chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/partial_sum_kernel_impl.h

@@ -0,0 +1,94 @@
+// 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.
+
+#pragma once
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void PartialSumKernel(const Context& dev_ctx,
+                      const std::vector<const DenseTensor*>& x,
+                      int start_index,
+                      int length,
+                      DenseTensor* out) {
+  PADDLE_ENFORCE_EQ(
+      x.size() > 0,
+      true,
+      common::errors::InvalidArgument("The input should not be null."));
+
+  auto* out_t = dev_ctx.template Alloc<T>(out);
+  auto batch_size = x[0]->dims()[0];
+  if (length == -1) {
+    length = x[0]->dims()[1] - start_index;
+  }
+
+  memset(out_t, 0, sizeof(T) * batch_size * length);
+
+  for (size_t i = 0; i < x.size(); ++i) {
+    auto* in_t = x[i]->data<T>();
+    auto total_len = x[i]->dims()[1];
+    for (auto bs_id = 0; bs_id < batch_size; ++bs_id) {
+      for (auto k = 0; k < length; ++k) {
+        out_t[bs_id * length + k] += in_t[bs_id * total_len + start_index + k];
+      }
+    }
+  }
+}
+
+template <typename T, typename Context>
+void PartialSumGradientOpKernel(const Context& dev_ctx,
+                                const std::vector<const DenseTensor*>& x,
+                                const DenseTensor& out_grad,
+                                int start_index,
+                                int length,
+                                std::vector<DenseTensor*> x_grad) {
+  PADDLE_ENFORCE_EQ(
+      x.size() > 0,
+      true,
+      common::errors::InvalidArgument("The input should not be null."));
+  auto batch_size = x[0]->dims()[0];
+  if (length == -1) {
+    length = x[0]->dims()[1] - start_index;
+  }
+
+  // initialize
+  auto& place = *dev_ctx.eigen_device();
+  for (size_t i = 0; i < x_grad.size(); ++i) {
+    dev_ctx.template Alloc<T>(x_grad[i]);
+    auto dxt = EigenVector<T>::Flatten(*x_grad[i]);
+    dxt.device(place) = dxt.constant(static_cast<T>(0));
+  }
+
+  auto* out_grad_t = out_grad.data<T>();
+  for (size_t i = 0; i < x_grad.size(); ++i) {
+    auto* out_t = x_grad[i]->data<T>();
+    auto total_len = x[i]->dims()[1];
+    for (auto bs_id = 0; bs_id < batch_size; ++bs_id) {
+      for (int len = 0; len < length; ++len) {
+        out_t[start_index + bs_id * total_len + len] =
+            out_grad_t[bs_id * length + len] * static_cast<T>(1);
+      }
+    }
+  }
+}
+
+}  // namespace phi