convert stride_per_key_per_rank to tensor inside KJT (#2959)

Summary:

# context
* this diff is part of the "variable-batch KJT refactoring" project ([doc](https://fburl.com/gdoc/svfysfai))
* previously the `stride_per_key_per_rank` variable is `List[List[int]] | None` which can't be handled correctly in PT2 IR (torch.export)
* this change makes the KJT class variable `_stride_per_key_per_rank` as `torch.IntTensor | None` so it would be compatible with PT2 IR.

# equivalency
* to check if `self._stride_per_key_per_rank` is `None`
this logic is used to differentiate variable_batch case, and should have the same behavior after this diff
* to use `self._stride_per_key_per_rank` as `List[List[int]]`
most of the callsite use the function to get the list: `def stride_per_key_per_rank(self) -> List[List[int]]:`, and this function is modified to covert the `torch.IntTensor` to list as ` _stride_per_key_per_rank.tolist()`, the results should be the same

NOTE: currently this `self._stride_per_key_per_rank` tensor is always on CPU since it's effective the meta data of a KJT. However, ideally it should be on GPU side since it's after input_dist and we'll should avoid move it to cpu unless really need it.

Reviewed By: jd7-tr

Differential Revision: D74366343

Author: TroyGarden

torchrec/distributed/tests/test_pt2_multiprocess.py

@@ -703,31 +703,31 @@ def disable_cuda_tf32(self) -> bool:
                     ShardingType.TABLE_WISE.value,
                     _InputType.SINGLE_BATCH,
                     _ConvertToVariableBatch.TRUE,
-                    "inductor",
+                    "aot_eager",
                     _TestConfig(),
                 ),
                 (
                     _ModelType.EBC,
                     ShardingType.COLUMN_WISE.value,
                     _InputType.SINGLE_BATCH,
                     _ConvertToVariableBatch.TRUE,
-                    "inductor",
+                    "aot_eager",
                     _TestConfig(),
                 ),
                 (
                     _ModelType.EBC,
                     ShardingType.TABLE_WISE.value,
                     _InputType.SINGLE_BATCH,
                     _ConvertToVariableBatch.FALSE,
-                    "inductor",
+                    "aot_eager",
                     _TestConfig(),
                 ),
                 (
                     _ModelType.EBC,
                     ShardingType.COLUMN_WISE.value,
                     _InputType.SINGLE_BATCH,
                     _ConvertToVariableBatch.FALSE,
-                    "inductor",
+                    "aot_eager",
                     _TestConfig(),
                 ),
             ]

torchrec/pt2/utils.py

@@ -54,7 +54,7 @@ def kjt_for_pt2_tracing(
             values=values,
             lengths=lengths,
             weights=kjt.weights_or_none(),
-            stride_per_key_per_rank=[[stride]] * n,
+            stride_per_key_per_rank=torch.IntTensor([[stride]] * n, device="cpu"),
             inverse_indices=(kjt.keys(), inverse_indices_tensor),
         )
 
@@ -85,7 +85,7 @@ def kjt_for_pt2_tracing(
         lengths=lengths,
         weights=weights,
         stride=stride if not is_vb else None,
-        stride_per_key_per_rank=kjt.stride_per_key_per_rank() if is_vb else None,
+        stride_per_key_per_rank=kjt._stride_per_key_per_rank if is_vb else None,
         inverse_indices=inverse_indices,
     )
 

torchrec/schema/api_tests/test_jagged_tensor_schema.py

@@ -9,7 +9,7 @@
 
 import inspect
 import unittest
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 from torchrec.schema.utils import is_signature_compatible
@@ -112,7 +112,9 @@ def __init__(
         lengths: Optional[torch.Tensor] = None,
         offsets: Optional[torch.Tensor] = None,
         stride: Optional[int] = None,
-        stride_per_key_per_rank: Optional[List[List[int]]] = None,
+        stride_per_key_per_rank: Optional[
+            Union[List[List[int]], torch.IntTensor]
+        ] = None,
         # Below exposed to ensure torch.script-able
         stride_per_key: Optional[List[int]] = None,
         length_per_key: Optional[List[int]] = None,

torchrec/sparse/jagged_tensor.py

@@ -1096,13 +1096,15 @@ def _maybe_compute_stride_kjt(
     stride: Optional[int],
     lengths: Optional[torch.Tensor],
     offsets: Optional[torch.Tensor],
-    stride_per_key_per_rank: Optional[List[List[int]]],
+    stride_per_key_per_rank: Optional[torch.IntTensor],
 ) -> int:
     if stride is None:
         if len(keys) == 0:
             stride = 0
-        elif stride_per_key_per_rank is not None and len(stride_per_key_per_rank) > 0:
-            stride = max([sum(s) for s in stride_per_key_per_rank])
+        elif (
+            stride_per_key_per_rank is not None and stride_per_key_per_rank.numel() > 0
+        ):
+            stride = int(stride_per_key_per_rank.sum(dim=1).max().item())
         elif offsets is not None and offsets.numel() > 0:
             stride = (offsets.numel() - 1) // len(keys)
         elif lengths is not None:
@@ -1481,8 +1483,8 @@ def _strides_from_kjt(
 def _kjt_empty_like(kjt: "KeyedJaggedTensor") -> "KeyedJaggedTensor":
     # empty like function fx wrapped, also avoids device hardcoding
     stride, stride_per_key_per_rank = (
-        (None, kjt.stride_per_key_per_rank())
-        if kjt.variable_stride_per_key()
+        (None, kjt._stride_per_key_per_rank)
+        if kjt._stride_per_key_per_rank is not None and kjt.variable_stride_per_key()
         else (kjt.stride(), None)
     )
 
@@ -1668,14 +1670,20 @@ def _maybe_compute_lengths_offset_per_key(
 
 def _maybe_compute_stride_per_key(
     stride_per_key: Optional[List[int]],
-    stride_per_key_per_rank: Optional[List[List[int]]],
+    stride_per_key_per_rank: Optional[torch.IntTensor],
     stride: Optional[int],
     keys: List[str],
 ) -> Optional[List[int]]:
     if stride_per_key is not None:
         return stride_per_key
     elif stride_per_key_per_rank is not None:
-        return [sum(s) for s in stride_per_key_per_rank]
+        if stride_per_key_per_rank.dim() != 2:
+            # after permute the kjt could be empty
+            return []
+        rt: List[int] = stride_per_key_per_rank.sum(dim=1).tolist()
+        if not torch.jit.is_scripting() and is_torchdynamo_compiling():
+            pt2_checks_all_is_size(rt)
+        return rt
     elif stride is not None:
         return [stride] * len(keys)
     else:
@@ -1766,7 +1774,9 @@ def __init__(
         lengths: Optional[torch.Tensor] = None,
         offsets: Optional[torch.Tensor] = None,
         stride: Optional[int] = None,
-        stride_per_key_per_rank: Optional[List[List[int]]] = None,
+        stride_per_key_per_rank: Optional[
+            Union[torch.IntTensor, List[List[int]]]
+        ] = None,
         # Below exposed to ensure torch.script-able
         stride_per_key: Optional[List[int]] = None,
         length_per_key: Optional[List[int]] = None,
@@ -1788,8 +1798,14 @@ def __init__(
         self._lengths: Optional[torch.Tensor] = lengths
         self._offsets: Optional[torch.Tensor] = offsets
         self._stride: Optional[int] = stride
-        self._stride_per_key_per_rank: Optional[List[List[int]]] = (
-            stride_per_key_per_rank
+        if not torch.jit.is_scripting() and is_torchdynamo_compiling():
+            # in pt2.compile the stride_per_key_per_rank has to be torch.Tensor or None
+            # does not take List[List[int]]
+            assert not isinstance(stride_per_key_per_rank, list)
+        self._stride_per_key_per_rank: Optional[torch.IntTensor] = (
+            torch.IntTensor(stride_per_key_per_rank, device="cpu")
+            if isinstance(stride_per_key_per_rank, list)
+            else stride_per_key_per_rank
         )
         self._stride_per_key: Optional[List[int]] = stride_per_key
         self._length_per_key: Optional[List[int]] = length_per_key
@@ -1815,10 +1831,11 @@ def _init_pt2_checks(self) -> None:
             return
         if self._stride_per_key is not None:
             pt2_checks_all_is_size(self._stride_per_key)
-        if self._stride_per_key_per_rank is not None:
-            # pyre-ignore [16]
-            for s in self._stride_per_key_per_rank:
-                pt2_checks_all_is_size(s)
+        _stride_per_key_per_rank = self._stride_per_key_per_rank
+        if _stride_per_key_per_rank is not None:
+            stride_per_key_per_rank = _stride_per_key_per_rank.tolist()
+            for stride_per_rank in stride_per_key_per_rank:
+                pt2_checks_all_is_size(stride_per_rank)
 
     @staticmethod
     def from_offsets_sync(
@@ -2028,7 +2045,7 @@ def from_jt_dict(jt_dict: Dict[str, JaggedTensor]) -> "KeyedJaggedTensor":
         kjt_stride, kjt_stride_per_key_per_rank = (
             (stride_per_key[0], None)
             if all(s == stride_per_key[0] for s in stride_per_key)
-            else (None, [[stride] for stride in stride_per_key])
+            else (None, torch.IntTensor(stride_per_key, device="cpu").reshape(-1, 1))
         )
         kjt = KeyedJaggedTensor(
             keys=kjt_keys,
@@ -2193,12 +2210,29 @@ def stride_per_key_per_rank(self) -> List[List[int]]:
         Returns:
             List[List[int]]: stride per key per rank of the KeyedJaggedTensor.
         """
-        stride_per_key_per_rank = self._stride_per_key_per_rank
-        return stride_per_key_per_rank if stride_per_key_per_rank is not None else []
+        # making a local reference to the class variable to make jit.script behave
+        _stride_per_key_per_rank = self._stride_per_key_per_rank
+        if (
+            not torch.jit.is_scripting()
+            and is_torchdynamo_compiling()
+            and _stride_per_key_per_rank is not None
+        ):
+            stride_per_key_per_rank = _stride_per_key_per_rank.tolist()
+            for stride_per_rank in stride_per_key_per_rank:
+                pt2_checks_all_is_size(stride_per_rank)
+            return stride_per_key_per_rank
+        return (
+            []
+            if _stride_per_key_per_rank is None
+            else _stride_per_key_per_rank.tolist()
+        )
 
     def variable_stride_per_key(self) -> bool:
         """
         Returns whether the KeyedJaggedTensor has variable stride per key.
+        NOTE: `self._variable_stride_per_key` could be `False` when `self._stride_per_key_per_rank`
+        is not `None`. It might be assigned to False externally/intentionally, usually the
+        `self._stride_per_key_per_rank` is trivial.
 
         Returns:
             bool: whether the KeyedJaggedTensor has variable stride per key.
@@ -2343,13 +2377,16 @@ def split(self, segments: List[int]) -> List["KeyedJaggedTensor"]:
         start_offset = 0
         _length_per_key = self.length_per_key()
         _offset_per_key = self.offset_per_key()
+        # use local copy/ref for self._stride_per_key_per_rank to satisfy jit.script
+        _stride_per_key_per_rank = self._stride_per_key_per_rank
         for segment in segments:
             end = start + segment
             end_offset = _offset_per_key[end]
             keys: List[str] = self._keys[start:end]
             stride_per_key_per_rank = (
-                self.stride_per_key_per_rank()[start:end]
+                _stride_per_key_per_rank[start:end, :]
                 if self.variable_stride_per_key()
+                and _stride_per_key_per_rank is not None
                 else None
             )
             if segment == len(self._keys):
@@ -2514,17 +2551,17 @@ def permute(
 
         length_per_key = self.length_per_key()
         permuted_keys: List[str] = []
-        permuted_stride_per_key_per_rank: List[List[int]] = []
         permuted_length_per_key: List[int] = []
         permuted_length_per_key_sum = 0
         for index in indices:
             key = self.keys()[index]
             permuted_keys.append(key)
             permuted_length_per_key.append(length_per_key[index])
-            if self.variable_stride_per_key():
-                permuted_stride_per_key_per_rank.append(
-                    self.stride_per_key_per_rank()[index]
-                )
+        _stride_per_key_per_rank = self._stride_per_key_per_rank
+        if self.variable_stride_per_key() and _stride_per_key_per_rank is not None:
+            permuted_stride_per_key_per_rank = _stride_per_key_per_rank[indices, :]
+        else:
+            permuted_stride_per_key_per_rank = None
 
         permuted_length_per_key_sum = sum(permuted_length_per_key)
         if not torch.jit.is_scripting() and is_non_strict_exporting():
@@ -2576,17 +2613,15 @@ def permute(
                 self.weights_or_none(),
                 permuted_length_per_key_sum,
             )
-        stride_per_key_per_rank = (
-            permuted_stride_per_key_per_rank if self.variable_stride_per_key() else None
-        )
+
         kjt = KeyedJaggedTensor(
             keys=permuted_keys,
             values=permuted_values,
             weights=permuted_weights,
             lengths=permuted_lengths.view(-1),
             offsets=None,
             stride=self._stride,
-            stride_per_key_per_rank=stride_per_key_per_rank,
+            stride_per_key_per_rank=permuted_stride_per_key_per_rank,
             stride_per_key=None,
             length_per_key=permuted_length_per_key if len(permuted_keys) > 0 else None,
             lengths_offset_per_key=None,
@@ -2904,7 +2939,7 @@ def dist_init(
 
         if variable_stride_per_key:
             assert stride_per_rank_per_key is not None
-            stride_per_key_per_rank_tensor: torch.Tensor = stride_per_rank_per_key.view(
+            stride_per_key_per_rank: torch.Tensor = stride_per_rank_per_key.view(
                 num_workers, len(keys)
             ).T.cpu()
 
@@ -2941,23 +2976,18 @@ def dist_init(
                         weights,
                     )
 
-            stride_per_key_per_rank = torch.jit.annotate(
-                List[List[int]], stride_per_key_per_rank_tensor.tolist()
-            )
+            if stride_per_key_per_rank.numel() == 0:
+                stride_per_key_per_rank = torch.zeros(
+                    (len(keys), 1), device="cpu", dtype=torch.int64
+                )
 
-            if not stride_per_key_per_rank:
-                stride_per_key_per_rank = [[0]] * len(keys)
             if stagger > 1:
-                stride_per_key_per_rank_stagger: List[List[int]] = []
                 local_world_size = num_workers // stagger
-                for i in range(len(keys)):
-                    stride_per_rank_stagger: List[int] = []
-                    for j in range(local_world_size):
-                        stride_per_rank_stagger.extend(
-                            stride_per_key_per_rank[i][j::local_world_size]
-                        )
-                    stride_per_key_per_rank_stagger.append(stride_per_rank_stagger)
-                stride_per_key_per_rank = stride_per_key_per_rank_stagger
+                indices = [
+                    list(range(i, num_workers, local_world_size))
+                    for i in range(local_world_size)
+                ]
+                stride_per_key_per_rank = stride_per_key_per_rank[:, indices]
 
             kjt = KeyedJaggedTensor(
                 keys=keys,