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train_model.py
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train_model.py
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import sys
import os
import json
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from data import TranslationDataset
from transformers import BertTokenizerFast
from transformers import BertModel, BertForMaskedLM, BertConfig, EncoderDecoderModel
# Identify the config file
if len(sys.argv) < 2:
print("No config file specified. Using the default config.")
configfile = "config.json"
else:
configfile = sys.argv[1]
# Read the params
with open(configfile, "r") as f:
config = json.load(f)
globalparams = config["global_params"]
encparams = config["encoder_params"]
decparams = config["decoder_params"]
modelparams = config["model_params"]
# Load the tokenizers
en_tok_path = encparams["tokenizer_path"]
en_tokenizer = BertTokenizerFast(os.path.join(en_tok_path, "vocab.txt"))
de_tok_path = decparams["tokenizer_path"]
de_tokenizer = BertTokenizerFast(os.path.join(de_tok_path, "vocab.txt"))
# Init the dataset
train_en_file = globalparams["train_en_file"]
train_de_file = globalparams["train_de_file"]
valid_en_file = globalparams["valid_en_file"]
valid_de_file = globalparams["valid_de_file"]
enc_maxlength = encparams["max_length"]
dec_maxlength = decparams["max_length"]
batch_size = modelparams["batch_size"]
train_dataset = TranslationDataset(train_en_file, train_de_file, en_tokenizer, de_tokenizer, enc_maxlength, dec_maxlength)
train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=False, \
drop_last=True, num_workers=1, collate_fn=train_dataset.collate_function)
valid_dataset = TranslationDataset(valid_en_file, valid_de_file, en_tokenizer, de_tokenizer, enc_maxlength, dec_maxlength)
valid_dataloader = torch.utils.data.DataLoader(dataset=valid_dataset, batch_size=batch_size, shuffle=False, \
drop_last=True, num_workers=1, collate_fn=valid_dataset.collate_function)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Using device:", device)
print("Loading models ..")
vocabsize = encparams["vocab_size"]
max_length = encparams["max_length"]
encoder_config = BertConfig(vocab_size = vocabsize,
max_position_embeddings = max_length+64, # this shuold be some large value
num_attention_heads = encparams["num_attn_heads"],
num_hidden_layers = encparams["num_hidden_layers"],
hidden_size = encparams["hidden_size"],
type_vocab_size = 1)
encoder = BertModel(config=encoder_config)
vocabsize = decparams["vocab_size"]
max_length = decparams["max_length"]
decoder_config = BertConfig(vocab_size = vocabsize,
max_position_embeddings = max_length+64, # this shuold be some large value
num_attention_heads = decparams["num_attn_heads"],
num_hidden_layers = decparams["num_hidden_layers"],
hidden_size = decparams["hidden_size"],
type_vocab_size = 1,
is_decoder=True) # Very Important
decoder = BertForMaskedLM(config=decoder_config)
# Define encoder decoder model
model = EncoderDecoderModel(encoder=encoder, decoder=decoder)
model.to(device)
def count_parameters(mdl):
return sum(p.numel() for p in mdl.parameters() if p.requires_grad)
print(f'The encoder has {count_parameters(encoder):,} trainable parameters')
print(f'The decoder has {count_parameters(decoder):,} trainable parameters')
print(f'The model has {count_parameters(model):,} trainable parameters')
optimizer = optim.Adam(model.parameters(), lr=modelparams['lr'])
criterion = nn.NLLLoss(ignore_index=de_tokenizer.pad_token_id)
num_train_batches = len(train_dataloader)
num_valid_batches = len(valid_dataloader)
def compute_loss(predictions, targets):
"""Compute our custom loss"""
predictions = predictions[:, :-1, :].contiguous()
targets = targets[:, 1:]
rearranged_output = predictions.view(predictions.shape[0]*predictions.shape[1], -1)
rearranged_target = targets.contiguous().view(-1)
loss = criterion(rearranged_output, rearranged_target)
return loss
def train_model():
model.train()
epoch_loss = 0
for i, (en_input, en_masks, de_output, de_masks) in enumerate(train_dataloader):
optimizer.zero_grad()
en_input = en_input.to(device)
de_output = de_output.to(device)
en_masks = en_masks.to(device)
de_masks = de_masks.to(device)
lm_labels = de_output.clone()
out = model(input_ids=en_input, attention_mask=en_masks,
decoder_input_ids=de_output, decoder_attention_mask=de_masks,lm_labels=lm_labels)
prediction_scores = out[1]
predictions = F.log_softmax(prediction_scores, dim=2)
loss = compute_loss(predictions, de_output)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
epoch_loss += loss.item()
print("Mean epoch loss:", (epoch_loss / num_train_batches))
def eval_model():
model.eval()
epoch_loss = 0
for i, (en_input, en_masks, de_output, de_masks) in enumerate(train_dataloader):
optimizer.zero_grad()
en_input = en_input.to(device)
de_output = de_output.to(device)
en_masks = en_masks.to(device)
de_masks = de_masks.to(device)
lm_labels = de_output.clone()
out = model(input_ids=en_input, attention_mask=en_masks,
decoder_input_ids=de_output, decoder_attention_mask=de_masks,lm_labels=lm_labels)
prediction_scores = out[1]
predictions = F.log_softmax(prediction_scores, dim=2)
loss = compute_loss(predictions, de_output)
epoch_loss += loss.item()
print("Mean validation loss:", (epoch_loss / num_valid_batches))
# MAIN TRAINING LOOP
for epoch in range(modelparams['num_epochs']):
print("Starting epoch", epoch+1)
train_model()
eval_model()
print("Saving model ..")
save_location = modelparams['model_path']
model_name = modelparams['model_name']
if not os.path.exists(save_location):
os.makedirs(save_location)
save_location = os.path.join(save_location, model_name)
torch.save(model, save_location)