[NPU] add clip and clip_grad on NPU, test=develop (#34429)

* [NPU] add clip and clip_grad on NPU, test=develop

* address review comments, test=develop

* update, test=develop

paddle/fluid/operators/clip_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 License. */
+
+#include "paddle/fluid/operators/clip_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ClipNPUKernel : 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");
+    out->mutable_data<T>(ctx.GetPlace());
+
+    auto min_tensor = ctx.HasInput("Min") ? ctx.Input<Tensor>("Min") : nullptr;
+    auto max_tensor = ctx.HasInput("Max") ? ctx.Input<Tensor>("Max") : nullptr;
+
+    Tensor min_tensor_temp(x->type());
+    Tensor max_tensor_temp(x->type());
+    if (min_tensor == nullptr) {
+      auto min_value = static_cast<T>(ctx.Attr<float>("min"));
+      min_tensor_temp.mutable_data<T>({1}, ctx.GetPlace());
+      FillNpuTensorWithConstant<T>(&min_tensor_temp, min_value);
+      min_tensor = &min_tensor_temp;
+    }
+
+    if (max_tensor == nullptr) {
+      auto max_value = static_cast<T>(ctx.Attr<float>("max"));
+      max_tensor_temp.mutable_data<T>({1}, ctx.GetPlace());
+      FillNpuTensorWithConstant<T>(&max_tensor_temp, max_value);
+      max_tensor = &max_tensor_temp;
+    }
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    const auto& runner =
+        NpuOpRunner("ClipByValue", {*x, *min_tensor, *max_tensor}, {*out}, {});
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ClipGradNPUKernel : 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());
+
+    auto* min_tensor = ctx.HasInput("Min") ? ctx.Input<Tensor>("Min") : nullptr;
+    auto* max_tensor = ctx.HasInput("Max") ? ctx.Input<Tensor>("Max") : nullptr;
+
+    auto min_val = ctx.Attr<float>("min");
+    if (min_tensor) {
+      Tensor min_data;
+      framework::TensorCopy(
+          *min_tensor, platform::CPUPlace(),
+          ctx.template device_context<platform::DeviceContext>(), &min_data);
+      ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
+      min_val = static_cast<float>(min_data.data<T>()[0]);
+    }
+
+    auto max_val = ctx.Attr<float>("max");
+    if (max_tensor) {
+      Tensor max_data;
+      framework::TensorCopy(
+          *max_tensor, platform::CPUPlace(),
+          ctx.template device_context<platform::DeviceContext>(), &max_data);
+      ctx.template device_context<paddle::platform::NPUDeviceContext>().Wait();
+      max_val = static_cast<float>(max_data.data<T>()[0]);
+    }
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    const auto& runner =
+        NpuOpRunner("HardtanhGrad", {*x, *dout}, {*dx},
+                    {{"min_val", min_val}, {"max_val", max_val}});
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_NPU_KERNEL(
+    clip, ops::ClipNPUKernel<plat::NPUDeviceContext, float>,
+    ops::ClipNPUKernel<plat::NPUDeviceContext, plat::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    clip_grad, ops::ClipGradNPUKernel<plat::NPUDeviceContext, float>,
+    ops::ClipGradNPUKernel<plat::NPUDeviceContext, plat::float16>);

python/paddle/fluid/tests/unittests/npu/test_clip_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 unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+import sys
+sys.path.append("..")
+from op_test import OpTest
+
+
+class TestClipOp(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def setUp(self):
+        self.set_npu()
+        self.max_relative_error = 0.006
+
+        self.inputs = {}
+        self.initTestCase()
+
+        self.op_type = "clip"
+        self.attrs = {}
+        self.attrs['min'] = self.min
+        self.attrs['max'] = self.max
+        if 'Min' in self.inputs:
+            min_v = self.inputs['Min']
+        else:
+            min_v = self.attrs['min']
+
+        if 'Max' in self.inputs:
+            max_v = self.inputs['Max']
+        else:
+            max_v = self.attrs['max']
+
+        input = np.random.random(self.shape).astype("float32")
+        input[np.abs(input - min_v) < self.max_relative_error] = 0.5
+        input[np.abs(input - max_v) < self.max_relative_error] = 0.5
+        self.inputs['X'] = input
+        self.outputs = {'Out': np.clip(self.inputs['X'], min_v, max_v)}
+
+    def test_check_output(self):
+        paddle.enable_static()
+        self.check_output_with_place(self.place)
+        paddle.disable_static()
+
+    def test_check_grad_normal(self):
+        paddle.enable_static()
+        self.check_grad_with_place(self.place, ['X'], 'Out')
+        paddle.disable_static()
+
+    def initTestCase(self):
+        self.shape = (4, 10, 10)
+        self.max = 0.8
+        self.min = 0.3
+        self.inputs['Max'] = np.array([0.8]).astype('float32')
+        self.inputs['Min'] = np.array([0.1]).astype('float32')
+
+
+class TestCase1(TestClipOp):
+    def initTestCase(self):
+        self.shape = (8, 16, 8)
+        self.max = 0.7
+        self.min = 0.0
+
+
+class TestCase2(TestClipOp):
+    def initTestCase(self):
+        self.shape = (8, 16)
+        self.max = 1.0
+        self.min = 0.0
+
+
+class TestCase3(TestClipOp):
+    def initTestCase(self):
+        self.shape = (4, 8, 16)
+        self.max = 0.7
+        self.min = 0.2
+
+
+class TestCase4(TestClipOp):
+    def initTestCase(self):
+        self.shape = (4, 8, 8)
+        self.max = 0.7
+        self.min = 0.2
+        self.inputs['Max'] = np.array([0.8]).astype('float32')
+        self.inputs['Min'] = np.array([0.3]).astype('float32')
+
+
+class TestCase5(TestClipOp):
+    def initTestCase(self):
+        self.shape = (4, 8, 16)
+        self.max = 0.5
+        self.min = 0.5
+
+
+class TestClipOpError(unittest.TestCase):
+    def test_errors(self):
+        paddle.enable_static()
+        with program_guard(Program(), Program()):
+            input_data = np.random.random((2, 4)).astype("float32")
+
+            def test_Variable():
+                fluid.layers.clip(x=input_data, min=-1.0, max=1.0)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_dtype():
+                x2 = fluid.layers.data(name='x2', shape=[1], dtype='int32')
+                fluid.layers.clip(x=x2, min=-1.0, max=1.0)
+
+            self.assertRaises(TypeError, test_dtype)
+        paddle.disable_static()
+
+
+class TestClipAPI(unittest.TestCase):
+    def _executed_api(self, x, min=None, max=None):
+        return paddle.clip(x, min, max)
+
+    def test_clip(self):
+        paddle.enable_static()
+        data_shape = [1, 9, 9, 4]
+        data = np.random.random(data_shape).astype('float32')
+        images = fluid.data(name='image', shape=data_shape, dtype='float32')
+        min = fluid.data(name='min', shape=[1], dtype='float32')
+        max = fluid.data(name='max', shape=[1], dtype='float32')
+
+        place = fluid.NPUPlace(0) if fluid.core.is_compiled_with_npu(
+        ) else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        out_1 = self._executed_api(images, min=min, max=max)
+        out_2 = self._executed_api(images, min=0.2, max=0.9)
+        out_3 = self._executed_api(images, min=0.3)
+        out_4 = self._executed_api(images, max=0.7)
+        out_5 = self._executed_api(images, min=min)
+        out_6 = self._executed_api(images, max=max)
+        out_7 = self._executed_api(images, max=-1.)
+        out_8 = self._executed_api(images)
+
+        res1, res2, res3, res4, res5, res6, res7, res8 = exe.run(
+            fluid.default_main_program(),
+            feed={
+                "image": data,
+                "min": np.array([0.2]).astype('float32'),
+                "max": np.array([0.8]).astype('float32')
+            },
+            fetch_list=[
+                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
+            ])
+
+        self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
+        self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
+        self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
+        self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
+        self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
+        self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
+        self.assertTrue(np.allclose(res7, data.clip(max=-1)))
+        self.assertTrue(np.allclose(res8, data))
+        paddle.disable_static()
+
+    def test_clip_dygraph(self):
+        paddle.disable_static()
+        place = fluid.NPUPlace(0) if fluid.core.is_compiled_with_npu(
+        ) else fluid.CPUPlace()
+        paddle.disable_static(place)
+        data_shape = [1, 9, 9, 4]
+        data = np.random.random(data_shape).astype('float32')
+        images = paddle.to_tensor(data, dtype='float32')
+        v_min = paddle.to_tensor(np.array([0.2], dtype=np.float32))
+        v_max = paddle.to_tensor(np.array([0.8], dtype=np.float32))
+
+        out_1 = self._executed_api(images, min=0.2, max=0.8)
+        images = paddle.to_tensor(data, dtype='float32')
+        out_2 = self._executed_api(images, min=0.2, max=0.9)
+        images = paddle.to_tensor(data, dtype='float32')
+        out_3 = self._executed_api(images, min=v_min, max=v_max)
+
+        self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8)))
+        self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9)))
+        self.assertTrue(np.allclose(out_3.numpy(), data.clip(0.2, 0.8)))
+
+    def test_errors(self):
+        paddle.enable_static()
+        x1 = fluid.data(name='x1', shape=[1], dtype="int16")
+        x2 = fluid.data(name='x2', shape=[1], dtype="int8")
+        self.assertRaises(TypeError, paddle.clip, x=x1, min=0.2, max=0.8)
+        self.assertRaises(TypeError, paddle.clip, x=x2, min=0.2, max=0.8)
+        paddle.disable_static()
+
+
+class TestInplaceClipAPI(TestClipAPI):
+    def _executed_api(self, x, min=None, max=None):
+        return x.clip_(min, max)
+
+
+if __name__ == '__main__':
+    unittest.main()