[XPU] Add kernels for VITDET (#50992)

* add support of int64 add for xpu

* add transpose support for int64

* add randperm kernel

* fix randperm

* add distribute_fpn_proposal kernel

* fix comment

* add reduce_sum_int32

paddle/phi/backends/xpu/xpu2_op_list.cc

@@ -168,6 +168,7 @@ XPUOpMap& get_kl2_ops() {
                      phi::DataType::FLOAT16,
                      phi::DataType::INT32,
                      phi::DataType::INT64})},
+      {"distribute_fpn_proposals", XPUKernelSet({phi::DataType::FLOAT32})},
       {"diagonal",
        XPUKernelSet({phi::DataType::INT64,
                      phi::DataType::INT32,
@@ -496,6 +497,16 @@ XPUOpMap& get_kl2_ops() {
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
       {"prod_raw", XPUKernelSet({phi::DataType::FLOAT32})},
       {"range", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT64})},
+      {"randperm_raw",
+       XPUKernelSet({phi::DataType::INT32,
+                     phi::DataType::INT64,
+                     phi::DataType::FLOAT32,
+                     phi::DataType::FLOAT64})},
+      {"randperm",
+       XPUKernelSet({phi::DataType::INT32,
+                     phi::DataType::INT64,
+                     phi::DataType::FLOAT32,
+                     phi::DataType::FLOAT64})},
       {"reciprocal", XPUKernelSet({phi::DataType::FLOAT32})},
       {"reciprocal_grad",
        XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
@@ -509,6 +520,7 @@ XPUOpMap& get_kl2_ops() {
       {"reduce_sum",
        XPUKernelSet({phi::DataType::FLOAT16,
                      phi::DataType::INT64,
+                     phi::DataType::INT32,
                      phi::DataType::INT8,
                      phi::DataType::FLOAT32})},
       {"relu6", XPUKernelSet({phi::DataType::FLOAT32})},

paddle/phi/kernels/elementwise_kernel.cc

@@ -324,7 +324,8 @@ PD_REGISTER_KERNEL(
     divide, XPU, ALL_LAYOUT, phi::DivideKernel, phi::dtype::float16, float) {}
 
 PD_REGISTER_KERNEL(
-    add, XPU, ALL_LAYOUT, phi::AddKernel, phi::dtype::float16, float) {}
+    add, XPU, ALL_LAYOUT, phi::AddKernel, phi::dtype::float16, float, int64_t) {
+}
 
 PD_REGISTER_KERNEL(multiply,
                    XPU,

paddle/phi/kernels/funcs/distribute_fpn_proposals_functor.h

@@ -35,7 +35,8 @@ inline std::vector<size_t> GetLodFromRoisNum(const Context& dev_ctx,
   std::vector<size_t> rois_lod;
   auto* rois_num_data = rois_num->data<int>();
   DenseTensor cpu_tensor;
-  if (paddle::platform::is_gpu_place(rois_num->place())) {
+  if (paddle::platform::is_gpu_place(rois_num->place()) ||
+      paddle::platform::is_xpu_place(rois_num->place())) {
     Copy<Context>(dev_ctx, *rois_num, phi::CPUPlace(), true, &cpu_tensor);
     rois_num_data = cpu_tensor.data<int>();
   }

paddle/phi/kernels/reduce_sum_kernel.cc

@@ -90,6 +90,7 @@ PD_REGISTER_KERNEL(sum,
                    float,
                    phi::dtype::float16,
                    int8_t,
+                   int,
                    int64_t) {
   kernel->OutputAt(0).SetDataType(paddle::experimental::DataType::UNDEFINED);
 }

paddle/phi/kernels/xpu/distribute_fpn_proposals_kernel.cc

+// Copyright (c) 2023 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/phi/kernels/distribute_fpn_proposals_kernel.h"
+#include "paddle/phi/kernels/funcs/distribute_fpn_proposals_functor.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T>
+static void Sort(const XPUContext& dev_ctx,
+                 const DenseTensor& value,
+                 DenseTensor* index_out) {
+  auto* value_data = value.data<T>();
+  auto place = dev_ctx.GetPlace();
+  auto cpu_place = phi::CPUPlace();
+
+  DenseTensor scores_slice_cpu;
+  scores_slice_cpu.Resize({value.numel()});
+  T* scores_slice_cpu_data = dev_ctx.template HostAlloc<T>(&scores_slice_cpu);
+
+  paddle::memory::Copy(cpu_place,
+                       scores_slice_cpu_data,
+                       place,
+                       value_data,
+                       sizeof(T) * value.numel());
+  // Sort index
+  DenseTensor index_t;
+  index_t.Resize({value.numel()});
+  int* index = dev_ctx.template HostAlloc<int>(&index_t);
+  for (int i = 0; i < value.numel(); ++i) {
+    index[i] = i;
+  }
+
+  auto compare = [scores_slice_cpu_data](const int64_t& i, const int64_t& j) {
+    return scores_slice_cpu_data[i] < scores_slice_cpu_data[j];
+  };
+
+  std::sort(index, index + value.numel(), compare);
+  index_out->Resize({index_t.numel()});
+  int* idx_out = dev_ctx.template Alloc<int>(index_out);
+  paddle::memory::Copy(
+      place, idx_out, cpu_place, index, sizeof(T) * index_t.numel());
+}
+
+template <typename T, typename Context>
+void DistributeFpnProposalsKernel(
+    const Context& dev_ctx,
+    const DenseTensor& fpn_rois,
+    const paddle::optional<DenseTensor>& rois_num,
+    int min_level,
+    int max_level,
+    int refer_level,
+    int refer_scale,
+    bool pixel_offset,
+    std::vector<DenseTensor*> multi_fpn_rois,
+    std::vector<DenseTensor*> multi_level_rois_num,
+    DenseTensor* restore_index) {
+  const int num_level = max_level - min_level + 1;
+  // check that the fpn_rois is not empty
+  if (!rois_num.get_ptr()) {
+    PADDLE_ENFORCE_EQ(
+        fpn_rois.lod().size(),
+        1UL,
+        errors::InvalidArgument("DistributeFpnProposalsOp needs LoD"
+                                "with one level"));
+  }
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+  std::vector<size_t> fpn_rois_lod;
+  if (rois_num.get_ptr()) {
+    fpn_rois_lod = funcs::GetLodFromRoisNum(dev_ctx, rois_num.get_ptr());
+  } else {
+    fpn_rois_lod = fpn_rois.lod().back();
+  }
+
+  int lod_size = fpn_rois_lod.size() - 1;
+  // the total num of roi
+  int roi_num = fpn_rois_lod[lod_size];
+
+  DenseTensor sub_lod_list;
+  sub_lod_list.Resize({num_level, lod_size});
+  int* sub_lod_list_data = dev_ctx.template Alloc<int>(&sub_lod_list);
+  phi::funcs::SetConstant<phi::XPUContext, int> set_zero;
+  set_zero(dev_ctx, &sub_lod_list, static_cast<int>(0));
+
+  DenseTensor target_lvls;
+  target_lvls.Resize({roi_num});
+  int* target_lvls_data = dev_ctx.template Alloc<int>(&target_lvls);
+
+  std::vector<int> rois_lod_vec(fpn_rois_lod.size(), 0);
+  for (size_t i = 0; i < fpn_rois_lod.size(); ++i) {
+    rois_lod_vec[i] = static_cast<int>(fpn_rois_lod[i]);
+  }
+  xpu::VectorParam<int> rois_lod = {
+      rois_lod_vec.data(), static_cast<int>(rois_lod_vec.size()), nullptr};
+
+  int r = xpu::distribute_fpn_proposals_helper<XPUType, int, int>(
+      dev_ctx.x_context(),
+      reinterpret_cast<const XPUType*>(fpn_rois.data<T>()),
+      rois_lod,
+      sub_lod_list_data,
+      target_lvls_data,
+      static_cast<int64_t>(min_level),
+      static_cast<int64_t>(max_level),
+      static_cast<int64_t>(refer_level),
+      static_cast<int64_t>(refer_scale),
+      pixel_offset);
+
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "distribute_fpn_proposals_helper");
+
+  DenseTensor index_out_t;
+  Sort<int>(dev_ctx, target_lvls, &index_out_t);
+  Sort<int>(dev_ctx, index_out_t, restore_index);
+  restore_index->Resize({roi_num, 1});
+
+  int start = 0;
+  std::vector<int> sub_lod_list_cpu(lod_size * num_level);
+  phi::TensorToVector<int>(sub_lod_list, dev_ctx, &sub_lod_list_cpu);
+
+  for (int i = 0; i < num_level; ++i) {
+    DenseTensor sub_lod = sub_lod_list.Slice(i, i + 1);
+    // transfer length-based lod to offset-based lod
+    std::vector<size_t> offset(1, 0);
+    for (int j = 0; j < lod_size; ++j) {
+      offset.emplace_back(offset.back() + sub_lod_list_cpu[i * lod_size + j]);
+    }
+    int sub_rois_num = offset.back();
+    int end = start + sub_rois_num;
+    if (end > start) {
+      DenseTensor sub_idx = index_out_t.Slice(start, end);
+      start = end;
+      multi_fpn_rois[i]->Resize({sub_rois_num, funcs::kBoxDim});
+      dev_ctx.template Alloc<T>(multi_fpn_rois[i]);
+
+      std::vector<int> fpn_rois_shape(fpn_rois.dims().size());
+      for (int i = 0; i < fpn_rois.dims().size(); ++i) {
+        fpn_rois_shape[i] = fpn_rois.dims()[i];
+      }
+      int r1 = xpu::gather<XPUType, int>(
+          dev_ctx.x_context(),
+          reinterpret_cast<const XPUType*>(fpn_rois.data<T>()),
+          sub_idx.data<int>(),
+          reinterpret_cast<XPUType*>(multi_fpn_rois[i]->data<T>()),
+          fpn_rois_shape,
+          sub_idx.numel(),
+          0);
+      PADDLE_ENFORCE_XDNN_SUCCESS(r1, "distribute_fpn_proposals_helper");
+    } else {
+      multi_fpn_rois[i]->Resize({sub_rois_num, funcs::kBoxDim});
+      dev_ctx.template Alloc<T>(multi_fpn_rois[i]);
+    }
+    if (multi_level_rois_num.size() > 0) {
+      DenseTensor* rois_num_t = multi_level_rois_num[i];
+      Copy(dev_ctx, sub_lod, dev_ctx.GetPlace(), true, rois_num_t);
+      rois_num_t->Resize({lod_size});
+    }
+    LoD lod;
+    lod.emplace_back(offset);
+    multi_fpn_rois[i]->set_lod(lod);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(distribute_fpn_proposals,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::DistributeFpnProposalsKernel,
+                   float) {}

paddle/phi/kernels/xpu/randperm_kernel.cc

+// Copyright (c) 2023 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/phi/kernels/randperm_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/backends/xpu/xpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void RandpermRawKernel(
+    const Context& dev_ctx, int n, DataType dtype, int seed, DenseTensor* out) {
+  std::shared_ptr<std::mt19937_64> engine;
+
+  if (seed) {
+    engine = std::make_shared<std::mt19937_64>();
+    engine->seed(seed);
+  } else {
+    engine = dev_ctx.GetGenerator()->GetCPUEngine();
+  }
+
+  if (dev_ctx.GetPlace().GetType() == phi::AllocationType::CPU) {
+    T* out_data = dev_ctx.template HostAlloc<T>(out);
+    for (int i = 0; i < n; ++i) {
+      out_data[i] = static_cast<T>(i);
+    }
+    std::shuffle(out_data, out_data + n, *engine);
+  } else {
+    dev_ctx.template Alloc<T>(out);
+    phi::DenseTensor tmp_tensor;
+    tmp_tensor.Resize(phi::make_ddim({n}));
+    T* tmp_data = dev_ctx.template HostAlloc<T>(&tmp_tensor);
+    for (int i = 0; i < n; ++i) {
+      tmp_data[i] = static_cast<T>(i);
+    }
+    std::shuffle(tmp_data, tmp_data + n, *engine);
+    Copy(dev_ctx, tmp_tensor, dev_ctx.GetPlace(), true, out);
+  }
+}
+
+template <typename T, typename Context>
+void RandpermKernel(const Context& dev_ctx,
+                    int n,
+                    DataType dtype,
+                    DenseTensor* out) {
+  RandpermRawKernel<T, Context>(dev_ctx, n, dtype, 0, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(randperm_raw,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::RandpermRawKernel,
+                   int,
+                   int64_t,
+                   float,
+                   double) {}
+
+PD_REGISTER_KERNEL(randperm,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::RandpermKernel,
+                   int,
+                   int64_t,
+                   float,
+                   double) {}

paddle/phi/kernels/xpu/reduce_sum_kernel.cc

@@ -57,4 +57,5 @@ PD_REGISTER_KERNEL(sum_raw,
                    float,
                    phi::dtype::float16,
                    int8_t,
+                   int,
                    int64_t) {}

python/paddle/fluid/tests/unittests/op_test.py

@@ -2164,7 +2164,10 @@ class OpTest(unittest.TestCase):
                 else:
                     abs_a = 1 if abs_a < 1e-3 else abs_a
 
-            diff_mat = np.abs(a - b) / abs_a
+            if self.dtype == np.bool:
+                diff_mat = np.abs(a ^ b) / abs_a
+            else:
+                diff_mat = np.abs(a - b) / abs_a
             max_diff = np.max(diff_mat)
 
             def err_msg():

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

+# Copyright (c) 2023 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 sys
+
+sys.path.append("..")
+import unittest
+
+import numpy as np
+from op_test_xpu import XPUOpTest
+
+import paddle
+
+paddle.enable_static()
+
+from xpu.get_test_cover_info import (
+    XPUOpTestWrapper,
+    create_test_class,
+    get_xpu_op_support_types,
+)
+
+
+def distribute_fpn_proposals_wrapper(
+    fpn_rois,
+    rois_num,
+    min_level,
+    max_level,
+    refer_level,
+    refer_scale,
+    pixel_offset,
+):
+    return paddle.vision.ops.distribute_fpn_proposals(
+        fpn_rois=fpn_rois,
+        min_level=min_level,
+        max_level=max_level,
+        refer_level=refer_level,
+        refer_scale=refer_scale,
+        rois_num=rois_num,
+    )
+
+
+class XPUTestDistributeFPNProposalsOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'distribute_fpn_proposals'
+        self.use_dynamic_create_class = False
+
+    class TestDistributeFPNProposalsOp(XPUOpTest):
+        def setUp(self):
+            self.op_type = "distribute_fpn_proposals"
+            self.python_api = distribute_fpn_proposals_wrapper
+            self.python_out_sig = ['MultiFpnRois', 'RestoreIndex']
+            self.dtype = self.in_type
+            self.init_test_case()
+            self.make_rois()
+            self.rois_fpn, self.rois_idx_restore = self.calc_rois_distribute()
+            self.inputs = {'FpnRois': (self.rois[:, 1:5], self.rois_lod)}
+            self.attrs = {
+                'max_level': self.roi_max_level,
+                'min_level': self.roi_min_level,
+                'refer_scale': self.canonical_scale,
+                'refer_level': self.canonical_level,
+                'pixel_offset': self.pixel_offset,
+            }
+            output = [
+                ('out%d' % i, self.rois_fpn[i])
+                for i in range(len(self.rois_fpn))
+            ]
+
+            self.outputs = {
+                'MultiFpnRois': output,
+                'RestoreIndex': self.rois_idx_restore.reshape(-1, 1),
+            }
+
+        def test_check_output(self):
+            self.check_output_with_place(paddle.XPUPlace(0))
+
+        def init_test_case(self):
+            self.roi_max_level = 5
+            self.roi_min_level = 2
+            self.canonical_scale = 224
+            self.canonical_level = 4
+            self.images_shape = [512, 512]
+            self.pixel_offset = True
+
+        def boxes_area(self, boxes):
+            offset = 1 if self.pixel_offset else 0
+            w = boxes[:, 2] - boxes[:, 0] + offset
+            h = boxes[:, 3] - boxes[:, 1] + offset
+            areas = w * h
+            assert np.all(areas >= 0), 'Negative areas founds'
+            return areas
+
+        def map_rois_to_fpn_levels(self, rois, lvl_min, lvl_max):
+            s = np.sqrt(self.boxes_area(rois))
+            s0 = self.canonical_scale
+            lvl0 = self.canonical_level
+            target_lvls = np.floor(lvl0 + np.log2(s / s0 + 1e-8))
+            target_lvls = np.clip(target_lvls, lvl_min, lvl_max)
+            return target_lvls
+
+        def get_sub_lod(self, sub_lvl):
+            sub_lod = [0, 0]
+            max_batch_id = sub_lvl[-1]
+            for i in range(max_batch_id.astype(np.int32) + 1):
+                sub_lod[i] = np.where(sub_lvl == i)[0].size
+            return sub_lod
+
+        def add_multilevel_roi(self, rois, target_lvls, lvl_min, lvl_max):
+            rois_idx_order = np.empty((0,))
+            rois_fpn = []
+            for lvl in range(lvl_min, lvl_max + 1):
+                idx_lvl = np.where(target_lvls == lvl)[0]
+                if len(idx_lvl) == 0:
+                    rois_fpn.append((np.empty(shape=(0, 4)), [[0, 0]]))
+                    continue
+                sub_lod = self.get_sub_lod(rois[idx_lvl, 0])
+                rois_fpn.append((rois[idx_lvl, 1:], [sub_lod]))
+                rois_idx_order = np.concatenate((rois_idx_order, idx_lvl))
+            rois_idx_restore = np.argsort(rois_idx_order).astype(
+                np.int32, copy=False
+            )
+            return rois_fpn, rois_idx_restore
+
+        def calc_rois_distribute(self):
+            lvl_min = self.roi_min_level
+            lvl_max = self.roi_max_level
+            target_lvls = self.map_rois_to_fpn_levels(
+                self.rois[:, 1:5], lvl_min, lvl_max
+            )
+            rois_fpn, rois_idx_restore = self.add_multilevel_roi(
+                self.rois, target_lvls, lvl_min, lvl_max
+            )
+            return rois_fpn, rois_idx_restore
+
+        def make_rois(self):
+            self.rois_lod = [[10, 4]]
+            rois = []
+            lod = self.rois_lod[0]
+            bno = 0
+            for roi_num in lod:
+                for i in range(roi_num):
+                    xywh = np.random.rand(4)
+                    xy1 = xywh[0:2] * 20
+                    wh = xywh[2:4] * (self.images_shape - xy1)
+                    xy2 = xy1 + wh
+                    roi = [bno, xy1[0], xy1[1], xy2[0], xy2[1]]
+                    rois.append(roi)
+                bno += 1
+            self.rois = np.array(rois).astype("float32")
+
+    class TestDistributeFPNProposalsOpWithRoisNum(TestDistributeFPNProposalsOp):
+        def setUp(self):
+            self.op_type = "distribute_fpn_proposals"
+            self.python_api = distribute_fpn_proposals_wrapper
+            self.python_out_sig = [
+                'MultiFpnRois',
+                'MultiLevelRoIsNum',
+                'RestoreIndex',
+            ]
+            self.dtype = self.in_type
+            self.init_test_case()
+            self.make_rois()
+            self.rois_fpn, self.rois_idx_restore = self.calc_rois_distribute()
+            self.inputs = {
+                'FpnRois': (self.rois[:, 1:5], self.rois_lod),
+                'RoisNum': np.array(self.rois_lod[0]).astype('int32'),
+            }
+            self.attrs = {
+                'max_level': self.roi_max_level,
+                'min_level': self.roi_min_level,
+                'refer_scale': self.canonical_scale,
+                'refer_level': self.canonical_level,
+                'pixel_offset': self.pixel_offset,
+            }
+            output = [
+                ('out%d' % i, self.rois_fpn[i])
+                for i in range(len(self.rois_fpn))
+            ]
+            rois_num_per_level = [
+                (
+                    'rois_num%d' % i,
+                    np.array(self.rois_fpn[i][1][0]).astype('int32'),
+                )
+                for i in range(len(self.rois_fpn))
+            ]
+
+            self.outputs = {
+                'MultiFpnRois': output,
+                'RestoreIndex': self.rois_idx_restore.reshape(-1, 1),
+                'MultiLevelRoIsNum': rois_num_per_level,
+            }
+
+    class TestDistributeFPNProposalsOpNoOffset(
+        TestDistributeFPNProposalsOpWithRoisNum
+    ):
+        def init_test_case(self):
+            self.roi_max_level = 5
+            self.roi_min_level = 2
+            self.canonical_scale = 224
+            self.canonical_level = 4
+            self.images_shape = [512, 512]
+            self.pixel_offset = False
+
+
+support_types = get_xpu_op_support_types('distribute_fpn_proposals')
+for stype in support_types:
+    create_test_class(globals(), XPUTestDistributeFPNProposalsOp, stype)
+
+if __name__ == '__main__':
+    unittest.main()

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

+#   Copyright (c) 2023 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 sys
+import unittest
+
+import numpy as np
+
+import paddle
+import paddle.fluid.core as core
+from paddle.static import Program, program_guard
+
+sys.path.append("..")
+from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import (
+    XPUOpTestWrapper,
+    create_test_class,
+    get_xpu_op_support_types,
+)
+
+paddle.enable_static()
+
+
+def check_randperm_out(n, data_np):
+    assert isinstance(
+        data_np, np.ndarray
+    ), "The input data_np should be np.ndarray."
+    gt_sorted = np.arange(n)
+    out_sorted = np.sort(data_np)
+    return list(gt_sorted == out_sorted)
+
+
+def error_msg(data_np):
+    return (
+        "The sorted ground truth and sorted out should "
+        + "be equal, out = "
+        + str(data_np)
+    )
+
+
+def convert_dtype(dtype_str):
+    dtype_str_list = [np.int32, np.int64, np.float32, np.float64]
+    dtype_num_list = [
+        core.VarDesc.VarType.INT32,
+        core.VarDesc.VarType.INT64,
+        core.VarDesc.VarType.FP32,
+        core.VarDesc.VarType.FP64,
+    ]
+    assert dtype_str in dtype_str_list, (
+        dtype_str + " should in " + str(dtype_str_list)
+    )
+    return dtype_num_list[dtype_str_list.index(dtype_str)]
+
+
+class XPUTestRandpermOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = "randperm"
+        self.use_dynamic_create_class = False
+
+    class TestXPURandpermOp(XPUOpTest):
+        """Test randperm op."""
+
+        def setUp(self):
+            self.init_op_type()
+            self.initTestCase()
+            self.dtype = self.in_type
+            self.use_xpu = True
+            self.use_mkldnn = False
+            self.inputs = {}
+            self.outputs = {"Out": np.zeros((self.n)).astype(self.dtype)}
+            self.attrs = {
+                "n": self.n,
+                "dtype": convert_dtype(self.dtype),
+            }
+
+        def init_op_type(self):
+            self.op_type = "randperm"
+            self.use_mkldnn = False
+
+        def initTestCase(self):
+            self.n = 200
+
+        def test_check_output(self):
+            if paddle.is_compiled_with_xpu():
+                paddle.enable_static()
+                place = paddle.XPUPlace(0)
+                self.check_output_customized(self.verify_output)
+
+        def verify_output(self, outs):
+            out_np = np.array(outs[0])
+            self.assertTrue(
+                check_randperm_out(self.n, out_np), msg=error_msg(out_np)
+            )
+
+    class TestXPURandpermOpN(TestXPURandpermOp):
+        def initTestCase(self):
+            self.n = 10000
+
+    class TestRandpermImperative(unittest.TestCase):
+        def test_out(self):
+            paddle.disable_static()
+            n = 10
+            dtype = self.in_type
+            data_p = paddle.randperm(n, dtype)
+            data_np = data_p.numpy()
+            self.assertTrue(
+                check_randperm_out(n, data_np), msg=error_msg(data_np)
+            )
+            paddle.enable_static()
+
+    class TestRandpermEager(unittest.TestCase):
+        def test_out(self):
+            paddle.disable_static()
+            n = 10
+            dtype = self.in_type
+            data_p = paddle.randperm(n, dtype)
+            data_np = data_p.numpy()
+            self.assertTrue(
+                check_randperm_out(n, data_np), msg=error_msg(data_np)
+            )
+            paddle.enable_static()
+
+
+support_types = get_xpu_op_support_types("randperm")
+for stype in support_types:
+    create_test_class(globals(), XPUTestRandpermOp, stype)
+
+
+class TestRandpermAPI(unittest.TestCase):
+    def test_out(self):
+        n = 10
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+        else:
+            place = paddle.CPUPlace()
+        with program_guard(Program(), Program()):
+            x1 = paddle.randperm(n)
+            x2 = paddle.randperm(n, 'float32')
+
+            exe = paddle.static.Executor(place)
+            res = exe.run(fetch_list=[x1, x2])
+
+            self.assertEqual(res[0].dtype, np.int64)
+            self.assertEqual(res[1].dtype, np.float32)
+            self.assertTrue(check_randperm_out(n, res[0]))
+            self.assertTrue(check_randperm_out(n, res[1]))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -19,119 +19,127 @@ import numpy as np
 
 sys.path.append("..")
 from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import (
+    XPUOpTestWrapper,
+    create_test_class,
+    get_xpu_op_support_types,
+)
 
 import paddle
 
 
-class TestXPUTransposeOp(XPUOpTest):
-    def setUp(self):
-        self.init_op_type()
-        self.initTestCase()
-        self.use_xpu = True
-        self.use_mkldnn = False
-        self.inputs = {'X': np.random.random(self.shape).astype("float32")}
-        self.attrs = {
-            'axis': list(self.axis),
-            'use_mkldnn': False,
-            'use_xpu': True,
-        }
-        self.outputs = {
-            'XShape': np.random.random(self.shape).astype("float32"),
-            'Out': self.inputs['X'].transpose(self.axis),
-        }
-
-    def init_op_type(self):
-        self.op_type = "transpose2"
-        self.use_mkldnn = False
-
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
-            paddle.enable_static()
-            place = paddle.XPUPlace(0)
-            self.check_output_with_place(place=place, no_check_set=['XShape'])
-
-    def test_check_grad(self):
-        if paddle.is_compiled_with_xpu():
-            paddle.enable_static()
-            place = paddle.XPUPlace(0)
-            self.check_grad_with_place(place, ['X'], 'Out')
-
-    def initTestCase(self):
-        self.shape = (3, 40)
-        self.axis = (1, 0)
-
-
-class TestCase_ZeroDim(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = ()
-        self.axis = ()
-
-
-class TestCase0(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (100,)
-        self.axis = (0,)
-
-
-class TestCase1(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (3, 4, 10)
-        self.axis = (0, 2, 1)
-
-
-class TestCase2(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 3, 4, 5)
-        self.axis = (0, 2, 3, 1)
-
-
-class TestCase3(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 3, 4, 5, 6)
-        self.axis = (4, 2, 3, 1, 0)
-
-
-class TestCase4(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 3, 4, 5, 6, 1)
-        self.axis = (4, 2, 3, 1, 0, 5)
-
-
-class TestCase5(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 16, 96)
-        self.axis = (0, 2, 1)
-
-
-class TestCase6(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 10, 12, 16)
-        self.axis = (3, 1, 2, 0)
-
-
-class TestCase7(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 10, 2, 16)
-        self.axis = (0, 1, 3, 2)
-
-
-class TestCase8(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
-        self.axis = (0, 1, 3, 2, 4, 5, 6, 7)
-
-
-class TestCase9(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
-        self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
-
-
-class TestCase10(TestXPUTransposeOp):
-    def initTestCase(self):
-        self.shape = (2, 3, 2)
-        self.axis = (-1, 1, -3)
-
+class XPUTestXPUTransposeOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'transpose'
+        self.use_dynamic_create_class = False
+
+    class TestXPUTransposeOp(XPUOpTest):
+        def setUp(self):
+            self.init_op_type()
+            self.init_type()
+            self.initTestCase()
+            self.use_xpu = True
+            self.use_mkldnn = False
+            self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
+            self.attrs = {
+                'axis': list(self.axis),
+                'use_mkldnn': False,
+                'use_xpu': True,
+            }
+            self.outputs = {
+                'XShape': np.random.random(self.shape).astype(self.dtype),
+                'Out': self.inputs['X'].transpose(self.axis),
+            }
+
+        def init_op_type(self):
+            self.op_type = "transpose2"
+            self.use_mkldnn = False
+
+        def init_type(self):
+            self.dtype = self.in_type
+
+        def test_check_output(self):
+            if paddle.is_compiled_with_xpu():
+                paddle.enable_static()
+                place = paddle.XPUPlace(0)
+                self.check_output_with_place(
+                    place=place, no_check_set=['XShape']
+                )
+
+        def test_check_grad(self):
+            if paddle.is_compiled_with_xpu():
+                paddle.enable_static()
+                place = paddle.XPUPlace(0)
+                self.check_grad_with_place(place, ['X'], 'Out')
+
+        def initTestCase(self):
+            self.shape = (3, 40)
+            self.axis = (1, 0)
+
+    class TestCase_ZeroDim(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = ()
+            self.axis = ()
+
+    class TestCase0(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (100,)
+            self.axis = (0,)
+
+    class TestCase1(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (3, 4, 10)
+            self.axis = (0, 2, 1)
+
+    class TestCase2(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 3, 4, 5)
+            self.axis = (0, 2, 3, 1)
+
+    class TestCase3(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 3, 4, 5, 6)
+            self.axis = (4, 2, 3, 1, 0)
+
+    class TestCase4(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 3, 4, 5, 6, 1)
+            self.axis = (4, 2, 3, 1, 0, 5)
+
+    class TestCase5(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 16, 96)
+            self.axis = (0, 2, 1)
+
+    class TestCase6(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 10, 12, 16)
+            self.axis = (3, 1, 2, 0)
+
+    class TestCase7(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 10, 2, 16)
+            self.axis = (0, 1, 3, 2)
+
+    class TestCase8(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
+            self.axis = (0, 1, 3, 2, 4, 5, 6, 7)
+
+    class TestCase9(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
+            self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
+
+    class TestCase10(TestXPUTransposeOp):
+        def initTestCase(self):
+            self.shape = (200, 3, 2)
+            self.axis = (-1, 1, -3)
+
+
+support_types = get_xpu_op_support_types('transpose')
+for stype in support_types:
+    create_test_class(globals(), XPUTestXPUTransposeOp, stype)
 
 if __name__ == "__main__":
     unittest.main()