Musicgen ONNX export (text-conditional only)

docs/source/exporters/onnx/overview.mdx

@@ -70,6 +70,7 @@ Supported architectures from [🤗 Transformers](https://huggingface.co/docs/tra
 - MobileNet v2
 - MPNet
 - MT5
+- Musicgen (text-conditional only)
 - Nystromformer
 - OWL-ViT
 - Pegasus

optimum/exporters/onnx/config.py

@@ -383,6 +383,13 @@ def __init__(
             )
 
         self._normalized_config.DECODER_NORMALIZED_CONFIG_CLASS = self._decoder_onnx_config._normalized_config
+        self._normalized_config.DECODER_NORMALIZED_CONFIG_CLASS = self._decoder_onnx_config._normalized_config
+        self._normalized_config.DECODER_NORMALIZED_CONFIG_CLASS.encoder_num_attention_heads = (
+            self._decoder_onnx_config._normalized_config.num_attention_heads
+        )
+        self._normalized_config.DECODER_NORMALIZED_CONFIG_CLASS.decoder_num_attention_heads = (
+            self._decoder_onnx_config._normalized_config.num_attention_heads
+        )
 
         if isinstance(self._decoder_onnx_config, OnnxSeq2SeqConfigWithPast):
             self._past_key_values_generator = (

optimum/exporters/onnx/constants.py

@@ -36,5 +36,6 @@
 
 SDPA_ARCHS_ONNX_EXPORT_NOT_SUPPORTED = [
     "bart",
+    "musicgen",
     "whisper",
 ]

optimum/exporters/onnx/convert.py

@@ -258,7 +258,7 @@ def _run_validation(
 
     model_kwargs = model_kwargs if model_kwargs is not None else {}
 
-    logger.info(f"Validating ONNX model {onnx_model.as_posix()}...")
+    logger.info(f"\nValidating ONNX model {onnx_model.as_posix()}...")
 
     if atol is None:
         atol = config.ATOL_FOR_VALIDATION
@@ -764,6 +764,9 @@ def export_models(
         output_path = output_dir / output_name
         output_path.parent.mkdir(parents=True, exist_ok=True)
 
+        logger.info(
+            f"\n***** Exporting submodel {i + 1}/{len(models_and_onnx_configs)}: {submodel.__class__.__name__} *****"
+        )
         outputs.append(
             export(
                 model=submodel,

optimum/exporters/onnx/model_patcher.py

@@ -254,7 +254,6 @@ def patched_forward(*args, **kwargs):
                         elif self.real_config._behavior == "decoder" and self.real_config.use_past_in_inputs:
                             # The filtering happens here. The decoder with use_past_in_inputs=True corresponds to the autoregressive one.
                             filterd_outputs[name] = tuple([v[:2] for v in value])
-
             return filterd_outputs
 
         self.patched_forward = patched_forward
@@ -796,3 +795,139 @@ def patched_forward(input_ids, attention_mask, pixel_values):
             return {"text_embeds": text_embeds, "image_embeds": image_embeds}
 
         self.patched_forward = patched_forward
+
+
+# Triu with possible dynamic `diagonal` argument. Not possible with torch.triu unfortunately.
+def triu_onnx(x, diagonal=0):
+    l, w = x.shape
+    arange_rows = torch.arange(l, device=x.device)
+
+    arange_cols = torch.arange(w, device=x.device)
+    mask = arange_cols.expand(l, w)
+
+    arange_rows = arange_rows[:, None] + diagonal
+    mask = mask >= arange_rows
+    return x.masked_fill(mask == 0, 0)
+
+
+def patched_build_delay_pattern_mask(self, input_ids: torch.Tensor, pad_token_id: int, max_length: int = None):
+    # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
+    input_ids = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
+    bsz, num_codebooks, seq_len = input_ids.shape
+
+    max_length = max_length if max_length is not None else self.generation_config.max_length
+    input_ids_shifted = torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1
+
+    channel_codebooks = num_codebooks // 2 if self.config.audio_channels == 2 else num_codebooks
+    # we only apply the mask if we have a large enough seq len - otherwise we return as is
+    if max_length < 2 * channel_codebooks - 1:
+        raise NotImplementedError("Not supported in ONNX export. Please open an issue in Optimum repository.")
+
+    # fill the shifted ids with the prompt entries, offset by the codebook idx
+    for codebook in range(channel_codebooks):
+        if self.config.audio_channels == 1:
+            # mono channel - loop over the codebooks one-by-one
+            input_ids_shifted[:, codebook, codebook : seq_len + codebook] = input_ids[:, codebook]
+        else:
+            # left/right channels are interleaved in the generated codebooks, so handle one then the other
+            input_ids_shifted[:, 2 * codebook, codebook : seq_len + codebook] = input_ids[:, 2 * codebook]
+            input_ids_shifted[:, 2 * codebook + 1, codebook : seq_len + codebook] = input_ids[:, 2 * codebook + 1]
+
+    # construct a pattern mask that indicates the positions of padding tokens for each codebook
+    # first fill the upper triangular part (the EOS padding)
+    # NOTE: We could use torch.bool here, but PyTorch the complains with `The exported ONNX model failed ONNX shape inference.`
+    # Using int8 leads to `Could not find an implementation for Where`
+    delay_pattern = triu_onnx(
+        torch.ones((channel_codebooks, max_length), dtype=torch.int32), diagonal=max_length - channel_codebooks + 1
+    )
+
+    # NOTE: We could use torch.bool here, but PyTorch the complains with `The exported ONNX model failed ONNX shape inference.`
+    # Using int32 leads to `Could not find an implementation for Trilu`, hence int64 here
+
+    # then fill the lower triangular part (the BOS padding)
+    delay_pattern = delay_pattern + torch.tril(torch.ones((channel_codebooks, max_length), dtype=torch.int64))
+    delay_pattern = delay_pattern.to(torch.bool)
+
+    if self.config.audio_channels == 2:
+        # for left/right channel we need to duplicate every row of the pattern mask in an interleaved fashion
+        delay_pattern = delay_pattern.repeat_interleave(2, dim=0)
+
+    mask = ~delay_pattern.to(input_ids.device)
+    input_ids = mask * input_ids_shifted + ~mask * pad_token_id
+
+    # find the first position to start generating - this is the first place we have the -1 token
+    # and will always be in the first codebook (since it has no codebook offset)
+    first_codebook_ids = input_ids[:, 0, :]
+    start_ids = (first_codebook_ids == -1).nonzero()[:, 1]
+
+    # TODO: Is this OK?
+    first_start_id = start_ids.min()
+
+    # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
+    pattern_mask = input_ids.reshape(bsz * num_codebooks, -1)
+    input_ids_edited = input_ids[..., :first_start_id].reshape(bsz * num_codebooks, -1)
+    return {"input_ids_edited": input_ids_edited, "delay_pattern_mask": pattern_mask}
+
+
+class MusicgenModelPatcher(Seq2SeqModelPatcher):
+    def __enter__(self):
+        self.patch_ops()
+        if self.real_config.model_part == "build_delay_pattern_mask":
+            # For build_delay_pattern_mask, we need to override the signature too.
+            self._model.forward = types.MethodType(patched_build_delay_pattern_mask, self._model)
+        else:
+            setattr(self._model, self.orig_forward_name, self.patched_forward)
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.restore_ops()
+        if self.real_config.model_part == "build_delay_pattern_mask":
+            self._model.forward = self.original_decoder_forward
+        else:
+            setattr(self._model, self.orig_forward_name, self.orig_forward)
+
+    def __init__(
+        self,
+        config: "OnnxConfig",
+        model: Union["PreTrainedModel", "TFPreTrainedModel"],
+        model_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__(config, model, model_kwargs)
+
+        if config.model_part == "build_delay_pattern_mask":
+            self.original_decoder_forward = self.orig_forward
+        elif config.model_part == "encodec_decode":
+            # EncodecModel.forward -> EncodecModel.decode
+            @functools.wraps(self.orig_forward)
+            def patched_forward(
+                input_values: Optional["torch.Tensor"] = None,
+                padding_mask: Optional["torch.Tensor"] = None,
+                audio_codes: Optional["torch.Tensor"] = None,
+                bandwidth: Optional[float] = None,
+                audio_scales: Optional["torch.Tensor"] = None,
+                return_dict: Optional[bool] = None,
+            ):
+                chunk_length = self.real_config._config.audio_encoder.chunk_length
+                if chunk_length is None:
+                    if audio_scales is not None:
+                        audio_scales = audio_scales[0]
+
+                    if len(audio_codes) != 1:
+                        raise ValueError(f"Expected one frame, got {len(audio_codes)}")
+                    audio_values = self._model._decode_frame(audio_codes[0], audio_scales)
+                else:
+                    raise ValueError("Not supported, a meaningful error should have been raised ahead.")
+                    decoded_frames = []
+
+                    for frame, scale in zip(audio_codes, audio_scales):
+                        frames = self._model._decode_frame(frame, scale)
+                        decoded_frames.append(frames)
+
+                    audio_values = self._model._linear_overlap_add(decoded_frames, self.config.chunk_stride or 1)
+
+                # truncate based on padding mask
+                if padding_mask is not None and padding_mask.shape[-1] < audio_values.shape[-1]:
+                    audio_values = audio_values[..., : padding_mask.shape[-1]]
+
+                return {"audio_values": audio_values}
+
+            self.patched_forward = patched_forward

optimum/exporters/tasks.py

@@ -177,7 +177,7 @@ class TasksManager:
             "object-detection": "AutoModelForObjectDetection",
             "question-answering": "AutoModelForQuestionAnswering",
             "semantic-segmentation": "AutoModelForSemanticSegmentation",
-            "text-to-audio": "AutoModelForTextToSpectrogram",
+            "text-to-audio": ("AutoModelForTextToSpectrogram", "AutoModelForTextToWaveform"),
             "text-generation": "AutoModelForCausalLM",
             "text2text-generation": "AutoModelForSeq2SeqLM",
             "text-classification": "AutoModelForSequenceClassification",
@@ -334,6 +334,7 @@ class TasksManager:
 
     # TODO: some models here support text-generation export but are not supported in ORTModelForCausalLM
     # Set of model topologies we support associated to the tasks supported by each topology and the factory
+    # TODO: remove `-with-past` tasks and rather rely on `variant`.
     _SUPPORTED_MODEL_TYPE = {
         "audio-spectrogram-transformer": supported_tasks_mapping(
             "feature-extraction",
@@ -806,6 +807,10 @@ class TasksManager:
             "text2text-generation-with-past",
             onnx="MT5OnnxConfig",
         ),
+        "musicgen": supported_tasks_mapping(
+            "text-to-audio",  # "variant" handles the "-with-past". We should generalize that.
+            onnx="MusicgenOnnxConfig",
+        ),
         "m2m-100": supported_tasks_mapping(
             "feature-extraction",
             "feature-extraction-with-past",

optimum/exporters/utils.py

@@ -345,6 +345,50 @@ def get_stable_diffusion_models_for_export(
     return models_for_export
 
 
+def get_musicgen_models_for_export(model: Union["PreTrainedModel", "TFPreTrainedModel"], config: "ExportConfig"):
+    models_for_export = {
+        "text_encoder": model.text_encoder,
+        "encodec_decode": model.audio_encoder,
+        # For the decoder, we do not pass model.decoder because we may need to export model.enc_to_dec_proj
+        DECODER_NAME: model,
+        DECODER_WITH_PAST_NAME: model,
+        "build_delay_pattern_mask": model.decoder,
+    }
+
+    text_encoder_config = config.__class__(
+        model.config, task=config.task, legacy=False, model_part="text_encoder", variant=config.variant
+    )
+    models_for_export["text_encoder"] = (models_for_export["text_encoder"], text_encoder_config)
+
+    audio_encoder_config = config.__class__(
+        model.config, task=config.task, legacy=False, model_part="encodec_decode", variant=config.variant
+    )
+    models_for_export["encodec_decode"] = (models_for_export["encodec_decode"], audio_encoder_config)
+
+    use_past = "with-past" in config.variant
+    decoder_export_config = config.with_behavior("decoder", use_past=use_past, use_past_in_inputs=False)
+    decoder_export_config.model_part = "decoder"
+    models_for_export[DECODER_NAME] = (models_for_export[DECODER_NAME], decoder_export_config)
+
+    if "with-past" in config.variant:
+        decoder_export_config_with_past = config.with_behavior("decoder", use_past=True, use_past_in_inputs=True)
+        decoder_export_config_with_past.model_part = "decoder"
+        models_for_export[DECODER_WITH_PAST_NAME] = (
+            models_for_export[DECODER_WITH_PAST_NAME],
+            decoder_export_config_with_past,
+        )
+
+    build_delay_pattern_mask_config = config.__class__(
+        model.config, task=config.task, legacy=False, model_part="build_delay_pattern_mask", variant=config.variant
+    )
+    models_for_export["build_delay_pattern_mask"] = (
+        models_for_export["build_delay_pattern_mask"],
+        build_delay_pattern_mask_config,
+    )
+
+    return models_for_export
+
+
 def _get_submodels_for_export_sam(model, variant):
     models_for_export = {}
 
@@ -513,6 +557,8 @@ def _get_submodels_and_export_configs(
                 models_and_export_configs = get_sam_models_for_export(model, export_config)
             elif model.config.model_type == "speecht5":
                 models_and_export_configs = get_speecht5_models_for_export(model, export_config, model_kwargs)
+            elif model.config.model_type == "musicgen":
+                models_and_export_configs = get_musicgen_models_for_export(model, export_config)
             else:
                 models_and_export_configs = {"model": (model, export_config)}
 

optimum/onnx/transformations_utils.py

@@ -160,7 +160,7 @@ def _unify_onnx_outputs(model1: ModelProto, model2: ModelProto, strict: bool):
         else:
             logger.info(
                 f"The two models proto have different outputs ({len(model1_outputs)} and {len(model2_outputs)} outputs)."
-                " Constant outputs will be added to unify the two models outputs."
+                " Constant outputs will be added to unify the two models outputs. This is expected for encoder-decoder models where cached cross-attention key/values are constant outputs, omitted in the model with KV cache."
             )
 
     if model2_outputs.issubset(model1_outputs) is False:

optimum/utils/__init__.py

@@ -48,8 +48,11 @@
     BloomDummyPastKeyValuesGenerator,
     DummyAudioInputGenerator,
     DummyBboxInputGenerator,
+    DummyCodegenDecoderTextInputGenerator,
     DummyDecoderTextInputGenerator,
+    DummyEncodecInputGenerator,
     DummyInputGenerator,
+    DummyIntGenerator,
     DummyLabelsGenerator,
     DummyPastKeyValuesGenerator,
     DummyPix2StructInputGenerator,