Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer. (NVIDIA#9347)

* Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer.

decoder_lengths is allowed to be on CPU even when decoder_output is on
GPU. This matches the behavior of GreedyCTCInfer. Even though that
behavior is unintentional, there is code depending on that behavior,
including our jupyter notebooks.

Signed-off-by: Daniel Galvez <dgalvez@nvidia.com>

* Apply isort and black reformatting

Signed-off-by: titu1994 <titu1994@users.noreply.github.com>

---------

Signed-off-by: Daniel Galvez <dgalvez@nvidia.com>
Signed-off-by: titu1994 <titu1994@users.noreply.github.com>
Co-authored-by: Somshubra Majumdar <titu1994@gmail.com>
Co-authored-by: titu1994 <titu1994@users.noreply.github.com>
Co-authored-by: Nithin Rao <nithinrao.koluguri@gmail.com>

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

@@ -394,7 +394,17 @@ def forward(
 
         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])
+            decoder_lengths = torch.tensor(
+                [decoder_output.shape[1]], dtype=torch.long, device=decoder_output.device
+            ).expand(decoder_output.shape[0])
+
+        # GreedyCTCInfer::forward(), by accident, works with
+        # decoder_lengths on either CPU or GPU when decoder_output is
+        # on GPU. For the sake of backwards compatibility, we also
+        # allow decoder_lengths to be on the CPU device. In this case,
+        # we simply copy the decoder_lengths from CPU to GPU. If both
+        # tensors are already on the same device, this is a no-op.
+        decoder_lengths = decoder_lengths.to(decoder_output.device)
 
         if decoder_output.ndim == 2:
             hypotheses = self._greedy_decode_labels_batched(decoder_output, decoder_lengths)

tests/collections/asr/decoding/test_ctc_decoding.py

@@ -200,8 +200,41 @@ def test_subword_decoding_greedy_forward_hypotheses(self, tmp_tokenizer, alignme
     @pytest.mark.parametrize('timestamps', [False, True])
     @pytest.mark.parametrize('preserve_frame_confidence', [False, True])
     @pytest.mark.parametrize('length_is_none', [False, True])
+    @pytest.mark.parametrize(
+        "logprobs_device",
+        [
+            torch.device("cpu"),
+            pytest.param(
+                torch.device("cuda"),
+                marks=pytest.mark.skipif(
+                    not torch.cuda.is_available(),
+                    reason='CUDA required for test.',
+                ),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "length_device",
+        [
+            torch.device("cpu"),
+            pytest.param(
+                torch.device("cuda"),
+                marks=pytest.mark.skipif(
+                    not torch.cuda.is_available(),
+                    reason='CUDA required for test.',
+                ),
+            ),
+        ],
+    )
     def test_batched_decoding_logprobs(
-        self, tmp_tokenizer, alignments, timestamps, preserve_frame_confidence, length_is_none
+        self,
+        tmp_tokenizer,
+        alignments,
+        timestamps,
+        preserve_frame_confidence,
+        length_is_none,
+        logprobs_device,
+        length_device,
     ):
         cfg = CTCBPEDecodingConfig(
             strategy='greedy',
@@ -217,15 +250,15 @@ def test_batched_decoding_logprobs(
         torch.manual_seed(1)
         B, T = 4, 20
         V = unbatched_decoding.tokenizer.tokenizer.vocab_size + 1
-        input_signal = torch.randn(size=(B, T, V))
+        input_signal = torch.randn(size=(B, T, V), device=logprobs_device)
         # Set the blank index to a very high probability to make sure
         # 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
         if length_is_none:
             length = None
         else:
-            length = torch.randint(low=1, high=T, size=[B])
+            length = torch.randint(low=1, high=T, size=[B], device=length_device)
 
         with torch.inference_mode():
             hyps, _ = unbatched_decoding.ctc_decoder_predictions_tensor(
@@ -249,7 +282,33 @@ def test_batched_decoding_logprobs(
     @pytest.mark.unit
     @pytest.mark.parametrize('timestamps', [False, True])
     @pytest.mark.parametrize('length_is_none', [False, True])
-    def test_batched_decoding_labels(self, tmp_tokenizer, timestamps, length_is_none):
+    @pytest.mark.parametrize(
+        "labels_device",
+        [
+            torch.device("cpu"),
+            pytest.param(
+                torch.device("cuda"),
+                marks=pytest.mark.skipif(
+                    not torch.cuda.is_available(),
+                    reason='CUDA required for test.',
+                ),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "length_device",
+        [
+            torch.device("cpu"),
+            pytest.param(
+                torch.device("cuda"),
+                marks=pytest.mark.skipif(
+                    not torch.cuda.is_available(),
+                    reason='CUDA required for test.',
+                ),
+            ),
+        ],
+    )
+    def test_batched_decoding_labels(self, tmp_tokenizer, timestamps, length_is_none, labels_device, length_device):
         cfg = CTCBPEDecodingConfig(strategy='greedy', compute_timestamps=timestamps)
         unbatched_decoding = CTCBPEDecoding(decoding_cfg=cfg, tokenizer=tmp_tokenizer)
         cfg.strategy = 'greedy_batched'
@@ -258,15 +317,15 @@ def test_batched_decoding_labels(self, tmp_tokenizer, timestamps, length_is_none
         torch.manual_seed(1)
         B, T = 4, 20
         V = unbatched_decoding.tokenizer.tokenizer.vocab_size + 1
-        input_labels = torch.randint(V, size=(B, T))
+        input_labels = torch.randint(V, size=(B, T), device=labels_device)
         # Set some indices to blank to make sure that we always handle
         # at least a few blanks.
         input_labels[:, 0] = unbatched_decoding.tokenizer.tokenizer.vocab_size
         input_labels[:, 1] = unbatched_decoding.tokenizer.tokenizer.vocab_size
         if length_is_none:
             length = None
         else:
-            length = torch.randint(low=1, high=T, size=[B])
+            length = torch.randint(low=1, high=T, size=[B], device=length_device)
 
         with torch.inference_mode():
             hyps, _ = unbatched_decoding.ctc_decoder_predictions_tensor(