Accept None as an argument to decoder_lengths in GreedyBatchedCTCInfe…

…r::forward (#9246)

* Accept None as an argument to decoder_lengths in GreedyBatchedCTCInfer::forward

GreedyCTCInfer::forward already allowed for this, so they did not
implement the exact same interface. Now, they do.

Also warn about not passing in the decoder_lengths argument. It is
likely an error on the user's part not to pass it in explicitly.

Signed-off-by: Daniel Galvez <[email protected]>

* Apply isort and black reformatting

Signed-off-by: titu1994 <[email protected]>

* Log warning only once for sanity.

Signed-off-by: Daniel Galvez <[email protected]>

---------

Signed-off-by: Daniel Galvez <[email protected]>
Signed-off-by: titu1994 <[email protected]>
Co-authored-by: titu1994 <[email protected]>
Co-authored-by: Somshubra Majumdar <[email protected]>

nemo/collections/asr/parts/submodules/ctc_greedy_decoding.py

@@ -58,6 +58,9 @@ def _states_to_device(dec_state, device='cpu'):
     return dec_state
 
 
+_DECODER_LENGTHS_NONE_WARNING = "Passing in decoder_lengths=None for CTC decoding is likely to be an error, since it is unlikely that each element of your batch has exactly the same length. decoder_lengths will default to decoder_output.shape[0]."
+
+
 class GreedyCTCInfer(Typing, ConfidenceMethodMixin):
     """A greedy CTC decoder.
 
@@ -148,7 +151,7 @@ def __init__(
     def forward(
         self,
         decoder_output: torch.Tensor,
-        decoder_lengths: torch.Tensor,
+        decoder_lengths: Optional[torch.Tensor],
     ):
         """Returns a list of hypotheses given an input batch of the encoder hidden embedding.
         Output token is generated auto-repressively.
@@ -167,6 +170,9 @@ def forward(
             mode=logging_mode.ONCE,
         )
 
+        if decoder_lengths is None:
+            logging.warning(_DECODER_LENGTHS_NONE_WARNING, mode=logging_mode.ONCE)
+
         with torch.inference_mode():
             hypotheses = []
             # Process each sequence independently
@@ -213,7 +219,7 @@ def forward(
         return (packed_result,)
 
     @torch.no_grad()
-    def _greedy_decode_logprobs(self, x: torch.Tensor, out_len: torch.Tensor):
+    def _greedy_decode_logprobs(self, x: torch.Tensor, out_len: Optional[torch.Tensor]):
         # x: [T, D]
         # out_len: [seq_len]
 
@@ -243,7 +249,7 @@ def _greedy_decode_logprobs(self, x: torch.Tensor, out_len: torch.Tensor):
         return hypothesis
 
     @torch.no_grad()
-    def _greedy_decode_labels(self, x: torch.Tensor, out_len: torch.Tensor):
+    def _greedy_decode_labels(self, x: torch.Tensor, out_len: Optional[torch.Tensor]):
         # x: [T]
         # out_len: [seq_len]
 
@@ -370,7 +376,7 @@ def __init__(
     def forward(
         self,
         decoder_output: torch.Tensor,
-        decoder_lengths: torch.Tensor,
+        decoder_lengths: Optional[torch.Tensor],
     ):
         """Returns a list of hypotheses given an input batch of the encoder hidden embedding.
         Output token is generated auto-repressively.
@@ -383,11 +389,18 @@ def forward(
         Returns:
             packed list containing batch number of sentences (Hypotheses).
         """
+
+        input_decoder_lengths = decoder_lengths
+
+        if decoder_lengths is None:
+            logging.warning(_DECODER_LENGTHS_NONE_WARNING, mode=logging_mode.ONCE)
+            decoder_lengths = torch.tensor([decoder_output.shape[1]], dtype=torch.long).expand(decoder_output.shape[0])
+
         if decoder_output.ndim == 2:
             hypotheses = self._greedy_decode_labels_batched(decoder_output, decoder_lengths)
         else:
             hypotheses = self._greedy_decode_logprobs_batched(decoder_output, decoder_lengths)
-        packed_result = pack_hypotheses(hypotheses, decoder_lengths)
+        packed_result = pack_hypotheses(hypotheses, input_decoder_lengths)
         return (packed_result,)
 
     @torch.no_grad()

tests/collections/asr/decoding/test_ctc_decoding.py

@@ -199,7 +199,10 @@ def test_subword_decoding_greedy_forward_hypotheses(self, tmp_tokenizer, alignme
     @pytest.mark.parametrize('alignments', [False, True])
     @pytest.mark.parametrize('timestamps', [False, True])
     @pytest.mark.parametrize('preserve_frame_confidence', [False, True])
-    def test_batched_decoding_logprobs(self, tmp_tokenizer, alignments, timestamps, preserve_frame_confidence):
+    @pytest.mark.parametrize('length_is_none', [False, True])
+    def test_batched_decoding_logprobs(
+        self, tmp_tokenizer, alignments, timestamps, preserve_frame_confidence, length_is_none
+    ):
         cfg = CTCBPEDecodingConfig(
             strategy='greedy',
             preserve_alignments=alignments,
@@ -219,7 +222,10 @@ def test_batched_decoding_logprobs(self, tmp_tokenizer, alignments, timestamps,
         # that we always handle at least a few blanks.
         input_signal[:, 0, unbatched_decoding.tokenizer.tokenizer.vocab_size] = 1000
         input_signal[:, 1, unbatched_decoding.tokenizer.tokenizer.vocab_size] = 1000
-        length = torch.randint(low=1, high=T, size=[B])
+        if length_is_none:
+            length = None
+        else:
+            length = torch.randint(low=1, high=T, size=[B])
 
         with torch.inference_mode():
             hyps, _ = unbatched_decoding.ctc_decoder_predictions_tensor(
@@ -242,7 +248,8 @@ def test_batched_decoding_logprobs(self, tmp_tokenizer, alignments, timestamps,
 
     @pytest.mark.unit
     @pytest.mark.parametrize('timestamps', [False, True])
-    def test_batched_decoding_labels(self, tmp_tokenizer, timestamps):
+    @pytest.mark.parametrize('length_is_none', [False, True])
+    def test_batched_decoding_labels(self, tmp_tokenizer, timestamps, length_is_none):
         cfg = CTCBPEDecodingConfig(strategy='greedy', compute_timestamps=timestamps)
         unbatched_decoding = CTCBPEDecoding(decoding_cfg=cfg, tokenizer=tmp_tokenizer)
         cfg.strategy = 'greedy_batch'
@@ -256,7 +263,10 @@ def test_batched_decoding_labels(self, tmp_tokenizer, timestamps):
         # at least a few blanks.
         input_labels[:, 0] = unbatched_decoding.tokenizer.tokenizer.vocab_size
         input_labels[:, 1] = unbatched_decoding.tokenizer.tokenizer.vocab_size
-        length = torch.randint(low=1, high=T, size=[B])
+        if length_is_none:
+            length = None
+        else:
+            length = torch.randint(low=1, high=T, size=[B])
 
         with torch.inference_mode():
             hyps, _ = unbatched_decoding.ctc_decoder_predictions_tensor(