[NPU] Add log_loss op (#35010)

* [NPU] Add log_loss op

* [NPU] Add log_loss op

* [NPU] Add log_loss op

paddle/fluid/operators/log_loss_op_npu.cc

+/* 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 Licnse. */
+
+#include "paddle/fluid/operators/log_loss_op.h"
+#include <cmath>
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+void LogLossAdds(const platform::Place& place, const aclrtStream& stream,
+                 const Tensor* x, float scale, Tensor* y) {
+  //  Calculate y = x + scale
+  y->mutable_data<T>(x->dims(), place);
+  const auto& runner = NpuOpRunner("Adds", {*x}, {*y}, {{"value", scale}});
+  runner.Run(stream);
+}
+
+template <typename T>
+void LogLossMuls(const platform::Place& place, const aclrtStream& stream,
+                 const Tensor* x, float scale, Tensor* y) {
+  //  Calculate y = x + scale
+  y->mutable_data<T>(x->dims(), place);
+  const auto& runner = NpuOpRunner("Muls", {*x}, {*y}, {{"value", scale}});
+  runner.Run(stream);
+}
+
+template <typename T>
+void LogLossBCE(const platform::Place& place, const aclrtStream& stream,
+                const Tensor* x, const Tensor* y, Tensor* z) {
+  z->mutable_data<T>(x->dims(), place);
+  const auto& runner =
+      NpuOpRunner("BinaryCrossEntropy", {*x, *y}, {*z},
+                  {{"reduction", static_cast<std::string>("none")}});
+  runner.Run(stream);
+}
+
+template <typename T>
+void LogLossBCEGrad(const platform::Place& place, const aclrtStream& stream,
+                    const Tensor* x, const Tensor* y, const Tensor* dout,
+                    Tensor* dx) {
+  dx->mutable_data<T>(x->dims(), place);
+  const auto& runner =
+      NpuOpRunner("BinaryCrossEntropyGrad", {*x, *y, *dout}, {*dx},
+                  {{"reduction", static_cast<std::string>("none")}});
+  runner.Run(stream);
+}
+
+template <typename T, typename AttrType = T>
+class LogLossNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* y = ctx.Output<Tensor>("Loss");
+    auto* pred = ctx.Input<Tensor>("Predicted");
+    auto* label = ctx.Input<Tensor>("Labels");
+    auto epsilon = static_cast<T>(ctx.Attr<AttrType>("epsilon"));
+
+    auto place = ctx.GetPlace();
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    float factor = 1 / (1 + 2 * epsilon);
+    float coef = std::log(factor);
+    LogLossAdds<T>(place, stream, pred, epsilon, y);
+    LogLossMuls<T>(place, stream, y, factor, y);
+    LogLossBCE<T>(place, stream, y, label, y);
+    LogLossAdds<T>(place, stream, y, coef, y);
+  }
+};
+
+template <typename T, typename AttrType = T>
+class LogLossGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* pred = ctx.Input<Tensor>("Predicted");
+    auto* label = ctx.Input<Tensor>("Labels");
+    auto* dloss = ctx.Input<Tensor>(framework::GradVarName("Loss"));
+    auto* dpred = ctx.Output<Tensor>(framework::GradVarName("Predicted"));
+    auto epsilon = static_cast<T>(ctx.Attr<AttrType>("epsilon"));
+
+    auto place = ctx.GetPlace();
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    if (dpred) {
+      LogLossBCEGrad<T>(place, stream, pred, label, dloss, dpred);
+      LogLossMuls<T>(place, stream, dpred, 1 / (1 + 2 * epsilon), dpred);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(log_loss, ops::LogLossNPUKernel<float>);
+
+REGISTER_OP_NPU_KERNEL(log_loss_grad, ops::LogLossGradNPUKernel<float>);

python/paddle/fluid/tests/unittests/npu/test_log_loss_op_npu.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
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+
+
+def sigmoid_array(x):
+    return 1 / (1 + np.exp(-x))
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestLogLossOp(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = 'log_loss'
+        self.place = paddle.NPUPlace(0)
+
+        self.init_dtype()
+
+        self.set_inputs()
+        self.set_attrs()
+        self.set_outputs()
+
+    def set_inputs(self):
+        samples_num = 100
+        x = np.random.random((samples_num, 1)).astype(self.dtype)
+        predicted = sigmoid_array(x)
+        labels = np.random.randint(0, 2, (samples_num, 1)).astype(self.dtype)
+        self.inputs = {'Predicted': predicted, 'Labels': labels}
+
+    def set_attrs(self):
+        epsilon = 1e-7
+        self.attrs = {'epsilon': epsilon}
+
+    def set_outputs(self):
+        epsilon = self.attrs['epsilon']
+        labels = self.inputs['Labels']
+        predicted = self.inputs['Predicted']
+        loss = -labels * np.log(predicted + epsilon) - (
+            1 - labels) * np.log(1 - predicted + epsilon)
+        self.outputs = {'Loss': loss}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['Predicted'], 'Loss')
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestLogLossOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_x_type():
+                input_data = np.random.random(100, 1).astype("float32")
+                fluid.layers.log_loss(input_data)
+
+            self.assertRaises(TypeError, test_x_type)
+
+            def test_x_dtype():
+                x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
+                fluid.layers.log_loss(x2)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+            def test_label_type():
+                input_data = np.random.random(100, 1).astype("float32")
+                fluid.layers.log_loss(input_data)
+
+            self.assertRaises(TypeError, test_label_type)
+
+            def test_label_dtype():
+                x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
+                fluid.layers.log_loss(x2)
+
+            self.assertRaises(TypeError, test_label_dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()