2024-12-23 nightly release (464a0e9)

torchrec/distributed/embedding.py

@@ -28,7 +28,7 @@
 import torch
 from torch import distributed as dist, nn
 from torch.autograd.profiler import record_function
-from torch.distributed._shard.sharding_spec.api import EnumerableShardingSpec
+from torch.distributed._shard.sharding_spec import EnumerableShardingSpec
 from torch.distributed._tensor import DTensor
 from torch.nn.parallel import DistributedDataParallel
 from torchrec.distributed.embedding_sharding import (

torchrec/distributed/embedding_sharding.py

@@ -47,6 +47,7 @@
 from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
 from torchrec.streamable import Multistreamable
 
+
 torch.fx.wrap("len")
 
 CACHE_LOAD_FACTOR_STR: str = "cache_load_factor"
@@ -61,6 +62,15 @@ def _fx_wrap_tensor_to_device_dtype(
     return t.to(device=tensor_device_dtype.device, dtype=tensor_device_dtype.dtype)
 
 
+@torch.fx.wrap
+def _fx_wrap_optional_tensor_to_device_dtype(
+    t: Optional[torch.Tensor], tensor_device_dtype: torch.Tensor
+) -> Optional[torch.Tensor]:
+    if t is None:
+        return None
+    return t.to(device=tensor_device_dtype.device, dtype=tensor_device_dtype.dtype)
+
+
 @torch.fx.wrap
 def _fx_wrap_batch_size_per_feature(kjt: KeyedJaggedTensor) -> Optional[torch.Tensor]:
     return (
@@ -121,6 +131,7 @@ def _fx_wrap_seq_block_bucketize_sparse_features_inference(
     block_sizes: torch.Tensor,
     bucketize_pos: bool = False,
     block_bucketize_pos: Optional[List[torch.Tensor]] = None,
+    total_num_blocks: Optional[torch.Tensor] = None,
 ) -> Tuple[
     torch.Tensor,
     torch.Tensor,
@@ -142,6 +153,7 @@ def _fx_wrap_seq_block_bucketize_sparse_features_inference(
         bucketize_pos=bucketize_pos,
         sequence=True,
         block_sizes=block_sizes,
+        total_num_blocks=total_num_blocks,
         my_size=num_buckets,
         weights=kjt.weights_or_none(),
         max_B=_fx_wrap_max_B(kjt),
@@ -289,6 +301,7 @@ def bucketize_kjt_inference(
     kjt: KeyedJaggedTensor,
     num_buckets: int,
     block_sizes: torch.Tensor,
+    total_num_buckets: Optional[torch.Tensor] = None,
     bucketize_pos: bool = False,
     block_bucketize_row_pos: Optional[List[torch.Tensor]] = None,
     is_sequence: bool = False,
@@ -303,6 +316,7 @@ def bucketize_kjt_inference(
     Args:
         num_buckets (int): number of buckets to bucketize the values into.
         block_sizes: (torch.Tensor): bucket sizes for the keyed dimension.
+        total_num_blocks: (Optional[torch.Tensor]): number of blocks per feature, useful for two-level bucketization
         bucketize_pos (bool): output the changed position of the bucketized values or
             not.
         block_bucketize_row_pos (Optional[List[torch.Tensor]]): The offsets of shard size for each feature.
@@ -318,6 +332,9 @@ def bucketize_kjt_inference(
         f"Expecting block sizes for {num_features} features, but {block_sizes.numel()} received.",
     )
     block_sizes_new_type = _fx_wrap_tensor_to_device_dtype(block_sizes, kjt.values())
+    total_num_buckets_new_type = _fx_wrap_optional_tensor_to_device_dtype(
+        total_num_buckets, kjt.values()
+    )
     unbucketize_permute = None
     bucket_mapping = None
     if is_sequence:
@@ -332,6 +349,7 @@ def bucketize_kjt_inference(
             kjt,
             num_buckets=num_buckets,
             block_sizes=block_sizes_new_type,
+            total_num_blocks=total_num_buckets_new_type,
             bucketize_pos=bucketize_pos,
             block_bucketize_pos=block_bucketize_row_pos,
         )