[NPU] Add leaky Relu (#34894)

* test=develop

* test=develop

paddle/fluid/operators/activation_op_npu.cc

@@ -207,6 +207,47 @@ class SqrtNPUKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+class LeakyReluNPUKernel : 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 alpha = ctx.Attr<float>("alpha");
+
+    out->mutable_data<T>(ctx.GetPlace());
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    const auto& runner =
+        NpuOpRunner("LeakyRelu", {*x}, {*out}, {{"negative_slope", alpha}});
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class LeakyReluGradNPUKernel : 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"));
+    auto alpha = ctx.Attr<float>("alpha");
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    dx->mutable_data<T>(ctx.GetPlace());
+    const auto& runner = NpuOpRunner("LeakyReluGrad", {*dout, *x}, {*dx},
+                                     {{"negative_slope", alpha}});
+
+    runner.Run(stream);
+  }
+};
+
 template <typename DeviceContext, typename T>
 class SqrtGradNPUKernel : public framework::OpKernel<T> {
  public:
@@ -778,6 +819,18 @@ REGISTER_OP_NPU_KERNEL(
     ops::Relu6GradNPUKernel<paddle::platform::NPUDeviceContext,
                             paddle::platform::float16>);
 
+REGISTER_OP_NPU_KERNEL(
+    leaky_relu,
+    ops::LeakyReluNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::LeakyReluNPUKernel<paddle::platform::NPUDeviceContext,
+                            paddle::platform::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    leaky_relu_grad,
+    ops::LeakyReluGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::LeakyReluGradNPUKernel<paddle::platform::NPUDeviceContext,
+                                paddle::platform::float16>);
+
 REGISTER_OP_NPU_KERNEL(
     sqrt, ops::SqrtNPUKernel<paddle::platform::NPUDeviceContext, float>,
     ops::SqrtNPUKernel<paddle::platform::NPUDeviceContext,

python/paddle/fluid/tests/unittests/npu/test_leaky_relu_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
+from test_activation_op import ref_leaky_relu
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+SEED = 2021
+
+
+class TestLeadyRelu(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "leaky_relu"
+        self.place = paddle.NPUPlace(0)
+
+        self.init_dtype()
+        np.random.seed(SEED)
+
+        self.set_inputs()
+        self.set_attrs()
+        self.set_outputs()
+
+    def set_inputs(self):
+        x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+
+    def set_attrs(self):
+        self.attrs = {}
+
+    def set_outputs(self):
+        alpha = 0.02 if 'alpha' not in self.attrs else self.attrs['alpha']
+        out = ref_leaky_relu(self.inputs['X'], alpha)
+        self.outputs = {'Out': out}
+
+    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):
+        if self.dtype == np.float16:
+            return
+        self.check_grad_with_place(self.place, ['X'], 'Out')
+
+
+class TestLeadyReluFP16(TestLeadyRelu):
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestLeadyRelu2(TestLeadyRelu):
+    def set_attrs(self):
+        self.attrs = {'alpha': 0.5}
+
+
+class TestLeadyRelu3(TestLeadyRelu):
+    def set_attrs(self):
+        self.attrs = {'alpha': -0.5}
+
+
+class TestLeakyReluNet(unittest.TestCase):
+    def _test(self, run_npu=True):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+        np.random.seed(SEED)
+
+        x_np = np.random.random(size=(32, 32)).astype('float32')
+        label_np = np.random.randint(2, size=(32, 1)).astype('int64')
+
+        with paddle.static.program_guard(main_prog, startup_prog):
+            x = paddle.static.data(name="x", shape=[32, 32], dtype='float32')
+            label = paddle.static.data(
+                name="label", shape=[32, 1], dtype='int64')
+
+            y = paddle.nn.functional.leaky_relu(x)
+
+            fc_1 = fluid.layers.fc(input=y, size=128)
+            prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
+
+            cost = fluid.layers.cross_entropy(input=prediction, label=label)
+            loss = fluid.layers.reduce_mean(cost)
+            sgd = fluid.optimizer.SGD(learning_rate=0.01)
+            sgd.minimize(loss)
+
+        if run_npu:
+            place = paddle.NPUPlace(0)
+        else:
+            place = paddle.CPUPlace()
+
+        exe = paddle.static.Executor(place)
+        exe.run(startup_prog)
+
+        print("Start run on {}".format(place))
+        for epoch in range(100):
+
+            pred_res, loss_res = exe.run(main_prog,
+                                         feed={"x": x_np,
+                                               "label": label_np},
+                                         fetch_list=[prediction, loss])
+            if epoch % 10 == 0:
+                print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
+                    epoch, pred_res[0], loss_res))
+
+        return pred_res, loss_res
+
+    def test_npu(self):
+        cpu_pred, cpu_loss = self._test(False)
+        npu_pred, npu_loss = self._test(True)
+
+        self.assertTrue(np.allclose(npu_pred, cpu_pred))
+        self.assertTrue(np.allclose(npu_loss, cpu_loss))
+
+
+if __name__ == '__main__':
+    unittest.main()