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mpt-7b-instruct-evaluate

Text Generation

text-generation-inference

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mpt-7b-instruct-evaluate / pipeline.py

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oleksandrfluxon

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	import torch
	import transformers
	from torch import cuda
	from accelerate import dispatch_model, infer_auto_device_map
	from accelerate.utils import get_balanced_memory
	from transformers import BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
	from typing import Dict, List, Any

	class PreTrainedPipeline():
	def __init__(self, path=""):
	path = "oleksandrfluxon/mpt-7b-instruct-evaluate"
	print("===> path", path)

	device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
	print("===> device", device)

	model = transformers.AutoModelForCausalLM.from_pretrained(
	'oleksandrfluxon/mpt-7b-instruct-evaluate',
	trust_remote_code=True,
	load_in_8bit=True, # this requires the `bitsandbytes` library
	max_seq_len=8192,
	init_device=device
	)
	model.eval()
	#model.to(device)
	print(f"===> Model loaded on {device}")

	tokenizer = transformers.AutoTokenizer.from_pretrained("mosaicml/mpt-7b")

	# we create a list of stopping criteria
	stop_token_ids = [
	tokenizer.convert_tokens_to_ids(x) for x in [
	['Human', ':'], ['AI', ':']
	]
	]
	stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
	print("===> stop_token_ids", stop_token_ids)

	# define custom stopping criteria object
	class StopOnTokens(StoppingCriteria):
	def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
	for stop_ids in stop_token_ids:
	if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
	return True
	return False

	stopping_criteria = StoppingCriteriaList([StopOnTokens()])

	self.pipeline = transformers.pipeline(
	model=model, tokenizer=tokenizer,
	return_full_text=True, # langchain expects the full text
	task='text-generation',
	# we pass model parameters here too
	stopping_criteria=stopping_criteria, # without this model rambles during chat
	temperature=0.1, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
	top_p=0.15, # select from top tokens whose probability add up to 15%
	top_k=0, # select from top 0 tokens (because zero, relies on top_p)
	max_new_tokens=1000, # mex number of tokens to generate in the output
	repetition_penalty=1.1 # without this output begins repeating
	)

	print("===> init finished")

	def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
	"""
	data args:
	inputs (:obj: `str`)
	parameters (:obj: `str`)
	Return:
	A :obj:`str`: todo
	"""
	# get inputs
	inputs = data.pop("inputs",data)
	parameters = data.pop("parameters", {})
	date = data.pop("date", None)
	print("===> inputs", inputs)
	print("===> parameters", parameters)

	result = self.pipeline(inputs, **parameters)
	print("===> result", result)

	return result