File size: 6,152 Bytes
76a12b2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
import datetime
from pathlib import Path
import pandas as pd
import torch
from datasets import load_dataset
from tqdm import tqdm
from modules import shared
from modules.logging_colors import logger
from modules.models import clear_torch_cache, load_model, unload_model
from modules.models_settings import get_model_metadata, update_model_parameters
from modules.text_generation import encode
def load_past_evaluations():
if Path('logs/evaluations.csv').exists():
df = pd.read_csv(Path('logs/evaluations.csv'), dtype=str)
df['Perplexity'] = pd.to_numeric(df['Perplexity'])
return df
else:
return pd.DataFrame(columns=['Model', 'LoRAs', 'Dataset', 'Perplexity', 'stride', 'max_length', 'Date', 'Comment'])
past_evaluations = load_past_evaluations()
def save_past_evaluations(df):
global past_evaluations
past_evaluations = df
filepath = Path('logs/evaluations.csv')
filepath.parent.mkdir(parents=True, exist_ok=True)
df.to_csv(filepath, index=False)
def calculate_perplexity(models, input_dataset, stride, _max_length):
'''
Based on:
https://huggingface.co/docs/transformers/perplexity#calculating-ppl-with-fixedlength-models
'''
if not shared.args.no_use_fast:
logger.warning("--no_use_fast is not being used. If tokenizing the input dataset takes a long time, consider loading the model with that option checked.")
global past_evaluations
cumulative_log = ''
cumulative_log += "Loading the input dataset...\n\n"
yield cumulative_log
# Copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/triton/utils/datautils.py
if input_dataset == 'wikitext':
data = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
text = "\n\n".join(data['text'])
elif input_dataset == 'ptb':
data = load_dataset('ptb_text_only', 'penn_treebank', split='validation')
text = "\n\n".join(data['sentence'])
elif input_dataset == 'ptb_new':
data = load_dataset('ptb_text_only', 'penn_treebank', split='test')
text = " ".join(data['sentence'])
else:
with open(Path(f'training/datasets/{input_dataset}.txt'), 'r', encoding='utf-8') as f:
text = f.read()
for model in models:
if is_in_past_evaluations(model, input_dataset, stride, _max_length):
cumulative_log += f"`{model}` has already been tested. Ignoring.\n\n"
yield cumulative_log
continue
if model != 'current model':
try:
yield cumulative_log + f"Loading `{model}`...\n\n"
model_settings = get_model_metadata(model)
shared.settings.update({k: v for k, v in model_settings.items() if k in shared.settings}) # hijacking the interface defaults
update_model_parameters(model_settings) # hijacking the command-line arguments
unload_model()
shared.model, shared.tokenizer = load_model(model)
except:
cumulative_log += f"Failed to load `{model}`. Moving on.\n\n"
yield cumulative_log
continue
cumulative_log += f"Processing `{shared.model_name}`...\n\n"
yield cumulative_log + "Tokenizing the input dataset...\n\n"
encodings = encode(text, add_special_tokens=False)
seq_len = encodings.shape[1]
if _max_length:
max_length = _max_length
elif hasattr(shared.model.config, 'max_position_embeddings'):
max_length = shared.model.config.max_position_embeddings
else:
max_length = 2048
nlls = []
prev_end_loc = 0
for begin_loc in tqdm(range(0, seq_len, stride)):
yield cumulative_log + f"Evaluating... {100*begin_loc/seq_len:.2f}%"
end_loc = min(begin_loc + max_length, seq_len)
trg_len = end_loc - prev_end_loc # may be different from stride on last loop
input_ids = encodings[:, begin_loc:end_loc]
target_ids = input_ids.clone()
target_ids[:, :-trg_len] = -100
clear_torch_cache()
with torch.no_grad():
outputs = shared.model(input_ids=input_ids, labels=target_ids)
# loss is calculated using CrossEntropyLoss which averages over valid labels
# N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels
# to the left by 1.
neg_log_likelihood = outputs.loss
nlls.append(neg_log_likelihood)
prev_end_loc = end_loc
if end_loc == seq_len:
break
ppl = torch.exp(torch.stack(nlls).mean())
add_entry_to_past_evaluations(float(ppl), shared.model_name, input_dataset, stride, _max_length)
save_past_evaluations(past_evaluations)
cumulative_log += f"The perplexity for `{shared.model_name}` is: {float(ppl)}\n\n"
yield cumulative_log
def add_entry_to_past_evaluations(perplexity, model, dataset, stride, max_length):
global past_evaluations
entry = {
'Model': model,
'LoRAs': ', '.join(shared.lora_names) or '-',
'Dataset': dataset,
'Perplexity': perplexity,
'stride': str(stride),
'max_length': str(max_length),
'Date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'Comment': ''
}
past_evaluations = pd.concat([past_evaluations, pd.DataFrame([entry])], ignore_index=True)
def is_in_past_evaluations(model, dataset, stride, max_length):
entries = past_evaluations[(past_evaluations['Model'] == model) &
(past_evaluations['Dataset'] == dataset) &
(past_evaluations['max_length'] == str(max_length)) &
(past_evaluations['stride'] == str(stride))]
if entries.shape[0] > 0:
return True
else:
return False
def generate_markdown_table():
sorted_df = past_evaluations.sort_values(by=['Dataset', 'stride', 'Perplexity', 'Date'])
return sorted_df
|