Merge remote-tracking branch 'k2-fsa/master'

egs/gigaspeech/ASR/conformer_ctc/decode.py

@@ -177,8 +177,8 @@ def post_processing(
 ) -> List[Tuple[List[str], List[str]]]:
     new_results = []
     for ref, hyp in results:
-        new_ref = asr_text_post_processing(" ".join(ref))
-        new_hyp = asr_text_post_processing(" ".join(hyp))
+        new_ref = asr_text_post_processing(" ".join(ref)).split()
+        new_hyp = asr_text_post_processing(" ".join(hyp)).split()
         new_results.append((new_ref, new_hyp))
     return new_results
 

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -276,7 +276,7 @@ def greedy_search(
     context_size = model.decoder.context_size
     unk_id = getattr(model, "unk_id", blank_id)
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size, device=device, dtype=torch.int64
@@ -350,7 +350,7 @@ def greedy_search_batch(
     assert encoder_out.ndim == 3
     assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    device = model.device
+    device = next(model.parameters()).device
 
     batch_size = encoder_out.size(0)
     T = encoder_out.size(1)
@@ -580,7 +580,7 @@ def modified_beam_search(
     blank_id = model.decoder.blank_id
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
-    device = model.device
+    device = next(model.parameters()).device
     B = [HypothesisList() for _ in range(batch_size)]
     for i in range(batch_size):
         B[i].add(
@@ -705,7 +705,7 @@ def _deprecated_modified_beam_search(
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     T = encoder_out.size(1)
 
@@ -813,7 +813,7 @@ def beam_search(
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size,

egs/librispeech/ASR/pruned_transducer_stateless2/train.py

@@ -1001,7 +1001,7 @@ def scan_pessimistic_batches_for_oom(
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()
-        except RuntimeError as e:
+        except Exception as e:
             if "CUDA out of memory" in str(e):
                 logging.error(
                     "Your GPU ran out of memory with the current "
@@ -1010,7 +1010,7 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-                display_and_save_batch(batch, params=params, sp=sp)
+            display_and_save_batch(batch, params=params, sp=sp)
             raise
 
 

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -250,7 +250,7 @@ def decode_one_batch(
       Return the decoding result. See above description for the format of
       the returned dict.
     """
-    device = model.device
+    device = next(model.parameters()).device
     feature = batch["inputs"]
     assert feature.ndim == 3
 
@@ -560,7 +560,6 @@ def main():
 
     model.to(device)
     model.eval()
-    model.device = device
 
     if params.decoding_method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)

egs/librispeech/ASR/pruned_transducer_stateless4/train.py

@@ -125,8 +125,8 @@ def get_parser():
         "--start-epoch",
         type=int,
         default=1,
-        help="""Resume training from from this epoch.
-        If it is positive, it will load checkpoint from
+        help="""Resume training from this epoch. It should be positive.
+        If larger than 1, it will load checkpoint from
         exp-dir/epoch-{start_epoch-1}.pt
         """,
     )
@@ -479,7 +479,7 @@ def load_checkpoint_if_available(
 
 def save_checkpoint(
     params: AttributeDict,
-    model: nn.Module,
+    model: Union[nn.Module, DDP],
     model_avg: Optional[nn.Module] = None,
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
@@ -529,7 +529,7 @@ def save_checkpoint(
 
 def compute_loss(
     params: AttributeDict,
-    model: nn.Module,
+    model: Union[nn.Module, DDP],
     sp: spm.SentencePieceProcessor,
     batch: dict,
     is_training: bool,
@@ -553,7 +553,11 @@ def compute_loss(
      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
-    device = model.device
+    device = (
+        model.device
+        if isinstance(model, DDP)
+        else next(model.parameters()).device
+    )
     feature = batch["inputs"]
     # at entry, feature is (N, T, C)
     assert feature.ndim == 3
@@ -609,7 +613,7 @@ def compute_loss(
 
 def compute_validation_loss(
     params: AttributeDict,
-    model: nn.Module,
+    model: Union[nn.Module, DDP],
     sp: spm.SentencePieceProcessor,
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
@@ -643,7 +647,7 @@ def compute_validation_loss(
 
 def train_one_epoch(
     params: AttributeDict,
-    model: nn.Module,
+    model: Union[nn.Module, DDP],
     optimizer: torch.optim.Optimizer,
     scheduler: LRSchedulerType,
     sp: spm.SentencePieceProcessor,
@@ -857,6 +861,7 @@ def run(rank, world_size, args):
         # model_avg is only used with rank 0
         model_avg = copy.deepcopy(model)
 
+    assert params.start_epoch > 0, params.start_epoch
     checkpoints = load_checkpoint_if_available(
         params=params, model=model, model_avg=model_avg
     )
@@ -865,11 +870,6 @@ def run(rank, world_size, args):
     if world_size > 1:
         logging.info("Using DDP")
         model = DDP(model, device_ids=[rank])
-    model.device = device
-
-    if rank == 0:
-        model_avg.to(device)
-        model_avg.device = device
 
     optimizer = Eve(model.parameters(), lr=params.initial_lr)
 
@@ -990,7 +990,7 @@ def run(rank, world_size, args):
 
 
 def scan_pessimistic_batches_for_oom(
-    model: nn.Module,
+    model: Union[nn.Module, DDP],
     train_dl: torch.utils.data.DataLoader,
     optimizer: torch.optim.Optimizer,
     sp: spm.SentencePieceProcessor,

icefall/checkpoint.py

@@ -467,5 +467,7 @@ def average_state_dict(
     uniqued_names = list(uniqued.values())
     for k in uniqued_names:
         state_dict_1[k] *= weight_1
-        state_dict_1[k] += state_dict_2[k] * weight_2
+        state_dict_1[k] += (
+            state_dict_2[k].to(device=state_dict_1[k].device) * weight_2
+        )
         state_dict_1[k] *= scaling_factor