processing_megrezo.py

# Copyright 2024 Infinigence AI Team. All rights reserved.
#
# 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.

import io
import re
import subprocess
from collections import UserDict
from typing import List, Literal, Optional, Tuple, Union

import numpy as np
import PIL
import PIL.Image
import torch
from torch.nn.utils.rnn import pad_sequence
from transformers import TensorType
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin

from .image_processing_megrezo import MegrezOImageProcessor  # noqa: F401

AudioInput = Union[str, bytes, np.ndarray, List[str], List[bytes], List[np.ndarray]]
ReturnTensorType = Union[str, TensorType]


class ImageBatchFeature(BatchFeature):
    r"""
    Holds the image features of a batch of images.
    """

    pixel_values: Union[np.ndarray, torch.Tensor]
    image_sizes: Union[np.ndarray, torch.Tensor]
    tgt_sizes: Union[np.ndarray, torch.Tensor]
    patch_attention_mask: Union[np.ndarray, torch.Tensor]
    image_bounds: Union[np.ndarray, torch.Tensor]


class AudioBatchFeature(BatchFeature):
    r"""
    Holds the audio features of a batch of audio.
    """

    input_audios: List[Union[np.ndarray, torch.Tensor]]
    input_audio_lengths: List[Union[np.ndarray, torch.Tensor]]
    audio_span_tokens: List[Union[np.ndarray, torch.Tensor]]
    audio_bounds: Union[np.ndarray, torch.Tensor]


class ConvContent(UserDict):
    text: Optional[str] = None
    image: Optional[ImageInput] = None
    audio: Optional[Union[str, bytes, List[Union[str, bytes]]]] = None


class Conversation(UserDict):
    role: Literal["user", "assistant"]
    content: Union[str, dict, ConvContent]


def load_audio(
    audio: Union[str, bytes],
    sample_rate: int = 16000,
) -> "np.ndarray":
    """Load audio from a file path or bytes and return as a numpy array.

    Args:
        audio (Union[str, bytes]): path to a audio file or audio bytes.
        sample_rate (int, optional): sample rate. Defaults to 16000.

    Raises:
        ValueError: if the input audio is neither a path nor bytes.

    Returns:
        np.ndarray: the audio as a numpy array.
    """
    if isinstance(audio, str):
        inp = audio
        out = "-"
        cmd_inp = None
    elif isinstance(audio, bytes):
        inp = "pipe:"
        out = "pipe:"
        cmd_inp = audio
    else:
        raise ValueError("input audio must be either a path or bytes")

    cmd = [
        "ffmpeg",
        "-nostdin",
        "-threads",
        "0",
        "-i",
        inp,
        "-f",
        "s16le",
        "-ac",
        "1",
        "-acodec",
        "pcm_s16le",
        "-ar",
        str(sample_rate),
        out,
    ]

    out = subprocess.check_output(cmd, input=cmd_inp, stderr=subprocess.PIPE)
    arr = np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
    return arr


def load_image(
    image: Union[str, bytes, PIL.Image.Image],
) -> PIL.Image.Image:
    """Load image from a file path or bytes and return as a PIL image.

    Args:
        image (Union[str, bytes, PIL.Image.Image]): path to an image file, image bytes or a PIL image.

    Raises:
        ValueError: if the input image is neither a path nor bytes.

    Returns:
        PIL.Image.Image: the image as a PIL image.
    """
    if isinstance(image, PIL.Image.Image):
        return image

    if isinstance(image, str):
        img = PIL.Image.open(image)
    elif isinstance(image, bytes):
        img = PIL.Image.open(io.BytesIO(image))
    else:
        raise ValueError("input image must be either a path or bytes")

    return img


class MegrezOProcessor(ProcessorMixin):
    attributes = ["image_processor", "audio_feature_extractor", "tokenizer"]
    image_processor_class = "AutoImageProcessor"
    audio_feature_extractor_class = "WhisperFeatureExtractor"
    tokenizer_class = "AutoTokenizer"

    _image_placeholder = r"(<image>./</image>)"
    _audio_placeholder = r"(<audio>./</audio>)"

    def __init__(self, image_processor=None, audio_feature_extractor=None, tokenizer=None):
        super().__init__(image_processor, audio_feature_extractor, tokenizer)
        self.chat_template = self.tokenizer.chat_template

    def _parse_and_check_inputs(self, inputs) -> List[Conversation]:
        if not isinstance(inputs, list):
            raise ValueError("inputs must be a list of conversations")

        conversations = []
        images = []
        audios = []

        for input in inputs:
            if not isinstance(input, dict) and not isinstance(input, Conversation):
                raise ValueError("each element of inputs must be a dictionary or a Conversation object")

            role = input.get("role")
            content = input.get("content")
            if role is None or content is None:
                raise ValueError("role and content must be provided in each conversation")

            if isinstance(content, str):
                content = content
            elif isinstance(content, dict):
                content = ConvContent({**content})
            elif not isinstance(content, ConvContent):
                raise ValueError("content must be a dictionary or a ConvContent object")

            if not isinstance(content, str):
                if content.get("image") is not None:
                    images.extend(content["image"] if isinstance(content["image"], list) else [content["image"]])

                if content.get("audio") is not None:
                    audios.extend(content["audio"] if isinstance(content["audio"], list) else [content["audio"]])

            conv = Conversation({"role": role, "content": content})
            conversations.append(conv)

        return conversations, images, audios

    def __call__(
        self,
        conversations: List[Conversation],
        apply_chat_template: bool = True,
        max_length: Optional[int] = None,
        return_tensors: ReturnTensorType = TensorType.PYTORCH,
        apply_data_collator: bool = True,
        **kwargs,
    ):
        assert return_tensors is TensorType.PYTORCH, "Only PyTorch tensors are supported for now."
        convs, images, audios = self._parse_and_check_inputs(conversations)
        add_generation_prompt = kwargs.pop("add_generation_prompt", True)
        if apply_chat_template:
            prompt = self.tokenizer.apply_chat_template(
                convs,
                tokenize=False,
                add_generation_prompt=add_generation_prompt,
            )
        else:  # (TODO) For clarification temporarily. Check whether this needs to be removed.
            prompt = "\n".join([conv["content"] for conv in convs])

        prompt, multimodal_inputs = self.process_multimodal_inputs(
            prompt,
            images=images,
            audios=audios,
            return_tensors=return_tensors,
            **kwargs,
        )
        text_encodings = self.tokenizer(
            prompt,
            return_tensors=return_tensors,
            max_length=max_length,
            padding=True,
            padding_side="left",
            truncation=True,
            **kwargs,
        )

        merged = self.merge_encodings(text_encodings, multimodal_inputs)

        if apply_data_collator:
            return self.data_collator([merged])

        return merged

    def merge_encodings(self, text_encodings, multimodal_inputs):

        result = {
            "image_encoding": None,
            "audio_encoding": None,
        }

        result["input_ids"] = text_encodings["input_ids"].reshape(-1).to(torch.int32)
        result["attention_mask"] = result["input_ids"].ne(0)
        result["position_ids"] = torch.arange(result["input_ids"].size(0)).long()

        if "image_encoding" in multimodal_inputs and multimodal_inputs["image_encoding"]:
            result["image_encoding"] = multimodal_inputs["image_encoding"]
            result["image_encoding"]["image_bounds"] = self.compute_bounds_image(result["input_ids"])

        if "audio_encoding" in multimodal_inputs and multimodal_inputs["audio_encoding"]:
            result["audio_encoding"] = multimodal_inputs["audio_encoding"]
            result["audio_encoding"]["audio_bounds"] = self.compute_bounds_audio(result["input_ids"])

        return result

    def compute_bounds_image(self, input_ids: torch.Tensor) -> List[torch.Tensor]:
        image_start_ids = (
            torch.where((input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id))[0] + 1
        )
        image_end_ids = torch.where(
            (input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id)
        )[0]

        valid_image_nums = max(len(image_start_ids), len(image_end_ids))
        bounds_image = torch.hstack(
            [
                image_start_ids[:valid_image_nums].unsqueeze(-1),
                image_end_ids[:valid_image_nums].unsqueeze(-1),
            ]
        )
        return bounds_image

    def compute_bounds_audio(self, input_ids: torch.Tensor) -> torch.Tensor:
        audio_bos_ids = torch.where(input_ids == self.tokenizer.audio_start_id)[0]
        audio_eos_ids = torch.where(input_ids == self.tokenizer.audio_end_id)[0]
        bounds_audio = torch.stack([audio_bos_ids, audio_eos_ids], 1)
        return bounds_audio

    def process_multimodal_inputs(
        self,
        text: str,
        images: Optional[ImageInput] = None,
        audios: Optional[Union[str, bytes, List[Union[str, bytes]]]] = None,
        return_tensors: ReturnTensorType = TensorType.PYTORCH,
        **kwargs,
    ):
        # (NOTE) Only single pair of multimodal input is allowed currently.
        # (TODO) Check whether single multimodal input is allowed.
        if text is None and images is None and audios is None:
            raise ValueError("At least one of text, images or audio must be provided")

        image_processor_kwargs, audio_feature_extractor_kwargs = {}, {}
        if kwargs:
            image_processor_kwargs = {
                k: v for k, v in kwargs.items() if k in self.image_processor._valid_processor_keys
            }
            audio_feature_extractor_kwargs = {
                k: v for k, v in kwargs.items() if k in self.audio_feature_extractor._valid_processor_keys
            }

        multimodal_encodings = {
            "image_encoding": None,
            "audio_encoding": None,
        }

        if images:
            image_encoding = self.process_image(
                images,
                return_tensors=return_tensors,
                **image_processor_kwargs,
            )
            text = self.insert_image_feature_placeholders(text, image_encoding)
            multimodal_encodings["image_encoding"] = image_encoding

        if audios:
            audio_encoding = self.process_audio(
                audios,
                **audio_feature_extractor_kwargs,
            )
            text = self.insert_audio_feature_placeholders(text, audio_encoding)
            multimodal_encodings["audio_encoding"] = audio_encoding

        return text, multimodal_encodings

    def insert_image_feature_placeholders(
        self,
        prompt: str,
        image_features: ImageBatchFeature,
        max_slice_nums: Optional[int] = None,
        use_image_id: Optional[bool] = None,
    ) -> List[str]:
        # Check the number of image tags and the number of images.
        img_tags = re.findall(self._image_placeholder, prompt)
        assert len(img_tags) == len(
            image_features.image_sizes
        ), f"the number of image tags must match the number of images, got {len(img_tags)} and {len(image_features.image_sizes)}"

        # Replace image tags with image placeholders.
        text_chunks = prompt.split(self._image_placeholder)
        final_text = ""
        for i in range(len(img_tags)):
            final_text += text_chunks[i] + self.image_processor.get_slice_image_placeholder(
                image_features.image_sizes[i],
                i,
                max_slice_nums,
                use_image_id,
            )
        final_text += text_chunks[-1]

        return final_text

    def insert_audio_feature_placeholders(
        self,
        prompt: str,
        audio_features: AudioBatchFeature,
    ) -> List[str]:
        # Check the number of audio tags and the number of audios.
        audio_tags = re.findall(self._audio_placeholder, prompt)
        assert len(audio_tags) == len(
            audio_features.input_audios
        ), "the number of audio tags must match the number of audios"

        # Replace audio tags with audio placeholders.
        text_chunks = prompt.split(self._audio_placeholder)
        final_text = ""
        for idx in range(len(audio_features.input_audios)):
            final_text += text_chunks[idx] + (
                self.tokenizer.audio_start
                + self.tokenizer.unk_token * audio_features.audio_span_tokens[idx]
                + self.tokenizer.audio_end
            )
        final_text += text_chunks[-1]

        return final_text

    def process_audio(
        self,
        audio_input: AudioInput,
        return_tensors: ReturnTensorType = TensorType.PYTORCH,
        **kwargs,
    ) -> AudioBatchFeature:
        if isinstance(audio_input, list):
            inputs = [load_audio(x) for x in audio_input]
        elif isinstance(audio_input, (str, bytes, np.ndarray)):
            inputs = [load_audio(audio_input)]
        else:
            raise ValueError("audio_input must be a path or bytes or a list of paths/bytes")

        features = self.audio_feature_extractor(
            inputs,
            sampling_rate=self.audio_feature_extractor.sampling_rate,
            return_attention_mask=True,
            return_token_timestamps=True,
            padding="max_length",
            return_tensors=return_tensors,
            **kwargs,
        )

        input_lengths = features["num_frames"]
        input_lengths = (input_lengths - 1) // 2 + 1
        output_lengths = (input_lengths - 2) // 2 + 1
        input_audio_lengths = torch.stack([input_lengths, output_lengths], dim=1)
        audio_span_tokens = (output_lengths + 2).tolist()  # add bos and eos tokens

        data = {
            "input_audios": features["input_features"],
            "input_audio_lengths": input_audio_lengths,
            "audio_span_tokens": audio_span_tokens,
        }

        # tensor types are already converted in `self.audio_feature_extractor`.
        return AudioBatchFeature(data={**data})

    def pad_images(
        self,
        pixel_values_list: List[torch.Tensor],
        tgt_sizes: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Pad images to the same size and return the padded pixel values and patch attention mask.

        Sliced pataches may have different sizes. We pad them to the same size and return the padded pixel values and corresponding patch attention mask.
        """

        all_pixel_values = []
        for pixel_value in pixel_values_list:
            all_pixel_values.append(pixel_value.flatten(end_dim=1).permute(1, 0))

        max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
        all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True, padding_value=0.0)
        B, L, _ = all_pixel_values.shape
        all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)

        patch_attention_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool)
        for i in range(B):
            patch_attention_mask[i, 0, : tgt_sizes[i][0] * tgt_sizes[i][1]] = True

        return all_pixel_values, patch_attention_mask

    def process_image(
        self,
        image_input: ImageInput,
        do_pad: bool = True,
        max_slice_nums: Optional[int] = None,
        return_tensors: ReturnTensorType = TensorType.PYTORCH,
        **kwargs,
    ) -> ImageBatchFeature:
        if isinstance(image_input, list):
            image_input = [load_image(x) for x in image_input]
        elif isinstance(image_input, (str, bytes, PIL.Image.Image)):
            image_input = [load_image(image_input)]
        else:
            raise ValueError(f"image_input must be a path or bytes or a list of paths/bytes, not: {type(image_input)}")

        image_features = self.image_processor(
            image_input,
            do_pad=do_pad,
            max_slice_nums=max_slice_nums,
            return_tensors=return_tensors,
            **kwargs,
        )

        # Multiple images are packed into first element of the list. We unpack them here.
        assert len(image_features.pixel_values) == 1, "images should be packed into one list."
        pixel_values = image_features.pixel_values[0]
        tgt_sizes = image_features.tgt_sizes[0]
        image_sizes = image_features.image_sizes[0]

        pixel_values, patch_attention_mask = self.pad_images(pixel_values, tgt_sizes)

        data = {
            "pixel_values": pixel_values,
            "image_sizes": image_sizes,
            "tgt_sizes": tgt_sizes,
            "patch_attention_mask": patch_attention_mask,
        }

        # tensor types are already converted in `self.image_processor`.
        return ImageBatchFeature(data=data)

    def data_collator(self, examples, padding_value=0, max_length=4096, collate_labels=False):
        """Collate data for MegrezO model.

        Batch data for MegrezO model. This function trims and pads the input_ids, position_ids, and attention_mask tensors. For bounds tensors, it adds batch index to the bounds.
        """
        # (TODO) Remove this function?

        def trim_and_pad(seq, batch_first, padding_value):
            return pad_sequence(
                [s[:max_length] for s in seq],
                batch_first=True,
                padding_value=padding_value,
            )

        input_ids = trim_and_pad(
            [example["input_ids"] for example in examples],
            batch_first=True,
            padding_value=padding_value,
        )
        position_ids = trim_and_pad(
            [example["position_ids"] for example in examples],
            batch_first=True,
            padding_value=padding_value,
        )

        attention_mask = trim_and_pad(
            [example["attention_mask"] for example in examples],
            batch_first=True,
            padding_value=padding_value,
        )

        image_encoding_list = {
            "pixel_values": [],
            "image_bounds": [],
            "tgt_sizes": [],
            "patch_attention_mask": [],
        }
        for bid, example in enumerate(examples):
            image_encoding = example.get("image_encoding")
            if not image_encoding:
                continue

            image_encoding_list["pixel_values"].append(image_encoding["pixel_values"])
            image_encoding_list["tgt_sizes"].append(image_encoding["tgt_sizes"])
            image_encoding_list["patch_attention_mask"].append(image_encoding["patch_attention_mask"])

            # (TODO) Remove?
            # add batch index to bounds (bid, start, end)
            bounds_with_bid = image_encoding["image_bounds"].clone()
            bounds_with_bid = torch.hstack(
                [
                    torch.full((bounds_with_bid.size(0), 1), bid, dtype=bounds_with_bid.dtype),
                    bounds_with_bid,
                ]
            )
            image_encoding_list["image_bounds"].append(bounds_with_bid)

        audio_encoding_list = {
            "input_audios": [],
            "input_audio_lengths": [],
            "audio_span_tokens": [],
            "audio_bounds": [],
        }
        for bid, example in enumerate(examples):
            audio_encoding = example.get("audio_encoding")
            if not audio_encoding:
                continue

            audio_encoding_list["input_audios"].append(audio_encoding["input_audios"])
            audio_encoding_list["input_audio_lengths"].append(audio_encoding["input_audio_lengths"])
            audio_encoding_list["audio_span_tokens"].extend(audio_encoding["audio_span_tokens"])
            bounds_with_bid = audio_encoding["audio_bounds"].clone()
            bounds_with_bid = torch.hstack(
                [
                    torch.full((bounds_with_bid.size(0), 1), bid, dtype=bounds_with_bid.dtype),
                    bounds_with_bid,
                ]
            )
            audio_encoding_list["audio_bounds"].append(bounds_with_bid)

        result = {
            "input_ids": input_ids,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "image_encoding": None,
            "audio_encoding": None,
        }

        if collate_labels:
            labels = trim_and_pad(
                [example["labels"] for example in examples],
                batch_first=True,
                padding_value=-100,
            )
            result["labels"] = labels

        if any(image_encoding_list.values()):
            result["image_encoding"] = {
                "pixel_values": torch.vstack(image_encoding_list["pixel_values"]),
                "tgt_sizes": torch.vstack(image_encoding_list["tgt_sizes"]),
                "patch_attention_mask": torch.vstack(image_encoding_list["patch_attention_mask"]),
                "image_bounds": torch.vstack(image_encoding_list["image_bounds"]),
            }
        if any(audio_encoding_list.values()):
            result["audio_encoding"] = {
                "input_audios": torch.vstack(audio_encoding_list["input_audios"]),
                "input_audio_lengths": torch.vstack(audio_encoding_list["input_audio_lengths"]),
                "audio_span_tokens": audio_encoding_list["audio_span_tokens"],
                "audio_bounds": torch.vstack(audio_encoding_list["audio_bounds"]),
            }
        return result