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import itertools
import json
import logging
import math
import os
import re
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Sequence
import torch
import transformers
from torch.utils.data import default_collate
from transformers.trainer_pt_utils import LabelSmoother
IGNORE_TOKEN_ID = LabelSmoother.ignore_index
@dataclass
class DataCollatorForSupervisedDataset(object):
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
input_ids = [instance["input_ids"] for instance in instances]
labels = [instance["labels"] for instance in instances]
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids,
batch_first=True,
padding_value=self.tokenizer.pad_token_id,
)
labels = torch.nn.utils.rnn.pad_sequence(
labels,
batch_first=True,
padding_value=IGNORE_TOKEN_ID,
)
input_ids = input_ids[:, : self.tokenizer.model_max_length]
labels = labels[:, : self.tokenizer.model_max_length]
batch = dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
)
if "images" in instances[0]:
images = [instance["images"] for instance in instances]
batch["images"] = torch.cat(images, dim=0)
if "doclm_images" in instances[0]:
images = [instance["doclm_images"] for instance in instances]
batch["doclm_images"] = torch.cat(images, dim=0)
if "image_paths" in instances[0]:
image_paths = [instance["image_paths"] for instance in instances]
batch["image_paths"] = image_paths
if "pixel_values" in instances[0]:
pixel_values = torch.cat([instance["pixel_values"] for instance in instances])
batch["pixel_values"] = pixel_values
if "image_flags" in instances[0]:
image_flags = torch.cat([instance["image_flags"] for instance in instances])
batch["image_flags"] = image_flags
return batch
def collate_fn_deepspeed_old(batch):
keys = list(set().union(*[set(x.keys()) for x in batch]))
tmp_batch = [{} for _ in range(len(batch))]
if "actual_seq_len" in batch[0]:
actual_seq_len = [x["actual_seq_len"] for x in batch]
else:
actual_seq_len = None
for k in keys:
if "images" in k or k == "image_indices":
for x, y in zip(tmp_batch, batch):
if k in y:
x[k] = y.pop(k)
# print("x[image_indices]", x["image_indices"].size())
new_batch = default_collate(batch)
for k in keys:
if "images" in k or k == "image_indices":
cat_dim = 0 if k != "image_indices" else 1
if k == "image_indices":
cnt = 0
for sample in tmp_batch:
if k in sample:
sample[k][0] = cnt
cnt += 1
new_batch[k] = torch.cat([x[k] for x in tmp_batch if k in x], dim=cat_dim)
# print("new_batch[image_indices]", new_batch["image_indices"].size())
if actual_seq_len is not None:
seq_len = actual_seq_len[0][-1]
actual_seq_len = [elem + i * seq_len for i, elem in enumerate(actual_seq_len)]
new_batch["actual_seq_len"] = torch.cat(actual_seq_len)
return new_batch
def collate_fn_deepspeed(batch):
keys = list(set().union(*[set(x.keys()) for x in batch]))
# print(f"{keys=}")
tmp_batch = [{} for _ in range(len(batch))]
if "actual_seq_len" in batch[0]:
actual_seq_len = [x["actual_seq_len"] for x in batch]
else:
actual_seq_len = None
if "images" in batch[0].keys():
for new_x, x in zip(tmp_batch, batch):
new_x["images"] = x.pop("images")
new_x["image_indices"] = x.pop("image_indices")
if "audios" in batch[0].keys():
for new_x, x in zip(tmp_batch, batch):
new_x["audios"] = x.pop("audios")
new_x["audio_indices"] = x.pop("audio_indices")
new_batch = default_collate(batch)
if "images" in tmp_batch[0].keys():
new_batch["images"] = torch.cat([x["images"] for x in tmp_batch], dim=0)
for sample_idx, sample in enumerate(tmp_batch):
for j in range(len(sample["image_indices"])):
sample["image_indices"][j][0, :, :] = sample_idx
new_batch["image_indices"] = torch.cat([x["image_indices"] for x in tmp_batch], dim=1)
if "audios" in tmp_batch[0].keys():
new_batch["audios"] = list(itertools.chain.from_iterable([x["audios"] for x in tmp_batch]))
# print(f"{[x.size() for x in sample['audios']]}")
for sample_idx, sample in enumerate(tmp_batch):
for j in range(len(sample["audio_indices"])):
sample["audio_indices"][j][0, :, :] = sample_idx
new_batch["audio_indices"] = list(
itertools.chain.from_iterable([x["audio_indices"] for x in tmp_batch])
)
# print(f"{[x.size() for x in sample['audio_indices']]}")
if actual_seq_len is not None:
seq_len = actual_seq_len[0][-1]
actual_seq_len = [elem + i * seq_len for i, elem in enumerate(actual_seq_len)]
new_batch["actual_seq_len"] = torch.cat(actual_seq_len)
return new_batch
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