Support encoder input chunking for SALM vLLM inference

nemo/collections/speechlm2/vllm/salm/audio.py

@@ -64,6 +64,17 @@
 _SAMPLING_RATE = 16000
 _AUDIO_CHANNELS = 1
 _DUMMY_AUDIO_DURATION_S = 40.0
+_DUMMY_AUDIO_MAX_DURATION_S = 3600.0
+_DUMMY_AUDIO_TEXT_TOKEN_RESERVE = 64
+# FastConformer preprocessor hop length, used to derive the smallest
+# chunk that produces ≥ 2 feature frames (per-feature normalization
+# breaks on a single frame). Mirrors
+# ``encoder_chunking._get_min_chunk_size_samples`` for the canonical
+# preprocessor we ship; the chunking helper probes the live featurizer
+# at training time, but the prompt processor here runs before the
+# perception module is loaded, so we use the same constant the helper
+# would derive.
+_MIN_CHUNK_SIZE_SAMPLES = 320
 
 
 # ── Helpers ─────────────────────────────────────────────────────────
@@ -123,11 +134,20 @@ def get_data_parser(self) -> MultiModalDataParser:
         )
 
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
-        return {"audio": 1}
+        return {"audio": None}
+
+    def _get_encoder_chunk_size_seconds(self) -> float | None:
+        """Return the per-encoder-call chunk size baked into the checkpoint.
+
+        Mirrors the training-time ``model.encoder_chunk_size_seconds`` field
+        (see ``encode_audio_with_optional_chunking``). ``None`` means the
+        encoder runs once over the full audio, matching legacy checkpoints.
+        """
+        return getattr(self.get_hf_config(), "encoder_chunk_size_seconds", None)
 
     @staticmethod
-    def _estimate_audio_tokens(audio_length_samples: int) -> int:
-        """Predict the encoder's output frame count for an audio of N samples.
+    def _estimate_audio_tokens_single_pass(audio_length_samples: int) -> int:
+        """Predict the encoder's output frame count for one perception forward.
 
         Mirrors the FastConformer preprocessing chain used by
         ``AudioPerceptionModule``: STFT (n_fft=512, hop_length=160) followed
@@ -151,6 +171,64 @@ def _estimate_audio_tokens(audio_length_samples: int) -> int:
             length = (length + add_pad) / stride + 1.0
         return max(1, int(length))
 
+    @classmethod
+    def _estimate_audio_tokens(
+        cls,
+        audio_length_samples: int,
+        chunk_size_seconds: float | None = None,
+    ) -> int:
+        """Predict the encoder's total output frame count for an audio of N samples.
+
+        When ``chunk_size_seconds`` is ``None`` or the audio fits in a single
+        chunk, returns the single-pass estimate. Otherwise mirrors
+        ``encode_audio_with_optional_chunking``'s split (with the same
+        tail-folding rule) and sums the per-chunk frame counts so the
+        placeholder count matches what the model emits at forward time.
+        """
+        if chunk_size_seconds is None or audio_length_samples <= 0:
+            return cls._estimate_audio_tokens_single_pass(audio_length_samples)
+        if chunk_size_seconds <= 0.0:
+            raise ValueError("encoder_chunk_size_seconds must be positive when set.")
+        chunk_size_samples = max(1, int(round(chunk_size_seconds * _SAMPLING_RATE)))
+        chunk_size_samples = max(chunk_size_samples, _MIN_CHUNK_SIZE_SAMPLES)
+        if audio_length_samples <= chunk_size_samples:
+            return cls._estimate_audio_tokens_single_pass(audio_length_samples)
+
+        spans: list[tuple[int, int]] = []
+        for begin in range(0, audio_length_samples, chunk_size_samples):
+            end = min(begin + chunk_size_samples, audio_length_samples)
+            spans.append((begin, end))
+        if len(spans) > 1 and spans[-1][1] - spans[-1][0] < _MIN_CHUNK_SIZE_SAMPLES:
+            spans[-2] = (spans[-2][0], spans[-1][1])
+            spans.pop()
+
+        return sum(cls._estimate_audio_tokens_single_pass(end - begin) for begin, end in spans)
+
+    @classmethod
+    def _samples_for_audio_tokens(cls, target_tokens: int, chunk_size_seconds: float | None = None) -> int:
+        """Return the smallest sample count estimated to produce ``target_tokens``.
+
+        vLLM sizes the multimodal encoder cache from dummy inputs.  The SALM
+        plugin supports arbitrarily long audio by chunking the encoder forward,
+        but the decoder still receives the concatenated full-audio embedding
+        sequence.  This inverse estimator lets ``--limit-mm-per-prompt`` audio
+        length hints reserve cache for that full sequence without hard-coding a
+        single maximum call duration.
+        """
+        target_tokens = max(1, int(target_tokens))
+        max_samples = int(_DUMMY_AUDIO_MAX_DURATION_S * _SAMPLING_RATE)
+        lo, hi = 1, min(_SAMPLING_RATE, max_samples)
+        while hi < max_samples and cls._estimate_audio_tokens(hi, chunk_size_seconds) < target_tokens:
+            hi = min(hi * 2, max_samples)
+
+        while lo < hi:
+            mid = (lo + hi) // 2
+            if cls._estimate_audio_tokens(mid, chunk_size_seconds) >= target_tokens:
+                hi = mid
+            else:
+                lo = mid + 1
+        return lo
+
 
 class NeMoSpeechLMMultiModalProcessor(
     BaseMultiModalProcessor[NeMoSpeechLMProcessingInfo],
@@ -182,10 +260,11 @@ def _get_prompt_updates(
         out_mm_kwargs: MultiModalKwargsItems,
     ) -> list[PromptUpdate]:
         audios = mm_items.get_items("audio", AudioProcessorItems)
+        chunk_size_seconds = self.info._get_encoder_chunk_size_seconds()
 
         def get_replacement(item_idx: int):
             audio = audios.get(item_idx)
-            n_tokens = self.info._estimate_audio_tokens(audio.shape[-1])
+            n_tokens = self.info._estimate_audio_tokens(audio.shape[-1], chunk_size_seconds)
             repl_full = _AUDIO_PLACEHOLDER * n_tokens
             return PromptUpdateDetails.select_text(repl_full, _AUDIO_PLACEHOLDER)
 
@@ -210,6 +289,7 @@ def _call_hf_processor(
         audios = mm_data.pop("audios", [])
 
         if audios:
+            chunk_size_seconds = self.info._get_encoder_chunk_size_seconds()
             audio_list: list[torch.Tensor] = []
             audio_lengths: list[int] = []
             parts = re.split(f"({re.escape(_AUDIO_PLACEHOLDER)})", prompt)
@@ -229,7 +309,7 @@ def _call_hf_processor(
                 )
                 if audio_tensor.dim() > 1:
                     audio_tensor = audio_tensor.squeeze()
-                n_tokens = self.info._estimate_audio_tokens(audio_tensor.shape[-1])
+                n_tokens = self.info._estimate_audio_tokens(audio_tensor.shape[-1], chunk_size_seconds)
                 parts[i] = _AUDIO_PLACEHOLDER * n_tokens
                 audio_list.append(audio_tensor)
                 audio_lengths.append(audio_tensor.shape[-1])
@@ -257,6 +337,19 @@ def get_dummy_mm_data(
     ) -> MultiModalDataDict:
         num_audios = mm_counts.get("audio", 0)
         dummy_audio_len = int(_DUMMY_AUDIO_DURATION_S * _SAMPLING_RATE)
+        audio_options = mm_options.get("audio") if mm_options else None
+        requested_audio_len = getattr(audio_options, "length", None)
+        if requested_audio_len:
+            chunk_size_seconds = self.info._get_encoder_chunk_size_seconds()
+            if seq_len > _DUMMY_AUDIO_TEXT_TOKEN_RESERVE:
+                max_audio_tokens = seq_len - _DUMMY_AUDIO_TEXT_TOKEN_RESERVE
+                max_audio_len = NeMoSpeechLMProcessingInfo._samples_for_audio_tokens(
+                    max_audio_tokens,
+                    chunk_size_seconds,
+                )
+            else:
+                max_audio_len = int(_DUMMY_AUDIO_MAX_DURATION_S * _SAMPLING_RATE)
+            dummy_audio_len = min(int(requested_audio_len), max_audio_len)
         return {
             "audio": self._get_dummy_audios(
                 length=dummy_audio_len,

nemo/collections/speechlm2/vllm/salm/config.py

@@ -76,6 +76,7 @@ def __init__(
         prompt_format: str | None = None,
         pretrained_weights: bool | None = None,
         lora: dict | None = None,
+        encoder_chunk_size_seconds: float | None = None,
         **kwargs,
     ):
         required_fields = {
@@ -88,6 +89,7 @@ def __init__(
         is_default_init = (
             perception is None
             and lora is None
+            and encoder_chunk_size_seconds is None
             and not kwargs
             and all(value is None for value in required_fields.values())
         )
@@ -112,6 +114,7 @@ def __init__(
             self.prompt_format = None
             self.pretrained_weights = None
             self.lora = None
+            self.encoder_chunk_size_seconds = None
             return
 
         for name, value in required_fields.items():
@@ -137,6 +140,7 @@ def __init__(
         self.prompt_format = prompt_format
         self.pretrained_weights = pretrained_weights
         self.lora = lora
+        self.encoder_chunk_size_seconds = encoder_chunk_size_seconds
 
         self.text_config = AutoConfig.from_pretrained(pretrained_llm, trust_remote_code=True)
 
@@ -214,6 +218,7 @@ def __getattr__(self, name):
             "text_config",
             "lora",
             "is_hybrid",
+            "encoder_chunk_size_seconds",
         ):
             raise AttributeError(name)
         alias = self._ATTR_ALIASES.get(name, name) if self.is_hybrid else name

nemo/collections/speechlm2/vllm/salm/model.py

@@ -49,7 +49,9 @@
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors
 
+from nemo.collections.speechlm2.parts.encoder_chunking import encode_audio_with_optional_chunking
 from nemo.collections.speechlm2.vllm.salm.audio import (
+    _SAMPLING_RATE,
     NeMoSpeechLMAudioInputs,
     NeMoSpeechLMDummyInputsBuilder,
     NeMoSpeechLMMultiModalProcessor,
@@ -87,6 +89,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
         self.config = config
+        self.encoder_chunk_size_seconds = getattr(config, "encoder_chunk_size_seconds", None)
 
         backend = make_backend(config)
         self._backend = backend
@@ -142,13 +145,21 @@ def _process_audio(self, audio_input: NeMoSpeechLMAudioInputs) -> tuple[torch.Te
         audio_signal = audio_signal.to(device=device, dtype=_AUDIO_INPUT_DTYPE)
         audio_lengths = audio_input.audio_signal_length.to(device=device)
 
+        # Mirrors training (``encode_audio_with_optional_chunking``): when the
+        # checkpoint was trained with a chunked encoder (e.g. SALMAutomodel
+        # default 30 s), long audios are split into chunks before the perception
+        # forward and the per-chunk embeddings are concatenated. ``None``
+        # disables chunking and runs a single forward over the full batch.
         with torch.no_grad():
-            audio_embeds, audio_embed_lens = self.perception(
-                input_signal=audio_signal,
-                input_signal_length=audio_lengths,
+            audio_embeds = encode_audio_with_optional_chunking(
+                self.perception,
+                audio_signal,
+                audio_lengths,
+                chunk_size_seconds=self.encoder_chunk_size_seconds,
+                sampling_rate=_SAMPLING_RATE,
             )
 
-        return tuple(audio_embeds[i, : audio_embed_lens[i]] for i in range(audio_embeds.shape[0]))
+        return tuple(emb.to(torch.bfloat16) for emb in audio_embeds)
 
     def embed_multimodal(self, **kwargs) -> MultiModalEmbeddings:
         audio_input = self._parse_audio_input(**kwargs)

tests/collections/speechlm2/test_vllm_audio_token_estimator.py

@@ -80,3 +80,53 @@ def test_estimator_matches_calc_length(samples: int) -> None:
 def test_estimator_min_one() -> None:
     """Even for very short audio the estimator must return at least 1."""
     assert NeMoSpeechLMProcessingInfo._estimate_audio_tokens(1) >= 1
+
+
+def test_estimator_chunking_disabled_matches_single_pass() -> None:
+    """``chunk_size_seconds=None`` must match the legacy single-pass estimate."""
+    samples = 30 * 16_000
+    assert NeMoSpeechLMProcessingInfo._estimate_audio_tokens(
+        samples, chunk_size_seconds=None
+    ) == NeMoSpeechLMProcessingInfo._estimate_audio_tokens_single_pass(samples)
+
+
+def test_estimator_short_audio_falls_back_to_single_pass() -> None:
+    """Audio shorter than the chunk size collapses to a single forward."""
+    samples = 5 * 16_000
+    assert NeMoSpeechLMProcessingInfo._estimate_audio_tokens(
+        samples, chunk_size_seconds=30.0
+    ) == NeMoSpeechLMProcessingInfo._estimate_audio_tokens_single_pass(samples)
+
+
+def test_estimator_chunked_sums_per_chunk_frames() -> None:
+    """Long audio is split into chunks and per-chunk frame counts are summed,
+    matching ``encode_audio_with_optional_chunking``'s concat behavior."""
+    samples = 90 * 16_000
+    chunk_size_seconds = 30.0
+    chunk_samples = int(round(chunk_size_seconds * 16_000))
+    expected = sum(
+        NeMoSpeechLMProcessingInfo._estimate_audio_tokens_single_pass(min(chunk_samples, samples - i))
+        for i in range(0, samples, chunk_samples)
+    )
+    assert (
+        NeMoSpeechLMProcessingInfo._estimate_audio_tokens(samples, chunk_size_seconds=chunk_size_seconds) == expected
+    )
+
+
+def test_estimator_chunked_tail_folded_into_previous_chunk() -> None:
+    """A tiny tail (< min chunk size) is folded into the previous chunk so
+    the total token count matches the runtime helper instead of producing a
+    spurious single-frame chunk that the audio preprocessor would reject."""
+    chunk_size_seconds = 30.0
+    chunk_samples = int(round(chunk_size_seconds * 16_000))
+    samples = chunk_samples + 100  # 100 sample tail < min_chunk_size_samples (320)
+    # Folded: one chunk of `samples` samples (no split).
+    expected = NeMoSpeechLMProcessingInfo._estimate_audio_tokens_single_pass(samples)
+    assert (
+        NeMoSpeechLMProcessingInfo._estimate_audio_tokens(samples, chunk_size_seconds=chunk_size_seconds) == expected
+    )
+
+
+def test_estimator_negative_chunk_size_raises() -> None:
+    with pytest.raises(ValueError, match="encoder_chunk_size_seconds"):
+        NeMoSpeechLMProcessingInfo._estimate_audio_tokens(16_000, chunk_size_seconds=-1.0)

tests/collections/speechlm2/test_vllm_plugin.py

@@ -209,6 +209,26 @@ def test_unknown_attr_raises(self):
         with pytest.raises(AttributeError):
             _ = cfg.nonexistent_attribute_xyz
 
+    def test_encoder_chunk_size_seconds_default_none(self):
+        """Legacy checkpoints without a chunk size keep the single-pass encoder path."""
+        cfg = NeMoSpeechLMConfig(**_DEFAULT_CONFIG_KWARGS)
+        assert cfg.encoder_chunk_size_seconds is None
+
+    def test_encoder_chunk_size_seconds_round_trips(self):
+        """Chunk size set in config.json (e.g. SALMAutomodel default 30 s) survives load."""
+        cfg = NeMoSpeechLMConfig(
+            **{
+                **_DEFAULT_CONFIG_KWARGS,
+                "encoder_chunk_size_seconds": 30.0,
+            }
+        )
+        assert cfg.encoder_chunk_size_seconds == 30.0
+
+    def test_encoder_chunk_size_seconds_default_init_inert(self):
+        """No-arg default init must still expose ``encoder_chunk_size_seconds=None``."""
+        cfg = NeMoSpeechLMConfig()
+        assert cfg.encoder_chunk_size_seconds is None
+
 
 @pytest.mark.skipif(not (_HAS_CONFIG and _HAS_VLLM), reason="NeMoSpeechLMConfig or vLLM not available")
 class TestBackendSelection:
@@ -332,7 +352,8 @@ def test_call_hf_processor_requires_matching_placeholder_count(self):
         processor = object.__new__(NeMoSpeechLMMultiModalProcessor)
         processor.info = SimpleNamespace(
             get_tokenizer=_FakeTokenizer,
-            _estimate_audio_tokens=lambda samples: 2,
+            _estimate_audio_tokens=lambda samples, chunk_size_seconds=None: 2,
+            _get_encoder_chunk_size_seconds=lambda: None,
         )
 
         with pytest.raises(ValueError, match="placeholders"):
@@ -351,7 +372,8 @@ def test_call_hf_processor_emits_true_audio_lengths(self):
         processor = object.__new__(NeMoSpeechLMMultiModalProcessor)
         processor.info = SimpleNamespace(
             get_tokenizer=_FakeTokenizer,
-            _estimate_audio_tokens=lambda samples: 2,
+            _estimate_audio_tokens=lambda samples, chunk_size_seconds=None: 2,
+            _get_encoder_chunk_size_seconds=lambda: None,
         )
 
         result = processor._call_hf_processor(