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import torch
import math
from abc import ABC, abstractmethod
from PIL import Image
from typing import Dict, List, Optional, Union, cast
from typing_extensions import Unpack
from transformers import BatchEncoding, BatchFeature
from transformers.processing_utils import (
AllKwargsForChatTemplate,
ImageInput,
PreTokenizedInput,
TextInput,
VideoInput,
)
from transformers.models.qwen2_vl import Qwen2VLProcessor
def get_torch_device(device: str = "auto") -> str:
"""
Returns the device (string) to be used by PyTorch.
`device` arg defaults to "auto" which will use:
- "cuda:0" if available
- else "mps" if available
- else "cpu".
"""
if device == "auto":
if torch.cuda.is_available():
device = "cuda:0"
elif torch.backends.mps.is_available(): # for Apple Silicon
device = "mps"
else:
device = "cpu"
return device
class BaseVisualRetrieverProcessor(ABC):
"""
Base class for visual retriever processors.
"""
@abstractmethod
def process_images(
self,
images: List[Image.Image],
) -> Union[BatchFeature, BatchEncoding]:
pass
@abstractmethod
def process_texts(
self,
texts: List[str],
max_length: int = 50,
suffix: Optional[str] = None,
prefix: Optional[str] = None,
) -> Union[BatchFeature, BatchEncoding]:
pass
@abstractmethod
def score(
self,
qs: List[torch.Tensor],
ps: List[torch.Tensor],
device: Optional[Union[str, torch.device]] = None,
**kwargs,
) -> torch.Tensor:
pass
@staticmethod
def score_single_vector(
qs: List[torch.Tensor],
ps: List[torch.Tensor],
device: Optional[Union[str, torch.device]] = None,
) -> torch.Tensor:
"""
Compute the dot product score for the given single-vector query and passage embeddings.
"""
device = device or get_torch_device("auto")
if len(qs) == 0:
raise ValueError("No queries provided")
if len(ps) == 0:
raise ValueError("No passages provided")
qs_stacked = torch.stack(qs).to(device)
ps_stacked = torch.stack(ps).to(device)
scores = torch.einsum("bd,cd->bc", qs_stacked, ps_stacked)
assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"
scores = scores.to(torch.float32)
return scores
@staticmethod
def score_multi_vector(
qs: List[torch.Tensor],
ps: List[torch.Tensor],
batch_size: int = 128,
device: Optional[Union[str, torch.device]] = None,
) -> torch.Tensor:
"""
Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
"""
device = device or get_torch_device("auto")
if len(qs) == 0:
raise ValueError("No queries provided")
if len(ps) == 0:
raise ValueError("No passages provided")
scores_list: List[torch.Tensor] = []
for i in range(0, len(qs), batch_size):
scores_batch = []
qs_batch = torch.nn.utils.rnn.pad_sequence(qs[i : i + batch_size], batch_first=True, padding_value=0).to(
device
)
for j in range(0, len(ps), batch_size):
ps_batch = torch.nn.utils.rnn.pad_sequence(
ps[j : j + batch_size], batch_first=True, padding_value=0
).to(device)
scores_batch.append(torch.einsum("bnd,csd->bcns", qs_batch, ps_batch).max(dim=3)[0].sum(dim=2))
scores_batch = torch.cat(scores_batch, dim=1).cpu()
scores_list.append(scores_batch)
scores = torch.cat(scores_list, dim=0)
assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"
scores = scores.to(torch.float32)
return scores
class QwenVLProcessor(ABC):
def __call__(
self,
images: Optional[ImageInput] = None,
text: Optional[Union[TextInput, PreTokenizedInput, List[PreTokenizedInput]]] = None,
videos: Optional[VideoInput] = None,
**kwargs,
) -> BatchFeature:
return super().__call__(images=images, text=text, videos=videos, **kwargs) # type: ignore
def apply_chat_template(
self,
conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
chat_template: Optional[str] = None,
**kwargs: Unpack[AllKwargsForChatTemplate],
) -> str:
return super().apply_chat_template(conversation=conversation, chat_template=chat_template, **kwargs) # type: ignore
class QwenVLEmbeddingProcessorBase(BaseVisualRetrieverProcessor, QwenVLProcessor):
assistant_prefix_len: int = 58 # length of prefix created by
# super().apply_chat_template(conversation=conversation, chat_template=chat_template, **kwargs)
@staticmethod
def round_by_factor(number: float, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
@staticmethod
def ceil_by_factor(number: float, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
@staticmethod
def floor_by_factor(number: float, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def process_images(
self,
images: Union[List[Image.Image], List[List[Image.Image]]],
) -> BatchFeature:
if isinstance(images[0], list):
images = cast(List[List[Image.Image]], images)
text_doc = []
for i in range(len(images)):
conversation = [{"role": "user", "content": [{"type": "image"}] * len(images[i])}]
template = self.apply_chat_template(conversation, add_generation_prompt=False)
text_doc.append(template[self.assistant_prefix_len :])
else:
images = cast(List[Image.Image], images)
text_doc = [
"<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n"
] * len(images)
# The following code is a hack to make sure the scatter in DDP is done correctly when training on multiple GPUs
batch_doc = self(text=text_doc, images=images, padding="longest", return_tensors="pt") # type: ignore
# Separate pixel_values for each image
offsets = batch_doc["image_grid_thw"][:, 1] * batch_doc["image_grid_thw"][:, 2]
# Pad pixel_values to the same length to be able to make it into a tensor
pixel_values = torch.split(batch_doc["pixel_values"], offsets.tolist())
max_length = max([len(pv) for pv in pixel_values])
pixel_values = [
torch.cat([pv, torch.zeros((max_length - len(pv), pv.shape[1]), dtype=pv.dtype, device=pv.device)])
for pv in pixel_values
]
batch_doc["pixel_values"] = torch.stack(pixel_values)
return batch_doc
def process_texts(
self,
texts: List[str],
max_length: int,
suffix: Optional[str] = None,
prefix: Optional[str] = None,
padding: Optional[str] = None,
) -> BatchFeature:
if suffix is None:
suffix = "<pad>" * 10
padded_texts: List[str] = []
for text in texts:
if prefix:
text = f"{prefix}: {text}"
text += suffix
padded_texts.append(text)
text_batch = self(
text=padded_texts,
return_tensors="pt",
padding=padding or "longest",
max_length=max_length,
truncation=True,
)
return text_batch
class ColQwenDuoProcessorBase(QwenVLEmbeddingProcessorBase):
"""
Processor for ColQwenDuo. Mirrors the `ColQwen2Processor` class.
"""
def score(
self,
qs: List[torch.Tensor],
ps: List[torch.Tensor],
vector_type: str,
device: Optional[Union[str, torch.device]] = None,
truncate: Optional[int] = None,
**kwargs,
) -> torch.Tensor:
"""
Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
"""
if truncate:
qs = [q[..., :truncate] for q in qs]
ps = [p[..., :truncate] for p in ps]
if vector_type == "single_vector":
return self.score_single_vector(qs, ps, device=device)
elif vector_type == "multi_vector":
return self.score_multi_vector(qs, ps, device=device, **kwargs)
else:
raise ValueError('vector_type must be one of the following: [`single_vector`, `multi_vector`]')
class ColQwen2DuoProcessor(ColQwenDuoProcessorBase, Qwen2VLProcessor):
def __init__(self, *args, **kwargs) -> None:
Qwen2VLProcessor.__init__(self, *args, **kwargs)
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