Revert D74366343

Summary:
This diff reverts D74366343
(The context such as a Sandcastle job, Task, SEV, etc. was not provided.)

Depends on D74366343

Reviewed By: jd7-tr

Differential Revision: D76870690

fbshipit-source-id: 8c08ef3272cdcf5345c3d98c0bccf2e02d5846a6

Author: generatedunixname89002005232357

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=torch.IntTensor([[stride]] * n, device="cpu"),
+            stride_per_key_per_rank=[[stride]] * n,
             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, Union
+from typing import Dict, List, Optional, Tuple
 
 import torch
 from torchrec.schema.utils import is_signature_compatible
@@ -112,9 +112,7 @@ def __init__(
         lengths: Optional[torch.Tensor] = None,
         offsets: Optional[torch.Tensor] = None,
         stride: Optional[int] = None,
-        stride_per_key_per_rank: Optional[
-            Union[List[List[int]], torch.IntTensor]
-        ] = None,
+        stride_per_key_per_rank: Optional[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,

torchrec/sparse/jagged_tensor.py

@@ -1098,15 +1098,13 @@ def _maybe_compute_stride_kjt(
     stride: Optional[int],
     lengths: Optional[torch.Tensor],
     offsets: Optional[torch.Tensor],
-    stride_per_key_per_rank: Optional[torch.IntTensor],
+    stride_per_key_per_rank: Optional[List[List[int]]],
 ) -> int:
     if stride is None:
         if len(keys) == 0:
             stride = 0
-        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 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 offsets is not None and offsets.numel() > 0:
             stride = (offsets.numel() - 1) // len(keys)
         elif lengths is not None:
@@ -1485,8 +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._stride_per_key_per_rank is not None and kjt.variable_stride_per_key()
+        (None, kjt.stride_per_key_per_rank())
+        if kjt.variable_stride_per_key()
         else (kjt.stride(), None)
     )
 
@@ -1672,20 +1670,14 @@ 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[torch.IntTensor],
+    stride_per_key_per_rank: Optional[List[List[int]]],
     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:
-        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
+        return [sum(s) for s in stride_per_key_per_rank]
     elif stride is not None:
         return [stride] * len(keys)
     else:
@@ -1776,9 +1768,7 @@ def __init__(
         lengths: Optional[torch.Tensor] = None,
         offsets: Optional[torch.Tensor] = None,
         stride: Optional[int] = None,
-        stride_per_key_per_rank: Optional[
-            Union[torch.IntTensor, List[List[int]]]
-        ] = None,
+        stride_per_key_per_rank: Optional[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,
@@ -1800,14 +1790,8 @@ def __init__(
         self._lengths: Optional[torch.Tensor] = lengths
         self._offsets: Optional[torch.Tensor] = offsets
         self._stride: Optional[int] = stride
-        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_per_rank: Optional[List[List[int]]] = (
+            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
@@ -1818,8 +1802,6 @@ def __init__(
         self._inverse_indices: Optional[Tuple[List[str], torch.Tensor]] = (
             inverse_indices
         )
-        # this is only needed for torch.compile case
-        self._pt2_stride_per_key_per_rank: Optional[List[List[int]]] = None
 
         # legacy attribute, for backward compatabilibity
         self._variable_stride_per_key: Optional[bool] = None
@@ -1835,6 +1817,10 @@ 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)
 
     @staticmethod
     def from_offsets_sync(
@@ -2044,7 +2030,7 @@ 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, torch.IntTensor(stride_per_key, device="cpu").reshape(-1, 1))
+            else (None, [[stride] for stride in stride_per_key])
         )
         kjt = KeyedJaggedTensor(
             keys=kjt_keys,
@@ -2209,32 +2195,12 @@ def stride_per_key_per_rank(self) -> List[List[int]]:
         Returns:
             List[List[int]]: stride per key per rank of the KeyedJaggedTensor.
         """
-        # 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
-        ):
-            if self._pt2_stride_per_key_per_rank is not None:
-                return self._pt2_stride_per_key_per_rank
-            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)
-            self._pt2_stride_per_key_per_rank = stride_per_key_per_rank
-            return stride_per_key_per_rank
-        return (
-            []
-            if _stride_per_key_per_rank is None
-            else _stride_per_key_per_rank.tolist()
-        )
+        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 []
 
     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.
@@ -2379,16 +2345,13 @@ 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 = (
-                _stride_per_key_per_rank[start:end, :]
+                self.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):
@@ -2536,24 +2499,17 @@ 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])
-
-        stride_per_key = self._stride_per_key
-        permuted_stride_per_key = (
-            [stride_per_key[i] for i in indices] if stride_per_key is not None else None
-        )
-
-        _stride_per_key_per_rank = self._stride_per_key_per_rank
-        permuted_stride_per_key_per_rank = (
-            _stride_per_key_per_rank[indices, :]
-            if self.variable_stride_per_key() and _stride_per_key_per_rank is not None
-            else None
-        )
+            if self.variable_stride_per_key():
+                permuted_stride_per_key_per_rank.append(
+                    self.stride_per_key_per_rank()[index]
+                )
 
         permuted_length_per_key_sum = sum(permuted_length_per_key)
         if not torch.jit.is_scripting() and is_non_strict_exporting():
@@ -2605,16 +2561,18 @@ 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=permuted_stride_per_key_per_rank,
-            stride_per_key=permuted_stride_per_key,
+            stride_per_key_per_rank=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,
             offset_per_key=None,
@@ -2933,7 +2891,7 @@ def dist_init(
 
         if variable_stride_per_key:
             assert stride_per_rank_per_key is not None
-            stride_per_key_per_rank: torch.Tensor = stride_per_rank_per_key.view(
+            stride_per_key_per_rank_tensor: torch.Tensor = stride_per_rank_per_key.view(
                 num_workers, len(keys)
             ).T.cpu()
 
@@ -2970,18 +2928,23 @@ def dist_init(
                         weights,
                     )
 
-            if stride_per_key_per_rank.numel() == 0:
-                stride_per_key_per_rank = torch.zeros(
-                    (len(keys), 1), device="cpu", dtype=torch.int64
-                )
+            stride_per_key_per_rank = torch.jit.annotate(
+                List[List[int]], stride_per_key_per_rank_tensor.tolist()
+            )
 
+            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
-                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]
+                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
 
             kjt = KeyedJaggedTensor(
                 keys=keys,