fix format error; test=develop

paddle/fluid/imperative/prepared_operator.h

@@ -412,7 +412,7 @@ 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))) {
+      } 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))) {

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/framework/op_registry.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/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/kernels/funcs/math_function.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 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();
+  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));
+  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;
+  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;
-    }
+  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));
+  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;
-      }
+  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;
     }
+  }
 }
 
-}  // namspace pten
-
+}  // namespace pten
 
 PT_REGISTER_KERNEL(histogram,
                    CPU,
@@ -74,4 +85,4 @@ PT_REGISTER_KERNEL(histogram,
                    float,
                    double,
                    int,
-                   int64_t) {}
\ No newline at end of file
+                   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/histogram_kernel.h"
 #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"
@@ -9,8 +21,8 @@
 #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/eigen_function.h"
 #include "paddle/pten/kernels/funcs/eigen/common.h"
+#include "paddle/pten/kernels/funcs/eigen/eigen_function.h"
 
 namespace pten {
 
@@ -22,7 +34,9 @@ inline int GET_BLOCKS(const int N) {
 }
 
 template <typename T, typename IndexType>
-__device__ static IndexType GetBin(T input_value, T min_value, T max_value,
+__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));
@@ -31,9 +45,12 @@ __device__ static IndexType GetBin(T input_value, T min_value, T max_value,
 }
 
 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) {
+__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;
@@ -58,83 +75,80 @@ __global__ void KernelHistogram(const T* input, const int total_elements,
 
 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;
-    }
+                     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);
+  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
-
+}  // namespace pten
 
 PT_REGISTER_KERNEL(histogram,
                    GPU,

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"});
+  return KernelSignature("histogram", {"X"}, {"bins", "min", "max"}, {"Out"});
 }
 
-
 }  // namespace pten
 
-PT_REGISTER_ARG_MAPPING_FN(histogram, pten::HistogramOpArgumentMapping);
\ No newline at end of file
+PT_REGISTER_ARG_MAPPING_FN(histogram, pten::HistogramOpArgumentMapping);