[MLU]add mlu kernel for scatter op (#43292)

paddle/fluid/operators/scatter_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 ScatterMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* x = ctx.Input<Tensor>("X");
+    auto* indices = 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);
+    MLUCnnlTensorDesc x_desc(*x);
+    MLUCnnlTensorDesc indices_desc(*indices);
+    MLUCnnlTensorDesc updates_desc(*updates);
+    MLUCnnlTensorDesc out_desc(*out);
+    cnnlScatterRefMode_t mode;
+    if (overwrite) {
+      mode = CNNL_SCATTERREF_UPDATE;
+      MLUCnnl::ScatterFunctor(ctx, x_desc.get(), GetBasePtr(x),
+                              updates_desc.get(), GetBasePtr(updates),
+                              indices_desc.get(), GetBasePtr(indices), mode);
+    } else {
+      Tensor tensor_zeros(updates->type());
+      tensor_zeros.mutable_data<T>(updates->dims(), ctx.GetPlace());
+      MLUCnnlTensorDesc tensor_zeros_desc(tensor_zeros);
+      float value = 0.0;
+      auto value_t = static_cast<T>(value);
+      MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &value_t,
+                    tensor_zeros_desc.get(), GetBasePtr(&tensor_zeros));
+      mode = CNNL_SCATTERREF_UPDATE;
+      MLUCnnl::ScatterFunctor(ctx, x_desc.get(), GetBasePtr(x),
+                              tensor_zeros_desc.get(),
+                              GetBasePtr(&tensor_zeros), indices_desc.get(),
+                              GetBasePtr(indices), mode);
+      mode = CNNL_SCATTERREF_ADD;
+      MLUCnnl::ScatterFunctor(ctx, x_desc.get(), GetBasePtr(x),
+                              updates_desc.get(), GetBasePtr(updates),
+                              indices_desc.get(), GetBasePtr(indices), mode);
+    }
+    paddle::framework::TensorCopy(*x, place, out);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_MLU_KERNEL(scatter, ops::ScatterMLUKernel<float>,
+                       ops::ScatterMLUKernel<paddle::platform::float16>);

python/paddle/fluid/tests/unittests/mlu/test_scatter_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 sys
+
+sys.path.append("..")
+import unittest
+import numpy as np
+import os
+import paddle
+import paddle.fluid as fluid
+from op_test import OpTest
+import paddle.fluid.core as core
+from paddle.fluid.dygraph.base import switch_to_static_graph
+
+
+class TestScatterOp(OpTest):
+
+    def setUp(self):
+        self.op_type = "scatter"
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.python_api = paddle.scatter
+        ref_np = np.ones((3, 50)).astype("float32")
+        index_np = np.array([1, 2]).astype("int32")
+        updates_np = np.random.random((2, 50)).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}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_eager=False)
+
+    def test_check_grad(self):
+        self.check_grad(["X", "Updates"], "Out", check_eager=False)
+
+
+class TestScatterOp0(OpTest):
+
+    def setUp(self):
+        self.op_type = "scatter"
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.python_api = paddle.scatter
+        ref_np = np.ones((3, 3)).astype("float32")
+        index_np = np.array([1, 2]).astype("int32")
+        updates_np = np.random.random((2, 3)).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.attrs = {'overwrite': True}
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_eager=False)
+
+    def test_check_grad(self):
+        self.check_grad(["X", "Updates"], "Out", check_eager=False)
+
+
+class TestScatterOp1(OpTest):
+
+    def setUp(self):
+        self.op_type = "scatter"
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.python_api = paddle.scatter
+        ref_np = np.ones((3, 3)).astype("float32")
+        zeros_np = np.zeros([2, 3]).astype('float32')
+        index_np = np.array([1, 1]).astype("int32")
+        updates_np = np.random.random((2, 3)).astype("float32")
+        output_np = np.copy(ref_np)
+        output_np[index_np] = zeros_np
+        for i in range(0, len(index_np)):
+            output_np[index_np[i]] += updates_np[i]
+        self.attrs = {'overwrite': False}
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_eager=False)
+
+    def test_check_grad(self):
+        self.check_grad(["X", "Updates"], "Out", check_eager=False)
+
+
+class TestScatterOp2(OpTest):
+
+    def setUp(self):
+        self.op_type = "scatter"
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.python_api = paddle.scatter
+        ref_np = np.ones((3, 3)).astype("float32")
+        index_np = np.array([1, 2]).astype("int64")
+        updates_np = np.random.random((2, 3)).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}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_eager=False)
+
+    def test_check_grad(self):
+        self.check_grad(['X', 'Updates'], 'Out', check_eager=False)
+
+
+class TestScatterAPI(unittest.TestCase):
+
+    def setUp(self):
+        self.places = [paddle.device.MLUPlace(0)]
+        self.__class__.use_mlu = True
+        self.executed_api()
+
+    def executed_api(self):
+        self.scatter = paddle.scatter
+
+    def check_static_result(self, place):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            input = fluid.data(name="input", shape=[3, 2], dtype="float32")
+            index = fluid.data(name="index", shape=[4], dtype="int64")
+            updates = fluid.data(name="updates", shape=[4, 2], dtype="float32")
+            result = self.scatter(input, index, updates, False)
+
+            input_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float32)
+            index_data = np.array([2, 1, 0, 1]).astype(np.int64)
+            updates_data = np.array([[1, 1], [2, 2], [3, 3],
+                                     [4, 4]]).astype(np.float32)
+
+            exe = fluid.Executor(place)
+            fetches = exe.run(fluid.default_main_program(),
+                              feed={
+                                  "input": input_data,
+                                  "index": index_data,
+                                  "updates": updates_data
+                              },
+                              fetch_list=[result])
+            self.assertEqual((fetches[0] == \
+                              np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
+
+    def test_static(self):
+        for place in self.places:
+            self.check_static_result(place=place)
+
+    def test_dygraph(self):
+        for place in self.places:
+            with fluid.dygraph.guard(place):
+                x_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float32)
+                index_data = np.array([2, 1, 0, 1]).astype(np.int64)
+                updates_data = np.array([[1, 1], [2, 2], [3, 3],
+                                         [4, 4]]).astype(np.float32)
+
+                x = fluid.dygraph.to_variable(x_data)
+                index = fluid.dygraph.to_variable(index_data)
+                updates = fluid.dygraph.to_variable(updates_data)
+
+                output1 = self.scatter(x, index, updates, overwrite=False)
+                self.assertEqual((output1.numpy() == \
+                                  np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
+
+    def test_large_data(self):
+        if os.name == "nt":
+            return
+
+        x = np.random.rand(183826, 256).astype("float32")
+        index = np.ones(8388608, dtype="int64")
+        updates = np.ones(shape=[8388608, 256], dtype="float32")
+
+        def test_dygraph():
+            with fluid.dygraph.guard():
+                mlu_out = paddle.scatter(paddle.to_tensor(x),
+                                         paddle.to_tensor(index),
+                                         paddle.to_tensor(updates))
+                return mlu_out.numpy()
+
+        @switch_to_static_graph
+        def test_static_graph():
+            with paddle.static.program_guard(paddle.static.Program(),
+                                             paddle.static.Program()):
+                x_t = paddle.static.data(name="x", dtype=x.dtype, shape=x.shape)
+                index_t = paddle.static.data(name="index",
+                                             dtype=index.dtype,
+                                             shape=index.shape)
+                updates_t = paddle.static.data(name="updates",
+                                               dtype=updates.dtype,
+                                               shape=updates.shape)
+                out_t = paddle.scatter(x_t, index_t, updates_t)
+                feed = {
+                    x_t.name: x,
+                    index_t.name: index,
+                    updates_t.name: updates
+                }
+                fetch = [out_t]
+
+                mlu_exe = paddle.static.Executor(paddle.device.MLUPlace(0))
+                mlu_value = mlu_exe.run(feed=feed, fetch_list=fetch)[0]
+                return mlu_value
+
+        self.assertTrue(np.array_equal(test_dygraph(), test_static_graph()))
+
+
+class TestScatterOpFp16(OpTest):
+
+    def setUp(self):
+        self.op_type = "scatter"
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.python_api = paddle.scatter
+        ref_np = np.ones((3, 3)).astype("float16")
+        index_np = np.array([1, 2]).astype("int32")
+        updates_np = np.random.random((2, 3)).astype("float16")
+        output_np = np.copy(ref_np)
+        output_np[index_np] = updates_np
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
+        self.attrs = {'overwrite': True}
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, check_eager=False)
+
+    def test_check_grad(self):
+        self.check_grad(["X", "Updates"], "Out", check_eager=False)
+
+
+class TestScatterInplaceAPI(TestScatterAPI):
+
+    def executed_api(self):
+        self.scatter = paddle.scatter_
+
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()