/
language_models.py
157 lines (141 loc) · 6.71 KB
/
language_models.py
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import openai
from openai import OpenAI
import anthropic
import os
import time
import torch
import gc
import tiktoken
from typing import Dict, List
# import google.generativeai as palm
class GPT:
API_RETRY_SLEEP = 10
API_ERROR_OUTPUT = "$ERROR$"
API_QUERY_SLEEP = 0.5
API_MAX_RETRY = 5
API_TIMEOUT = 20
API_LOGPROBS = True
API_TOP_LOGPROBS = 20
def __init__(self, model_name):
self.model_name = model_name
if 'gpt' in model_name:
self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
elif 'together' in model_name:
TOGETHER_API_KEY = os.environ.get("TOGETHER_API_KEY")
self.client = OpenAI(api_key=TOGETHER_API_KEY, base_url='https://api.together.xyz')
else:
raise ValueError(f"Unknown model name: {model_name}")
self.tokenizer = tiktoken.encoding_for_model("gpt-4") # same as for gpt-3.5
self.tokenizer.vocab_size = 100256 # note values from 100256 to 100275 (tokenizer.max_token_value) throw an error
def generate(
self, convs: List[List[Dict]], max_n_tokens: int, temperature: float, top_p: float
):
"""
Args:
convs: List of conversations (each of them is a List[Dict]), OpenAI API format
max_n_tokens: int, max number of tokens to generate
temperature: float, temperature for sampling
top_p: float, top p for sampling
Returns:
str: generated response
"""
outputs = []
for conv in convs:
output = self.API_ERROR_OUTPUT
for _ in range(self.API_MAX_RETRY):
try:
response = self.client.chat.completions.create(
model=self.model_name,
messages=conv,
max_tokens=max_n_tokens,
temperature=temperature,
top_p=top_p,
timeout=self.API_TIMEOUT,
logprobs=self.API_LOGPROBS,
top_logprobs=self.API_TOP_LOGPROBS,
seed=0,
)
response_logprobs = [
dict((response.choices[0].logprobs.content[i_token].top_logprobs[i_top_logprob].token,
response.choices[0].logprobs.content[i_token].top_logprobs[i_top_logprob].logprob)
for i_top_logprob in range(self.API_TOP_LOGPROBS)
)
for i_token in range(len(response.choices[0].logprobs.content))
]
output = {'text': response.choices[0].message.content,
'logprobs': response_logprobs,
'n_input_tokens': response.usage.prompt_tokens,
'n_output_tokens': response.usage.completion_tokens,
}
break
except openai.OpenAIError as e:
print(type(e), e)
time.sleep(self.API_RETRY_SLEEP)
time.sleep(self.API_QUERY_SLEEP)
outputs.append(output)
return outputs
class HuggingFace:
def __init__(self,model_name, model, tokenizer):
self.model_name = model_name
self.model = model
self.tokenizer = tokenizer
# substitute '▁Sure' with 'Sure' (note: can lead to collisions for some target_tokens)
self.pos_to_token_dict = {v: k.replace('▁', ' ') for k, v in self.tokenizer.get_vocab().items()}
# self.pos_to_token_dict = {v: k for k, v in self.tokenizer.get_vocab().items()}
self.eos_token_ids = [self.tokenizer.eos_token_id]
if 'llama3' in self.model_name.lower():
self.eos_token_ids.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
self.pad_token_id = self.tokenizer.pad_token_id
def generate(self,
full_prompts_list: List[str],
max_n_tokens: int,
temperature: float,
top_p: float = 1.0) -> List[Dict]:
if 'llama2' in self.model_name.lower():
max_n_tokens += 1 # +1 to account for the first special token (id=29871) for llama2 models
batch_size = len(full_prompts_list)
vocab_size = len(self.tokenizer.get_vocab())
inputs = self.tokenizer(full_prompts_list, return_tensors='pt', padding=True)
inputs = {k: v.to(self.model.device.index) for k, v in inputs.items()}
input_ids = inputs["input_ids"]
# Batch generation
output = self.model.generate(
**inputs,
max_new_tokens=max_n_tokens,
do_sample=False if temperature == 0 else True,
temperature=None if temperature == 0 else temperature,
eos_token_id=self.eos_token_ids,
pad_token_id=self.tokenizer.pad_token_id, # added for Mistral
top_p=top_p,
output_scores=True,
return_dict_in_generate=True,
)
output_ids = output.sequences
# If the model is not an encoder-decoder type, slice off the input tokens
if not self.model.config.is_encoder_decoder:
output_ids = output_ids[:, input_ids.shape[1]:]
if 'llama2' in self.model_name.lower():
output_ids = output_ids[:, 1:] # ignore the first special token (id=29871)
generated_texts = self.tokenizer.batch_decode(output_ids)
# output.scores: n_output_tokens x batch_size x vocab_size (can be counter-intuitive that batch_size doesn't go first)
logprobs_tokens = [torch.nn.functional.log_softmax(output.scores[i_out_token], dim=-1).cpu().numpy()
for i_out_token in range(len(output.scores))]
if 'llama2' in self.model_name.lower():
logprobs_tokens = logprobs_tokens[1:] # ignore the first special token (id=29871)
logprob_dicts = [[{self.pos_to_token_dict[i_vocab]: logprobs_tokens[i_out_token][i_batch][i_vocab]
for i_vocab in range(vocab_size)}
for i_out_token in range(len(logprobs_tokens))
] for i_batch in range(batch_size)]
outputs = [{'text': generated_texts[i_batch],
'logprobs': logprob_dicts[i_batch],
'n_input_tokens': len(input_ids[i_batch][input_ids[i_batch] != 0]), # don't count zero-token padding
'n_output_tokens': len(output_ids[i_batch]),
} for i_batch in range(batch_size)
]
for key in inputs:
inputs[key].to('cpu')
output_ids.to('cpu')
del inputs, output_ids
gc.collect()
torch.cuda.empty_cache()
return outputs