add mlu stack kernel (#43423)

paddle/fluid/operators/stack_op_mlu.cc

+/* Copyright (c) 2022 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/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class StackMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto x = ctx.MultiInput<Tensor>("X");
+    auto* y = ctx.Output<Tensor>("Y");
+    int axis = ctx.Attr<int>("axis");
+    if (axis < 0) axis += (x[0]->dims().size() + 1);
+    int num = static_cast<int>(x.size());
+
+    PADDLE_ENFORCE_GT(
+        num, 0,
+        platform::errors::InvalidArgument("number of input Tensor <= 0"));
+
+    std::vector<MLUCnnlTensorDesc> x_descs;
+    std::vector<cnnlTensorDescriptor_t> x_raw_descs;
+    std::vector<const void*> x_ptrs;
+    for (int i = 0; i < num; i++) {
+      if (x[i]->dims().size() != 0) {
+        std::vector<int64_t> in_dims = phi::vectorize(x[i]->dims());
+        in_dims.insert(in_dims.begin() + axis, 1);
+        x_descs.emplace_back(MLUCnnlTensorDesc(in_dims.size(), in_dims.data(),
+                                               ToCnnlDataType<T>()));
+      } else {
+        int input_dims = 1;
+        x_descs.emplace_back(
+            MLUCnnlTensorDesc(1, &input_dims, ToCnnlDataType<T>()));
+      }
+      x_raw_descs.push_back(x_descs.back().get());
+      x_ptrs.push_back(GetBasePtr(x[i]));
+    }
+    y->mutable_data<T>(ctx.GetPlace());
+
+    MLUCnnlTensorDesc y_desc(*y);
+    MLUCnnl::Concat(ctx, num, axis, x_raw_descs.data(), x_ptrs.data(),
+                    y_desc.get(), GetBasePtr(y));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_MLU_KERNEL(
+    stack, paddle::operators::StackMLUKernel<int64_t>,
+    paddle::operators::StackMLUKernel<int>,
+    paddle::operators::StackMLUKernel<float>,
+    paddle::operators::StackMLUKernel<paddle::platform::float16>);

python/paddle/fluid/tests/unittests/mlu/test_stack_op_mlu.py

+# Copyright (c) 2022 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.fluid as fluid
+import paddle
+
+paddle.enable_static()
+
+
+class TestStackOpBase(OpTest):
+
+    def initDefaultParameters(self):
+        self.num_inputs = 4
+        self.input_dim = (5, 6, 7)
+        self.axis = 0
+
+    def initParameters(self):
+        pass
+
+    def get_x_names(self):
+        x_names = []
+        for i in range(self.num_inputs):
+            x_names.append('x{}'.format(i))
+        return x_names
+
+    def setUp(self):
+        self.initDefaultParameters()
+        self.initParameters()
+        self.op_type = 'stack'
+        self.set_mlu()
+        self.init_dtype()
+        self.x = []
+        for i in range(self.num_inputs):
+            self.x.append(
+                np.random.random(size=self.input_dim).astype(self.dtype))
+
+        tmp = []
+        x_names = self.get_x_names()
+        for i in range(self.num_inputs):
+            tmp.append((x_names[i], self.x[i]))
+
+        self.inputs = {'X': tmp}
+        self.outputs = {'Y': np.stack(self.x, axis=self.axis)}
+        self.attrs = {'axis': self.axis}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.MLUPlace(0)
+        self.__class__.no_need_check_grad = True
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestStackOp1(TestStackOpBase):
+
+    def initParameters(self):
+        self.num_inputs = 16
+
+
+class TestStackOp2(TestStackOpBase):
+
+    def initParameters(self):
+        self.num_inputs = 20
+
+
+class TestStackOp3(TestStackOpBase):
+
+    def initParameters(self):
+        self.axis = -1
+
+
+class TestStackOp4(TestStackOpBase):
+
+    def initParameters(self):
+        self.axis = -4
+
+
+class TestStackOp5(TestStackOpBase):
+
+    def initParameters(self):
+        self.axis = 1
+
+
+class TestStackOp6(TestStackOpBase):
+
+    def initParameters(self):
+        self.axis = 3
+
+
+class TestStackOpINT32(TestStackOpBase):
+
+    def init_dtype(self):
+        self.dtype = np.int32
+
+
+class TestStackOpINT64(TestStackOpBase):
+
+    def init_dtype(self):
+        self.dtype = np.int64
+
+
+class TestStackOpHalf(TestStackOpBase):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+
+class API_test(unittest.TestCase):
+
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data('data1', shape=[1, 2], dtype='float32')
+            data2 = fluid.layers.data('data2', shape=[1, 2], dtype='float32')
+            data3 = fluid.layers.data('data3', shape=[1, 2], dtype='float32')
+            result_stack = paddle.stack([data1, data2, data3], axis=0)
+            place = paddle.MLUPlace(0)
+            exe = fluid.Executor(place)
+            input1 = np.random.random([1, 2]).astype('float32')
+            input2 = np.random.random([1, 2]).astype('float32')
+            input3 = np.random.random([1, 2]).astype('float32')
+            result, = exe.run(feed={
+                "data1": input1,
+                "data2": input2,
+                "data3": input3
+            },
+                              fetch_list=[result_stack])
+            expected_result = np.stack([input1, input2, input3], axis=0)
+            self.assertTrue(np.allclose(expected_result, result))
+
+    def test_single_tensor_error(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            x = paddle.rand([2, 3])
+            self.assertRaises(TypeError, paddle.stack, x)
+
+
+class API_DygraphTest(unittest.TestCase):
+
+    def test_out(self):
+        data1 = np.array([[1.0, 2.0]]).astype("float32")
+        data2 = np.array([[3.0, 4.0]]).astype("float32")
+        data3 = np.array([[5.0, 6.0]]).astype("float32")
+        with fluid.dygraph.guard(place=paddle.MLUPlace(0)):
+            x1 = fluid.dygraph.to_variable(data1)
+            x2 = fluid.dygraph.to_variable(data2)
+            x3 = fluid.dygraph.to_variable(data3)
+            result = paddle.stack([x1, x2, x3])
+            result_np = result.numpy()
+        expected_result = np.stack([data1, data2, data3])
+        self.assertTrue(np.allclose(expected_result, result_np))
+
+        with fluid.dygraph.guard(place=paddle.MLUPlace(0)):
+            y1 = fluid.dygraph.to_variable(data1)
+            result = paddle.stack([y1], axis=0)
+            result_np_2 = result.numpy()
+        expected_result_2 = np.stack([data1], axis=0)
+        self.assertTrue(np.allclose(expected_result_2, result_np_2))
+
+    def test_single_tensor_error(self):
+        with fluid.dygraph.guard(place=paddle.MLUPlace(0)):
+            x = paddle.to_tensor([1, 2, 3])
+            self.assertRaises(Exception, paddle.stack, x)
+
+
+if __name__ == '__main__':
+    unittest.main()