causal=False, # auto-regressive or not
bucket_size=64, # average size of qk per bucket, 64 was recommended in paper
n_hashes=4, # 4 is permissible per author, 8 is the best but slower
ff_chunks=
200, # number of chunks for feedforward layer, make higher if there are memory issues
weight_tie=
False, # tie parameters of each layer for no memory per additional depth
attn_chunks=
8, # process lsh attention in chunks, only way for memory to fit when scaling to 16k tokens
num_mem_kv=
128, # persistent learned memory key values, from all-attention paper
twin_attention=
False, # both branches of the reversible network will be attention
use_full_attn=False, # use full self attention, for comparison
full_attn_thres=
128, # use full attention if context length is less than set value
use_scale_norm=
True, # use scale norm from 'Transformers without tears' paper
axial_position_emb=True,
axial_position_shape=(640, 64),
axial_position_dims=(384, 384))
model.train()
model.to(devices)
print("starting test")
inputs = torch.randint(low=0,
high=tokenizer.vocab_size - 1,
size=(10, tokenizer.max_len)).to(devices)
output = model(inputs)
print(output)
print("test pass")
pred = model(inputs)
pred.shape
tokenizer.decode(torch.argmax(pred, dim=-1).squeeze(0))
loss_fn = nn.CrossEntropyLoss() #
masked_lm_loss = loss_fn(pred.view(-1, tokenizer.vocab_size), labels.view(-1))
masked_lm_loss
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
total_loss = 0.0
model.train()
model.to(device)
inputs = inputs.to(device)
labels = labels.to(device)
loss = []
optimizer = AdamW(params=model.parameters())
for _ in tqdm(range(100000)):
pred = model(inputs)
mlm_loss = loss_fn(pred.view(-1, tokenizer.vocab_size), labels.view(-1))
total_loss += mlm_loss.item()
loss.append(mlm_loss.item())
mlm_loss.backward()
optimizer.step()
def test_encdec_v1(input_lang, target_lang, dim, bucket_size, depth, heads,
n_hashes, vir_seq_len, ff_chunks, attn_chunks, mol_seq_len,
cmd_args, train_dataset, test_dataset, output_folder,
train_batch_size, epochs, validate_every, save_every,
checkpoint_id, deepspeed_optimizer, use_full_attn,
gradient_accumulation_steps, filter_thres):
results = {
'generated_seq': [],
'generated_mol': [],
'target_mol': [],
'input_genome': []
}
encoder = ReformerLM(
num_tokens=input_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
max_seq_len=vir_seq_len,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
weight_tie=True,
weight_tie_embedding=True,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(vir_seq_len),
axial_position_dims=(dim // 2, dim // 2),
return_embeddings=True,
use_full_attn=use_full_attn).to(device)
decoder = ReformerLM(
num_tokens=target_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
max_seq_len=mol_seq_len,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(mol_seq_len),
axial_position_dims=(dim // 2, dim // 2),
weight_tie=True,
weight_tie_embedding=True,
causal=True,
use_full_attn=use_full_attn).to(device)
SAVE_DIR = os.sep.join([output_folder, 'saved_model'])
if checkpoint_id:
enc_ckp_max = checkpoint_id
dec_ckp_max = checkpoint_id
else:
try:
enc_ckp_max = np.max([
int(ckp)
for ckp in os.listdir(os.sep.join([SAVE_DIR, 'encoder']))
])
except Exception as e:
print('Exception:', e)
enc_ckp_max = 0
try:
dec_ckp_max = np.max([
int(ckp)
for ckp in os.listdir(os.sep.join([SAVE_DIR, 'decoder']))
])
except:
dec_ckp_max = 0
encoder = TrainingWrapper(encoder, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
decoder = TrainingWrapper(decoder, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
'''
encoder_params = filter(lambda p: p.requires_grad, encoder.parameters())
decoder_params = filter(lambda p: p.requires_grad, decoder.parameters())
if deepspeed_optimizer == False:
print('No DeepSpeed optimizer found. Using RangerLars.')
encoder_optimizer = RangerLars(encoder.parameters())
decoder_optimizer = RangerLars(decoder.parameters())
encoder_engine, encoder_optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=encoder,
optimizer=encoder_optimizer,
model_parameters=encoder_params,
training_data=train_dataset,
dist_init_required=True
)
decoder_engine, decoder_optimizer, testloader, _ = deepspeed.initialize(
args=cmd_args,
model=decoder,
optimizer=decoder_optimizer,
model_parameters=decoder_params,
training_data=test_dataset,
dist_init_required=False
)
else:
print('Found optimizer in the DeepSpeed configurations. Using it.')
encoder_engine, encoder_optimizer, trainloader, _ = deepspeed.initialize(args=cmd_args, model=encoder, model_parameters=encoder_params, training_data=train_dataset, dist_init_required=True)
decoder_engine, decoder_optimizer, testloader, _ = deepspeed.initialize(args=cmd_args, model=decoder, model_parameters=decoder_params, training_data=test_dataset, dist_init_required=False)
_, encoder_client_sd = encoder_engine.load_checkpoint(os.sep.join([SAVE_DIR,'encoder']), enc_ckp_max)
_, decoder_client_sd = decoder_engine.load_checkpoint(os.sep.join([SAVE_DIR,'decoder']), dec_ckp_max)
gpus_mini_batch = (train_batch_size// gradient_accumulation_steps) // torch.cuda.device_count()
print('gpus_mini_batch:', gpus_mini_batch, 'with gradient_accumulation_steps:', gradient_accumulation_steps)
for pair in tqdm(testloader):
encoder_engine.eval()
decoder_engine.eval()
encoder.eval()
decoder.eval()
with torch.no_grad():
ts_src = pair[0]
ts_trg = pair[1]
input_genome = [[input_lang.index2word[gen_idx.item()] for gen_idx in smpl] for smpl in pair[0]]
target_mol = [[target_lang.index2word[mol_idx.item()] for mol_idx in smpl] for smpl in pair[1]]
ts_src = ts_src.to(encoder_engine.local_rank) #ts_src.to(device) #
ts_trg = ts_trg.to(decoder_engine.local_rank) #ts_trg.to(device) #
print('ts_src.shape', ts_src.shape)
print('ts_src.shape', ts_trg.shape)
enc_keys = encoder(ts_src) #encoder_engine(ts_src)
yi = torch.tensor([[SOS_token] for _ in range(gpus_mini_batch)]).long().to(decoder_engine.local_rank) #to(device) #
#sample = decoder_engine.generate(yi, mol_seq_len, filter_logits_fn=top_p, filter_thres=0.95, keys=enc_keys, eos_token = EOS_token)
sample = decoder.generate(yi, mol_seq_len, filter_logits_fn=top_p, filter_thres=0.95, keys=enc_keys, eos_token = EOS_token)
actual_mol = []
for mol_seq in sample.cpu().numpy():
for mol_idx in mol_seq:
actual_mol.append(target_lang.index2word[mol_idx])
print('Generated Seq:', sample)
print('Generated Mol:', actual_mol)
print('Real Mol:', target_mol[:target_mol.index(target_lang.index2word[EOS_token])])
results['generated_seq'].append(sample)
results['generated_mol'].append(actual_mol)
results['target_mol'].append(target_mol)
results['input_genome'].append(input_genome)
print('Saving Test Results..')
pickle.dump(results, open(os.sep.join([output_folder,'test_results.pkl']), 'wb'))
'''
encoder_checkpoint = os.sep.join([
output_folder, 'saved_model', 'encoder', enc_ckp_max,
'mp_rank_00_model_states.pt'
])
decoder_checkpoint = os.sep.join([
output_folder, 'saved_model', 'decoder', dec_ckp_max,
'mp_rank_00_model_states.pt'
])
encoder.load_state_dict(
torch.load(encoder_checkpoint,
map_location=torch.device(device))['module'])
decoder.load_state_dict(
torch.load(decoder_checkpoint,
map_location=torch.device(device))['module'])
real_batch_size = train_batch_size // gradient_accumulation_steps
test_loader = DataLoader(dataset=test_dataset,
batch_size=real_batch_size,
shuffle=True)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
encoder = nn.DataParallel(encoder)
decoder = nn.DataParallel(decoder)
encoder.to(device)
decoder.to(device)
for pair in tqdm(test_loader):
encoder.eval()
decoder.eval()
with torch.no_grad():
ts_src = torch.tensor(np.array([pair[0].numpy()])).to(device)
ts_trg = torch.tensor(np.array([pair[1].numpy()])).to(device)
input_genome = [
input_lang.index2word[gen_idx.item()] for gen_idx in pair[0]
]
target_mol = [
target_lang.index2word[mol_idx.item()] for mol_idx in pair[1]
]
enc_keys = encoder(ts_src)
yi = torch.tensor([[SOS_token]]).long().to(device)
sample = decoder.generate(yi,
mol_seq_len,
filter_logits_fn=top_p,
filter_thres=filter_thres,
keys=enc_keys,
eos_token=EOS_token)
actual_mol = []
for mol_seq in sample.cpu().numpy():
for mol_idx in mol_seq:
actual_mol.append(target_lang.index2word[mol_idx])
print('Generated Seq:', sample)
print('Generated Mol:', actual_mol)
print(
'Real Mol:',
target_mol[:target_mol.index(target_lang.
index2word[EOS_token])])
results['generated_seq'].append(sample)
results['generated_mol'].append(actual_mol)
results['target_mol'].append(target_mol)
results['input_genome'].append(input_genome)
print('Saving Test Results..')
pickle.dump(results,
open(os.sep.join([output_folder, 'test_results.pkl']), 'wb'))
'''
