | ||
| return vbe_metadata |