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calculate sample length fixes and SFT splitting fixes (#2351)

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* fix chat template splitting long samples across multiple rows

* make the preprocessing faster
This commit is contained in:
Wing Lian
2025-02-20 14:29:58 -05:00
committed by GitHub
parent 954e192f38
commit 02f45e94be
3 changed files with 13 additions and 9 deletions
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3
src/axolotl/prompt_strategies/chat_template.py
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@@ -272,8 +272,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
dict(zip(feature_names, row)) dict(zip(feature_names, row))
) )
for key, val in tokenized_prompt.items(): for key, val in tokenized_prompt.items():
for i in range(0, len(val), self.sequence_len): res[key].append(val)
res[key].append(val[i : i + self.sequence_len])
# If there are no examples left, return an empty dictionary # If there are no examples left, return an empty dictionary
if not res: if not res:

9
src/axolotl/utils/data/utils.py
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@@ -172,10 +172,11 @@ def drop_long_seq_in_dataset(dataset: Dataset, cfg: DictDefault):
) )
try: try:
min_input_len = np.min(get_dataset_lengths(dataset)) ds_lengths = get_dataset_lengths(dataset, from_arrow=True)
LOG.debug(f"min_input_len: {min_input_len}") min_input_len = np.min(ds_lengths)
max_input_len = np.max(get_dataset_lengths(dataset)) LOG.info(f"min_input_len: {min_input_len}")
LOG.debug(f"max_input_len: {max_input_len}") max_input_len = np.max(ds_lengths)
LOG.info(f"max_input_len: {max_input_len}")
except AttributeError: except AttributeError:
pass pass

10
src/axolotl/utils/samplers/utils.py
View File

@@ -4,13 +4,17 @@ helper util to calculate dataset lengths
import numpy as np import numpy as np
def get_dataset_lengths(dataset): def get_dataset_lengths(dataset, from_arrow=False):
if "length" in dataset.column_names: if "length" in dataset.column_names:
lengths = np.array(dataset["length"]) lengths = np.array(dataset["length"])
elif "position_ids" in dataset.column_names: elif "position_ids" in dataset.column_names:
position_ids = dataset["position_ids"] position_ids = dataset["position_ids"]
lengths = np.array([x[-1] + 1 for x in position_ids]) lengths = np.array([x[-1] + 1 for x in position_ids])
else: else:
input_ids = dataset["input_ids"] if from_arrow:
lengths = np.array([len(seq) for seq in input_ids]) input_ids = dataset.data.column("input_ids")
lengths = np.vectorize(len)(np.array(input_ids, dtype=object))
else:
input_ids = dataset["input_ids"]
lengths = np.array([len(seq) for seq in input_ids])
return lengths return lengths