Add abstract impl fbgemm::dense_to_jagged

fbgemm_gpu/fbgemm_gpu/sparse_ops.py

@@ -371,3 +371,31 @@ def segment_sum_csr_abstract(
     output_size = csr_seg.numel() - 1
     output = values.new_empty(output_size)
     return output
+
+
+@impl_abstract("fbgemm::dense_to_jagged_forward")
+def dense_to_jagged_forward(
+    dense: torch.Tensor,
+    offsets: List[torch.Tensor],
+    total_L: Optional[torch.SymInt] = None,
+) -> torch.Tensor:
+    if not total_L:
+        total_L = torch.library.get_ctx().new_dynamic_size()
+    return dense.new_zeros(
+        total_L,
+        dense.size()[-1],
+        dtype=dense.dtype,
+        device=dense.device,
+        layout=dense.layout,
+    )
+
+
+@impl_abstract("fbgemm::dense_to_jagged")
+def dense_to_jagged(
+    dense: torch.Tensor,
+    offsets: List[torch.Tensor],
+    total_L: Optional[torch.SymInt] = None,
+) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+    if not total_L:
+        total_L = torch.library.get_ctx().new_dynamic_size()
+    return (dense_to_jagged_forward(dense, offsets, total_L), offsets)

fbgemm_gpu/src/jagged_tensor_ops/jagged_tensor_ops_cpu.cpp

@@ -1635,6 +1635,9 @@ TORCH_LIBRARY_FRAGMENT(fbgemm, m) {
   // SymInt is a new PyTorch 2.0 feature to support dynamic shape. See more
   // details at https://pytorch.org/get-started/pytorch-2.0/#dynamic-shapes. If
   // you find it doesn't compile, please pull the new PyTorch 2.0 code
+  m.impl_abstract_pystub(
+      "fbgemm_gpu.sparse_ops",
+      "//deeplearning/fbgemm/fbgemm_gpu:sparse_ops_py");
   m.def(
       "dense_to_jagged(Tensor dense, Tensor[] x_offsets, SymInt? total_L=None) -> (Tensor, Tensor[])",
       {PT2_COMPLIANT_TAG});

fbgemm_gpu/src/jagged_tensor_ops/jagged_tensor_ops_meta.cpp

@@ -92,28 +92,6 @@ Tensor jagged_dense_elementwise_add_meta(
   return at::empty_like(y);
 }
 
-Tensor dense_to_jagged_forward_meta(
-    const Tensor& dense,
-    const std::vector<Tensor>& offsets,
-    c10::optional<at::SymInt> total_L) {
-  auto dense_values = dense;
-  at::SymInt D = dense_values.sym_size(-1);
-  TORCH_CHECK_NOT_IMPLEMENTED(
-      total_L.has_value(), "total_L is required for meta backend");
-  auto& total_L_computed = total_L.value();
-  auto values = at::zeros_symint({total_L_computed, D}, dense_values.options());
-
-  TORCH_CHECK(values.is_meta());
-  return values;
-}
-
-std::tuple<Tensor, std::vector<Tensor>> dense_to_jagged_meta(
-    const Tensor& dense,
-    const std::vector<Tensor>& offsets,
-    c10::optional<at::SymInt> total_L) {
-  return {dense_to_jagged_forward_meta(dense, offsets, total_L), offsets};
-}
-
 std::tuple<Tensor, std::vector<Tensor>> jagged_dense_elementwise_mul_meta(
     const Tensor& x_values,
     const std::vector<Tensor>& x_offsets,
@@ -241,10 +219,6 @@ TORCH_LIBRARY_IMPL(fbgemm, Meta, m) {
   m.impl(
       "jagged_to_padded_dense_backward",
       TORCH_FN(fbgemm_gpu::jagged_to_padded_dense_backward_meta));
-  m.impl(
-      "dense_to_jagged_forward",
-      TORCH_FN(fbgemm_gpu::dense_to_jagged_forward_meta));
-  m.impl("dense_to_jagged", TORCH_FN(fbgemm_gpu::dense_to_jagged_meta));
   m.impl(
       "jagged_dense_dense_elementwise_add_jagged_output_forward",
       TORCH_FN(

fbgemm_gpu/test/failures_dict.json

@@ -115,40 +115,7 @@
         "status": "xfail"
       }
     },
-    "fbgemm::dense_to_jagged": {
-      "JaggedTensorOpsTest.test_aot_dispatch_dynamic__test_dense_to_jagged": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_aot_dispatch_dynamic__test_dense_to_jagged_meta_backend": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_aot_dispatch_dynamic__test_dense_to_jagged_opt": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_aot_dispatch_dynamic__test_dense_to_jagged_opt_large_batch": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_faketensor__test_dense_to_jagged": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_faketensor__test_dense_to_jagged_meta_backend": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_faketensor__test_dense_to_jagged_opt": {
-        "comment": "",
-        "status": "xfail"
-      },
-      "JaggedTensorOpsTest.test_faketensor__test_dense_to_jagged_opt_large_batch": {
-        "comment": "",
-        "status": "xfail"
-      }
-    },
+    "fbgemm::dense_to_jagged": {},
     "fbgemm::expand_into_jagged_permute": {},
     "fbgemm::generic_histogram_binning_calibration_by_feature": {
       "SparseOpsTest.test_aot_dispatch_dynamic__test_generic_histogram_binning_calibration_by_feature": {

fbgemm_gpu/test/jagged_tensor_ops_test.py

@@ -132,10 +132,6 @@ def hash_size_cumsum_to_offsets(hash_size_cum_sum_list: List[int]) -> List[int]:
 # skips and failures in deeplearning/fbgemm/fbgemm_gpu/test/failures_dict.json
 # pyre-ignore[24]: Generic type `Callable` expects 2 type parameters.
 additional_decorators: Dict[str, List[Callable]] = {
-    "test_pt2_compliant_tag_fbgemm_dense_to_jagged": [
-        # This operator has been grandfathered in. We need to fix this test failure.
-        unittest.expectedFailure,
-    ],
     "test_pt2_compliant_tag_fbgemm_jagged_dense_elementwise_add": [
         # This operator has been grandfathered in. We need to fix this test failure.
         unittest.expectedFailure,