h2ogpt-chatbot / generate.py
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import functools
import sys
import os
import traceback
import typing
from utils import set_seed, clear_torch_cache, save_generate_output, NullContext, KThread, wrapped_partial
SEED = 1236
set_seed(SEED)
os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
from typing import Union
import numpy as np
import pandas as pd
import fire
import torch
from peft import PeftModel
from transformers import GenerationConfig, StoppingCriteriaList, AutoModel, TextIteratorStreamer
from accelerate import init_empty_weights, infer_auto_device_map
from prompter import Prompter
from finetune import get_loaders, example_data_points, generate_prompt, human, bot, inv_prompt_type_to_model_lower
from stopping import StoppingCriteriaSub
eval_extra_columns = ['prompt', 'response', 'score']
def main(
load_8bit: bool = False,
load_half: bool = True,
infer_devices: bool = True, # really if to "control" devices now
base_model: str = '',
tokenizer_base_model: str = '',
lora_weights: str = "",
gpu_id: int = 0, # if infer_devices = True and gpu_id != -1
prompt_type: Union[int, str] = None,
# input to generation
temperature: float = None,
top_p: float = None,
top_k: int = None,
num_beams: int = None,
repetition_penalty: float = None,
num_return_sequences: int = None,
do_sample: bool = None,
max_new_tokens: int = None,
min_new_tokens: int = None,
early_stopping: Union[bool, str] = None,
max_time: float = None,
debug: bool = False,
save_dir: str = None,
share: bool = True,
local_files_only: bool = False,
resume_download: bool = True,
use_auth_token: Union[str, bool] = False, # True requires CLI did huggingface-cli login before running
src_lang: str = "English",
tgt_lang: str = "Russian",
gradio: bool = True,
gradio_avoid_processing_markdown: bool = False,
chat: bool = True,
chat_history: int = 4096, # character length of chat context/history
chat_context: bool = False, # use default context if human_bot
stream_output: bool = True,
show_examples: bool = None,
verbose: bool = False,
h2ocolors: bool = True,
height: int = 400,
show_lora: bool = True,
# set to True to load --base_model after client logs in,
# to be able to free GPU memory when model is swapped
login_mode_if_model0: bool = False,
block_gradio_exit: bool = True,
concurrency_count: int = 1,
api_open: bool = False, # don't let API skip queue
allow_api: bool = True,
input_lines: int = 1,
sanitize_user_prompt: bool = True,
sanitize_bot_response: bool = True,
extra_model_options: typing.List[str] = [],
extra_lora_options: typing.List[str] = [],
score_model: str = 'OpenAssistant/reward-model-deberta-v3-large-v2',
auto_score: bool = True,
eval_sharegpt_prompts_only: int = 0,
eval_sharegpt_prompts_only_seed: int = 1234,
eval_sharegpt_as_output: bool = False,
hard_stop_list: typing.List[str] = [],
):
is_hf = bool(os.getenv("HUGGINGFACE_SPACES"))
is_gpth2oai = bool(os.getenv("GPT_H2O_AI"))
is_public = is_hf or is_gpth2oai # multi-user case with fixed model and disclaimer
is_low_mem = is_hf # assumes run on 24GB consumer GPU
admin_pass = os.getenv("ADMIN_PASS")
# will sometimes appear in UI or sometimes actual generation, but maybe better than empty result
# but becomes unrecoverable sometimes if raise, so just be silent for now
raise_generate_gpu_exceptions = not is_public
# allow set token directly
use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token)
if is_public:
input_lines = 1 # ensure set, for ease of use
temperature = 0.4
top_p = 0.85
top_k = 70
do_sample = True
if is_low_mem:
base_model = 'h2oai/h2ogpt-oasst1-512-12b'
load_8bit = True
else:
base_model = 'h2oai/h2ogpt-oasst1-512-20b'
if is_low_mem:
load_8bit = True
if is_hf:
# must override share if in spaces
share = False
save_dir = os.getenv('SAVE_DIR', save_dir)
score_model = os.getenv('SCORE_MODEL', score_model)
if score_model == 'None':
score_model = ''
concurrency_count = int(os.getenv('CONCURRENCY_COUNT', concurrency_count))
api_open = bool(int(os.getenv('API_OPEN', api_open)))
allow_api = bool(int(os.getenv('ALLOW_API', allow_api)))
n_gpus = torch.cuda.device_count()
# get defaults
model_lower = base_model.lower()
if not gradio:
# force, else not single response like want to look at
stream_output = False
# else prompt removal can mess up output
chat = False
placeholder_instruction, placeholder_input, \
stream_output, show_examples, \
prompt_type, temperature, top_p, top_k, num_beams, \
max_new_tokens, min_new_tokens, early_stopping, max_time, \
repetition_penalty, num_return_sequences, \
do_sample, \
src_lang, tgt_lang, \
examples, \
task_info = \
get_generate_params(model_lower, chat,
stream_output, show_examples,
prompt_type, temperature, top_p, top_k, num_beams,
max_new_tokens, min_new_tokens, early_stopping, max_time,
repetition_penalty, num_return_sequences,
do_sample,
)
if not gradio:
if eval_sharegpt_prompts_only > 0:
# override default examples with shareGPT ones for human-level eval purposes only
eval_filename = 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json'
if not os.path.isfile(eval_filename):
os.system(
'wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s' % eval_filename)
import json
data = json.load(open(eval_filename, 'rt'))
# focus on data that starts with human, else likely chopped from other data
turn_start = 0 # odd in general
data = [x for x in data if len(x['conversations']) > turn_start + 1 and
x['conversations'][turn_start]['from'] == 'human' and
x['conversations'][turn_start + 1]['from'] == 'gpt']
np.random.seed(eval_sharegpt_prompts_only_seed)
example1 = examples[-1] # pick reference example
examples = []
responses = []
for i in list(np.random.randint(0, len(data), size=eval_sharegpt_prompts_only)):
assert data[i]['conversations'][turn_start]['from'] == 'human'
instruction = data[i]['conversations'][turn_start]['value']
assert data[i]['conversations'][turn_start + 1]['from'] == 'gpt'
output = data[i]['conversations'][turn_start + 1]['value']
examplenew = example1.copy()
assert not chat, "No gradio must use chat=False, uses nochat instruct"
examplenew[eval_func_param_names.index('instruction_nochat')] = instruction
examplenew[eval_func_param_names.index('iinput_nochat')] = '' # no input
examplenew[eval_func_param_names.index('context')] = get_context(chat_context, prompt_type)
examples.append(examplenew)
responses.append(output)
num_examples = len(examples)
scoring_path = 'scoring'
os.makedirs(scoring_path, exist_ok=True)
if eval_sharegpt_as_output:
used_base_model = 'gpt35'
used_lora_weights = ''
else:
used_base_model = str(base_model.split('/')[-1])
used_lora_weights = str(lora_weights.split('/')[-1])
eval_filename = "df_scores_%s_%s_%s_%s_%s_%s.parquet" % (num_examples, eval_sharegpt_prompts_only,
eval_sharegpt_prompts_only_seed,
eval_sharegpt_as_output,
used_base_model,
used_lora_weights)
eval_filename = os.path.join(scoring_path, eval_filename)
# torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
context_class = NullContext() if n_gpus > 1 else torch.device("cuda")
with context_class:
# ensure was set right above before examples generated
assert not stream_output, "stream_output=True does not make sense with example loop"
import time
from functools import partial
# get score model
smodel, stokenizer, sdevice = get_score_model(**locals())
if not eval_sharegpt_as_output:
model, tokenizer, device = get_model(**locals())
model_state = [model, tokenizer, device, base_model]
fun = partial(evaluate, model_state, debug=debug, save_dir=save_dir, is_low_mem=is_low_mem,
raise_generate_gpu_exceptions=raise_generate_gpu_exceptions,
chat_context=chat_context,
concurrency_count=concurrency_count)
else:
assert eval_sharegpt_prompts_only > 0
def get_response(*args, exi=0):
# assumes same ordering of examples and responses
yield responses[exi]
fun = get_response
t0 = time.time()
score_dump = []
import matplotlib.pyplot as plt
for exi, ex in enumerate(examples):
instruction = ex[eval_func_param_names.index('instruction_nochat')]
iinput = ex[eval_func_param_names.index('iinput_nochat')]
context = ex[eval_func_param_names.index('context')]
clear_torch_cache()
print("")
print("START" + "=" * 100)
print("Question: %s %s" % (instruction, ('input=%s' % iinput if iinput else '')))
print("-" * 105)
# fun yields as generator, so have to iterate over it
# Also means likely do NOT want --stream_output=True, else would show all generations
gener = fun(*tuple(ex), exi=exi) if eval_sharegpt_as_output else fun(*tuple(ex))
for res in gener:
print(res)
if smodel:
score_with_prompt = False
if score_with_prompt:
data_point = dict(instruction=instruction, input=iinput, context=context)
prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output)
prompt = prompter.generate_prompt(data_point)
else:
# just raw input and output
if eval_sharegpt_prompts_only > 0:
# only our own examples have this filled at moment
assert iinput in [None, ''], iinput # should be no iinput
if not (chat_context and prompt_type == 'human_bot'):
assert context in [None, ''], context # should be no context
prompt = instruction
cutoff_len = 768 if is_low_mem else 2048
inputs = stokenizer(prompt, res,
return_tensors="pt",
truncation=True,
max_length=cutoff_len)
try:
score = torch.sigmoid(smodel(**inputs).logits[0]).cpu().detach().numpy()[0]
except torch.cuda.OutOfMemoryError as e:
print("GPU OOM 1: question: %s answer: %s exception: %s" % (prompt, res, str(e)), flush=True)
traceback.print_exc()
score = 0.0
clear_torch_cache()
except (Exception, RuntimeError) as e:
if 'Expected all tensors to be on the same device' in str(e) or \
'expected scalar type Half but found Float' in str(e) or \
'probability tensor contains either' in str(e) or \
'cublasLt ran into an error!' in str(e):
print("GPU error: question: %s answer: %s exception: %s" % (prompt, res, str(e)),
flush=True)
traceback.print_exc()
score = 0.0
clear_torch_cache()
else:
raise
print("SCORE %s: %s" % (exi, score), flush=True)
score_dump.append(ex + [prompt, res, score])
# dump every score in case abort
df_scores = pd.DataFrame(score_dump,
columns=eval_func_param_names + eval_extra_columns)
df_scores.to_parquet(eval_filename, index=False)
# plot histogram so far
plt.figure(figsize=(10, 10))
plt.hist(df_scores['score'], bins=20)
score_avg = np.mean(df_scores['score'])
score_median = np.median(df_scores['score'])
plt.title("Score avg: %s median: %s" % (score_avg, score_median))
plt.savefig(eval_filename.replace('.parquet', '.png'))
plt.close()
print("END" + "=" * 102)
print("")
t2 = time.time()
print("Time taken so far: %.4f about %.4g per example" % (t2 - t0, (t2 - t0) / (1 + exi)))
t1 = time.time()
print("Total time taken: %.4f about %.4g per example" % (t1 - t0, (t1 - t0) / num_examples))
return eval_filename
if gradio:
# imported here so don't require gradio to run generate
from gradio_runner import go_gradio
# get default model
all_kwargs = locals().copy()
if all_kwargs.get('base_model') and not all_kwargs['login_mode_if_model0']:
model0, tokenizer0, device = get_model(**all_kwargs)
else:
# if empty model, then don't load anything, just get gradio up
model0, tokenizer0, device = None, None, None
model_state0 = [model0, tokenizer0, device, all_kwargs['base_model']]
# get score model
smodel, stokenizer, sdevice = get_score_model(**all_kwargs)
score_model_state0 = [smodel, stokenizer, sdevice, score_model]
go_gradio(**locals())
def get_device():
if torch.cuda.is_available():
device = "cuda"
else:
raise RuntimeError("only cuda supported")
return device
def get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type,
gpu_id=0,
use_auth_token=False):
"""
Ensure model gets on correct device
:param base_model:
:param model_loader:
:param load_half:
:param model_kwargs:
:param reward_type:
:param gpu_id:
:param use_auth_token:
:return:
"""
with init_empty_weights():
from transformers import AutoConfig
config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token)
model = AutoModel.from_config(
config,
)
# NOTE: Can specify max_memory={0: max_mem, 1: max_mem}, to shard model
# NOTE: Some models require avoiding sharding some layers,
# then would pass no_split_module_classes and give list of those layers.
device_map = infer_auto_device_map(
model,
dtype=torch.float16 if load_half else torch.float32,
)
if hasattr(model, 'model'):
device_map_model = infer_auto_device_map(
model.model,
dtype=torch.float16 if load_half else torch.float32,
)
device_map.update(device_map_model)
print('device_map: %s' % device_map, flush=True)
if gpu_id >= 0:
# FIXME: If really distributes model, tend to get things like: ValueError: gpt_neox.embed_in.weight doesn't have any device set.
# So avoid for now, just put on first GPU, unless score_model, put on last
n_gpus = torch.cuda.device_count()
if reward_type:
device_map = {'': n_gpus - 1}
else:
device_map = {'': min(n_gpus - 1, gpu_id)}
if gpu_id == -1:
device_map = {'': 'cuda'}
load_in_8bit = model_kwargs.get('load_in_8bit', False)
model_kwargs['device_map'] = device_map
if load_in_8bit or not load_half:
model = model_loader.from_pretrained(
base_model,
**model_kwargs,
)
else:
model = model_loader.from_pretrained(
base_model,
**model_kwargs,
).half()
return model
def get_model(
load_8bit: bool = False,
load_half: bool = True,
infer_devices: bool = True,
base_model: str = '',
tokenizer_base_model: str = '',
lora_weights: str = "",
gpu_id: int = 0,
reward_type: bool = None,
local_files_only: bool = False,
resume_download: bool = True,
use_auth_token: Union[str, bool] = False,
compile: bool = True,
**kwargs,
):
"""
:param load_8bit: load model in 8-bit, not supported by all models
:param load_half: load model in 16-bit
:param infer_devices: Use torch infer of optimal placement of layers on devices (for non-lora case)
For non-LORA case, False will spread shards across multiple GPUs, but this can lead to cuda:x cuda:y mismatches
So it is not the default
:param base_model: name/path of base model
:param tokenizer_base_model: name/path of tokenizer
:param lora_weights: name/path
:param gpu_id: which GPU (0..n_gpus-1) or allow all GPUs if relevant (-1)
:param reward_type: reward type model for sequence classification
:param local_files_only: use local files instead of from HF
:param resume_download: resume downloads from HF
:param use_auth_token: assumes user did on CLI `huggingface-cli login` to access private repo
:param compile: whether to compile torch model
:param kwargs:
:return:
"""
print("Get %s model" % base_model, flush=True)
if lora_weights is not None and lora_weights.strip():
print("Get %s lora weights" % lora_weights, flush=True)
device = get_device()
if 'gpt2' in base_model.lower():
# RuntimeError: where expected condition to be a boolean tensor, but got a tensor with dtype Half
load_8bit = False
assert base_model.strip(), (
"Please choose a base model with --base_model (CLI) or in Models Tab (gradio)"
)
from transformers import AutoConfig
config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token)
llama_type_from_config = 'llama' in str(config).lower()
llama_type_from_name = "llama" in base_model.lower()
llama_type = llama_type_from_config or llama_type_from_name
if llama_type:
print("Detected as llama type from"
" config (%s) or name (%s)" % (llama_type_from_config, llama_type_from_name), flush=True)
model_loader, tokenizer_loader = get_loaders(llama_type=llama_type, model_name=base_model, reward_type=reward_type)
if not tokenizer_base_model:
tokenizer_base_model = base_model
if tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
local_files_only=local_files_only,
resume_download=resume_download,
use_auth_token=use_auth_token,
)
else:
tokenizer = tokenizer_loader
if isinstance(tokenizer, str):
# already a pipeline, tokenizer_loader is string for task
model = model_loader(tokenizer,
model=base_model,
device=0 if device == "cuda" else -1,
torch_dtype=torch.float16)
else:
assert device == "cuda", "Unsupported device %s" % device
model_kwargs = dict(local_files_only=local_files_only,
torch_dtype=torch.float16,
resume_download=resume_download,
use_auth_token=use_auth_token)
if 'mbart-' not in base_model.lower():
model_kwargs.update(dict(load_in_8bit=load_8bit,
device_map={"": 0} if load_8bit else "auto",
))
if 'OpenAssistant/reward-model'.lower() in base_model.lower():
# could put on other GPUs
model_kwargs['device_map'] = {"": 0}
model_kwargs.pop('torch_dtype', None)
if not lora_weights:
with torch.device("cuda"):
if infer_devices:
model = get_non_lora_model(base_model, model_loader, load_half, model_kwargs, reward_type,
gpu_id=gpu_id, use_auth_token=use_auth_token)
else:
if load_half and not load_8bit:
model = model_loader.from_pretrained(
base_model,
**model_kwargs).half()
else:
model = model_loader.from_pretrained(
base_model,
**model_kwargs)
elif load_8bit:
model = model_loader.from_pretrained(
base_model,
**model_kwargs
)
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
local_files_only=local_files_only,
resume_download=resume_download,
use_auth_token=use_auth_token,
device_map={"": 0}, # seems to be required
)
else:
with torch.device("cuda"):
model = model_loader.from_pretrained(
base_model,
**model_kwargs
)
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
local_files_only=local_files_only,
resume_download=resume_download,
use_auth_token=use_auth_token,
device_map="auto",
)
if load_half:
model.half()
# unwind broken decapoda-research config
if llama_type:
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
if 'gpt2' in base_model.lower():
# add special tokens that otherwise all share the same id
tokenizer.add_special_tokens({'bos_token': '<bos>',
'eos_token': '<eos>',
'pad_token': '<pad>'})
if not isinstance(tokenizer, str):
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32" and compile:
model = torch.compile(model)
return model, tokenizer, device
def get_score_model(**kwargs):
# score model
if kwargs.get('score_model') is not None and kwargs.get('score_model').strip():
score_all_kwargs = kwargs.copy()
score_all_kwargs['load_8bit'] = False
score_all_kwargs['load_half'] = False
score_all_kwargs['base_model'] = kwargs.get('score_model').strip()
score_all_kwargs['tokenizer_base_model'] = ''
score_all_kwargs['lora_weights'] = ''
score_all_kwargs['llama_type'] = False
score_all_kwargs['compile'] = False
smodel, stokenizer, sdevice = get_model(**score_all_kwargs)
else:
smodel, stokenizer, sdevice = None, None, None
return smodel, stokenizer, sdevice
eval_func_param_names = ['instruction',
'iinput',
'context',
'stream_output',
'prompt_type',
'temperature',
'top_p',
'top_k',
'num_beams',
'max_new_tokens',
'min_new_tokens',
'early_stopping',
'max_time',
'repetition_penalty',
'num_return_sequences',
'do_sample',
'chat',
'instruction_nochat',
'iinput_nochat',
]
def evaluate(
model_state,
# START NOTE: Examples must have same order of parameters
instruction,
iinput,
context,
stream_output,
prompt_type,
temperature,
top_p,
top_k,
num_beams,
max_new_tokens,
min_new_tokens,
early_stopping,
max_time,
repetition_penalty,
num_return_sequences,
do_sample,
chat,
instruction_nochat,
iinput_nochat,
# END NOTE: Examples must have same order of parameters
src_lang=None,
tgt_lang=None,
debug=False,
concurrency_count=None,
save_dir=None,
hard_stop_list=None,
sanitize_bot_response=True,
model_state0=None,
is_low_mem=None,
raise_generate_gpu_exceptions=None,
chat_context=None,
):
# ensure passed these
assert concurrency_count is not None
assert is_low_mem is not None
assert raise_generate_gpu_exceptions is not None
assert chat_context is not None
if debug:
locals_dict = locals().copy()
locals_dict.pop('model_state', None)
locals_dict.pop('model_state0', None)
print(locals_dict)
no_model_msg = "Please choose a base model with --base_model (CLI) or in Models Tab (gradio).\nThen start New Conversation"
if model_state0 is None:
# e.g. for no gradio case, set dummy value, else should be set
model_state0 = [None, None, None, None]
if model_state is not None and len(model_state) == 4 and not isinstance(model_state[0], str):
# try to free-up original model (i.e. list was passed as reference)
if model_state0 is not None and model_state0[0] is not None:
model_state0[0].cpu()
model_state0[0] = None
# try to free-up original tokenizer (i.e. list was passed as reference)
if model_state0 is not None and model_state0[1] is not None:
model_state0[1] = None
clear_torch_cache()
model, tokenizer, device, base_model = model_state
elif model_state0 is not None and len(model_state0) == 4 and model_state0[0] is not None:
assert isinstance(model_state[0], str)
model, tokenizer, device, base_model = model_state0
else:
raise AssertionError(no_model_msg)
if base_model is None:
raise AssertionError(no_model_msg)
assert base_model.strip(), no_model_msg
assert model, "Model is missing"
assert tokenizer, "Tokenizer is missing"
# choose chat or non-chat mode
if not chat:
instruction = instruction_nochat
iinput = iinput_nochat
if not context:
# get hidden context if have one
context = get_context(chat_context, prompt_type)
data_point = dict(context=context, instruction=instruction, input=iinput)
prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output)
prompt = prompter.generate_prompt(data_point)
if hard_stop_list is None:
# acts like undo on user entry and bot response
hard_stop_list = []
if isinstance(tokenizer, str):
# pipeline
if tokenizer == "summarization":
key = 'summary_text'
else:
raise RuntimeError("No such task type %s" % tokenizer)
# NOTE: uses max_length only
yield model(prompt, max_length=max_new_tokens)[0][key]
if 'mbart-' in base_model.lower():
assert src_lang is not None
tokenizer.src_lang = languages_covered()[src_lang]
if chat:
# override, ignore user change
num_return_sequences = 1
if prompt_type in ['human_bot', 'instruct_vicuna', 'instruct_with_end']:
if prompt_type == 'human_bot':
# 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 == 'instruct_vicuna':
# 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:
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)])
else:
stopping_criteria = StoppingCriteriaList()
# help to avoid errors like:
# RuntimeError: The size of tensor a (2048) must match the size of tensor b (2049) at non-singleton dimension 3
# RuntimeError: expected scalar type Half but found Float
# with - 256
max_length_tokenize = 768 - 256 if is_low_mem else 2048 - 256
cutoff_len = max_length_tokenize * 4 # if reaches limit, then can't generate new tokens
output_smallest = 30 * 4
prompt = prompt[-cutoff_len - output_smallest:]
inputs = tokenizer(prompt,
return_tensors="pt",
truncation=True,
max_length=max_length_tokenize)
if debug and len(inputs["input_ids"]) > 0:
print('input_ids length', len(inputs["input_ids"][0]), flush=True)
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=float(temperature),
top_p=float(top_p),
top_k=top_k,
num_beams=num_beams,
do_sample=do_sample,
repetition_penalty=float(repetition_penalty),
num_return_sequences=num_return_sequences,
renormalize_logits=True,
remove_invalid_values=True,
)
gen_kwargs = dict(input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens, # prompt + new
min_new_tokens=min_new_tokens, # prompt + new
early_stopping=early_stopping, # False, True, "never"
max_time=max_time,
stopping_criteria=stopping_criteria,
)
if 'gpt2' in base_model.lower():
gen_kwargs.update(dict(bos_token_id=tokenizer.bos_token_id, pad_token_id=tokenizer.eos_token_id))
elif 'mbart-' in base_model.lower():
assert tgt_lang is not None
tgt_lang = languages_covered()[tgt_lang]
gen_kwargs.update(dict(forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang]))
else:
gen_kwargs.update(dict(pad_token_id=tokenizer.eos_token_id))
decoder = functools.partial(tokenizer.decode,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
decoder_raw = functools.partial(tokenizer.decode,
skip_special_tokens=False,
clean_up_tokenization_spaces=True,
)
with torch.no_grad():
# decoded tokenized prompt can deviate from prompt due to special characters
inputs_decoded = decoder(input_ids[0])
inputs_decoded_raw = decoder_raw(input_ids[0])
if inputs_decoded == prompt:
# normal
pass
elif inputs_decoded.lstrip() == prompt.lstrip():
# sometimes extra space in front, make prompt same for prompt removal
prompt = inputs_decoded
elif inputs_decoded_raw == prompt:
# some models specify special tokens that are part of normal prompt, so can't skip them
inputs_decoded_raw = inputs_decoded
decoder = decoder_raw
else:
print("WARNING: Special characters in prompt", flush=True)
if stream_output:
#skip_prompt = prompt_type != 'plain'
skip_prompt = False
streamer = TextIteratorStreamer(tokenizer, skip_prompt=skip_prompt)
gen_kwargs.update(dict(streamer=streamer))
if debug:
KThread.show_threads()
target_func = generate_with_exceptions
if concurrency_count == 1:
# otherwise can't do this
KThread.kill_threads(target_func.__name__, debug=debug)
target = wrapped_partial(generate_with_exceptions, model.generate, prompt, inputs_decoded,
raise_generate_gpu_exceptions, **gen_kwargs)
thread = KThread(target=target)
thread.start()
outputs = ""
for new_text in streamer:
outputs += new_text
yield prompter.get_response(outputs, prompt=inputs_decoded,
sanitize_bot_response=sanitize_bot_response)
else:
outputs = model.generate(**gen_kwargs)
outputs = [decoder(s) for s in outputs.sequences]
yield prompter.get_response(outputs, prompt=inputs_decoded,
sanitize_bot_response=sanitize_bot_response)
if save_dir and outputs and len(outputs) >= 1:
decoded_output = prompt + outputs[0]
save_generate_output(output=decoded_output, base_model=base_model, save_dir=save_dir)
def generate_with_exceptions(func, prompt, inputs_decoded, raise_generate_gpu_exceptions, **kwargs):
try:
func(**kwargs)
except torch.cuda.OutOfMemoryError as e:
print("GPU OOM 2: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
flush=True)
if kwargs['input_ids'] is not None:
kwargs['input_ids'].cpu()
kwargs['input_ids'] = None
traceback.print_exc()
clear_torch_cache()
return
except (Exception, RuntimeError) as e:
if 'Expected all tensors to be on the same device' in str(e) or \
'expected scalar type Half but found Float' in str(e) or \
'probability tensor contains either' in str(e) or \
'cublasLt ran into an error!' in str(e) or \
'mat1 and mat2 shapes cannot be multiplied' in str(e):
print(
"GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
flush=True)
traceback.print_exc()
clear_torch_cache()
if raise_generate_gpu_exceptions:
raise
return
else:
clear_torch_cache()
raise
def get_generate_params(model_lower, chat,
stream_output, show_examples,
prompt_type, temperature, top_p, top_k, num_beams,
max_new_tokens, min_new_tokens, early_stopping, max_time,
repetition_penalty, num_return_sequences,
do_sample):
use_defaults = False
use_default_examples = True
examples = []
task_info = f"{prompt_type}"
if model_lower:
print(f"Using Model {model_lower}", flush=True)
else:
print("No model defined yet", flush=True)
min_new_tokens = min_new_tokens if min_new_tokens is not None else 0
early_stopping = early_stopping if early_stopping is not None else False
max_time_defaults = 60 * 3
max_time = max_time if max_time is not None else max_time_defaults
if not prompt_type and model_lower in inv_prompt_type_to_model_lower:
prompt_type = inv_prompt_type_to_model_lower[model_lower]
# examples at first don't include chat, instruction_nochat, iinput_nochat, added at end
if show_examples is None:
if chat:
show_examples = False
else:
show_examples = True
summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker?
Philipp: Sure you can use the new Hugging Face Deep Learning Container.
Jeff: ok.
Jeff: and how can I get started?
Jeff: where can I find documentation?
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"""
if 'bart-large-cnn-samsum' in model_lower or 'flan-t5-base-samsum' in model_lower:
placeholder_instruction = summarize_example1
placeholder_input = ""
use_defaults = True
use_default_examples = False
examples += [
[placeholder_instruction, "", "", stream_output, 'plain', 1.0, 1.0, 50, 1, 128, 0, False, max_time_defaults,
1.0, 1,
False]]
task_info = "Summarization"
elif 't5-' in model_lower or 't5' == model_lower or 'flan-' in model_lower:
placeholder_instruction = "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
placeholder_input = ""
use_defaults = True
use_default_examples = True
task_info = "Multi-Task: Q/A, translation, Chain-of-Thought, Logical Reasoning, Summarization, etc. Best to use task prefix as trained on, e.g. `translate English to German: ` (space after colon)"
elif 'mbart-' in model_lower:
placeholder_instruction = "The girl has long hair."
placeholder_input = ""
use_defaults = True
use_default_examples = False
examples += [
[placeholder_instruction, "", "", stream_output, 'plain', 1.0, 1.0, 50, 1, 128, 0, False, max_time_defaults,
1.0, 1,
False]]
elif 'gpt2' in model_lower:
placeholder_instruction = "The sky is"
placeholder_input = ""
prompt_type = prompt_type or 'plain'
use_default_examples = True # some will be odd "continuations" but can be ok
examples += [
[placeholder_instruction, "", "", stream_output, 'plain', 1.0, 1.0, 50, 1, 128, 0, False, max_time_defaults,
1.0, 1,
False]]
task_info = "Auto-complete phrase, code, etc."
use_defaults = True
else:
if chat:
placeholder_instruction = "Enter a question or imperative."
else:
placeholder_instruction = "Give detailed answer for whether Einstein or Newton is smarter."
placeholder_input = ""
if model_lower:
prompt_type = prompt_type or 'human_bot'
else:
prompt_type = ''
examples += [[summarize_example1, 'Summarize' if prompt_type not in ['plain', 'instruct_simple'] else '', "",
stream_output, prompt_type or 'plain', 0.1, 0.75, 40, 4, 256, 0, False, max_time_defaults, 1.0, 1,
False]]
task_info = "No task"
if prompt_type == 'instruct':
task_info = "Answer question or follow imperative as instruction with optionally input."
elif prompt_type == 'plain':
task_info = "Auto-complete phrase, code, etc."
elif prompt_type == 'human_bot':
if chat:
task_info = "Chat (Shift-Enter to give question/imperative, input concatenated with instruction)"
else:
task_info = "Ask question/imperative (input concatenated with instruction)"
# revert to plain if still nothing
prompt_type = prompt_type or 'plain'
if use_defaults:
temperature = 1.0 if temperature is None else temperature
top_p = 1.0 if top_p is None else top_p
top_k = 40 if top_k is None else top_k
num_beams = num_beams or 1
max_new_tokens = max_new_tokens or 128
repetition_penalty = repetition_penalty or 1.07
num_return_sequences = min(num_beams, num_return_sequences or 1)
do_sample = False if do_sample is None else do_sample
else:
temperature = 0.4 if temperature is None else temperature
top_p = 0.85 if top_p is None else top_p
top_k = 70 if top_k is None else top_k
if chat:
num_beams = num_beams or 1
else:
num_beams = num_beams or 4
max_new_tokens = max_new_tokens or 256
repetition_penalty = repetition_penalty or 1.07
num_return_sequences = min(num_beams, num_return_sequences or 1)
do_sample = True if do_sample is None else do_sample
# doesn't include chat, instruction_nochat, iinput_nochat, added later
params_list = ["", stream_output, prompt_type, temperature, top_p, top_k, num_beams, max_new_tokens, min_new_tokens,
early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample]
if use_default_examples:
examples += [
["Translate English to French", "Good morning"] + params_list,
["Give detailed answer for whether Einstein or Newton is smarter.", ''] + params_list,
["Explain in detailed list, all the best practices for coding in python.", ''] + params_list,
[
"Create a markdown table with 3 rows for the primary colors, and 2 columns, with color name and hex codes.",
''] + params_list,
['Translate to German: My name is Arthur', ''] + params_list,
["Please answer to the following question. Who is going to be the next Ballon d'or?", ''] + params_list,
['Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.',
''] + params_list,
['Please answer the following question. What is the boiling point of Nitrogen?', ''] + params_list,
['Answer the following yes/no question. Can you write a whole Haiku in a single tweet?', ''] + params_list,
["Simplify the following expression: (False or False and True). Explain your answer.", ''] + params_list,
[
"Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?",
''] + params_list,
['The square root of x is the cube root of y. What is y to the power of 2, if x = 4?', ''] + params_list,
[
'Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?',
''] + params_list,
["""def area_of_rectangle(a: float, b: float):
\"\"\"Return the area of the rectangle.\"\"\"""", ''] + params_list,
["""# a function in native python:
def mean(a):
return sum(a)/len(a)
# the same function using numpy:
import numpy as np
def mean(a):""", ''] + params_list,
["""X = np.random.randn(100, 100)
y = np.random.randint(0, 1, 100)
# fit random forest classifier with 20 estimators""", ''] + params_list,
]
src_lang = "English"
tgt_lang = "Russian"
# move to correct position
for example in examples:
example += [chat, '', '']
# adjust examples if non-chat mode
if not chat:
example[eval_func_param_names.index('instruction_nochat')] = example[
eval_func_param_names.index('instruction')]
example[eval_func_param_names.index('instruction')] = ''
example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')]
example[eval_func_param_names.index('iinput')] = ''
return placeholder_instruction, placeholder_input, \
stream_output, show_examples, \
prompt_type, temperature, top_p, top_k, num_beams, \
max_new_tokens, min_new_tokens, early_stopping, max_time, \
repetition_penalty, num_return_sequences, \
do_sample, \
src_lang, tgt_lang, \
examples, \
task_info
def languages_covered():
# https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered
covered = """Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)"""
covered = covered.split(', ')
covered = {x.split(' ')[0]: x.split(' ')[1].replace(')', '').replace('(', '') for x in covered}
return covered
def get_context(chat_context, prompt_type):
if chat_context and prompt_type == 'human_bot':
context0 = """<bot>: I am an intelligent, helpful, truthful, and fair assistant named h2oGPT, who will give accurate, balanced, and reliable responses. I will not respond with I don't know or I don't understand.
<human>: I am a human person seeking useful assistance and request all questions be answered completely, and typically expect detailed responses. Give answers in numbered list format if several distinct but related items are being listed."""
else:
context0 = ''
return context0
def test_test_prompt(prompt_type='instruct', data_point=0):
example_data_point = example_data_points[data_point]
example_data_point.pop('output', None)
return generate_prompt(example_data_point, prompt_type, False, False)
def score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len):
question = question[-cutoff_len:]
answer = answer[-cutoff_len:]
inputs = stokenizer(question, answer,
return_tensors="pt",
truncation=True,
max_length=max_length_tokenize).to(smodel.device)
try:
score = torch.sigmoid(smodel(**inputs).logits[0]).cpu().detach().numpy()[0]
except torch.cuda.OutOfMemoryError as e:
print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True)
del inputs
traceback.print_exc()
clear_torch_cache()
return 'Response Score: GPU OOM'
except (Exception, RuntimeError) as e:
if 'Expected all tensors to be on the same device' in str(e) or \
'expected scalar type Half but found Float' in str(e) or \
'probability tensor contains either' in str(e) or \
'cublasLt ran into an error!' in str(e):
print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)),
flush=True)
traceback.print_exc()
clear_torch_cache()
return 'Response Score: GPU Error'
else:
raise
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
return score
if __name__ == "__main__":
print("""
WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 --master_port=1234 generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights=lora-alpaca_6B
python generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights='lora-alpaca_6B'
python generate.py --base_model='EleutherAI/gpt-neox-20b' --lora_weights='lora-alpaca_20B'
# generate without lora weights, no prompt
python generate.py --base_model='EleutherAI/gpt-neox-20b' --prompt_type='plain'
python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq'
python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' --lora_weights='lora_20B_daifaq'
# OpenChatKit settings:
python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0
python generate.py --base_model='distilgpt2' --prompt_type='plain' --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 --share=False
python generate.py --base_model='t5-large' --prompt_type='simple_instruct'
python generate.py --base_model='philschmid/bart-large-cnn-samsum'
python generate.py --base_model='philschmid/flan-t5-base-samsum'
python generate.py --base_model='facebook/mbart-large-50-many-to-many-mmt'
python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot' --lora_weights='GPT-NeoXT-Chat-Base-20B.merged.json.8_epochs.57b2892c53df5b8cefac45f84d019cace803ef26.28'
must have 4*48GB GPU and run without 8bit in order for sharding to work with infer_devices=False
can also pass --prompt_type='human_bot' and model can somewhat handle instructions without being instruct tuned
python generate.py --base_model=decapoda-research/llama-65b-hf --load_8bit=False --infer_devices=False --prompt_type='human_bot'
python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b
""", flush=True)
fire.Fire(main)