[NPU] Support npu op:(1)arg_min (2)arg_max (#34867)

* [NPU] Support npu op:(1)arg_min (2)arg_max

* Modify and add unit test cases

* Modify unit test cases

paddle/fluid/operators/arg_max_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/arg_min_max_op_base.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ArgMaxNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    int64_t axis = ctx.Attr<int64_t>("axis");
+    auto dtype = ctx.Attr<int>("dtype");
+
+    auto* out = ctx.Output<Tensor>("Out");
+    out->mutable_data<int32_t>(ctx.GetPlace());
+
+    NpuOpRunner runner;
+    runner.SetType("ArgMaxV2")
+        .AddInput(*x)
+        .AddInput(std::vector<int64_t>{axis})
+        .AddOutput(*out)
+        .AddAttr("dtype", dtype);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_NPU_KERNEL(
+    arg_max, ops::ArgMaxNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::ArgMaxNPUKernel<paddle::platform::NPUDeviceContext,
+                         paddle::platform::float16>);

paddle/fluid/operators/arg_min_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/arg_min_max_op_base.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ArgMinNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("X");
+    int64_t axis = ctx.Attr<int64_t>("axis");
+    auto dtype = ctx.Attr<int>("dtype");
+
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    out->mutable_data<int32_t>(ctx.GetPlace());
+
+    NpuOpRunner runner;
+    runner.SetType("ArgMin")
+        .AddInput(*x)
+        .AddInput(std::vector<int64_t>{axis})
+        .AddOutput(*out)
+        .AddAttr("dtype", dtype);
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_NPU_KERNEL(
+    arg_min, ops::ArgMinNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::ArgMinNPUKernel<paddle::platform::NPUDeviceContext,
+                         paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_arg_max_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 sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid.core as core
+
+paddle.enable_static()
+
+
+class BaseTestCase(OpTest):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+        np.random.seed(2021)
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        if self.op_type == "arg_min":
+            self.outputs = {'Out': np.argmin(self.x, axis=self.axis)}
+        else:
+            self.outputs = {'Out': np.argmax(self.x, axis=self.axis)}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+# test argmax, dtype: float16
+class TestArgMaxFloat16Case1(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = -1
+
+
+class TestArgMaxFloat16Case2(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case3(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 1
+
+
+class TestArgMaxFloat16Case4(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 2
+
+
+class TestArgMaxFloat16Case5(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = -1
+
+
+class TestArgMaxFloat16Case6(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case7(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = 1
+
+
+class TestArgMaxFloat16Case8(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (1, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case9(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (2, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMaxFloat16Case10(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+# test argmax, dtype: float32
+class TestArgMaxFloat32Case1(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMaxFloat32Case2(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case3(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMaxFloat32Case4(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 2
+
+
+class TestArgMaxFloat32Case5(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMaxFloat32Case6(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case7(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMaxFloat32Case8(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (1, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case9(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (2, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxFloat32Case10(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMaxAPI(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = [paddle.NPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(numpy_input, axis=self.axis)
+            paddle_output = paddle.argmax(tensor_input, axis=self.axis)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+class TestArgMaxAPI_2(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+        self.keep_dims = True
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = [paddle.NPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(
+                numpy_input, axis=self.axis).reshape(1, 4, 5)
+            paddle_output = paddle.argmax(
+                tensor_input, axis=self.axis, keepdim=self.keep_dims)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/npu/test_arg_min_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 sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid.core as core
+
+paddle.enable_static()
+
+
+class BaseTestCase(OpTest):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+        np.random.seed(2021)
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        if self.op_type == "arg_min":
+            self.outputs = {'Out': np.argmin(self.x, axis=self.axis)}
+        else:
+            self.outputs = {'Out': np.argmax(self.x, axis=self.axis)}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+# test argmin, dtype: float16
+class TestArgMinFloat16Case1(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = -1
+
+
+class TestArgMinFloat16Case2(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMinFloat16Case3(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 1
+
+
+class TestArgMinFloat16Case4(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float16'
+        self.axis = 2
+
+
+class TestArgMinFloat16Case5(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = -1
+
+
+class TestArgMinFloat16Case6(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMinFloat16Case7(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float16'
+        self.axis = 1
+
+
+class TestArgMinFloat16Case8(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (1, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMinFloat16Case9(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (2, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+class TestArgMinFloat16Case10(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, )
+        self.dtype = 'float16'
+        self.axis = 0
+
+
+# test argmin, dtype: float32
+class TestArgMinFloat32Case1(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMinFloat32Case2(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMinFloat32Case3(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMinFloat32Case4(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 2
+
+
+class TestArgMinFloat32Case5(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = -1
+
+
+class TestArgMinFloat32Case6(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMinFloat32Case7(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, 4)
+        self.dtype = 'float32'
+        self.axis = 1
+
+
+class TestArgMinFloat32Case8(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (1, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMinFloat32Case9(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (2, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMinFloat32Case10(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (3, )
+        self.dtype = 'float32'
+        self.axis = 0
+
+
+class TestArgMinAPI(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = [paddle.NPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmin(numpy_input, axis=self.axis)
+            paddle_output = paddle.argmin(tensor_input, axis=self.axis)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+class TestArgMaxAPI_2(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (3, 4, 5)
+        self.dtype = 'float32'
+        self.axis = 0
+        self.keep_dims = True
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = [paddle.NPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmin(
+                numpy_input, axis=self.axis).reshape(1, 4, 5)
+            paddle_output = paddle.argmin(
+                tensor_input, axis=self.axis, keepdim=self.keep_dims)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+if __name__ == '__main__':
+    unittest.main()