ScatterMax support dynamic shape

mindspore/core/ops/scatter_max.cc

@@ -0,0 +1,95 @@
+/**
+ * Copyright 2022 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "ops/scatter_max.h"
+#include <set>
+#include <map>
+#include <string>
+#include "abstract/ops/primitive_infer_map.h"
+#include "ops/op_utils.h"
+#include "utils/check_convert_utils.h"
+#include "mindapi/src/helper.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+abstract::ShapePtr ScatterMaxInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
+  BaseShapePtr input_x_shape_ptr = input_args[kInputIndex0]->BuildShape();
+  MS_EXCEPTION_IF_NULL(input_x_shape_ptr);
+  BaseShapePtr indices_shape_ptr = input_args[kInputIndex1]->BuildShape();
+  MS_EXCEPTION_IF_NULL(indices_shape_ptr);
+  BaseShapePtr updates_shape_ptr = input_args[kInputIndex2]->BuildShape();
+  MS_EXCEPTION_IF_NULL(updates_shape_ptr);
+
+  if (input_x_shape_ptr->IsDynamic()) {
+    MS_EXCEPTION(ValueError) << "For " << primitive->name() << ", "
+                             << "the 'input_x' does not support dynamic shape, but got the shape of 'input_x' is "
+                             << input_x_shape_ptr->ToString();
+  }
+
+  if (indices_shape_ptr->IsDynamic() || updates_shape_ptr->IsDynamic()) {
+    return input_args[kInputIndex0]->BuildShape()->cast<abstract::ShapePtr>();
+  }
+
+  std::vector<int64_t> input_x_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_x_shape_ptr)[kShape];
+  std::vector<int64_t> indices_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(indices_shape_ptr)[kShape];
+  std::vector<int64_t> updates_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(updates_shape_ptr)[kShape];
+  std::vector<int64_t> check_update_shape(indices_shape);
+  for (int64_t i = 1; i < SizeToLong(input_x_shape.size()); ++i) {
+    check_update_shape.push_back(input_x_shape[i]);
+  }
+  if (updates_shape != check_update_shape) {
+    MS_EXCEPTION(ValueError) << "For " << primitive->name() << ", "
+                             << "updates_shape = indices_shape + x_shape[1:], but got x_shape: "
+                             << input_x_shape_ptr->ToString() << ", indices_shape: " << indices_shape_ptr->ToString()
+                             << ", updates_shape: " << updates_shape_ptr->ToString() << ".";
+  }
+
+  auto output_shape = input_args[kInputIndex0]->BuildShape()->cast<abstract::ShapePtr>();
+  return output_shape;
+}
+
+TypePtr ScatterMaxInferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
+  auto input_x_type_ptr = input_args[kInputIndex0]->BuildType();
+  auto indiecs_type_ptr = input_args[kInputIndex1]->BuildType();
+  auto updates_type_ptr = input_args[kInputIndex2]->BuildType();
+  auto prim_name = primitive->name();
+  std::set<TypePtr> type_set = {kInt32};
+  (void)CheckAndConvertUtils::CheckTensorTypeValid("indices type", indiecs_type_ptr, type_set, prim_name);
+  (void)CheckAndConvertUtils::CheckTensorTypeValid("input_x type", input_x_type_ptr, common_valid_types_with_complex,
+                                                   prim_name);
+  (void)CheckAndConvertUtils::CheckTensorTypeValid("updates type", updates_type_ptr, common_valid_types_with_complex,
+                                                   prim_name);
+
+  std::map<std::string, TypePtr> type_dict;
+  type_dict.emplace("input_x", input_x_type_ptr);
+  type_dict.emplace("updates", updates_type_ptr);
+  return CheckAndConvertUtils::CheckTensorTypeSame(type_dict, common_valid_types, prim_name);
+}
+}  // namespace
+
+MIND_API_OPERATOR_IMPL(ScatterMax, BaseOperator);
+AbstractBasePtr ScatterMaxInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  const int64_t input_num = 3;
+  (void)CheckAndConvertUtils::CheckInputArgs(input_args, kGreaterEqual, input_num, primitive->name());
+  auto infer_type = ScatterMaxInferType(primitive, input_args);
+  auto infer_shape = ScatterMaxInferShape(primitive, input_args);
+  return abstract::MakeAbstract(infer_shape, infer_type);
+}
+REGISTER_PRIMITIVE_EVAL_IMPL(ScatterMax, prim::kPrimScatterMax, ScatterMaxInfer, nullptr, true);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/scatter_max.h

@@ -0,0 +1,39 @@
+/**
+ * Copyright 2022 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CORE_OPS_SCATTER_MAX_H_
+#define MINDSPORE_CORE_OPS_SCATTER_MAX_H_
+
+#include <vector>
+#include "ops/base_operator.h"
+#include "mindapi/base/types.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameScatterMax = "ScatterMax";
+class MIND_API ScatterMax : public BaseOperator {
+ public:
+  MIND_API_BASE_MEMBER(ScatterMax);
+  /// \brief Constructor.
+  ScatterMax() : BaseOperator(kNameScatterMax) { InitIOName({"input_x", "indices", "updates"}, {"output"}); }
+};
+
+abstract::AbstractBasePtr ScatterMaxInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                          const std::vector<abstract::AbstractBasePtr> &input_args);
+}  // namespace ops
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CORE_OPS_SCATTER_MAX_H_

mindspore/core/ops/scatter_min.cc

@@ -33,7 +33,13 @@ abstract::ShapePtr ScatterMinInferShape(const PrimitivePtr &primitive, const std
   BaseShapePtr updates_shape_ptr = input_args[kInputIndex2]->BuildShape();
   MS_EXCEPTION_IF_NULL(updates_shape_ptr);
 
-  if (input_x_shape_ptr->IsDynamic() || indices_shape_ptr->IsDynamic() || updates_shape_ptr->IsDynamic()) {
+  if (input_x_shape_ptr->IsDynamic()) {
+    MS_EXCEPTION(ValueError) << "For " << primitive->name() << ", "
+                             << "the 'input_x' does not support dynamic shape, but got the shape of 'input_x' is "
+                             << input_x_shape_ptr->ToString();
+  }
+
+  if (indices_shape_ptr->IsDynamic() || updates_shape_ptr->IsDynamic()) {
     return input_args[kInputIndex0]->BuildShape()->cast<abstract::ShapePtr>();
   }
 

mindspore/python/mindspore/ops/operations/array_ops.py

@@ -4230,7 +4230,7 @@ class ScatterNdUpdate(Primitive):
         self.add_prim_attr('side_effect_mem', True)
 
 
-class ScatterMax(_ScatterOp):
+class ScatterMax(_ScatterOpDynamic):
     r"""
     Updates the value of the input tensor through the maximum operation.
 
@@ -4284,7 +4284,7 @@ class ScatterMax(_ScatterOp):
     """
 
 
-class ScatterMin(_ScatterOp):
+class ScatterMin(_ScatterOpDynamic):
     r"""
     Updates the value of the input tensor through the minimum operation.
 

tests/st/ops/gpu/test_scatter_func_op.py

@@ -722,6 +722,13 @@ def test_scatter_func_disordered_dynamic_int32():
     ).astype(np.int32)
     np.testing.assert_array_almost_equal(output.asnumpy(), expected)
 
+    # max
+    output = scatter_func_d_net("max", inputx, indices, updates)
+    expected = np.array(
+        [[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
+    ).astype(np.int32)
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
     # min
     output = scatter_func_d_net("min", inputx, indices, updates)
     expected = np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int32))
@@ -761,6 +768,13 @@ def test_scatter_func_disordered_dynamic_int8():
     ).astype(np.int8)
     np.testing.assert_array_almost_equal(output.asnumpy(), expected)
 
+    # max
+    output = scatter_func_d_net("max", inputx, indices, updates)
+    expected = np.array(
+        [[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
+    ).astype(np.int8)
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
     # min
     output = scatter_func_d_net("min", inputx, indices, updates)
     expected = np.flip(np.arange(34, 46).reshape(3, 4).astype(np.int8))
@@ -796,6 +810,13 @@ def test_scatter_func_disordered_dynamic_uint8():
     ).astype(np.uint8)
     np.testing.assert_array_almost_equal(output.asnumpy(), expected)
 
+    # max
+    output = scatter_func_d_net("max", inputx, indices, updates)
+    expected = np.array(
+        [[95.0, 96.0, 97.0, 98.0], [67.0, 68.0, 69.0, 70.0], [99.0, 100.0, 101.0, 102.0]]
+    ).astype(np.uint8)
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
     # min
     output = scatter_func_d_net("min", inputx, indices, updates)
     expected = np.flip(np.arange(34, 46).reshape(3, 4).astype(np.uint8))
@@ -848,6 +869,13 @@ def test_scatter_func_input_less_than_1_dynamic_float32():
     )
     np.testing.assert_array_almost_equal(output.asnumpy(), expected)
 
+    # max
+    output = scatter_func_d_net("max", inputx, indices, updates)
+    expected = np.array(
+        [[37.0, 38.0, 39.0], [34.0, 35.0, 66.0], [67.0, 68.0, 69.0],], dtype=np.float32,
+    )
+    np.testing.assert_array_almost_equal(output.asnumpy(), expected)
+
     # min
     output = scatter_func_d_net("min", inputx, indices, updates)
     expected = inputx_np
@@ -891,6 +919,15 @@ def test_scatter_func_dynamic_two_inputs():
     np.testing.assert_array_almost_equal(output_1.asnumpy(), expected_1)
     np.testing.assert_array_almost_equal(output_2.asnumpy(), expected_2)
 
+    # max
+    output_1, output_2 = scatter_func_d2_net(
+        "max", inputx, indices_1, updates_1, indices_2, updates_2
+    )
+    expected_1 = np.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]])
+    expected_2 = np.array([[17.0, 16.0, 15.0], [11.0, 10.0, 9.0]])
+    np.testing.assert_array_almost_equal(output_1.asnumpy(), expected_1)
+    np.testing.assert_array_almost_equal(output_2.asnumpy(), expected_2)
+
     # min
     output_1, output_2 = scatter_func_d2_net(
         "min", inputx, indices_1, updates_1, indices_2, updates_2
@@ -902,13 +939,8 @@ def test_scatter_func_dynamic_two_inputs():
 
 
 if __name__ == "__main__":
-    test_scatter_func_small_float32()
+    test_scatter_func_disordered_dynamic_int32()
-    test_scatter_func_input_updated()
+    test_scatter_func_disordered_dynamic_int8()
-    test_scatter_func_large_shape_float32()
+    test_scatter_func_disordered_dynamic_uint8()
-    test_scatter_func_small_float32_use_locking_false()
+    test_scatter_func_input_less_than_1_dynamic_float32()
-    test_scatter_func_input_less_than_1_float32()
+    test_scatter_func_dynamic_two_inputs()
-    test_scatter_func_float16()
-    test_scatter_func_large_float16()
-    test_scatter_func_disordered_float16()
-    test_scatter_func_large_int32()
-    test_scatter_func_disordered_int32()