!26028 [feat] [assistant] [I48OAM] MulNoNan with dynamic infer shape

Merge pull request !26028 from 郑鹏飞/mulnonan_1

mindspore/core/base/core_ops.h

@@ -54,6 +54,7 @@ constexpr auto kNotEqual = "NotEqual";
 constexpr auto kNeg = "Neg";
 constexpr auto kSub = "Sub";
 constexpr auto kMul = "Mul";
+constexpr auto kMulNoNan = "MulNoNan";
 constexpr auto kRealDiv = "RealDiv";
 constexpr auto kReciprocal = "Reciprocal";
 constexpr auto kLog = "Log";
@@ -533,6 +534,7 @@ inline const PrimitivePtr kPrimSin = std::make_shared<Primitive>("Sin");
 inline const PrimitivePtr kPrimCos = std::make_shared<Primitive>(kCos);
 inline const PrimitivePtr kPrimSub = std::make_shared<Primitive>(kSub);
 inline const PrimitivePtr kPrimMul = std::make_shared<Primitive>(kMul);
+inline const PrimitivePtr kPrimMulNoNan = std::make_shared<Primitive>(kMulNoNan);
 inline const PrimitivePtr kPrimDiv = std::make_shared<Primitive>("Div");
 inline const PrimitivePtr kPrimMod = std::make_shared<Primitive>("Mod");
 inline const PrimitivePtr kPrimFloor = std::make_shared<Primitive>("Floor");

mindspore/core/ops/mulnonan.cc

@@ -0,0 +1,83 @@
+/**
+ * Copyright 2021 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/mulnonan.h"
+#include <string>
+#include <algorithm>
+#include <memory>
+#include <set>
+#include <vector>
+#include "ops/op_utils.h"
+#include "utils/check_convert_utils.h"
+#include "abstract/primitive_infer_map.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+abstract::ShapePtr MulNoNanInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  return BroadCastInferShape(prim_name, input_args);
+}
+
+TypePtr MulNoNanInferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
+  for (const auto &item : input_args) {
+    MS_EXCEPTION_IF_NULL(item);
+  }
+  auto op_name = prim->name();
+  const int64_t kInputNum = 2;
+  (void)CheckAndConvertUtils::CheckInteger("input number", SizeToLong(input_args.size()), kGreaterEqual, kInputNum,
+                                           op_name);
+  std::map<std::string, TypePtr> types;
+  (void)types.emplace("x", input_args[0]->BuildType());
+  (void)types.emplace("y", input_args[1]->BuildType());
+  auto type_x = input_args[0]->BuildType();
+  auto type_y = input_args[1]->BuildType();
+  MS_EXCEPTION_IF_NULL(type_x);
+  MS_EXCEPTION_IF_NULL(type_y);
+  if (type_x->isa<Complex>() || type_y->isa<Complex>()) {
+    if (type_x->type_id() == kNumberTypeComplex64 && type_y->type_id() == kNumberTypeComplex64) {
+      return type_x;
+    } else if (type_x->type_id() == kNumberTypeComplex64 && type_y->type_id() == kNumberTypeFloat32) {
+      return type_x;
+    } else if (type_x->type_id() == kNumberTypeComplex128 && type_y->type_id() == kNumberTypeComplex128) {
+      return type_x;
+    } else if (type_x->type_id() == kNumberTypeComplex128 && type_y->type_id() == kNumberTypeFloat64) {
+      return type_x;
+    } else if (type_x->type_id() == kNumberTypeFloat32 && type_y->type_id() == kNumberTypeComplex64) {
+      return type_y;
+    } else if (type_x->type_id() == kNumberTypeFloat64 && type_y->type_id() == kNumberTypeComplex128) {
+      return type_y;
+    } else {
+      MS_EXCEPTION(TypeError)
+        << "Complex math binary op expecting Tensor [complex64, complex64],[complex64, float32], [float32, "
+           "complex64],[complex128, complex128],[complex128, float64], [float64, complex128], but got["
+        << type_x->ToString() << ", " << type_y->ToString() << "].";
+    }
+  }
+  return CheckAndConvertUtils::CheckTensorTypeSame(types, common_valid_types_with_complex, prim->name());
+}
+}  // namespace
+
+AbstractBasePtr MulNoNanInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                              const std::vector<AbstractBasePtr> &input_args) {
+  auto infer_type = MulNoNanInferType(primitive, input_args);
+  auto infer_shape = MulNoNanInferShape(primitive, input_args);
+  return abstract::MakeAbstract(infer_shape, infer_type);
+}
+REGISTER_PRIMITIVE_EVAL_IMPL(MulNoNan, prim::kPrimMulNoNan, MulNoNanInfer, nullptr, true);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/mulnonan.h

@@ -0,0 +1,44 @@
+/**
+ * Copyright 2021 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_MULNONAN_H_
+#define MINDSPORE_CORE_OPS_MULNONAN_H_
+#include <map>
+#include <vector>
+#include <string>
+#include <memory>
+#include "ops/primitive_c.h"
+#include "abstract/abstract_value.h"
+#include "utils/check_convert_utils.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameMulNoNan = prim::kMulNoNan;
+class MulNoNan : public PrimitiveC {
+ public:
+  MulNoNan() : PrimitiveC(kNameMulNoNan) { InitIOName({"x", "y"}, {"output"}); }
+  explicit MulNoNan(const std::string k_name) : PrimitiveC(k_name) { InitIOName({"x", "y"}, {"output"}); }
+  ~MulNoNan() = default;
+  MS_DECLARE_PARENT(MulNoNan, PrimitiveC);
+  void Init() {}
+};
+AbstractBasePtr MulNoNanInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                              const std::vector<AbstractBasePtr> &input_args);
+using kPrimMulNoNanPtr = std::shared_ptr<MulNoNan>;
+}  // namespace ops
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CORE_OPS_MULNONAN_H_

mindspore/python/mindspore/ops/_op_impl/tbe/__init__.py

@@ -476,6 +476,7 @@ from .nll_loss_grad import _nll_loss_grad_tbe
 from .masked_fill import _masked_fill_tbe
 from .mish import _mish_tbe
 from .mul_no_nan import _mul_no_nan_tbe
+from .mul_no_nan_ds import _mul_no_nan_ds_tbe
 from .selu import _selu_tbe
 from .centralization import _centralization_tbe
 from .exp_ds import _exp_ds_tbe

mindspore/python/mindspore/ops/_op_impl/tbe/mul_no_nan_ds.py

@@ -0,0 +1,40 @@
+# Copyright 2021 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.
+# ============================================================================
+
+"""MulNoNan op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+mul_no_nan_op_info = TBERegOp("MulNoNan") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("mul_no_nan.so") \
+    .compute_cost(10) \
+    .kernel_name("mul_no_nan") \
+    .partial_flag(True) \
+    .dynamic_shape(True) \
+    .input(0, "x1", False, "required", "all") \
+    .input(1, "x2", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .op_pattern("broadcast") \
+    .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None) \
+    .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None) \
+    .dtype_format(DataType.I32_None, DataType.I32_None, DataType.I32_None) \
+    .get_op_info()
+
+
+@op_info_register(mul_no_nan_op_info)
+def _mul_no_nan_ds_tbe():
+    """MulNoNan TBE register"""
+    return

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -2788,16 +2788,6 @@ class MulNoNan(_MathBinaryOp):
         """Initialize _BinaryOp"""
         self.init_prim_io_names(inputs=['x', 'y'], outputs=['output'])
 
-    def infer_value(self, x, y):
-        if x is not None and y is not None:
-            x = x.asnumpy()
-            y = y.asnumpy()
-            with np.errstate(divide='ignore', invalid='ignore'):
-                out = np.multiply(x, y)
-                out[y == 0] = 0
-            return out
-        return None
-
 
 class FloorDiv(Primitive):
     """