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import torch
import numpy as np
import logging
import torch.distributed as dist
from funasr_detach.register import tables
@tables.register("batch_sampler_classes", "DynamicBatchLocalShuffleSampler")
class BatchSampler(torch.utils.data.BatchSampler):
def __init__(
self,
dataset,
batch_type: str = "example",
batch_size: int = 100,
buffer_size: int = 30,
drop_last: bool = False,
shuffle: bool = True,
is_training: bool = True,
**kwargs
):
self.drop_last = drop_last
self.pre_idx = -1
self.dataset = dataset
self.total_samples = len(dataset)
self.batch_type = batch_type
self.batch_size = int(batch_size)
self.buffer_size = buffer_size
self.max_token_length = kwargs.get("max_token_length", 5000)
self.shuffle_idx = np.arange(self.total_samples)
self.shuffle = shuffle and is_training
self.length_scale_source = kwargs.get("length_scale_source", 1.0)
def __len__(self):
return (self.total_samples - 1) // self.batch_size + 1
def set_epoch(self, epoch):
np.random.seed(epoch)
def __iter__(self):
if self.shuffle:
np.random.shuffle(self.shuffle_idx)
batch = []
max_token = 0
num_sample = 0
iter_num = (self.total_samples - 1) // self.buffer_size + 1
# print("iter_num: ", iter_num)
for iter in range(self.pre_idx + 1, iter_num):
datalen_with_index = []
for i in range(self.buffer_size):
idx = iter * self.buffer_size + i
if idx >= self.total_samples:
continue
idx_map = self.shuffle_idx[idx]
# prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
target_len = (
self.dataset.get_target_len(idx_map)
if self.batch_type == "length"
else 0.0
)
source_len = (
self.dataset.get_source_len(idx_map) / self.length_scale_source
)
sample_len_cur = source_len + target_len
datalen_with_index.append([idx, sample_len_cur])
datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
for item in datalen_with_index_sort:
idx, sample_len_cur_raw = item
if sample_len_cur_raw > self.max_token_length:
continue
max_token_cur = max(max_token, sample_len_cur_raw)
max_token_padding = 1 + num_sample
if self.batch_type != "example":
max_token_padding *= max_token_cur
if max_token_padding <= self.batch_size:
batch.append(idx)
max_token = max_token_cur
num_sample += 1
else:
yield batch
batch = [idx]
max_token = sample_len_cur_raw
num_sample = 1
@tables.register("batch_sampler_classes", "BatchSampler")
@tables.register("batch_sampler_classes", "RankFullLocalShuffleBatchSampler")
class RankFullLocalShuffleBatchSampler(torch.utils.data.BatchSampler):
def __init__(
self,
dataset,
batch_type: str = "example",
batch_size: int = 100,
buffer_size: int = 30,
drop_last: bool = True,
shuffle: bool = True,
is_training: bool = True,
**kwargs
):
self.drop_last = drop_last
self.pre_idx = -1
self.dataset = dataset
self.total_samples = len(dataset)
self.batch_type = batch_type
self.batch_size = int(batch_size)
self.buffer_size = buffer_size
self.max_token_length = kwargs.get("max_token_length", 1500)
self.shuffle_idx = np.arange(self.total_samples)
self.shuffle = shuffle and is_training
self.length_scale_source = kwargs.get("length_scale_source", 1.0)
try:
rank = dist.get_rank()
world_size = dist.get_world_size()
except:
rank = 0
world_size = 1
self.rank = rank
self.world_size = world_size
def __len__(self):
return (self.total_samples - 1) // (self.batch_size * self.world_size) + 1
def set_epoch(self, epoch):
np.random.seed(epoch)
def __iter__(self):
batch_size_total = self.batch_size * self.world_size
if self.shuffle:
np.random.shuffle(self.shuffle_idx)
batch = []
max_token = 0
num_sample = 0
iter_num = (self.total_samples - 1) // self.buffer_size + 1
# print("iter_num: ", iter_num)
for iter in range(self.pre_idx + 1, iter_num):
# if iter == iter_num -1 and self.drop_last:
# continue
datalen_with_index = []
for i in range(self.buffer_size):
idx = iter * self.buffer_size + i
if idx >= self.total_samples:
continue
idx_map = self.shuffle_idx[idx]
# prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
source_len = (
self.dataset.get_source_len(idx_map) / self.length_scale_source
)
target_len = (
self.dataset.get_target_len(idx_map)
if self.batch_type == "length"
else 0.0
)
sample_len_cur = source_len + target_len
datalen_with_index.append([idx, sample_len_cur])
datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
for item in datalen_with_index_sort:
idx, sample_len_cur_raw = item
if sample_len_cur_raw > self.max_token_length:
continue
max_token_cur = max(max_token, sample_len_cur_raw)
max_token_padding = 1 + num_sample
# if self.batch_type != 'example':
# max_token_padding *= max_token_cur
if max_token_padding <= batch_size_total:
batch.append(idx)
max_token = max_token_cur
num_sample += 1
else:
batch_rank = batch[
self.rank * self.batch_size : (self.rank + 1) * self.batch_size
]
yield batch_rank
batch = [idx]
max_token = sample_len_cur_raw
num_sample = 1
@tables.register("batch_sampler_classes", "RankFullLocalShuffleDynamicBatchSampler")
class RankFullLocalShuffleDynamicBatchSampler(torch.utils.data.BatchSampler):
def __init__(
self,
dataset,
batch_type: str = "example",
batch_size: int = 100,
buffer_size: int = 30,
drop_last: bool = True,
shuffle: bool = True,
is_training: bool = True,
**kwargs
):
self.drop_last = drop_last
self.pre_idx = -1
self.dataset = dataset
self.total_samples = len(dataset)
self.batch_type = batch_type
self.batch_size = int(batch_size)
self.buffer_size = buffer_size
self.max_token_length = kwargs.get("max_token_length", 1500)
self.shuffle_idx = np.arange(self.total_samples)
self.shuffle = shuffle and is_training
self.length_scale_source = kwargs.get("length_scale_source", 1.0)
try:
rank = dist.get_rank()
world_size = dist.get_world_size()
except:
rank = 0
world_size = 1
self.rank = rank
self.world_size = world_size
def __len__(self):
return (self.total_samples - 1) // (self.batch_size * self.world_size) + 1
def set_epoch(self, epoch):
np.random.seed(epoch)
def __iter__(self):
batch_size_total = self.batch_size * self.world_size
if self.shuffle:
np.random.shuffle(self.shuffle_idx)
batch_list_all_rank = []
batch_list_cur = []
max_token = 0
num_sample = 0
iter_num = (self.total_samples - 1) // self.buffer_size + 1
# print("iter_num: ", iter_num)
for iter in range(self.pre_idx + 1, iter_num):
# if iter == iter_num - 1 and self.drop_last:
# continue
datalen_with_index = []
for i in range(self.buffer_size):
idx = iter * self.buffer_size + i
if idx >= self.total_samples:
continue
idx_map = self.shuffle_idx[idx]
# prompt = self.dataset.indexed_dataset[idx_map]["prompt"]
source_len = (
self.dataset.get_source_len(idx_map) / self.length_scale_source
)
target_len = (
self.dataset.get_target_len(idx_map)
if self.batch_type == "length"
else 0.0
)
sample_len_cur = source_len + target_len
datalen_with_index.append([idx, sample_len_cur])
datalen_with_index_sort = sorted(datalen_with_index, key=lambda x: x[1])
for ii, item in enumerate(datalen_with_index_sort):
is_last_batch = iter == iter_num - 1 and ii == len(
datalen_with_index_sort
)
idx, sample_len_cur_raw = item
if sample_len_cur_raw > self.max_token_length:
continue
max_token_cur = max(max_token, sample_len_cur_raw)
max_token_padding = 1 + num_sample
if self.batch_type != "example":
max_token_padding *= max_token_cur
if len(batch_list_all_rank) < self.world_size:
if max_token_padding <= self.batch_size:
batch_list_cur.append(idx)
max_token = max_token_cur
num_sample += 1
else:
batch_list_all_rank.append(batch_list_cur)
batch_list_cur = []
else:
batch_rank = batch_list_all_rank[self.rank]
yield batch_rank
batch_list_all_rank = [idx]
max_token = sample_len_cur_raw
num_sample = 1
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