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# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Generation support."""
from typing import List, Tuple, Union
import torch
from transformers import PreTrainedTokenizer
# Types.
HistoryType = List[Tuple[str, str]]
TokensType = List[int]
BatchTokensType = List[List[int]]
def pad_batch(batch: BatchTokensType, pad_id: int,
seq_length: int) -> BatchTokensType:
for tokens in batch:
context_length = len(tokens)
if context_length < seq_length:
tokens.extend([pad_id] * (seq_length - context_length))
return batch
def get_ltor_masks_and_position_ids(
data: torch.Tensor,
eod_token: int,
reset_position_ids: bool,
reset_attention_mask: bool,
eod_mask_loss: bool,
):
"""Build masks and position id for left to right model."""
# Extract batch size and sequence length.
micro_batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = micro_batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(
torch.ones((att_mask_batch, seq_length, seq_length),
device=data.device)).view(att_mask_batch, 1, seq_length,
seq_length)
# Loss mask.
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
if eod_mask_loss:
loss_mask[data == eod_token] = 0.0
# Position ids.
position_ids = torch.arange(seq_length,
dtype=torch.long,
device=data.device)
position_ids = position_ids.unsqueeze(0).expand_as(data)
# We need to clone as the ids will be modified based on batch index.
if reset_position_ids:
position_ids = position_ids.clone()
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(micro_batch_size):
# Find indices where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
# Detach indices from positions if going to modify positions.
if reset_position_ids:
eod_index = eod_index.clone()
# Loop through EOD indices:
prev_index = 0
for j in range(eod_index.size()[0]):
i = eod_index[j]
# Mask attention loss.
if reset_attention_mask:
attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
# Reset positions.
if reset_position_ids:
position_ids[b, (i + 1):] -= i + 1 - prev_index
prev_index = i + 1
# Convert attention mask to binary:
attention_mask = attention_mask < 0.5
return attention_mask, loss_mask, position_ids
def get_batch(context_tokens: torch.LongTensor, eod_id: int):
"""Generate batch from context tokens."""
# Move to GPU.
tokens = context_tokens.contiguous().to(context_tokens.device)
# Get the attention mask and position ids.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
tokens,
eod_id,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False,
)
return tokens, attention_mask, position_ids
def get_stop_words_ids(chat_format: str, tokenizer: PreTrainedTokenizer):
if chat_format == 'raw':
stop_words_ids = [tokenizer.encode('Human:'), [tokenizer.eod_id]]
elif chat_format == 'chatml':
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
else:
raise NotImplementedError(f'Unknown chat format {chat_format!r}')
return stop_words_ids
def make_context(
tokenizer: PreTrainedTokenizer,
query: str,
history: List[Tuple[str, str]] = None,
system: str = '',
max_window_size: int = 6144,
chat_format: str = 'chatml',
):
if history is None:
history = []
if chat_format == 'chatml':
im_start, im_end = '<|im_start|>', '<|im_end|>'
im_start_tokens = [tokenizer.im_start_id]
im_end_tokens = [tokenizer.im_end_id]
nl_tokens = tokenizer.encode('\n')
def _tokenize_str(role, content):
return f'{role}\n{content}', tokenizer.encode(
role, allowed_special=set(
tokenizer.IMAGE_ST)) + nl_tokens + tokenizer.encode(
content, allowed_special=set(tokenizer.IMAGE_ST))
system_text, system_tokens_part = _tokenize_str('system', system)
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
raw_text = ''
context_tokens = []
for turn_query, turn_response in reversed(history):
query_text, query_tokens_part = _tokenize_str('user', turn_query)
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
if turn_response is not None:
response_text, response_tokens_part = _tokenize_str(
'assistant', turn_response)
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens # noqa
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens # noqa
prev_chat = (
f'\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}' # noqa
)
else:
next_context_tokens = nl_tokens + query_tokens + nl_tokens
prev_chat = f'\n{im_start}{query_text}{im_end}\n'
current_context_size = (len(system_tokens) +
len(next_context_tokens) +
len(context_tokens))
if current_context_size < max_window_size:
context_tokens = next_context_tokens + context_tokens
raw_text = prev_chat + raw_text
else:
break
context_tokens = system_tokens + context_tokens
raw_text = f'{im_start}{system_text}{im_end}' + raw_text
context_tokens += (nl_tokens + im_start_tokens +
_tokenize_str('user', query)[1] + im_end_tokens +
nl_tokens + im_start_tokens +
tokenizer.encode('assistant') + nl_tokens)
raw_text += f'\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n'
elif chat_format == 'raw':
raw_text = query
context_tokens = tokenizer.encode(raw_text)
else:
raise NotImplementedError(f'Unknown chat format {chat_format!r}')
return raw_text, context_tokens
def _decode_default(
tokens: List[int],
*,
stop_words: List[str],
eod_words: List[str],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
verbose: bool = False,
return_end_reason: bool = False,
errors: str = 'replace',
):
trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
if verbose:
print('\nRaw Generate: ', trim_decode_tokens)
end_reason = f'Gen length {len(tokens)}'
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, '').strip()
for eod_word in eod_words:
if eod_word in trim_decode_tokens:
end_reason = f'Gen {eod_word!r}'
trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print('\nEnd Reason:', end_reason)
print('\nGenerate: ', trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def _decode_chatml(tokens: List[int],
*,
stop_words: List[str],
eod_token_ids: List[int],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
verbose: bool = False,
return_end_reason: bool = False,
errors: str = 'replace'):
end_reason = f'Gen length {len(tokens)}'
eod_token_idx = context_length
for eod_token_idx in range(context_length, len(tokens)):
if tokens[eod_token_idx] in eod_token_ids:
end_reason = f'Gen {tokenizer.decode([tokens[eod_token_idx]])!r}'
break
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx],
errors=errors)[raw_text_len:]
if verbose:
print('\nRaw Generate w/o EOD:',
tokenizer.decode(tokens, errors=errors)[raw_text_len:])
print('\nRaw Generate:', trim_decode_tokens)
print('\nEnd Reason:', end_reason)
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, '').strip()
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print('\nGenerate:', trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def decode_tokens(
tokens: Union[torch.LongTensor, TokensType],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
chat_format: str,
verbose: bool = False,
return_end_reason: bool = False,
errors: str = 'replace',
) -> str:
if torch.is_tensor(tokens):
tokens = tokens.cpu().numpy().tolist()
if chat_format == 'chatml':
return _decode_chatml(
tokens,
stop_words=[],
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
context_length=context_length,
verbose=verbose,
return_end_reason=return_end_reason,
errors=errors,
)
elif chat_format == 'raw':
return _decode_default(
tokens,
stop_words=['<|endoftext|>'],
eod_words=['<|endoftext|>'],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
verbose=verbose,
return_end_reason=return_end_reason,
errors=errors,
)
else:
raise NotImplementedError(f'Unknown chat format {chat_format!r}')
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