chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/crf_decoding_kernel.cc

@@ -0,0 +1,165 @@
+// 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/crf_decoding_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/jit/kernels.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void Decode(const Context& dev_ctx,
+            const DenseTensor& emission_weights,
+            const DenseTensor& transition_weights,
+            DenseTensor* decoded_path) {
+  auto emission_dims = emission_weights.dims();
+  const size_t seq_len = emission_dims[0];
+  const size_t tag_num = emission_dims[1];
+  const T* x = emission_weights.data<T>();
+  const T* w = transition_weights.data<T>();
+  int64_t* path = decoded_path->data<int64_t>();
+
+  // alpha is a memo table. An element alpha(k, v) records the score of the
+  // best sequence of tags from position 1 to position k with v being the end
+  // tag.
+  DenseTensor alpha;
+  alpha.Resize(emission_dims);
+  T* alpha_value = dev_ctx.template Alloc<T>(&alpha);
+  DenseTensor track;
+  track.Resize(emission_dims);
+  int* track_value = dev_ctx.template Alloc<int>(&track);
+  auto ker =
+      jit::KernelFuncs<jit::CRFDecodingTuple<T>, CPUPlace>::Cache().At(tag_num);
+  ker(static_cast<int>(seq_len), x, w, alpha_value, track_value, tag_num);
+  T max_score = -std::numeric_limits<T>::max();
+  int max_i = 0;
+  for (size_t i = 0; i < tag_num; ++i) {
+    T score = alpha_value[(seq_len - 1) * tag_num + i] + w[tag_num + i];
+    if (score > max_score) {
+      max_score = score;
+      max_i = i;
+    }
+  }
+  path[seq_len - 1] = max_i;
+  for (int k = seq_len - 1; k >= 1; --k) {
+    path[k - 1] = max_i = track_value[k * tag_num + max_i];
+  }
+}
+
+// Slice() needs to be used in *.cc files, otherwise there is a error in
+// test/custom_runtime/extension_header_test.cc
+template <typename T, typename Context>
+void CRFDecodingOpKernel(const Context& dev_ctx,
+                         const DenseTensor& emission,
+                         const DenseTensor& transition,
+                         const optional<DenseTensor>& label,
+                         const optional<DenseTensor>& length,
+                         DenseTensor* viterbi_path) {
+  auto* emission_weights = &emission;
+  auto* transition_weights = &transition;
+  auto* label_p = label.get_ptr();
+  auto* decoded_path = viterbi_path;
+
+  int64_t* path = dev_ctx.template Alloc<int64_t>(decoded_path);
+  funcs::SetConstant<Context, int64_t>()(dev_ctx, decoded_path, 0);
+
+  bool has_length = length.get_ptr() != nullptr;
+  if (has_length) {
+    auto* length_p = length.get_ptr();
+    const size_t seq_num = length_p->numel();
+    const int64_t* length_data = length_p->data<int64_t>();
+    auto in_dims = emission_weights->dims();
+
+    DenseTensor emission_weights_tmp = *emission_weights;
+    emission_weights_tmp.Resize(
+        make_ddim({in_dims[0] * in_dims[1], in_dims[2]}));
+
+    decoded_path->Resize({in_dims[0] * in_dims[1], 1});
+    for (size_t i = 0; i < seq_num; ++i) {
+      if (length_data[i] == 0) continue;
+      int64_t start_pos = i * in_dims[1];
+      int64_t end_pos = start_pos + static_cast<int64_t>(length_data[i]);
+      DenseTensor decoded_path_one_seq =
+          decoded_path->Slice(start_pos, end_pos);
+      Decode<T, Context>(dev_ctx,
+                         emission_weights_tmp.Slice(start_pos, end_pos),
+                         *transition_weights,
+                         &decoded_path_one_seq);
+    }
+    decoded_path->Resize({in_dims[0], in_dims[1]});
+
+    if (label) {
+      const int64_t* label_value = label_p->data<int64_t>();
+      for (size_t i = 0; i < seq_num; ++i) {
+        for (int64_t j = 0; j < in_dims[1]; ++j) {
+          int64_t start_pos = i * in_dims[1];
+          if (j < length_data[i]) {
+            path[start_pos + j] =
+                label_value[start_pos + j] == path[start_pos + j] ? 1 : 0;
+          } else {
+            path[start_pos + j] = 0;
+          }
+        }
+      }
+    }
+  } else {
+    PADDLE_ENFORCE_EQ(emission_weights->NumLevels(),
+                      1UL,
+                      common::errors::InvalidArgument(
+                          "The Input(Emission) should be a sequence with lod "
+                          "level 1. But received: lod level %u.",
+                          emission_weights->NumLevels()));
+    auto lod = emission_weights->lod();
+    PADDLE_ENFORCE_GT(
+        lod.size(),
+        0,
+        common::errors::InvalidArgument(
+            "Input(Emission) must be a sequence. But received: lod level %u.",
+            lod.size()));
+    const size_t level = 0;
+    const size_t seq_num = lod[level].size() - 1;
+
+    for (size_t i = 0; i < seq_num; ++i) {
+      if (lod[level][i] == lod[level][i + 1]) continue;
+      int64_t start_pos = static_cast<int64_t>(lod[level][i]);
+      int64_t end_pos = static_cast<int64_t>(lod[level][i + 1]);
+      DenseTensor decoded_path_one_seq =
+          decoded_path->Slice(start_pos, end_pos);
+      Decode<T, Context>(dev_ctx,
+                         emission_weights->Slice(start_pos, end_pos),
+                         *transition_weights,
+                         &decoded_path_one_seq);
+    }
+    if (label) {
+      PADDLE_ENFORCE_EQ(label_p->NumLevels(),
+                        1UL,
+                        common::errors::InvalidArgument(
+                            "The Input(label) should be a sequence with lod "
+                            "level 1. But received: lod level %u.",
+                            label_p->NumLevels()));
+      const int64_t* label_value = label_p->data<int64_t>();
+      size_t numel = label->numel();
+      for (size_t i = 0; i < numel; ++i) {
+        path[i] = label_value[i] == path[i] ? 1 : 0;
+      }
+    }
+  }
+}
+}  // namespace phi
+PD_REGISTER_KERNEL(
+    crf_decoding, CPU, ALL_LAYOUT, phi::CRFDecodingOpKernel, float, double) {
+  kernel->OutputAt(0).SetDataType(phi::DataType::INT64);
+}