[NPU] add hard_sigmoid (#34094)

* [NPU] add hard_sigmoid

* [NPU] delete check_dygraph=False and max_relative_error

* [NPU] delete debug codes

* [NPU] add more test cases

* [NPU] add api test TestHardsigmoidAPI

* [NPU] temp delete hard_sigmoid for resovle conficts

* [NPU] resolve conflicts

paddle/fluid/operators/activation_op_npu.cc

@@ -347,6 +347,56 @@ class SigmoidGradNPUKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+class HardSigmoidNPUKernel : 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");
+    float slope = ctx.Attr<float>("slope");
+    float offset = ctx.Attr<float>("offset");
+
+    out->mutable_data<T>(ctx.GetPlace());
+
+    framework::NPUAttributeMap attr_input = {{"alpha", slope},
+                                             {"beta", offset}};
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    const auto& runner = NpuOpRunner("HardSigmoid", {*x}, {*out}, attr_input);
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class HardSigmoidGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* out = ctx.Input<Tensor>("Out");
+
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    float slope = ctx.Attr<float>("slope");
+    float offset = ctx.Attr<float>("offset");
+
+    dx->mutable_data<T>(ctx.GetPlace());
+
+    framework::NPUAttributeMap attr_input = {{"alpha", slope},
+                                             {"beta", offset}};
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    const auto& runner_dx =
+        NpuOpRunner("HardSigmoidGrad", {*dout, *out}, {*dx}, attr_input);
+    runner_dx.Run(stream);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -421,3 +471,15 @@ REGISTER_OP_NPU_KERNEL(
     ops::SigmoidGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
     ops::SigmoidGradNPUKernel<paddle::platform::NPUDeviceContext,
                               paddle::platform::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    hard_sigmoid,
+    ops::HardSigmoidNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::HardSigmoidNPUKernel<paddle::platform::NPUDeviceContext,
+                              paddle::platform::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    hard_sigmoid_grad,
+    ops::HardSigmoidGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::HardSigmoidGradNPUKernel<paddle::platform::NPUDeviceContext,
+                                  paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_hard_sigmoid_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
+from paddle.fluid.tests.unittests.op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.nn.functional as F
+from paddle.fluid import Program, program_guard
+
+paddle.enable_static()
+SEED = 2021
+
+
+def ref_hardsigmoid(x, slope=0.166666666666667, offset=0.5):
+    return np.maximum(np.minimum(x * slope + offset, 1.), 0.).astype(x.dtype)
+
+
+class TestNPUHardSigmoid(OpTest):
+    def setUp(self):
+        paddle.enable_static()
+
+        self.op_type = "hard_sigmoid"
+        self.set_npu()
+        self.init_dtype()
+        self.set_attrs()
+
+        x = np.random.uniform(-5, 5, [10, 12]).astype(self.dtype)
+        lower_threshold = -self.offset / self.slope
+        upper_threshold = (1. - self.offset) / self.slope
+
+        # Same reason as TestAbs
+        delta = 0.005
+        x[np.abs(x - lower_threshold) < delta] = lower_threshold - 0.02
+        x[np.abs(x - upper_threshold) < delta] = upper_threshold - 0.02
+
+        out = ref_hardsigmoid(x, self.slope, self.offset)
+
+        self.attrs = {'slope': self.slope, 'offset': self.offset}
+        self.inputs = {'X': x}
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-5)
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+
+        self.check_grad_with_place(self.place, ['X'], 'Out')
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def set_attrs(self):
+        self.slope = 0.166666666666667
+        self.offset = 0.5
+
+
+class TestNPUHardSigmoid2(TestNPUHardSigmoid):
+    def set_attrs(self):
+        self.slope = 0.2
+        self.offset = 0.5
+
+
+class TestNPUHardSigmoid3(TestNPUHardSigmoid):
+    def set_attrs(self):
+        self.slope = 0.2
+        self.offset = 0.4
+
+
+class TestNPUHardSigmoidFp16(TestNPUHardSigmoid):
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-3)
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class TestHardsigmoidAPI(unittest.TestCase):
+    # test paddle.nn.Hardsigmoid, paddle.nn.functional.hardsigmoid
+    def setUp(self):
+        self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(np.float32)
+        self.place = paddle.NPUPlace(0)
+
+    def test_static_api(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.static.data('X', self.x_np.shape, self.x_np.dtype)
+            out1 = F.hardsigmoid(x)
+            m = paddle.nn.Hardsigmoid()
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = ref_hardsigmoid(self.x_np)
+        for r in res:
+            self.assertTrue(np.allclose(out_ref, r))
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out1 = F.hardsigmoid(x)
+        m = paddle.nn.Hardsigmoid()
+        out2 = m(x)
+        out_ref = ref_hardsigmoid(self.x_np)
+        for r in [out1, out2]:
+            self.assertTrue(np.allclose(out_ref, r.numpy()))
+        paddle.enable_static()
+
+    def test_fluid_api(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
+            out = fluid.layers.hard_sigmoid(x)
+            exe = fluid.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
+        out_ref = ref_hardsigmoid(self.x_np, 0.2, 0.5)
+        self.assertTrue(np.allclose(out_ref, res[0]))
+
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out = paddle.fluid.layers.hard_sigmoid(x)
+        self.assertTrue(np.allclose(out_ref, out.numpy()))
+        paddle.enable_static()
+
+    def test_errors(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            # The input type must be Variable.
+            self.assertRaises(TypeError, F.hardsigmoid, 1)
+            # The input dtype must be float16, float32, float64.
+            x_int32 = paddle.fluid.data(
+                name='x_int32', shape=[12, 10], dtype='int32')
+            self.assertRaises(TypeError, F.hardsigmoid, x_int32)
+            # support the input dtype is float16
+            x_fp16 = paddle.fluid.data(
+                name='x_fp16', shape=[12, 10], dtype='float16')
+            F.hardsigmoid(x_fp16)
+
+
+if __name__ == '__main__':
+    unittest.main()