aphrodite-engine/aphrodite/modeling/models/ultravox.py

# Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_model.py
"""PyTorch Ultravox model."""

import itertools
import math
from array import array
from functools import lru_cache
from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                    TypedDict, Union, cast)

import librosa
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from transformers.models.whisper import WhisperFeatureExtractor
from transformers.models.whisper.modeling_whisper import WhisperEncoder

from aphrodite.attention import AttentionMetadata
from aphrodite.common.config import CacheConfig, MultiModalConfig
from aphrodite.common.sequence import (APHRODITE_TOKEN_ID_ARRAY_TYPE,
                                       SequenceData)
from aphrodite.inputs import INPUT_REGISTRY
from aphrodite.inputs.data import LLMInputs
from aphrodite.inputs.registry import InputContext
from aphrodite.modeling.layers.activation import SiluAndMul, get_act_fn
from aphrodite.modeling.layers.layernorm import RMSNorm
from aphrodite.modeling.layers.sampler import SamplerOutput
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.models.interfaces import SupportsMultiModal
from aphrodite.modeling.models.utils import (filter_weights, flatten_bn,
                                             init_aphrodite_registered_model,
                                             merge_multimodal_embeddings)
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.multimodal import MULTIMODAL_REGISTRY
from aphrodite.multimodal.base import MultiModalInputs, NestedTensors
from aphrodite.multimodal.utils import (cached_get_tokenizer,
                                        repeat_and_pad_placeholder_tokens)
from aphrodite.quantization.base_config import QuantizationConfig
from aphrodite.transformers_utils.configs.ultravox import UltravoxConfig

_AUDIO_PLACEHOLDER_TOKEN = 128002
_AUDIO_TOKENS_PER_SECOND = 6.25


class UltravoxAudioFeatureInputs(TypedDict):
    type: Literal["audio_features"]
    data: NestedTensors
    """Shape: `(batch_size, num_audios, 80, M)"""


class UltravoxAudioEmbeddingInputs(TypedDict):
    type: Literal["audio_embeds"]
    data: NestedTensors
    """Shape: `(batch_size, num_audios, audio_feature_size, hidden_size)"""


UltravoxAudioInputs = Union[UltravoxAudioFeatureInputs,
                            UltravoxAudioEmbeddingInputs]


@lru_cache
def cached_feature_extractor(model_id: str) -> WhisperFeatureExtractor:
    return WhisperFeatureExtractor.from_pretrained(model_id)


def whisper_feature_extractor(ctx: InputContext) -> WhisperFeatureExtractor:
    return cached_feature_extractor(
        ctx.get_hf_config(UltravoxConfig).audio_model_id)


def get_ultravox_max_audio_tokens(ctx: InputContext):
    feature_extractor = whisper_feature_extractor(ctx)
    return math.ceil(feature_extractor.chunk_length * _AUDIO_TOKENS_PER_SECOND)


def dummy_data_for_ultravox(
    ctx: InputContext,
    seq_len: int,
    mm_counts: Mapping[str, int],
):
    feature_extractor = whisper_feature_extractor(ctx)

    audio_count = mm_counts["audio"]

    audio_placeholder = array(
        APHRODITE_TOKEN_ID_ARRAY_TYPE,
        [_AUDIO_PLACEHOLDER_TOKEN]) * get_ultravox_max_audio_tokens(ctx)
    # Add a separator between each chunk.
    audio_token_ids = (audio_placeholder +
                       array(APHRODITE_TOKEN_ID_ARRAY_TYPE, [0])) * audio_count
    other_token_ids = array(APHRODITE_TOKEN_ID_ARRAY_TYPE,
                            [0]) * (seq_len - len(audio_token_ids))

    audio_and_sr = (np.array([0.0] * feature_extractor.chunk_length), 1)
    mm_dict = {"audio": [audio_and_sr] * audio_count}

    return (SequenceData(audio_token_ids + other_token_ids), mm_dict)


def input_mapper_for_ultravox(ctx: InputContext, data: object):
    if not isinstance(data, list):
        data = [data]
    audio_features = []
    for audio_input in data:
        if not isinstance(audio_input, tuple):
            raise NotImplementedError(
                f"Unsupported data type: {type(audio_input)}")
        (audio, sr) = cast(Tuple[np.ndarray, Union[float, int]], audio_input)
        feature_extractor = whisper_feature_extractor(ctx)

        if sr != feature_extractor.sampling_rate:
            audio = librosa.resample(audio,
                                     orig_sr=sr,
                                     target_sr=feature_extractor.sampling_rate)
            sr = feature_extractor.sampling_rate

        minimum_audio_length = feature_extractor.n_fft // 2 + 1
        if len(audio) < minimum_audio_length:
            # Not enough audio; pad it.
            audio = np.pad(audio, (0, minimum_audio_length - len(audio)))

        single_audio_features = feature_extractor(
            audio, sampling_rate=sr, padding="longest",
            return_tensors="pt")["input_features"]

        # Remove the batch dimension because we're wrapping it in a list.
        audio_features.append(single_audio_features.squeeze(0))
    return MultiModalInputs({"audio_features": audio_features})


def input_processor_for_ultravox(ctx: InputContext, llm_inputs: LLMInputs):
    multi_modal_data = llm_inputs.get("multi_modal_data")
    if multi_modal_data is None or "audio" not in multi_modal_data:
        return llm_inputs

    feature_extractor = whisper_feature_extractor(ctx)
    audios = multi_modal_data["audio"]
    if not isinstance(audios, list):
        audios = [audios]
    audio_token_counts = []
    for audio_data, sample_rate in audios:
        audio_length = audio_data.shape[0]
        if sample_rate != feature_extractor.sampling_rate:
            # Account for resampling.
            adjustment = feature_extractor.sampling_rate / sample_rate
            audio_length = math.ceil(adjustment * audio_length)
        feature_extractor_output_length = math.ceil(
            (audio_length - (feature_extractor.hop_length - 1)) /
            feature_extractor.hop_length)
        uv_config = ctx.get_hf_config(UltravoxConfig)
        audio_num_tokens = min(
            max(
                1,
                math.ceil(feature_extractor_output_length /
                          (uv_config.stack_factor * 2))),
            get_ultravox_max_audio_tokens(ctx))
        audio_token_counts.append(audio_num_tokens)

    tokenizer = cached_get_tokenizer(ctx.model_config.tokenizer)

    new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
        tokenizer,
        llm_inputs.get("prompt"),
        llm_inputs["prompt_token_ids"],
        placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN,
        repeat_count=audio_token_counts,
    )

    # NOTE: Create a defensive copy of the original inputs
    return LLMInputs(prompt_token_ids=new_token_ids,
                     prompt=new_prompt,
                     multi_modal_data=multi_modal_data)


class StackAudioFrames(nn.Module):
    """
    Stack the audio embedding frames to reduce the sequence length by a factor
    of `stack_factor`.
    """

    def __init__(self, stack_factor: int = 8):
        super().__init__()
        self.stack_factor = stack_factor

    def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
        B, T, C = audio_embeds.shape
        T_pad = (T + self.stack_factor -
                 1) // self.stack_factor * self.stack_factor
        audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T))
        B, T, C = audio_embeds.shape
        audio_embeds = audio_embeds.view(B, T // self.stack_factor,
                                         C * self.stack_factor)
        return audio_embeds


class FlippedSiluAndMul(SiluAndMul):
    """Ultravox is trained with SwiGLU with flipped halves."""

    def forward(self, x: torch.Tensor):
        a, b = x.chunk(2, dim=-1)
        flipped = torch.cat((b, a), dim=-1)
        return super().forward(flipped)


class UltravoxProjector(nn.Module):

    def __init__(self, config: UltravoxConfig):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self._pad_and_stack = StackAudioFrames(config.stack_factor)
        dim = config.audio_config.hidden_size * config.stack_factor
        self.ln_pre = RMSNorm(dim)
        self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
        dim = self.hidden_dim

        if config.projector_act == "swiglu":
            self.act = FlippedSiluAndMul()
            dim = dim // 2
        else:
            self.act = get_act_fn(config.projector_act)

        self.linear_2 = nn.Linear(dim,
                                  config.text_config.hidden_size,
                                  bias=False)
        self.ln_post = RMSNorm(config.text_config.hidden_size)

    def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
        audio_features = self._pad_and_stack(audio_features)
        audio_features = self.ln_pre(audio_features)
        hidden_states = self.linear_1(audio_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        hidden_states = self.ln_post(hidden_states)
        return hidden_states


class ModifiedWhisperEncoder(WhisperEncoder):
    """
    Encoder portion of OpenAI's Whisper model.

    This implementation is a slightly modified version of HF Transformers'
    Whisper Encoder, with only a few fixes:
    1. base_model_prefix updated to allow for doing `.from_pretrained`
       directly on the encoder
    2. allow less than 30 second of audio padding to be passed in:
        - relaxed ValueError check for `input_features` length to be less
           than or equal to `expected_seq_length` instead of strictly equal
        - embed_pos is now sliced to match the length of `inputs_embeds`

    Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
    See commentary: https://github.com/huggingface/transformers/issues/25744
    """

    base_model_prefix = "model.encoder"

    def forward(
        self,
        input_features,
    ):
        expected_seq_length = (self.config.max_source_positions *
                               self.conv1.stride[0] * self.conv2.stride[0])
        if input_features.shape[-1] > expected_seq_length:
            raise ValueError(
                f"Whisper expects the mel input features to be of length "
                f"{expected_seq_length} or less, but found "
                f"{input_features.shape[-1]}. Make sure to pad the input mel "
                f"features to {expected_seq_length}.")

        inputs_embeds = nn.functional.gelu(self.conv1(input_features))
        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))

        inputs_embeds = inputs_embeds.permute(0, 2, 1)
        embed_pos = self.embed_positions.weight[:inputs_embeds.size(-2)]

        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(hidden_states,
                                              p=self.dropout,
                                              training=self.training)

        for encoder_layer in self.layers:
            layer_outputs = encoder_layer(
                hidden_states,
                None,
                layer_head_mask=None,
            )

            hidden_states = layer_outputs[0]

        hidden_states = self.layer_norm(hidden_states)
        return hidden_states


@MULTIMODAL_REGISTRY.register_input_mapper("audio", input_mapper_for_ultravox)
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
    "audio", get_ultravox_max_audio_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_ultravox)
@INPUT_REGISTRY.register_input_processor(input_processor_for_ultravox)
class UltravoxModel(nn.Module, SupportsMultiModal):

    def __init__(self,
                 config: UltravoxConfig,
                 multimodal_config: MultiModalConfig,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional["QuantizationConfig"] = None):
        super().__init__()
        self.config = config
        self.multi_modal_config = multimodal_config
        assert self.multi_modal_config

        if config.audio_model_id is not None:
            self.audio_tower = ModifiedWhisperEncoder.from_pretrained(
                config.audio_model_id)
        else:
            self.audio_tower = ModifiedWhisperEncoder(config.audio_config)
        self.multi_modal_projector = UltravoxProjector(config)
        self.language_model = init_aphrodite_registered_model(
            config.text_config, cache_config, quant_config)

    def _audio_features_to_embeddings(
            self, input_features: torch.Tensor) -> torch.Tensor:
        audio_input = input_features.to(self.audio_tower.dtype)
        audio_features = self.audio_tower(audio_input)
        audio_features = audio_features.to(self.audio_tower.dtype)
        audio_embeddings = self.multi_modal_projector(audio_features)
        return audio_embeddings

    def _parse_and_validate_audio_input(
            self, **kwargs: object) -> Optional[UltravoxAudioInputs]:
        audio_features = kwargs.pop("audio_features", None)
        audio_embeds = kwargs.pop("audio_embeds", None)

        if audio_features is None and audio_embeds is None:
            return None

        if audio_features is not None:
            if not isinstance(audio_features, (torch.Tensor, list)):
                raise ValueError("Incorrect type of audio features. "
                                 f"Got type: {type(audio_features)}")

            return UltravoxAudioFeatureInputs(type="audio_features",
                                              data=audio_features)

        if audio_embeds is not None:
            if not isinstance(audio_embeds, (torch.Tensor, list)):
                raise ValueError("Incorrect type of audio embeds. "
                                 f"Got type: {type(audio_embeds)}")

            return UltravoxAudioEmbeddingInputs(type="audio_embeds",
                                                data=audio_embeds)

        raise AssertionError("This line should be unreachable.")


    def _process_audio_input(
            self, audio_input: UltravoxAudioInputs) -> NestedTensors:
        if audio_input["type"] == "audio_embeds":
            return audio_input["data"]

        audio_features = audio_input["data"]
        if isinstance(audio_features, torch.Tensor):
            # Combine the B and N dimensions for the encoder/projector
            flattened = flatten_bn(audio_features)
            flattened_embeddings = self._audio_features_to_embeddings(
                flattened)
            # Restore the original dimensions
            embeddings = flattened_embeddings.unflatten(
                0, audio_features.shape[:2])
            return embeddings
        result = []
        # TODO: Batch heterogeneous tensors through the encoder/projector
        for audio_features_item in audio_features:
            if isinstance(audio_features_item, torch.Tensor):
                result.append(
                    self._audio_features_to_embeddings(audio_features_item))
            else:
                embeddings = [
                    # Add a batch dimension to embed it, then remove it.
                    self._audio_features_to_embeddings(tensor.unsqueeze(0)
                                                       ).squeeze(0)
                    for tensor in audio_features_item
                ]
                result.append(embeddings)
        return result

    def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
                kv_caches: List[torch.Tensor],
                attn_metadata: AttentionMetadata,
                intermediate_tensors: Optional[torch.Tensor],
                **kwargs) -> SamplerOutput:
        """Run forward pass for Ultravox

        One key thing to understand is the `input_ids` already accounts for the
        positions of the to-be-inserted audio embeddings. The to-be-inserted
        audio has a size that is essentially 6.25 tokens per second of audio.

        This way, the `positions` and `attn_metadata` are consistent
        with the `input_ids`.

        Args:
            audio_features: A batch of audio inputs [B, N, 80, M].
        """
        audio_input = self._parse_and_validate_audio_input(**kwargs)
        if audio_input is not None:
            audio_embeddings = self._process_audio_input(audio_input)
            inputs_embeds = self.language_model.model.get_input_embeddings(
                input_ids)

            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, audio_embeddings,
                _AUDIO_PLACEHOLDER_TOKEN)
            input_ids = None
        else:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            input_ids=input_ids,
            positions=positions,
            kv_caches=kv_caches,
            attn_metadata=attn_metadata,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds)
        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        return self.language_model.sample(logits, sampling_metadata)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        # prepare weight iterators for components
        projector_weights, llm_weights = itertools.tee(weights, 2)

        # load projector weights
        projector_weights = filter_weights(projector_weights,
                                           "multi_modal_projector")
        projector_params_dict = dict(
            self.multi_modal_projector.named_parameters())
        for name, loaded_weight in projector_weights:
            param = projector_params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)

        # load llm backbone
        llm_weights = filter_weights(llm_weights, "language_model")
        self.language_model.load_weights(llm_weights)