minicpmo.py

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

# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only MiniCPM-O model compatible with HuggingFace weights."""
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Any, Callable, Literal, Optional, Union

import torch
from torch import nn
from transformers import BatchFeature, PretrainedConfig
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.models.whisper.modeling_whisper import (ACT2FN,
                                                          WhisperAttention,
                                                          WhisperConfig,
                                                          WhisperEncoder)

from vllm.config import VllmConfig
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.gptq_marlin import (
    GPTQMarlinConfig)
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    NestedTensors)
from vllm.multimodal.parse import (AudioItem, AudioProcessorItems,
                                   DictEmbeddingItems, ModalityData,
                                   ModalityDataItems, MultiModalDataItems,
                                   MultiModalDataParser)
from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
                                        PromptUpdateDetails)
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .minicpmv import (_MAX_FRAMES_PER_VIDEO, MiniCPMV2_6,
                       MiniCPMVDummyInputsBuilder,
                       MiniCPMVMultiModalDataParser,
                       MiniCPMVMultiModalProcessor, MiniCPMVProcessingInfo,
                       _minicpmv_field_config)
from .utils import (AutoWeightsLoader, cast_overflow_tensors, flatten_bn,
                    maybe_prefix)

CPU_DEVICE = torch.device("cpu")


class MiniCPMOAudioFeatureInputs(TensorSchema):
    """
    Dimensions:
        - bns: Batch size * number of audios * number of slices
        - bn: Batch size * number of audios
        - c: Number of channels
        - l: Length
        - s: Number of slices
    """
    type: Literal["audio_features"] = "audio_features"

    audio_features: Annotated[
        Union[torch.Tensor, list[torch.Tensor]],
        TensorShape("bns", "c", "l", dynamic_dims={"l"}),
    ]
    """
    Slice here means chunk. Audio that is too long will be split into slices,
    which is the same as image. Padding is used therefore `audio_features` is 
    `torch.Tensor`.
    """

    audio_feature_lens: Annotated[
        Union[torch.Tensor, list[torch.Tensor]],
        TensorShape("bn", "s"),
    ]
    """
    This should be feature length of each audio slice, 
    which equals to `audio_features.shape[-1]`
    """


class MiniCPMOAudioEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of audios
        - s: Number of slices
        - h: Hidden size (must match language model backbone)
    
    Length of each slice may vary, so pass it as a list.
    """
    type: Literal["audio_embeds"] = "audio_embeds"

    audio_embeds: Annotated[
        Union[torch.Tensor, list[torch.Tensor]],
        TensorShape("bn", "s", "h", dynamic_dims={"s"}),
    ]


MiniCPMOAudioInputs = Union[MiniCPMOAudioFeatureInputs,
                            MiniCPMOAudioEmbeddingInputs]


def _minicpmo_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    audio_features = hf_inputs.get("audio_features", torch.empty(0))
    num_audios = len(audio_features)

    return dict(
        **_minicpmv_field_config(hf_inputs),
        audio_features=MultiModalFieldConfig.batched("audio"),
        audio_feature_lens=MultiModalFieldConfig.batched("audio"),
        audio_embeds=MultiModalFieldConfig.batched("audio"),
        audio_token_id=MultiModalFieldConfig.shared("audio", num_audios),
    )


class MiniCPMOAudioEmbeddingItems(DictEmbeddingItems):

    def __init__(
        self,
        data: Mapping[str, torch.Tensor],
        fields_factory: Callable[
            [Mapping[str, torch.Tensor]],
            Mapping[str, MultiModalFieldConfig],
        ],
    ) -> None:
        super().__init__(
            data,
            modality="image",
            required_fields={"audio_embeds"},
            fields_factory=fields_factory,
        )


class MiniCPMOMultiModalDataParser(MiniCPMVMultiModalDataParser):

    def _parse_audio_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[AudioItem]],
    ) -> Optional[ModalityDataItems[Any, Any]]:
        if isinstance(data, dict):
            return MiniCPMOAudioEmbeddingItems(
                data,
                fields_factory=_minicpmo_field_config,
            )

        return super()._parse_audio_data(data)


class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
    audio_pattern = "(<audio>./</audio>)"

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {**super().get_supported_mm_limits(), "audio": None}

    def get_audio_placeholder(
        self,
        audio_lens: int,
        chunk_input: bool = True,
        chunk_length: int = 1,
    ) -> str:
        hf_processor = self.get_hf_processor()

        return hf_processor.get_audio_placeholder(
            audio_lens,
            chunk_input=chunk_input,
            chunk_length=chunk_length,
        )

    def get_default_audio_pool_step(self) -> int:
        return 2

    def get_default_audio_sampling_rate(self) -> int:
        return 16000

    def get_chunk_length(self) -> int:
        return self.get_hf_config().audio_chunk_length

    def get_max_audio_tokens_per_chunk(self) -> int:
        pool_step = self.get_default_audio_pool_step()
        fbank_feat_in_chunk = 100
        cnn_feat_in_chunk = (fbank_feat_in_chunk - 1) // 2 + 1
        return (cnn_feat_in_chunk - pool_step) // pool_step + 1

    def get_max_audio_chunks_with_most_features(self) -> int:
        return 30

    def get_max_audio_tokens(self) -> int:
        num_chunks = self.get_max_audio_chunks_with_most_features()
        return self.get_max_audio_tokens_per_chunk() * num_chunks

    def get_audio_len_by_num_chunks(self, num_chunks: int) -> int:
        sampling_rate = self.get_default_audio_sampling_rate()
        num_tokens_per_chunk = self.get_max_audio_tokens_per_chunk()
        return int(num_chunks * sampling_rate / num_tokens_per_chunk) + 1

    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        max_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)
        max_audios = mm_counts.get("audio", 0)

        max_image_tokens = self.get_max_image_tokens() * max_images
        max_audio_tokens = self.get_max_audio_tokens() * max_audios
        max_total_frames = self.get_max_video_frames(seq_len -
                                                     max_image_tokens -
                                                     max_audio_tokens)
        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
                                   _MAX_FRAMES_PER_VIDEO)

        return max(max_frames_per_video, 1)


class MiniCPMODummyInputsBuilder(
        MiniCPMVDummyInputsBuilder[MiniCPMOProcessingInfo]):

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

        audio_prompt_texts = self.info.audio_pattern * num_audios

        return super().get_dummy_text(mm_counts) + audio_prompt_texts

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

        audio_mm_data = {
            "audio":
            self._get_dummy_audios(length=audio_len, num_audios=num_audios)
        }

        return {
            **super().get_dummy_mm_data(seq_len, mm_counts),
            **audio_mm_data,
        }


class MiniCPMOMultiModalProcessor(
        MiniCPMVMultiModalProcessor[MiniCPMOProcessingInfo]):

    def _get_data_parser(self) -> MultiModalDataParser:
        return MiniCPMOMultiModalDataParser(
            target_sr=self.info.get_default_audio_sampling_rate())

    def get_audio_prompt_texts(
        self,
        audio_lens: int,
        chunk_input: bool = True,
        chunk_length: int = 1,
    ) -> str:
        return self.info.get_audio_placeholder(
            audio_lens,
            chunk_input=chunk_input,
            chunk_length=chunk_length,
        )

    def process_audios(
        self,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> Mapping[str, NestedTensors]:
        if (audios := mm_data.get("audios")) is None:
            return {}

        parsed_audios = (self._get_data_parser().parse_mm_data({
            "audio": audios
        }).get_items("audio",
                     (MiniCPMOAudioEmbeddingItems, AudioProcessorItems)))

        if isinstance(parsed_audios, MiniCPMOAudioEmbeddingItems):
            audio_inputs = {}
        else:
            audio_inputs = self._base_call_hf_processor(
                prompts=[self.info.audio_pattern] * len(parsed_audios),
                mm_data={"audios": [[audio] for audio in parsed_audios]},
                mm_kwargs={
                    **mm_kwargs, "chunk_input": True
                },
                tok_kwargs=tok_kwargs,
                out_keys={"audio_features", "audio_feature_lens"},
            )

            # Avoid padding since we need the output for each audio to be
            # independent of other audios for the cache to work correctly
            unpadded_audio_features = [
                feat[:, :feature_len] for feat, feature_len in zip(
                    audio_inputs["audio_features"],
                    audio_inputs["audio_feature_lens"],
                )
            ]
            audio_inputs["audio_features"] = unpadded_audio_features

        tokenizer = self.info.get_tokenizer()
        unk_token_id = tokenizer.get_vocab()["<unk>"]
        audio_inputs["audio_token_id"] = torch.tensor(unk_token_id)

        return audio_inputs

    def process_mm_inputs(
        self,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> Mapping[str, NestedTensors]:
        return {
            **super().process_mm_inputs(mm_data, mm_kwargs, tok_kwargs),
            **self.process_audios(mm_data, mm_kwargs, tok_kwargs),
        }

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        base_updates = super()._get_prompt_updates(
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            out_mm_kwargs=out_mm_kwargs,
        )

        audio_placeholder = self.info.audio_pattern

        def get_audio_replacement(item_idx: int):
            audios = mm_items.get_items(
                "audio", (MiniCPMOAudioEmbeddingItems, AudioProcessorItems))

            if isinstance(audios, MiniCPMOAudioEmbeddingItems):
                single_audio_embeds = audios.get(item_idx)["audio_embeds"]
                audio_len = self.info.get_audio_len_by_num_chunks(
                    sum(map(len, single_audio_embeds)))
            else:
                audio_len = audios.get_audio_length(item_idx)

            return PromptUpdateDetails.select_text(
                self.get_audio_prompt_texts(audio_len),
                "<unk>",
            )

        return [
            *base_updates,
            PromptReplacement(modality="audio",
                              target=audio_placeholder,
                              replacement=get_audio_replacement),
        ]

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _minicpmo_field_config(hf_inputs)


class MultiModalProjector(nn.Module):

    def __init__(self, in_dim: int, out_dim: int):
        super().__init__()
        self.linear1 = nn.Linear(in_features=in_dim,
                                 out_features=out_dim,
                                 bias=True)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(in_features=out_dim,
                                 out_features=out_dim,
                                 bias=True)

    def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
        hidden_states = self.relu(self.linear1(audio_features))
        hidden_states = self.linear2(hidden_states)
        return hidden_states


class MiniCPMWhisperEncoderLayer(nn.Module):

    def __init__(self, config: WhisperConfig, layer_idx: int):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = WhisperAttention(
            embed_dim=self.embed_dim,
            num_heads=config.encoder_attention_heads,
            dropout=config.attention_dropout,
            config=config,
            layer_idx=layer_idx,
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
    ) -> torch.Tensor:
        residual = hidden_states
        past_key_values = None
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, past_key_values = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_value=past_key_values,
        )
        hidden_states = nn.functional.dropout(hidden_states,
                                              p=self.dropout,
                                              training=self.training)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states,
                                              p=self.activation_dropout,
                                              training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states,
                                              p=self.dropout,
                                              training=self.training)
        hidden_states = residual + hidden_states

        if hidden_states.dtype == torch.float16:
            hidden_states = cast_overflow_tensors(hidden_states)

        outputs = (hidden_states, )

        return outputs


class MiniCPMWhisperEncoder(WhisperEncoder):

    def __init__(self, config: WhisperConfig):
        super().__init__(config)
        self.layers = nn.ModuleList([
            MiniCPMWhisperEncoderLayer(config, layer_idx=i)
            for i in range(config.encoder_layers)
        ])

    def forward(
        self,
        input_features: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> BaseModelOutputWithPast:
        # Ignore copy
        input_features = input_features.to(dtype=self.conv1.weight.dtype,
                                           device=self.conv1.weight.device)

        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

        embed_pos = embed_pos[:inputs_embeds.shape[1], :]

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

        encoder_states = ()

        for idx, encoder_layer in enumerate(self.layers):
            encoder_states = encoder_states + (hidden_states, )
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:  # skip the layer
                    to_drop = True

            # Ignore copy
            if to_drop:
                layer_outputs = (None, None)
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                )

                hidden_states = layer_outputs[0]

        hidden_states = self.layer_norm(hidden_states)
        encoder_states = encoder_states + (hidden_states, )

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            hidden_states=encoder_states,
        )


@MULTIMODAL_REGISTRY.register_processor(
    MiniCPMOMultiModalProcessor,
    info=MiniCPMOProcessingInfo,
    dummy_inputs=MiniCPMODummyInputsBuilder)
class MiniCPMO(MiniCPMV2_6):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "(<image>./</image>)"
        if modality.startswith("video"):
            return "(<video>./</video>)"
        if modality.startswith("audio"):
            return "(<audio>./</audio>)"

        raise ValueError("Only image, video or audio modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)
        self.apm = self.init_audio_module(vllm_config=vllm_config,
                                          prefix=maybe_prefix(prefix, "apm"))

        self.audio_token_id = None

    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
        # seems to avoid vision encoder sections for some models.
        # See: https://huggingface.co/openbmb/MiniCPM-o-2_6-int4
        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
            return None
        return quant_config

    def init_vision_module(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> nn.Module:
        # MiniCPMO GPTQ model leave vpm unquantized.
        quant_config = self._maybe_ignore_quant_config(quant_config)
        return super().init_vision_module(config, quant_config, prefix)

    def init_resampler(
        self,
        embed_dim: int,
        vision_dim: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> nn.Module:
        # MiniCPMO GPTQ model leave resampler unquantized.
        quant_config = self._maybe_ignore_quant_config(quant_config)
        return super().init_resampler(embed_dim, vision_dim, quant_config,
                                      prefix)

    def init_audio_module(self, *, vllm_config: VllmConfig, prefix: str = ""):
        # Do not use parameters temporarily
        audio_config = self.config.audio_config
        model = MiniCPMWhisperEncoder(audio_config)
        audio_output_dim = int(audio_config.encoder_ffn_dim // 4)
        self.audio_avg_pooler = \
            nn.AvgPool1d(self.config.audio_pool_step,
                         stride=self.config.audio_pool_step)
        self.audio_projection_layer = \
            MultiModalProjector(in_dim=audio_output_dim,out_dim=self.embed_dim)
        self.audio_encoder_layer = -1
        return model

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self, skip_prefixes=["tts"])
        return loader.load_weights(weights)

    def subsequent_chunk_mask(
        self,
        size: int,
        chunk_size: int,
        num_left_chunks: int = -1,
        device: torch.device = CPU_DEVICE,
        num_lookhead: int = 0,
    ) -> torch.Tensor:
        ret = torch.zeros(size, size, device=device, dtype=torch.bool)
        # Vectorized computation of row indices and chunk boundaries
        row_indices = torch.arange(size, device=device)
        chunk_indices = row_indices // chunk_size
        if num_left_chunks < 0:
            # If num_left_chunks < 0, start is always 0 for all rows
            start_indices = torch.zeros_like(row_indices)
        else:
            # Compute start indices vectorially
            start_chunk_indices = torch.clamp(chunk_indices - num_left_chunks,
                                              min=0)
            start_indices = start_chunk_indices * chunk_size
        # Compute ending indices vectorially
        end_chunk_indices = chunk_indices + 1
        end_indices = torch.clamp(end_chunk_indices * chunk_size +
                                  num_lookhead,
                                  max=size)
        # Create column indices for broadcasting
        col_indices = torch.arange(size, device=device).unsqueeze(0)
        start_indices = start_indices.unsqueeze(1)
        end_indices = end_indices.unsqueeze(1)
        # Vectorized mask creation
        ret = (col_indices >= start_indices) & (col_indices < end_indices)
        return ret

    def _get_feat_extract_output_lengths(self,
                                         input_lengths: torch.LongTensor):
        input_lengths_after_cnn = (input_lengths - 1) // 2 + 1
        input_lengths_after_pooling = (
            input_lengths_after_cnn -
            self.config.audio_pool_step) // self.config.audio_pool_step + 1
        input_lengths_after_pooling = input_lengths_after_pooling.to(
            dtype=torch.int32)

        return input_lengths_after_cnn, input_lengths_after_pooling

    def get_audio_hidden_states(
            self, data: MiniCPMOAudioFeatureInputs) -> list[torch.Tensor]:
        chunk_length = self.config.audio_chunk_length

        # (bs, 80, frames) or [], multi audios need filled in advance
        wavforms_raw = data["audio_features"]
        if isinstance(wavforms_raw, list):
            B = len(wavforms_raw)
            C = wavforms_raw[0].shape[-2]
            L = max(item.shape[-1] for item in wavforms_raw)
            device = wavforms_raw[0].device
            dtype = wavforms_raw[0].dtype

            wavforms = torch.zeros((B, C, L), dtype=dtype, device=device)
            for i, wavforms_item in enumerate(wavforms_raw):
                L_item = wavforms_item.shape[-1]
                wavforms[i, ..., :L_item] = wavforms_item
        else:
            wavforms = wavforms_raw

        # list, [[x1, x2], [y1], [z1]]
        audio_feature_lens_raw = data["audio_feature_lens"]
        if isinstance(audio_feature_lens_raw, torch.Tensor):
            audio_feature_lens_raw = audio_feature_lens_raw.unbind(0)

        audio_feature_lens = torch.hstack(audio_feature_lens_raw)
        batch_size, _, max_mel_seq_len = wavforms.shape
        max_seq_len = (max_mel_seq_len - 1) // 2 + 1

        # Create a sequence tensor of shape (batch_size, max_seq_len)
        seq_range = (torch.arange(
            0,
            max_seq_len,
            dtype=audio_feature_lens.dtype,
            device=audio_feature_lens.device).unsqueeze(0).expand(
                batch_size, max_seq_len))
        lengths_expand = audio_feature_lens.unsqueeze(1).expand(
            batch_size, max_seq_len)
        # Create mask
        padding_mask = seq_range >= lengths_expand  # 1 for padded values

        audio_attention_mask_ = padding_mask.view(
            batch_size, 1, 1, max_seq_len).expand(batch_size, 1, max_seq_len,
                                                  max_seq_len)
        audio_attention_mask = audio_attention_mask_.to(
            dtype=self.apm.conv1.weight.dtype,
            device=self.apm.conv1.weight.device)

        if chunk_length > 0:
            chunk_num_frame = int(chunk_length * 50)
            chunk_mask = self.subsequent_chunk_mask(
                size=max_seq_len,
                chunk_size=chunk_num_frame,
                num_left_chunks=-1,
                device=audio_attention_mask_.device,
            )
            audio_attention_mask_ = torch.logical_or(
                audio_attention_mask_, torch.logical_not(chunk_mask))

        audio_attention_mask[audio_attention_mask_] = float("-inf")
        audio_states = self.apm(
            wavforms, attention_mask=audio_attention_mask).hidden_states[
                self.audio_encoder_layer]
        audio_embeds = self.audio_projection_layer(audio_states)

        audio_embeds = audio_embeds.transpose(1, 2)
        audio_embeds = self.audio_avg_pooler(audio_embeds)
        audio_embeds = audio_embeds.transpose(1, 2)

        _, feature_lens_after_pooling = \
            self._get_feat_extract_output_lengths(audio_feature_lens)

        num_audio_tokens = feature_lens_after_pooling

        final_audio_embeds = list[torch.Tensor]()
        idx = 0
        for i in range(len(audio_feature_lens_raw)):
            target_audio_embeds_lst = list[torch.Tensor]()
            for _ in range(len(audio_feature_lens_raw[i])):
                target_audio_embeds_lst.append(
                    audio_embeds[idx, :num_audio_tokens[idx], :])
                idx += 1

            final_audio_embeds.append(torch.cat(target_audio_embeds_lst))

        return final_audio_embeds

    def _parse_and_validate_audio_input(
            self, **kwargs: object) -> Optional[MiniCPMOAudioInputs]:
        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

        audio_token_id = kwargs.pop("audio_token_id")
        if audio_token_id is not None:
            assert isinstance(audio_token_id, torch.Tensor)
            self.mm_token_ids.add(audio_token_id.flatten().unique().item())

        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)}")

            audio_embeds_flat = flatten_bn(audio_embeds)

            return MiniCPMOAudioEmbeddingInputs(
                type="audio_embeds",
                audio_embeds=audio_embeds_flat,
            )

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

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

        audio_features_flat = flatten_bn(audio_features)
        audio_feature_lens_flat = flatten_bn(audio_feature_lens)

        return MiniCPMOAudioFeatureInputs(
            type="audio_features",
            audio_features=audio_features_flat,
            audio_feature_lens=audio_feature_lens_flat,
        )

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = super()._parse_and_validate_multimodal_inputs(**kwargs)

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
            if input_key in ("audio_features",
                             "audio_embeds") and "audios" not in modalities:
                modalities["audios"] = self._parse_and_validate_audio_input(
                    **kwargs)

        return modalities

    def _process_audio_input(
        self,
        audio_input: MiniCPMOAudioInputs,
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        if audio_input["type"] == "audio_embeds":
            return audio_input["audio_embeds"]

        return self.get_audio_hidden_states(audio_input)

    def _process_multimodal_inputs(self, modalities: dict):
        multimodal_embeddings = super()._process_multimodal_inputs(modalities)

        for modality in modalities:
            if modality == "audios":
                audio_input = modalities["audios"]
                audio_features = self._process_audio_input(audio_input)
                multimodal_embeddings += tuple(audio_features)

        return multimodal_embeddings