add gather_op xpu, test=kunlun (#27822)

* add gather_op xpu, test=develop, test=kunlun

* fix ut, test=develop, test=kunlun

* fix the ut,test=develop, test=kunlun

paddle/fluid/operators/gather_op_xpu.cc

@@ -0,0 +1,153 @@
+/* Copyright (c) 2020 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_XPU
+#include "paddle/fluid/operators/gather_op.h"
+#include <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/ddim.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class GatherOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_xpu_place(ctx.GetPlace()), true,
+        platform::errors::PreconditionNotMet("This kernel only runs on XPU."));
+
+    auto *x = ctx.Input<Tensor>("X");
+    auto *index = ctx.Input<Tensor>("Index");
+    auto *output = ctx.Output<Tensor>("Out");
+    if (ctx.HasInput("Axis")) {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Now, it doesn't support XPU with Axis."));
+    }
+
+    output->mutable_data<T>(ctx.GetPlace());
+    if (x->numel() == 0) return;
+    // check index type is INT32
+    const auto &index_type = index->type();
+    bool index_type_match = index_type == framework::proto::VarType::INT32;
+    PADDLE_ENFORCE_EQ(
+        index_type_match, true,
+        platform::errors::InvalidArgument(
+            "XPU only support INT32, it holds %s, but desires to be %s",
+            paddle::framework::DataTypeToString(index_type),
+            paddle::framework::DataTypeToString(
+                framework::proto::VarType::INT32)));
+
+    const auto index_dims = index->dims();
+    if (index_dims.size() == 2) {
+      PADDLE_ENFORCE_EQ(
+          index_dims[1], 1,
+          platform::errors::InvalidArgument(
+              "The last dim of index should be 1 when it is 2D, but we get %d",
+              index_dims[1]));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          index_dims.size(), 1,
+          platform::errors::InvalidArgument(
+              "The index should be 1D, when it is not 2D, but we get %d",
+              index_dims.size()));
+    }
+    int slice_size = x->numel() / x->dims()[0];
+    auto &dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
+    int r =
+        xpu::gather<T>(dev_ctx.x_context(), x->data<T>(), index->data<int>(),
+                       index->dims()[0], slice_size, output->data<T>());
+    PADDLE_ENFORCE_EQ(
+        r, xpu::Error_t::SUCCESS,
+        platform::errors::External("XPU kernel error! error code=%d", r));
+  }
+};
+
+template <typename T>
+class GatherGradOpXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    PADDLE_ENFORCE_EQ(
+        platform::is_xpu_place(ctx.GetPlace()), true,
+        platform::errors::PreconditionNotMet("This kernel only runs on XPU."));
+
+    auto *index = ctx.Input<Tensor>("Index");
+    auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto &dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
+
+    if (ctx.HasInput("Axis")) {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Now, it doesn't support XPU with Axis."));
+    }
+
+    dx->mutable_data<T>(ctx.GetPlace());
+    const int zero = 0;
+    int r_dx = xpu::memset(dev_ctx.x_context(), dx->data<T>(), zero,
+                           dx->numel() * sizeof(T));
+    PADDLE_ENFORCE_EQ(
+        r_dx, xpu::Error_t::SUCCESS,
+        platform::errors::External("XPU kernel error! error code=%d", r_dx));
+
+    if (dout->numel() == 0) {
+      return;
+    }
+    bool overwrite = ctx.Attr<bool>("overwrite");
+    // check index type is INT32
+    const auto &index_type = index->type();
+    bool index_type_match = index_type == framework::proto::VarType::INT32;
+    PADDLE_ENFORCE_EQ(
+        index_type_match, true,
+        platform::errors::InvalidArgument(
+            "XPU only support INT32, it holds %s, but desires to be %s",
+            paddle::framework::DataTypeToString(index_type),
+            paddle::framework::DataTypeToString(
+                framework::proto::VarType::INT32)));
+
+    const auto index_dims = index->dims();
+    if (index_dims.size() == 2) {
+      PADDLE_ENFORCE_EQ(
+          index_dims[1], 1,
+          platform::errors::InvalidArgument(
+              "The last dim of index should be 1 when it is 2D, but we get %d",
+              index_dims[1]));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          index_dims.size(), 1,
+          platform::errors::InvalidArgument(
+              "The index should be 1D, when it is not 2D, but we get %d",
+              index_dims.size()));
+    }
+
+    int index_size = index_dims[0];
+    int slice_size = dout->numel() / dout->dims()[0];
+
+    int r = xpu::scatter<T>(dev_ctx.x_context(), dout->data<T>(),
+                            index->data<int>(), index_size, slice_size,
+                            dx->data<T>(), overwrite);
+    PADDLE_ENFORCE_EQ(
+        r, xpu::Error_t::SUCCESS,
+        platform::errors::External("XPU kernel error! error code=%d", r));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_XPU_KERNEL(gather, ops::GatherOpXPUKernel<float>);
+REGISTER_OP_XPU_KERNEL(gather_grad, ops::GatherGradOpXPUKernel<float>);
+#endif

python/paddle/fluid/tests/unittests/xpu/test_gather_op_xpu.py

@@ -0,0 +1,154 @@
+#   Copyright (c) 2020 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
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+
+
+def gather_numpy(x, index, axis):
+    x_transpose = np.swapaxes(x, 0, axis)
+    tmp_gather = x_transpose[index, ...]
+    gather = np.swapaxes(tmp_gather, 0, axis)
+    return gather
+
+
+class TestGatherOp(OpTest):
+    def setUp(self):
+        self.op_type = "gather"
+        self.config()
+        xnp = np.random.random(self.x_shape).astype(self.x_type)
+        self.inputs = {
+            'X': xnp,
+            'Index': np.array(self.index).astype(self.index_type)
+        }
+        self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+    def config(self):
+        """
+        For multi-dimension input
+        """
+        self.x_shape = (10, 20)
+        self.x_type = "float64"
+        self.index = [1, 3, 5]
+        self.index_type = "int32"
+
+
+class TestXPUGatherOp(OpTest):
+    def setUp(self):
+        self.op_type = "gather"
+        self.dtype = np.float32
+        self.attrs = {'use_xpu': True}
+
+        self.config()
+        xnp = np.random.random(self.x_shape).astype(self.x_type)
+        self.inputs = {
+            'X': xnp,
+            'Index': np.array(self.index).astype(self.index_type)
+        }
+        self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
+
+    def test_check_output(self):
+        if self.dtype == np.float32 and paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if self.dtype == np.float32 and paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(place, ['X'], 'Out')
+
+    def config(self):
+        """
+        For multi-dimension input
+        """
+        self.x_shape = (10, 20)
+        self.x_type = self.dtype
+        self.index = [1, 3, 5]
+        self.index_type = "int32"
+
+
+class TestCase1(TestXPUGatherOp):
+    def config(self):
+        """
+        For one dimension input
+        """
+        self.x_shape = (100)
+        self.x_type = "float32"
+        self.index = [1, 3, 5]
+        self.index_type = "int32"
+
+
+class TestCase2(TestXPUGatherOp):
+    def config(self):
+        """
+        For int64_t index type
+        """
+        self.x_shape = (100)
+        self.x_type = "float32"
+        self.index = [1, 3, 5]
+        self.index_type = "int32"
+
+
+class TestCase3(TestXPUGatherOp):
+    def config(self):
+        """
+        For other input type
+        """
+        self.x_shape = (10, 20)
+        self.x_type = "float32"
+        self.index = [1, 3, 5]
+        self.index_type = "int32"
+
+
+class TestCase4(TestXPUGatherOp):
+    def config(self):
+        self.x_shape = (10, 20)
+        self.attrs = {'use_xpu': True, 'overwrite': False}
+        self.x_type = "float32"
+        self.index = [1, 1]
+        self.index_type = "int32"
+
+
+class TestCase5(TestXPUGatherOp):
+    def config(self):
+        self.x_shape = (10, 20)
+        self.attrs = {'use_xpu': True, 'overwrite': False}
+        self.x_type = "float32"
+        self.index = [1, 1, 3]
+        self.index_type = "int32"
+
+
+class TestCase6(TestXPUGatherOp):
+    def config(self):
+        self.x_shape = (10, 20)
+        self.attrs = {'use_xpu': True, 'overwrite': True}
+        self.x_type = "float32"
+        self.index = [1, 3]
+        self.index_type = "int32"
+
+
+if __name__ == "__main__":
+    unittest.main()