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import re
import random
import torch
import torch.utils.checkpoint
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import BatchEncoding
from transformers.models.clip.image_processing_clip import CLIPImageProcessor
from .tokenization_mplug_owl import MplugOwlTokenizer
from decord import VideoReader
import numpy as np
from PIL import Image
def get_index(num_frames, num_segments):
seg_size = float(num_frames - 1) / num_segments
start = int(seg_size / 2)
offsets = np.array([
start + int(np.round(seg_size * idx)) for idx in range(num_segments)
])
return offsets
def load_video(path, num_frames=4):
vr = VideoReader(path, height=224, width=224)
total_frames = len(vr)
frame_indices = get_index(total_frames, num_frames)
images_group = list()
for frame_index in frame_indices:
img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
images_group.append(img)
return images_group
class MplugOwlProcessor(ProcessorMixin):
attributes = []
tokenizer_class = ("MplugOwlTokenizer")
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
super().__init__(**kwargs)
self.tokens_to_generate = 0
self.image_processor = image_processor
self.tokenizer = tokenizer
self.add_BOS = True
def __call__(self, videos=None, text=None, num_frames=4, return_tensors=None, **kwargs):
if text is not None:
encoding = tokenize_prompts(
prompts=text,
tokens_to_generate=self.tokens_to_generate,
add_BOS=self.add_BOS,
tokenizer=self.tokenizer,
ignore_dist=True,
**kwargs,
)
if videos is not None:
video_features = []
for video in videos:
video_frames = load_video(video, num_frames)
video_feature = self.image_processor(video_frames, return_tensors=return_tensors, **kwargs)['pixel_values']
video_features.append(video_feature)
video_features = torch.stack(video_features, dim=0)
video_features = video_features.permute(0, 2, 1, 3, 4)
if text is not None and videos is not None:
encoding["video_pixel_values"] = video_features
return encoding
if text is not None and videos is None:
return encoding
return video_features
def batch_decode(self, skip_special_tokens=True, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, skip_special_tokens=skip_special_tokens, **kwargs)
def decode(self, skip_special_tokens=True, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, skip_special_tokens=skip_special_tokens, **kwargs)
class MplugOwlImageProcessor(CLIPImageProcessor):
pass
def detokenize_generations(tokens_gpu_tensor, lengths_gpu_tensor, return_segments, tokenizer):
"""Detokenize the generated tokens."""
prompts_plus_generations = []
if return_segments:
prompts_plus_generations_segments = []
tokens = tokens_gpu_tensor.cpu().numpy().tolist()
lengths = lengths_gpu_tensor.cpu().numpy().tolist()
for sequence_tokens, length in zip(tokens, lengths):
sequence_tokens = sequence_tokens[:length]
prompts_plus_generations.append(tokenizer.detokenize(sequence_tokens))
if return_segments:
from tokenizers.decoders import Metaspace
if hasattr(tokenizer, "tokenizer"):
if isinstance(tokenizer.tokenizer.decoder, Metaspace):
words = tokenizer.tokenizer.decode(sequence_tokens)
else:
words = []
for token in sequence_tokens:
word = tokenizer.tokenizer.decoder[token]
word = bytearray([tokenizer.tokenizer.byte_decoder[c] for c in word]).decode(
"utf-8", errors="replace"
)
words.append(word)
prompts_plus_generations_segments.append(words)
else:
words = tokenizer.detokenize(sequence_tokens)
# else:
# words = []
# for token in sequence_tokens:
# word = tokenizer.tokenizer.decoder[token]
# word = bytearray(
# [tokenizer.tokenizer.byte_decoder[c] for c in word]).decode(
# 'utf-8', errors='replace')
# words.append(word)
prompts_plus_generations_segments.append(words)
if return_segments:
return tokens, prompts_plus_generations, prompts_plus_generations_segments
return tokens, prompts_plus_generations
def tokenize_prompts(
prompts=None, tokens_to_generate=None, add_BOS=None, rank=0, tokenizer=None, ignore_dist=False, **kwargs
):
"""Tokenize prompts and make them avaiable on all ranks."""
# On all ranks set to None so we can pass them to functions
prompts_tokens_cuda_long_tensor = None
prompts_length_cuda_long_tensor = None
# On the specified rank, build the above.
attention_mask = None
if ignore_dist or torch.distributed.get_rank() == rank:
assert prompts is not None
assert tokens_to_generate is not None
# Tensor of tokens padded and their unpadded length.
prompts_tokens_cuda_long_tensor, prompts_length_cuda_long_tensor, attention_mask = _tokenize_prompts_and_batch(
prompts, tokens_to_generate, add_BOS, tokenizer, **kwargs
)
# We need the sizes of these tensors for the boradcast
[
prompts_tokens_cuda_long_tensor.size(0), # Batch size
prompts_tokens_cuda_long_tensor.size(1),
] # Sequence lenght
return {
"input_ids": prompts_tokens_cuda_long_tensor,
"attention_mask": attention_mask,
# "prompt_length": prompts_length_cuda_long_tensor,
}
def _tokenize_prompts_and_batch(prompts, tokens_to_generate, add_BOS, tokenizer, **kwargs):
"""Given a set of prompts and number of tokens to generate:
- tokenize prompts
- set the sequence length to be the max of length of prompts
plus the number of tokens we would like to generate
- pad all the sequences to this length so we can convert them
into a 2D tensor.
"""
# Tokenize all the prompts.
# if add_BOS:
# prompts_tokens = [[tokenizer.bos] + tokenizer.tokenize(prompt)
# for prompt in prompts]
# else:
# prompts_tokens = [tokenizer.tokenize(prompt) for prompt in prompts]
prompts_tokens = [_tokenize_prompt(prompt, tokenizer, add_BOS, **kwargs) for prompt in prompts]
# Now we have a list of list of tokens which each list has a different
# size. We want to extend this list to:
# - incorporate the tokens that need to be generated
# - make all the sequences equal length.
# Get the prompts length.
prompts_length = [len(prompt_tokens) for prompt_tokens in prompts_tokens]
# Get the max prompts length.
max_prompt_len = max(prompts_length)
# Number of tokens in the each sample of the batch.
samples_length = max_prompt_len + tokens_to_generate
# Now update the list of list to be of the same size: samples_length.
for prompt_tokens, prompt_length in zip(prompts_tokens, prompts_length):
padding_size = samples_length - prompt_length
prompt_tokens.extend([tokenizer.eos_token_id] * padding_size)
# Now we are in a structured format, we can convert to tensors.
prompts_tokens_tensor = torch.LongTensor(prompts_tokens)
prompts_length_tensor = torch.LongTensor(prompts_length)
attention_mask = torch.zeros(prompts_tokens_tensor.shape[:2])
for i, l in enumerate(prompts_length_tensor):
attention_mask[i, :l] = 1
return prompts_tokens_tensor, prompts_length_tensor, attention_mask
def _tokenize_prompt(
prompt, tokenizer, add_BOS=False,
media_info={"<image>": 65, "<|video|>": 65},
**kwargs
):
media_tokens = {k: -int(i + 1) for i, k in enumerate(media_info.keys())}
media_lengths = media_info.copy()
if add_BOS:
prompt_chunk = [tokenizer.bos_token_id]
else:
prompt_chunk = []
# Pure Text
if all([media_token not in prompt for media_token in media_tokens.keys()]):
enc_chunk = prompt_chunk + tokenizer(prompt, add_special_tokens=False, **kwargs)["input_ids"]
# Multi-Modal Text
else:
enc_chunk = prompt_chunk
pattern = "|".join(map(re.escape, list(media_tokens.keys())))
chunk_strs = re.split(f"({pattern})", prompt)
chunk_strs = [x for x in chunk_strs if len(x) > 0]
for idx, chunk_str in enumerate(chunk_strs):
if chunk_str in media_tokens:
enc_chunk += [media_tokens[chunk_str]] * media_lengths[chunk_str]
else:
tmp_chunk = tokenizer(chunk_str, add_special_tokens=False)["input_ids"]
# if idx < len(chunk_strs) - 1: # Last chunk should not have eos
# tmp_chunk += [tokenizer.eod_id]
enc_chunk += tmp_chunk
return enc_chunk
if __name__ == "__main__":
pass |