SpeechLM2 SALM: load ckpt faster, with less GPU memory

examples/speechlm2/salm_eval.py

@@ -47,6 +47,7 @@ class SalmEvalConfig:
     verbose: bool = True
     use_normalizer: Optional[str] = "english"  # "english", "basic", or "none" / "None"
     device: str = "cuda"
+    dtype: str = "bfloat16"
     extra_eos_tokens: Optional[list[str]] = None
     system_prompt: Optional[str] = None
     user_prompt: Optional[str] = None
@@ -56,10 +57,7 @@ class SalmEvalConfig:
 def main(cfg: SalmEvalConfig):
     logging.info(f'Hydra config:\n{OmegaConf.to_yaml(cfg)}')
 
-    with torch.device(cfg.device):
-        torch.set_default_dtype(torch.bfloat16)
-        model = SALM.from_pretrained(cfg.pretrained_name).eval().to(torch.bfloat16).to(cfg.device)
-        torch.set_default_dtype(torch.float32)
+    model = SALM.from_pretrained(cfg.pretrained_name).eval().to(getattr(torch, cfg.dtype)).to(cfg.device)
 
     cuts = guess_parse_cutset(cfg.inputs).sort_by_duration()
     dloader = torch.utils.data.DataLoader(

examples/speechlm2/salm_generate.py

@@ -42,6 +42,7 @@ class SalmEvalConfig:
     output_manifest: str = "generations.jsonl"
     verbose: bool = True
     device: str = "cuda"
+    dtype: str = "bfloat16"
     extra_eos_tokens: Optional[list[str]] = None
     system_prompt: Optional[str] = None
     user_prompt: Optional[str] = None
@@ -51,10 +52,7 @@ class SalmEvalConfig:
 def main(cfg: SalmEvalConfig):
     logging.info(f"Hydra config:\n{OmegaConf.to_yaml(cfg)}")
 
-    with torch.device(cfg.device):
-        torch.set_default_dtype(torch.bfloat16)
-        model = SALM.from_pretrained(cfg.pretrained_name).eval().to(torch.bfloat16).to(cfg.device)
-        torch.set_default_dtype(torch.float32)
+    model = SALM.from_pretrained(cfg.pretrained_name).eval().to(getattr(torch, cfg.dtype)).to(cfg.device)
 
     conversations = (
         guess_parse_cutset(cfg.inputs)

examples/speechlm2/to_hf.py

@@ -35,6 +35,9 @@ class HfExportConfig:
     # Path where we should save the HuggingFace Hub compatible checkpoint
     output_dir: str
 
+    # Dtype used for stored parameters
+    dtype: str = "bfloat16"
+
 
 def load_checkpoint(model: torch.nn.Module, checkpoint_path: str):
     if Path(checkpoint_path).is_dir():
@@ -60,6 +63,7 @@ def main(cfg: HfExportConfig):
     cls = import_class_by_path(cfg.class_path)
     model = cls(OmegaConf.to_container(model_cfg, resolve=True))
     load_checkpoint(model, cfg.ckpt_path)
+    model = model.to(getattr(torch, cfg.dtype))
     model.save_pretrained(cfg.output_dir)
 
 

nemo/collections/speechlm2/models/duplex_s2s_model.py

@@ -73,7 +73,7 @@ def __init__(self, cfg: dict) -> None:
         maybe_install_lora(self)
 
         # Load the pretrained ASR model.
-        setup_speech_encoder(self)
+        setup_speech_encoder(self, pretrained_weights=self.cfg.pretrained_weights)
 
         self.embed_audio_tokens = torch.nn.ModuleList(
             [

nemo/collections/speechlm2/models/duplex_s2s_speech_decoder_model.py

@@ -74,7 +74,7 @@ def __init__(self, cfg: dict) -> None:
         maybe_install_lora(self)
 
         # Load the pretrained streaming ASR model and copy its parameters into the audio perception module.
-        setup_speech_encoder(self)
+        setup_speech_encoder(self, pretrained_weights=self.cfg.pretrained_weights)
 
         self.speech_generation = TransformerARSpeechDecoder(
             speech_decoder_parms=OmegaConf.to_container(self.cfg.speech_decoder),

nemo/collections/speechlm2/models/salm.py

@@ -67,7 +67,7 @@ def __init__(self, cfg) -> None:
         maybe_install_lora(self)
 
         # Load the pretrained streaming ASR model and copy its parameters into the audio perception module.
-        setup_speech_encoder(self)
+        setup_speech_encoder(self, pretrained_weights=self.cfg.pretrained_weights)
 
         self._use_fsdp = False
         self._use_tp = False

nemo/collections/speechlm2/parts/pretrained.py

@@ -83,16 +83,19 @@ def setup_audio_codec(model: torch.nn.Module):
     del model.audio_codec.discriminator  # free up some memory
 
 
-def setup_speech_encoder(model: torch.nn.Module):
+def setup_speech_encoder(model: torch.nn.Module, pretrained_weights: bool = True):
     """
     Sets up an ``AudioPerceptionModule``, initializing its ``encoder`` and ``preprocessor``
     with a pretrained NeMo ``ASRModel``.
     The result is assigned to ``model.perception`` attribute and is trainable.
     """
-    asr = load_pretrained_nemo(ASRModel, model.cfg.pretrained_asr).eval()
-    with open_dict(model.cfg):
-        model.cfg.perception.preprocessor = asr.cfg.preprocessor
-        model.cfg.perception.encoder = asr.cfg.encoder
-        model.cfg.perception.output_dim = model.llm.config.hidden_size
-    model.perception = AudioPerceptionModule(model.cfg.perception).train()
-    model.perception.load_state_dict(asr.state_dict(), strict=False)
+    if pretrained_weights:
+        asr = load_pretrained_nemo(ASRModel, model.cfg.pretrained_asr).eval()
+        with open_dict(model.cfg):
+            model.cfg.perception.preprocessor = asr.cfg.preprocessor
+            model.cfg.perception.encoder = asr.cfg.encoder
+            model.cfg.perception.output_dim = model.llm.config.hidden_size
+        model.perception = AudioPerceptionModule(model.cfg.perception).train()
+        model.perception.load_state_dict(asr.state_dict(), strict=False)
+    else:
+        model.perception = AudioPerceptionModule(model.cfg.perception).train()

tests/collections/speechlm2/test_duplex_s2s.py

@@ -54,14 +54,49 @@ def model():
         "audio_loss_weight": 1,
         "text_loss_weight": 3,
         "perception": {
-            "_target_": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
-            "modality_adapter": {
+            "target": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
+            "output_dim": 2048,
+            "encoder": {
                 "_target_": "nemo.collections.asr.modules.ConformerEncoder",
-                "feat_in": 512,
+                "att_context_size": [-1, -1],
+                "causal_downsampling": False,
+                "conv_context_size": None,
+                "conv_kernel_size": 9,
+                "conv_norm_type": "batch_norm",
+                "d_model": 1024,
+                "dropout": 0.1,
+                "dropout_att": 0.1,
+                "dropout_emb": 0.0,
+                "dropout_pre_encoder": 0.1,
+                "feat_in": 128,
                 "feat_out": -1,
-                "n_layers": 1,
-                "d_model": 512,
-                "subsampling_factor": 1,
+                "ff_expansion_factor": 4,
+                "n_heads": 8,
+                "n_layers": 2,
+                "pos_emb_max_len": 5000,
+                "self_attention_model": "rel_pos",
+                "subsampling": "dw_striding",
+                "subsampling_conv_channels": 256,
+                "subsampling_factor": 8,
+            },
+            "modality_adapter": {
+                "_target_": "nemo.collections.speechlm2.modules.perception.IdentityConnector",
+                "d_model": 1024,
+            },
+            "preprocessor": {
+                "_target_": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
+                "dither": 1e-05,
+                "features": 128,
+                "frame_splicing": 1,
+                "log": True,
+                "n_fft": 512,
+                "normalize": "per_feature",
+                "pad_to": 0,
+                "pad_value": 0.0,
+                "sample_rate": 16000,
+                "window": "hann",
+                "window_size": 0.025,
+                "window_stride": 0.01,
             },
         },
         "optimizer": {"_target_": "torch.optim.AdamW"},
@@ -177,13 +212,13 @@ def test_s2s_offline_generation(model):
     assert isinstance(ans["text"][0], str)
 
     gen_text = ans["tokens_text"]
-    assert gen_text.shape == (1, 14)
+    assert gen_text.shape == (1, 13)
     assert gen_text.dtype == torch.long
     assert (gen_text >= 0).all()
     assert (gen_text < model.text_vocab_size).all()
 
     gen_audio_codes = ans["tokens_audio"]
-    assert gen_audio_codes.shape == (1, 14, 8)
+    assert gen_audio_codes.shape == (1, 13, 8)
     assert gen_audio_codes.dtype == torch.long
     assert (gen_audio_codes >= 0).all()
     assert (gen_audio_codes < model.speech_vocab_size).all()

tests/collections/speechlm2/test_duplex_s2s_speech_decoder.py

@@ -54,14 +54,49 @@ def model():
         "audio_loss_weight": 1,
         "text_loss_weight": 3,
         "perception": {
-            "_target_": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
-            "modality_adapter": {
+            "target": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
+            "output_dim": 2048,
+            "encoder": {
                 "_target_": "nemo.collections.asr.modules.ConformerEncoder",
-                "feat_in": 512,
+                "att_context_size": [-1, -1],
+                "causal_downsampling": False,
+                "conv_context_size": None,
+                "conv_kernel_size": 9,
+                "conv_norm_type": "batch_norm",
+                "d_model": 1024,
+                "dropout": 0.1,
+                "dropout_att": 0.1,
+                "dropout_emb": 0.0,
+                "dropout_pre_encoder": 0.1,
+                "feat_in": 128,
                 "feat_out": -1,
-                "n_layers": 1,
-                "d_model": 512,
-                "subsampling_factor": 1,
+                "ff_expansion_factor": 4,
+                "n_heads": 8,
+                "n_layers": 2,
+                "pos_emb_max_len": 5000,
+                "self_attention_model": "rel_pos",
+                "subsampling": "dw_striding",
+                "subsampling_conv_channels": 256,
+                "subsampling_factor": 8,
+            },
+            "modality_adapter": {
+                "_target_": "nemo.collections.speechlm2.modules.perception.IdentityConnector",
+                "d_model": 1024,
+            },
+            "preprocessor": {
+                "_target_": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
+                "dither": 1e-05,
+                "features": 128,
+                "frame_splicing": 1,
+                "log": True,
+                "n_fft": 512,
+                "normalize": "per_feature",
+                "pad_to": 0,
+                "pad_value": 0.0,
+                "sample_rate": 16000,
+                "window": "hann",
+                "window_size": 0.025,
+                "window_stride": 0.01,
             },
         },
         "speech_decoder": {
@@ -164,13 +199,13 @@ def test_s2s_speech_decoder_offline_generation(model):
     assert isinstance(ans["text"][0], str)
 
     gen_text = ans["tokens_text"]
-    assert gen_text.shape == (1, 14)
+    assert gen_text.shape == (1, 13)
     assert gen_text.dtype == torch.long
     assert (gen_text >= 0).all()
     assert (gen_text < model.text_vocab_size).all()
 
     gen_audio_codes = ans["tokens_audio"]
-    assert gen_audio_codes.shape == (1, 14, 8)
+    assert gen_audio_codes.shape == (1, 13, 8)
     assert gen_audio_codes.dtype == torch.long
     assert (gen_audio_codes >= 0).all()
     assert (gen_audio_codes < model.speech_vocab_size).all()

tests/collections/speechlm2/test_salm.py

@@ -57,12 +57,50 @@ def model():
         "prompt_format": PROMPT,
         "audio_locator_tag": AUDIO_LOCATOR_TAG,
         "perception": {
-            "_target_": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
+            "target": "nemo.collections.speechlm2.modules.perception.AudioPerceptionModule",
             "output_dim": 2048,
+            "encoder": {
+                "_target_": "nemo.collections.asr.modules.ConformerEncoder",
+                "att_context_size": [-1, -1],
+                "causal_downsampling": False,
+                "conv_context_size": None,
+                "conv_kernel_size": 9,
+                "conv_norm_type": "batch_norm",
+                "d_model": 1024,
+                "dropout": 0.1,
+                "dropout_att": 0.1,
+                "dropout_emb": 0.0,
+                "dropout_pre_encoder": 0.1,
+                "feat_in": 128,
+                "feat_out": -1,
+                "ff_expansion_factor": 4,
+                "n_heads": 8,
+                "n_layers": 2,
+                "pos_emb_max_len": 5000,
+                "self_attention_model": "rel_pos",
+                "subsampling": "dw_striding",
+                "subsampling_conv_channels": 256,
+                "subsampling_factor": 8,
+            },
             "modality_adapter": {
                 "_target_": "nemo.collections.speechlm2.modules.perception.IdentityConnector",
                 "d_model": 1024,
             },
+            "preprocessor": {
+                "_target_": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
+                "dither": 1e-05,
+                "features": 128,
+                "frame_splicing": 1,
+                "log": True,
+                "n_fft": 512,
+                "normalize": "per_feature",
+                "pad_to": 0,
+                "pad_value": 0.0,
+                "sample_rate": 16000,
+                "window": "hann",
+                "window_size": 0.025,
+                "window_stride": 0.01,
+            },
         },
         "optimizer": {"_target_": "torch.optim.AdamW"},
     }