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import os | |
from transformers import TextGenerationPipeline | |
from transformers.pipelines.text_generation import ReturnType | |
from stopping import get_stopping | |
from prompter import Prompter, PromptType | |
class H2OTextGenerationPipeline(TextGenerationPipeline): | |
def __init__(self, *args, debug=False, chat=False, stream_output=False, | |
sanitize_bot_response=False, | |
use_prompter=True, prompter=None, | |
context='', iinput='', | |
prompt_type=None, prompt_dict=None, | |
max_input_tokens=2048 - 256, **kwargs): | |
""" | |
HF-like pipeline, but handle instruction prompting and stopping (for some models) | |
:param args: | |
:param debug: | |
:param chat: | |
:param stream_output: | |
:param sanitize_bot_response: | |
:param use_prompter: Whether to use prompter. If pass prompt_type, will make prompter | |
:param prompter: prompter, can pass if have already | |
:param prompt_type: prompt_type, e.g. human_bot. See prompt_type to model mapping in from prompter.py. | |
If use_prompter, then will make prompter and use it. | |
:param prompt_dict: dict of get_prompt(, return_dict=True) for prompt_type=custom | |
:param max_input_tokens: | |
:param kwargs: | |
""" | |
super().__init__(*args, **kwargs) | |
self.prompt_text = None | |
self.use_prompter = use_prompter | |
self.prompt_type = prompt_type | |
self.prompt_dict = prompt_dict | |
self.prompter = prompter | |
self.context = context | |
self.iinput = iinput | |
if self.use_prompter: | |
if self.prompter is not None: | |
assert self.prompter.prompt_type is not None | |
else: | |
self.prompter = Prompter(self.prompt_type, self.prompt_dict, debug=debug, chat=chat, | |
stream_output=stream_output) | |
self.human = self.prompter.humanstr | |
self.bot = self.prompter.botstr | |
self.can_stop = True | |
else: | |
self.prompter = None | |
self.human = None | |
self.bot = None | |
self.can_stop = False | |
self.sanitize_bot_response = sanitize_bot_response | |
self.max_input_tokens = max_input_tokens # not for generate, so ok that not kwargs | |
def limit_prompt(prompt_text, tokenizer, max_prompt_length=None): | |
verbose = bool(int(os.getenv('VERBOSE_PIPELINE', '0'))) | |
if hasattr(tokenizer, 'model_max_length'): | |
# model_max_length only defined for generate.py, not raw use of h2oai_pipeline.py | |
model_max_length = tokenizer.model_max_length | |
if max_prompt_length is not None: | |
model_max_length = min(model_max_length, max_prompt_length) | |
# cut at some upper likely limit to avoid excessive tokenization etc | |
# upper bound of 10 chars/token, e.g. special chars sometimes are long | |
if len(prompt_text) > model_max_length * 10: | |
len0 = len(prompt_text) | |
prompt_text = prompt_text[-model_max_length * 10:] | |
if verbose: | |
print("Cut of input: %s -> %s" % (len0, len(prompt_text)), flush=True) | |
else: | |
# unknown | |
model_max_length = None | |
num_prompt_tokens = None | |
if model_max_length is not None: | |
# can't wait for "hole" if not plain prompt_type, since would lose prefix like <human>: | |
# For https://github.com/h2oai/h2ogpt/issues/192 | |
for trial in range(0, 3): | |
prompt_tokens = tokenizer(prompt_text)['input_ids'] | |
num_prompt_tokens = len(prompt_tokens) | |
if num_prompt_tokens > model_max_length: | |
# conservative by using int() | |
chars_per_token = int(len(prompt_text) / num_prompt_tokens) | |
# keep tail, where question is if using langchain | |
prompt_text = prompt_text[-model_max_length * chars_per_token:] | |
if verbose: | |
print("reducing %s tokens, assuming average of %s chars/token for %s characters" % ( | |
num_prompt_tokens, chars_per_token, len(prompt_text)), flush=True) | |
else: | |
if verbose: | |
print("using %s tokens with %s chars" % (num_prompt_tokens, len(prompt_text)), flush=True) | |
break | |
# Why Below False: don't limit max_new_tokens more, just rely upon stopping to reach limit of model | |
if False: | |
# if input prompt is some number of tokens, despite user request, can't have max_new_tokens more | |
# | |
assert num_prompt_tokens is not None | |
if self.prompt_type not in [PromptType.plain.name, PromptType.plain.value]: | |
# then give room for prompt | |
fudge = 20 | |
else: | |
fudge = 0 | |
max_new_tokens = max(0, min(generate_kwargs['max_new_tokens'], | |
model_max_length - (num_prompt_tokens + fudge))) | |
if max_new_tokens < generate_kwargs['max_new_tokens']: | |
if verbose: | |
print("Reduced max_new_tokens from %s -> %s" % ( | |
generate_kwargs['max_new_tokens'], max_new_tokens)) | |
generate_kwargs['max_new_tokens'] = max_new_tokens | |
return prompt_text, num_prompt_tokens | |
def preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs): | |
prompt_text, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt_text, self.tokenizer) | |
data_point = dict(context=self.context, instruction=prompt_text, input=self.iinput) | |
if self.prompter is not None: | |
prompt_text = self.prompter.generate_prompt(data_point) | |
self.prompt_text = prompt_text | |
if handle_long_generation is None: | |
# forces truncation of inputs to avoid critical failure | |
handle_long_generation = None # disable with new approaches | |
return super().preprocess(prompt_text, prefix=prefix, handle_long_generation=handle_long_generation, | |
**generate_kwargs) | |
def postprocess(self, model_outputs, return_type=ReturnType.FULL_TEXT, clean_up_tokenization_spaces=True): | |
records = super().postprocess(model_outputs, return_type=return_type, | |
clean_up_tokenization_spaces=clean_up_tokenization_spaces) | |
for rec in records: | |
if self.use_prompter: | |
outputs = rec['generated_text'] | |
outputs = self.prompter.get_response(outputs, prompt=self.prompt_text, | |
sanitize_bot_response=self.sanitize_bot_response) | |
elif self.bot and self.human: | |
outputs = rec['generated_text'].split(self.bot)[1].split(self.human)[0] | |
else: | |
outputs = rec['generated_text'] | |
rec['generated_text'] = outputs | |
print("prompt: %s\noutputs: %s\n\n" % (self.prompt_text, outputs), flush=True) | |
return records | |
def _forward(self, model_inputs, **generate_kwargs): | |
if self.can_stop: | |
stopping_criteria = get_stopping(self.prompt_type, self.prompt_dict, | |
self.tokenizer, self.device, | |
human=self.human, bot=self.bot, | |
model_max_length=self.tokenizer.model_max_length) | |
generate_kwargs['stopping_criteria'] = stopping_criteria | |
# return super()._forward(model_inputs, **generate_kwargs) | |
return self.__forward(model_inputs, **generate_kwargs) | |
# FIXME: Copy-paste of original _forward, but removed copy.deepcopy() | |
# FIXME: https://github.com/h2oai/h2ogpt/issues/172 | |
def __forward(self, model_inputs, **generate_kwargs): | |
input_ids = model_inputs["input_ids"] | |
attention_mask = model_inputs.get("attention_mask", None) | |
# Allow empty prompts | |
if input_ids.shape[1] == 0: | |
input_ids = None | |
attention_mask = None | |
in_b = 1 | |
else: | |
in_b = input_ids.shape[0] | |
prompt_text = model_inputs.pop("prompt_text") | |
## If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying | |
## generate_kwargs, as some of the parameterization may come from the initialization of the pipeline. | |
# generate_kwargs = copy.deepcopy(generate_kwargs) | |
prefix_length = generate_kwargs.pop("prefix_length", 0) | |
if prefix_length > 0: | |
has_max_new_tokens = "max_new_tokens" in generate_kwargs or ( | |
"generation_config" in generate_kwargs | |
and generate_kwargs["generation_config"].max_new_tokens is not None | |
) | |
if not has_max_new_tokens: | |
generate_kwargs["max_length"] = generate_kwargs.get("max_length") or self.model.config.max_length | |
generate_kwargs["max_length"] += prefix_length | |
has_min_new_tokens = "min_new_tokens" in generate_kwargs or ( | |
"generation_config" in generate_kwargs | |
and generate_kwargs["generation_config"].min_new_tokens is not None | |
) | |
if not has_min_new_tokens and "min_length" in generate_kwargs: | |
generate_kwargs["min_length"] += prefix_length | |
# BS x SL | |
generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs) | |
out_b = generated_sequence.shape[0] | |
if self.framework == "pt": | |
generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:]) | |
elif self.framework == "tf": | |
from transformers import is_tf_available | |
if is_tf_available(): | |
import tensorflow as tf | |
generated_sequence = tf.reshape(generated_sequence, | |
(in_b, out_b // in_b, *generated_sequence.shape[1:])) | |
else: | |
raise ValueError("TF not avaialble.") | |
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text} | |