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import datetime
import traceback
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.models import load_model, unload_model
from modules.text_generation import encode
from server import get_model_specific_settings, update_model_parameters
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
df.to_csv(Path('logs/evaluations.csv'), index=False)
def calculate_perplexity(models, input_dataset, stride, _max_length):
'''
Based on:
https://huggingface.co/docs/transformers/perplexity#calculating-ppl-with-fixedlength-models
'''
global past_evaluations
cumulative_log = ''
cumulative_log += "Loading the input dataset...\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"
yield cumulative_log
continue
if model != 'current model':
try:
yield cumulative_log + f"Loading {model}...\n"
model_settings = get_model_specific_settings(model)
shared.settings.update(model_settings) # hijacking the interface defaults
update_model_parameters(model_settings) # hijacking the command-line arguments
shared.model_name = model
unload_model()
shared.model, shared.tokenizer = load_model(shared.model_name)
except:
cumulative_log += f"Failed to load {model}. Moving on.\n"
yield cumulative_log
continue
cumulative_log += f"Processing {model}...\n"
yield cumulative_log + "Tokenizing the input dataset...\n"
encodings = encode(text, add_special_tokens=False)
seq_len = encodings.shape[1]
max_length = _max_length or shared.model.config.max_position_embeddings
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
with torch.no_grad():
outputs = shared.model(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"Done. The perplexity 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
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