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import torch
from torch import nn
import torch.nn.functional as F
from typing import Union, List, Dict, Optional
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTJForCausalLM
from transformers.generation_logits_process import (
LogitsProcessorList,
NoBadWordsLogitsProcessor,
NoRepeatNGramLogitsProcessor,
)
from utils import (
NEGATIVE_INF, HALF_NEGATIVE_INF,
logits_to_entropy, mask_pad
)
from clipcap import ClipCap
class Policy(nn.Module):
def __init__(self, model_name, temperature, device, clipcap_path='', fix_gpt=False,
use_transformer_mapper: bool = False, use_ptuning_v2: bool = False,
prefix_length=10, clipcap_num_layers: int = 1,
label_path: str = '', model_weight: str = 'None', use_label_prefix: bool = False):
super().__init__()
self.device = device
self.model = ClipCap(model_name, device,
model_path=clipcap_path, fix_gpt=fix_gpt,
prefix_length=prefix_length,
num_layers=clipcap_num_layers,
label_path=label_path, model_weight=model_weight,
use_transformer_mapper=use_transformer_mapper,
use_ptuning_v2=use_ptuning_v2,
use_label_prefix=use_label_prefix)
self.tokenizer = AutoTokenizer.from_pretrained(model_name, pad_token="<|endoftext|>")
self.model.gpt.config.pad_token_id = self.tokenizer.pad_token_id
self.temperature = temperature
def get_processor(self, no_repeat_ngram_size: int = 3):
logits_processor = LogitsProcessorList()
if no_repeat_ngram_size > 0:
logits_processor.append(NoRepeatNGramLogitsProcessor(ngram_size=no_repeat_ngram_size))
'''
logits_processor.append(NoBadWordsLogitsProcessor([[self.tokenizer.pad_token_id]],
self.tokenizer.pad_token_id))
'''
return logits_processor
def sample(self,
input_ids: torch.Tensor = None,
features: torch.Tensor = None,
attention_mask: torch.Tensor = None,
labels: Optional[torch.Tensor] = None,
max_len: int = 20,
sample: bool = True,
top_k: int = None,
top_p: float = None,
temperature: float = None,
no_repeat_ngram_size: int = 0,
invalidate_eos: bool = True,
device = None) -> Dict[str, Union[torch.Tensor, List[str]]]:
if device is None:
device = self.device
if temperature is None:
temperature = self.temperature
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
model_kwargs = {'attention_mask': attention_mask}
batch_size, input_seq_len = input_ids.shape
logits_processor = self.get_processor(no_repeat_ngram_size=no_repeat_ngram_size)
logits_warper = self.model.gpt._get_logits_warper(
top_k=top_k, top_p=top_p, temperature=temperature, num_beams=1
)
unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=device)
output_logprob = torch.zeros([batch_size, 0], device=device)
eos_logprobs = torch.zeros([batch_size, 0], device=device)
output_mask = torch.ones([batch_size, 0], dtype=torch.long, device=device)
self.model.eval()
with torch.no_grad():
for step in range(max_len):
# prepare model inputs
model_inputs = self.model.prepare_inputs_for_generation(input_ids,
features=features,
labels=labels,
**model_kwargs)
# forward pass to get next token
outputs = self.model(
**model_inputs,
device=device
)
# in the first decoding step, we want to use the 'real' last position for each sentence
if step == 0:
last_non_masked_idx = torch.sum(attention_mask, dim=1) - 1
next_token_logits = outputs.logits[range(batch_size), last_non_masked_idx, :]
else:
next_token_logits = outputs.logits[:, -1, :]
negative_inf = HALF_NEGATIVE_INF if next_token_logits.dtype == torch.half else NEGATIVE_INF
next_token_scores = logits_processor(input_ids, next_token_logits)
if invalidate_eos:
next_token_scores[:, self.tokenizer.eos_token_id] = negative_inf # no endoftext
log_prob = F.log_softmax(next_token_scores, dim=-1) # authentic sampling distribution
next_token_scores = logits_warper(input_ids, next_token_scores)
if sample:
# Temperature (higher temperature => more likely to sample low probability tokens)
probs = F.softmax(next_token_scores, dim=-1)
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
else:
# Greedy decoding
next_tokens = torch.argmax(next_token_scores, dim=-1)
# finished sentences should have their next token be a padding token
next_tokens = next_tokens * unfinished_sequences + self.tokenizer.pad_token_id * (1 - unfinished_sequences)
# update output mask
output_mask = torch.cat([output_mask, unfinished_sequences[:, None]], dim=-1)
# update output log probability
eos_logprob = log_prob[:, self.tokenizer.eos_token_id]
eos_logprob = eos_logprob * unfinished_sequences + negative_inf * (1 - unfinished_sequences)
eos_logprobs = torch.cat([eos_logprobs, eos_logprob[:, None]], dim=-1)
token_logprob = torch.gather(log_prob, 1, next_tokens[:, None]).squeeze(1)
token_logprob = token_logprob * unfinished_sequences + negative_inf * (1 - unfinished_sequences)
output_logprob = torch.cat([output_logprob, token_logprob[:, None]], dim=-1)
# update generated ids, model inputs for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
model_kwargs = self.model.gpt._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.model.gpt.config.is_encoder_decoder
)
# if eos_token was found in one sentence, set sentence to finished
unfinished_sequences = unfinished_sequences.mul((next_tokens != self.tokenizer.eos_token_id).long())
if unfinished_sequences.max() == 0:
break
response_ids = input_ids[:, input_seq_len:]
response_text = [self.tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
for output in response_ids]
prompt_ids = input_ids[:, :input_seq_len]
prompts = [self.tokenizer.decode(query, skip_special_tokens=True, clean_up_tokenization_spaces=True)
for query in prompt_ids]
eos_probs = eos_logprobs.exp()
return {
'query/input_ids': prompt_ids,
'query/text': prompts,
'query/mask': attention_mask,
'response/input_ids': response_ids,
'response/text': response_text,
'response/mask': output_mask,
'response/log_prob': output_logprob,
'response/eos_prob': eos_probs,
}
def forward_pass(self,
query_input_ids: torch.Tensor,
query_mask: torch.Tensor,
response_input_ids: torch.Tensor,
response_mask: torch.Tensor,
features: torch.Tensor,
labels: Optional[torch.Tensor] = None,
invalidate_eos: bool = True,
device = None):
if device is None:
device = self.device
batch_size, query_seq_len = query_input_ids.shape
input_ids = torch.cat([query_input_ids, response_input_ids], dim=-1)
attention_mask = torch.cat([query_mask, response_mask], dim=-1)
# forward pass to get next token
outputs = self.model(
input_ids,
features,
attention_mask,
labels,
device=device
)
# get the first logit
query_logits = outputs.logits[:, :query_seq_len, :]
last_non_masked_idx = torch.sum(query_mask, dim=1) - 1
first_logits = query_logits[range(batch_size), last_non_masked_idx, :]
# get the second to last logit
response_logits = outputs.logits[:, query_seq_len:-1, :]
logits = torch.cat([first_logits[:, None], response_logits], dim=1)
negative_inf = HALF_NEGATIVE_INF if logits.dtype == torch.half else NEGATIVE_INF
if invalidate_eos:
logits[:, :, self.tokenizer.eos_token_id] = negative_inf # no endoftext
log_prob = F.log_softmax(logits, dim=-1)
output_logprob = torch.gather(log_prob, 2, response_input_ids[:, :, None]).squeeze(2)
output_entropy = logits_to_entropy(logits)
eos_prob = F.softmax(logits, dim=-1)[:, :, self.tokenizer.eos_token_id]
pos_logit = torch.gather(logits, 2, response_input_ids[:, :, None]).squeeze(2)
return {
'response/log_prob': mask_pad(output_logprob, response_mask),
'response/eos_prob': mask_pad(eos_prob, response_mask),
'response/entropy': mask_pad(output_entropy, response_mask),
'response/pos_logit': mask_pad(pos_logit, response_mask),
'response/logits': logits,
}
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