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# coding=utf-8
# Copyright 2024 Sourab Mangrulkar. All rights reserved.
#
# 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.
import functools
import numpy as np
# this is expensive so we cache it
@functools.lru_cache(maxsize=None)
def get_fim_token_ids(tokenizer):
if "codellama" in tokenizer.name_or_path:
return (
tokenizer.bos_token_id,
tokenizer.suffix_id,
tokenizer.prefix_id,
tokenizer.middle_id,
0,
)
elif "deepseek-coder" in tokenizer.name_or_path:
return (
tokenizer.bos_token_id,
tokenizer.encode("<｜fim▁hole｜>", add_special_tokens=False)[0],
tokenizer.encode("<｜fim▁begin｜>", add_special_tokens=False)[0],
tokenizer.encode("<｜fim▁end｜>", add_special_tokens=False)[0],
tokenizer.encode("<pad>", add_special_tokens=False)[0],
)
elif "stable-code" in tokenizer.name_or_path:
return (
tokenizer.bos_token_id,
tokenizer.encode("<fim_suffix>")[0],
tokenizer.encode("<fim_prefix>")[0],
tokenizer.encode("<fim_middle>")[0],
tokenizer.encode("<fim_pad>")[0],
)
else:
bos_token_id = None
try:
FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[
"additional_special_tokens"
][1:5]
suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (
tokenizer.vocab[tok]
for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]
)
except KeyError:
suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (
None,
None,
None,
None,
)
return bos_token_id, suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id
def _bos_token_processing(prefix_token_list, bos_token):
if bos_token is not None:
# add the BOS token to the beginning of the list
prefix_token_list.insert(0, bos_token)
return prefix_token_list
## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py
def permute(
sample,
np_rng,
suffix_tok_id,
prefix_tok_id,
middle_tok_id,
pad_tok_id,
fim_rate=0.5,
fim_spm_rate=0.5,
truncate_or_pad=False,
bos_token_id=None,
):
"""
Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:
PSM and SPM (with a probability of fim_spm_rate).
"""
if np_rng.binomial(1, fim_rate):
boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))
boundaries.sort()
prefix = np.array(sample[: boundaries[0]], dtype=np.int64)
middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)
suffix = np.array(sample[boundaries[1] :], dtype=np.int64)
if truncate_or_pad:
new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3
diff = new_length - len(sample)
if diff > 0:
if suffix.shape[0] <= diff:
return sample, np_rng
suffix = suffix[: suffix.shape[0] - diff]
elif diff < 0:
suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])
if np_rng.binomial(1, fim_spm_rate):
prefix_special_tokens = _bos_token_processing(
[prefix_tok_id, suffix_tok_id], bos_token_id
)
# SPM (variant 2 from FIM paper)
new_sample = np.concatenate(
[
prefix_special_tokens,
suffix,
[middle_tok_id],
prefix,
middle,
]
)
else:
prefix_special_tokens = _bos_token_processing([prefix_tok_id], bos_token_id)
# PSM
new_sample = np.concatenate(
[
prefix_special_tokens,
prefix,
[suffix_tok_id],
suffix,
[middle_tok_id],
middle,
]
)
else:
# don't do FIM preproc
new_sample = sample
return list(new_sample), np_rng
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