Move permute_sparse_features tests and abstract impl to fbgemm (pytor…

…ch#2129)

Summary:
Pull Request resolved: pytorch#2129

Title

Reviewed By: zou3519

Differential Revision: D51211981

fbshipit-source-id: 14d4201670b9998288bc2bcf9b476730cd66c6c1

fbgemm_gpu/fbgemm_gpu/sparse_ops.py

@@ -251,3 +251,27 @@ def bounds_check_indices(
     max_B: int = -1,
 ) -> None:
     pass
+
+
+@impl_abstract("fbgemm::permute_sparse_features")
+def permute_sparse_features_abstract(
+    permute: Tensor, lengths: Tensor, indices: Tensor, weights: Optional[Tensor] = None
+) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
+    torch._check(lengths.dtype == indices.dtype)
+    torch._check(permute.device == lengths.device)
+    torch._check(permute.device == indices.device)
+    if weights is not None:
+        torch._check(permute.device == weights.device)
+    num_output_features = permute.numel()
+    B = lengths.size(1)
+    permuted_lengths = lengths.new_empty(num_output_features, B)
+    output_size = torch.library.get_ctx().new_dynamic_size()
+    # pyre-fixme[6]: In call `torch._C.TensorBase.new_empty`, for 1st positional argument,
+    # expected `Sequence[Union[int, types.SymInt]]` but got `Union[int, torch.SymInt]`
+    permuted_indices = indices.new_empty(output_size)
+    permuted_weights = None
+    if weights is not None:
+        # pyre-fixme[6]: In call `torch._C.TensorBase.new_empty`, for 1st positional argument,
+        # expected `Sequence[Union[int, types.SymInt]]` but got `Union[int, torch.SymInt]`
+        permuted_weights = weights.new_empty(output_size)
+    return (permuted_lengths, permuted_indices, permuted_weights)

fbgemm_gpu/src/sparse_ops/sparse_ops_cpu.cpp

@@ -2738,7 +2738,8 @@ TORCH_LIBRARY_FRAGMENT(fbgemm, m) {
   m.def(
       "lengths_range_out(Tensor output, Tensor t_in, SymInt[]? shape=None) -> Tensor");
   m.def(
-      "permute_sparse_features(Tensor permute, Tensor lengths, Tensor indices, Tensor? weights=None) -> (Tensor, Tensor, Tensor?)");
+      "permute_sparse_features(Tensor permute, Tensor lengths, Tensor indices, Tensor? weights=None) -> (Tensor, Tensor, Tensor?)",
+      {PT2_COMPLIANT_TAG});
   m.def("Bfloat16QuantizedToFloat(Tensor input) -> Tensor");
   m.def("FloatToBfloat16Quantized(Tensor input) -> Tensor");
   m.def(

fbgemm_gpu/test/sparse_ops_test.py

@@ -15,7 +15,7 @@
 import random
 import unittest
 from itertools import accumulate
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Type, Union
 
 import fbgemm_gpu
 
@@ -2402,6 +2402,167 @@ def validate(
             "grad",
         )
 
+    def permute_sparse_features_ref_(
+        self,
+        lengths: torch.Tensor,
+        indices: torch.Tensor,
+        weights: Optional[torch.Tensor],
+        permute: torch.LongTensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+        T = lengths.size(0)
+        B = lengths.size(1)
+        permuted_lengths = torch.index_select(lengths.view(T, B), 0, permute)
+
+        original_segment_lengths = lengths.view(T, B).sum(dim=1, dtype=torch.int32)
+        original_segment_start = torch.ops.fbgemm.asynchronous_exclusive_cumsum(
+            original_segment_lengths.view(-1)
+        )
+
+        permuted_indices = []
+        permuted_weights = []
+        for i in range(permute.size(0)):
+            start = original_segment_start[permute[i]]
+            end = start + original_segment_lengths[permute[i]]
+            permuted_indices.append(indices[start:end])
+            if weights is not None:
+                permuted_weights.append(weights[start:end])
+
+        permuted_indices = torch.cat(permuted_indices, dim=0).flatten()
+
+        if weights is None:
+            permuted_weights = None
+        else:
+            permuted_weights = torch.cat(permuted_weights, dim=0).flatten()
+
+        return permuted_lengths, permuted_indices, permuted_weights
+
+    @given(
+        B=st.integers(min_value=1, max_value=20),
+        T=st.integers(min_value=1, max_value=20),
+        L=st.integers(min_value=2, max_value=20),
+        long_index=st.booleans(),
+        has_weight=st.booleans(),
+    )
+    @settings(max_examples=20, deadline=None)
+    def test_permute_sparse_features(
+        self, B: int, T: int, L: int, long_index: bool, has_weight: bool
+    ) -> None:
+        index_dtype = torch.int64 if long_index else torch.int32
+        lengths = torch.randint(low=1, high=L, size=(T, B)).type(index_dtype)
+        weights = torch.rand(int(lengths.sum().item())).float() if has_weight else None
+        indices = torch.randint(
+            low=1,
+            high=int(1e5),
+            size=cast(Tuple[int, ...], (lengths.sum().item(),)),
+        ).type(index_dtype)
+        permute_list = list(range(T))
+        random.shuffle(permute_list)
+        permute = torch.IntTensor(permute_list)
+
+        (
+            permuted_lengths_cpu,
+            permuted_indices_cpu,
+            permuted_weights_cpu,
+        ) = torch.ops.fbgemm.permute_sparse_features(permute, lengths, indices, weights)
+        (
+            permuted_lengths_ref,
+            permuted_indices_ref,
+            permuted_weights_ref,
+            # pyre-fixme[6]: For 4th param expected `LongTensor` but got `Tensor`.
+        ) = self.permute_indices_ref_(lengths, indices, weights, permute.long())
+        torch.testing.assert_close(permuted_indices_cpu, permuted_indices_ref)
+        torch.testing.assert_close(permuted_lengths_cpu, permuted_lengths_ref)
+        if has_weight:
+            torch.testing.assert_close(permuted_weights_cpu, permuted_weights_ref)
+        else:
+            assert permuted_weights_cpu is None and permuted_weights_ref is None
+
+        if gpu_available:
+            (
+                permuted_lengths_gpu,
+                permuted_indices_gpu,
+                permuted_weights_gpu,
+            ) = torch.ops.fbgemm.permute_sparse_features(
+                permute.cuda(),
+                lengths.cuda(),
+                indices.cuda(),
+                weights.cuda() if has_weight and weights is not None else None,
+            )
+            torch.testing.assert_close(permuted_indices_gpu.cpu(), permuted_indices_cpu)
+            torch.testing.assert_close(permuted_lengths_gpu.cpu(), permuted_lengths_cpu)
+            if has_weight:
+                torch.testing.assert_close(
+                    permuted_weights_gpu.cpu(), permuted_weights_cpu
+                )
+            else:
+                assert permuted_weights_gpu is None
+
+    @given(
+        B=st.integers(min_value=1, max_value=20),
+        T=st.integers(min_value=1, max_value=20),
+        L=st.integers(min_value=2, max_value=20),
+        long_index=st.booleans(),
+        has_weight=st.booleans(),
+    )
+    @settings(max_examples=20, deadline=None)
+    def test_permute_sparse_features_with_repeats(
+        self, B: int, T: int, L: int, long_index: bool, has_weight: bool
+    ) -> None:
+        index_dtype = torch.int64 if long_index else torch.int32
+        lengths = torch.randint(low=1, high=L, size=(T, B)).type(index_dtype)
+        weights = torch.rand(int(lengths.sum().item())).float() if has_weight else None
+        indices = torch.randint(
+            low=1,
+            high=int(1e5),
+            size=cast(Tuple[int, ...], (lengths.sum().item(),)),
+        ).type(index_dtype)
+        permute_list = list(range(T))
+
+        num_repeats = random.randint(0, T)
+        for _ in range(num_repeats):
+            permute_list.append(random.randint(0, T - 1))
+
+        random.shuffle(permute_list)
+        permute = torch.IntTensor(permute_list)
+
+        (
+            permuted_lengths_cpu,
+            permuted_indices_cpu,
+            permuted_weights_cpu,
+        ) = torch.ops.fbgemm.permute_sparse_features(permute, lengths, indices, weights)
+        (
+            permuted_lengths_ref,
+            permuted_indices_ref,
+            permuted_weights_ref,
+            # pyre-fixme[6]: For 4th param expected `LongTensor` but got `Tensor`.
+        ) = self.permute_indices_ref_(lengths, indices, weights, permute.long())
+        torch.testing.assert_close(permuted_indices_cpu, permuted_indices_ref)
+        torch.testing.assert_close(permuted_lengths_cpu, permuted_lengths_ref)
+        if has_weight:
+            torch.testing.assert_close(permuted_weights_cpu, permuted_weights_ref)
+        else:
+            assert permuted_weights_cpu is None and permuted_weights_ref is None
+
+        if gpu_available:
+            (
+                permuted_lengths_gpu,
+                permuted_indices_gpu,
+                permuted_weights_gpu,
+            ) = torch.ops.fbgemm.permute_sparse_features(
+                permute.cuda(),
+                lengths.cuda(),
+                indices.cuda(),
+                weights.cuda() if has_weight and weights is not None else None,
+            )
+            torch.testing.assert_close(permuted_indices_gpu.cpu(), permuted_indices_cpu)
+            torch.testing.assert_close(permuted_lengths_gpu.cpu(), permuted_lengths_cpu)
+            if has_weight:
+                torch.testing.assert_close(
+                    permuted_weights_gpu.cpu(), permuted_weights_cpu
+                )
+            else:
+                assert permuted_weights_cpu is None
+
 
 failures_dict_path: str = get_file_path_2(
     "", os.path.dirname(__file__), "failures_dict.json"