voxtral.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import math
from collections.abc import Iterable, Mapping, Sequence
from functools import cached_property
from math import ceil
from typing import Literal, Optional, Union, cast

import numpy as np
import regex as re
import torch
import torch.nn as nn
from mistral_common.audio import mel_filter_bank
from mistral_common.protocol.instruct.messages import (AudioChunk, RawAudio,
                                                       TextChunk, UserMessage)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.protocol.transcription.request import TranscriptionRequest
from mistral_common.tokens.tokenizers.audio import Audio, AudioEncoder
from transformers import BatchFeature, TensorType, WhisperConfig
from transformers.tokenization_utils_base import TextInput

from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
from vllm.inputs.data import PromptType
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models import SupportsPP
from vllm.model_executor.models.module_mapping import MultiModelKeys
# yapf: disable
from vllm.model_executor.models.whisper import WhisperEncoder
# yapf: enable
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalKwargsItems, MultiModalUUIDDict,
                                    NestedTensors)
from vllm.multimodal.parse import (AudioProcessorItems, MultiModalDataItems,
                                   MultiModalDataParser)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo,
                                        MultiModalProcessingInfo,
                                        PromptReplacement, PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.tokenizer import (MistralTokenizer,
                                               cached_tokenizer_from_config)

from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsTranscription
from .utils import flatten_bn, init_vllm_registered_model, maybe_prefix

logger = init_logger(__name__)

ISO639_1_SUPPORTED_LANGS = {
    "ar": "Arabic",
    "nl": "Dutch",
    "en": "English",
    "fr": "French",
    "de": "German",
    "hi": "Hindi",
    "it": "Italian",
    "pt": "Portuguese",
    "es": "Spanish",
}


class VoxtralProcessorAdapter:
    """
    Provide a HF-compatible interface for
    :class:`mistral_common.tokens.tokenizers.multimodal.AudioEncoder`.
    """

    def __init__(self, tokenizer: MistralTokenizer) -> None:
        super().__init__()
        self.tokenizer = tokenizer

    @cached_property
    def _audio_processor(self) -> AudioEncoder:
        audio_encoder = self.tokenizer.instruct.audio_encoder
        assert isinstance(audio_encoder, AudioEncoder)
        return audio_encoder

    @cached_property
    def audio_token_id(self) -> int:
        return self._audio_processor.special_ids.audio

    @cached_property
    def begin_audio_token_id(self) -> int:
        return self._audio_processor.special_ids.begin_audio

    # @cached_property
    # def begin_transcript_token_id(self) -> int:
    #     return self._audio_processor.special_ids.begin_transcript

    # @cached_property
    # def end_transcript_token_id(self) -> int:
    #     return self._audio_processor.special_ids.end_transcript

    @cached_property
    def sampling_rate(self) -> int:
        return self._audio_processor.audio_config.sampling_rate

    @cached_property
    def frame_rate(self) -> float:
        return self._audio_processor.audio_config.frame_rate

    def get_num_audio_tokens(
        self,
        audio_length: int,
    ) -> int:
        pad_audio_length = self._audio_processor.next_multiple_of_chunk_frames(
            audio_length, self.sampling_rate)
        return ceil(pad_audio_length / (self.sampling_rate // self.frame_rate))

    def __call__(
        self,
        text: Optional[Union[TextInput, list[TextInput]]] = None,
        audios: Optional[Union[np.ndarray, list[np.ndarray]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> Mapping[str, NestedTensors]:
        if text is None:
            text = []
        if not isinstance(text, list):
            text = [text]
        if audios is None:
            audios = []
        if not isinstance(audios, list):
            audios = [audios]

        if not audios:
            input_ids = self.tokenizer(text).input_ids
            return {"input_ids": torch.tensor(input_ids)}

        # Allow dummy text, which is used for profiling as well as token inputs
        if any(len(t) > 0 for t in text):
            raise ValueError(
                "You've passed text inputs instead of token inputs. "
                "Make sure to process your input via `mistral_common`'s "
                "tokenizer or pass a chat completion request. "
                "For more info, see: "
                "https://github.com/vllm-project/vllm/issues/8411.")

        audios_tokens = list[torch.Tensor]()
        audios_processed = list[torch.Tensor]()
        for audio in audios:
            assert isinstance(audio, np.ndarray)
            assert audio.ndim == 1

            # pad if necessary
            audio = self._audio_processor.pad(audio, self.sampling_rate)

            audio_tokens = [
                self.begin_audio_token_id
            ] + [self.audio_token_id] * self.get_num_audio_tokens(len(audio))

            audios_tokens.append(torch.tensor(audio_tokens))
            audios_processed.append(torch.tensor(audio))

        return BatchFeature({
            "input_ids":
            torch.cat(audios_tokens)[None].expand(len(text), -1),
            "audio_arrays":
            audios_processed,
        })


class VoxtralProcessingInfo(BaseProcessingInfo):

    def get_tokenizer(self) -> MistralTokenizer:
        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
        if not isinstance(tokenizer, MistralTokenizer):
            raise ValueError("This model requires `--tokenizer-mode mistral`")

        return tokenizer

    def get_hf_processor(self) -> VoxtralProcessorAdapter:
        return VoxtralProcessorAdapter(self.get_tokenizer())

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"audio": 5}  # Performance tends to degrade after 5

    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:
        return {"audio": self.get_max_audio_tokens()}

    def get_max_audio_tokens(self) -> int:
        return self.ctx.model_config.max_model_len

    def get_max_audio_array_len(self) -> int:
        processor = self.get_hf_processor()
        return self.get_max_audio_tokens() * int(
            processor.sampling_rate // processor.frame_rate)


class VoxtralDummyInputsBuilder(BaseDummyInputsBuilder[VoxtralProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_audios = mm_counts.get("audio", 0)

        target_length = self.info.get_max_audio_array_len()

        return {
            "audio":
            self._get_dummy_audios(length=target_length, num_audios=num_audios)
        }

    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        tokenizer = self.info.get_tokenizer()

        dummy_text = self.get_dummy_text(mm_counts)
        dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts)
        dummy_audios = dummy_mm_data.get("audio", [])

        audio_chunks: list[AudioChunk] = []
        format = "wav"
        for audio in dummy_audios:
            audio_item = Audio(
                audio_array=audio,
                sampling_rate=self.info.get_hf_processor().sampling_rate,
                format=format,
            )
            chunk = AudioChunk(input_audio=RawAudio.from_audio(audio_item))
            audio_chunks.append(chunk)

        request = ChatCompletionRequest(messages=[
            UserMessage(content=[TextChunk(text=dummy_text), *audio_chunks]),
        ])
        res = tokenizer.mistral.encode_chat_completion(request)
        dummy_tokens = res.tokens
        # whixtral tokenizer adds padding to the audio
        # so we need to update the audio arrays
        dummy_mm_data["audio"] = [a.audio_array for a in res.audios]

        return ProcessorInputs(prompt=dummy_tokens, mm_data=dummy_mm_data)


class VoxtralMultiModalProcessor(BaseMultiModalProcessor[VoxtralProcessingInfo]
                                 ):

    def _get_mm_fields_config(
        self,
        hf_inputs: Mapping[str, NestedTensors],
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(audio_arrays=MultiModalFieldConfig.batched("audio"))

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        audio_id = processor.audio_token_id

        def get_replacement(item_idx: int):
            audios = mm_items.get_items("audio", AudioProcessorItems)
            audio_len = audios.get_audio_length(item_idx)

            nb_audio_tokens = processor.get_num_audio_tokens(audio_len)

            return [audio_id] * nb_audio_tokens

        return [
            PromptReplacement(
                modality="audio",
                target="",  # Never match the prompt (see below note)
                replacement=get_replacement,
            ),
        ]

    def _cached_apply_hf_processor(
        self,
        prompt: Union[str, list[int]],
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
        mm_uuids: Optional[MultiModalUUIDDict] = None,
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
        prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
            prompt=prompt,
            mm_data_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
            mm_uuids=mm_uuids,
        )

        # NOTE: The tokens are already inserted by the chat template
        return prompt_ids, mm_info, True

    def _get_data_parser(self) -> MultiModalDataParser:
        sampling_rate = self.info.get_hf_processor().sampling_rate
        return MultiModalDataParser(target_sr=sampling_rate)


@MULTIMODAL_REGISTRY.register_processor(VoxtralMultiModalProcessor,
                                        info=VoxtralProcessingInfo,
                                        dummy_inputs=VoxtralDummyInputsBuilder)
class VoxtralForConditionalGeneration(nn.Module, SupportsMultiModal,
                                      SupportsPP, SupportsLoRA,
                                      SupportsTranscription):
    supported_languages = ISO639_1_SUPPORTED_LANGS

    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"]
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.tokenizer = cached_tokenizer_from_config(vllm_config.model_config)

        # update quant config to so that ignored module and target module names
        # match the vLLM model names
        if hasattr(vllm_config, "quant_config"):
            vllm_config.quant_config = self.maybe_update_quant_config(
                vllm_config.quant_config)

        config = vllm_config.model_config.hf_config
        self.config = config
        self.downsample_factor = self.config.audio_config.downsample_factor

        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )
        self.whisper_encoder = VoxtralEncoderModel(
            vllm_config.with_hf_config(config.audio_config),
            prefix=maybe_prefix(prefix, "whisper_encoder"),
        )
        self.audio_language_adapter = AudioLanguageAdapter(
            hidden_size=config.audio_config.d_model * self.downsample_factor,
            dim=config.text_config.hidden_size,
        )

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def get_mm_mapping(self) -> MultiModelKeys:
        """Get module prefix for multimodal models to filter LoRA modules."""
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="audio_language_adapter",
            tower_model=["whisper_encoder"],
        )

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if intermediate_tensors is not None:
            inputs_embeds = None

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

        return hidden_states

    def get_multimodal_embeddings(
        self, **kwargs
    ) -> Union[list[torch.Tensor], torch.Tensor, tuple[torch.Tensor, ...],
               None]:
        audio_inputs = self._parse_and_validate_audio_arrays(**kwargs)
        if audio_inputs is None:
            return None

        audio_embeddings = self.whisper_encoder(audio_inputs)

        for i, audio_embedding in enumerate(audio_embeddings):
            seq_len, dim = audio_embedding.shape
            # Pad such that seq_len is divisible by downsample_factor
            target_seq_len = self.downsample_factor * math.ceil(
                seq_len / self.downsample_factor)
            audio_embedding = torch.nn.functional.pad(
                audio_embedding,
                (0, 0, 0, target_seq_len - seq_len),
            )
            audio_embeddings[i] = audio_embedding.reshape(
                target_seq_len // self.downsample_factor,
                dim * self.downsample_factor)

        # Concat, project and resplit
        audio_embeddings_packed = torch.cat(audio_embeddings, dim=0)
        audio_embeddings_packed = self.audio_language_adapter(
            audio_embeddings_packed)
        audio_embeddings = torch.split(audio_embeddings_packed,
                                       [a.shape[0] for a in audio_embeddings],
                                       dim=0)

        return audio_embeddings

    def _parse_and_validate_audio_arrays(
            self, **kwargs: object) -> Union[list[torch.Tensor], None]:
        audio_arrays = kwargs.pop("audio_arrays", None)
        if audio_arrays is None:
            return None

        if not isinstance(audio_arrays, (torch.Tensor, list)):
            raise ValueError("Incorrect type of audio_arrays. "
                             f"Got type: {type(audio_arrays)}")

        audio_arrays = flatten_bn(audio_arrays)
        if isinstance(audio_arrays, torch.Tensor):
            audio_arrays = list(audio_arrays.unbind(0))
        return audio_arrays

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states)

    @classmethod
    def get_speech_to_text_config(cls, model_config: ModelConfig,
                                  task_type: str) -> SpeechToTextConfig:
        tokenizer = cached_tokenizer_from_config(model_config)
        audio_config = tokenizer.instruct.audio_encoder.audio_config
        max_audio_clip_s = audio_config.chunk_length_s
        sample_rate = audio_config.sampling_rate
        return SpeechToTextConfig(
            max_audio_clip_s=max_audio_clip_s,
            sample_rate=sample_rate,
            # mistral_common and whisper encoder take care of chunking
            min_energy_split_window_size=None,
        )

    @classmethod
    # for speech-to-text transcription
    def get_generation_prompt(cls, audio: np.ndarray,
                              model_config: ModelConfig,
                              stt_config: SpeechToTextConfig,
                              language: Optional[str],
                              task_type: Literal["transcribe", "translate"],
                              request_prompt: str,
                              to_language: Optional[str]) -> PromptType:
        tokenizer = cached_tokenizer_from_config(model_config)
        audio = Audio(audio, int(stt_config.sample_rate),
                      format="wav")  # lossless
        req = TranscriptionRequest(model=model_config.model,
                                   audio=RawAudio.from_audio(audio),
                                   language=language)

        tokenized = tokenizer.instruct.encode_transcription(req)
        audio = (tokenized.audios[0].audio_array, stt_config.sample_rate)
        prompts_dict = {"multi_modal_data": {"audio": audio}}
        prompts_dict["prompt_token_ids"] = tokenized.tokens
        return cast(PromptType, prompts_dict)

    @classmethod
    def get_num_audio_tokens(cls, audio_duration_s: float,
                             stt_config: SpeechToTextConfig,
                             model_config: ModelConfig) -> Optional[int]:
        """
        Map from audio duration to number of audio tokens produced by the ASR 
        model, without running a forward pass.
        This is used for estimating the amount of processing for this audio.
        """
        tokenizer = cached_tokenizer_from_config(model_config)
        adapter = VoxtralProcessorAdapter(tokenizer)
        return adapter.get_num_audio_tokens(
            int(audio_duration_s * stt_config.sample_rate))

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        # fmt: off
        remapping_rules = [
            (r"mm_whisper_embeddings\.(.*)", r"\1"),
            (r"audio_language_projection\.(.*)", r"audio_language_adapter.\1"),
            (r"audio_language_adapter\.0\.weight", r"audio_language_adapter.w_in.weight"),  # noqa: E501
            (r"audio_language_adapter\.2\.weight", r"audio_language_adapter.w_out.weight"),  # noqa: E501
        ]
        # fmt: on

        audio_params = dict(
            nn.ModuleDict({
                "audio_language_adapter":
                self.audio_language_adapter,
            }).named_parameters())

        loaded_weights = set()

        def llm_weights_generator():
            nonlocal loaded_weights
            for name, w in weights:
                is_encoder = (
                    name.startswith("mm_whisper_embeddings") and
                    not name.startswith("mm_whisper_embeddings.tok_embeddings")
                    and not name.startswith(
                        "mm_whisper_embeddings.audio_language_projection"))

                for pattern, repl in remapping_rules:
                    if re.fullmatch(pattern, name):
                        name = re.sub(pattern, repl, name)

                if is_encoder:
                    name = self.whisper_encoder.load_weight((name, w))
                    loaded_weights.add(f"whisper_encoder.{name}")
                    continue

                if name in audio_params:
                    param = audio_params[name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                    loaded_weights.add(name)
                else:
                    yield (name, w)

        for name in self.language_model.load_weights(llm_weights_generator()):
            loaded_weights.add(f"language_model.{name}")

        # potentially manually add position embeddings
        sin_key = "whisper_encoder.whisper_encoder.embed_positions.weight"
        if sin_key not in loaded_weights:
            # make sure we don't hit an error here
            loaded_weights.add(sin_key)

        return loaded_weights

    def maybe_update_quant_config(
            self, quant_config: QuantizationConfig) -> QuantizationConfig:
        """
        Update quant config to so that ignored module and target module names
        match the vLLM model names.
        Right now this is specific for compressed-tensors format and
        load_format mistral.
        """
        remapping_rules = [
            (r"output", r"language_model.lm_head"),
            (r"layers\.(\d+)\.attention\.wo",
             r"language_model.model.layers.\1.self_attn.out_proj"),
            (r"layers\.(\d+)\.attention\.w(.*)",
             r"language_model.model.layers.\1.self_attn.\2_proj"),
            (r"layers\.(\d+)\.feed_forward\.w1",
             r"language_model.model.layers.\1.mlp.gate_proj"),
            (r"layers\.(\d+)\.feed_forward\.w2",
             r"language_model.model.layers.\1.mlp.down_proj"),
            (r"layers\.(\d+)\.feed_forward\.w3",
             r"language_model.model.layers.\1.mlp.up_proj"),
            (r"mm_whisper_embeddings\.whisper_encoder\.transformer\.layers\.(\d+)\.attention.w(.*)",
             r"whisper_encoder.whisper_encoder.layers.\1.layers.self_attn.\2_proj"
             ),
            (r"mm_whisper_embeddings\.whisper_encoder\.transformer\.layers\.(\d+)\.attention.wo",
             r"whisper_encoder.whisper_encoder.layers.\1.layers.self_attn.out_proj"
             ),
            (r"mm_whisper_embeddings\.whisper_encoder\.transformer\.layers\.(\d+)\.feed_forward.w(\d+)",
             r"whisper_encoder.whisper_encoder.layers.\1.layers.mlp.fc\2"),
            (r"mm_whisper_embeddings\.whisper_encoder\.conv_layers\.0",
             r"whisper_encoder.whisper_encoder.conv1"),
            (r"mm_whisper_embeddings\.whisper_encoder\.conv_layers\.1",
             r"whisper_encoder.whisper_encoder.conv2"),
            (r"mm_whisper_embeddings\.audio_language_projection\.0",
             r"audio_language_adapter.w_in"),
            (r"mm_whisper_embeddings\.audio_language_projection\.2",
             r"audio_language_adapter.w_out"),
        ]

        # Update ignore list
        if hasattr(quant_config, "ignore"):
            mistral_ignore = []
            for name in quant_config.ignore:
                mistral_name = name
                for pattern, repl in remapping_rules:
                    if re.fullmatch(pattern, name):
                        mistral_name = re.sub(pattern, repl, name)
                mistral_ignore.append(mistral_name)
            quant_config.ignore = mistral_ignore

        # Update target list
        if hasattr(quant_config, "config_groups"):
            config_groups = quant_config.config_groups
            for group_name in config_groups:
                if "targets" in config_groups[group_name]:
                    targets = []
                    for name in config_groups[group_name]["targets"]:
                        mistral_name = name
                        for pattern, repl in remapping_rules:
                            if re.fullmatch(pattern, name):
                                mistral_name = re.sub(pattern, repl, name)
                        targets.append(mistral_name)
                config_groups[group_name]["targets"] = targets
            quant_config.config_groups = config_groups

        return quant_config


class AudioLanguageAdapter(nn.Module):

    def __init__(self, hidden_size: int, dim: int) -> None:
        super().__init__()
        self.w_in = nn.Linear(hidden_size, dim, bias=False)
        self.gelu = nn.GELU()
        self.w_out = nn.Linear(dim, dim, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w_out(self.gelu(self.w_in(x)))


class VoxtralEncoderModel(nn.Module):
    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}

    # fmt: off
    mistral_remapping = [
        (r"whisper_encoder\.conv_layers\.0\.(weight|bias)", r"whisper_encoder.conv1.\1"), # noqa: E501
        (r"whisper_encoder\.conv_layers\.1\.(weight|bias)", r"whisper_encoder.conv2.\1"), # noqa: E501
        (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention\.w([qkv])\.(weight|bias)", r"whisper_encoder.layers.\1.self_attn.\2_proj.\3"), # noqa: E501
        (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention\.wo\.(weight|bias)", r"whisper_encoder.layers.\1.self_attn.out_proj.\2"), # noqa: E501
        (r"whisper_encoder\.transformer\.layers\.(\d+)\.attention_norm\.(weight|bias)", r"whisper_encoder.layers.\1.self_attn_layer_norm.\2"), # noqa: E501
        (r"whisper_encoder\.transformer\.layers\.(\d+)\.feed_forward\.w1\.(weight|bias)", r"whisper_encoder.layers.\1.mlp.fc1.\2"), # noqa: E501
        (r"whisper_encoder\.transformer\.layers\.(\d+)\.feed_forward\.w2\.(weight|bias)", r"whisper_encoder.layers.\1.mlp.fc2.\2"), # noqa: E501
        (r"whisper_encoder\.transformer\.layers\.(\d+)\.ffn_norm\.(weight|bias)", r"whisper_encoder.layers.\1.final_layer_norm.\2"), # noqa: E501
        (r"whisper_encoder\.transformer\.norm\.(weight|bias)", r"whisper_encoder.layer_norm.\1"), # noqa: E501
    ]
    # fmt: on

    def __init__(
        self,
        vllm_config: VllmConfig,
        *,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = cast(WhisperConfig, vllm_config.model_config.hf_config)
        self.dtype: torch.dtype = vllm_config.model_config.dtype
        self.whisper_encoder = WhisperEncoder(vllm_config=vllm_config,
                                              prefix=maybe_prefix(
                                                  prefix, "whisper_encoder"),
                                              init_in_fp32=True)
        mel_filters = mel_filter_bank(
            num_frequency_bins=1 + self.config.window_size // 2,
            num_mel_bins=self.config.num_mel_bins,
            min_frequency=0.0,
            max_frequency=8000.0,
            sampling_rate=self.config.sampling_rate,
        )
        self.mel_filters = torch.tensor(mel_filters, dtype=torch.float32)

    def compute_whisper_melspec(
        self,
        audio_waveforms: torch.Tensor,
    ) -> torch.Tensor:
        input_dtype = audio_waveforms.dtype
        window = torch.hann_window(self.config.window_size).to(
            audio_waveforms.device)
        stft = torch.stft(
            audio_waveforms,
            self.config.window_size,
            self.config.hop_length,
            window=window,
            return_complex=True,
        )
        magnitudes = stft[..., :-1].abs()**2
        mel_spec = self.mel_filters.T @ magnitudes
        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
        log_spec = (log_spec + 4.0) / 4.0
        return log_spec.to(input_dtype)

    @property
    def downsample_factor(self) -> int:
        return self.whisper_encoder.conv1.stride[
            0] * self.whisper_encoder.conv2.stride[0]

    @property
    def chunk_size(self) -> int:
        return self.config.max_source_positions * self.downsample_factor

    def prepare_inputs_for_conv(
        self,
        audio_waveforms: list[torch.Tensor],
    ) -> tuple[torch.Tensor, list[int]]:
        assert isinstance(audio_waveforms, list)
        # list[num_mel_bins, seq_len]
        input_features = [
            self.compute_whisper_melspec(audio).to(self.dtype)
            for audio in audio_waveforms
        ]

        chunked_features: list[torch.Tensor] = []
        chunks_per_example: list[int] = []
        for feature in input_features:
            chunks = feature.split(self.chunk_size, dim=-1)
            chunked_features += chunks
            chunks_per_example.append(len(chunks))

        # [total_num_chunks, num_mel_bins, chunk_size]
        return torch.stack(chunked_features), chunks_per_example

    def forward(
        self, input_features: Union[torch.Tensor, list[torch.Tensor]]
    ) -> list[torch.Tensor]:
        if not isinstance(input_features, list):
            input_features = [input_features]

        # Split long inputs into chunks
        input_embeds, chunks_per_example = (
            self.prepare_inputs_for_conv(input_features))

        # [total_num_chunks, ceil(chunk_size / downsample_factor), hidden_size]
        out = self.whisper_encoder([input_embeds])

        # Re-concatenate the chunks
        chunk_idx = 0
        results = []
        for n_chunks in chunks_per_example:
            result = out[chunk_idx:chunk_idx + n_chunks].flatten(0, 1)
            results.append(result)
            chunk_idx += n_chunks

        return results

    def load_weight(self, weight: tuple[str, torch.Tensor]) -> str:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())

        name, loaded_weight = weight
        for pattern, repl in self.mistral_remapping:
            if re.fullmatch(pattern, name):
                name = re.sub(pattern, repl, name)

        for (param_name, weight_name, shard_id) in stacked_params_mapping:
            if weight_name not in name:
                continue
            name = name.replace(weight_name, param_name)

            param = params_dict[name]
            weight_loader = param.weight_loader
            weight_loader(param, loaded_weight, shard_id)
            break
        else:
            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)

        return name