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from typing import Any, Dict, Tuple
import warnings
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import (
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
END_KEY = "### End"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
class InstructionTextGenerationPipeline:
def __init__(
self,
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
use_auth_token=None,
) -> None:
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch_dtype,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
)
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=trust_remote_code,
use_auth_token=use_auth_token,
)
if tokenizer.pad_token_id is None:
warnings.warn(
"pad_token_id is not set for the tokenizer. Using eos_token_id as pad_token_id."
)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
self.tokenizer = tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.eval()
self.model.to(device=device, dtype=torch_dtype)
self.generate_kwargs = {
"temperature": 0.1,
"top_p": 0.92,
"top_k": 0,
"max_new_tokens": 1024,
"use_cache": True,
"do_sample": True,
"eos_token_id": self.tokenizer.eos_token_id,
"pad_token_id": self.tokenizer.pad_token_id,
"repetition_penalty": 1.1, # 1.0 means no penalty, > 1.0 means penalty, 1.2 from CTRL paper
}
def format_instruction(self, instruction):
return PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction)
def __call__(
self, instruction: str, **generate_kwargs: Dict[str, Any]
) -> Tuple[str, str, float]:
s = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction)
input_ids = self.tokenizer(s, return_tensors="pt").input_ids
input_ids = input_ids.to(self.model.device)
gkw = {**self.generate_kwargs, **generate_kwargs}
with torch.no_grad():
output_ids = self.model.generate(input_ids, **gkw)
# Slice the output_ids tensor to get only new tokens
new_tokens = output_ids[0, len(input_ids[0]) :]
output_text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
return output_text
# Initialize the model and tokenizer
generate = InstructionTextGenerationPipeline(
"mosaicml/mpt-7b-instruct",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
stop_token_ids = generate.tokenizer.convert_tokens_to_ids(["<|endoftext|>"])
# Define a custom stopping criteria
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
for stop_id in stop_token_ids:
if input_ids[0][-1] == stop_id:
return True
return False
"""### The prompt & response"""
import json
import textwrap
def get_prompt(instruction):
prompt_template = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
return prompt_template
# print(get_prompt('What is the meaning of life?'))
def parse_text(text):
wrapped_text = textwrap.fill(text, width=100)
print(wrapped_text +'\n\n') |