chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/reduce_kernel.cu

@@ -0,0 +1,410 @@
+// 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/reduce_kernel.h"
+#include "paddle/phi/kernels/reduce_nansum_grad_kernel.h"
+
+#include "paddle/phi/kernels/funcs/for_range.h"
+#include "paddle/phi/kernels/gpu/reduce.h"
+#include "paddle/phi/kernels/gpu/reduce_amin_amax_common.h"
+#include "paddle/phi/kernels/reduce_amin_grad_kernel.h"
+#include "paddle/phi/kernels/reduce_max_grad_kernel.h"
+#include "paddle/phi/kernels/reduce_mean_grad_kernel.h"
+#include "paddle/phi/kernels/reduce_min_grad_kernel.h"
+#include "paddle/phi/kernels/reduce_sum_grad_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
+#include "paddle/phi/kernels/funcs/broadcast_function.h"
+#include "paddle/phi/kernels/funcs/compare_functors.h"
+#include "paddle/phi/kernels/funcs/elementwise_functor.h"
+#include "paddle/phi/kernels/funcs/reduce_function.h"
+#include "paddle/phi/kernels/gpu/reduce_grad.h"
+
+#include "paddle/phi/backends/all_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
+#include "paddle/phi/core/distributed/nccl_comm_context.h"
+#endif
+
+namespace phi {
+
+template <typename T, typename Context>
+void ReduceSumGradKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& out_grad,
+                         const IntArray& dims,
+                         bool keep_dim,
+                         bool reduce_all,
+                         DenseTensor* x_grad) {
+  reduce_all = recompute_reduce_all(x, dims, reduce_all);
+  if (x_grad && x_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(x_grad);
+    return;
+  }
+  // get reduce_dim for reduce_mean_grad
+  int dim_size = x.dims().size();
+  std::vector<int> reduce_dims =
+      funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
+
+  auto update_dims = vectorize(x.dims());
+  for (auto i : reduce_dims) {
+    update_dims[i] = 1;
+  }
+
+  // make new tensor
+  DenseTensor new_out_grad(out_grad.dtype());
+  new_out_grad.ShareDataWith(out_grad);
+  new_out_grad.Resize(update_dims);
+
+  // call ReduceGrad
+  dev_ctx.Alloc(x_grad, x.dtype());
+  using MT = typename MPTypeTrait<T>::Type;
+  ReduceGrad<kps::IdentityFunctor<T, MT>>(
+      dev_ctx, &new_out_grad, x_grad, x.dtype(), kps::IdentityFunctor<T, MT>());
+}
+
+template <typename T, typename Context>
+void ReduceMeanGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& out_grad,
+                          const IntArray& dims,
+                          bool keep_dim,
+                          bool reduce_all,
+                          DenseTensor* x_grad) {
+  if (x_grad && x_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(x_grad);
+    return;
+  }
+
+  reduce_all = recompute_reduce_all(x, dims, reduce_all);
+  // get reduce_dim and reduce_num for reduce_mean_grad
+  int dim_size = x.dims().size();
+  std::vector<int> reduce_dims =
+      funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
+
+  auto update_dims = vectorize(x.dims());
+  int64_t reduce_num = 1;
+  for (auto i : reduce_dims) {
+    reduce_num *= (x.dims())[i];
+    update_dims[i] = 1;
+  }
+
+  // make new tensor
+  DenseTensor new_out_grad(out_grad.dtype());
+  new_out_grad.ShareDataWith(out_grad);
+  new_out_grad.Resize(update_dims);
+
+  // call BroadcastKernel
+  dev_ctx.Alloc(x_grad, x.dtype());
+  std::vector<const DenseTensor*> inputs = {&new_out_grad};
+  std::vector<DenseTensor*> outputs = {x_grad};
+
+  using MT = typename MPTypeTrait<T>::Type;
+  funcs::BroadcastKernel<T>(dev_ctx,
+                            inputs,
+                            &outputs,
+                            kps::MPTypeDivideFunctor<T, MT>(reduce_num),
+                            0);
+}
+
+template <typename T, typename Context>
+void ReduceAMinGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& out,
+                          const DenseTensor& out_grad,
+                          const std::vector<int64_t>& dims,
+                          bool keep_dim,
+                          bool reduce_all,
+                          DenseTensor* x_grad) {
+  reduce_all = recompute_reduce_all(x, dims, reduce_all);
+  ReduceCudaAMaxAMinGrad<T, Context>(
+      dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
+}
+
+template <typename T, typename Context>
+void ReduceMinGradKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& out,
+                         const DenseTensor& out_grad,
+                         const IntArray& dims,
+                         bool keep_dim,
+                         bool reduce_all,
+                         DenseTensor* x_grad) {
+  ReduceAMinGradKernel<T, Context>(
+      dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
+}
+
+template <typename T, typename Context>
+void ReduceAMaxGradKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& out,
+                          const DenseTensor& out_grad,
+                          const std::vector<int64_t>& dims,
+                          bool keep_dim,
+                          bool reduce_all,
+                          DenseTensor* x_grad) {
+  reduce_all = recompute_reduce_all(x, dims, reduce_all);
+  ReduceCudaAMaxAMinGrad<T, Context>(
+      dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
+}
+
+template <typename T, typename Context>
+void ReduceMaxGradKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& out,
+                         const DenseTensor& out_grad,
+                         const IntArray& dims,
+                         bool keep_dim,
+                         bool reduce_all,
+                         DenseTensor* x_grad) {
+  ReduceAMaxGradKernel<T, Context>(
+      dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
+}
+
+template <typename T, typename Context>
+void ReduceKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  int root,
+                  int reduce_type,
+                  DenseTensor* out) {
+  PADDLE_ENFORCE_GT(x.numel(),
+                    0,
+                    common::errors::InvalidArgument(
+                        "Tensor need be reduced must not empty."));
+#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
+  out->Resize(x.dims());
+  dev_ctx.template Alloc<T>(out);
+
+  auto comm_ctx =
+      static_cast<distributed::NCCLCommContext*>(dev_ctx.GetCommContext());
+  PADDLE_ENFORCE_NE(
+      comm_ctx,
+      nullptr,
+      errors::Unavailable("NCCLCommContext is nullptr, collective op should "
+                          "has ring_id attr."));
+  gpuStream_t stream = dev_ctx.stream();
+  PADDLE_ENFORCE_NOT_NULL(stream,
+                          errors::NotFound("Should initialize NCCL firstly."));
+
+  ncclRedOp_t red_type = ncclSum;
+  switch (static_cast<ReduceType>(reduce_type)) {
+    case ReduceType::kRedSum:
+      red_type = ncclSum;
+      break;
+    case ReduceType::kRedMax:
+      red_type = ncclMax;
+      break;
+    case ReduceType::kRedMin:
+      red_type = ncclMin;
+      break;
+    case ReduceType::kRedProd:
+      red_type = ncclProd;
+      break;
+#if NCCL_VERSION_CODE >= 21000
+    case ReduceType::kRedAvg:
+      red_type = ncclAvg;
+      break;
+#endif
+  }
+  comm_ctx->Reduce(out, x, red_type, root, stream);
+#else
+  PADDLE_THROW(
+      errors::PreconditionNotMet("PaddlePaddle should compile with GPU."));
+#endif
+}
+
+template <typename T>
+struct NanMaskFunctor {
+  const T* x_data;
+  T* x_grad_data;
+
+  NanMaskFunctor(const T* x_data, T* x_grad_data)
+      : x_data(x_data), x_grad_data(x_grad_data) {}
+
+  HOSTDEVICE void operator()(size_t idx) const {
+    // NaN != NaN for floating-point; always false for integral types
+    if (x_data[idx] != x_data[idx]) {
+      x_grad_data[idx] = static_cast<T>(0);
+    }
+  }
+};
+
+template <typename T, typename Context>
+void NansumGradKernel(const Context& dev_ctx,
+                      const DenseTensor& x,
+                      const DenseTensor& out_grad,
+                      const IntArray& dims,
+                      bool keep_dim,
+                      bool reduce_all,
+                      DenseTensor* x_grad) {
+  reduce_all = recompute_reduce_all(x, dims, reduce_all);
+  if (x_grad && x_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(x_grad);
+    return;
+  }
+
+  // Step 1: broadcast out_grad to x_grad shape (same as sum_grad)
+  int dim_size = x.dims().size();
+  std::vector<int> reduce_dims =
+      funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
+
+  auto update_dims = vectorize(x.dims());
+  for (auto i : reduce_dims) {
+    update_dims[i] = 1;
+  }
+
+  DenseTensor new_out_grad(out_grad.dtype());
+  new_out_grad.ShareDataWith(out_grad);
+  new_out_grad.Resize(update_dims);
+
+  dev_ctx.Alloc(x_grad, x.dtype());
+  using MT = typename MPTypeTrait<T>::Type;
+  ReduceGrad<kps::IdentityFunctor<T, MT>>(
+      dev_ctx, &new_out_grad, x_grad, x.dtype(), kps::IdentityFunctor<T, MT>());
+
+  // Step 2: zero out gradient where x is NaN
+  const T* x_data = x.data<T>();
+  T* x_grad_data = x_grad->data<T>();
+  int64_t numel = x.numel();
+  funcs::ForRange<Context> for_range(dev_ctx, numel);
+  for_range(NanMaskFunctor<T>(x_data, x_grad_data));
+}
+
+}  // namespace phi
+
+#if NCCL_VERSION_CODE >= 21000
+PD_REGISTER_KERNEL(reduce,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceKernel,
+                   float,
+                   double,
+                   int,
+                   bool,
+                   int8_t,
+                   uint8_t,
+                   int64_t,
+                   phi::bfloat16,
+                   phi::float16) {}
+#else
+PD_REGISTER_KERNEL(reduce,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceKernel,
+                   float,
+                   double,
+                   int,
+                   bool,
+                   int8_t,
+                   uint8_t,
+                   int64_t,
+                   phi::float16) {}
+#endif
+
+PD_REGISTER_KERNEL(amax_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceAMaxGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   phi::float16,
+                   phi::bfloat16) {}
+
+PD_REGISTER_KERNEL(amin_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceAMinGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {}
+
+PD_REGISTER_KERNEL(max_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceMaxGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   phi::float16,
+                   phi::bfloat16) {}
+
+PD_REGISTER_KERNEL(mean_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceMeanGradKernel,
+                   bool,
+                   float,
+                   double,
+                   phi::float8_e4m3fn,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128,
+                   int,
+                   int64_t) {}
+
+PD_REGISTER_KERNEL(min_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceMinGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   phi::float16,
+                   phi::bfloat16) {}
+
+PD_REGISTER_KERNEL(sum_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::ReduceSumGradKernel,
+                   bool,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::bfloat16,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {
+  kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
+}
+
+PD_REGISTER_KERNEL(nansum_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::NansumGradKernel,
+                   bool,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::bfloat16,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {
+  kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
+}