def train(model: Transformer, optimizer, criterion, clip, device):
model.train()
epoches_loss = 0
for index, batch in tqdm(enumerate(dataset_pro.train_iter)):
shang_lian, shang_lian_length = batch.shang_lian
shang_lian = shang_lian.permute(1, 0).to(device)
# shang_lian_length = shang_lian_length.permute(1, 0).to(device)
# shang_lian_length = shang_lian_length.numpy()
# shang_lian_pos = torch.LongTensor(get_pos_ids(shang_lian_length, shang_lian.shape[1])).to(device)
xia_lian, xia_lian_length = batch.xia_lian
xia_lian = xia_lian.permute(1, 0).to(device)
# xia_lian_length = xia_lian_length.numpy()
# xia_lian_pos = torch.LongTensor(get_pos_ids(xia_lian_length, xia_lian.shape[1])).to(device)
optimizer.zero_grad()
outputs = model(shang_lian, xia_lian[:, :-1])
outputs = outputs.contiguous().view(-1, outputs.shape[-1])
xia_lian = xia_lian[:, 1:].contiguous().view(-1)
loss = criterion(outputs, xia_lian)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
# print(loss.item())
optimizer.step()
epoches_loss += loss.item()
result_loss = epoches_loss / len(dataset_pro.train_iter)
return result_loss
def main():
device = torch.device("cuda:0" if USE_CUDA else "cpu")
env = Environment()
END_TAG_IDX = env.lang.word2idx[END_TAG]
SAY_HI = "hello"
targ_lang = env.lang
vocab_inp_size = len(env.lang.word2idx)
vocab_tar_size = len(targ_lang.word2idx)
print("vocab_inp_size", vocab_inp_size)
print("vocab_tar_size", vocab_tar_size)
model = Transformer(
vocab_inp_size,
vocab_tar_size,
MAX_TARGET_LEN,
d_word_vec=32,
d_model=32,
d_inner=32,
n_layers=3,
n_head=4,
d_k=32,
d_v=32,
dropout=0.1,
).to(device)
# baseline = Baseline(UNITS)
history = []
l_optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
batch = None
def maybe_pad_sentence(s):
return tf.keras.preprocessing.sequence.pad_sequences(
s, maxlen=MAX_TARGET_LEN, padding='post')
def get_returns(r: float, seq_len: int):
return list(reversed([r * (GAMMA**t) for t in range(seq_len)]))
def sentence_to_idxs(sentence: str):
return [
env.lang.word2idx[token] for token in tokenize_sentence(sentence)
]
for episode in range(EPISODES):
# Start of Episode
env.reset()
model.eval()
# get first state from the env
state, _, done = env.step(SAY_HI)
while not done:
src_seq = [
env.lang.word2idx[token] for token in tokenize_sentence(state)
]
src_seq, src_pos = collate_fn([src_seq])
src_seq, src_pos = src_seq.to(device), src_pos.to(device)
enc_output, *_ = model.encoder(src_seq, src_pos)
actions_t = []
actions = []
actions_idx = []
while len(actions) == 0 or actions[len(actions) -
1] != END_TAG_IDX and len(
actions) < MAX_TARGET_LEN:
# construct new tgt_seq based on what's outputed so far
if len(actions_t) == 0:
tgt_seq = [env.lang.word2idx[Constants.UNK_WORD]]
else:
tgt_seq = actions_idx
tgt_seq, tgt_pos = collate_fn([tgt_seq])
tgt_seq, tgt_pos = tgt_seq.to(device), tgt_pos.to(device)
# dec_output dims: [1, pos, hidden]
dec_output, * \
_ = model.decoder(tgt_seq, tgt_pos, src_seq, enc_output)
# pick last step
dec_output = dec_output[:, -1, :]
# w_logits dims: [1, vocab_size]
w_logits = model.tgt_word_prj(dec_output)
# w_probs dims: [1, vocab_size]
w_probs = torch.nn.functional.softmax(w_logits, dim=1)
w_dist = torch.distributions.categorical.Categorical(
probs=w_probs)
w_idx_t = w_dist.sample()
w_idx = w_idx_t.cpu().numpy()[0]
actions_t.append(w_idx_t)
actions_idx.append(w_idx)
actions.append(env.lang.idx2word[w_idx])
# action is a sentence (string)
action_str = ' '.join(actions)
next_state, reward, done = env.step(action_str)
# print(reward)
history.append((state, actions_t, action_str, reward))
state = next_state
# record history (to be used for gradient updating after the episode is done)
# End of Episode
# Update policy
model.train()
while len(history) >= BATCH_SIZE:
batch = history[:BATCH_SIZE]
state_inp_b, action_inp_b, reward_b, ret_seq_b = zip(*[[
sentence_to_idxs(state), actions_b, reward,
get_returns(reward, MAX_TARGET_LEN)
] for state, actions_b, _, reward in batch])
action_inp_b = [torch.stack(sent) for sent in action_inp_b]
action_inp_b = torch.stack(action_inp_b)
ret_seq_b = np.asarray(ret_seq_b)
# ret_mean = np.mean(ret_seq_b)
# ret_std = np.std(ret_seq_b)
# ret_seq_b = (ret_seq_b - ret_mean) / ret_std
ret_seq_b = np.exp((ret_seq_b - 0.5) * 5)
ret_seq_b = torch.tensor(ret_seq_b, dtype=torch.float32).to(device)
loss = 0
# loss_bl=0
l_optimizer.zero_grad()
# accumulate gradient with GradientTape
src_seq, src_pos = collate_fn(list(state_inp_b))
src_seq, src_pos = src_seq.to(device), src_pos.to(device)
enc_output_b, *_ = model.encoder(src_seq, src_pos)
max_sentence_len = action_inp_b.shape[1]
tgt_seq = [[Constants.BOS] for i in range(BATCH_SIZE)]
for t in range(max_sentence_len):
# _b stands for batch
prev_w_idx_b, tgt_pos = collate_fn(tgt_seq)
prev_w_idx_b, tgt_pos = prev_w_idx_b.to(device), tgt_pos.to(
device)
# dec_output_b dims: [batch, pos, hidden]
dec_output_b, *_ = \
model.decoder(prev_w_idx_b, tgt_pos, src_seq, enc_output_b)
# pick last step
dec_output_b = dec_output_b[:, -1, :]
# w_logits_b dims: [batch, vocab_size]
w_logits_b = model.tgt_word_prj(dec_output_b)
# w_probs dims: [batch, vocab_size]
w_probs_b = torch.nn.functional.softmax(w_logits_b, dim=1)
dist_b = torch.distributions.categorical.Categorical(
probs=w_probs_b)
curr_w_idx_b = action_inp_b[:, t, :]
log_probs_b = torch.transpose(
dist_b.log_prob(torch.transpose(curr_w_idx_b, 0, 1)), 0, 1)
# bl_val_b = baseline(tf.cast(dec_hidden_b, 'float32'))
# delta_b = ret_b - bl_val_b
# cost_b = -tf.math.multiply(log_probs_b, delta_b)
# cost_b = -tf.math.multiply(log_probs_b, ret_b)
ret_b = torch.reshape(ret_seq_b[:, t],
(BATCH_SIZE, 1)).to(device)
# alternatively, use torch.mul() but it is overloaded. Might need to try log_probs_b*vec.expand_as(A)
cost_b = -torch.mul(log_probs_b, ret_b)
# log_probs_b*vec.expand_as(A)
# cost_b = -torch.bmm() #if we are doing batch multiplication
loss += cost_b
# loss_bl += -tf.math.multiply(delta_b, bl_val_b)
prev_w_idx_b = curr_w_idx_b
tgt_seq = np.append(tgt_seq,
prev_w_idx_b.data.cpu().numpy(),
axis=1).tolist()
# calculate cumulative gradients
# model_vars = encoder.variables + decoder.variables
loss = loss.mean()
loss.backward()
# loss_bl.backward()
# finally, apply gradient
l_optimizer.step()
# bl_optimizer.step()
# Reset everything for the next episode
history = history[BATCH_SIZE:]
if episode % max(BATCH_SIZE, 32) == 0 and batch != None:
print(">>>>>>>>>>>>>>>>>>>>>>>>>>")
print("Episode # ", episode)
print("Samples from episode with rewards > 0: ")
good_rewards = [(s, a_str, r) for s, _, a_str, r in batch]
for s, a, r in random.sample(good_rewards,
min(len(good_rewards), 3)):
print("prev_state: ", s)
print("actions: ", a)
print("reward: ", r)
# print("return: ", get_returns(r, MAX_TARGET_LEN))
ret_seq_b_np = ret_seq_b.cpu().numpy()
print("all returns: min=%f, max=%f, median=%f" %
(np.min(ret_seq_b_np), np.max(ret_seq_b_np),
np.median(ret_seq_b_np)))
print("avg reward: ", sum(reward_b) / len(reward_b))
print("avg loss: ", np.mean(loss.cpu().detach().numpy()))
torch.cuda.manual_seed_all(random_seed)
print('[Info] Setting up random seed using CUDA.')
else:
torch.manual_seed(random_seed)
# begin training
for i in range(config["num_epochs"]):
start = time.time()
epoch_metrics = dict()
# output an example
greedy_output_example(model, val_dataset, device, vocab)
# run each phase per epoch
for phase in ["train", "val"]:
if phase == "train":
# set model to training mode
model.train()
dataloader = data_loader_train
batch_size = config["train_batch_size"]
else:
# set model to evaluation mode
model.eval()
dataloader = data_loader_val
batch_size = config["val_batch_size"]
# initialize metrics
phase_metrics = dict()
epoch_loss = list()
average_epoch_loss = None
n_word_total = 0
n_correct = 0
