423A35C7/LLaMA-Factory
1
0
Fork 0
mirror of https://github.com/hiyouga/LLaMA-Factory.git synced 2025-12-17 12:20:37 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix #944

Browse Source
This commit is contained in:
hiyouga
2023-09-21 19:51:02 +08:00
parent ace3f85a72
commit 338b8664ed
11 changed files with 116 additions and 101 deletions
Download Patch File Download Diff File Expand all files Collapse all files

60
src/llmtuner/dsets/preprocess.py
View File

@@ -44,10 +44,10 @@ def preprocess_dataset(
tokenized_examples = tokenizer(examples["prompt"], **kwargs)
concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
block_size = data_args.max_source_length
block_size = data_args.cutoff_len
# we drop the small remainder, and if the total_length < block_size, we exclude this batch
total_length = (total_length // block_size) * block_size
# split by chunks of max_source_length
# split by chunks of cutoff_len
result = {
k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
@@ -58,7 +58,6 @@ def preprocess_dataset(
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
# for multiturn examples, we only mask the prompt part in each prompt-response pair.
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
max_length = data_args.max_source_length + data_args.max_target_length
for query, response, history, system in construct_example(examples):
input_ids, labels = [], []
@@ -66,13 +65,14 @@ def preprocess_dataset(
for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
tokenizer, query, response, history, system
)):
if len(source_ids) > data_args.max_source_length:
source_ids = source_ids[:data_args.max_source_length]
if len(target_ids) > data_args.max_target_length:
target_ids = target_ids[:data_args.max_target_length]
total_len = len(source_ids) + len(target_ids)
max_source_len = int(data_args.cutoff_len * (len(source_ids) / total_len))
max_target_len = int(data_args.cutoff_len * (len(target_ids) / total_len))
if len(input_ids) + len(source_ids) + len(target_ids) > max_length:
break
if len(source_ids) > max_source_len:
source_ids = source_ids[:max_source_len]
if len(target_ids) > max_target_len:
target_ids = target_ids[:max_target_len]
if turn_idx != 0 and template.efficient_eos:
source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
@@ -86,6 +86,10 @@ def preprocess_dataset(
input_ids += [tokenizer.eos_token_id]
labels += [tokenizer.eos_token_id]
if len(input_ids) > data_args.cutoff_len:
input_ids = input_ids[:data_args.cutoff_len]
labels = labels[:data_args.cutoff_len]
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
@@ -97,19 +101,19 @@ def preprocess_dataset(
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
for query, response, history, system in construct_example(examples):
source_ids, target_ids = template.encode_oneturn(tokenizer, query, response, history, system)
if len(source_ids) > data_args.max_source_length:
source_ids = source_ids[:data_args.max_source_length]
if len(target_ids) > data_args.max_target_length:
target_ids = target_ids[:data_args.max_target_length]
input_ids, labels = template.encode_oneturn(tokenizer, query, response, history, system)
if template.efficient_eos:
target_ids += [tokenizer.eos_token_id]
labels += [tokenizer.eos_token_id]
model_inputs["input_ids"].append(source_ids)
model_inputs["attention_mask"].append([1] * len(source_ids))
model_inputs["labels"].append(target_ids)
if len(input_ids) > data_args.cutoff_len:
input_ids = input_ids[:data_args.cutoff_len]
if len(labels) > data_args.cutoff_len:
labels = labels[:data_args.cutoff_len]
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
return model_inputs
@@ -120,17 +124,21 @@ def preprocess_dataset(
prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, system)
_, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, system)
if len(prompt_ids) > data_args.max_source_length:
prompt_ids = prompt_ids[:data_args.max_source_length]
if len(chosen_ids) > data_args.max_target_length:
chosen_ids = chosen_ids[:data_args.max_target_length]
if len(rejected_ids) > data_args.max_target_length:
rejected_ids = rejected_ids[:data_args.max_target_length]
if template.efficient_eos:
chosen_ids += [tokenizer.eos_token_id]
rejected_ids += [tokenizer.eos_token_id]
total_len = len(prompt_ids) + max(len(chosen_ids), len(rejected_ids))
max_source_len = int(data_args.cutoff_len * (len(prompt_ids) / total_len))
max_target_len = int(data_args.cutoff_len * (max(len(chosen_ids), len(rejected_ids)) / total_len))
if len(prompt_ids) > max_source_len:
prompt_ids = prompt_ids[:max_source_len]
if len(chosen_ids) > max_target_len:
chosen_ids = chosen_ids[:max_target_len]
if len(rejected_ids) > max_target_len:
rejected_ids = rejected_ids[:max_target_len]
model_inputs["prompt_ids"].append(prompt_ids)
model_inputs["chosen_ids"].append(chosen_ids)
model_inputs["rejected_ids"].append(rejected_ids)