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deepspeed-chat: filter stage3 too long prompts #782

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Nov 21, 2023
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deepspeed-chat: filter stage3 too long prompts #782

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77 changes: 53 additions & 24 deletions applications/DeepSpeed-Chat/dschat/utils/data/data_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,12 +92,19 @@ def get_shuffle_idx(seed, size):
return shuffle_idx


def get_raw_dataset_split_index(local_rank, output_path, dataset_name, seed,
split_name, data_split, split_index,
data_size):
def get_raw_dataset_split_index(local_rank,
output_path,
dataset_name,
seed,
split_name,
data_split,
split_index,
data_size,
rebuild=False):
index_file_name = f"{output_path}/{dataset_name}_seed{seed}_{split_name}_{data_split}_{split_index}.npy"
# reindex each time when using local jsonfile since it's more likely to get modified
if (not os.path.isfile(index_file_name)) or (dataset_name == 'jsonfile'):
if rebuild or (not os.path.isfile(index_file_name)) or (dataset_name
== 'jsonfile'):
splits = [float(s) for s in data_split.split(',')]
splits_sum = sum(splits)
splits = [split / splits_sum for split in splits]
Expand Down Expand Up @@ -176,6 +183,9 @@ def create_dataset_split(current_dataset, raw_dataset, train_phase, tokenizer,
chosen_token["attention_mask"] = chosen_token[
"attention_mask"].squeeze(0)
chosen_dataset.append(chosen_token)
print(
f'Creating dataset {raw_dataset.dataset_name_clean} for {train_phase=} size={len(chosen_dataset)}'
)

elif train_phase == 2:
for i, tmp_data in enumerate(current_dataset):
Expand Down Expand Up @@ -204,39 +214,41 @@ def create_dataset_split(current_dataset, raw_dataset, train_phase, tokenizer,
reject_token["input_ids"] = reject_token["input_ids"]
reject_token["attention_mask"] = reject_token["attention_mask"]
reject_dataset.append(reject_token)
print(
f'Creating dataset {raw_dataset.dataset_name_clean} for {train_phase=} size={len(chosen_dataset)}'
)

elif train_phase == 3:
filtered = 0
for i, tmp_data in enumerate(current_dataset):
# tokenize the text
prompt = raw_dataset.get_prompt(tmp_data)
if prompt is not None:
prompt_token = tokenizer(prompt, return_tensors="pt")
prompt_token["input_ids"] = prompt_token["input_ids"]
prompt_token["attention_mask"] = prompt_token["attention_mask"]
for key_word in ["input_ids", "attention_mask"]:
length = prompt_token[key_word].size()[-1]
if length > max_seq_len:
y = prompt_token[key_word].squeeze(0)[length -
(max_seq_len -
1):].flip(0)
else:
y = prompt_token[key_word].squeeze(0).flip(0)
prompt_token[key_word] = y
prompt_dataset.append(prompt_token)
if prompt_token["input_ids"].size()[-1] <= max_seq_len:
for key_word in ["input_ids", "attention_mask"]:
prompt_token[key_word] = prompt_token[
key_word].squeeze(0).flip(0)
prompt_dataset.append(prompt_token)
else:
filtered += 1
print(f'Creating dataset {raw_dataset.dataset_name_clean} '
f'for {train_phase=} size={len(prompt_dataset)} {filtered=}')

return PromptDataset(prompt_dataset, chosen_dataset, reject_dataset,
tokenizer.pad_token_id, train_phase)


def create_dataset(local_rank, dataset_name, data_split, output_path,
train_phase, seed, tokenizer, end_of_conversation_token,
max_seq_len):
max_seq_len, rebuild):
raw_dataset = get_raw_dataset(dataset_name, output_path, seed, local_rank)
train_dataset = raw_dataset.get_train_data()
train_index = get_raw_dataset_split_index(local_rank, output_path,
raw_dataset.dataset_name_clean,
seed, "train", data_split,
train_phase - 1,
len(train_dataset))
len(train_dataset), rebuild)
train_dataset = Subset(train_dataset, train_index)
train_dataset = create_dataset_split(train_dataset, raw_dataset,
train_phase, tokenizer,
Expand All @@ -248,7 +260,7 @@ def create_dataset(local_rank, dataset_name, data_split, output_path,
raw_dataset.dataset_name_clean,
seed, "eval",
data_split, train_phase - 1,
len(eval_dataset))
len(eval_dataset), rebuild)
eval_dataset = Subset(eval_dataset, eval_index)
eval_dataset = create_dataset_split(eval_dataset, raw_dataset, train_phase,
tokenizer, end_of_conversation_token,
Expand Down Expand Up @@ -287,19 +299,36 @@ def create_prompt_dataset(local_rank,
torch.distributed.all_reduce(buf_create_cache)

if local_rank <= 0 and (buf_create_cache.item() != 0 or reload):
print(f'Creating prompt dataset {data_path}, {reload=}')
if len(data_path) == 1: # Single dataset.
train_dataset, eval_dataset = create_dataset(
local_rank, data_path[0], data_split, output_path, train_phase,
seed, tokenizer, end_of_conversation_token, max_seq_len)
local_rank,
data_path[0],
data_split,
output_path,
train_phase,
seed,
tokenizer,
end_of_conversation_token,
max_seq_len,
rebuild=reload)
else: # Blending datasets.
train_datasets = []
eval_datasets = []
train_size = 0
eval_size = 0
for d_path in data_path:
train_dataset, eval_dataset = create_dataset(
local_rank, d_path, data_split, output_path, train_phase,
seed, tokenizer, end_of_conversation_token, max_seq_len)
local_rank,
d_path,
data_split,
output_path,
train_phase,
seed,
tokenizer,
end_of_conversation_token,
max_seq_len,
rebuild=reload)
train_datasets.append(train_dataset)
eval_datasets.append(eval_dataset)
train_size += len(train_dataset)
Expand Down Expand Up @@ -328,7 +357,7 @@ def create_prompt_dataset(local_rank,
tokenizer,
end_of_conversation_token,
max_seq_len,
)
rebuild=reload)
sft_train_datasets.append(sft_train_dataset)
sft_eval_datasets.append(sft_eval_dataset)
sft_train_size += len(sft_train_dataset)
Expand Down
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