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import re
from functools import partial
from typing import Dict, List, Optional, Union
import numpy as np
import torch
from opencompass.models.base import BaseModel, LMTemplateParser
from opencompass.registry import MODELS
from opencompass.utils.prompt import PromptList
PromptType = Union[PromptList, str]
@MODELS.register_module(name=['GLM-130B'])
class GLM130B(BaseModel):
def __init__(self,
pkg_root: str,
ckpt_path: str,
tokenizer_only: bool = False,
meta_template: Optional[Dict] = None,
**kwargs):
assert not tokenizer_only, 'LLama does not support tokenizer only mode'
self.pkg_root = pkg_root
self.ckpt_path = ckpt_path
self._load_model(**kwargs)
self.template_parser = LMTemplateParser(meta_template)
self.eos_token_id = None
if meta_template and 'eos_token_id' in meta_template:
self.eos_token_id = meta_template['eos_token_id']
def _load_model(self, **kwargs):
import sys
sys.path.insert(0, self.pkg_root)
from argparse import Namespace
from evaluation.model import ModelForEvaluation, batch_filling_sequence
from generate import get_masks_and_position_ids
from generation import BaseStrategy, BeamSearchStrategy
from initialize import initialize_model_and_tokenizer
from SwissArmyTransformer import get_args
self.get_masks_and_position_ids = get_masks_and_position_ids
self.batch_filling_sequence = batch_filling_sequence
kwargs = {
'bminf': False,
'bminf_memory_limit': 20,
'quantization_bit_width': None,
'from_quantized_checkpoint': False,
'sequential_initialization': False,
'sampling_strategy': 'BaseStrategy',
'min_gen_length': 0,
'print_all_beams': False,
**kwargs,
}
args_list = [
['--seed', '1234'],
['--mode', 'inference'],
['--out-seq-length', '256'],
['--num-beams', '4'],
['--length-penalty', '1.0'],
['--no-repeat-ngram-size', '3'],
['--temperature', '1.0'],
['--top_k', '0'],
['--top_p', '0'],
['--output-path', 'samples'],
['--model-parallel-size', '8'],
['--num-layers', '70'],
['--hidden-size', '12288'],
['--inner-hidden-size', '32768'],
['--vocab-size', '150528'],
['--num-attention-heads', '96'],
['--max-sequence-length', '2048'],
['--tokenizer-type', 'icetk-glm-130B'],
['--layernorm-order', 'post'],
['--load', self.ckpt_path],
['--skip-init'],
['--fp16'],
['--input-source', 'interactive'],
] # Come from the default initialize arguments of official repo
args = get_args(sum(args_list, []))
args = Namespace(**vars(args), **kwargs)
args.do_train = False
self.args = args
model, tokenizer = initialize_model_and_tokenizer(args)
self.model = model
self.model_for_eval = ModelForEvaluation(model)
self.tokenizer = tokenizer
self.device = args.device
end_tokens = [
tokenizer.get_command('eop'),
tokenizer.get_command('eos')
]
if args.sampling_strategy == 'BaseStrategy':
self.strategy = BaseStrategy(batch_size=1,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
end_tokens=end_tokens)
elif args.sampling_strategy == 'BeamSearchStrategy':
self.strategy = BeamSearchStrategy(
1,
args.num_beams,
length_penalty=args.length_penalty,
consider_end=True,
end_tokens=end_tokens,
no_repeat_ngram_size=args.no_repeat_ngram_size,
min_gen_length=args.min_gen_length,
)
else:
raise ValueError(f'unknown strategy {args.sampling_strategy}')
sys.path.pop(0)
def get_token_len(self, prompt: str) -> int:
"""Get lengths of the tokenized strings.
Args:
prompt (str): Input string.
Returns:
int: Length of the input tokens
"""
return len(self.tokenizer.tokenize(prompt))
def choice(self, inputs, choices):
import sys
sys.path.insert(0, self.pkg_root)
from unittest.mock import MagicMock
from evaluation.dataset import MultiChoiceTaskDataset
sys.path.pop(0)
choice_tokens = [self.tokenizer.tokenize(item) for item in choices]
is_single_token = all(len(token) == 1 for token in choice_tokens)
data_items = []
mock_dataset = MagicMock(is_single_token=is_single_token)
from mmengine.dist import is_main_process
for text in inputs:
if is_main_process():
print(f"\033[92m'text'\033[0m: {text}")
data_item = MultiChoiceTaskDataset.build_multiple_choice_sample(
text=self.tokenizer.tokenize(text),
# text=self.tokenizer.tokenize(text) + [20019],
choices=[self.tokenizer.tokenize(item) for item in choices],
is_single_token=is_single_token,
)
data_items.append(data_item)
batch = MultiChoiceTaskDataset.collate_fn(mock_dataset, data_items)
log_probs = self.model_for_eval.cond_log_prob(batch)
answers = []
for log_prob in zip(log_probs):
answers.append(choices[np.argmax(log_prob).item()])
return answers
def generate(self, inputs: List[str], max_out_len: int) -> List[str]:
"""Generate results given a list of inputs.
Args:
inputs (List[str]): A list of strings.
max_out_len (int): The maximum length of the output.
Returns:
List[str]: A list of generated strings.
"""
if isinstance(inputs, list):
return sum((self.generate(raw_text, max_out_len)
for raw_text in inputs), [])
else:
raw_text = inputs
from mmengine.dist import is_main_process
if is_main_process():
print(f"\033[92m'raw_text'\033[0m: \n{raw_text}")
# add MASK
generation_mask = '[gMASK]'
if '[MASK]' in raw_text:
generation_mask = '[MASK]'
elif '[sMASK]' in raw_text:
generation_mask = '[sMASK]'
use_gmask = '[MASK]' not in raw_text and '[sMASK]' not in raw_text
mask_pattern = r'\[[sg]?MASK\]'
text_list = re.split(mask_pattern, raw_text)
pattern_list = re.compile(mask_pattern).findall(raw_text)
seq = []
for i in range(len(pattern_list)):
pattern = pattern_list[i]
sub_text = text_list[i]
seq.extend(self.tokenizer.tokenize(sub_text))
seq.append(self.tokenizer.get_command(pattern))
seq.extend(self.tokenizer.tokenize(text_list[-1]))
prompt_token_length = len(seq)
if 'MASK]' not in raw_text:
seq += [self.tokenizer.get_command(generation_mask)]
raw_text += ' ' + generation_mask
if not raw_text.endswith('MASK]'):
seq = seq + [self.tokenizer.get_command('eos')]
if len(seq) > self.args.max_sequence_length:
raise ValueError('text too long.')
# generation
output_list = [seq]
if self.args.sampling_strategy == 'BeamSearchStrategy':
num_output = self.args.num_beams
else:
num_output = 1
last_pos = [0] * num_output
# continually detect the first mark position
while True:
seq = output_list[0]
# detect mask position
mask_token = self.tokenizer.get_command(generation_mask)
if mask_token not in seq:
break
mask_position = seq.index(mask_token)
output_list = []
input_seq = torch.cuda.LongTensor(
[seq + [self.tokenizer.get_command('sop')]],
device=self.device,
)
output, _ = self.batch_filling_sequence(
self.model,
input_seq,
torch.cuda.LongTensor([input_seq.shape[-1]],
device=self.device),
strategy=self.strategy,
get_masks_and_position_ids=partial(
self.get_masks_and_position_ids,
mask_position=mask_position,
max_gen_length=max_out_len,
gmask=use_gmask,
),
)
if isinstance(output, torch.Tensor): # different strategies
output = output.tolist()
output = output[0] # batch_size = 1
output_list.extend(output)
# clip -1s and fill back generated things into seq
for i in range(len(output_list)):
output = output_list[i].tolist() if isinstance(
output_list[i], torch.Tensor) else output_list[i]
try:
unfinished = output.index(-1)
except ValueError:
unfinished = len(output)
if output[unfinished - 1] in self.strategy.end_tokens:
unfinished -= 1
bog = output.index(self.tokenizer.get_command('sop'))
last_pos[i] = mask_position + unfinished - (bog + 1)
output_list[i] = output[:mask_position] + output[
bog + 1:unfinished] + output[mask_position + 1:bog]
# Select the best answer
output = output_list[0]
if output[-1] == self.tokenizer.get_command('eos'):
output = output[:-1]
# Avoid generate out-of-range id, replace to unk
output = np.array(output)
output[output < 20000] = 20000
output = output.tolist()
answer = self.tokenizer.detokenize(output[prompt_token_length:])
if is_main_process():
print(f"\033[92m'answer'\033[0m: \n{answer}")
return [answer]
def get_logits(self, inputs: List[str]):
mask_id = self.tokenizer.get_command('[MASK]')
sop_id = self.tokenizer.get_command('sop')
tokens = []
targets = []
position_ids = []
attn_masks = []
from mmengine.dist import is_main_process
for raw_text in inputs:
mask_pattern = r'\[MASK\]'
text_list = re.split(mask_pattern, raw_text, 1)
token = sum([
self.tokenizer.tokenize(text_list[0]),
[mask_id, sop_id],
self.tokenizer.tokenize(text_list[1]),
], [])[:-1]
target = sum([
self.tokenizer.tokenize(text_list[0]),
[mask_id],
self.tokenizer.tokenize(text_list[1]),
], [])
if is_main_process():
print(f"\033[92m'raw_text'\033[0m: {raw_text}")
print(f"\033[92m'token'\033[0m: {token}")
seq_length = len(token)
attn_mask = np.ones((seq_length, seq_length), dtype=np.int64)
tokens.append(np.array(token, dtype=np.int64))
targets.append(np.array(target, dtype=np.int64))
position_ids.append(np.arange(0, seq_length, dtype=np.int64))
attn_masks.append(attn_mask)
TILE = 32
length_to_pad = (max(map(len, tokens)) + TILE - 1) // TILE * TILE
token_batch, target_batch, position_id_batch, attention_mask_batch = [], [], [], [] # noqa: E501
for token, target, position_id, attn_mask in zip(
tokens, targets, position_ids, attn_masks):
attn_mask = np.pad(
attn_mask,
pad_width=((0, length_to_pad - len(token)), ),
mode='constant',
constant_values=0,
)
token = np.concatenate(
(token, np.zeros(length_to_pad - len(token), dtype=np.int64)))
target = np.concatenate((target,
np.full(length_to_pad - len(target),
-1,
dtype=np.int64)))
position_id = np.concatenate(
(position_id,
np.zeros(length_to_pad - len(position_id), dtype=np.int64)))
token_batch.append(token)
target_batch.append(target)
position_id_batch.append(position_id)
attention_mask_batch.append(attn_mask)
token_batch = torch.tensor(np.array(token_batch),
dtype=torch.int64).to(self.device)
target_batch = torch.tensor(np.array(target_batch),
dtype=torch.int64).to(self.device)
position_id_batch = torch.tensor(np.array(position_id_batch),
dtype=torch.int64).to(self.device)
attention_mask_batch = (torch.tensor(
np.array(attention_mask_batch), dtype=torch.int64) < 0.5).to(
self.device).bool().unsqueeze(1)
logits, *out_per_layers = self.model(token_batch,
position_id_batch,
attention_mask_batch,
log_attention_weights=None)
if is_main_process():
print(f"\033[92m'target_batch'\033[0m: {target_batch}")
return logits, target_batch
def get_ppl(self,
inputs: List[str],
mask_length: Optional[List[int]] = None) -> List[float]:
"""Get perplexity scores given a list of inputs.
Args:
inputs (List[str]): A list of strings.
mask_length (Optional[List[int]]): A list of mask lengths. If
provided, the perplexity scores will be calculated with the
first mask_length[i] tokens masked out. It's okay to skip
its implementation if advanced features in PPLInfernecer is
not needed.
Returns:
List[float]: A list of perplexity scores.
"""
logits, targets = self.get_logits(inputs)
loss_fn = torch.nn.CrossEntropyLoss(reduction='none', ignore_index=-1)
loss = loss_fn(logits.view(-1, logits.size(-1)),
targets.view(-1)).view(targets.size())
from mmengine.dist import is_main_process
if is_main_process():
print(f"\033[92m'loss'\033[0m: {loss}")
if mask_length is not None:
mask = torch.zeros_like(targets) # [batch,seqlen]
for i in range(len(mask)):
for j in range(mask_length[i] - 1, len(mask[i])):
mask[i][j] = 1
loss = loss * mask
lens = (targets != -1).sum(-1).cpu().numpy()
if mask_length is not None:
lens -= np.array(mask_length)
ce_loss = loss.sum(-1).cpu().detach().numpy() / lens
if is_main_process():
print(f"\033[92m'lens'\033[0m: {lens}")
print(f"\033[92m'ce_loss'\033[0m: {ce_loss}")
return ce_loss
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