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Model: h2oai/h2ogpt-oasst1-512-12b
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2026-07-20 19:17:11 +08:00
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from transformers import TextGenerationPipeline
from transformers.pipelines.text_generation import ReturnType
class H2OTextGenerationPipeline(TextGenerationPipeline):
def __init__(self, *args, debug=False, chat=False, stream_output=False,
sanitize_bot_response=True,
use_prompter=True, prompter=None, prompt_type=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 max_input_tokens:
:param kwargs:
"""
super().__init__(*args, **kwargs)
self.prompt_text = None
self.use_prompter = use_prompter
self.prompt_type = prompt_type
self.prompter = prompter
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, 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 preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs):
data_point = dict(context='', instruction=prompt_text, input='')
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 = 'hole'
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].strip().split(self.human)[0].strip()
else:
outputs = rec['generated_text']
rec['generated_text'] = outputs
return records
def _forward(self, model_inputs, **generate_kwargs):
if self.can_stop:
stopping_criteria = get_stopping(self.prompt_type, self.tokenizer, self.device, human=self.human,
bot=self.bot)
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}
import torch
from transformers import StoppingCriteria, StoppingCriteriaList
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=[], device="cuda"):
super().__init__()
assert len(stops) % len(encounters) == 0, "Number of stops and encounters must match"
self.encounters = encounters
self.stops = [stop.to(device) for stop in stops]
self.num_stops = [0] * len(stops)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
for stopi, stop in enumerate(self.stops):
if torch.all((stop == input_ids[0][-len(stop):])).item():
self.num_stops[stopi] += 1
if self.num_stops[stopi] >= self.encounters[stopi % len(self.encounters)]:
# print("Stopped", flush=True)
return True
# print("Tokens: %s" % input_ids[0].cpu().numpy(), flush=True)
# print("Stop Tokens: %s" % [x.cpu().numpy() for x in self.stops], flush=True)
return False
def get_stopping(prompt_type, tokenizer, device, human='<human>:', bot="<bot>:"):
if prompt_type in [PromptType.human_bot.name, PromptType.instruct_vicuna.name, PromptType.instruct_with_end.name]:
if prompt_type == PromptType.human_bot.name:
# encounters = [prompt.count(human) + 1, prompt.count(bot) + 1]
# stopping only starts once output is beyond prompt
# 1 human is enough to trigger, but need 2 bots, because very first view back will be bot we added
stop_words = [human, bot, '\n' + human, '\n' + bot]
encounters = [1, 2]
elif prompt_type == PromptType.instruct_vicuna.name:
# even below is not enough, generic strings and many ways to encode
stop_words = [
'### Human:',
"""
### Human:""",
"""
### Human:
""",
'### Assistant:',
"""
### Assistant:""",
"""
### Assistant:
""",
]
encounters = [1, 2]
else:
# some instruct prompts have this as end, doesn't hurt to stop on it since not common otherwise
stop_words = ['### End']
encounters = [1]
stop_words_ids = [
tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words]
# handle single token case
stop_words_ids = [x if len(x.shape) > 0 else torch.tensor([x]) for x in stop_words_ids]
stop_words_ids = [x for x in stop_words_ids if x.shape[0] > 0]
# avoid padding in front of tokens
if tokenizer._pad_token: # use hidden variable to avoid annoying properly logger bug
stop_words_ids = [x[1:] if x[0] == tokenizer.pad_token_id and len(x) > 1 else x for x in stop_words_ids]
# handle fake \n added
stop_words_ids = [x[1:] if y[0] == '\n' else x for x, y in zip(stop_words_ids, stop_words)]
# build stopper
stopping_criteria = StoppingCriteriaList(
[StoppingCriteriaSub(stops=stop_words_ids, encounters=encounters, device=device)])
else:
stopping_criteria = StoppingCriteriaList()
return stopping_criteria
import time
from enum import Enum
non_hf_types = ['gpt4all_llama', 'llama', 'gptj']
class PromptType(Enum):
plain = 0
instruct = 1
quality = 2
human_bot = 3
dai_faq = 4
summarize = 5
simple_instruct = 6
instruct_vicuna = 7
instruct_with_end = 8
human_bot_orig = 9
prompt_answer = 10
open_assistant = 11
wizard_lm = 12
wizard_mega = 13
instruct_vicuna2 = 14
instruct_vicuna3 = 15
wizard2 = 16
wizard3 = 17
prompt_type_to_model_name = {
'plain': [
'EleutherAI/gpt-j-6B',
'EleutherAI/pythia-6.9b',
'EleutherAI/pythia-12b',
'EleutherAI/pythia-12b-deduped',
'EleutherAI/gpt-neox-20b',
'openlm-research/open_llama_7b_700bt_preview',
'decapoda-research/llama-7b-hf',
'decapoda-research/llama-13b-hf',
'decapoda-research/llama-30b-hf',
'decapoda-research/llama-65b-hf',
'facebook/mbart-large-50-many-to-many-mmt',
'philschmid/bart-large-cnn-samsum',
'philschmid/flan-t5-base-samsum',
'gpt2',
'distilgpt2',
'mosaicml/mpt-7b-storywriter',
'mosaicml/mpt-7b-instruct', # internal code handles instruct
'mosaicml/mpt-7b-chat', # NC, internal code handles instruct
'gptj', # internally handles prompting
'llama', # plain, or need to choose prompt_type for given TheBloke model
'gpt4all_llama', # internally handles prompting
],
'prompt_answer': [
'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
],
'instruct': [],
'instruct_with_end': ['databricks/dolly-v2-12b'],
'quality': [],
'human_bot': [
'h2oai/h2ogpt-oasst1-512-12b',
'h2oai/h2ogpt-oasst1-512-20b',
'h2oai/h2ogpt-oig-oasst1-256-6_9b',
'h2oai/h2ogpt-oig-oasst1-512-6_9b',
'h2oai/h2ogpt-oig-oasst1-256-6.9b', # legacy
'h2oai/h2ogpt-oig-oasst1-512-6.9b', # legacy
'h2oai/h2ogpt-research-oasst1-512-30b',
'h2oai/h2ogpt-oasst1-falcon-40b',
],
'dai_faq': [],
'summarize': [],
'simple_instruct': ['t5-small', 't5-large', 'google/flan-t5', 'google/flan-t5-xxl', 'google/flan-ul2'],
'instruct_vicuna': ['AlekseyKorshuk/vicuna-7b', 'TheBloke/stable-vicuna-13B-HF', 'junelee/wizard-vicuna-13b'],
'human_bot_orig': ['togethercomputer/GPT-NeoXT-Chat-Base-20B'],
"open_assistant": ['OpenAssistant/oasst-sft-7-llama-30b-xor', 'oasst-sft-7-llama-30b'],
"wizard_lm": ['ehartford/WizardLM-7B-Uncensored', 'ehartford/WizardLM-13B-Uncensored'],
"wizard_mega": ['openaccess-ai-collective/wizard-mega-13b'],
}
inv_prompt_type_to_model_name = {v.strip(): k for k, l in prompt_type_to_model_name.items() for v in l}
inv_prompt_type_to_model_lower = {v.strip().lower(): k for k, l in prompt_type_to_model_name.items() for v in l}
prompt_types_strings = []
for p in PromptType:
prompt_types_strings.extend([p.name])
prompt_types = []
for p in PromptType:
prompt_types.extend([p.name, p.value, str(p.value)])
def get_prompt(prompt_type, chat, context, reduced):
if prompt_type in [PromptType.plain.value, str(PromptType.plain.value),
PromptType.plain.name]:
promptA = promptB = PreInstruct = PreInput = PreResponse = ''
terminate_response = []
chat_sep = ''
humanstr = ''
botstr = ''
elif prompt_type == 'simple_instruct':
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_sep = '\n'
humanstr = ''
botstr = ''
elif prompt_type in [PromptType.instruct.value, str(PromptType.instruct.value),
PromptType.instruct.name] + [PromptType.instruct_with_end.value,
str(PromptType.instruct_with_end.value),
PromptType.instruct_with_end.name]:
promptA = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n' if not (
chat and reduced) else ''
promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not (
chat and reduced) else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
if prompt_type in [PromptType.instruct_with_end.value, str(PromptType.instruct_with_end.value),
PromptType.instruct_with_end.name]:
terminate_response = ['### End']
else:
terminate_response = None
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.quality.value, str(PromptType.quality.value),
PromptType.quality.name]:
promptA = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction as applied on the Input.\n' if not (
chat and reduced) else ''
promptB = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction.\n' if not (
chat and reduced) else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
terminate_response = None
chat_sep = '\n'
humanstr = PreInstruct # first thing human says
botstr = PreResponse # first thing bot says
elif prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
PromptType.human_bot.name] + [PromptType.human_bot_orig.value,
str(PromptType.human_bot_orig.value),
PromptType.human_bot_orig.name]:
human = '<human>:'
bot = "<bot>:"
if reduced or context or prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
PromptType.human_bot.name]:
preprompt = ''
else:
cur_date = time.strftime('%Y-%m-%d')
cur_time = time.strftime('%H:%M:%S %p %Z')
PRE_PROMPT = """\
Current Date: {}
Current Time: {}
"""
preprompt = PRE_PROMPT.format(cur_date, cur_time)
start = human
promptB = promptA = '%s%s ' % (preprompt, start)
PreInstruct = ""
PreInput = None
if reduced:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = bot + ' '
else:
# normally LLM adds space after this, because was how trained.
# if add space here, non-unique tokenization will often make LLM produce wrong output
PreResponse = bot
terminate_response = [start, PreResponse]
chat_sep = '\n'
humanstr = human # tag before human talks
botstr = bot # tag before bot talks
elif prompt_type in [PromptType.dai_faq.value, str(PromptType.dai_faq.value),
PromptType.dai_faq.name]:
promptA = ''
promptB = 'Answer the following Driverless AI question.\n'
PreInstruct = """
### Driverless AI frequently asked question:
"""
PreInput = None
PreResponse = """
### Driverless AI documentation answer:
"""
terminate_response = ['\n\n']
chat_sep = terminate_response
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.summarize.value, str(PromptType.summarize.value),
PromptType.summarize.name]:
promptA = promptB = PreInput = ''
PreInstruct = '## Main Text\n\n'
PreResponse = '\n\n## Summary\n\n'
terminate_response = None
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna.value, str(PromptType.instruct_vicuna.value),
PromptType.instruct_vicuna.name]:
promptA = promptB = "A chat between a curious human and an artificial intelligence assistant. " \
"The assistant gives helpful, detailed, and polite answers to the human's questions." if not (
chat and reduced) else ''
PreInstruct = """
### Human:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [
'### Human:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.prompt_answer.value, str(PromptType.prompt_answer.value),
PromptType.prompt_answer.name]:
preprompt = ''
prompt_tokens = "<|prompt|>"
answer_tokens = "<|answer|>"
start = prompt_tokens
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = answer_tokens
eos = '<|endoftext|>' # neox eos
terminate_response = [start, PreResponse, eos]
chat_sep = eos
humanstr = prompt_tokens
botstr = answer_tokens
elif prompt_type in [PromptType.open_assistant.value, str(PromptType.open_assistant.value),
PromptType.open_assistant.name]:
# From added_tokens.json
preprompt = ''
prompt_tokens = "<|prompter|>"
answer_tokens = "<|assistant|>"
start = prompt_tokens
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = answer_tokens
pend = "<|prefix_end|>"
eos = "</s>"
terminate_response = [start, PreResponse, pend, eos]
chat_sep = eos
humanstr = prompt_tokens
botstr = answer_tokens
elif prompt_type in [PromptType.wizard_lm.value, str(PromptType.wizard_lm.value),
PromptType.wizard_lm.name]:
# https://github.com/ehartford/WizardLM/blob/main/src/train_freeform.py
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = "\n\n### Response\n"
eos = "</s>"
terminate_response = [PreResponse, eos]
chat_sep = eos
humanstr = promptA
botstr = PreResponse
elif prompt_type in [PromptType.wizard_mega.value, str(PromptType.wizard_mega.value),
PromptType.wizard_mega.name]:
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """
### Instruction:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [PreResponse]
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna2.value, str(PromptType.instruct_vicuna2.value),
PromptType.instruct_vicuna2.name]:
promptA = promptB = "" if not (
chat and reduced) else ''
PreInstruct = """
HUMAN:
"""
PreInput = None
PreResponse = """
ASSISTANT:
"""
terminate_response = [
'HUMAN:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna3.value, str(PromptType.instruct_vicuna3.value),
PromptType.instruct_vicuna3.name]:
promptA = promptB = "" if not (
chat and reduced) else ''
PreInstruct = """
### User:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [
'### User:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard2.value, str(PromptType.wizard2.value),
PromptType.wizard2.name]:
# https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML
preprompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request."""
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """
### Instruction:
"""
PreInput = None
PreResponse = """
### Response:
"""
terminate_response = [PreResponse]
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard3.value, str(PromptType.wizard3.value),
PromptType.wizard3.name]:
# https://huggingface.co/TheBloke/wizardLM-13B-1.0-GGML
preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."""
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT: """
terminate_response = [PreResponse]
chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
else:
raise RuntimeError("No such prompt_type=%s" % prompt_type)
return promptA, promptB, PreInstruct, PreInput, PreResponse, terminate_response, chat_sep, humanstr, botstr
def generate_prompt(data_point, prompt_type, chat, reduced):
context = data_point.get('context')
if context is None:
context = ''
instruction = data_point.get('instruction')
input = data_point.get('input')
output = data_point.get('output')
prompt_type = data_point.get('prompt_type', prompt_type)
assert prompt_type in prompt_types, "Bad prompt type: %s" % prompt_type
promptA, promptB, PreInstruct, PreInput, PreResponse, \
terminate_response, chat_sep, humanstr, botstr = get_prompt(prompt_type, chat, context, reduced)
prompt = context if not reduced else ''
if input and promptA:
prompt += f"""{promptA}"""
elif promptB:
prompt += f"""{promptB}"""
if instruction and PreInstruct is not None and input and PreInput is not None:
prompt += f"""{PreInstruct}{instruction}{PreInput}{input}"""
prompt = inject_newline(prompt_type, prompt)
elif instruction and input and PreInstruct is None and PreInput is not None:
prompt += f"""{PreInput}{instruction}
{input}"""
prompt = inject_newline(prompt_type, prompt)
elif input and instruction and PreInput is None and PreInstruct is not None:
prompt += f"""{PreInstruct}{instruction}
{input}"""
prompt = inject_newline(prompt_type, prompt)
elif instruction and PreInstruct is not None:
prompt += f"""{PreInstruct}{instruction}"""
prompt = inject_newline(prompt_type, prompt)
elif input and PreInput is not None:
prompt += f"""{PreInput}{input}"""
prompt = inject_newline(prompt_type, prompt)
elif input and instruction and PreInput is not None:
prompt += f"""{PreInput}{instruction}{input}"""
prompt = inject_newline(prompt_type, prompt)
elif input and instruction and PreInstruct is not None:
prompt += f"""{PreInstruct}{instruction}{input}"""
prompt = inject_newline(prompt_type, prompt)
elif input and instruction:
# i.e. for simple_instruct
prompt += f"""{instruction}: {input}"""
prompt = inject_newline(prompt_type, prompt)
elif input:
prompt += f"""{input}"""
prompt = inject_newline(prompt_type, prompt)
elif instruction:
prompt += f"""{instruction}"""
prompt = inject_newline(prompt_type, prompt)
if PreResponse is not None:
prompt += f"""{PreResponse}"""
pre_response = PreResponse # Don't use strip
else:
pre_response = ''
if output:
prompt += f"""{output}"""
return prompt, pre_response, terminate_response, chat_sep
def inject_newline(prompt_type, prompt):
if prompt_type not in [-1, '-1', 'plain', 'simple_instruct']:
# only add new line if structured prompt, while 'plain' is just generation of next tokens from input
prompt += '\n'
return prompt
class Prompter(object):
def __init__(self, prompt_type, debug=False, chat=False, stream_output=False, repeat_penalty=True,
allowed_repeat_line_length=10):
self.prompt_type = prompt_type
data_point = dict(instruction='', input='', output='')
_, self.pre_response, self.terminate_response, self.chat_sep = \
generate_prompt(data_point, prompt_type, chat, False)
self.debug = debug
self.chat = chat
self.stream_output = stream_output
self.repeat_penalty = repeat_penalty
self.allowed_repeat_line_length = allowed_repeat_line_length
self.prompt = None
context = "" # not for chat context
reduced = False # not for chat context
self.promptA, self.promptB, self.PreInstruct, self.PreInput, self.PreResponse, \
self.terminate_response, self.chat_sep, self.humanstr, self.botstr = \
get_prompt(prompt_type, chat, context, reduced)
def generate_prompt(self, data_point):
reduced = False
prompt, _, _, _ = generate_prompt(data_point, self.prompt_type, self.chat, reduced)
if self.debug:
print("prompt: ", prompt, flush=True)
self.prompt = prompt
return prompt
def get_response(self, outputs, prompt=None, sanitize_bot_response=True):
if isinstance(outputs, str):
outputs = [outputs]
if self.debug:
print("output:\n", '\n\n'.join(outputs), flush=True)
if prompt is not None:
self.prompt = prompt
def clean_response(response):
meaningless_words = ['<pad>', '</s>', '<|endoftext|>']
for word in meaningless_words:
response = response.replace(word, "")
if sanitize_bot_response:
from better_profanity import profanity
response = profanity.censor(response)
response = response.strip("\n")
return response
def clean_repeats(response):
lines = response.split('\n')
new_lines = []
[new_lines.append(line) for line in lines if
line not in new_lines or len(line) < self.allowed_repeat_line_length]
if self.debug and len(lines) != len(new_lines):
print("cleaned repeats: %s %s" % (len(lines), len(new_lines)), flush=True)
response = '\n'.join(new_lines)
return response
multi_output = len(outputs) > 1
for oi, output in enumerate(outputs):
if self.prompt_type in [PromptType.plain.value, str(PromptType.plain.value), PromptType.plain.name]:
output = clean_response(output)
elif prompt is None:
# then use most basic parsing like pipeline
if self.botstr in output:
if self.humanstr:
output = clean_response(output.split(self.botstr)[1].strip().split(self.humanstr)[0].strip())
else:
# i.e. use after bot but only up to next bot
output = clean_response(output.split(self.botstr)[1].strip().split(self.botstr)[0].strip())
else:
# output = clean_response(output.strip())
# assume just not printed yet
output = ""
else:
# find first instance of prereponse
# prompt sometimes has odd characters, that mutate length,
# so can't go by length alone
if self.pre_response:
outputi = output.find(prompt)
if outputi >= 0:
output = output[outputi + len(prompt):]
allow_terminate = True
else:
# subtraction is risky due to space offsets sometimes, so only do if necessary
output = output[len(prompt) - len(self.pre_response):]
# [1] to avoid repeated pre_response, just take first (after prompt - pre_response for chat)
if self.pre_response in output:
output = output.split(self.pre_response)[1]
allow_terminate = True
else:
if output:
print("Failure of parsing or not enough output yet: %s" % output, flush=True)
allow_terminate = False
else:
allow_terminate = True
output = output[len(prompt):]
# clean after subtract prompt out, so correct removal of pre_response
output = clean_response(output).strip()
if self.repeat_penalty:
output = clean_repeats(output).strip()
if self.terminate_response and allow_terminate:
finds = []
for term in self.terminate_response:
finds.append(output.find(term))
finds = [x for x in finds if x >= 0]
if len(finds) > 0:
termi = finds[0]
output = output[:termi].strip()
else:
output = output.strip()
else:
output = output.strip()
if multi_output:
# prefix with output counter
output = "\n=========== Output %d\n\n" % (1 + oi) + output
if oi > 0:
# post fix outputs with seperator
output += '\n'
outputs[oi] = output
# join all outputs, only one extra new line between outputs
output = '\n'.join(outputs)
if self.debug:
print("outputclean:\n", '\n\n'.join(outputs), flush=True)
return output