chore: import upstream snapshot with attribution

paddle/phi/kernels/custom/assign_pos_kernel.cc

@@ -0,0 +1,72 @@
+// 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/assign_pos_kernel.h"
+#include "paddle/phi/backends/all_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_utils.h"
+
+#ifdef PADDLE_WITH_CUSTOM_DEVICE
+namespace phi {
+
+template <typename T, typename Context>
+void AssignPosKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const DenseTensor& cum_count_in,
+                     const DenseTensor& eff_num_len_in,
+                     DenseTensor* out) {
+  // assign pos decides which tokens should be fetched belong to specially
+  // counter orderly.
+  auto cum_count = &cum_count_in;      // (counter number) int32 | int64
+  auto numbers = &x;                   // (batch_size * seq_len, topk) int32
+  auto eff_num_len = &eff_num_len_in;  // (sum(cum_count))
+  // out: (cum_count) value ranges
+  // from 0 to batch_size *
+  // seq_len * topk
+
+  DenseTensor cpu_eff_num_len;
+  int64_t cpu_eff_num_len_data = 0;
+  if (eff_num_len->place().GetType() == phi::AllocationType::CPU) {
+    cpu_eff_num_len_data = eff_num_len->data<T>()[0];
+  } else {
+    phi::Copy(dev_ctx, *eff_num_len, phi::CPUPlace(), true, &cpu_eff_num_len);
+    cpu_eff_num_len_data = cpu_eff_num_len.data<T>()[0];
+  }
+
+  out->Resize({cpu_eff_num_len_data});
+  dev_ctx.template Alloc<T>(out);
+
+  DenseTensor numbers_cpu, cum_count_cpu;
+  phi::Copy(dev_ctx, *numbers, phi::CPUPlace(), true, &numbers_cpu);
+  phi::Copy(dev_ctx, *cum_count, phi::CPUPlace(), true, &cum_count_cpu);
+  auto* numbers_data = numbers_cpu.data<T>();
+  auto* cum_count_data = cum_count_cpu.data<T>();
+
+  std::vector<T> out_data(cpu_eff_num_len_data);
+  for (int64_t i = 0; i < numbers->numel(); ++i) {
+    int number_idx = numbers_data[i];
+    if (number_idx > -1) {
+      cum_count_data[number_idx] -= 1;
+      int p = cum_count_data[number_idx];
+      out_data[p] = i;
+    }
+  }
+  TensorFromVector<int64_t>(out_data, dev_ctx, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    assign_pos, Custom, ALL_LAYOUT, phi::AssignPosKernel, int64_t) {}
+#endif