add mlu expand_as_v2 kernel (#43393)

paddle/fluid/operators/expand_as_v2_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/framework/tensor.h"
+#include "paddle/fluid/operators/expand_as_v2_op.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class ExpandAsV2MLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto rank = context.Input<Tensor>("X")->dims().size();
+    auto target_shape = context.Attr<std::vector<int>>("target_shape");
+    auto target_rank = target_shape.size();
+    PADDLE_ENFORCE_GE(target_rank, rank,
+                      platform::errors::InvalidArgument(
+                          "The rank (%d) of the input 'target_tensor' for "
+                          "expand_as_v2 op must be greater than or equal to "
+                          "the rank (%d) of the input 'x'.",
+                          target_rank, rank));
+    PADDLE_ENFORCE_GE(
+        rank, 1,
+        platform::errors::InvalidArgument("The rank (%d) of the input 'x' for "
+                                          "expand_as_v2 op must be positive.",
+                                          rank));
+    PADDLE_ENFORCE_LE(target_rank, MAX_RANK_SUPPORTED,
+                      platform::errors::InvalidArgument(
+                          "The rank (%d) of the input 'target_tensor' for "
+                          "expand_as_v2 op must be less than or equal to %d.",
+                          target_rank, MAX_RANK_SUPPORTED));
+    ExpandAs(context);
+  }
+
+ protected:
+  void ExpandAs(const framework::ExecutionContext& context) const {
+    auto* in0 = context.Input<Tensor>("X");
+    auto in_dims = in0->dims();
+    auto target_shape = context.Attr<std::vector<int>>("target_shape");
+    auto vec_in_dims = phi::vectorize<int>(in_dims);
+    auto diff = target_shape.size() - vec_in_dims.size();
+    vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
+
+    for (size_t i = 0; i < vec_in_dims.size(); ++i) {
+      PADDLE_ENFORCE_NE(target_shape[i], 0,
+                        platform::errors::InvalidArgument(
+                            "The value of target shape cannot be zero."));
+      if (vec_in_dims[i] != 1) {
+        PADDLE_ENFORCE_EQ(
+            vec_in_dims[i], target_shape[i],
+            platform::errors::InvalidArgument(
+                "The value (%d) of the non-singleton dimension does not match"
+                " the corresponding value (%d) in "
+                "target tensor for expand_as_v2 op.",
+                vec_in_dims[i], target_shape[i]));
+      }
+    }
+    auto* out0 = context.Output<Tensor>("Out");
+
+    framework::DDim out_dims = phi::make_ddim(target_shape);
+
+    out0->Resize(out_dims);
+    out0->mutable_data<T>(context.GetPlace());
+
+    MLUCnnlTensorDesc x_desc(*in0);
+    MLUCnnlTensorDesc out_desc(*out0);
+
+    MLUCnnl::BroadcastTo(context, x_desc.get(), GetBasePtr(in0), out_desc.get(),
+                         GetBasePtr(out0));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_MLU_KERNEL(expand_as_v2, ops::ExpandAsV2MLUKernel<float>,
+                       ops::ExpandAsV2MLUKernel<int>,
+                       ops::ExpandAsV2MLUKernel<int64_t>,
+                       ops::ExpandAsV2MLUKernel<int8_t>,
+                       ops::ExpandAsV2MLUKernel<uint8_t>,
+                       ops::ExpandAsV2MLUKernel<bool>,
+                       ops::ExpandAsV2MLUKernel<paddle::platform::float16>);

python/paddle/fluid/tests/unittests/mlu/test_expand_as_v2_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 unittest
+import sys
+
+sys.path.append('..')
+import numpy as np
+from op_test import OpTest
+import paddle.fluid as fluid
+import paddle
+
+paddle.enable_static()
+
+
+def test_class1(op_type, typename):
+
+    class TestExpandAsBasic(OpTest):
+
+        def setUp(self):
+            self.set_mlu()
+            self.op_type = "expand_as_v2"
+            self.python_api = paddle.expand_as
+            x = np.random.rand(100).astype(typename)
+            target_tensor = np.random.rand(2, 100).astype(typename)
+            self.inputs = {'X': x}
+            self.attrs = {'target_shape': target_tensor.shape}
+            bcast_dims = [2, 1]
+            output = np.tile(self.inputs['X'], bcast_dims)
+            self.outputs = {'Out': output}
+
+        def set_mlu(self):
+            self.__class__.use_mlu = True
+            self.place = paddle.device.MLUPlace(0)
+            self.__class__.no_need_check_grad = True
+
+        def test_check_output(self):
+            self.check_output_with_place(self.place)
+
+    cls_name = str(op_type) + "_" + str(typename) + "_1"
+    TestExpandAsBasic.__name__ = cls_name
+    globals()[cls_name] = TestExpandAsBasic
+
+
+def test_class2(op_type, typename):
+
+    class TestExpandAsOpRank2(OpTest):
+
+        def setUp(self):
+            self.set_mlu()
+            self.op_type = "expand_as_v2"
+            self.python_api = paddle.expand_as
+            x = np.random.rand(10, 12).astype(typename)
+            target_tensor = np.random.rand(10, 12).astype(typename)
+            self.inputs = {'X': x}
+            self.attrs = {'target_shape': target_tensor.shape}
+            bcast_dims = [1, 1]
+            output = np.tile(self.inputs['X'], bcast_dims)
+            self.outputs = {'Out': output}
+
+        def set_mlu(self):
+            self.__class__.use_mlu = True
+            self.place = paddle.device.MLUPlace(0)
+            self.__class__.no_need_check_grad = True
+
+        def test_check_output(self):
+            self.check_output_with_place(self.place)
+
+    cls_name = str(op_type) + "_" + str(typename) + "_2"
+    TestExpandAsOpRank2.__name__ = cls_name
+    globals()[cls_name] = TestExpandAsOpRank2
+
+
+def test_class3(op_type, typename):
+
+    class TestExpandAsOpRank3(OpTest):
+
+        def setUp(self):
+            self.set_mlu()
+            self.op_type = "expand_as_v2"
+            self.python_api = paddle.expand_as
+            x = np.random.rand(2, 3, 20).astype(typename)
+            target_tensor = np.random.rand(2, 3, 20).astype(typename)
+            self.inputs = {'X': x}
+            self.attrs = {'target_shape': target_tensor.shape}
+            bcast_dims = [1, 1, 1]
+            output = np.tile(self.inputs['X'], bcast_dims)
+            self.outputs = {'Out': output}
+
+        def set_mlu(self):
+            self.__class__.use_mlu = True
+            self.place = paddle.device.MLUPlace(0)
+            self.__class__.no_need_check_grad = True
+
+        def test_check_output(self):
+            self.check_output_with_place(self.place)
+
+    cls_name = str(op_type) + "_" + str(typename) + "_3"
+    TestExpandAsOpRank3.__name__ = cls_name
+    globals()[cls_name] = TestExpandAsOpRank3
+
+
+def test_class4(op_type, typename):
+
+    class TestExpandAsOpRank4(OpTest):
+
+        def setUp(self):
+            self.set_mlu()
+            self.op_type = "expand_as_v2"
+            self.python_api = paddle.expand_as
+            x = np.random.rand(1, 1, 7, 16).astype(typename)
+            target_tensor = np.random.rand(4, 6, 7, 16).astype(typename)
+            self.inputs = {'X': x}
+            self.attrs = {'target_shape': target_tensor.shape}
+            bcast_dims = [4, 6, 1, 1]
+            output = np.tile(self.inputs['X'], bcast_dims)
+            self.outputs = {'Out': output}
+
+        def set_mlu(self):
+            self.__class__.use_mlu = True
+            self.place = paddle.device.MLUPlace(0)
+            self.__class__.no_need_check_grad = True
+
+        def test_check_output(self):
+            self.check_output_with_place(self.place)
+
+    cls_name = str(op_type) + "_" + str(typename) + "_4"
+    TestExpandAsOpRank4.__name__ = cls_name
+    globals()[cls_name] = TestExpandAsOpRank4
+
+
+# Test python API
+class TestExpandAsV2API(unittest.TestCase):
+
+    def test_api(self):
+        input1 = np.random.random([12, 14]).astype("float32")
+        input2 = np.random.random([2, 12, 14]).astype("float32")
+        x = fluid.layers.data(name='x',
+                              shape=[12, 14],
+                              append_batch_size=False,
+                              dtype="float32")
+
+        y = fluid.layers.data(name='target_tensor',
+                              shape=[2, 12, 14],
+                              append_batch_size=False,
+                              dtype="float32")
+
+        out_1 = paddle.expand_as(x, y=y)
+
+        exe = fluid.Executor(place=fluid.MLUPlace(0))
+        res_1 = exe.run(fluid.default_main_program(),
+                        feed={
+                            "x": input1,
+                            "target_tensor": input2
+                        },
+                        fetch_list=[out_1])
+        assert np.array_equal(res_1[0], np.tile(input1, (2, 1, 1)))
+
+
+for _typename in {
+        'float16', 'float32', 'int64', 'int32', 'int8', 'uint8', 'bool'
+}:
+    test_class1('expand_as_v2', _typename)
+    test_class2('expand_as_v2', _typename)
+    test_class3('expand_as_v2', _typename)
+    test_class4('expand_as_v2', _typename)
+
+if __name__ == "__main__":
+    unittest.main()