Support pow's second input could be tensor and fix bug in bprop of pow

mindspore/ccsrc/parallel/ops_info/arithmetic_info.h

@@ -98,6 +98,13 @@ class FloorDivInfo : public ArithmeticBase {
   ~FloorDivInfo() override = default;
 };
 
+class PowInfo : public ArithmeticBase {
+ public:
+  PowInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
+      : ArithmeticBase(name, inputs_shape, outputs_shape, attrs) {}
+  ~PowInfo() override = default;
+};
+
 class GreaterInfo : public ArithmeticBase {
  public:
   GreaterInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape,

mindspore/ccsrc/parallel/ops_info/elementary_function_info.cc

@@ -1,47 +0,0 @@
-/**
- * Copyright 2019 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 "parallel/ops_info/elementary_function_info.h"
-
-namespace mindspore {
-namespace parallel {
-Status PowInfo::InferMirrorOps() {
-  mirror_ops_.clear();
-
-  Shape tensor_map = inputs_tensor_map_[0];
-  std::vector<Group> group;
-  if (CreateGroupByTensorMap(tensor_map, &group) != SUCCESS) {
-    MS_LOG(ERROR) << name_ << " : Create group failed.";
-    return FAILED;
-  }
-
-  OperatorVector mirror_op;
-  OperatorVector op_for_value;
-  if (group.empty()) {
-    MS_LOG(INFO) << name_ << " : The mirror ops is empty.";
-    return SUCCESS;
-  } else {
-    mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
-    mirror_ops_.push_back(mirror_op);
-    mirror_ops_.push_back(op_for_value);
-    std::string group_name = group[0].name();
-    MS_LOG(INFO) << name_ << " : Create the mirror ops success, the group name is " << group_name;
-  }
-
-  return SUCCESS;
-}
-}  // namespace parallel
-}  // namespace mindspore

mindspore/ccsrc/parallel/ops_info/elementary_function_info.h

@@ -27,16 +27,6 @@
 
 namespace mindspore {
 namespace parallel {
-class PowInfo : public ActivationOther {
- public:
-  PowInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)
-      : ActivationOther(name, inputs_shape, outputs_shape, attrs) {}
-  ~PowInfo() override = default;
-
- protected:
-  Status InferMirrorOps() override;
-};
-
 class ExpInfo : public ActivationOther {
  public:
   ExpInfo(const std::string& name, const Shapes& inputs_shape, const Shapes& outputs_shape, const PrimitiveAttrs& attrs)

mindspore/nn/layer/pooling.py

@@ -58,7 +58,7 @@ class _PoolNd(Cell):
         pass
 
     def extend_repr(self):
-        return 'kernel_size={kernel_size}, strides={strides}, pad_mode={pad_mode}'.format(**self.__dict__)
+        return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__)
 
 
 class MaxPool2d(_PoolNd):

mindspore/ops/_grad/grad_math_ops.py

@@ -336,14 +336,13 @@ def get_bprop_log(self):
 @bprop_getters.register(P.Pow)
 def get_bprop_pow(self):
     """Grad definition for `Pow` operation."""
-    pow_ = P.Pow()
-    cast = P.Cast()
-    dtype = P.DType()
+    pow_op = P.Pow()
+    ln = P.Log()
 
     def bprop(x, power, out, dout):
-        g = cast(F.tuple_to_array((power,)), dtype(x)) * pow_(x, power-1.0)
-        dx = g * dout
-        return dx, 0
+        dx = power * pow_op(x, power - 1.0) * dout
+        dpower = pow_op(x, power) * ln(x) * dout
+        return dx, dpower
     return bprop
 
 

mindspore/ops/operations/array_ops.py

@@ -1097,7 +1097,7 @@ class ArgMaxWithValue(PrimitiveWithInfer):
         axis = self.axis
         x_rank = len(x_shape)
         validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT)
-        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name())
+        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.name)
         return ouput_shape, ouput_shape
 
     def infer_dtype(self, x_dtype):
@@ -1143,7 +1143,7 @@ class ArgMinWithValue(PrimitiveWithInfer):
         axis = self.axis
         x_rank = len(x_shape)
         validator.check_int_range("axis", axis, -x_rank, x_rank, Rel.INC_LEFT)
-        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.prim_name())
+        ouput_shape = _infer_shape_reduce(x_shape, self.axis, self.keep_dims, self.name)
         return ouput_shape, ouput_shape
 
     def infer_dtype(self, x_dtype):

mindspore/ops/primitive.py

@@ -194,9 +194,6 @@ class PrimitiveWithInfer(Primitive):
         Primitive.__init__(self, name)
         self.set_prim_type(prim_type.py_infer_shape)
 
-    def prim_name(self):
-        return self.__class__.__name__
-
     def _clone(self):
         """
         Deeply clones the primitive object.

tests/ut/cpp/parallel/ops_info/pow_info_test.cc

@@ -19,7 +19,7 @@
 #include <vector>
 #include "common/common_test.h"
 #include "parallel/strategy.h"
-#include "parallel/ops_info/elementary_function_info.h"
+#include "parallel/ops_info/arithmetic_info.h"
 #include "parallel/device_manager.h"
 #include "parallel/step_parallel.h"
 
@@ -56,14 +56,14 @@ void TestPowInfo::SetUp() {
 
   std::unordered_map<std::string, ValuePtr> attr;
 
-  Shapes inputs_shape = {{32, 64, 128}};
+  Shapes inputs_shape = {{32, 64, 128}, {32, 64, 128}};
   Shapes outputs_shape = {{32, 64, 128}};
 
   pow = std::make_shared<PowInfo>("pow_info", inputs_shape, outputs_shape, attr);
 }
 
 TEST_F(TestPowInfo, InferDevMatrixShape1) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -74,7 +74,7 @@ TEST_F(TestPowInfo, InferDevMatrixShape1) {
 }
 
 TEST_F(TestPowInfo, InferSliceShape1) {
-  std::vector<Dimensions> str = {{2, 4, 8}};
+  std::vector<Dimensions> str = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   pow->Init(strategy);
@@ -95,7 +95,7 @@ TEST_F(TestPowInfo, InferSliceShape1) {
 }
 
 TEST_F(TestPowInfo, GetTensorLayout1) {
-  std::vector<Dimensions> str = {{2, 4, 8}};
+  std::vector<Dimensions> str = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, str);
 
   pow->Init(strategy);
@@ -116,7 +116,7 @@ TEST_F(TestPowInfo, GetTensorLayout1) {
 }
 
 TEST_F(TestPowInfo, GetForwardOp1) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -127,7 +127,7 @@ TEST_F(TestPowInfo, GetForwardOp1) {
 }
 
 TEST_F(TestPowInfo, GetMirrorOPs1) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   pow->Init(strategy);
@@ -147,7 +147,7 @@ TEST_F(TestPowInfo, CheckStrategy1) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy2) {
-  std::vector<Dimensions> inputs = {{2, 4, 8, 16}};
+  std::vector<Dimensions> inputs = {{2, 4, 8, 16}, {2, 4, 8, 16}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);
@@ -155,7 +155,7 @@ TEST_F(TestPowInfo, CheckStrategy2) {
 }
 
 TEST_F(TestPowInfo, CheckStrategy3) {
-  std::vector<Dimensions> inputs = {{2, 4, 8}};
+  std::vector<Dimensions> inputs = {{2, 4, 8}, {2, 4, 8}};
   StrategyPtr strategy = NewStrategy(0, inputs);
 
   Status ret = pow->Init(strategy);

tests/ut/python/ops/test_math_ops.py

@@ -82,9 +82,10 @@ def test_sqrt():
 def test_pow():
     """ test_pow """
     input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
+    power = Tensor(np.array(3.0, np.int64))
     testpow = P.Pow()
     expect = np.array([[8, 8], [27, 27]])
-    result = testpow(input_tensor, 3.0)
+    result = testpow(input_tensor, power)
     assert np.all(result.asnumpy() == expect)
 
 

tests/ut/python/ops/test_ops.py

@@ -224,11 +224,15 @@ test_case_math_ops = [
         'block': P.Minimum(),
         'desc_inputs': [[2, 3, 3, 5], [2, 3, 3, 5]],
         'desc_bprop': [[2, 3, 3, 5]]}),
-    ('Pow', {
+    ('Pow_0', {
         'block': P.Pow(),
         'desc_const': [2.0],
         'desc_inputs': [[2, 3, 3, 5]],
         'desc_bprop': [[2, 3, 3, 5]]}),
+    ('Pow_1', {
+        'block': P.Pow(),
+        'desc_inputs': [[3, 5], [2, 3, 3, 5]],
+        'desc_bprop': [[2, 3, 3, 5]]}),
     ('Exp', {
         'block': P.Exp(),
         'desc_inputs': [[2, 3]],

tests/ut/python/parallel/test_element_wise_function.py

@@ -59,7 +59,7 @@ def test_matmul_pow():
 
     context.set_auto_parallel_context(device_num=8, global_rank=0)
     strategy1 = ((2, 2), (2, 2))
-    strategy2 = ((4, 2), )
+    strategy2 = ((4, 2), ())
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
 

tests/vm_impl/math_ops_vm_impl.py

@@ -117,6 +117,7 @@ def vm_impl_pow(self):
     """Generate vm_impl function for Pow."""
     def vm_impl(x, y):
         x = x.asnumpy()
+        y = y.asnumpy()
         res = vm.power(x, y)
         return Tensor(res)
     return vm_impl