qwen2_audio.py

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

# Copyright 2024 The Qwen team.
# 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 Qwen2-Audio model compatible with HuggingFace weights."""
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Any, Literal, Optional, Union

import torch
import torch.nn as nn
from transformers import BatchFeature
from transformers.models.qwen2_audio import (Qwen2AudioConfig,
                                             Qwen2AudioEncoder,
                                             Qwen2AudioProcessor)
from transformers.models.whisper import WhisperFeatureExtractor

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (AudioItem, ModalityData,
                                    MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalKwargsItems)
from vllm.multimodal.parse import (AudioProcessorItems, DictEmbeddingItems,
                                   ModalityDataItems, MultiModalDataItems,
                                   MultiModalDataParser)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo, PromptReplacement,
                                        PromptUpdate, PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix


# # === Audio Inputs === #
class Qwen2AudioFeatureInputs(TensorSchema):
    """
    Dimensions:
        - na: Number of audios
        - nmb: Number of mel bins
    """
    type: Literal["audio_features"]
    input_features: Annotated[
        Union[torch.Tensor, list[torch.Tensor]],
        TensorShape("na", "nmb", 3000),
    ]

    feature_attention_mask: Annotated[
        torch.Tensor,
        TensorShape("na", 3000),
    ]


class Qwen2AudioEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size
        - naf: Number of audio features
        - hs: Hidden size (must match the hidden size of language model
          backbone)
    """
    type: Literal["audio_embeds"] = "audio_embeds"

    audio_embeds: Annotated[
        list[torch.Tensor],
        TensorShape("bn", "naf", "hs"),
    ]


Qwen2AudioInputs = Union[Qwen2AudioFeatureInputs, Qwen2AudioEmbeddingInputs]

# === Audio Encoder === #


class Qwen2AudioMultiModalProjector(nn.Module):

    def __init__(self, audio_hidden_size: int, text_hidden_size: int):
        super().__init__()
        self.linear = nn.Linear(audio_hidden_size, text_hidden_size, bias=True)

    def forward(self, audio_features):
        hidden_states = self.linear(audio_features)
        return hidden_states


# From Qwen2AudioEncoder._get_feat_extract_output_lengths
def _get_feat_extract_output_lengths(input_lengths: torch.Tensor):
    feat_lengths = (input_lengths - 1) // 2 + 1
    output_lengths = (feat_lengths - 2) // 2 + 1
    return feat_lengths, output_lengths


class Qwen2AudioProcessingInfo(BaseProcessingInfo):

    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen2AudioConfig)

    def get_hf_processor(self, **kwargs: object) -> Qwen2AudioProcessor:
        return self.ctx.get_hf_processor(Qwen2AudioProcessor, **kwargs)

    def get_feature_extractor(self,
                              **kwargs: object) -> WhisperFeatureExtractor:
        hf_processor = self.get_hf_processor(**kwargs)
        feature_extractor = hf_processor.feature_extractor  # type: ignore
        assert isinstance(feature_extractor, WhisperFeatureExtractor)
        return feature_extractor

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


class Qwen2AudioDummyInputsBuilder(
        BaseDummyInputsBuilder[Qwen2AudioProcessingInfo]):

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

        hf_processor = self.info.get_hf_processor()
        audio_token = hf_processor.audio_token

        return audio_token * num_audios

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
    ) -> MultiModalDataDict:
        feature_extractor = self.info.get_feature_extractor()

        sampling_rate = feature_extractor.sampling_rate
        audio_len = feature_extractor.chunk_length * sampling_rate
        num_audios = mm_counts.get("audio", 0)

        audio_overrides = mm_options.get("audio") if mm_options else None

        return {
            "audio":
            self._get_dummy_audios(length=audio_len,
                                   num_audios=num_audios,
                                   overrides=audio_overrides)
        }


def _qwen2audio_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    return dict(
        audio_embeds=MultiModalFieldConfig.batched("audio"),
        input_features=MultiModalFieldConfig.batched("audio"),
        feature_attention_mask=MultiModalFieldConfig.batched("audio"),
    )


class Qwen2AudioMultiModalDataParser(MultiModalDataParser):

    def _parse_audio_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[AudioItem]],
    ) -> Optional[ModalityDataItems[Any, Any]]:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="audio",
                required_fields={"audio_embeds"},
                fields_factory=_qwen2audio_field_config,
            )

        return super()._parse_audio_data(data)


class Qwen2AudioMultiModalProcessor(
        BaseMultiModalProcessor[Qwen2AudioProcessingInfo]):

    def _get_data_parser(self) -> MultiModalDataParser:
        feature_extractor = self.info.get_feature_extractor()
        return Qwen2AudioMultiModalDataParser(
            target_sr=feature_extractor.sampling_rate)

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, Any],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        # NOTE - we rename audios -> audio in mm data because transformers has
        # deprecated audios for the qwen2audio processor and will remove
        # support for it in transformers 4.54.
        audios = mm_data.pop("audios", [])
        if audios:
            mm_data["audio"] = audios

        # Text-only input not supported in composite processor
        if not mm_data.get("audio", []):
            prompt_ids = self.info.get_tokenizer().encode(prompt)
            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        feature_extractor = self.info.get_feature_extractor(**mm_kwargs)
        mm_kwargs = dict(
            **mm_kwargs,
            sampling_rate=feature_extractor.sampling_rate,
        )

        return super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _qwen2audio_field_config(hf_inputs)

    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)
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        # Use getattr with default to be compatible with transformers<4.48
        audio_token = getattr(processor, "audio_token", "<|AUDIO|>")
        audio_bos_token = getattr(processor, "audio_bos_token",
                                  "<|audio_bos|>")
        audio_eos_token = getattr(processor, "audio_eos_token",
                                  "<|audio_eos|>")

        audio_token_id = vocab[audio_token]
        audio_bos_id = vocab[audio_bos_token]
        audio_eos_id = vocab[audio_eos_token]

        out_mm_data = out_mm_kwargs.get_data()
        feature_attention_mask = out_mm_data.get("feature_attention_mask")
        if feature_attention_mask is None:
            audio_output_lengths = []
        else:
            assert isinstance(feature_attention_mask, torch.Tensor)
            _, audio_output_lens = _get_feat_extract_output_lengths(
                feature_attention_mask.sum(-1))

            audio_output_lengths = audio_output_lens.tolist()

        def get_replacement_qwen2_audio(item_idx: int):

            if audio_output_lengths:
                num_features = audio_output_lengths[item_idx]
            else:
                audio_embeds = out_mm_data["audio_embeds"][item_idx]
                assert len(audio_embeds.shape
                           ) == 2, "audio_embeds must be a 2D tensor"
                num_features = audio_embeds.shape[0]

            if num_features == 0:
                audios = mm_items.get_items("audio", AudioProcessorItems)
                audio_len = audios.get_audio_length(item_idx)

                raise ValueError(f"The audio (len={audio_len}) is too short "
                                 "to be represented inside the model")

            audio_tokens = [audio_token_id] * num_features

            return PromptUpdateDetails.select_token_id(
                [audio_bos_id] + audio_tokens + [audio_eos_id],
                embed_token_id=audio_token_id,
            )

        return [
            PromptReplacement(
                modality="audio",
                target=audio_token,
                replacement=get_replacement_qwen2_audio,
            )
        ]


@MULTIMODAL_REGISTRY.register_processor(
    Qwen2AudioMultiModalProcessor,
    info=Qwen2AudioProcessingInfo,
    dummy_inputs=Qwen2AudioDummyInputsBuilder)
class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal,
                                         SupportsPP):

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("audio"):
            return f"Audio {i}: <|audio_bos|><|AUDIO|><|audio_eos|>"

        raise ValueError("Only audio modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = config
        self.multimodal_config = multimodal_config

        self.audio_tower = Qwen2AudioEncoder(config.audio_config)
        self.multi_modal_projector = Qwen2AudioMultiModalProjector(
            config.audio_config.d_model, config.text_config.hidden_size)

        self.quant_config = quant_config

        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
            architectures=["Qwen2ForCausalLM"],
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def _validate_and_reshape_mm_tensor(self, mm_input: object,
                                        name: str) -> torch.Tensor:
        if not isinstance(mm_input, (torch.Tensor, list)):
            raise ValueError(f"Incorrect type of {name}. "
                             f"Got type: {type(mm_input)}")
        if isinstance(mm_input, torch.Tensor):
            return mm_input.reshape(-1, *mm_input.shape[2:])
        else:
            return torch.concat(mm_input)

    def _parse_and_validate_audio_input(
            self, **kwargs: object) -> Optional[Qwen2AudioInputs]:
        input_features = kwargs.pop('input_features', None)
        audio_embeds = kwargs.pop('audio_embeds', None)
        feature_attention_mask = kwargs.pop('feature_attention_mask', None)

        if input_features is None and audio_embeds is None:
            return None

        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 = self._validate_and_reshape_mm_tensor(
                audio_embeds, "audio_embeds")
            return Qwen2AudioEmbeddingInputs(type="audio_embeds",
                                             audio_embeds=audio_embeds)

        if input_features is not None:
            input_features = self._validate_and_reshape_mm_tensor(
                input_features, 'input_features')
            feature_attention_mask = self._validate_and_reshape_mm_tensor(
                feature_attention_mask, 'feature_attention_mask')
            return Qwen2AudioFeatureInputs(
                type="audio_features",
                input_features=input_features,
                feature_attention_mask=feature_attention_mask)

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

    def _process_audio_input(
        self, audio_input: Qwen2AudioInputs
    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
        if audio_input["type"] == "audio_embeds":
            audio_embeds = audio_input["audio_embeds"]
            return tuple(audio_embeds)

        input_features = audio_input["input_features"]
        feature_attention_mask = audio_input["feature_attention_mask"]

        audio_feat_lengths, audio_output_lengths = (
            self.audio_tower._get_feat_extract_output_lengths(
                feature_attention_mask.sum(-1)))

        batch_size, _, max_mel_seq_len = input_features.shape
        max_seq_len = (max_mel_seq_len - 2) // 2 + 1
        # Create a sequence tensor of shape (batch_size, max_seq_len)
        seq_range = (torch.arange(
            0,
            max_seq_len,
            dtype=audio_feat_lengths.dtype,
            device=audio_feat_lengths.device).unsqueeze(0).expand(
                batch_size, max_seq_len))
        lengths_expand = audio_feat_lengths.unsqueeze(-1).expand(
            batch_size, max_seq_len)
        # Create mask
        padding_mask = seq_range >= lengths_expand

        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.audio_tower.conv1.weight.dtype,
            device=self.audio_tower.conv1.weight.device)
        audio_attention_mask[audio_attention_mask_] = float("-inf")

        audio_outputs = self.audio_tower(input_features,
                                         attention_mask=audio_attention_mask)
        selected_audio_feature = audio_outputs.last_hidden_state
        audio_features = self.multi_modal_projector(selected_audio_feature)
        num_audios, max_audio_tokens, embed_dim = audio_features.shape
        audio_output_lengths = audio_output_lengths.unsqueeze(1)
        audio_features_mask = torch.arange(max_audio_tokens).expand(
            num_audios, max_audio_tokens).to(
                audio_output_lengths.device) < audio_output_lengths
        masked_audio_features = audio_features[audio_features_mask].view(
            -1, embed_dim)

        # Split to tuple of embeddings for individual audio input.
        return torch.split(masked_audio_features,
                           audio_output_lengths.flatten().tolist())

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

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
        audio_input = self._parse_and_validate_audio_input(**kwargs)
        if audio_input is None:
            return []
        masked_audio_features = self._process_audio_input(audio_input)
        return masked_audio_features

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

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