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import torch
import torch.nn as nn
from labml import experiment, monit, logger, tracker
from labml.configs import option
from labml.logger import Text
from labml_helpers.device import DeviceConfigs
from labml_helpers.metrics.accuracy import Accuracy
from labml_helpers.metrics.simple_state import SimpleStateModule
from labml_helpers.module import Module
from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
from labml_nn.optimizers.configs import OptimizerConfigs
from labml_nn.transformers import TransformerConfigs
from python_autocomplete.dataset.dataset import SourceCodeDataConfigs
class Configs(TrainValidConfigs):
optimizer: torch.optim.Adam
device: torch.device = DeviceConfigs()
model: Module
text = SourceCodeDataConfigs()
n_tokens: int
n_layers: int = 2
dropout: float = 0.2
d_model: int = 512
rnn_size: int = 512
rhn_depth: int = 1
inner_iterations = 100
is_save_models = True
transformer: TransformerConfigs
accuracy = Accuracy()
loss_func: 'CrossEntropyLoss'
state_updater: 'StateUpdater'
state = SimpleStateModule()
mem_len: int = 512
grad_norm_clip: float = 1.0
is_token_by_token: bool = False
def init(self):
tracker.set_queue("loss.*", 20, True)
tracker.set_scalar("accuracy.*", True)
hook_model_outputs(self.mode, self.model, 'model')
self.state_modules = [self.accuracy, self.state]
def step(self, batch: any, batch_idx: BatchIndex):
data, target = batch[0].to(self.device), batch[1].to(self.device)
if self.mode.is_train:
tracker.add_global_step(target.shape[0] * target.shape[1])
with self.mode.update(is_log_activations=batch_idx.is_last):
state = self.state.get()
output, new_state = self.model(data, state)
state = self.state_updater(state, new_state)
self.state.set(state)
loss = self.loss_func(output, target)
tracker.add("loss.", loss)
self.accuracy(output, target)
self.accuracy.track()
if self.mode.is_train:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=self.grad_norm_clip)
self.optimizer.step()
if batch_idx.is_last:
tracker.add('model', self.model)
self.optimizer.zero_grad()
tracker.save()
def sample(self):
prompt = 'def train('
log = [(prompt, Text.subtle)]
state = None
for i in monit.iterate('Sample', 25):
data = self.text.text_to_i(prompt).unsqueeze(-1)
data = data.to(self.device)
output, new_state = self.model(data, state)
output = output.argmax(dim=-1).squeeze(1)
prompt += '' + self.text.tokenizer.itos[output[-1]]
if self.is_token_by_token:
prompt = self.text.tokenizer.itos[output[-1]]
else:
prompt += '' + self.text.tokenizer.itos[output[-1]]
log += [('' + self.text.tokenizer.itos[output[-1]], Text.value)]
state = self.state_updater(state, new_state)
logger.log(log)
@option(Configs.transformer)
def default_transformer(c: Configs):
conf = TransformerConfigs()
conf.d_model = c.d_model
conf.n_layers = c.n_layers
conf.n_src_vocab = c.n_tokens
conf.n_tgt_vocab = c.n_tokens
conf.dropout = c.dropout
return conf
@option(Configs.optimizer)
def _optimizer(c: Configs):
optimizer = OptimizerConfigs()
optimizer.parameters = c.model.parameters()
optimizer.optimizer = 'Adam'
optimizer.d_model = c.d_model
return optimizer
class CrossEntropyLoss(Module):
def __init__(self, n_tokens: int):
super().__init__()
self.n_tokens = n_tokens
self.loss = nn.CrossEntropyLoss()
def __call__(self, outputs, targets):
return self.loss(outputs.view(-1, self.n_tokens), targets.view(-1))
@option(Configs.loss_func)
def _loss_func(c: Configs):
return CrossEntropyLoss(c.n_tokens)
@option(Configs.n_tokens)
def _n_tokens(c: Configs):
return c.text.tokenizer.n_tokens
@option(Configs.model)
def lstm_model(c: Configs):
from python_autocomplete.models.lstm import LstmModel
m = LstmModel(n_tokens=c.n_tokens,
embedding_size=c.d_model,
hidden_size=c.rnn_size,
n_layers=c.n_layers)
return m.to(c.device)
@option(Configs.model)
def rhn_model(c: Configs):
from python_autocomplete.models.highway import RhnModel
m = RhnModel(n_tokens=c.n_tokens,
embedding_size=c.d_model,
hidden_size=c.rnn_size,
n_layers=c.n_layers,
depth=c.rhn_depth)
return m.to(c.device)
@option(Configs.model)
def transformer_model(c: Configs):
from python_autocomplete.models.transformer import TransformerModel
m = TransformerModel(n_tokens=c.n_tokens,
d_model=c.d_model,
encoder=c.transformer.encoder,
src_embed=c.transformer.src_embed)
return m.to(c.device)
@option(Configs.model)
def transformer_xl_model(c: Configs):
from labml_nn.transformers.xl import RelativeMultiHeadAttention
from labml_nn.transformers.feed_forward import FeedForward
from labml_nn.transformers.xl import TransformerXL
from labml_nn.transformers.xl import TransformerXLLayer
from python_autocomplete.models.xl import TransformerXLModel
m = TransformerXLModel(c.n_tokens, c.d_model, TransformerXL(
TransformerXLLayer(d_model=c.d_model,
self_attn=RelativeMultiHeadAttention(c.transformer.n_heads, c.d_model, c.dropout),
feed_forward=FeedForward(c.d_model, c.transformer.ffn.d_ff, c.dropout),
dropout_prob=c.dropout), c.n_layers))
return m.to(c.device)
class StateUpdater:
def __call__(self, old_state, new_state):
return new_state
def get_from_batch(self, state, batch_idx):
if state is None:
return None
elif isinstance(state, torch.Tensor):
return state[batch_idx]
elif isinstance(state, tuple):
return tuple(s[batch_idx] for s in state)
elif isinstance(state, list):
return [s[batch_idx] for s in state]
def make_batch(self, batch):
assert isinstance(batch, list)
if batch[0] is None:
return None
elif isinstance(batch[0], torch.Tensor):
return torch.stack(batch)
elif isinstance(batch[0], tuple):
return tuple(torch.stack([b[n] for b in batch]) for n in range(len(batch[0])))
elif isinstance(batch[0], list):
return [torch.stack([b[n] for b in batch]) for n in range(len(batch[0]))]
class MemoryUpdater(StateUpdater):
def __init__(self, mem_len: int):
self.mem_len = mem_len
def __call__(self, old_mem, new_mem):
if self.mem_len == 0:
return []
if old_mem:
mem = [torch.cat((m, x), dim=0) for m, x in zip(old_mem, new_mem)]
else:
mem = new_mem
if len(mem[0]) > self.mem_len:
mem = [m[-self.mem_len:] for m in mem]
return mem
def get_from_batch(self, state, batch_idx):
if state is None:
return None
return [m[:, batch_idx] for m in state]
def make_batch(self, batch):
if batch[0] is None:
return None
return [torch.stack([b[n] for b in batch], dim=1) for n in range(len(batch[0]))]
@option(Configs.state_updater)
def simple():
return StateUpdater()
@option(Configs.state_updater)
def transformer_memory(c: Configs):
return MemoryUpdater(c.mem_len)
@option(Configs.train_loader)
def _train_loader(c: Configs):
return c.text.train_loader
@option(Configs.valid_loader)
def _valid_loader(c: Configs):
return c.text.valid_loader
def main():
conf = Configs()
# Assign one of transformer_mode, lstm_model, or rhn_model
experiment.create(name="source_code",
comment='bpe')
experiment.configs(conf, {
# 'model': 'transformer_model',
'model': 'transformer_xl_model',
'n_layers': 6,
'epochs': 32,
'optimizer.optimizer': 'AdamW',
'optimizer.learning_rate': 1.25e-4,
'device.cuda_device': 0,
'is_token_by_token': True,
'state_updater': 'transformer_memory',
'mem_len': 256,
'text.is_shuffle': False,
'text.tokenizer': 'bpe',
'text.batch_size': 12,
'text.seq_len': 256,
#
# 'inner_iterations': 10,
# 'text.truncate_data': 100_000,
})
experiment.add_pytorch_models(model=conf.model)
with experiment.start():
conf.run()
if __name__ == '__main__':
main()