chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/dot_kernel.cu

@@ -0,0 +1,99 @@
+// Copyright (c) 2021 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/dot_kernel.h"
+#include "paddle/common/enforce.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+namespace phi {
+
+template <typename T, typename Context>
+void DotKernel(const Context& dev_ctx,
+               const DenseTensor& x,
+               const DenseTensor& y,
+               DenseTensor* out) {
+  if (x.numel() == 0 || y.numel() == 0) {
+    // x[2, 1], y[2, 0], out[2]
+    Full<T, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+  if (out->numel() <= 0) {
+    return;
+  }
+  auto x_data = x.data<T>();
+  auto y_data = y.data<T>();
+  dev_ctx.template Alloc<T>(out);
+  auto out_data = out->data<T>();
+  if (out->dims().size() == 0) {
+#ifdef PADDLE_WITH_CUDA
+    if constexpr (std::is_same_v<T, int> || std::is_same_v<T, int64_t>) {
+      auto eigen_out = EigenScalar<T>::From(*out);
+      auto eigen_x = EigenVector<T>::Flatten(x);
+      auto eigen_y = EigenVector<T>::Flatten(y);
+
+      auto& dev = *dev_ctx.eigen_device();
+      eigen_out.device(dev) = (eigen_x * eigen_y).sum();
+    } else {
+      PADDLE_ENFORCE_LE_INT_MAX(x.numel(), "dot CUDOT n");
+      PADDLE_ENFORCE_LE_INT_MAX(x.strides()[0], "dot CUDOT incx");
+      const int n = static_cast<int>(x.numel());
+      int incx = static_cast<int>(x.strides()[0]);
+      int incy = static_cast<int>(x.strides()[0]);
+      if (n == 1) {
+        incx = 1;
+        incy = 1;
+      }
+
+      auto blas = funcs::GetBlas<GPUContext, T>(dev_ctx);
+      blas.CUDOT(n, x_data, incx, y_data, incy, out_data);
+    }
+#else
+    auto eigen_out = EigenScalar<T>::From(*out);
+    auto eigen_x = EigenVector<T>::Flatten(x);
+    auto eigen_y = EigenVector<T>::Flatten(y);
+
+    auto& dev = *dev_ctx.eigen_device();
+    eigen_out.device(dev) = (eigen_x * eigen_y).sum();
+#endif
+  } else {
+    auto eigen_out = EigenVector<T>::From(*out);
+    auto eigen_x = EigenMatrix<T>::From(x);
+    auto eigen_y = EigenMatrix<T>::From(y);
+
+    auto& dev = *dev_ctx.eigen_device();
+    eigen_out.device(dev) = (eigen_x * eigen_y).sum(Eigen::DSizes<int, 1>(1));
+  }
+}
+}  // namespace phi
+
+using complex64 = phi::complex64;
+using complex128 = phi::complex128;
+
+PD_REGISTER_KERNEL(dot,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::DotKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   complex64,
+                   complex128,
+                   phi::float16,
+                   phi::bfloat16) {}