[MLU] add size kernel for mlu (#43450)

paddle/fluid/operators/size_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/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class SizeMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<framework::Tensor>("Input");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    out->mutable_data<int64_t>(ctx.GetPlace());
+
+    int64_t size = x->numel();
+    FillMLUTensorWithHostValue<int64_t>(ctx, size, out);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_MLU_KERNEL(size, ops::SizeMLUKernel<int>,
+                       ops::SizeMLUKernel<int64_t>,
+                       ops::SizeMLUKernel<paddle::platform::float16>,
+                       ops::SizeMLUKernel<float>, ops::SizeMLUKernel<double>,
+                       ops::SizeMLUKernel<bool>);

python/paddle/fluid/tests/unittests/mlu/test_size_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.
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import sys
+
+sys.path.append('..')
+from op_test import OpTest
+
+paddle.enable_static()
+
+
+class TestSizeOp(OpTest):
+
+    def setUp(self):
+        self.op_type = "size"
+        self.shape = []
+        self.config()
+        input = np.zeros(self.shape, dtype='bool')
+        self.inputs = {'Input': input}
+        self.outputs = {'Out': np.array([np.size(input)], dtype='int64')}
+
+    def config(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output_with_place(paddle.device.MLUPlace(0))
+
+
+class TestRank1Tensor(TestSizeOp):
+
+    def config(self):
+        self.shape = [2]
+
+
+class TestRank2Tensor(TestSizeOp):
+
+    def config(self):
+        self.shape = [2, 3]
+
+
+class TestRank3Tensor(TestSizeOp):
+
+    def config(self):
+        self.shape = [2, 3, 100]
+
+
+class TestLargeTensor(TestSizeOp):
+
+    def config(self):
+        self.shape = [2**10]
+
+
+class TestSizeAPI(unittest.TestCase):
+
+    def test_size_static(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+            shape1 = [2, 1, 4, 5]
+            shape2 = [1, 4, 5]
+            x_1 = paddle.fluid.data(shape=shape1, dtype='int32', name='x_1')
+            x_2 = paddle.fluid.data(shape=shape2, dtype='int32', name='x_2')
+            input_1 = np.random.random(shape1).astype("int32")
+            input_2 = np.random.random(shape2).astype("int32")
+            out_1 = paddle.fluid.layers.size(x_1)
+            out_2 = paddle.fluid.layers.size(x_2)
+            exe = paddle.static.Executor(place=paddle.MLUPlace(0))
+            res_1, res_2 = exe.run(feed={
+                "x_1": input_1,
+                "x_2": input_2,
+            },
+                                   fetch_list=[out_1, out_2])
+            assert (np.array_equal(res_1,
+                                   np.array([np.size(input_1)
+                                             ]).astype("int64")))
+            assert (np.array_equal(res_2,
+                                   np.array([np.size(input_2)
+                                             ]).astype("int64")))
+
+    def test_size_imperative(self):
+        paddle.disable_static(paddle.MLUPlace(0))
+        input_1 = np.random.random([2, 1, 4, 5]).astype("int32")
+        input_2 = np.random.random([1, 4, 5]).astype("int32")
+        x_1 = paddle.to_tensor(input_1)
+        x_2 = paddle.to_tensor(input_2)
+        out_1 = paddle.fluid.layers.size(x_1)
+        out_2 = paddle.fluid.layers.size(x_2)
+        assert (np.array_equal(out_1.numpy().item(0), np.size(input_1)))
+        assert (np.array_equal(out_2.numpy().item(0), np.size(input_2)))
+        paddle.enable_static()
+
+    def test_error(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+
+            def test_x_type():
+                shape = [1, 4, 5]
+                input_1 = np.random.random(shape).astype("int32")
+                out_1 = paddle.fluid.layers.size(input_1)
+
+            self.assertRaises(TypeError, test_x_type)
+
+
+if __name__ == '__main__':
+    unittest.main()