chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/row_conv_kernel.cc

@@ -0,0 +1,101 @@
+// 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.
+
+#include "paddle/phi/kernels/row_conv_kernel.h"
+#include <memory>
+#include <string>
+#include <vector>
+#include "paddle/phi/core/enforce.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/mixed_vector.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void RowConvKernel(const Context &dev_ctx,
+                   const DenseTensor &x_in,
+                   const DenseTensor &filter_in,
+                   DenseTensor *out) {
+  auto *x = &x_in;
+  auto *filter = &filter_in;
+
+  dev_ctx.template Alloc<T>(out);
+
+  bool is_tensor = x->lod().empty();
+  int batch_size = 0;
+  if (is_tensor) {
+    batch_size = static_cast<int>(x->dims()[0]);
+  } else {
+    batch_size = static_cast<int>(x->lod()[0].size() - 1);
+  }
+  Vector<size_t> batch_indices(batch_size + 1);
+  int input_dim = 0;
+  int timesteps = 0;
+  if (is_tensor) {
+    for (int i = 0; i < batch_size + 1; i++) {
+      batch_indices[i] = i;
+    }
+    input_dim = static_cast<int>(x->dims()[2]);
+    timesteps = static_cast<int>(x->dims()[1]);
+  } else {
+    batch_indices = x->lod()[0];
+    input_dim = static_cast<int>(x->dims()[1]);
+  }
+  size_t num_sequence = batch_indices.size() - 1;
+
+  auto future_context = filter->dims()[0];
+  auto weights = EigenMatrix<T>::From(*filter);
+
+  for (size_t i = 0; i < num_sequence; i++) {
+    int start = static_cast<int>(batch_indices[i]);
+    int end = static_cast<int>(batch_indices[i + 1]);
+    int current_timesteps = 0;
+    if (is_tensor) {
+      current_timesteps = timesteps;
+    } else {
+      current_timesteps = end - start;
+    }
+    // int current_timesteps = end - start;
+    DenseTensor cur_input_sequence =
+        x->Slice(start, end);  // Current input sequence
+    cur_input_sequence =
+        cur_input_sequence.Resize({current_timesteps, input_dim});
+
+    DenseTensor cur_output_sequence =
+        out->Slice(start, end);  // Current output sequence
+    cur_output_sequence =
+        cur_output_sequence.Resize({current_timesteps, input_dim});
+
+    auto cip_seq = EigenMatrix<T>::From(cur_input_sequence);
+    auto cot_seq = EigenMatrix<T>::From(cur_output_sequence);
+
+    for (int k = 0; k < current_timesteps;
+         k++) {  // For different time steps in the same sequence
+      for (int w = 0; (w < future_context) && ((k + w) < current_timesteps);
+           w++) {
+        for (int d = 0; d < input_dim; d++) {
+          if (w == 0) {
+            cot_seq(k, d) = weights(w, d) * cip_seq(k + w, d);
+          } else {
+            cot_seq(k, d) += weights(w, d) * cip_seq(k + w, d);
+          }
+        }
+      }
+    }
+  }
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(row_conv, CPU, ALL_LAYOUT, phi::RowConvKernel, float) {}