[torch][segment_reduce] Add support for initial value (#56923)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56923

Next Steps in order:
- Add backward support for CUDA
- Add support for more aggregation types
- Benchmarking (for cuda mainly)/more testing/documentation
- Support for multi dimension

Test Plan: Updated unit test to include 0 length segment as well.

Reviewed By: ngimel

Differential Revision: D27992228

fbshipit-source-id: 28851811f8a784a63162721c511d69e617a93727

aten/src/ATen/native/SegmentReduce.cpp

@@ -17,45 +17,32 @@ Tensor _segment_reduce_cpu_kernel(
     const Tensor& data,
     const Tensor& lengths,
     int64_t axis,
-    bool unsafe) {
-  const auto lengths_contig = lengths.contiguous();
-  const auto data_contig = data.contiguous();
-
-  int64_t batch_size = lengths_contig.numel();
+    const c10::optional<Scalar>& initial) {
+  int64_t batch_size = lengths.numel();
   auto output = at::empty({batch_size}, data.options());
 
-  const auto* lengths_data = lengths_contig.data_ptr<int64_t>();
-  if (!unsafe) {
-    int64_t sum = 0;
-    for (int64_t i = 0; i < batch_size; ++i) {
-      TORCH_CHECK(
-          (lengths_data[i] > 0), "lengths contains non positive value!");
-      sum += lengths_data[i];
-    }
-    TORCH_CHECK(sum == data.numel());
-  }
+  const auto* lengths_data = lengths.data_ptr<int64_t>();
 
   AT_DISPATCH_ALL_TYPES_AND2(
-      kBFloat16,
-      kHalf,
-      data_contig.scalar_type(),
-      "_segment_reduce_cpu",
-      ([&]() {
+      kBFloat16, kHalf, data.scalar_type(), "_segment_reduce_cpu", ([&]() {
         auto* output_data = output.data_ptr<scalar_t>();
-        const auto* values_data = data_contig.data_ptr<scalar_t>();
+        const auto* values_data = data.data_ptr<scalar_t>();
         int64_t k = 0;
         for (int64_t i = 0; i < batch_size; ++i) {
-          scalar_t reduction = std::numeric_limits<scalar_t>::lowest();
+          scalar_t initial_value = initial.has_value()
+              ? initial.value().to<scalar_t>()
+              : std::numeric_limits<scalar_t>::lowest();
           for (int64_t j = 0; j < lengths_data[i]; ++j) {
             const auto data = values_data[k];
-            reduction =
-                at::_isnan(data) ? data : std::max<scalar_t>(reduction, data);
+            initial_value = at::_isnan(data)
+                ? data
+                : std::max<scalar_t>(initial_value, data);
             k++;
           }
           // If unsafe is false, check on lengths or indices should cover cases
-          // where lengths for a particular segment is non-positive. If unsafe
-          // is true, simply set to numerical limits for particular reduction
-          output_data[i] = reduction;
+          // where lengths for a particular segment is negative. If unsafe
+          // is true, simply set to initial_value for particular reduction
+          output_data[i] = initial_value;
         }
       }));
 
@@ -93,8 +80,7 @@ Tensor _segment_reduce_cpu_backward_kernel(
             }
             k++;
           }
-          // Average gradient output based on number of maximum in the segment
-          TORCH_INTERNAL_ASSERT(counter > 0);
+          // Average gradient based on number of maximum elements in the segment
           if (counter < 2) {
             continue;
           }
@@ -124,7 +110,8 @@ Tensor segment_reduce_kernel(
     const c10::optional<Tensor>& lengths,
     const c10::optional<Tensor>& indices,
     int64_t axis,
-    bool unsafe) {
+    bool unsafe,
+    const c10::optional<Scalar>& initial) {
   axis = maybe_wrap_dim(axis, data.ndimension());
   TORCH_CHECK(axis == 0, "Currently only dim=0 is supported!");
   TORCH_CHECK(data.dim() == 1);
@@ -142,8 +129,22 @@ Tensor segment_reduce_kernel(
   TORCH_CHECK(data.get_device() == lengths_value.get_device());
   TORCH_CHECK(data.dim() >= lengths_value.dim());
 
+  if (!unsafe) {
+    auto min_length = lengths_value.min().item<int64_t>();
+    TORCH_CHECK((min_length >= 0), "lengths contains negative value!");
+    TORCH_CHECK(min_length != 0 || initial.has_value());
+    TORCH_CHECK(lengths_value.sum().item<int64_t>() == data.numel());
+  }
+
+  const auto data_contig = data.contiguous();
+  const auto lengths_contig = lengths_value.contiguous();
+
   return _segment_reduce_stub(
-      data.device().type(), data, lengths_value, axis, unsafe);
+      data_contig.device().type(),
+      data_contig,
+      lengths_contig,
+      axis,
+      initial);
 }
 
 REGISTER_ARCH_DISPATCH(

aten/src/ATen/native/SegmentReduce.h

@@ -7,8 +7,11 @@
 namespace at {
 namespace native {
 
-using segment_reduce_fn =
-    Tensor (*)(const Tensor&, const Tensor&, int64_t, bool);
+using segment_reduce_fn = Tensor (*)(
+    const Tensor&,
+    const Tensor&,
+    int64_t,
+    const c10::optional<Scalar>&);
 DECLARE_DISPATCH(segment_reduce_fn, _segment_reduce_stub);
 
 using segment_reduce_backward_fn =

aten/src/ATen/native/cuda/SegmentReduce.cu

@@ -45,20 +45,11 @@ Tensor _segment_reduce_cuda_kernel(
     const Tensor& data,
     const Tensor& lengths,
     int64_t axis,
-    bool unsafe) {
-  if (!unsafe) {
-    TORCH_CHECK(
-        (lengths.min().item<int64_t>() > 0),
-        "lengths contains non positive value!");
-    TORCH_CHECK(lengths.sum().item<int64_t>() == data.numel());
-  }
-
+    const c10::optional<Scalar>& initial) {
   int64_t segment_count = lengths.numel();
-  const auto data_contig = data.contiguous();
   auto output = at::empty({segment_count}, data.options());
 
-  const auto lengths_contig = lengths.contiguous();
-  auto offsets = _get_complete_sum(lengths_contig);
+  auto offsets = _get_complete_sum(lengths);
   auto* offsets_data_ptr = offsets.data_ptr<int64_t>();
 
   AT_DISPATCH_ALL_TYPES_AND2(
@@ -67,14 +58,16 @@ Tensor _segment_reduce_cuda_kernel(
       data.scalar_type(),
       "segment_reduce_cuda",
       [&]() {
-        auto* data_contig_data_ptr = data_contig.data_ptr<scalar_t>();
+        auto* data_data_ptr = data.data_ptr<scalar_t>();
         auto* output_data_ptr = output.data_ptr<scalar_t>();
 
         CustomMax max_op{};
-        scalar_t initial_value = std::numeric_limits<scalar_t>::lowest();
+        scalar_t initial_value = initial.has_value()
+            ? initial.value().to<scalar_t>()
+            : std::numeric_limits<scalar_t>::lowest();
         CUB_WRAPPER(
             cub::DeviceSegmentedReduce::Reduce,
-            data_contig_data_ptr,
+            data_data_ptr,
             output_data_ptr,
             segment_count,
             offsets_data_ptr,

aten/src/ATen/native/native_functions.yaml

@@ -9003,7 +9003,7 @@
   cpp_no_default_args: ['a', 'b']
   python_module: nn
 
-- func: segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, int axis=0, bool unsafe=False) -> Tensor
+- func: segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, int axis=0, bool unsafe=False, Scalar? initial=None) -> Tensor
   variants: function
   dispatch:
     CPU, CUDA: segment_reduce_kernel

test/test_segment_reductions.py

@@ -13,16 +13,24 @@ from torch.testing._internal.common_utils import (
 
 class TestSegmentReductions(TestCase):
     def _test_max_simple_1d(self, device, dtype, unsafe, axis):
-        lengths = torch.tensor([1, 2, 3], device=device)
+        lengths = torch.tensor([1, 2, 3, 0], device=device)
         data = torch.tensor(
             [1, float("nan"), 3, 4, 5, 5],
             device=device,
             dtype=dtype,
             requires_grad=True,
         )
-        expected_result = torch.tensor([1, float("nan"), 5], device=device, dtype=dtype)
+        initial_value = 0
+        expected_result = torch.tensor(
+            [1, float("nan"), 5, initial_value], device=device, dtype=dtype
+        )
         actual_result = torch.segment_reduce(
-            data=data, reduce="max", lengths=lengths, axis=axis, unsafe=unsafe
+            data=data,
+            reduce="max",
+            lengths=lengths,
+            axis=axis,
+            unsafe=unsafe,
+            initial=initial_value,
         )
         self.assertEqual(
             expected_result, actual_result, rtol=1e-03, atol=1e-05, equal_nan=True
@@ -52,7 +60,12 @@ class TestSegmentReductions(TestCase):
             self.assertTrue(
                 gradcheck(
                     lambda x: torch.segment_reduce(
-                        data=x, reduce="max", lengths=lengths, axis=axis, unsafe=unsafe
+                        data=x,
+                        reduce="max",
+                        lengths=lengths,
+                        axis=axis,
+                        unsafe=unsafe,
+                        initial=initial_value,
                     ),
                     (data,),
                 )

tools/autograd/derivatives.yaml

@@ -2062,5 +2062,5 @@
 - name: nonzero(Tensor self) -> Tensor
   output_differentiability: [False]
 
-- name: segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, int axis=0, bool unsafe=False) -> Tensor
+- name: segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, int axis=0, bool unsafe=False, Scalar? initial=None) -> Tensor
   data: segment_reduce_backward(grad, result, data, lengths)