Skip to content
Snippets Groups Projects
Commit 85e492e2 authored by Caroline DE POURTALES's avatar Caroline DE POURTALES
Browse files

starting train

parent 0eb359e3
No related branches found
No related tags found
2 merge requests!6Linker with transformer,!5Linker with transformer
Hide whitespace changes
Inline Side-by-side
Showing
with 51 additions and 181 deletions
train.py
+ 51
− 181
View file @ 85e492e2
import os
import pickle
import time
from datetime import datetime
import numpy as np
import torch
import torch.nn.functional as F
import transformers
from torch.optim import SGD, Adam, AdamW
from torch.utils.data import Dataset, TensorDataset, random_split
from transformers import (AutoTokenizer, get_cosine_schedule_with_warmup)
from transformers import (CamembertModel)
from transformers import get_cosine_schedule_with_warmup
from Configuration import Configuration
from SuperTagger.Encoder.EncoderInput import EncoderInput
from SuperTagger.EncoderDecoder import EncoderDecoder
from SuperTagger.Symbol.SymbolTokenizer import SymbolTokenizer
from SuperTagger.Symbol.symbol_map import symbol_map
from SuperTagger.eval import NormCrossEntropy
from SuperTagger.Linker.Linker import Linker
from SuperTagger.Linker.atom_map import atom_map
from SuperTagger.eval import NormCrossEntropy, SinkhornLoss
from SuperTagger.utils import format_time, read_csv_pgbar, checkpoint_save, checkpoint_load
from torch.utils.tensorboard import SummaryWriter
transformers.TOKENIZERS_PARALLELISM = True
torch.cuda.empty_cache()
# region ParamsModel
max_len_sentence = int(Configuration.modelDecoderConfig['max_len_sentence'])
symbol_vocab_size = int(Configuration.modelDecoderConfig['symbols_vocab_size'])
num_gru_layers = int(Configuration.modelDecoderConfig['num_rnn_layers'])
max_len_sentence = int(Configuration.datasetConfig['max_len_sentence'])
atom_vocab_size = int(Configuration.datasetConfig['atoms_vocab_size'])
# endregion ParamsModel
# region ParamsTraining
file_path = 'Datasets/m2_dataset.csv'
batch_size = int(Configuration.modelTrainingConfig['batch_size'])
nb_sentences = batch_size * 40
epochs = int(Configuration.modelTrainingConfig['epoch'])
seed_val = int(Configuration.modelTrainingConfig['seed_val'])
learning_rate = float(Configuration.modelTrainingConfig['learning_rate'])
loss_scaled_by_freq = True
# endregion ParamsTraining
# region OutputTraining
outpout_path = str(Configuration.modelTrainingConfig['output_path'])
training_dir = os.path.join(outpout_path, 'Tranning_' + datetime.today().strftime('%d-%m_%H-%M'))
logs_dir = os.path.join(training_dir, 'logs')
checkpoint_dir = training_dir
writer = SummaryWriter(log_dir=logs_dir)
use_checkpoint_SAVE = bool(Configuration.modelTrainingConfig.getboolean('use_checkpoint_SAVE'))
# endregion OutputTraining
# region InputTraining
input_path = str(Configuration.modelTrainingConfig['input_path'])
model_to_load = str(Configuration.modelTrainingConfig['model_to_load'])
model_to_load_path = os.path.join(input_path, model_to_load)
use_checkpoint_LOAD = bool(Configuration.modelTrainingConfig.getboolean('use_checkpoint_LOAD'))
# endregion InputTraining
# region Print config
print("##" * 15 + "\nConfiguration : \n")
print("ParamsModel\n")
print("\tsymbol_vocab_size :", symbol_vocab_size)
print("\tbidirectional : ", False)
print("\tnum_gru_layers : ", num_gru_layers)
print("\n ParamsTraining\n")
print("\tDataset :", file_path)
print("\tb_sentences :", nb_sentences)
print("\tbatch_size :", batch_size)
print("\tepochs :", epochs)
print("\tseed_val :", seed_val)
print("\n Output\n")
print("\tuse checkpoint save :", use_checkpoint_SAVE)
print("\tcheckpoint_dir :", checkpoint_dir)
print("\tlogs_dir :", logs_dir)
print("\n Input\n")
print("\tModel to load :", model_to_load_path)
print("\tLoad checkpoint :", use_checkpoint_LOAD)
print("\nLoss and optimizer : ")
print("\tlearning_rate :", learning_rate)
print("\twith loss scaled by freq :", loss_scaled_by_freq)
print("\n Device\n")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("\t", device)
print()
print("##" * 15)
# endregion Print config
# region Model
# region Data loader
file_path = 'Datasets/m2_dataset.csv'
BASE_TOKENIZER = AutoTokenizer.from_pretrained(
'camembert-base',
do_lower_case=True)
BASE_MODEL = CamembertModel.from_pretrained("camembert-base")
symbols_tokenizer = SymbolTokenizer(symbol_map, max_len_sentence, max_len_sentence)
sents_tokenizer = EncoderInput(BASE_TOKENIZER)
model = EncoderDecoder(BASE_TOKENIZER, BASE_MODEL, symbol_map)
model = model.to("cuda" if torch.cuda.is_available() else "cpu")
file_path_axiom_links = 'Datasets/axiom_links.csv'
# endregion Model
# region Data loader
df = read_csv_pgbar(file_path, nb_sentences)
df_axiom_links = read_csv_pgbar(file_path_axiom_links, nb_sentences)
symbols_tokenized = symbols_tokenizer.convert_batchs_to_ids(df['sub_tree'])
sents_tokenized, sents_mask = sents_tokenizer.fit_transform_tensors(df['Sentences'].tolist())
dataset = TensorDataset(sents_tokenized, sents_mask, symbols_tokenized)
dataset = TensorDataset(df, df, df_axiom_links)
# Calculate the number of samples to include in each set.
train_size = int(0.9 * len(dataset))
......@@ -137,46 +54,34 @@ val_size = len(dataset) - train_size
# Divide the dataset by randomly selecting samples.
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
print('{:>5,} training samples'.format(train_size))
print('{:>5,} validation samples'.format(val_size))
training_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
validation_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
# endregion Data loader
# region Models
supertagger_path = ""
supertagger = pickle.load(supertagger_path)
linker = Linker()
# endregion Models
# region Fit tunning
# Optimizer
optimizer_encoder = AdamW(model.encoder.parameters(),
weight_decay=1e-5,
lr=5e-5)
optimizer_decoder = AdamW(model.decoder.parameters(),
weight_decay=1e-5,
lr=learning_rate)
# Total number of training steps is [number of batches] x [number of epochs].
# (Note that this is not the same as the number of training samples).
total_steps = len(training_dataloader) * epochs
optimizer_linker = AdamW(linker.parameters(),
weight_decay=1e-5,
lr=learning_rate)
# Create the learning rate scheduler.
scheduler_encoder = get_cosine_schedule_with_warmup(optimizer_encoder,
num_warmup_steps=0,
num_training_steps=5)
scheduler_decoder = get_cosine_schedule_with_warmup(optimizer_decoder,
num_warmup_steps=0,
num_training_steps=total_steps)
scheduler_linker = get_cosine_schedule_with_warmup(optimizer_linker,
num_warmup_steps=0,
num_training_steps=100)
# Loss
if loss_scaled_by_freq:
weights = torch.as_tensor(
[6.9952, 1.0763, 1.0317, 43.274, 16.5276, 11.8821, 28.2416, 2.7548, 1.0728, 3.1847, 8.4521, 6.77, 11.1887,
6.6692, 23.1277, 11.8821, 4.4338, 1.2303, 5.0238, 8.4376, 1.0656, 4.6886, 1.028, 4.273, 4.273, 0],
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
cross_entropy_loss = NormCrossEntropy(symbols_tokenizer.pad_token_id, symbols_tokenizer.sep_token_id,
weights=weights)
else:
cross_entropy_loss = NormCrossEntropy(symbols_tokenizer.pad_token_id, symbols_tokenizer.sep_token_id)
cross_entropy_loss = SinkhornLoss()
np.random.seed(seed_val)
torch.manual_seed(seed_val)
......@@ -192,10 +97,6 @@ total_t0 = time.time()
validate = True
if use_checkpoint_LOAD:
model, optimizer_decoder, last_epoch, loss = checkpoint_load(model, optimizer_decoder, model_to_load_path)
epochs = epochs - last_epoch
def run_epochs(epochs):
# For each epoch...
......@@ -216,60 +117,38 @@ def run_epochs(epochs):
# Reset the total loss for this epoch.
total_train_loss = 0
model.train()
linker.train()
# For each batch of training data...
for step, batch in enumerate(training_dataloader):
# Unpack this training batch from our dataloader.
batch_categories = batch[0].to("cuda" if torch.cuda.is_available() else "cpu")
batch_sentences = batch[1].to("cuda" if torch.cuda.is_available() else "cpu")
batch_axiom_links = batch[2].to("cuda" if torch.cuda.is_available() else "cpu")
optimizer_linker.zero_grad()
# Find the prediction of categories to feed the linker and the sentences embedding
category_logits_pred, sents_embedding, sents_mask = supertagger(batch_categories, batch_sentences)
# Predict the categories from prediction with argmax and softmax
category_batch = torch.argmax(torch.nn.functional.softmax(category_logits_pred, dim=2), dim=2)
# if epoch_i == 0 and step == 0:
# writer.add_graph(model, input_to_model=batch[0], verbose=False)
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# Report progress.
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(training_dataloader), elapsed))
# Unpack this training batch from our dataloader.
b_sents_tokenized = batch[0].to("cuda" if torch.cuda.is_available() else "cpu")
b_sents_mask = batch[1].to("cuda" if torch.cuda.is_available() else "cpu")
b_symbols_tokenized = batch[2].to("cuda" if torch.cuda.is_available() else "cpu")
optimizer_encoder.zero_grad()
optimizer_decoder.zero_grad()
logits_predictions = model(b_sents_tokenized, b_sents_mask, b_symbols_tokenized)
predict_trad = [{v: k for k, v in symbol_map.items()}[int(i)] for i in
torch.argmax(F.softmax(logits_predictions, dim=2), dim=2)[0]]
true_trad = [{v: k for k, v in symbol_map.items()}[int(i)] for i in b_symbols_tokenized[0]]
l = len([i for i in true_trad if i != '[PAD]'])
if step % 40 == 0 and not step == 0:
writer.add_text("Sample", "\ntrain true (" + str(l) + ") : " + str(
[token for token in true_trad if token != '[PAD]']) + "\ntrain predict (" + str(
len([i for i in predict_trad if i != '[PAD]'])) + ") : " + str(
[token for token in predict_trad[:l] if token != '[PAD]']))
loss = cross_entropy_loss(logits_predictions, b_symbols_tokenized)
# Run the kinker on the categories predictions
logits_predictions = linker(category_batch, sents_embedding, sents_mask)
linker_loss = cross_entropy_loss(logits_predictions, batch_axiom_links)
# Perform a backward pass to calculate the gradients.
total_train_loss += float(loss)
loss.backward()
total_train_loss += float(linker_loss)
linker_loss.backward()
# This is to help prevent the "exploding gradients" problem.
# torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=5.0, norm_type=2)
# Update parameters and take a step using the computed gradient.
optimizer_encoder.step()
optimizer_decoder.step()
scheduler_encoder.step()
scheduler_decoder.step()
# checkpoint
optimizer_linker.step()
if use_checkpoint_SAVE:
checkpoint_save(model, optimizer_decoder, epoch_i, checkpoint_dir, loss)
scheduler_linker.step()
avg_train_loss = total_train_loss / len(training_dataloader)
......@@ -277,27 +156,18 @@ def run_epochs(epochs):
training_time = format_time(time.time() - t0)
if validate:
model.eval()
linker.eval()
with torch.no_grad():
print("Start eval")
accuracy_sents, accuracy_symbol, v_loss = model.eval_epoch(validation_dataloader, cross_entropy_loss)
accuracy_sents, accuracy_atom, v_loss = linker.eval_epoch(validation_dataloader, cross_entropy_loss)
print("")
print(" Average accuracy sents on epoch: {0:.2f}".format(accuracy_sents))
print(" Average accuracy symbol on epoch: {0:.2f}".format(accuracy_symbol))
writer.add_scalar('Accuracy/sents', accuracy_sents, epoch_i + 1)
writer.add_scalar('Accuracy/symbol', accuracy_symbol, epoch_i + 1)
print(" Average accuracy atom on epoch: {0:.2f}".format(accuracy_atom))
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epcoh took: {:}".format(training_time))
# writer.add_scalar('Loss/train', total_train_loss, epoch_i+1)
writer.add_scalars('Training vs. Validation Loss',
{'Training': avg_train_loss, 'Validation': v_loss},
epoch_i + 1)
writer.flush()
run_epochs(epochs)
# endregion Train
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment