[Phi] move gaussian_random

paddle/fluid/operators/gaussian_random_op.cc

@@ -26,27 +26,6 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
-template <typename T>
-class CPUGaussianRandomKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    float mean = context.Attr<float>("mean");
-    float std = context.Attr<float>("std");
-    auto* tensor = context.Output<framework::Tensor>("Out");
-
-    std::normal_distribution<T> dist(mean, std);
-    auto shape = GetShape(context);
-    tensor->Resize(shape);
-    int64_t size = tensor->numel();
-    T* data = tensor->mutable_data<T>(context.GetPlace());
-    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
-    auto engine = framework::GetCPURandomEngine(seed);
-
-    for (int64_t i = 0; i < size; ++i) {
-      data[i] = dist(*engine);
-    }
-  }
-};  // namespace operators
 
 template <typename T>
 class CPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel<T> {
@@ -194,8 +173,6 @@ Used to initialize tensors with gaussian random generator.
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
                              ops::GaussianRandomOpMaker);
-REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel<float>,
-                       ops::CPUGaussianRandomKernel<double>);
 REGISTER_OP_CPU_KERNEL(gaussian_random_batch_size_like,
                        ops::CPUGaussianRandomBatchSizeLikeKernel<float>,
                        ops::CPUGaussianRandomBatchSizeLikeKernel<double>);

paddle/fluid/operators/gaussian_random_op.cu

@@ -52,53 +52,6 @@ struct GaussianGenerator {
   }
 };
 
-template <typename T>
-class GPUGaussianRandomKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>("Out");
-    unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
-    bool seed_flag = false;
-    if (seed == 0) {
-      std::random_device rd;
-      seed = rd();
-      seed_flag = true;
-    }
-    T mean = static_cast<T>(context.Attr<float>("mean"));
-    T std = static_cast<T>(context.Attr<float>("std"));
-    auto shape = GetShape(context);
-    tensor->Resize(shape);
-
-    auto& dev_cxt =
-        context.template device_context<platform::CUDADeviceContext>();
-    T* data = tensor->mutable_data<T>(dev_cxt.GetPlace());
-
-    int64_t size = tensor->numel();
-
-    int device_id = context.GetPlace().GetDeviceId();
-    auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);
-
-    if (gen_cuda->GetIsInitPy() && seed_flag) {
-      if (FLAGS_use_curand) {
-        using MT = typename details::MPTypeTrait<T>::Type;
-        distribution::normal_distribution<MT> dist;
-        distribution::normal_transform<MT> trans(mean, std);
-        distribution::distribution_and_transform<T>(dev_cxt, tensor, dist,
-                                                    trans);
-      } else {
-        auto seed_offset = gen_cuda->IncrementOffset(1);
-        int64_t gen_offset = size * seed_offset.second;
-        auto func =
-            GaussianGenerator<T>(mean, std, seed_offset.first, gen_offset);
-        IndexKernel<T, GaussianGenerator<T>>(dev_cxt, tensor, func);
-      }
-    } else {
-      auto func = GaussianGenerator<T>(mean, std, seed);
-      IndexKernel<T, GaussianGenerator<T>>(dev_cxt, tensor, func);
-    }
-  }
-};
-
 template <typename T>
 class GPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel<T> {
  public:
@@ -136,11 +89,6 @@ class GPUGaussianRandomBatchSizeLikeKernel : public framework::OpKernel<T> {
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_CUDA_KERNEL(
-    gaussian_random,
-    paddle::operators::GPUGaussianRandomKernel<paddle::platform::float16>,
-    paddle::operators::GPUGaussianRandomKernel<float>,
-    paddle::operators::GPUGaussianRandomKernel<double>);
 REGISTER_OP_CUDA_KERNEL(
     gaussian_random_batch_size_like,
     paddle::operators::GPUGaussianRandomBatchSizeLikeKernel<

paddle/phi/kernels/cpu/gaussian_random_kernel.cc

@@ -0,0 +1,53 @@
+// 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/phi/kernels/gaussian_random_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+#include "paddle/fluid/framework/generator.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void GaussianRandomKernel(const Context& dev_ctx,
+                          const ScalarArray& shape,
+                          float mean,
+                          float std,
+                          int seed,
+                          DataType dtype,
+                          DenseTensor* out) {
+  auto tensor = out;
+
+  std::normal_distribution<T> dist(mean, std);
+
+  tensor->Resize(phi::make_ddim(shape.GetData()));
+  int64_t size = tensor->numel();
+  T* data = dev_ctx.template Alloc<T>(tensor);
+  auto engine = paddle::framework::GetCPURandomEngine(seed);
+
+  for (int64_t i = 0; i < size; ++i) {
+    data[i] = dist(*engine);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(gaussian_random,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::GaussianRandomKernel,
+                   float,
+                   double) {}

paddle/phi/kernels/gaussian_random_kernel.h

@@ -0,0 +1,32 @@
+// 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.
+
+#pragma once
+
+#include "paddle/phi/common/scalar_array.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/device_context.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void GaussianRandomKernel(const Context& ctx,
+                          const ScalarArray& shape,
+                          float mean,
+                          float std,
+                          int seed,
+                          DataType dtype,
+                          DenseTensor* out);
+
+}  // namespace phi

paddle/phi/kernels/gpu/gaussian_random_kernel.cu

@@ -0,0 +1,111 @@
+// 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/phi/kernels/gaussian_random_kernel.h"
+
+#include <thrust/device_vector.h>
+#include <thrust/host_vector.h>
+#include <thrust/random.h>
+#include <thrust/transform.h>
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/distribution_helper.h"
+#include "paddle/phi/kernels/funcs/index_impl.cu.h"
+
+#include "paddle/fluid/framework/generator.h"
+
+DECLARE_bool(use_curand);
+
+namespace phi {
+
+template <typename T>
+struct GaussianGenerator {
+  T mean_, std_;
+  unsigned int seed_;
+  unsigned int offset_ = 0;
+
+  __host__ __device__ GaussianGenerator(T mean, T std, int seed)
+      : mean_(mean), std_(std), seed_(seed) {}
+
+  __host__ __device__ GaussianGenerator(T mean, T std, int seed, int offset)
+      : mean_(mean), std_(std), seed_(seed), offset_(offset) {}
+
+  __host__ __device__ T operator()(const unsigned int n) const {
+    thrust::minstd_rand rng;
+    rng.seed(seed_);
+    using MT = typename phi::kps::details::MPTypeTrait<T>::Type;
+    thrust::normal_distribution<MT> dist(mean_, std_);
+    unsigned int new_n = n + offset_;
+    rng.discard(new_n);
+    MT out = dist(rng);
+    return static_cast<T>(out);
+  }
+};
+
+template <typename T, typename Context>
+void GaussianRandomKernel(const Context& dev_ctx,
+                          const ScalarArray& shape,
+                          float mean,
+                          float std,
+                          int seed,
+                          DataType dtype,
+                          DenseTensor* out) {
+  auto tensor = out;
+
+  bool seed_flag = false;
+  if (seed == 0) {
+    std::random_device rd;
+    seed = rd();
+    seed_flag = true;
+  }
+
+  tensor->Resize(phi::make_ddim(shape.GetData()));
+
+  T* data = dev_ctx.template Alloc<T>(tensor);
+
+  int64_t size = tensor->numel();
+
+  int device_id = dev_ctx.GetPlace().GetDeviceId();
+  auto gen_cuda = paddle::framework::GetDefaultCUDAGenerator(device_id);
+
+  using MT = typename phi::kps::details::MPTypeTrait<T>::Type;
+  if (gen_cuda->GetIsInitPy() && seed_flag) {
+    if (FLAGS_use_curand) {
+      funcs::normal_distribution<MT> dist;
+      funcs::normal_transform<MT> trans(mean, std);
+      funcs::distribution_and_transform<T>(dev_ctx, tensor, dist, trans);
+    } else {
+      auto seed_offset = gen_cuda->IncrementOffset(1);
+      int64_t gen_offset = size * seed_offset.second;
+      auto func =
+          GaussianGenerator<MT>(mean, std, seed_offset.first, gen_offset);
+      IndexKernel<T, GaussianGenerator<MT>>(dev_ctx, tensor, func);
+    }
+  } else {
+    auto func = GaussianGenerator<MT>(mean, std, seed);
+    IndexKernel<T, GaussianGenerator<MT>>(dev_ctx, tensor, func);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(gaussian_random,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::GaussianRandomKernel,
+                   phi::dtype::float16,
+                   float,
+                   double) {}

paddle/phi/ops/compat/gaussian_random_sig.cc

@@ -0,0 +1,45 @@
+// 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/phi/core/compat/op_utils.h"
+
+namespace phi {
+
+KernelSignature GaussianRandomOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  if (ctx.InputSize("ShapeTensorList") > 0) {
+    return KernelSignature("gaussian_random",
+                           {},
+                           {"ShapeTensorList", "mean", "std", "seed", "dtype"},
+                           {"Out"});
+  }
+
+  const auto& shape = paddle::any_cast<std::vector<int64_t>>(ctx.Attr("shape"));
+  if (ctx.HasInput("ShapeTensor") && shape.empty()) {
+    return KernelSignature("gaussian_random",
+                           {},
+                           {"ShapeTensor", "mean", "std", "seed", "dtype"},
+                           {"Out"});
+  }
+
+  return KernelSignature("gaussian_random",
+                         {},
+                         {"shape", "mean", "std", "seed", "dtype"},
+                         {"Out"});
+}
+
+}  // namespace phi
+
+PD_REGISTER_ARG_MAPPING_FN(gaussian_random,
+                           phi::GaussianRandomOpArgumentMapping);