move histogram to pten (#39496)

* move histogram to pten; test=develop

* fix format error; test=develop

* fix histogram kernel format; test=develop

.gitignore

@@ -51,3 +51,5 @@ paddle/infrt/dialect/pd_ops_info.h
 .lit_test_times.txt
 paddle/infrt/tests/dialect/Output
 paddle/infrt/tests/lit.cfg.py
+paddle/fluid/pybind/eager_final_state_op_function_impl.h
+paddle/fluid/pybind/tmp_eager_final_state_op_function_impl.h

paddle/fluid/framework/operator.cc

@@ -2165,6 +2165,8 @@ void OperatorWithKernel::BuildPtenKernelContext(
         pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(float, attr));
       } else if (attr_defs[i].type_index == std::type_index(typeid(bool))) {
         pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(bool, attr));
+      } else if (attr_defs[i].type_index == std::type_index(typeid(int64_t))) {
+        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(int64_t, attr));
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::string))) {
         pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(std::string, attr));

paddle/fluid/imperative/prepared_operator.h

@@ -421,6 +421,8 @@ void BuildDygraphPtenKernelContext(
         kernel_ctx->EmplaceBackAttr(BOOST_GET_CONST(float, attr));
       } else if (attr_defs[i].type_index == std::type_index(typeid(bool))) {
         kernel_ctx->EmplaceBackAttr(BOOST_GET_CONST(bool, attr));
+      } else if (attr_defs[i].type_index == std::type_index(typeid(int64_t))) {
+        kernel_ctx->EmplaceBackAttr(BOOST_GET_CONST(int64_t, attr));
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::string))) {
         kernel_ctx->EmplaceBackAttr(BOOST_GET_CONST(std::string, attr));

paddle/fluid/operators/histogram_op.cc

@@ -12,12 +12,12 @@ 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/operators/histogram_op.h"
-
 #include <string>
 #include <unordered_map>
 #include <vector>
 
+#include "paddle/fluid/framework/op_registry.h"
+
 namespace paddle {
 namespace operators {
 
@@ -85,8 +85,3 @@ REGISTER_OPERATOR(
     histogram, ops::HistogramOp, ops::HistogramOpMaker,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
-REGISTER_OP_CPU_KERNEL(
-    histogram, ops::HistogramKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::HistogramKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::HistogramKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::HistogramKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/histogram_op.cu

-/* 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. */
-
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/operators/histogram_op.h"
-#include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
-#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
-#include "paddle/pten/core/hostdevice.h"
-
-namespace paddle {
-namespace operators {
-
-using IndexType = int64_t;
-using Tensor = framework::Tensor;
-using platform::PADDLE_CUDA_NUM_THREADS;
-
-inline int GET_BLOCKS(const int N) {
-  return (N + PADDLE_CUDA_NUM_THREADS - 1) / PADDLE_CUDA_NUM_THREADS;
-}
-
-template <typename T, typename IndexType>
-__device__ static IndexType GetBin(T input_value, T min_value, T max_value,
-                                   int64_t nbins) {
-  IndexType bin = static_cast<int>((input_value - min_value) * nbins /
-                                   (max_value - min_value));
-  IndexType output_index = bin < nbins - 1 ? bin : nbins - 1;
-  return output_index;
-}
-
-template <typename T, typename IndexType>
-__global__ void KernelHistogram(const T* input, const int total_elements,
-                                const int64_t nbins, const T min_value,
-                                const T max_value, int64_t* output) {
-  extern __shared__ int64_t buf_hist[];
-  for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
-    buf_hist[i] = 0;
-  }
-  __syncthreads();
-
-  CUDA_KERNEL_LOOP(input_index, total_elements) {
-    // const IndexType input_index = threadIdx.x + blockIdx.x * blockDim.x;
-    const auto input_value = input[input_index];
-    if (input_value >= min_value && input_value <= max_value) {
-      const IndexType output_index =
-          GetBin<T, IndexType>(input_value, min_value, max_value, nbins);
-      paddle::platform::CudaAtomicAdd(&buf_hist[output_index], 1);
-    }
-  }
-  __syncthreads();
-
-  for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
-    paddle::platform::CudaAtomicAdd(&output[i], buf_hist[i]);
-  }
-}
-
-template <typename DeviceContext, typename T>
-class HistogramCUDAKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    PADDLE_ENFORCE_EQ(
-        platform::is_gpu_place(context.GetPlace()), true,
-        platform::errors::InvalidArgument("It must use CUDAPlace."));
-
-    const Tensor* input = context.Input<framework::Tensor>("X");
-    Tensor* output = context.Output<framework::Tensor>("Out");
-    auto& nbins = context.Attr<int64_t>("bins");
-    auto& minval = context.Attr<int>("min");
-    auto& maxval = context.Attr<int>("max");
-
-    const T* input_data = input->data<T>();
-    const int input_numel = input->numel();
-
-    int64_t* out_data = output->mutable_data<int64_t>(context.GetPlace());
-    pten::funcs::SetConstant<platform::CUDADeviceContext, int64_t>()(
-        context.template device_context<platform::CUDADeviceContext>(), output,
-        static_cast<int64_t>(0));
-
-    if (input_data == nullptr) return;
-
-    T output_min = static_cast<T>(minval);
-    T output_max = static_cast<T>(maxval);
-
-    if (output_min == output_max) {
-      auto input_x = framework::EigenVector<T>::Flatten(*input);
-
-      framework::Tensor input_min_t, input_max_t;
-      auto* input_min_data =
-          input_min_t.mutable_data<T>({1}, context.GetPlace());
-      auto* input_max_data =
-          input_max_t.mutable_data<T>({1}, context.GetPlace());
-      auto input_min_scala = framework::EigenScalar<T>::From(input_min_t);
-      auto input_max_scala = framework::EigenScalar<T>::From(input_max_t);
-
-      auto* place =
-          context.template device_context<DeviceContext>().eigen_device();
-      input_min_scala.device(*place) = input_x.minimum();
-      input_max_scala.device(*place) = input_x.maximum();
-
-      Tensor input_min_cpu, input_max_cpu;
-      paddle::framework::TensorCopySync(input_min_t, platform::CPUPlace(),
-                                        &input_min_cpu);
-      paddle::framework::TensorCopySync(input_max_t, platform::CPUPlace(),
-                                        &input_max_cpu);
-
-      output_min = input_min_cpu.data<T>()[0];
-      output_max = input_max_cpu.data<T>()[0];
-    }
-    if (output_min == output_max) {
-      output_min = output_min - 1;
-      output_max = output_max + 1;
-    }
-
-    PADDLE_ENFORCE_EQ(
-        (std::isinf(static_cast<float>(output_min)) ||
-         std::isnan(static_cast<float>(output_max)) ||
-         std::isinf(static_cast<float>(output_min)) ||
-         std::isnan(static_cast<float>(output_max))),
-        false, platform::errors::OutOfRange("range of min, max is not finite"));
-    PADDLE_ENFORCE_GE(
-        output_max, output_min,
-        platform::errors::InvalidArgument(
-            "max must be larger or equal to min. If min and max are both zero, "
-            "the minimum and maximum values of the data are used. "
-            "But received max is %d, min is %d",
-            maxval, minval));
-
-    auto stream =
-        context.template device_context<platform::CUDADeviceContext>().stream();
-    KernelHistogram<
-        T, IndexType><<<GET_BLOCKS(input_numel), PADDLE_CUDA_NUM_THREADS,
-                        nbins * sizeof(int64_t), stream>>>(
-        input_data, input_numel, nbins, output_min, output_max, out_data);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    histogram,
-    ops::HistogramCUDAKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::HistogramCUDAKernel<paddle::platform::CUDADeviceContext, int64_t>,
-    ops::HistogramCUDAKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::HistogramCUDAKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/histogram_op.h

-/* 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. */
-
-#pragma once
-
-#include <algorithm>
-
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/pten/kernels/funcs/math_function.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-template <typename DeviceContext, typename T>
-class HistogramKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor* input = context.Input<framework::Tensor>("X");
-    Tensor* output = context.Output<framework::Tensor>("Out");
-    auto& nbins = context.Attr<int64_t>("bins");
-    auto& minval = context.Attr<int>("min");
-    auto& maxval = context.Attr<int>("max");
-
-    const T* input_data = input->data<T>();
-    auto input_numel = input->numel();
-
-    int64_t* out_data = output->mutable_data<int64_t>(context.GetPlace());
-    pten::funcs::SetConstant<DeviceContext, int64_t>()(
-        context.template device_context<DeviceContext>(), output,
-        static_cast<int64_t>(0));
-
-    if (input_data == nullptr) return;
-
-    T output_min = static_cast<T>(minval);
-    T output_max = static_cast<T>(maxval);
-    if (output_min == output_max) {
-      output_min = *std::min_element(input_data, input_data + input_numel);
-      output_max = *std::max_element(input_data, input_data + input_numel);
-    }
-    if (output_min == output_max) {
-      output_min = output_min - 1;
-      output_max = output_max + 1;
-    }
-
-    PADDLE_ENFORCE_EQ(
-        (std::isinf(static_cast<float>(output_min)) ||
-         std::isnan(static_cast<float>(output_max)) ||
-         std::isinf(static_cast<float>(output_min)) ||
-         std::isnan(static_cast<float>(output_max))),
-        false, platform::errors::OutOfRange("range of min, max is not finite"));
-    PADDLE_ENFORCE_GE(
-        output_max, output_min,
-        platform::errors::InvalidArgument(
-            "max must be larger or equal to min. If min and max are both zero, "
-            "the minimum and maximum values of the data are used. "
-            "But received max is %d, min is %d",
-            maxval, minval));
-
-    for (int64_t i = 0; i < input_numel; i++) {
-      if (input_data[i] >= output_min && input_data[i] <= output_max) {
-        const int64_t bin = (int64_t)((input_data[i] - output_min) * nbins /
-                                      (output_max - output_min));
-        out_data[std::min(bin, nbins - 1)] += 1;
-      }
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/pten/kernels/cpu/histogram_kernel.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/pten/kernels/histogram_kernel.h"
+#include "paddle/pten/backends/cpu/cpu_context.h"
+#include "paddle/pten/core/kernel_registry.h"
+#include "paddle/pten/kernels/funcs/math_function.h"
+
+namespace pten {
+
+template <typename T, typename Context>
+void HistogramKernel(const Context& dev_ctx,
+                     const DenseTensor& input,
+                     int64_t bins,
+                     int min,
+                     int max,
+                     DenseTensor* output) {
+  auto& nbins = bins;
+  auto& minval = min;
+  auto& maxval = max;
+
+  const T* input_data = input.data<T>();
+  auto input_numel = input.numel();
+
+  int64_t* out_data = output->mutable_data<int64_t>(dev_ctx.GetPlace());
+  pten::funcs::SetConstant<Context, int64_t>()(
+      dev_ctx, output, static_cast<int64_t>(0));
+
+  if (input_data == nullptr) return;
+
+  T output_min = static_cast<T>(minval);
+  T output_max = static_cast<T>(maxval);
+  if (output_min == output_max) {
+    output_min = *std::min_element(input_data, input_data + input_numel);
+    output_max = *std::max_element(input_data, input_data + input_numel);
+  }
+  if (output_min == output_max) {
+    output_min = output_min - 1;
+    output_max = output_max + 1;
+  }
+
+  PADDLE_ENFORCE_EQ(
+      (std::isinf(static_cast<float>(output_min)) ||
+       std::isnan(static_cast<float>(output_max)) ||
+       std::isinf(static_cast<float>(output_min)) ||
+       std::isnan(static_cast<float>(output_max))),
+      false,
+      pten::errors::OutOfRange("range of min, max is not finite"));
+  PADDLE_ENFORCE_GE(
+      output_max,
+      output_min,
+      pten::errors::InvalidArgument(
+          "max must be larger or equal to min. If min and max are both zero, "
+          "the minimum and maximum values of the data are used. "
+          "But received max is %d, min is %d",
+          maxval,
+          minval));
+
+  for (int64_t i = 0; i < input_numel; i++) {
+    if (input_data[i] >= output_min && input_data[i] <= output_max) {
+      const int64_t bin = (int64_t)((input_data[i] - output_min) * nbins /
+                                    (output_max - output_min));
+      out_data[std::min(bin, nbins - 1)] += 1;
+    }
+  }
+}
+
+}  // namespace pten
+
+PT_REGISTER_KERNEL(histogram,
+                   CPU,
+                   ALL_LAYOUT,
+                   pten::HistogramKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}

paddle/pten/kernels/gpu/histogram_kernel.cu

+// 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/pten/kernels/funcs/math_function.h"
+#include "paddle/pten/kernels/histogram_kernel.h"
+
+#include "paddle/pten/backends/gpu/gpu_context.h"
+#include "paddle/pten/core/kernel_registry.h"
+
+#include "paddle/fluid/platform/device/gpu/gpu_launch_config.h"
+#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
+
+#include "paddle/pten/kernels/funcs/eigen/common.h"
+#include "paddle/pten/kernels/funcs/eigen/eigen_function.h"
+
+namespace pten {
+
+using IndexType = int64_t;
+using paddle::platform::PADDLE_CUDA_NUM_THREADS;
+
+inline int GET_BLOCKS(const int N) {
+  return (N + PADDLE_CUDA_NUM_THREADS - 1) / PADDLE_CUDA_NUM_THREADS;
+}
+
+template <typename T, typename IndexType>
+__device__ static IndexType GetBin(T input_value,
+                                   T min_value,
+                                   T max_value,
+                                   int64_t nbins) {
+  IndexType bin = static_cast<int>((input_value - min_value) * nbins /
+                                   (max_value - min_value));
+  IndexType output_index = bin < nbins - 1 ? bin : nbins - 1;
+  return output_index;
+}
+
+template <typename T, typename IndexType>
+__global__ void KernelHistogram(const T* input,
+                                const int total_elements,
+                                const int64_t nbins,
+                                const T min_value,
+                                const T max_value,
+                                int64_t* output) {
+  extern __shared__ int64_t buf_hist[];
+  for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
+    buf_hist[i] = 0;
+  }
+  __syncthreads();
+
+  CUDA_KERNEL_LOOP(input_index, total_elements) {
+    // const IndexType input_index = threadIdx.x + blockIdx.x * blockDim.x;
+    const auto input_value = input[input_index];
+    if (input_value >= min_value && input_value <= max_value) {
+      const IndexType output_index =
+          GetBin<T, IndexType>(input_value, min_value, max_value, nbins);
+      paddle::platform::CudaAtomicAdd(&buf_hist[output_index], 1);
+    }
+  }
+  __syncthreads();
+
+  for (int i = threadIdx.x; i < nbins; i += blockDim.x) {
+    paddle::platform::CudaAtomicAdd(&output[i], buf_hist[i]);
+  }
+}
+
+template <typename T, typename Context>
+void HistogramKernel(const Context& dev_ctx,
+                     const DenseTensor& input,
+                     int64_t bins,
+                     int min,
+                     int max,
+                     DenseTensor* output) {
+  auto& nbins = bins;
+  auto& minval = min;
+  auto& maxval = max;
+
+  const T* input_data = input.data<T>();
+  const int input_numel = input.numel();
+
+  int64_t* out_data = output->mutable_data<int64_t>(dev_ctx.GetPlace());
+  pten::funcs::SetConstant<Context, int64_t>()(
+      dev_ctx, output, static_cast<int64_t>(0));
+
+  if (input_data == nullptr) return;
+
+  T output_min = static_cast<T>(minval);
+  T output_max = static_cast<T>(maxval);
+
+  if (output_min == output_max) {
+    auto input_x = pten::EigenVector<T>::Flatten(input);
+
+    DenseTensor input_min_t, input_max_t;
+    auto* input_min_data = input_min_t.mutable_data<T>({1}, dev_ctx.GetPlace());
+    auto* input_max_data = input_max_t.mutable_data<T>({1}, dev_ctx.GetPlace());
+    auto input_min_scala = pten::EigenScalar<T>::From(input_min_t);
+    auto input_max_scala = pten::EigenScalar<T>::From(input_max_t);
+
+    auto* place = dev_ctx.eigen_device();
+    input_min_scala.device(*place) = input_x.minimum();
+    input_max_scala.device(*place) = input_x.maximum();
+
+    DenseTensor input_min_cpu, input_max_cpu;
+    paddle::framework::TensorCopySync(
+        input_min_t, paddle::platform::CPUPlace(), &input_min_cpu);
+    paddle::framework::TensorCopySync(
+        input_max_t, paddle::platform::CPUPlace(), &input_max_cpu);
+
+    output_min = input_min_cpu.data<T>()[0];
+    output_max = input_max_cpu.data<T>()[0];
+  }
+  if (output_min == output_max) {
+    output_min = output_min - 1;
+    output_max = output_max + 1;
+  }
+
+  PADDLE_ENFORCE_EQ(
+      (std::isinf(static_cast<float>(output_min)) ||
+       std::isnan(static_cast<float>(output_max)) ||
+       std::isinf(static_cast<float>(output_min)) ||
+       std::isnan(static_cast<float>(output_max))),
+      false,
+      pten::errors::OutOfRange("range of min, max is not finite"));
+  PADDLE_ENFORCE_GE(
+      output_max,
+      output_min,
+      pten::errors::InvalidArgument(
+          "max must be larger or equal to min. If min and max are both zero, "
+          "the minimum and maximum values of the data are used. "
+          "But received max is %d, min is %d",
+          maxval,
+          minval));
+
+  auto stream = dev_ctx.stream();
+  KernelHistogram<T, IndexType><<<GET_BLOCKS(input_numel),
+                                  PADDLE_CUDA_NUM_THREADS,
+                                  nbins * sizeof(int64_t),
+                                  stream>>>(
+      input_data, input_numel, nbins, output_min, output_max, out_data);
+}
+
+}  // namespace pten
+
+PT_REGISTER_KERNEL(histogram,
+                   GPU,
+                   ALL_LAYOUT,
+                   pten::HistogramKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}

paddle/pten/kernels/histogram_kernel.h

+// 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/pten/core/dense_tensor.h"
+namespace pten {
+
+template <typename T, typename Context>
+void HistogramSelectKernel(const Context& dev_ctx,
+                           const DenseTensor& input,
+                           int64_t bins,
+                           int min,
+                           int max,
+                           DenseTensor* out);
+
+}  // namespace pten

paddle/pten/ops/compat/histogram_sig.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/pten/core/compat/op_utils.h"
+
+namespace pten {
+
+KernelSignature HistogramOpArgumentMapping(const ArgumentMappingContext& ctx) {
+  return KernelSignature("histogram", {"X"}, {"bins", "min", "max"}, {"Out"});
+}
+
+}  // namespace pten
+
+PT_REGISTER_ARG_MAPPING_FN(histogram, pten::HistogramOpArgumentMapping);