[NPU] Support npu kernel scatter op (#31624)

* Support npu kernel scatter op

* Add more test

paddle/fluid/operators/scatter_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. */
+
+#ifdef PADDLE_WITH_ASCEND_CL
+#include <memory>
+#include <string>
+
+#include "paddle/fluid/operators/scatter_op.h"
+#include "paddle/fluid/operators/kron_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class ScatterNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* index = ctx.Input<Tensor>("Ids");
+    auto* updates = ctx.Input<Tensor>("Updates");
+    bool overwrite = ctx.Attr<bool>("overwrite");
+
+    auto* out = ctx.Output<Tensor>("Out");
+    
+    auto place = ctx.GetPlace();
+    out->mutable_data<T>(place);
+
+    framework::Tensor tmp_tensor(index->type());
+    const auto index_dims = index->dims();
+    if (index_dims.size() == 1) {
+      tmp_tensor.ShareDataWith(*index);
+      std::vector<int64_t> new_dim = {index_dims[0], 1};
+      tmp_tensor.Resize(framework::make_ddim(new_dim));
+      index = &tmp_tensor;
+    }
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    if (overwrite){
+        auto runner_update = NpuOpRunner("TensorScatterUpdate", {*x, *index, *updates}, {*out}, {});
+        runner_update.Run(stream);
+    }
+    else{
+        auto runner_add = NpuOpRunner("TensorScatterAdd", {*x, *index, *updates}, {*out}, {});
+        runner_add.Run(stream);
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    scatter,
+    ops::ScatterNPUKernel<paddle::platform::NPUDeviceContext, float>,
+    ops::ScatterNPUKernel<paddle::platform::NPUDeviceContext,
+    paddle::platform::float16>);
+#endif

python/paddle/fluid/tests/unittests/npu/test_scatter_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 numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_npu(),
+                 "core is not compiled with NPU")
+class TestCast1(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "scatter"
+        self.place = paddle.NPUPlace(0)
+
+        ref_np = np.ones((3, 2)).astype("float32")
+        index_np = np.array([1]).astype("int32")
+        updates_np = np.random.random((1, 2)).astype("float32")
+
+        output_np = np.copy(ref_np)
+        output_np[index_np] = updates_np
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
+        self.outputs = {'Out': output_np}
+        self.attrs = {'overwrite': True}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+
+class TestCast2(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "scatter"
+        self.place = paddle.NPUPlace(0)
+       
+        ref_np = np.ones((3, 2)).astype("int32")
+        index_np = np.array([1]).astype("int32")
+        updates_np = np.zeros((1, 2)).astype("int32")
+
+        output_np = np.copy(ref_np)
+        output_np[index_np] = updates_np
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
+        self.outputs = {'Out': output_np}
+        self.attrs = {'overwrite': True}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+class TestCast3(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "scatter"
+        self.place = paddle.NPUPlace(0)
+       
+        ref_np = np.ones((3, 2)).astype("float32")
+        index_np = np.array([1]).astype("int32")
+        updates_np = np.random.random((1, 2)).astype("float32")
+
+        output_np = np.copy(ref_np)
+        output_np[index_np] += updates_np
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
+        self.outputs = {'Out': output_np}
+        self.attrs = {'overwrite': False}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+
+class TestCast4(OpTest):
+    def setUp(self):
+        self.set_npu()
+        self.op_type = "scatter"
+        self.place = paddle.NPUPlace(0)
+
+        ref_np = np.ones((3, 2)).astype("float32")
+        index_np = np.array([1, 2]).astype("int32")
+        updates_np = np.random.random((2, 2)).astype("float32")
+
+        output_np = np.copy(ref_np)
+        output_np[1] = updates_np[0]
+        output_np[2] = updates_np[1]
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
+        self.outputs = {'Out': output_np}
+        self.attrs = {'overwrite': True}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_dygraph=False)
+
+if __name__ == '__main__':
+    unittest.main()