[XPU] add group_norm, sin, cos, linspace, randint kernels (#50465)

* [XPU] add group_norm kernel

* update

* add xpu sin, cos, randint, linspace kernels

* update

* update

paddle/phi/backends/xpu/xpu2_op_list.cc

@@ -711,6 +711,12 @@ XPUOpMap& get_kl2_ops() {
        XPUKernelSet({phi::DataType::INT32,
                      phi::DataType::INT64,
                      phi::DataType::FLOAT32})},
+      {"sin", XPUKernelSet({phi::DataType::FLOAT32})},
+      {"cos", XPUKernelSet({phi::DataType::FLOAT32})},
+      {"linspace",
+       XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32})},
+      {"randint", XPUKernelSet({phi::DataType::INT32, phi::DataType::INT64})},
+      {"group_norm", XPUKernelSet({phi::DataType::FLOAT32})},
 
       // AddMore
       {"sequence_conv", XPUKernelSet({phi::DataType::FLOAT32})},

paddle/phi/kernels/xpu/activation_kernel.cc

@@ -457,6 +457,32 @@ struct XPUFloorFunctor : public funcs::BaseActivationFunctor<T> {
   }
 };
 
+template <typename T>
+struct XPUSinFunctor : public funcs::BaseActivationFunctor<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  template <typename Context>
+  void operator()(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  DenseTensor* out) const {
+    int r = xpu_activation_func<Context, T, XPUType>(
+        dev_ctx, x, out, xpu::sin<XPUType>);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "sin");
+  }
+};
+
+template <typename T>
+struct XPUCosFunctor : public funcs::BaseActivationFunctor<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  template <typename Context>
+  void operator()(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  DenseTensor* out) const {
+    int r = xpu_activation_func<Context, T, XPUType>(
+        dev_ctx, x, out, xpu::cos<XPUType>);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "cos");
+  }
+};
+
 DEFINE_XPU_ACTIVATION_KERNEL(Exp, XPUExpFunctor)
 DEFINE_XPU_ACTIVATION_KERNEL(Floor, XPUFloorFunctor)
 DEFINE_XPU_ACTIVATION_KERNEL(Log, XPULogFunctor)
@@ -467,6 +493,8 @@ DEFINE_XPU_ACTIVATION_KERNEL(Square, XPUSquareFunctor)
 DEFINE_XPU_ACTIVATION_KERNEL(Sqrt, XPUSqrtFunctor)
 DEFINE_XPU_ACTIVATION_KERNEL(Tanh, XPUTanhFunctor)
 DEFINE_XPU_ACTIVATION_KERNEL(Silu, XPUSiluFunctor)
+DEFINE_XPU_ACTIVATION_KERNEL(Sin, XPUSinFunctor)
+DEFINE_XPU_ACTIVATION_KERNEL(Cos, XPUCosFunctor)
 
 DEFINE_XPU_ACTIVATION_KERNEL_WITH_ONE_ATTRS(Mish, XPUMishFunctor, threshold)
 DEFINE_XPU_ACTIVATION_KERNEL_WITH_ONE_ATTRS(LeakyRelu,
@@ -531,3 +559,5 @@ PD_REGISTER_ACTIVATION_KERNEL(sigmoid, SigmoidKernel)
 PD_REGISTER_ACTIVATION_KERNEL(sqrt, SqrtKernel)
 PD_REGISTER_ACTIVATION_KERNEL(swish_raw, SwishRawKernel)
 PD_REGISTER_ACTIVATION_KERNEL(softplus, SoftplusKernel)
+PD_REGISTER_ACTIVATION_KERNEL(sin, SinKernel)
+PD_REGISTER_ACTIVATION_KERNEL(cos, CosKernel)

paddle/phi/kernels/xpu/group_norm_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/group_norm_kernel.h"
+
+#include <algorithm>
+#include <array>
+#include <numeric>
+#include <string>
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/common/layout.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void GroupNormKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const paddle::optional<DenseTensor>& scale,
+                     const paddle::optional<DenseTensor>& bias,
+                     float epsilon,
+                     int groups,
+                     const std::string& data_layout_str,
+                     DenseTensor* y,
+                     DenseTensor* mean,
+                     DenseTensor* var) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+  const DataLayout data_layout = phi::StringToDataLayout(data_layout_str);
+  const auto scale_ptr = scale.get_ptr();
+  const auto bias_ptr = bias.get_ptr();
+
+  const auto x_dims = phi::vectorize<int>(x.dims());
+  const int N = x_dims[0];
+  const bool channel_first =
+      data_layout == DataLayout::kNCHW || data_layout == DataLayout::kNCDHW;
+  const int C = (channel_first ? x_dims[1] : x_dims[x_dims.size() - 1]);
+  const int L =
+      (channel_first
+           ? std::accumulate(
+                 x_dims.begin() + 2, x_dims.end(), 1, std::multiplies<int>())
+           : std::accumulate(x_dims.begin() + 1,
+                             x_dims.end() - 1,
+                             1,
+                             std::multiplies<int>()));
+
+  dev_ctx.template Alloc<T>(y);
+  dev_ctx.template Alloc<T>(mean);
+  dev_ctx.template Alloc<T>(var);
+
+  auto* x_data = x.data<T>();
+  auto* y_data = y->data<T>();
+  auto* mean_data = mean->data<T>();
+  auto* var_data = var->data<T>();
+
+  const T* scale_data = nullptr;
+  if (scale_ptr) scale_data = scale_ptr->data<T>();
+  const T* bias_data = nullptr;
+  if (bias_ptr) bias_data = bias_ptr->data<T>();
+
+  auto r =
+      xpu::group_norm<XPUType>(dev_ctx.x_context(),
+                               reinterpret_cast<const XPUType*>(x_data),
+                               reinterpret_cast<XPUType*>(y_data),
+                               N,
+                               C,
+                               L,
+                               1,
+                               groups,
+                               static_cast<XPUType>(epsilon),
+                               reinterpret_cast<const XPUType*>(scale_data),
+                               reinterpret_cast<const XPUType*>(bias_data),
+                               reinterpret_cast<XPUType*>(mean_data),
+                               reinterpret_cast<XPUType*>(var_data),
+                               channel_first);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "group_norm");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(group_norm, XPU, ALL_LAYOUT, phi::GroupNormKernel, float) {}

paddle/phi/kernels/xpu/linspace_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/linspace_kernel.h"
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/data_type_transform.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+T GetValueOfExpectedType(const Context& ctx, const DenseTensor& x) {
+  switch (x.dtype()) {
+    case DataType::FLOAT32:
+      return static_cast<T>(GetValue<float, Context>(ctx, x));
+    case DataType::FLOAT64:
+      return static_cast<T>(GetValue<double, Context>(ctx, x));
+    case DataType::INT32:
+      return static_cast<T>(GetValue<int32_t, Context>(ctx, x));
+    case DataType::INT64:
+      return static_cast<T>(GetValue<int64_t, Context>(ctx, x));
+    case DataType::FLOAT16:
+      return static_cast<T>(GetValue<phi::dtype::float16, Context>(ctx, x));
+    case DataType::BFLOAT16:
+      return static_cast<T>(GetValue<phi::dtype::bfloat16, Context>(ctx, x));
+    case DataType::BOOL:
+      return static_cast<T>(GetValue<bool, Context>(ctx, x));
+    case DataType::INT16:
+      return static_cast<T>(GetValue<int16_t, Context>(ctx, x));
+    case DataType::UINT8:
+      return static_cast<T>(GetValue<uint8_t, Context>(ctx, x));
+    default:
+      PADDLE_THROW(phi::errors::Unimplemented(
+          "Data type (%s) is not supported when casting data type.",
+          x.dtype()));
+  }
+}
+
+template <typename T, typename Context>
+void LinspaceKernel(const Context& ctx,
+                    const DenseTensor& start,
+                    const DenseTensor& stop,
+                    const DenseTensor& number,
+                    DataType dtype,
+                    DenseTensor* out) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  T start_value = GetValueOfExpectedType<T, Context>(ctx, start);
+  T stop_value = GetValueOfExpectedType<T, Context>(ctx, stop);
+  int32_t num = GetValueOfExpectedType<int32_t, Context>(ctx, number);
+
+  PADDLE_ENFORCE_GT(
+      num,
+      0,
+      phi::errors::InvalidArgument("The num of linspace op should be larger "
+                                   "than 0, but received num is %d",
+                                   num));
+
+  out->Resize(phi::make_ddim({num}));
+  T* out_data = ctx.template Alloc<T>(out);
+
+  int r = xpu::linspace(ctx.x_context(),
+                        reinterpret_cast<XPUType*>(out_data),
+                        static_cast<XPUType>(start_value),
+                        static_cast<XPUType>(stop_value),
+                        num);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "linspace");
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    linspace, XPU, ALL_LAYOUT, phi::LinspaceKernel, float, int32_t) {}

paddle/phi/kernels/xpu/randint_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/randint_kernel.h"
+
+#include <random>
+
+#include "paddle/fluid/memory/memcpy.h"
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/generator.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void RandintRawKernel(const Context& dev_ctx,
+                      int low,
+                      int high,
+                      const IntArray& shape,
+                      DataType dtype,
+                      int seed,
+                      DenseTensor* out) {
+  int64_t size = out->numel();
+  out->Resize(phi::make_ddim(shape.GetData()));
+  T* data = dev_ctx.template Alloc<T>(out);
+  auto numel = out->numel();
+  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();
+  }
+  std::unique_ptr<T[]> data_cpu(new T[size]);
+  std::uniform_int_distribution<T> dist(low, high - 1);
+  for (int64_t i = 0; i < numel; ++i) {
+    data_cpu[i] = dist(*engine);
+  }
+  paddle::memory::Copy(dev_ctx.GetPlace(),
+                       data,
+                       phi::CPUPlace(),
+                       reinterpret_cast<void*>(data_cpu.get()),
+                       size * sizeof(T));
+}
+
+template <typename T, typename Context>
+void RandintKernel(const Context& dev_ctx,
+                   int low,
+                   int high,
+                   const IntArray& shape,
+                   DataType dtype,
+                   DenseTensor* out) {
+  RandintRawKernel<T>(dev_ctx, low, high, shape, dtype, 0, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    randint_raw, XPU, ALL_LAYOUT, phi::RandintRawKernel, int, int64_t) {}
+
+PD_REGISTER_KERNEL(randint, XPU, ALL_LAYOUT, phi::RandintKernel, int, int64_t) {
+}

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

@@ -1222,5 +1222,104 @@ def ref_mish(x, threshold=20):
     return out
 
 
+class XPUTestSinOP(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'sin'
+        self.use_dynamic_create_class = False
+
+    class XPUTestSinBase(TestActivationOPBase):
+        def set_case(self):
+            self.op_type = "sin"
+            self.dtype = self.in_type
+
+            self.init_config()
+            out = np.sin(self.x)
+
+            self.inputs = {'X': self.x}
+            self.outputs = {'Out': out}
+            self.attrs = {'use_xpu': True}
+
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [11, 17]).astype(
+                self.dtype
+            )
+
+    class XPUTestSin_ZeroDim(XPUTestSinBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, []).astype(self.dtype)
+
+    class XPUTestSin2(XPUTestSinBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [1024, 8]).astype(
+                self.dtype
+            )
+
+    class XPUTestSin3(XPUTestSinBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [4, 512, 15, 15]).astype(
+                self.dtype
+            )
+
+    class XPUTestSin4(XPUTestSinBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [4, 256, 22, 22]).astype(
+                self.dtype
+            )
+
+
+support_types = get_xpu_op_support_types('sin')
+for stype in support_types:
+    create_test_class(globals(), XPUTestSinOP, stype)
+
+
+class XPUTestCosOP(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'cos'
+        self.use_dynamic_create_class = False
+
+    class XPUTestCosBase(TestActivationOPBase):
+        def set_case(self):
+            self.op_type = "cos"
+            self.dtype = self.in_type
+
+            self.init_config()
+            out = np.cos(self.x)
+
+            self.inputs = {'X': self.x}
+            self.outputs = {'Out': out}
+            self.attrs = {'use_xpu': True}
+
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [11, 17]).astype(
+                self.dtype
+            )
+
+    class XPUTestCos_ZeroDim(XPUTestCosBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, []).astype(self.dtype)
+
+    class XPUTestCos2(XPUTestCosBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [1024, 8]).astype(
+                self.dtype
+            )
+
+    class XPUTestCos3(XPUTestCosBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [4, 512, 15, 15]).astype(
+                self.dtype
+            )
+
+    class XPUTestCos4(XPUTestCosBase):
+        def init_config(self):
+            self.x = np.random.uniform(-np.pi, np.pi, [4, 256, 22, 22]).astype(
+                self.dtype
+            )
+
+
+support_types = get_xpu_op_support_types('cos')
+for stype in support_types:
+    create_test_class(globals(), XPUTestCosOP, stype)
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_group_norm_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
+
+sys.path.append("..")
+
+from op_test import OpTest
+from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import (
+    XPUOpTestWrapper,
+    create_test_class,
+    get_xpu_op_support_types,
+)
+
+import paddle
+
+paddle.enable_static()
+
+
+def group_norm_naive(x, scale, bias, epsilon, groups, data_layout):
+    if data_layout == "NHWC":
+        x = np.transpose(x, (0, 3, 1, 2))  # NHWC => NCHW
+    N, C, H, W = x.shape
+    G = groups
+    x = x.reshape((N * G, -1))
+    mean = np.mean(x, axis=1, keepdims=True)
+    var = np.var(x, axis=1, keepdims=True)
+    output = (x - mean) / np.sqrt(var + epsilon)
+    output = output.reshape((N, C, H, W)) * scale.reshape(
+        (-1, 1, 1)
+    ) + bias.reshape((-1, 1, 1))
+    if data_layout == "NHWC":
+        output = np.transpose(output, (0, 2, 3, 1))  # NCHW => NHWC
+    return output, mean.reshape((N, G)), var.reshape((N, G))
+
+
+class XPUTestGroupNormOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'group_norm'
+        self.use_dynamic_create_class = False
+
+    class TestGroupNormOp(XPUOpTest):
+        def init_test_case(self):
+            self.data_format = "NCHW"
+            self.attrs = {'epsilon': 1e-5, 'groups': 2, 'data_layout': "NCHW"}
+
+        def setUp(self):
+            '''Test GroupNorm Op with supplied attributes'''
+            self.__class__.op_type = 'group_norm'
+            self.dtype = self.in_type
+            self.shape = (2, 100, 3, 5)
+            self.init_test_case()
+            input = np.random.random(self.shape).astype(self.dtype)
+            if self.data_format == "NHWC":
+                input = np.transpose(input, (0, 2, 3, 1))
+            scale = np.random.random([self.shape[1]]).astype(self.dtype)
+            bias = np.random.random([self.shape[1]]).astype(self.dtype)
+
+            output, mean, var = group_norm_naive(
+                input,
+                scale,
+                bias,
+                self.attrs['epsilon'],
+                self.attrs['groups'],
+                self.data_format,
+            )
+
+            self.inputs = {
+                'X': OpTest.np_dtype_to_fluid_dtype(input),
+                'Scale': OpTest.np_dtype_to_fluid_dtype(scale),
+                'Bias': OpTest.np_dtype_to_fluid_dtype(bias),
+            }
+            self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
+            self.attrs['data_layout'] = self.data_format
+
+        def test_check_output(self):
+            self.check_output_with_place(paddle.XPUPlace(0))
+
+        def test_check_grad(self):
+            pass
+
+    class TestGroupNormOp2(TestGroupNormOp):
+        def init_test_case(self):
+            self.data_format = "NHWC"
+            self.attrs = {'epsilon': 1e-5, 'groups': 2, 'data_layout': "NHWC"}
+
+
+support_types = get_xpu_op_support_types('group_norm')
+for stype in support_types:
+    create_test_class(globals(), XPUTestGroupNormOp, stype)
+
+if __name__ == "__main__":
+    paddle.enable_static()
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_linspace_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
+
+sys.path.append("..")
+
+from op_test_xpu import XPUOpTest, convert_np_dtype_to_dtype_
+from xpu.get_test_cover_info import (
+    XPUOpTestWrapper,
+    create_test_class,
+    get_xpu_op_support_types,
+)
+
+import paddle
+
+paddle.enable_static()
+
+
+class XPUTestLinspaceOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'linspace'
+        self.use_dynamic_create_class = False
+
+    class TestXPULinespaceOp(XPUOpTest):
+        def setUp(self):
+            self.op_type = "linspace"
+            self.dtype = self.in_type
+            self.set_attrs()
+
+            self.atol = 1e-4
+            np.random.seed(10)
+            self.inputs = {
+                'Start': np.array([0]).astype(self.dtype),
+                'Stop': np.array([10]).astype(self.dtype),
+                'Num': np.array([11]).astype('int32'),
+            }
+            self.outputs = {'Out': np.arange(0, 11).astype(self.dtype)}
+            self.attrs = {'dtype': int(convert_np_dtype_to_dtype_(self.dtype))}
+
+        def set_attrs(self):
+            pass
+
+        def test_check_output(self):
+            self.check_output_with_place(paddle.XPUPlace(0), atol=self.atol)
+
+    class TestXPULinespace2(TestXPULinespaceOp):
+        def set_attrs(self):
+            self.inputs = {
+                'Start': np.array([10]).astype(self.dtype),
+                'Stop': np.array([0]).astype(self.dtype),
+                'Num': np.array([11]).astype('int32'),
+            }
+
+            self.outputs = {'Out': np.arange(10, -1, -1).astype(self.dtype)}
+
+    class TestXPULinespace3(TestXPULinespaceOp):
+        def set_attrs(self):
+            self.inputs = {
+                'Start': np.array([10]).astype(self.dtype),
+                'Stop': np.array([0]).astype(self.dtype),
+                'Num': np.array([1]).astype('int32'),
+            }
+            self.outputs = {'Out': np.array(10, dtype=self.dtype)}
+
+
+support_types = get_xpu_op_support_types('linspace')
+for stype in support_types:
+    create_test_class(globals(), XPUTestLinspaceOp, stype)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_randint_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
+
+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
+
+paddle.enable_static()
+
+
+def output_hist(out):
+    hist, _ = np.histogram(out, range=(-10, 10))
+    hist = hist.astype("float32")
+    hist /= float(out.size)
+    prob = 0.1 * np.ones((10))
+    return hist, prob
+
+
+class XPUTestRandIntOp(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = 'randint'
+        self.use_dynamic_create_class = False
+
+    class TestXPURandIntOp(XPUOpTest):
+        def setUp(self):
+            self.op_type = "randint"
+            self.dtype = self.in_type
+            self.set_attrs()
+
+            self.atol = 1e-4
+            np.random.seed(10)
+            self.inputs = {}
+            self.outputs = {"Out": np.zeros((10000, 784)).astype("float32")}
+            self.attrs = {
+                "shape": [10000, 784],
+                "low": -10,
+                "high": 10,
+                "seed": 10,
+            }
+            self.output_hist = output_hist
+
+        def set_attrs(self):
+            pass
+
+        def test_check_output(self):
+            self.check_output_customized(self.verify_output)
+
+        def verify_output(self, outs):
+            hist, prob = self.output_hist(np.array(outs[0]))
+            np.testing.assert_allclose(hist, prob, rtol=0, atol=0.001)
+
+
+support_types = get_xpu_op_support_types('randint')
+for stype in support_types:
+    create_test_class(globals(), XPUTestRandIntOp, stype)
+
+
+if __name__ == "__main__":
+    unittest.main()