chopt-research-125m / instruct_pipeline.py

Update instruct_pipeline.py

67e3ee8 12 months ago

No virus

6.92 kB

	import re

	import numpy as np
	from transformers import Pipeline, PreTrainedTokenizer


	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."
	)

	# This is the prompt that is used for generating responses using an already trained model. It ends with the response
	# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
	PROMPT_FOR_GENERATION_FORMAT = """{intro}

	{instruction_key}
	{instruction}

	{response_key}
	""".format(
	intro=INTRO_BLURB,
	instruction_key=INSTRUCTION_KEY,
	instruction="{instruction}",
	response_key=RESPONSE_KEY,
	)


	def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
	"""Gets the token ID for a given string that has been added to the tokenizer as a special token.

	When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
	treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to.

	Args:
	tokenizer (PreTrainedTokenizer): the tokenizer
	key (str): the key to convert to a single token

	Raises:
	RuntimeError: if more than one ID was generated

	Returns:
	int: the token ID for the given key
	"""
	token_ids = tokenizer.encode(key)
	if len(token_ids) > 1:
	raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
	return token_ids[0]


	class InstructionTextGenerationPipeline(Pipeline):
	def __init__(
	self, args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, *kwargs
	):
	super().__init__(args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, *kwargs)

	def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs):
	preprocess_params = {}

	# newer versions of the tokenizer configure the response key as a special token. newer versions still may
	# append a newline to yield a single token. find whatever token is configured for the response key.
	tokenizer_response_key = next(
	(token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
	)

	response_key_token_id = None
	end_key_token_id = None
	if tokenizer_response_key:
	try:
	response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
	end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)

	# Ensure generation stops once it generates "### End"
	generate_kwargs["eos_token_id"] = end_key_token_id
	except ValueError:
	pass

	forward_params = generate_kwargs
	postprocess_params = {
	"response_key_token_id": response_key_token_id,
	"end_key_token_id": end_key_token_id,
	"return_instruction_text": return_instruction_text,
	}

	return preprocess_params, forward_params, postprocess_params

	def preprocess(self, instruction_text, **generate_kwargs):
	prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
	inputs = self.tokenizer(
	prompt_text,
	return_tensors="pt",
	)
	inputs["prompt_text"] = prompt_text
	inputs["instruction_text"] = instruction_text
	return inputs

	def _forward(self, model_inputs, **generate_kwargs):
	input_ids = model_inputs["input_ids"]
	attention_mask = model_inputs.get("attention_mask", None)
	generated_sequence = self.model.generate(
	input_ids=input_ids.to(self.model.device),
	attention_mask=attention_mask,
	pad_token_id=self.tokenizer.pad_token_id,
	**generate_kwargs,
	)[0].cpu()
	instruction_text = model_inputs.pop("instruction_text")
	return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}

	def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_instruction_text):
	sequence = model_outputs["generated_sequence"]
	instruction_text = model_outputs["instruction_text"]

	# The response will be set to this variable if we can identify it.
	decoded = None

	# If we have token IDs for the response and end, then we can find the tokens and only decode between them.
	if response_key_token_id and end_key_token_id:
	# Find where "### Response:" is first found in the generated tokens. Considering this is part of the
	# prompt, we should definitely find it. We will return the tokens found after this token.
	response_pos = None
	response_positions = np.where(sequence == response_key_token_id)[0]
	if len(response_positions) == 0:
	pass
	else:
	response_pos = response_positions[0]

	if response_pos:
	# Next find where "### End" is located. The model has been trained to end its responses with this
	# sequence (or actually, the token ID it maps to, since it is a special token). We may not find
	# this token, as the response could be truncated. If we don't find it then just return everything
	# to the end. Note that even though we set eos_token_id, we still see the this token at the end.
	end_pos = None
	end_positions = np.where(sequence == end_key_token_id)[0]
	if len(end_positions) > 0:
	end_pos = end_positions[0]

	decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
	else:
	# Otherwise we'll decode everything and use a regex to find the response and end.

	fully_decoded = self.tokenizer.decode(sequence)

	# The response appears after "### Response:". The model has been trained to append "### End" at the
	# end.
	m = re.search(r"#+\sResponse:\s(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)

	if m:
	decoded = m.group(1).strip()
	else:
	# The model might not generate the "### End" sequence before reaching the max tokens. In this case,
	# return everything after "### Response:".
	m = re.search(r"#+\sResponse:\s(.+)", fully_decoded, flags=re.DOTALL)
	if m:
	decoded = m.group(1).strip()

	if return_instruction_text:
	return {"instruction_text": instruction_text, "generated_text": decoded}

	return decoded