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text-generation-webui/extensions/openai/completions.py
oobabooga 8f97e87cac Lint the openai extension
2023-09-15 20:11:16 -07:00

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import time
import tiktoken
import torch
import torch.nn.functional as F
import yaml
from extensions.openai.defaults import clamp, default, get_default_req_params
from extensions.openai.errors import InvalidRequestError
from extensions.openai.utils import debug_msg, end_line
from modules import shared
from modules.text_generation import decode, encode, generate_reply
from transformers import LogitsProcessor, LogitsProcessorList
# Thanks to @Cypherfox [Cypherfoxy] for the logits code, blame to @matatonic
class LogitsBiasProcessor(LogitsProcessor):
def __init__(self, logit_bias={}):
self.logit_bias = logit_bias
if self.logit_bias:
self.keys = list([int(key) for key in self.logit_bias.keys()])
values = [self.logit_bias[str(key)] for key in self.keys]
self.values = torch.tensor(values, dtype=torch.float, device=shared.model.device)
debug_msg(f"{self})")
def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
if self.logit_bias:
debug_msg(logits[0, self.keys], " + ", self.values)
logits[0, self.keys] += self.values
debug_msg(" --> ", logits[0, self.keys])
debug_msg(" max/min ", float(torch.max(logits[0])), float(torch.min(logits[0])))
return logits
def __repr__(self):
return f"<{self.__class__.__name__}(logit_bias={self.logit_bias})>"
class LogprobProcessor(LogitsProcessor):
def __init__(self, logprobs=None):
self.logprobs = logprobs
self.token_alternatives = {}
def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
if self.logprobs is not None: # 0-5
log_e_probabilities = F.log_softmax(logits, dim=1)
top_values, top_indices = torch.topk(log_e_probabilities, k=self.logprobs + 1)
top_tokens = [decode(tok) for tok in top_indices[0]]
top_probs = [float(x) for x in top_values[0]]
self.token_alternatives = dict(zip(top_tokens, top_probs))
debug_msg(repr(self))
return logits
def __repr__(self):
return f"<{self.__class__.__name__}(logprobs={self.logprobs}, token_alternatives={self.token_alternatives})>"
def convert_logprobs_to_tiktoken(model, logprobs):
# more problems than it's worth.
# try:
# encoder = tiktoken.encoding_for_model(model)
# # just pick the first one if it encodes to multiple tokens... 99.9% not required and maybe worse overall.
# return dict([(encoder.decode([encoder.encode(token)[0]]), prob) for token, prob in logprobs.items()])
# except KeyError:
# # assume native tokens if we can't find the tokenizer
# return logprobs
return logprobs
def marshal_common_params(body):
# Request Parameters
# Try to use openai defaults or map them to something with the same intent
req_params = get_default_req_params()
# Common request parameters
req_params['truncation_length'] = shared.settings['truncation_length']
req_params['add_bos_token'] = shared.settings.get('add_bos_token', req_params['add_bos_token'])
req_params['seed'] = shared.settings.get('seed', req_params['seed'])
req_params['custom_stopping_strings'] = shared.settings['custom_stopping_strings']
# OpenAI API Parameters
# model - ignored for now, TODO: When we can reliably load a model or lora from a name only change this
req_params['requested_model'] = body.get('model', shared.model_name)
req_params['suffix'] = default(body, 'suffix', req_params['suffix'])
req_params['temperature'] = clamp(default(body, 'temperature', req_params['temperature']), 0.01, 1.99) # fixup absolute 0.0/2.0
req_params['top_p'] = clamp(default(body, 'top_p', req_params['top_p']), 0.01, 1.0)
n = default(body, 'n', 1)
if n != 1:
raise InvalidRequestError(message="Only n = 1 is supported.", param='n')
if 'stop' in body: # str or array, max len 4 (ignored)
if isinstance(body['stop'], str):
req_params['stopping_strings'] = [body['stop']] # non-standard parameter
elif isinstance(body['stop'], list):
req_params['stopping_strings'] = body['stop']
# presence_penalty - ignored
# frequency_penalty - ignored
# pass through unofficial params
req_params['repetition_penalty'] = default(body, 'repetition_penalty', req_params['repetition_penalty'])
req_params['encoder_repetition_penalty'] = default(body, 'encoder_repetition_penalty', req_params['encoder_repetition_penalty'])
# user - ignored
logits_processor = []
logit_bias = body.get('logit_bias', None)
if logit_bias: # {str: float, ...}
# XXX convert tokens from tiktoken based on requested model
# Ex.: 'logit_bias': {'1129': 100, '11442': 100, '16243': 100}
try:
encoder = tiktoken.encoding_for_model(req_params['requested_model'])
new_logit_bias = {}
for logit, bias in logit_bias.items():
for x in encode(encoder.decode([int(logit)]), add_special_tokens=False)[0]:
if int(x) in [0, 1, 2, 29871]: # XXX LLAMA tokens
continue
new_logit_bias[str(int(x))] = bias
debug_msg('logit_bias_map', logit_bias, '->', new_logit_bias)
logit_bias = new_logit_bias
except KeyError:
pass # assume native tokens if we can't find the tokenizer
logits_processor = [LogitsBiasProcessor(logit_bias)]
logprobs = None # coming to chat eventually
if 'logprobs' in body:
logprobs = default(body, 'logprobs', 0) # maybe cap at topk? don't clamp 0-5.
req_params['logprob_proc'] = LogprobProcessor(logprobs)
logits_processor.extend([req_params['logprob_proc']])
else:
logprobs = None
if logits_processor: # requires logits_processor support
req_params['logits_processor'] = LogitsProcessorList(logits_processor)
return req_params
def messages_to_prompt(body: dict, req_params: dict, max_tokens):
# functions
if body.get('functions', []): # chat only
raise InvalidRequestError(message="functions is not supported.", param='functions')
if body.get('function_call', ''): # chat only, 'none', 'auto', {'name': 'func'}
raise InvalidRequestError(message="function_call is not supported.", param='function_call')
if 'messages' not in body:
raise InvalidRequestError(message="messages is required", param='messages')
messages = body['messages']
role_formats = {
'user': 'User: {message}\n',
'assistant': 'Assistant: {message}\n',
'system': '{message}',
'context': 'You are a helpful assistant. Answer as concisely as possible.\nUser: I want your assistance.\nAssistant: Sure! What can I do for you?',
'prompt': 'Assistant:',
}
if 'stopping_strings' not in req_params:
req_params['stopping_strings'] = []
# Instruct models can be much better
if shared.settings['instruction_template']:
try:
instruct = yaml.safe_load(open(f"instruction-templates/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
system_message_template = "{message}"
system_message_default = instruct.get('context', '') # can be missing
bot_start = template.find('<|bot|>') # So far, 100% of instruction templates have this token
user_message_template = template[:bot_start].replace('<|user-message|>', '{message}').replace('<|user|>', instruct.get('user', ''))
bot_message_template = template[bot_start:].replace('<|bot-message|>', '{message}').replace('<|bot|>', instruct.get('bot', ''))
bot_prompt = bot_message_template[:bot_message_template.find('{message}')].rstrip(' ')
role_formats = {
'user': user_message_template,
'assistant': bot_message_template,
'system': system_message_template,
'context': system_message_default,
'prompt': bot_prompt,
}
if 'Alpaca' in shared.settings['instruction_template']:
req_params['stopping_strings'].extend(['\n###'])
elif instruct['user']: # WizardLM and some others have no user prompt.
req_params['stopping_strings'].extend(['\n' + instruct['user'], instruct['user']])
debug_msg(f"Loaded instruction role format: {shared.settings['instruction_template']}")
except Exception as e:
req_params['stopping_strings'].extend(['\nUser:', 'User:']) # XXX User: prompt here also
print(f"Exception: When loading instruction-templates/{shared.settings['instruction_template']}.yaml: {repr(e)}")
print("Warning: Loaded default instruction-following template for model.")
else:
req_params['stopping_strings'].extend(['\nUser:', 'User:']) # XXX User: prompt here also
print("Warning: Loaded default instruction-following template for model.")
system_msgs = []
chat_msgs = []
# You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
context_msg = role_formats['system'].format(message=role_formats['context']) if role_formats['context'] else ''
context_msg = end_line(context_msg)
# Maybe they sent both? This is not documented in the API, but some clients seem to do this.
if 'prompt' in body:
context_msg = end_line(role_formats['system'].format(message=body['prompt'])) + context_msg
for m in messages:
if 'role' not in m:
raise InvalidRequestError(message="messages: missing role", param='messages')
if 'content' not in m:
raise InvalidRequestError(message="messages: missing content", param='messages')
role = m['role']
content = m['content']
# name = m.get('name', None)
# function_call = m.get('function_call', None) # user name or function name with output in content
msg = role_formats[role].format(message=content)
if role == 'system':
system_msgs.extend([msg])
elif role == 'function':
raise InvalidRequestError(message="role: function is not supported.", param='messages')
else:
chat_msgs.extend([msg])
system_msg = '\n'.join(system_msgs)
system_msg = end_line(system_msg)
prompt = system_msg + context_msg + ''.join(chat_msgs) + role_formats['prompt']
token_count = len(encode(prompt)[0])
if token_count >= req_params['truncation_length']:
err_msg = f"This model maximum context length is {req_params['truncation_length']} tokens. However, your messages resulted in over {token_count} tokens."
raise InvalidRequestError(message=err_msg, param='messages')
if max_tokens > 0 and token_count + max_tokens > req_params['truncation_length']:
err_msg = f"This model maximum context length is {req_params['truncation_length']} tokens. However, your messages resulted in over {token_count} tokens and max_tokens is {max_tokens}."
print(f"Warning: ${err_msg}")
# raise InvalidRequestError(message=err_msg, params='max_tokens')
return prompt, token_count
def chat_completions(body: dict, is_legacy: bool = False) -> dict:
# Chat Completions
object_type = 'chat.completions'
created_time = int(time.time())
cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
resp_list = 'data' if is_legacy else 'choices'
# common params
req_params = marshal_common_params(body)
req_params['stream'] = False
requested_model = req_params.pop('requested_model')
logprob_proc = req_params.pop('logprob_proc', None)
req_params['top_k'] = 20 # There is no best_of/top_k param for chat, but it is much improved with a higher top_k.
# chat default max_tokens is 'inf', but also flexible
max_tokens = 0
max_tokens_str = 'length' if is_legacy else 'max_tokens'
if max_tokens_str in body:
max_tokens = default(body, max_tokens_str, req_params['truncation_length'])
req_params['max_new_tokens'] = max_tokens
else:
req_params['max_new_tokens'] = req_params['truncation_length']
# format the prompt from messages
prompt, token_count = messages_to_prompt(body, req_params, max_tokens) # updates req_params['stopping_strings']
# set real max, avoid deeper errors
if req_params['max_new_tokens'] + token_count >= req_params['truncation_length']:
req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
stopping_strings = req_params.pop('stopping_strings', [])
# generate reply #######################################
debug_msg({'prompt': prompt, 'req_params': req_params})
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
answer = ''
for a in generator:
answer = a
# strip extra leading space off new generated content
if answer and answer[0] == ' ':
answer = answer[1:]
completion_token_count = len(encode(answer)[0])
stop_reason = "stop"
if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= req_params['max_new_tokens']:
stop_reason = "length"
resp = {
"id": cmpl_id,
"object": object_type,
"created": created_time,
"model": shared.model_name, # TODO: add Lora info?
resp_list: [{
"index": 0,
"finish_reason": stop_reason,
"message": {"role": "assistant", "content": answer}
}],
"usage": {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
}
if logprob_proc: # not official for chat yet
top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
resp[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
# else:
# resp[resp_list][0]["logprobs"] = None
return resp
# generator
def stream_chat_completions(body: dict, is_legacy: bool = False):
# Chat Completions
stream_object_type = 'chat.completions.chunk'
created_time = int(time.time())
cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
resp_list = 'data' if is_legacy else 'choices'
# common params
req_params = marshal_common_params(body)
req_params['stream'] = True
requested_model = req_params.pop('requested_model')
logprob_proc = req_params.pop('logprob_proc', None)
req_params['top_k'] = 20 # There is no best_of/top_k param for chat, but it is much improved with a higher top_k.
# chat default max_tokens is 'inf', but also flexible
max_tokens = 0
max_tokens_str = 'length' if is_legacy else 'max_tokens'
if max_tokens_str in body:
max_tokens = default(body, max_tokens_str, req_params['truncation_length'])
req_params['max_new_tokens'] = max_tokens
else:
req_params['max_new_tokens'] = req_params['truncation_length']
# format the prompt from messages
prompt, token_count = messages_to_prompt(body, req_params, max_tokens) # updates req_params['stopping_strings']
# set real max, avoid deeper errors
if req_params['max_new_tokens'] + token_count >= req_params['truncation_length']:
req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
def chat_streaming_chunk(content):
# begin streaming
chunk = {
"id": cmpl_id,
"object": stream_object_type,
"created": created_time,
"model": shared.model_name,
resp_list: [{
"index": 0,
"finish_reason": None,
# So yeah... do both methods? delta and messages.
"message": {'role': 'assistant', 'content': content},
"delta": {'role': 'assistant', 'content': content},
}],
}
if logprob_proc: # not official for chat yet
top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
chunk[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
# else:
# chunk[resp_list][0]["logprobs"] = None
return chunk
yield chat_streaming_chunk('')
# generate reply #######################################
debug_msg({'prompt': prompt, 'req_params': req_params})
stopping_strings = req_params.pop('stopping_strings', [])
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
answer = ''
seen_content = ''
completion_token_count = 0
for a in generator:
answer = a
len_seen = len(seen_content)
new_content = answer[len_seen:]
if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
continue
seen_content = answer
# strip extra leading space off new generated content
if len_seen == 0 and new_content[0] == ' ':
new_content = new_content[1:]
chunk = chat_streaming_chunk(new_content)
yield chunk
# to get the correct token_count, strip leading space if present
if answer and answer[0] == ' ':
answer = answer[1:]
completion_token_count = len(encode(answer)[0])
stop_reason = "stop"
if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= req_params['max_new_tokens']:
stop_reason = "length"
chunk = chat_streaming_chunk('')
chunk[resp_list][0]['finish_reason'] = stop_reason
chunk['usage'] = {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
yield chunk
def completions(body: dict, is_legacy: bool = False):
# Legacy
# Text Completions
object_type = 'text_completion'
created_time = int(time.time())
cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
resp_list = 'data' if is_legacy else 'choices'
# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
prompt_str = 'context' if is_legacy else 'prompt'
if prompt_str not in body:
raise InvalidRequestError("Missing required input", param=prompt_str)
prompt_arg = body[prompt_str]
if isinstance(prompt_arg, str) or (isinstance(prompt_arg, list) and isinstance(prompt_arg[0], int)):
prompt_arg = [prompt_arg]
# common params
req_params = marshal_common_params(body)
req_params['stream'] = False
max_tokens_str = 'length' if is_legacy else 'max_tokens'
max_tokens = default(body, max_tokens_str, req_params['max_new_tokens'])
req_params['max_new_tokens'] = max_tokens
requested_model = req_params.pop('requested_model')
logprob_proc = req_params.pop('logprob_proc', None)
stopping_strings = req_params.pop('stopping_strings', [])
# req_params['suffix'] = default(body, 'suffix', req_params['suffix'])
req_params['echo'] = default(body, 'echo', req_params['echo'])
req_params['top_k'] = default(body, 'best_of', req_params['top_k'])
resp_list_data = []
total_completion_token_count = 0
total_prompt_token_count = 0
for idx, prompt in enumerate(prompt_arg, start=0):
if isinstance(prompt[0], int):
# token lists
if requested_model == shared.model_name:
prompt = decode(prompt)[0]
else:
try:
encoder = tiktoken.encoding_for_model(requested_model)
prompt = encoder.decode(prompt)
except KeyError:
prompt = decode(prompt)[0]
token_count = len(encode(prompt)[0])
total_prompt_token_count += token_count
if token_count + max_tokens > req_params['truncation_length']:
err_msg = f"The token count of your prompt ({token_count}) plus max_tokens ({max_tokens}) cannot exceed the model's context length ({req_params['truncation_length']})."
# print(f"Warning: ${err_msg}")
raise InvalidRequestError(message=err_msg, param=max_tokens_str)
# generate reply #######################################
debug_msg({'prompt': prompt, 'req_params': req_params})
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
answer = ''
for a in generator:
answer = a
# strip extra leading space off new generated content
if answer and answer[0] == ' ':
answer = answer[1:]
completion_token_count = len(encode(answer)[0])
total_completion_token_count += completion_token_count
stop_reason = "stop"
if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
stop_reason = "length"
respi = {
"index": idx,
"finish_reason": stop_reason,
"text": answer,
"logprobs": {'top_logprobs': [logprob_proc.token_alternatives]} if logprob_proc else None,
}
resp_list_data.extend([respi])
resp = {
"id": cmpl_id,
"object": object_type,
"created": created_time,
"model": shared.model_name, # TODO: add Lora info?
resp_list: resp_list_data,
"usage": {
"prompt_tokens": total_prompt_token_count,
"completion_tokens": total_completion_token_count,
"total_tokens": total_prompt_token_count + total_completion_token_count
}
}
return resp
# generator
def stream_completions(body: dict, is_legacy: bool = False):
# Legacy
# Text Completions
# object_type = 'text_completion'
stream_object_type = 'text_completion.chunk'
created_time = int(time.time())
cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
resp_list = 'data' if is_legacy else 'choices'
# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
prompt_str = 'context' if is_legacy else 'prompt'
if prompt_str not in body:
raise InvalidRequestError("Missing required input", param=prompt_str)
prompt = body[prompt_str]
req_params = marshal_common_params(body)
requested_model = req_params.pop('requested_model')
if isinstance(prompt, list):
if prompt and isinstance(prompt[0], int):
try:
encoder = tiktoken.encoding_for_model(requested_model)
prompt = encoder.decode(prompt)
except KeyError:
prompt = decode(prompt)[0]
else:
raise InvalidRequestError(message="API Batched generation not yet supported.", param=prompt_str)
# common params
req_params['stream'] = True
max_tokens_str = 'length' if is_legacy else 'max_tokens'
max_tokens = default(body, max_tokens_str, req_params['max_new_tokens'])
req_params['max_new_tokens'] = max_tokens
logprob_proc = req_params.pop('logprob_proc', None)
stopping_strings = req_params.pop('stopping_strings', [])
# req_params['suffix'] = default(body, 'suffix', req_params['suffix'])
req_params['echo'] = default(body, 'echo', req_params['echo'])
req_params['top_k'] = default(body, 'best_of', req_params['top_k'])
token_count = len(encode(prompt)[0])
if token_count + max_tokens > req_params['truncation_length']:
err_msg = f"The token count of your prompt ({token_count}) plus max_tokens ({max_tokens}) cannot exceed the model's context length ({req_params['truncation_length']})."
# print(f"Warning: ${err_msg}")
raise InvalidRequestError(message=err_msg, param=max_tokens_str)
def text_streaming_chunk(content):
# begin streaming
chunk = {
"id": cmpl_id,
"object": stream_object_type,
"created": created_time,
"model": shared.model_name,
resp_list: [{
"index": 0,
"finish_reason": None,
"text": content,
"logprobs": {'top_logprobs': [logprob_proc.token_alternatives]} if logprob_proc else None,
}],
}
return chunk
yield text_streaming_chunk('')
# generate reply #######################################
debug_msg({'prompt': prompt, 'req_params': req_params})
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
answer = ''
seen_content = ''
completion_token_count = 0
for a in generator:
answer = a
len_seen = len(seen_content)
new_content = answer[len_seen:]
if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
continue
seen_content = answer
# strip extra leading space off new generated content
if len_seen == 0 and new_content[0] == ' ':
new_content = new_content[1:]
chunk = text_streaming_chunk(new_content)
yield chunk
# to get the correct count, we strip the leading space if present
if answer and answer[0] == ' ':
answer = answer[1:]
completion_token_count = len(encode(answer)[0])
stop_reason = "stop"
if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
stop_reason = "length"
chunk = text_streaming_chunk('')
chunk[resp_list][0]["finish_reason"] = stop_reason
chunk["usage"] = {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
yield chunk
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