Adding API fix for handling dynamic_shape correctly plus STs for SegSum dyn_shape

Updating InferImpl function

adding check to inferImpl function

lint

lint2

mindspore/core/abstract/prim_arrays.cc

@@ -205,24 +205,23 @@ AbstractBasePtr InferImplUnsortedSegmentSum(const AnalysisEnginePtr &, const Pri
                                             const AbstractBasePtrList &args_spec_list) {
   const std::string op_name = primitive->name();
   CheckArgsSize(op_name, args_spec_list, 3);
-  // input x
   auto x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
   MS_EXCEPTION_IF_NULL(x);
   MS_EXCEPTION_IF_NULL(x->shape());
-  auto x_shape = x->shape()->shape();
-  // segment_ids
   auto segment_ids = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
   MS_EXCEPTION_IF_NULL(segment_ids);
   MS_EXCEPTION_IF_NULL(segment_ids->shape());
   auto segment_ids_shape = segment_ids->shape()->shape();
-  // checks on Tensors 0 and 1 types
+  (void)CheckTensorDType(x, {kFloat16, kFloat32, kInt32}, "Input 0 (x) for UnsortedSegmentSum should be %s");
-  (void)CheckTensorDType(x, {kFloat32, kInt32}, "Input 0 (x) for SequenceMask should be %s");
+  (void)CheckTensorDType(segment_ids, {kInt32, kInt64}, "Input 1 (segment_ids) for UnsortedSegmentSum should be %s");
-  (void)CheckTensorDType(segment_ids, {kInt32, kInt64}, "Input 1 (segment_ids) for SequenceMask should be %s");
+  // check if dynamic shape
+  bool x_is_dyn = (!x->shape()->min_shape().empty() && !x->shape()->max_shape().empty());
+  bool ids_is_dyn = (!segment_ids->shape()->min_shape().empty() && !segment_ids->shape()->max_shape().empty());
+  bool op_is_dynamic = x_is_dyn && ids_is_dyn;
+  auto x_shape = x->shape()->shape();
   ShapeVector shape;
-  ShapeVector max_shape;
+  int64_t num_segments_value = 0;
-  ShapeVector min_shape;
+  if (args_spec_list[2]->isa<AbstractTensor>()) {  // num_segments is Tensor
-  int64_t num_segments_value;
-  if (args_spec_list[2]->isa<AbstractTensor>()) {  // Num segments is Tensor
     auto num_segments = args_spec_list[2]->cast<AbstractTensorPtr>();
     MS_EXCEPTION_IF_NULL(num_segments);
     auto num_segments_value_ptr = num_segments->BuildValue();
@@ -230,26 +229,48 @@ AbstractBasePtr InferImplUnsortedSegmentSum(const AnalysisEnginePtr &, const Pri
     auto num_segments_tensor = num_segments_value_ptr->cast<tensor::TensorPtr>();
     MS_EXCEPTION_IF_NULL(num_segments_tensor);
     num_segments_value = *static_cast<int64_t *>(num_segments_tensor->data_c());
-    shape.emplace_back(num_segments_value);
+  } else if (args_spec_list[2]->isa<AbstractScalar>()) {  // num_segments is Scalar
-  } else if (args_spec_list[2]->isa<AbstractScalar>()) {  // Num segments is Scalar
     auto num_segments = CheckArg<AbstractScalar>(op_name, args_spec_list, 2);
     num_segments_value = GetValue<int64_t>(num_segments->BuildValue());
-    shape.emplace_back(num_segments_value);
   } else {
     MS_LOG(EXCEPTION) << "num_segments incorrect type in UnsortedSegmentSum";
   }
-  shape.insert(shape.end(), x_shape.begin() + segment_ids_shape.size(), x_shape.end());
+  if (num_segments_value <= 0) {
-  // calc max shape
+    MS_LOG(EXCEPTION) << "num_segments must be > 0 in UnsortedSegmentSum";
-  if (!x->shape()->max_shape().empty()) {  // copy max shape from x if present
-    std::copy(x->shape()->max_shape().begin(), x->shape()->max_shape().end(), std::back_inserter(max_shape));
-  } else {  // copy x shape directly if not present
-    std::copy(x->shape()->shape().begin(), x->shape()->shape().end(), std::back_inserter(max_shape));
   }
-  // calc min shape
+  shape.emplace_back(num_segments_value);
-  min_shape.push_back(segment_ids_shape.size());
+  shape.insert(shape.end(), x_shape.begin() + segment_ids_shape.size(), x_shape.end());
-  std::copy(x->shape()->shape().begin() + segment_ids_shape.size(), x->shape()->shape().end(),
+  // dims check
-            back_inserter(min_shape));
+  if (!op_is_dynamic) {
-  // return shape, min shape, max shape
+    for (size_t i = 0; i < segment_ids_shape.size(); i++) {
+      if (x_shape[i] != segment_ids_shape[i]) {
+        MS_LOG(EXCEPTION) << "Shape values of segments_ids must match with corresponding x shape values";
+      }
+    }
+    return std::make_shared<AbstractTensor>(x->element(), std::make_shared<Shape>(shape));
+  }
+  // is dynamic
+  ShapeVector min_shape;
+  ShapeVector max_shape;
+  min_shape.emplace_back(num_segments_value);
+  max_shape.emplace_back(num_segments_value);
+  // only run validation if shape values are known
+  bool x_any_shape = std::any_of(x_shape.begin(), x_shape.end(), [](int64_t dim) { return dim == Shape::SHP_ANY; });
+  bool ids_any_shape =
+    std::any_of(segment_ids_shape.begin(), segment_ids_shape.end(), [](int64_t dim) { return dim == Shape::SHP_ANY; });
+  if (!x_any_shape && !ids_any_shape) {
+    for (size_t i = 0; i < segment_ids_shape.size(); i++) {
+      if (x_shape[i] != segment_ids_shape[i]) {
+        MS_LOG(EXCEPTION) << "Shape values of segments_ids must match with corresponding x shape values";
+      }
+    }
+  }
+  ShapeVector x_shape_min;
+  ShapeVector x_shape_max;
+  x_shape_min = (x_is_dyn) ? x->shape()->min_shape() : x->shape()->shape();
+  x_shape_max = (x_is_dyn) ? x->shape()->max_shape() : x->shape()->shape();
+  min_shape.insert(min_shape.end(), x_shape_min.begin() + segment_ids_shape.size(), x_shape_min.end());
+  max_shape.insert(max_shape.end(), x_shape_max.begin() + segment_ids_shape.size(), x_shape_max.end());
   return std::make_shared<AbstractTensor>(x->element(), std::make_shared<Shape>(shape, min_shape, max_shape));
 }
 

mindspore/ops/operations/array_ops.py

@@ -1904,7 +1904,9 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
 
         shp += x_shp[segment_ids_shp_len:]
         if 'max_shape' in x:
-            output_max_shape = x['max_shape']
+            output_incoming = x['max_shape']
+            output_max_shape = [num_segments_v]
+            output_max_shape += output_incoming[segment_ids_shp_len:]
         else:
             output_max_shape = x_shp
         out = {'shape': shp,

tests/st/ops/gpu/test_unsorted_segment_sum.py

@@ -20,11 +20,11 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
+from mindspore.ops.operations import _inner_ops as inner
 from mindspore.ops import operations as P
 
 context.set_context(device_target='GPU')
 
-
 class UnsortedSegmentSumNet(nn.Cell):
     def __init__(self, num_segments):
         super(UnsortedSegmentSumNet, self).__init__()
@@ -108,3 +108,139 @@ def test_3D():
                [0., 0., 0.],
                [0., 0., 0.]]]
     assert (output.asnumpy() == expect).all()
+
+
+# Testing Dynamic Shape
+class UnsortedSegmentSumDynNet(nn.Cell):
+    def __init__(self, num_segments):
+        super(UnsortedSegmentSumDynNet, self).__init__()
+        self.unsorted_segment_sum = P.UnsortedSegmentSum()
+        self.to_dyn_op = inner.GpuConvertToDynamicShape()
+        self.num_segments = num_segments
+
+    def construct(self, data, ids):
+        data_dyn = self.to_dyn_op(data)
+        ids_dyn = self.to_dyn_op(ids)
+        return self.unsorted_segment_sum(data_dyn, ids_dyn, self.num_segments)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_dyn():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    num_segments = 4
+    net = UnsortedSegmentSumDynNet(num_segments)
+
+    input_x = Tensor([1, 2, 3, 4], mstype.float32)
+    segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
+    output = net(input_x, segment_ids)
+    expect = [3, 3, 4, 0]
+    assert (output.asnumpy() == expect).all()
+
+    input_x = Tensor([[1, 2, 3, 4],
+                      [5, 6, 7, 8],
+                      [9, 10, 11, 12]], mstype.float32)
+    segment_ids = Tensor([2, 1, 1], mstype.int32)
+    output = net(input_x, segment_ids)
+    expect = [[0, 0, 0, 0],
+              [14, 16, 18, 20],
+              [1, 2, 3, 4],
+              [0, 0, 0, 0]]
+    assert (output.asnumpy() == expect).all()
+
+    input_x = Tensor(np.arange(4 * 5 * 3, dtype=np.float32).reshape(4, 5, 3))
+    segment_ids = Tensor([2, 1, 1, -1], mstype.int32)
+    output = net(input_x, segment_ids)
+    expect = [[[0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.]],
+
+              [[45., 47., 49.],
+               [51., 53., 55.],
+               [57., 59., 61.],
+               [63., 65., 67.],
+               [69., 71., 73.]],
+
+              [[0., 1., 2.],
+               [3., 4., 5.],
+               [6., 7., 8.],
+               [9., 10., 11.],
+               [12., 13., 14.]],
+
+              [[0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.]]]
+    assert (output.asnumpy() == expect).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_dyn_1():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    num_segments = 6
+    net = UnsortedSegmentSumDynNet(num_segments)
+
+    input_x = Tensor([1, 2, 3, 4], mstype.float32)
+    segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
+    output = net(input_x, segment_ids)
+    expect = [3, 3, 4, 0, 0, 0]
+    assert (output.asnumpy() == expect).all()
+
+    input_x = Tensor([[1, 2, 3, 4],
+                      [5, 6, 7, 8],
+                      [9, 10, 11, 12]], mstype.float32)
+    segment_ids = Tensor([2, 1, 1], mstype.int32)
+    output = net(input_x, segment_ids)
+    expect = [[0, 0, 0, 0],
+              [14, 16, 18, 20],
+              [1, 2, 3, 4],
+              [0, 0, 0, 0],
+              [0, 0, 0, 0],
+              [0, 0, 0, 0]]
+    assert (output.asnumpy() == expect).all()
+
+    input_x = Tensor(np.arange(4 * 5 * 3, dtype=np.float32).reshape(4, 5, 3))
+    segment_ids = Tensor([2, 1, 1, -1], mstype.int32)
+    output = net(input_x, segment_ids)
+    expect = [[[0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.]],
+
+              [[45., 47., 49.],
+               [51., 53., 55.],
+               [57., 59., 61.],
+               [63., 65., 67.],
+               [69., 71., 73.]],
+
+              [[0., 1., 2.],
+               [3., 4., 5.],
+               [6., 7., 8.],
+               [9., 10., 11.],
+               [12., 13., 14.]],
+
+              [[0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.]],
+
+              [[0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.]],
+
+              [[0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.],
+               [0., 0., 0.]]]
+    assert (output.asnumpy() == expect).all()