Resolve comments. (#871)

* Resolve comments.

* Minor fixes.

Author: csukuangfj

k2/torch/bin/attention_rescore.cu

@@ -281,55 +281,14 @@ int main(int argc, char *argv[]) {
   std::vector<std::vector<int32_t>> token_ids = tokens.ToVecVec();
   // convert std::vector<std::vector<int32_t>>
   // to
-  // torch::List<torch::IValue> where torch::IValue is torch::List<int32_t>
-#if 0
-  // clang-format off
-  //
-  // This branch is used when `token_ids` is of type List[List[int]],
-  // but it throws the following error during runtime:
-  //
-/*
-terminate called after throwing an instance of 'std::runtime_error'
-  what():  The following operation failed in the TorchScript interpreter.
-Traceback of TorchScript, serialized code (most recent call last):
-  File "code/__torch__/conformer.py", line 97, in decoder_nll
-    for _22 in range(torch.len(ys_out)):
-      y1 = ys_out[_22]
-      _23 = torch.tensor(y1, dtype=None, device=None, requires_grad=False)
-            ~~~~~~~~~~~~ <--- HERE
-      _24 = torch.append(ys_out1, _23)
-    ys_out_pad = _15(ys_out1, True, -1., )
-
-Traceback of TorchScript, original code (most recent call last):
-  File "/ceph-fj/open-source/icefall-torchscript/egs/librispeech/ASR/conformer_ctc/transformer.py", line 340, in decoder_nll
-
-        ys_out = add_eos(token_ids, eos_id=eos_id)
-        ys_out = [torch.tensor(y) for y in ys_out]
-                  ~~~~~~~~~~~~ <--- HERE
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
-RuntimeError: Add new condition, expected Float, Complex, Int, or Bool but gotint
- */
-  torch::List<torch::IValue> token_ids_list(torch::ListType::ofInts());
-
-  token_ids_list.reserve(token_ids.size());
-  for (const auto tids : token_ids) {
-    torch::List<torch::IValue> tmp(torch::IntType::create());
-    tmp.reserve(tids.size());
-    for (auto i : tids) {
-      tmp.emplace_back(torch::IValue(i));
-    }
-    token_ids_list.push_back(std::move(tmp));
-  }
-  // clang-format on
-#else
+  // torch::List<torch::IValue> where torch::IValue is torch::Tensor
   torch::List<torch::IValue> token_ids_list(torch::TensorType::get());
 
   token_ids_list.reserve(token_ids.size());
   for (const auto tids : token_ids) {
     torch::Tensor tids_tensor = torch::tensor(tids);
     token_ids_list.emplace_back(tids_tensor);
   }
-#endif
 
   K2_LOG(INFO) << "Run attention decoder";
   torch::Tensor nll =

k2/torch/csrc/fsa_algo.cu

@@ -237,4 +237,19 @@ Nbest RandomPaths(FsaClass &lattice, int32_t num_paths) {
   return {ans_lattice, utt_to_path_shape};
 }
 
+FsaClass IntersectDevice(FsaClass &a_fsas, FsaClass &b_fsas,
+                         const Array1<int32_t> &b_to_a_map,
+                         bool sorted_match_a) {
+  Array1<int32_t> arc_map_a, arc_map_b;
+
+  Fsa c_fsas = IntersectDevice(a_fsas.fsa, a_fsas.Properties(), b_fsas.fsa,
+                               b_fsas.Properties(), b_to_a_map, &arc_map_a,
+                               &arc_map_b, sorted_match_a);
+
+  FsaClass ans(c_fsas);
+  ans.CopyAttrs(a_fsas, Array1ToTorch(arc_map_a));
+  ans.CopyAttrs(b_fsas, Array1ToTorch(arc_map_b));
+  return ans;
+}
+
 }  // namespace k2

k2/torch/csrc/fsa_algo.h

@@ -126,6 +126,12 @@ void TopSort(FsaClass *lattice);
  */
 Nbest RandomPaths(FsaClass &lattice, int32_t num_paths);
 
+/// Wrapper for k2::IntersectDevice() in k2/csrc/fsa_algo.h
+/// to support attribute propagation.
+FsaClass IntersectDevice(FsaClass &a_fsas, FsaClass &b_fsas,
+                         const Array1<int32_t> &b_to_a_map,
+                         bool sorted_match_a);
+
 }  // namespace k2
 
 #endif  // K2_TORCH_CSRC_FSA_ALGO_H_

k2/torch/csrc/nbest.cu

@@ -66,15 +66,13 @@ void Nbest::Intersect(FsaClass *lattice) {
   // Now lattice has word IDs as labels and token IDs as aux_labels
   ArcSort(lattice);
 
-  Array1<int32_t> arc_map_a, arc_map_b;
+  FsaClass word_fsa_with_epsilon_self_loops_wrapper(
+      word_fsa_with_epsilon_self_loops);
 
-  Fsa path_lattice = k2::IntersectDevice(
-      lattice->fsa, lattice->Properties(), word_fsa_with_epsilon_self_loops,
-      FsaClass(word_fsa_with_epsilon_self_loops).Properties(), path_to_utt_map,
-      &arc_map_a, &arc_map_b, true);
+  FsaClass ans =
+      IntersectDevice(*lattice, word_fsa_with_epsilon_self_loops_wrapper,
+                      path_to_utt_map, true);
 
-  FsaClass ans(path_lattice);
-  ans.CopyAttrs(*lattice, k2::Array1ToTorch(arc_map_a));
   Connect(&ans);
   TopSort(&ans);
   ans = ShortestPath(ans);

k2/torch/csrc/nbest.h

@@ -78,6 +78,7 @@ struct Nbest {
   /// `this` nbest.
   ///
   ///  @param lattice The lattice to intersect. Note it is modified in-place.
+  ///                 You should not use it after invoking this function.
   ///
   /// Note: The scores for the return value of FromLattice() are
   ///  all 0s.