chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/histogram_kernel.cc

@@ -0,0 +1,136 @@
+// Copyright (c) 2024 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/histogram_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+#include "paddle/phi/kernels/reduce_sum_kernel.h"
+#include "paddle/utils/optional.h"
+
+namespace phi {
+template <typename T, typename Context>
+void HistogramKernel(const Context& dev_ctx,
+                     const DenseTensor& input,
+                     const optional<DenseTensor>& weight,
+                     int64_t bins,
+                     float min,
+                     float max,
+                     bool density,
+                     DenseTensor* output) {
+  auto& nbins = bins;
+  auto& minval = min;
+  auto& maxval = max;
+
+  const T* input_data = input.data<T>();
+  auto weight_data = weight.get_ptr() ? weight.get_ptr()->data<T>() : nullptr;
+  auto input_numel = input.numel();
+  if (input_numel == 0) {
+    Full<T, Context>(dev_ctx, output->dims(), 0, output);
+    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;
+  }
+
+  // check if out of range
+  double range =
+      static_cast<double>(output_max) - static_cast<double>(output_min);
+  PADDLE_ENFORCE_LT(
+      range,
+      static_cast<double>(std::numeric_limits<T>::max()),
+      common::errors::InvalidArgument(
+          "The range of max - min is out of range for target type, "
+          "current kernel type is %s, the range should less than %f "
+          "but now min is %f, max is %f.",
+          typeid(T).name(),
+          std::numeric_limits<T>::max(),
+          output_min,
+          output_max));
+
+  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,
+      common::errors::OutOfRange("range of min, max is not finite"));
+  PADDLE_ENFORCE_GE(
+      output_max,
+      output_min,
+      common::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));
+
+  if (density || weight_data) {
+    float* out_data = dev_ctx.template Alloc<float>(output);
+    funcs::SetConstant<Context, float>()(
+        dev_ctx, output, static_cast<float>(0));
+    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)] +=
+            weight_data ? static_cast<float>(weight_data[i]) : 1;
+      }
+    }
+    if (density) {
+      DenseTensor sum = Sum<float, Context>(
+          dev_ctx, *output, IntArray({0}), DataType::FLOAT32, false);
+      float* sum_data = sum.data<float>();
+      float gap = static_cast<float>(nbins) /
+                  static_cast<float>((output_max - output_min)) / *sum_data;
+      for (int64_t i = 0; i < nbins; i++) {
+        out_data[i] *= gap;
+      }
+    }
+  } else {
+    int64_t* out_data = dev_ctx.template Alloc<int64_t>(output);
+    funcs::SetConstant<Context, int64_t>()(
+        dev_ctx, output, static_cast<int64_t>(0));
+    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 phi
+
+PD_REGISTER_KERNEL(histogram,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::HistogramKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}