chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/logsumexp_kernel.cu

@@ -0,0 +1,183 @@
+// 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/logsumexp_kernel.h"
+#include "paddle/phi/kernels/gpu/logsumexp_function.cu.h"
+
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/activation_kernel.h"
+#include "paddle/phi/kernels/elementwise_add_kernel.h"
+#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/activation_functor.h"
+#include "paddle/phi/kernels/funcs/elementwise_base.h"
+#include "paddle/phi/kernels/funcs/transpose_function.cuh"
+#include "paddle/phi/kernels/gpu/reduce.h"
+#include "paddle/phi/kernels/reduce_max_kernel.h"
+#include "paddle/phi/kernels/transpose_kernel.h"
+
+namespace phi {
+
+template <typename T>
+struct ComputeType {
+  using type = T;
+};
+
+template <>
+struct ComputeType<float16> {
+  using type = float;
+};
+
+template <>
+struct ComputeType<bfloat16> {
+  using type = float;
+};
+
+template <typename T, typename Context>
+void LogsumexpFallbackKernel(const Context& dev_ctx,
+                             const DenseTensor& x,
+                             const std::vector<int>& axis_vec,
+                             const std::vector<int64_t>& outdim_vec,
+                             const std::vector<int64_t>& keep_outdim_vec,
+                             bool keepdim,
+                             bool reduce_all,
+                             DenseTensor* out) {
+  auto* in_x = &x;
+  auto* out_y = out;
+
+  auto outdim = make_ddim(outdim_vec);
+  auto keep_outdim = make_ddim(keep_outdim_vec);
+  out->Resize(outdim);
+  dev_ctx.template Alloc<T>(out_y);
+
+  DenseTensor max_x;
+  max_x.Resize(outdim);
+  dev_ctx.template Alloc<T>(&max_x);
+
+  MaxKernel<T, Context>(dev_ctx, *in_x, axis_vec, false, &max_x);
+
+  max_x.Resize(keep_outdim);
+  DenseTensor temp_x = Subtract<T, Context>(dev_ctx, *in_x, max_x);
+  funcs::ReduceKernel<T, T, kps::AddFunctor, kps::ExpFunctor<T>>(
+      dev_ctx, temp_x, out_y, kps::ExpFunctor<T>(), axis_vec);
+
+  DenseTensor log_out;
+  log_out.Resize(outdim);
+  dev_ctx.template Alloc<T>(&log_out);
+  LogKernel<T, Context>(dev_ctx, *out_y, &log_out);
+  log_out.Resize(outdim);
+  out->Resize(outdim);
+  AddKernel<T, Context>(dev_ctx, log_out, max_x, out);
+}
+
+template <typename T, typename Context>
+void LogsumexpKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const std::vector<int>& axis_in,
+                     bool keepdim,
+                     bool reduce_all,
+                     DenseTensor* out) {
+  if (x.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), -INFINITY, out);
+    return;
+  }
+  std::vector<int64_t> axis;
+  axis.reserve(axis_in.size());
+  std::for_each(axis_in.begin(), axis_in.end(), [&axis](const int& t) {
+    axis.push_back(static_cast<int64_t>(t));
+  });
+  auto xdim = x.dims();
+  for (size_t i = 0; i < xdim.size(); i++)
+    PADDLE_ENFORCE_LT(0,
+                      xdim[i],
+                      errors::InvalidArgument(
+                          "The dims of Input(X) should be greater than 0."));
+
+  reduce_all = recompute_reduce_all(x, axis, reduce_all);
+  std::vector<int64_t> outdim_vec, keep_outdim_vec, transpose_shape;
+  std::vector<int> axis_vec, perm;
+  int64_t compute_size = 1, other_size = 1;
+  for (auto i : axis) {
+    auto v = i >= 0 ? i : i + xdim.size();
+    axis_vec.push_back(v);
+  }
+  if (axis.size() == 0 || reduce_all) {
+    axis_vec.clear();
+    for (size_t i = 0; i < xdim.size(); i++) {
+      axis_vec.push_back(i);
+    }
+  }
+  for (size_t i = 0; i < xdim.size(); i++) {
+    bool flag = false;
+    for (auto v : axis_vec) {
+      if (v == i) {
+        flag = true;
+        break;
+      }
+    }
+    if (flag) {
+      compute_size *= xdim[i];
+      keep_outdim_vec.push_back(1);
+      if (keepdim) outdim_vec.push_back(1);
+    } else {
+      other_size *= xdim[i];
+      transpose_shape.push_back(xdim[i]);
+      perm.push_back(i);
+      outdim_vec.push_back(xdim[i]);
+      keep_outdim_vec.push_back(xdim[i]);
+    }
+  }
+
+  auto outdim = make_ddim(outdim_vec);
+  if (compute_size <= 1024) {
+    if (perm.size() != xdim.size())
+      perm.insert(perm.end(), axis_vec.begin(), axis_vec.end());
+    for (auto i : axis_vec) transpose_shape.push_back(xdim[i]);
+    DenseTensor transpose_x;
+    if (xdim.size() == 0 ||
+        (axis_vec.size() == 1 && axis_vec[0] == xdim.size())) {
+      transpose_x = x;
+    } else {
+      transpose_x.Resize(transpose_shape);
+      dev_ctx.template Alloc<T>(&transpose_x);
+      funcs::TransposeGPUKernelDriver<T>(dev_ctx, x, perm, &transpose_x);
+    }
+    dev_ctx.template Alloc<T>(out);
+    using compute_type = typename ComputeType<T>::type;
+    const int64_t num_col = compute_size, num_row = other_size;
+    funcs::DispatchLogsumexpWarp<compute_type, T, Context>(
+        dev_ctx, num_row, num_col, transpose_x.data<T>(), out->data<T>());
+    out->Resize(outdim);
+  } else {
+    LogsumexpFallbackKernel<T, Context>(dev_ctx,
+                                        x,
+                                        axis_vec,
+                                        outdim_vec,
+                                        keep_outdim_vec,
+                                        keepdim,
+                                        reduce_all,
+                                        out);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(logsumexp,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::LogsumexpKernel,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::bfloat16) {}