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import functools
import inspect
import sys
import os
import traceback
import typing
from utils import set_seed, flatten_list, clear_torch_cache, system_info_print
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
from accelerate import init_empty_weights, infer_auto_device_map
from prompter import Prompter
from finetune import get_loaders, example_data_points, generate_prompt, get_githash, prompt_types_strings, \
human, bot, prompt_type_to_model_name, inv_prompt_type_to_model_lower
from stopping import CallbackToGenerator, Stream, StoppingCriteriaSub
def main(
load_8bit: bool = False,
load_half: bool = True,
infer_devices: bool = True,
base_model: str = '',
tokenizer_base_model: str = '',
lora_weights: str = "",
force_1_gpu: bool = True,
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,
llama_type: bool = None,
debug: bool = False,
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 = True,
chat: bool = True,
chat_history: int = 4096, # character length of chat context/history
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,
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,
):
# allow set token directly
use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token)
# override share if in spaces
if os.environ.get("HUGGINGFACE_SPACES"):
share = False
base_model = 'h2oai/h2ogpt-oasst1-512-12b'
load_8bit = True
# 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
filename = 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json'
if not os.path.isfile(filename):
os.system('wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s' % filename)
import json
data = json.load(open(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()
examplenew[0] = instruction
examplenew[1] = '' # no input
examplenew[2] = '' # no context
examples.append(examplenew)
responses.append(output)
with torch.device("cuda"):
# 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, chat=chat)
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 = []
num_examples = len(examples)
import matplotlib.pyplot as plt
for exi, ex in enumerate(examples):
clear_torch_cache()
print("")
print("START" + "=" * 100)
print("Question: %s %s" % (ex[0], ('input=%s' % ex[1] if ex[1] 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
for res in fun(*tuple(ex), exi=exi):
print(res)
if smodel:
score_with_prompt = False
if score_with_prompt:
data_point = dict(instruction=ex[0], input=ex[1])
prompter = Prompter(prompt_type, debug=debug, chat=chat, stream_output=stream_output)
prompt = prompter.generate_prompt(data_point)
else:
# just raw input and output
assert ex[1] in [None, ''] # should be no iinput
assert ex[2] in [None, ''] # should be no context
prompt = ex[0]
cutoff_len = 768 if os.environ.get("HUGGINGFACE_SPACES") 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: question: %s answer: %s exception: %s" % (prompt, res, str(e)), flush=True)
traceback.print_exc()
score = 0.0
clear_torch_cache()
except 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):
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
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])
df_scores = pd.DataFrame(score_dump, columns=eval_func_param_names + ['prompt', 'response', 'score'])
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)
filename = os.path.join(scoring_path, filename)
df_scores.to_parquet(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(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
if gradio:
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, force_1_gpu=True, 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:
: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 force_1_gpu:
# 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 = {'': 0}
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 = "",
force_1_gpu: bool = False,
llama_type: bool = None,
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 force_1_gpu:
:param llama_type: whether LLaMa type model
: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
:parm 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)"
)
llama_type = llama_type or "llama" in base_model
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,
force_1_gpu=force_1_gpu, 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
def go_gradio(**kwargs):
# get default model
all_kwargs = kwargs.copy()
all_kwargs.update(locals())
if kwargs.get('base_model') and not 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, kwargs['base_model']]
# get score model
smodel, stokenizer, sdevice = get_score_model(**all_kwargs)
if 'mbart-' in kwargs['model_lower']:
instruction_label = "Text to translate"
else:
instruction_label = "Instruction"
if kwargs['chat']:
instruction_label = "You (Shift-Enter or push Submit to send message)"
title = 'h2oGPT'
if kwargs['verbose']:
description = f"""Model {kwargs['base_model']} Instruct dataset.
For more information, visit [the project's website](https://github.com/h2oai/h2ogpt).
Command: {str(' '.join(sys.argv))}
Hash: {get_githash()}
"""
else:
description = "For more information, visit [the project's website](https://github.com/h2oai/h2ogpt).<br>"
if os.environ.get("HUGGINGFACE_SPACES"):
description += """<p><b> DISCLAIMERS: </b><ul><i><li>The data used to train this model include The Pile and other sources. These may contain objectionable content, so the model may reproduce that material. Use application and responses at own risk.</i></li>"""
if kwargs['load_8bit']:
description += """<i><li> Model is loaded in 8-bit and HF spaces version has other limitations in order to fit on HF GPUs, so UX can be worse than native app.</i></li>"""
description += """<i><li>Model loading and unloading disabled on HF SPACES to avoid GPU OOM for multi-user environment.</i></li></ul></p>"""
if kwargs['verbose']:
task_info_md = f"""
### Task: {kwargs['task_info']}"""
else:
task_info_md = ''
css_code = """footer {visibility: hidden}
body{background-image:url("https://h2o.ai/content/experience-fragments/h2o/us/en/site/header/master/_jcr_content/root/container/header_copy/logo.coreimg.svg/1678976605175/h2o-logo.svg");}}"""
from gradio.themes.utils import colors, fonts, sizes
if kwargs['h2ocolors']:
colors_dict = dict(primary_hue=colors.yellow,
secondary_hue=colors.yellow,
neutral_hue=colors.gray,
spacing_size=sizes.spacing_md,
radius_size=sizes.radius_md,
text_size=sizes.text_md,
)
else:
colors_dict = dict(primary_hue=colors.indigo,
secondary_hue=colors.indigo,
neutral_hue=colors.gray,
spacing_size=sizes.spacing_md,
radius_size=sizes.radius_md,
text_size=sizes.text_md,
)
import gradio as gr
if kwargs['gradio_avoid_processing_markdown']:
from gradio_client import utils as client_utils
from gradio.components import Chatbot
# gradio has issue with taking too long to process input/output for markdown etc.
# Avoid for now, allow raw html to render, good enough for chatbot.
def _postprocess_chat_messages(self, chat_message: str):
if chat_message is None:
return None
elif isinstance(chat_message, (tuple, list)):
filepath = chat_message[0]
mime_type = client_utils.get_mimetype(filepath)
filepath = self.make_temp_copy_if_needed(filepath)
return {
"name": filepath,
"mime_type": mime_type,
"alt_text": chat_message[1] if len(chat_message) > 1 else None,
"data": None, # These last two fields are filled in by the frontend
"is_file": True,
}
elif isinstance(chat_message, str):
return chat_message
else:
raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
Chatbot._postprocess_chat_messages = _postprocess_chat_messages
demo = gr.Blocks(theme=gr.themes.Soft(**colors_dict), css=css_code, title="h2oGPT", analytics_enabled=False)
callback = gr.CSVLogger()
# css_code = 'body{background-image:url("https://h2o.ai/content/experience-fragments/h2o/us/en/site/header/master/_jcr_content/root/container/header_copy/logo.coreimg.svg/1678976605175/h2o-logo.svg");}'
# demo = gr.Blocks(theme='gstaff/xkcd', css=css_code)
model_options = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
if kwargs['base_model'].strip() not in model_options:
lora_options = [kwargs['base_model'].strip()] + model_options
lora_options = kwargs['extra_lora_options']
if kwargs['lora_weights'].strip() not in lora_options:
lora_options = [kwargs['lora_weights'].strip()] + lora_options
# always add in no lora case
# add fake space so doesn't go away in gradio dropdown
lora_options = [' '] + kwargs['extra_lora_options']
output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get('base_model') else 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]'
with demo:
# avoid actual model/tokenizer here or anything that would be bad to deepcopy
# https://github.com/gradio-app/gradio/issues/3558
model_state = gr.State(['model', 'tokenizer', device, kwargs['base_model']])
model_options_state = gr.State([model_options])
lora_options_state = gr.State([lora_options])
gr.Markdown(
f"""
<h1 align="center"> {title}</h1>
{description}
{task_info_md}
""")
# go button visible if
base_wanted = bool(kwargs['base_model']) and kwargs['login_mode_if_model0']
go_btn = gr.Button(value="LOGIN", visible=base_wanted, variant="primary")
normal_block = gr.Row(visible=not base_wanted)
with normal_block:
with gr.Tabs():
with gr.Row():
if not kwargs['chat']:
with gr.Column():
instruction = gr.Textbox(
lines=4, label=instruction_label,
placeholder=kwargs['placeholder_instruction'],
)
iinput = gr.Textbox(lines=4, label="Input",
placeholder=kwargs['placeholder_input'])
flag_btn = gr.Button("Flag")
if kwargs['score_model']:
if not kwargs['auto_score']:
with gr.Column():
score_btn = gr.Button("Score last prompt & response")
score_text = gr.Textbox("Response Score: NA", show_label=False)
else:
score_text = gr.Textbox("Response Score: NA", show_label=False)
with gr.Column():
if kwargs['chat']:
text_output = gr.Chatbot(label=output_label0).style(height=kwargs['height'] or 400)
with gr.Row():
with gr.Column(scale=50):
instruction = gr.Textbox(
lines=4, label=instruction_label,
placeholder=kwargs['placeholder_instruction'],
)
with gr.Row(): # .style(equal_height=False, equal_width=False):
submit = gr.Button(value='Submit').style(full_width=False, size='sm')
stop_btn = gr.Button(value="Stop").style(full_width=False, size='sm')
with gr.Row():
clear = gr.Button("New Conversation")
flag_btn = gr.Button("Flag")
if kwargs['score_model']:
if not kwargs['auto_score']:
with gr.Column():
score_btn = gr.Button("Score last prompt & response").style(full_width=False, size='sm')
score_text = gr.Textbox("Response Score: NA", show_label=False)
else:
score_text = gr.Textbox("Response Score: NA", show_label=False)
retry = gr.Button("Regenerate")
undo = gr.Button("Undo")
else:
text_output = gr.Textbox(lines=5, label=output_label0)
with gr.TabItem("Input/Output"):
with gr.Row():
if 'mbart-' in kwargs['model_lower']:
src_lang = gr.Dropdown(list(languages_covered().keys()),
value=kwargs['src_lang'],
label="Input Language")
tgt_lang = gr.Dropdown(list(languages_covered().keys()),
value=kwargs['tgt_lang'],
label="Output Language")
with gr.TabItem("Expert"):
with gr.Row():
with gr.Column():
stream_output = gr.components.Checkbox(label="Stream output",
value=kwargs['stream_output'])
prompt_type = gr.Dropdown(prompt_types_strings,
value=kwargs['prompt_type'], label="Prompt Type",
visible=not os.environ.get("HUGGINGFACE_SPACES"))
temperature = gr.Slider(minimum=0, maximum=3,
value=kwargs['temperature'],
label="Temperature",
info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
top_p = gr.Slider(minimum=0, maximum=1,
value=kwargs['top_p'], label="Top p",
info="Cumulative probability of tokens to sample from")
top_k = gr.Slider(
minimum=0, maximum=100, step=1,
value=kwargs['top_k'], label="Top k",
info='Num. tokens to sample from'
)
max_beams = 8 if not os.environ.get("HUGGINGFACE_SPACES") else 2
num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
value=min(max_beams, kwargs['num_beams']), label="Beams",
info="Number of searches for optimal overall probability. "
"Uses more GPU memory/compute")
max_max_new_tokens = 2048 if not os.environ.get("HUGGINGFACE_SPACES") else kwargs['max_new_tokens']
max_new_tokens = gr.Slider(
minimum=1, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
)
min_new_tokens = gr.Slider(
minimum=0, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length",
)
early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
value=kwargs['early_stopping'])
max_max_time = 60 * 5 if not os.environ.get("HUGGINGFACE_SPACES") else 60
max_time = gr.Slider(minimum=0, maximum=max_max_time, step=1,
value=min(max_max_time, kwargs['max_time']), label="Max. time",
info="Max. time to search optimal output.")
repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
value=kwargs['repetition_penalty'],
label="Repetition Penalty")
num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
value=kwargs['num_return_sequences'],
label="Number Returns", info="Must be <= num_beams",
visible=not os.environ.get("HUGGINGFACE_SPACES"))
do_sample = gr.Checkbox(label="Sample", info="Sample, for diverse output(s)",
value=kwargs['do_sample'])
if kwargs['chat']:
iinput = gr.Textbox(lines=4, label="Input",
placeholder=kwargs['placeholder_input'],
visible=not os.environ.get("HUGGINGFACE_SPACES"))
# nominally empty for chat mode
context = gr.Textbox(lines=1, label="Context",
info="Ignored in chat mode.",
visible=not os.environ.get("HUGGINGFACE_SPACES"))
with gr.TabItem("Models"):
with gr.Row():
with gr.Column():
with gr.Row(scale=1):
with gr.Column(scale=50):
model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model", value=kwargs['base_model'])
lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA", value=kwargs['lora_weights'], visible=kwargs['show_lora'])
with gr.Column(scale=1):
load_msg = "Load Model/LORA" if not os.environ.get("HUGGINGFACE_SPACES") \
else "LOAD DISABLED ON HF SPACES"
load_model_button = gr.Button(load_msg)
model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'])
lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'], visible=kwargs['show_lora'])
with gr.Row(scale=1):
with gr.Column(scale=50):
new_model = gr.Textbox(label="New Model HF name/path")
new_lora = gr.Textbox(label="New LORA HF name/path", visible=kwargs['show_lora'])
with gr.Column(scale=1):
add_model_button = gr.Button("Add new model name")
add_lora_button = gr.Button("Add new LORA name", visible=kwargs['show_lora'])
with gr.TabItem("System"):
with gr.Row():
with gr.Column():
system_text = gr.Textbox(label='System Info')
system_btn = gr.Button(value='Get System Info')
inputs_list = get_inputs_list(locals(), kwargs['model_lower'])
from functools import partial
all_kwargs = kwargs.copy()
all_kwargs.update(locals())
kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list}
fun = partial(evaluate,
**kwargs_evaluate)
dark_mode_btn = gr.Button("Dark Mode", variant="primary").style(
size="sm",
)
dark_mode_btn.click(
None,
None,
None,
_js="""() => {
if (document.querySelectorAll('.dark').length) {
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
} else {
document.querySelector('body').classList.add('dark');
}
}""",
api_name="dark",
)
if not kwargs['chat']:
submit = gr.Button("Submit")
submit_event = submit.click(fun, inputs=[model_state] + inputs_list, outputs=text_output, api_name='submit')
# examples after submit or any other buttons for chat or no chat
if kwargs['examples'] is not None and kwargs['show_examples']:
gr.Examples(examples=kwargs['examples'], inputs=inputs_list)
# Score
def score_last_response(*args):
""" Similar to user() """
args_list = list(args)
history = args_list[-1]
if history is None:
print("Bad history in scoring last response, fix for now", flush=True)
history = []
if smodel is not None and \
stokenizer is not None and \
sdevice is not None and \
history is not None and len(history) > 0 and \
history[-1] is not None and \
len(history[-1]) >= 2:
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
max_length_tokenize = 512 if os.environ.get("HUGGINGFACE_SPACES") else 2048
cutoff_len = max_length_tokenize*4 # restrict deberta related to max for LLM
question = history[-1][0]
question = question[-cutoff_len:]
answer = history[-1][1]
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: 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 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):
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 'Response Score: {:.1%}'.format(score)
else:
return 'Response Score: NA'
if kwargs['score_model']:
score_args = dict(fn=score_last_response,
inputs=inputs_list + [text_output],
outputs=[score_text],
)
if not kwargs['auto_score']:
score_event = score_btn.click(**score_args, queue=stream_output, api_name='score')
if kwargs['chat']:
def user(*args, undo=False, sanitize_user_prompt=True):
args_list = list(args)
user_message = args_list[0]
input1 = args_list[1]
context1 = args_list[2]
if input1 and not user_message.endswith(':'):
user_message1 = user_message + ":" + input1
elif input1:
user_message1 = user_message + input1
else:
user_message1 = user_message
if sanitize_user_prompt:
from better_profanity import profanity
user_message1 = profanity.censor(user_message1)
history = args_list[-1]
if undo and history:
history.pop()
args_list = args_list[:-1]
if history is None:
print("Bad history, fix for now", flush=True)
history = []
if undo:
return "", history
else:
return "", history + [[user_message1, None]]
def bot(*args, retry=False):
args_list = list(args)
history = args_list[-1]
if retry and history:
history.pop()
if not history:
print("No history", flush=True)
return
instruction1 = history[-1][0]
context1 = ''
if kwargs['chat_history'] > 0:
prompt_type1 = args_list[prompt_type_arg_id]
context1 = ''
for histi in range(len(history) - 1):
data_point = dict(instruction=history[histi][0], input='', output=history[histi][1])
context1 += generate_prompt(data_point, prompt_type1, kwargs['chat'], reduced=True)[0].replace(
'<br>', '\n')
if not context1.endswith('\n'):
context1 += '\n'
if context1 and not context1.endswith('\n'):
context1 += '\n' # ensure if terminates abruptly, then human continues on next line
args_list[0] = instruction1
# only include desired chat history
args_list[2] = context1[-kwargs['chat_history']:]
model_state1 = args_list[-2]
args_list = args_list[:-2]
fun1 = partial(evaluate,
model_state1,
**kwargs_evaluate)
try:
for output in fun1(*tuple(args_list)):
bot_message = output
history[-1][1] = bot_message
yield history
except StopIteration:
yield history
except RuntimeError as e:
if "generator raised StopIteration" in str(e):
# assume last entry was bad, undo
history.pop()
yield history
raise
except Exception as e:
# put error into user input
history[-1][0] = "Exception: %s" % str(e)
yield history
raise
return
user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
inputs=inputs_list + [text_output],
outputs=[instruction, text_output],
)
bot_args = dict(fn=bot,
inputs=inputs_list + [model_state] + [text_output],
outputs=[text_output],
)
retry_bot_args = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list + [model_state] + [text_output],
outputs=[text_output],
)
undo_user_args = dict(fn=functools.partial(user, undo=True),
inputs=inputs_list + [text_output],
outputs=[instruction, text_output],
)
if kwargs['auto_score']:
submit_event = instruction.submit(**user_args, queue=stream_output, api_name='instruction').then(
**bot_args, api_name='instruction_bot',
).then(**score_args, api_name='instruction_bot_score').then(clear_torch_cache)
submit_event2 = submit.click(**user_args, queue=stream_output, api_name='submit').then(
**bot_args, api_name='submit_bot',
).then(**score_args, api_name='submit_bot_score').then(clear_torch_cache)
submit_event3 = retry.click(**user_args, queue=stream_output, api_name='retry').then(
**retry_bot_args, api_name='retry_bot',
).then(**score_args, api_name='retry_bot_score').then(clear_torch_cache)
submit_event4 = undo.click(**undo_user_args, queue=stream_output, api_name='undo').then(**score_args, api_name='undo_score')
else:
submit_event = instruction.submit(**user_args, queue=stream_output, api_name='instruction').then(
**bot_args, api_name='instruction_bot',
).then(clear_torch_cache)
submit_event2 = submit.click(**user_args, queue=stream_output, api_name='submit').then(
**bot_args, api_name='submit_bot',
).then(clear_torch_cache)
submit_event3 = retry.click(**user_args, queue=stream_output, api_name='retry').then(
**retry_bot_args, api_name='retry_bot',
).then(clear_torch_cache)
submit_event4 = undo.click(**undo_user_args, queue=stream_output, api_name='undo')
clear.click(lambda: None, None, text_output, queue=False, api_name='clear')
def load_model(model_name, lora_weights, model_state_old, prompt_type_old):
# ensure old model removed from GPU memory
if kwargs['debug']:
print("Pre-switch pre-del GPU memory: %s" % torch.cuda.memory_allocated(), flush=True)
if isinstance(model_state_old[0], str) and model0 is not None:
# best can do, move model loaded at first to CPU
model0.cpu()
if model_state_old[0] is not None and not isinstance(model_state_old[0], str):
try:
model_state_old[0].cpu()
except Exception as e:
# sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data!
print("Unable to put model on CPU: %s" % str(e), flush=True)
del model_state_old[0]
model_state_old[0] = None
if model_state_old[1] is not None and not isinstance(model_state_old[1], str):
del model_state_old[1]
model_state_old[1] = None
clear_torch_cache()
if kwargs['debug']:
print("Pre-switch post-del GPU memory: %s" % torch.cuda.memory_allocated(), flush=True)
all_kwargs['base_model'] = model_name.strip()
model_lower = model_name.strip().lower()
if model_lower in inv_prompt_type_to_model_lower:
prompt_type1 = inv_prompt_type_to_model_lower[model_lower]
else:
prompt_type1 = prompt_type_old
all_kwargs['lora_weights'] = lora_weights.strip()
model1, tokenizer1, device1 = get_model(**all_kwargs)
clear_torch_cache()
if kwargs['debug']:
print("Post-switch GPU memory: %s" % torch.cuda.memory_allocated(), flush=True)
return {model_state: [model1, tokenizer1, device1, model_name],
model_used: model_name,
lora_used: lora_weights,
prompt_type: prompt_type1}
def dropdown_prompt_type_list(x):
return gr.Dropdown.update(value=x)
def chatbot_list(x, model_used_in):
return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]')
load_model_args = dict(fn=load_model,
inputs=[model_choice, lora_choice, model_state, prompt_type],
outputs=[model_state, model_used, lora_used, prompt_type])
prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
if not os.environ.get("HUGGINGFACE_SPACES"):
load_model_event = load_model_button.click(**load_model_args) \
.then(**prompt_update_args) \
.then(**chatbot_update_args) \
.then(clear_torch_cache)
def dropdown_model_list(list0, x):
new_state = [list0[0] + [x]]
new_options = [*new_state[0]]
return gr.Dropdown.update(value=x, choices=new_options), '', new_state
add_model_event = add_model_button.click(fn=dropdown_model_list,
inputs=[model_options_state, new_model],
outputs=[model_choice, new_model, model_options_state])
def dropdown_lora_list(list0, x):
new_state = [list0[0] + [x]]
new_options = [*new_state[0]]
return gr.Dropdown.update(value=x, choices=new_options), '', new_state
add_lora_event = add_lora_button.click(fn=dropdown_lora_list,
inputs=[lora_options_state, new_lora],
outputs=[lora_choice, new_lora, lora_options_state])
go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go") \
.then(lambda: gr.update(visible=True), None, normal_block) \
.then(**load_model_args).then(**prompt_update_args)
# callback for logging flagged input/output
callback.setup(inputs_list + [text_output], "flagged_data_points")
flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output], None, preprocess=False,
api_name='flag')
def get_system_info():
return gr.Textbox.update(value=system_info_print())
system_event = system_btn.click(get_system_info, outputs=system_text, api_name='system_info')
if kwargs['chat']:
# don't pass text_output, don't want to clear output, just stop it
# FIXME: have to click once to stop output and second time to stop GPUs going
stop_btn.click(lambda: None, None, None, cancels=[submit_event, submit_event2, submit_event3],
queue=False, api_name='stop').then(clear_torch_cache)
demo.queue(concurrency_count=1)
favicon_path = "h2o-logo.svg"
demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
favicon_path=favicon_path, prevent_thread_lock=True) # , enable_queue=True)
print("Started GUI", flush=True)
demo.block_thread()
input_args_list = ['model_state']
inputs_kwargs_list = ['debug', 'chat', 'hard_stop_list', 'sanitize_bot_response', 'model_state0']
def get_inputs_list(inputs_dict, model_lower):
inputs_list_names = list(inspect.signature(evaluate).parameters)
inputs_list = []
for k in inputs_list_names:
if k == 'kwargs':
continue
if k in input_args_list + inputs_kwargs_list:
# these are added via partial, not taken as input
continue
if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']:
continue
inputs_list.append(inputs_dict[k])
return inputs_list
# index of prompt_type in evaluate function, after model_state
prompt_type_arg_id = 4
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',
]
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,
# END NOTE: Examples must have same order of parameters
src_lang=None,
tgt_lang=None,
debug=False,
chat=False,
hard_stop_list=None,
sanitize_bot_response=True,
model_state0=None,
**kwargs,
):
if debug:
locals_dict = locals().copy()
locals_dict.pop('model_state', 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_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)
assert base_model.strip(), no_model_msg
assert model, "Model is missing"
assert tokenizer, "Tokenizer is missing"
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]
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]
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 os.environ.get("HUGGINGFACE_SPACES") 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,
**kwargs,
)
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:
def generate(callback=None, **kwargs):
# re-order stopping so Stream first and get out all chunks before stop for other reasons
stopping_criteria0 = kwargs.get('stopping_criteria', StoppingCriteriaList()).copy()
kwargs['stopping_criteria'] = StoppingCriteriaList()
kwargs['stopping_criteria'].append(Stream(func=callback))
for stopping_criteria1 in stopping_criteria0:
kwargs['stopping_criteria'].append(stopping_criteria1)
try:
model.generate(**kwargs)
except torch.cuda.OutOfMemoryError as e:
print("GPU OOM: 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 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):
print(
"GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
flush=True)
traceback.print_exc()
clear_torch_cache()
return
else:
raise
for output in CallbackToGenerator(generate, callback=None, **gen_kwargs):
decoded_output = decoder(output)
if output[-1] in [tokenizer.eos_token_id]:
if debug:
print("HIT EOS", flush=True)
break
if any(ele in decoded_output for ele in hard_stop_list):
raise StopIteration
yield prompter.get_response(decoded_output, prompt=inputs_decoded,
sanitize_bot_response=sanitize_bot_response)
return
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)
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]
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 = 0.1 if temperature is None else temperature
top_p = 0.75 if top_p is None else top_p
top_k = 40 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 = False if do_sample is None else do_sample
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"
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 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)
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'
""", flush=True)
fire.Fire(main)
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