chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/partial_concat_kernel_impl.h

@@ -0,0 +1,118 @@
+// 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/device_context.h"
+#include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/partial_concat_funcs.h"
+#include "paddle/phi/kernels/funcs/strided_memcpy.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void PartialConcatKernel(const Context& dev_ctx,
+                         const std::vector<const DenseTensor*>& x,
+                         int start_index,
+                         int length,
+                         DenseTensor* out) {
+  auto ins = x;
+  PADDLE_ENFORCE_EQ(ins[0] != nullptr,
+                    true,
+                    common::errors::InvalidArgument(
+                        "The input of partial concat should not be null."));
+
+  auto input_dim = ins[0]->dims();
+  PADDLE_ENFORCE_EQ(input_dim.size(),
+                    2,
+                    common::errors::InvalidArgument(
+                        "Only supports 2-D array with batch size in the 1st "
+                        "dimension and data in the 2nd."));
+  auto in_size = input_dim[1];
+
+  // may be negative
+  start_index = ComputeStartIndex(start_index, in_size);
+
+  auto partial_len = length;
+  if (partial_len < 0) {
+    partial_len = in_size - start_index;
+  }
+
+  int batch = input_dim[0];
+  int out_size = partial_len * ins.size();
+  out->Resize({batch, out_size});
+  T* out_data = dev_ctx.template Alloc<T>(out);
+
+  for (size_t i = 0; i < ins.size(); ++i) {
+    for (int j = 0; j < batch; ++j) {
+      const T* in_data = ins[i]->data<T>();
+      memcpy(out_data + out_size * j + partial_len * i,
+             in_data + in_size * j + start_index,
+             partial_len * sizeof(T));
+    }
+  }
+}
+
+template <typename T, typename Context>
+void PartialConcatGradientOpKernel(const Context& dev_ctx,
+                                   const std::vector<const DenseTensor*>& x,
+                                   const DenseTensor& out_grad,
+                                   int start_index,
+                                   int length,
+                                   std::vector<DenseTensor*> x_grad) {
+  auto ins = x;
+  auto outs = x_grad;
+
+  PADDLE_ENFORCE_EQ(ins[0] != nullptr,
+                    true,
+                    common::errors::InvalidArgument(
+                        "The input of partial concat should not be null."));
+  // all parameters
+  auto batch_size = ins[0]->dims()[0];
+  auto in_size = ins[0]->dims()[1];
+  // may be negative
+  start_index = ComputeStartIndex(start_index, in_size);
+  auto partial_len = length;
+  if (partial_len < 0) partial_len = in_size - start_index;
+
+  auto in_num = ins.size();
+  auto grad_batch_len = partial_len * in_num;
+  auto all_length = grad_batch_len * batch_size;
+
+  // initialize
+  auto& place = *dev_ctx.eigen_device();
+  for (size_t i = 0; i < outs.size(); ++i) {
+    dev_ctx.template Alloc<T>(outs[i]);
+    auto dxt = EigenVector<T>::Flatten(*outs[i]);
+    dxt.device(place) = dxt.constant(static_cast<T>(0));
+  }
+
+  auto* out_grad_t = out_grad.data<T>();
+  for (size_t id = 0; id < all_length; id += partial_len) {
+    int bs_id = id / grad_batch_len;
+    int bs_index = id % grad_batch_len;
+    int var_id = bs_index / partial_len;
+    auto* out_t = outs[var_id]->data<T>();
+    memcpy(out_t + bs_id * in_size + start_index,
+           out_grad_t + id,
+           partial_len * sizeof(T));
+  }
+}
+}  // namespace phi