[MLU]add mlu kernel for reciprocal and reciprocal grad op (#43855)

paddle/fluid/operators/activation_op_mlu.cc

@@ -399,11 +399,81 @@ class HardSigmoidGradMLUKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+class ReciprocalMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* out = ctx.Output<Tensor>("Out");
+    auto place = ctx.GetPlace();
+    out->mutable_data<T>(place);
+    MLUCnnlTensorDesc x_desc(*x);
+    MLUCnnlTensorDesc out_desc(*out);
+    MLUCnnl::Reciprocal(
+        ctx, x_desc.get(), GetBasePtr(x), out_desc.get(), GetBasePtr(out));
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ReciprocalGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto place = ctx.GetPlace();
+    dx->mutable_data<T>(place);
+    Tensor square_out;
+    square_out.Resize(out->dims());
+    square_out.mutable_data<T>(place);
+    MLUCnnlTensorDesc out_desc(*out);
+    MLUCnnlTensorDesc dout_desc(*dout);
+    MLUCnnlTensorDesc dx_desc(*dx);
+    MLUCnnlTensorDesc square_out_desc(square_out);
+    MLUCnnl::Square(ctx,
+                    out_desc.get(),
+                    GetBasePtr(out),
+                    square_out_desc.get(),
+                    GetBasePtr(&square_out));
+    cnnlOpTensorDesc_t op_tensor_op = CNNL_OP_TENSOR_MUL;
+    cnnlDataType_t op_tensor_comp_type = CNNL_DTYPE_FLOAT;
+    cnnlNanPropagation_t op_tensor_nan_opt = CNNL_NOT_PROPAGATE_NAN;
+    MLUCnnlOpTensorDesc op_tensor_desc(
+        op_tensor_op, op_tensor_comp_type, op_tensor_nan_opt);
+    float alpha1_float = -1;
+    float alpha2_float = 1;
+    float beta_float = 0;
+    MLUCnnl::OpTensor(ctx,
+                      op_tensor_desc.get(),
+                      dout_desc.get(),
+                      GetBasePtr(dout),
+                      square_out_desc.get(),
+                      GetBasePtr(&square_out),
+                      dx_desc.get(),
+                      GetBasePtr(dx),
+                      op_tensor_comp_type,
+                      alpha1_float,
+                      alpha2_float,
+                      beta_float);
+  }
+};
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 
+// reciprocal
+REGISTER_OP_MLU_KERNEL(
+    reciprocal,
+    ops::ReciprocalMLUKernel<paddle::platform::MLUDeviceContext, float>,
+    ops::ReciprocalMLUKernel<paddle::platform::MLUDeviceContext,
+                             paddle::platform::float16>);
+
+REGISTER_OP_MLU_KERNEL(
+    reciprocal_grad,
+    ops::ReciprocalGradMLUKernel<paddle::platform::MLUDeviceContext, float>,
+    ops::ReciprocalGradMLUKernel<paddle::platform::MLUDeviceContext,
+                                 paddle::platform::float16>);
 // relu
 REGISTER_OP_MLU_KERNEL(
     relu,

python/paddle/fluid/tests/unittests/mlu/test_reciprocal_op_mlu.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function, division
+
+import numpy as np
+import unittest
+import sys
+
+sys.path.append("..")
+from op_test import OpTest, skip_check_grad_ci
+import paddle
+
+paddle.enable_static()
+
+
+class TestMLUReciprocal(OpTest):
+
+    def setUp(self):
+        self.op_type = "reciprocal"
+        self.set_mlu()
+        self.init_dtype()
+
+        np.random.seed(1024)
+        x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
+        out = np.reciprocal(x)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'],
+                                   'Out',
+                                   max_relative_error=0.01)
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+
+class TestMLUReciprocalFp16(TestMLUReciprocal):
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+if __name__ == '__main__':
+    unittest.main()