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#
# 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.
from typing import Any, Dict
class DynBszBuffer:
"""
A buffer to store samples for dynamic batch size.
"""
def __init__(self):
self._buffer = []
self._buffer_sample_lens = []
self.del_idxs = []
self.cur_idx = 0
self.all_token_cnt = 0
def append(self, item: Dict[str, Any]):
"""
Append a sample to the buffer.
Args:
item: a sample to append to the buffer.
The sample should be a dict with the following keys:
- input_ids: torch.Tensor of shape (seq_len, )
- attention_mask: torch.Tensor of shape (seq_len, )
"""
self._buffer.append(item)
if 'attention_mask' in item:
self._buffer_sample_lens.append(item["attention_mask"].sum())
self.all_token_cnt += self._buffer_sample_lens[-1]
elif 'lang_masks' in item:
self._buffer_sample_lens.append(item["lang_masks"].sum())
self.all_token_cnt += self._buffer_sample_lens[-1]
def get_samples(self, n_token_per_iter: int, force: bool = True):
"""
get samples from the buffer.
Args:
n_token_per_iter: the number of tokens to get.
force: if True, the first sample will be returned even if it is not full.
Returns:
samples: a list of samples.
"""
cum_seq_len = 0
samples = []
while self.cur_idx < len(self._buffer) and cum_seq_len < n_token_per_iter:
seq_len = self._buffer_sample_lens[self.cur_idx]
if self.cur_idx not in self.del_idxs and (
(force is True and cum_seq_len == 0) or (seq_len <= n_token_per_iter - cum_seq_len)
):
cum_seq_len += seq_len
samples.append(self._buffer[self.cur_idx])
self.del_idxs.append(self.cur_idx)
self.cur_idx += 1
assert len(samples) > 0
return samples
def __len__(self):
return len(self._buffer)
def flush(self):
""" "
Flush the buffer.
"""
self.cur_idx = 0
self.all_token_cnt -= sum([self._buffer_sample_lens[idx] for idx in self.del_idxs])
buffer_len = len(self._buffer)
self._buffer = [self._buffer[idx] for idx in range(buffer_len) if idx not in self.del_idxs]
self._buffer_sample_lens = [
self._buffer_sample_lens[idx] for idx in range(buffer_len) if idx not in self.del_idxs
]
self.del_idxs = []
def merge(self, buffer_to_merge: "DynBszBuffer"):
""" "
Merge the buffer with another buffer.
Args:
buffer_to_merge: the buffer to merge.
"""
self.flush()
buffer_to_merge.flush()
for item in buffer_to_merge._buffer:
self.append(item)
class BaseBatchingStrategy:
"""
Base class for batching strategy.s
"""
def is_full_filled(self) -> bool:
raise NotImplementedError("should implement `is_full_filled`")
def put_item(self, item: Dict[str, Any]):
raise NotImplementedError("should implement `put_item`")
def get_micro_batch(self, step: int) -> Any:
raise NotImplementedError("should implement `get_micro_batch` ")
def empty(self) -> bool:
raise NotImplementedError("should implement `empty`")
class IdentityPacker:
def __init__(self, token_micro_bsz, bsz_warmup_steps, bsz_warmup_init_mbtoken):
self.token_micro_bsz = token_micro_bsz
self.bsz_warmup_steps = bsz_warmup_steps
self.bsz_warmup_init_mbtoken = bsz_warmup_init_mbtoken
def __call__(self, samples):
return samples
def get_token_num_to_request(self, cur_step, warmup):
return (
(self.token_micro_bsz - self.bsz_warmup_init_mbtoken) * cur_step // self.bsz_warmup_steps
+ self.bsz_warmup_init_mbtoken
if warmup
else self.token_micro_bsz
)
class TextBatchingStrategy(BaseBatchingStrategy):
""" "
Batching strategy for text data.
Args:
token_micro_bsz: the number of tokens to get for each request.
buffer_size: the size of the buffer.
bsz_warmup_steps: the number of steps to warm up the batch size.
bsz_warmup_init_mbtoken: the initial number of tokens to get for each request.
"""
def __init__(
self,
token_micro_bsz,
buffer_size: int = 500,
bsz_warmup_steps: int = -1,
bsz_warmup_init_mbtoken: int = 200,
) -> None:
super().__init__()
self._step = 0
self.token_micro_bsz = token_micro_bsz
self.bsz_warmup_steps = bsz_warmup_steps
self.buffer_size = buffer_size # minimum samples in buffer
self.buffer = DynBszBuffer()
self.bsz_warmup_init_mbtoken = bsz_warmup_init_mbtoken
assert self.bsz_warmup_init_mbtoken >= 0
self.packer = IdentityPacker(
token_micro_bsz=token_micro_bsz,
bsz_warmup_steps=bsz_warmup_steps,
bsz_warmup_init_mbtoken=bsz_warmup_init_mbtoken,
)
def is_full_filled(self) -> bool:
return len(self.buffer) >= self.buffer_size and self.buffer.all_token_cnt >= self.token_micro_bsz
def put_item(self, item: Dict[str, Any]):
if "input_ids" in item:
if len(item["input_ids"]) == 1:
print("WARNING: EMPTY STRING.")
return
elif "lang_tokens" in item:
if all (item["lang_tokens"] == 0):
print("WARNING: EMPTY STRING.")
return
self.buffer.append(item)
def get_token_num_to_request(self):
if self.packer is not None:
warmup = self._step <= self.bsz_warmup_steps and self.bsz_warmup_steps > 0
return self.packer.get_token_num_to_request(self._step, warmup=warmup)
else:
return self.get_cur_token_micro_bsz()
def get_cur_token_micro_bsz(self):
warmup = self._step <= self.bsz_warmup_steps and self.bsz_warmup_steps > 0
if warmup:
return (
self.token_micro_bsz - self.bsz_warmup_init_mbtoken
) * self._step // self.bsz_warmup_steps + self.bsz_warmup_init_mbtoken
else:
return self.token_micro_bsz
def get_micro_batch(self, step) -> Any:
"""
Get a micro batch from the buffer according to the current step.
Args:
step: the current step.
Returns:
data: a list of samples.
"""
self._step = step
n_token_per_iter = self.get_token_num_to_request()
cur_token_micro_bsz = self.get_cur_token_micro_bsz()
assert cur_token_micro_bsz % n_token_per_iter == 0, (
"The token num to get for each request should be divisible by token micro bsz."
)
n_iter = int(cur_token_micro_bsz // n_token_per_iter)
data = []
for i in range(n_iter):
samples = self.buffer.get_samples(n_token_per_iter)
if self.packer:
samples = self.packer(samples) # maybe packed into one sample, but wrapped in list.
data.extend(samples)
self.buffer.flush() # remove the selected samples.
return data
def empty(self) -> bool:
return len(self.buffer) == 0
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