[MLU] add mlu kernel for abs op (#43099)

paddle/fluid/operators/abs_op_mlu.cc

+/* Copyright (c) 2022 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 Licnse. */
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class AbsMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+
+    output->mutable_data<T>(ctx.GetPlace());
+
+    MLUCnnlTensorDesc input_desc(*input);
+    MLUCnnlTensorDesc output_desc(*output);
+
+    MLUCnnl::Abs(ctx, input_desc.get(), GetBasePtr(input), output_desc.get(),
+                 GetBasePtr(output));
+  }
+};
+
+template <typename T>
+class AbsGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    dx->mutable_data<T>(ctx.GetPlace());
+
+    MLUCnnlTensorDesc input_desc(*x);
+    MLUCnnlOpTensorDesc mul_op_desc(CNNL_OP_TENSOR_MUL, ToCnnlDataType<T>(),
+                                    CNNL_NOT_PROPAGATE_NAN);
+
+    Tensor sign_x;
+    sign_x.mutable_data<T>(x->dims(), ctx.GetPlace());
+
+    MLUCnnl::Sign(ctx, input_desc.get(), GetBasePtr(x), input_desc.get(),
+                  GetBasePtr(&sign_x));
+    MLUCnnl::OpTensor(ctx, mul_op_desc.get(), input_desc.get(),
+                      GetBasePtr(&sign_x), input_desc.get(), GetBasePtr(dout),
+                      input_desc.get(), GetBasePtr(dx), ToCnnlDataType<T>());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(abs, ops::AbsMLUKernel<float>,
+                       ops::AbsMLUKernel<plat::float16>);
+
+REGISTER_OP_MLU_KERNEL(abs_grad, ops::AbsGradMLUKernel<float>,
+                       ops::AbsGradMLUKernel<plat::float16>);

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

+#   Copyright (c) 2022 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
+
+import unittest
+import numpy as np
+import sys
+sys.path.append('..')
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+import paddle
+import paddle.nn.functional as F
+
+paddle.enable_static()
+np.random.seed(10)
+
+
+class TestAbs(OpTest):
+    def setUp(self):
+        self.op_type = "abs"
+        self.set_mlu()
+        self.dtype = 'float32'
+        self.shape = [4, 25]
+
+        np.random.seed(1024)
+        x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        # Because we set delta = 0.005 in calculating numeric gradient,
+        # if x is too small, such as 0.002, x_neg will be -0.003
+        # x_pos will be 0.007, so the numeric gradient is inaccurate.
+        # we should avoid this
+        x[np.abs(x) < 0.005] = 0.02
+        out = np.abs(x)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.outputs = {'Out': out}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    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'], check_eager=False)
+
+
+class TestAbsHalf(OpTest):
+    def setUp(self):
+        self.op_type = "abs"
+        self.set_mlu()
+        self.dtype = 'float16'
+        self.shape = [7, 9, 13, 19]
+
+        np.random.seed(1024)
+        x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        # Because we set delta = 0.005 in calculating numeric gradient,
+        # if x is too small, such as 0.002, x_neg will be -0.003
+        # x_pos will be 0.007, so the numeric gradient is inaccurate.
+        # we should avoid this
+        x[np.abs(x) < 0.005] = 0.02
+        out = np.abs(x)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.outputs = {'Out': out}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    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'], check_eager=False)
+
+
+if __name__ == "__main__":
+    unittest.main()