Always avoid CPU blocking D2H in JaggedIndexSelect2dOp backward (pyto…

…rch#2510)

Summary:
Pull Request resolved: pytorch#2510

Since the value of `num_dense_output_rows` has been computed in forward. Directly save this number (not optional) in context and reuse it to avoid D2H syncs in backward.

Reviewed By: sryap

Differential Revision: D56212536

fbshipit-source-id: 3008745310f9056192dac417c775b553428f9bfc

fbgemm_gpu/src/jagged_tensor_ops/jagged_tensor_ops_autograd.cpp

@@ -604,7 +604,7 @@ class JaggedIndexSelect2dOp
       const Tensor& values,
       const Tensor& lengths,
       const Tensor& indices,
-      const c10::optional<int64_t> num_dense_output_rows) {
+      const c10::optional<int64_t> optional_num_dense_output_rows) {
     TORCH_CHECK(
         values.dim() == 2, "jagged_index_select supports only 2D inputs")
     TENSORS_ON_SAME_DEVICE(lengths, indices);
@@ -616,7 +616,6 @@ class JaggedIndexSelect2dOp
 
     ctx->save_for_backward({indices, output_offsets, input_offsets});
     ctx->saved_data["num_input_rows"] = values.sym_size(0);
-    ctx->saved_data["num_dense_output_rows"] = num_dense_output_rows;
 
     static auto op =
         c10::Dispatcher::singleton()
@@ -628,14 +627,17 @@ class JaggedIndexSelect2dOp
                 const Tensor& output_offsets,
                 const c10::optional<int64_t>)>();
 
-    return {
-        op.call(
-            values,
-            indices,
-            input_offsets,
-            output_offsets,
-            num_dense_output_rows),
-        output_lengths};
+    auto out = op.call(
+        values,
+        indices,
+        input_offsets,
+        output_offsets,
+        optional_num_dense_output_rows);
+
+    // Always save output size to avoid triggering D2H sync in backward
+    ctx->saved_data["num_dense_output_rows"] = out.sym_size(0);
+
+    return {out, output_lengths};
   }
 
   static torch::autograd::variable_list backward(
@@ -654,7 +656,7 @@ class JaggedIndexSelect2dOp
 
     auto num_output_rows = ctx->saved_data["num_input_rows"].toSymInt();
     auto num_dense_input_rows =
-        ctx->saved_data["num_dense_output_rows"].toOptional<int64_t>();
+        ctx->saved_data["num_dense_output_rows"].toSymInt();
 
     static auto op =
         c10::Dispatcher::singleton()
@@ -665,7 +667,7 @@ class JaggedIndexSelect2dOp
                 const Tensor& input_offsets,
                 const Tensor& output_offsets,
                 c10::SymInt num_output_rows,
-                const c10::optional<int64_t> optional_num_dense_input_rows)>();
+                c10::SymInt num_dense_input_rows)>();
 
     return {
         op.call(

fbgemm_gpu/src/jagged_tensor_ops/jagged_tensor_ops_cpu.cpp

@@ -1161,13 +1161,7 @@ Tensor jagged_index_add_2d_forward_v2_impl(
     const Tensor& input_offsets,
     const Tensor& output_offsets,
     const int64_t num_output_rows,
-    const c10::optional<int64_t> optional_num_dense_input_rows) {
-  // Intentionally not using optional::value_or here to avoid materializing
-  // .item() call when possible.
-  int64_t num_dense_input_rows = optional_num_dense_input_rows.has_value()
-      ? optional_num_dense_input_rows.value()
-      : input_offsets[input_offsets.numel() - 1].item<int64_t>();
-
+    const int64_t num_dense_input_rows) {
   static auto v1_op =
       c10::Dispatcher::singleton()
           .findSchemaOrThrow("fbgemm::jagged_index_add_2d_forward", "")
@@ -1681,7 +1675,7 @@ TORCH_LIBRARY_FRAGMENT(fbgemm, m) {
   m.def(
       "jagged_index_add_2d_forward(Tensor values, Tensor indices, Tensor input_offsets, Tensor output_offsets, int num_dense_input_rows, int num_output_rows) -> Tensor");
   m.def(
-      "jagged_index_add_2d_forward_v2(Tensor values, Tensor indices, Tensor input_offsets, Tensor output_offsets, SymInt num_output_rows, int? num_dense_input_rows) -> Tensor",
+      "jagged_index_add_2d_forward_v2(Tensor values, Tensor indices, Tensor input_offsets, Tensor output_offsets, SymInt num_output_rows, SymInt num_dense_input_rows) -> Tensor",
       {PT2_COMPLIANT_TAG});
   m.def(
       "jagged_1d_to_truncated_values(Tensor values, Tensor lengths, int max_truncated_length) -> Tensor");