)
lang_loss, dec_output, encoder_hidden = return_dict.loss, return_dict.logits, return_dict.encoder_last_hidden_state
tot_val_loss += lang_loss * len(inputs['input_ids'])
n_val += len(inputs['input_ids'])
print("n_val", n_val)
avg_val_loss = tot_val_loss.item() / n_val
return n_val, avg_val_loss
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
if pretrained:
model = BartForConditionalGeneration.from_pretrained('facebook/bart-base', dropout=args.dropout)
else:
config = BartConfig.from_pretrained('facebook/bart-base')
config.dropout = args.dropout
model = BartForConditionalGeneration(config)
model.to(DEVICE)
optimizer = AdamW(list(model.parameters()), lr=args.lr)
print("Loaded model")
# TODO load data
dataset = load_data(args.data, ["walkthrough0"] + [f"randcmd{i}" for i in range(100)], tokenizer, max_seq_len, max_data_size=4000)
print("Loaded train data")
dev_dataset = load_data(args.data, [f"randcmd{i}" for i in range(100,200)], tokenizer, max_seq_len, max_data_size=500)
print("Loaded dev data")
# initial eval
print("Initial eval")
n_val, avg_val_loss = eval_model(args, model, dev_dataset, tokenizer, eval_batchsize)
print(f"number of valid examples: {len(valid_idxs)}")
train_loader = DataLoader(train_idxs, batch_size=args.train_batch_size, shuffle=True)
valid_loader = DataLoader(valid_idxs, batch_size=args.valid_batch_size, shuffle=False)
print("==== preparing data ====")
make_path(args.cache_dir)
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base', cache_dir=args.cache_dir)
with open('synt_vocab.pkl', 'rb') as f:
synt_vocab = pickle.load(f)
dataset = prepare_dataset(para_data, tokenizer, num)
print("==== loading model ====")
config = BartConfig.from_pretrained('facebook/bart-base', cache_dir=args.cache_dir)
config.word_dropout = args.word_dropout
config.max_sent_len = args.max_sent_len
config.max_synt_len = args.max_synt_len
bart = BartModel.from_pretrained('facebook/bart-base', cache_dir=args.cache_dir)
model = ParaBart(config)
model.load_state_dict(bart.state_dict(), strict=False)
model.zero_grad()
del bart
no_decay_params = []
no_decay_fast_params = []
fast_params = []
all_other_params = []
def create_model(encoder_name="xlm-roberta-base",
dec_vocabsize=None,
dec_layers=6,
dec_dim=640,
dec_heads=8,
dropout=0.,
dropoutdec=0.,
maxlen=50,
smoothing=0.,
numbeam=1,
tensor2tree=None,
statesimweight=0.,
probsimweight=0.,
projmode="simple"):
# if encoder_name != "bert-base-uncased":
# raise NotImplemented(f"encoder '{encoder_name}' not supported yet.")
pretrained = AutoModel.from_pretrained(encoder_name)
encoder = pretrained
class BertEncoderWrapper(torch.nn.Module):
def __init__(self, model, dropout=0., **kw):
super(BertEncoderWrapper, self).__init__(**kw)
self.model = model
self.proj = self.create_proj(pretrained.config.hidden_size,
dec_dim,
mode=projmode)
self.dropout = torch.nn.Dropout(dropout)
@classmethod
def create_proj(cls, indim, outdim, mode="simple"):
if mode == "simple" or mode == "simpleshared":
proj = torch.nn.Linear(indim, outdim, bias=False)
elif mode == "twolayer":
proj = torch.nn.Sequential(torch.nn.Linear(indim, indim * 4),
torch.nn.LeakyReLU(0.1),
torch.nn.Linear(indim * 4, outdim))
return proj
def forward(self, input_ids, attention_mask=None):
ret, _ = self.model(input_ids, attention_mask=attention_mask)
# if pretrained.config.hidden_size != dec_dim:
ret = self.proj(ret)
# ret = self.dropout(ret)
ret = (ret, None, None)
return ret
encoder = BertEncoderWrapper(encoder, dropout=dropout)
decoder_config = BartConfig(
d_model=dec_dim,
pad_token_id=0,
bos_token_id=1,
vocab_size=dec_vocabsize,
decoder_attention_heads=dec_heads // 2,
decoder_layers=dec_layers,
dropout=dropoutdec,
attention_dropout=min(0.1, dropout / 2),
decoder_ffn_dim=dec_dim * 4,
encoder_attention_heads=dec_heads,
encoder_layers=dec_layers,
encoder_ffn_dim=dec_dim * 4,
relative_position=True,
)
model = BartGenerator(decoder_config, encoder.model.config)
model.model.encoder = encoder
if "shared" in projmode:
model2 = model
else:
model2 = q.copy(model)
model2.model = q.copy(model.model)
model2.model.encoder = q.copy(model.model.encoder)
model2.model.encoder.proj = BertEncoderWrapper.create_proj(
pretrained.config.hidden_size, dec_dim, mode=projmode)
orderless = {"op:and", "SW:concat"}
trainmodel = BartGeneratorTrain(model,
model2,
smoothing=smoothing,
tensor2tree=tensor2tree,
orderless=orderless,
statesimweight=statesimweight,
probsimweight=probsimweight)
testmodel = BartGeneratorTest(model,
model2,
maxlen=maxlen,
numbeam=numbeam,
tensor2tree=tensor2tree,
orderless=orderless)
return trainmodel, testmodel
def test_xsum_config_generation_params(self):
config = BartConfig.from_pretrained("facebook/bart-large-xsum")
expected_params = dict(num_beams=6, do_sample=False, early_stopping=True, length_penalty=1.0)
config_params = {k: getattr(config, k, "MISSING") for k, v in expected_params.items()}
self.assertDictEqual(expected_params, config_params)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_bart_dlm",
model_args,
data_args,
framework="flax")
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome.")
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
level=logging.INFO,
datefmt="[%X]",
)
# Log on each process the small summary:
logger = logging.getLogger(__name__)
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Handle the repository creation
if training_args.push_to_hub:
if training_args.hub_model_id is None:
repo_name = get_full_repo_name(Path(
training_args.output_dir).absolute().name,
token=training_args.hub_token)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
extension,
data_files=data_files,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
use_auth_token=True if model_args.use_auth_token else None,
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.config_name:
config = BartConfig.from_pretrained(
model_args.config_name,
cache_dir=model_args.cache_dir,
vocab_size=len(tokenizer),
use_auth_token=True if model_args.use_auth_token else None,
)
elif model_args.model_name_or_path:
config = BartConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning(
"You are instantiating a new config instance from scratch.")
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
# Use Punkt Sentence Tokenizer to divide a document into a list of sentences
nltk.download("punkt")
sentence_tokenizer = nltk.data.load("tokenizers/punkt/english.pickle")
def sentence_split_function(example):
sents = sentence_tokenizer.tokenize(example["text"])
# use pad token as end of sentence indicator
new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(
sents) + tokenizer.eos_token
return {"text": new_text}
split_datasets = datasets.map(
sentence_split_function,
batched=False,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
# Tokenize every text, then concatenate them together before splitting them in smaller parts.
# Since we make sure that all sequences are of the same length, no attention_mask is needed.
def tokenize_function(examples):
return tokenizer(examples[text_column_name],
add_special_tokens=False,
return_attention_mask=False)
tokenized_datasets = split_datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=text_column_name,
load_from_cache_file=not data_args.overwrite_cache,
)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of
# max_seq_length.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: list(chain(*examples[k]))
for k in examples.keys()
}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= max_seq_length:
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [
t[i:i + max_seq_length]
for i in range(0, total_length, max_seq_length)
]
for k, t in concatenated_examples.items()
}
return result
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
# might be slower to preprocess.
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
# Enable tensorboard only on the master node
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(
log_dir=Path(training_args.output_dir))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable.")
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
if model_args.model_name_or_path:
model = FlaxBartForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
use_auth_token=True if model_args.use_auth_token else None,
)
else:
config.vocab_size = len(tokenizer)
model = FlaxBartForConditionalGeneration(
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
)
# Data collator
# This one will take care of randomly masking the tokens and permuting the sentences.
data_collator = FlaxDataCollatorForBartDenoisingLM(
tokenizer=tokenizer,
decoder_start_token_id=model.config.decoder_start_token_id,
mask_ratio=data_args.mlm_probability,
poisson_lambda=data_args.poisson_lambda,
permute_sentence_ratio=data_args.permute_sentence_ratio,
)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(
training_args.per_device_train_batch_size) * jax.device_count()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = per_device_eval_batch_size * jax.device_count()
num_train_steps = len(
tokenized_datasets["train"]) // train_batch_size * num_epochs
# Create learning rate schedule
warmup_fn = optax.linear_schedule(
init_value=0.0,
end_value=training_args.learning_rate,
transition_steps=training_args.warmup_steps)
decay_fn = optax.linear_schedule(
init_value=training_args.learning_rate,
end_value=0,
transition_steps=num_train_steps - training_args.warmup_steps,
)
linear_decay_lr_schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn],
boundaries=[training_args.warmup_steps])
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
# find out all LayerNorm parameters
layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
layer_norm_named_params = set([
layer[-2:] for layer_norm_name in layer_norm_candidates
for layer in flat_params.keys()
if layer_norm_name in "".join(layer).lower()
])
flat_mask = {
path: (path[-1] != "bias"
and path[-2:] not in layer_norm_named_params)
for path in flat_params
}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
if training_args.adafactor:
# We use the default parameters here to initialize adafactor,
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
optimizer = optax.adafactor(
learning_rate=linear_decay_lr_schedule_fn, )
else:
optimizer = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# Setup train state
state = train_state.TrainState.create(apply_fn=model.__call__,
params=model.params,
tx=optimizer)
# Define gradient update step fn
def train_step(state, batch, dropout_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def loss_fn(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch,
params=params,
dropout_rng=dropout_rng,
train=True)[0]
# compute loss, ignore padded input tokens and special tokens
label_mask = jnp.where(labels > 0, 1.0, 0.0)
loss = optax.softmax_cross_entropy(
logits, onehot(labels, logits.shape[-1])) * label_mask
# take average
loss = loss.sum() / label_mask.sum()
return loss
grad_fn = jax.value_and_grad(loss_fn)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = jax.lax.pmean(
{
"loss": loss,
"learning_rate": linear_decay_lr_schedule_fn(state.step)
},
axis_name="batch")
return new_state, metrics, new_dropout_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0, ))
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
# compute loss, ignore padded input tokens and special tokens
label_mask = jnp.where(labels > 0, 1.0, 0.0)
loss = optax.softmax_cross_entropy(
logits, onehot(labels, logits.shape[-1])) * label_mask
# compute accuracy
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
# summarize metrics
metrics = {
"loss": loss.sum(),
"accuracy": accuracy.sum(),
"normalizer": label_mask.sum()
}
metrics = jax.lax.psum(metrics, axis_name="batch")
return metrics
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0, ))
# Replicate the train state on each device
state = jax_utils.replicate(state)
train_time = 0
epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
train_metrics = []
# Create sampling rng
rng, input_rng = jax.random.split(rng)
# Generate an epoch by shuffling sampling indices from the train dataset
num_train_samples = len(tokenized_datasets["train"])
# Avoid using jax.numpy here in case of TPU training
train_samples_idx = np.random.permutation(np.arange(num_train_samples))
train_batch_idx = generate_batch_splits(train_samples_idx,
train_batch_size)
# Gather the indexes for creating the batch and do a training step
for step, batch_idx in enumerate(
tqdm(train_batch_idx, desc="Training...", position=1)):
samples = [
tokenized_datasets["train"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
model_inputs = shard(model_inputs.data)
state, train_metric, dropout_rngs = p_train_step(
state, model_inputs, dropout_rngs)
train_metrics.append(train_metric)
cur_step = epoch * (num_train_samples // train_batch_size) + step
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
# Save metrics
train_metric = jax_utils.unreplicate(train_metric)
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics,
train_time, cur_step)
epochs.write(
f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:"
f" {train_metric['learning_rate']})")
train_metrics = []
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
# ======================== Evaluating ==============================
num_eval_samples = len(tokenized_datasets["validation"])
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size,
drop_last=False)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...",
position=2)):
samples = [
tokenized_datasets["validation"][int(idx)]
for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params,
model_inputs.data,
min_device_batch=per_device_eval_batch_size)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.sum, eval_metrics)
eval_normalizer = eval_metrics.pop("normalizer")
eval_metrics = jax.tree_map(lambda x: x / eval_normalizer,
eval_metrics)
# Update progress bar
epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
# Save metrics
if has_tensorboard and jax.process_index() == 0:
write_eval_metric(summary_writer, eval_metrics, cur_step)
if cur_step % training_args.save_steps == 0 and cur_step > 0:
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(
jax.tree_map(lambda x: x[0], state.params))
model.save_pretrained(training_args.output_dir,
params=params)
tokenizer.save_pretrained(training_args.output_dir)
if training_args.push_to_hub:
repo.push_to_hub(
commit_message=
f"Saving weights and logs of step {cur_step}",
blocking=False)
# Eval after training
if training_args.do_eval:
num_eval_samples = len(tokenized_datasets["validation"])
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size,
drop_last=False)
eval_metrics = []
for _, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [
tokenized_datasets["validation"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params,
model_inputs.data,
min_device_batch=per_device_eval_batch_size)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(lambda metric: jnp.sum(metric).item(),
eval_metrics)
eval_normalizer = eval_metrics.pop("normalizer")
eval_metrics = jax.tree_map(lambda x: x / eval_normalizer,
eval_metrics)
try:
perplexity = math.exp(eval_metrics["loss"])
except OverflowError:
perplexity = float("inf")
eval_metrics["perplexity"] = perplexity
if jax.process_index() == 0:
eval_metrics = {
f"eval_{metric_name}": value
for metric_name, value in eval_metrics.items()
}
path = os.path.join(training_args.output_dir, "eval_results.json")
with open(path, "w") as f:
json.dump(eval_metrics, f, indent=4, sort_keys=True)
def bart_or_mbart_model_heuristic(
model_config: transformers.BartConfig) -> ModelArchitectures:
if model_config.is_valid_mbart():
return ModelArchitectures.MBART
else:
return ModelArchitectures.BART
def __init__(
self,
pretrained_model=None,
additional_special_tokens_encoder=None,
additional_special_tokens_decoder=None,
model_config=None,
vocab_file=None,
args=None,
use_cuda=True,
cuda_device=-1,
**kwargs,
):
self.args = self._load_model_args()
if isinstance(args, dict):
self.args.update_from_dict(args)
elif isinstance(args, Seq2SeqArgs):
self.args = args
if "sweep_config" in kwargs:
self.is_sweeping = True
sweep_config = kwargs.pop("sweep_config")
sweep_values = sweep_config_to_sweep_values(sweep_config)
self.args.update_from_dict(sweep_values)
else:
self.is_sweeping = False
if self.args.manual_seed:
random.seed(self.args.manual_seed)
np.random.seed(self.args.manual_seed)
torch.manual_seed(self.args.manual_seed)
if self.args.n_gpu > 0:
torch.cuda.manual_seed_all(self.args.manual_seed)
if use_cuda:
if torch.cuda.is_available():
if cuda_device == -1:
self.device = torch.device("cuda")
else:
self.device = torch.device(f"cuda:{cuda_device}")
else:
raise ValueError(
"'use_cuda' set to True when cuda is unavailable."
"Make sure CUDA is available or set `use_cuda=False`.")
else:
self.device = "cpu"
self.results = {}
if not use_cuda:
self.args.fp16 = False
# BartConfig, BartForConditionalGeneration, BartTokenizer
# config = EncoderDecoderConfig.from_encoder_decoder_configs(config, config)
model_config = BartConfig.from_json_file(model_config)
if pretrained_model is None:
self.model = BartForConditionalGeneration(config=model_config)
self.encoder_tokenizer = BartTokenizer.from_pretrained(vocab_file)
else:
self.model = BartForConditionalGeneration.from_pretrained(
pretrained_model)
self.encoder_tokenizer = BartTokenizer.from_pretrained(vocab_file)
self.decoder_tokenizer = self.encoder_tokenizer
# special AST token
# additional_special_tokens_encoder = {'additional_special_tokens': ['Assertion', 'RegExp', 'Repetition', 'Quantifier', 'ClassRange', 'CharacterClass']}
# additional_special_tokens_decoder = {'additional_special_tokens': ['Assertion', 'RegExp', 'Repetition', 'Quantifier', 'ClassRange', 'CharacterClass']}
self.config = self.model.config
if additional_special_tokens_encoder is not None:
self.encoder_tokenizer.add_special_tokens(
additional_special_tokens_encoder)
if additional_special_tokens_decoder is not None:
self.decoder_tokenizer.add_special_tokens(
additional_special_tokens_decoder)
if self.args.wandb_project and not wandb_available:
warnings.warn(
"wandb_project specified but wandb is not available. Wandb disabled."
)
self.args.wandb_project = None
self.args.model_type = 'bart'
self.args.model_name = 'ExplainREGEX'
def create_model(encoder_name="xlm-roberta-base",
dec_vocabsize=None,
dec_layers=6,
dec_dim=640,
dec_heads=8,
dropout=0.,
dropoutdec=0.,
maxlen=50,
smoothing=0.,
numbeam=1,
tensor2tree=None):
# if encoder_name != "bert-base-uncased":
# raise NotImplemented(f"encoder '{encoder_name}' not supported yet.")
pretrained = AutoModel.from_pretrained(encoder_name)
encoder = pretrained
class BertEncoderWrapper(torch.nn.Module):
def __init__(self, model, dropout=0., **kw):
super(BertEncoderWrapper, self).__init__(**kw)
self.model = model
self.proj = torch.nn.Linear(pretrained.config.hidden_size,
dec_dim,
bias=False)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, input_ids, attention_mask=None):
ret, _ = self.model(input_ids, attention_mask=attention_mask)
if pretrained.config.hidden_size != dec_dim:
ret = self.proj(ret)
# ret = self.dropout(ret)
ret = (ret, None, None)
return ret
encoder = BertEncoderWrapper(encoder, dropout=dropout)
decoder_config = BartConfig(
d_model=dec_dim,
pad_token_id=0,
bos_token_id=1,
vocab_size=dec_vocabsize,
decoder_attention_heads=dec_heads // 2,
decoder_layers=dec_layers,
dropout=dropoutdec,
attention_dropout=min(0.1, dropout / 2),
decoder_ffn_dim=dec_dim * 4,
encoder_attention_heads=dec_heads,
encoder_layers=dec_layers,
encoder_ffn_dim=dec_dim * 4,
relative_position=True,
)
model = BartGenerator(decoder_config, encoder.model.config)
model.model.encoder = encoder
orderless = {"op:and", "SW:concat"}
trainmodel = BartGeneratorTrain(model,
smoothing=smoothing,
tensor2tree=tensor2tree,
orderless=orderless)
testmodel = BartGeneratorTest(model,
maxlen=maxlen,
numbeam=numbeam,
tensor2tree=tensor2tree,
orderless=orderless)
return trainmodel, testmodel
import sys, io
import numpy as np
import torch
from transformers import BartTokenizer, BartConfig, BartModel
from tqdm import tqdm
from sklearn.metrics import f1_score, roc_auc_score
import pickle, random
from parabart import ParaBart
print("==== loading model ====")
config = BartConfig.from_pretrained('facebook/bart-base',
cache_dir='../para-data/bart-base')
model = ParaBart(config)
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base',
cache_dir='../para-data/bart-base')
model.load_state_dict(torch.load("./model/model.pt", map_location='cpu'))
model = model.cuda()
def build_embeddings(model, tokenizer, sents):
model.eval()
embeddings = torch.ones((len(sents), model.config.d_model))
with torch.no_grad():
for i, sent in enumerate(sents):
sent_inputs = tokenizer(sent, return_tensors="pt")
sent_token_ids = sent_inputs['input_ids']
from flask import Flask, request, render_template
from regression import model1, tokenizer_new, tokenize_new
import numpy as np
from transformers import BartForConditionalGeneration, BartTokenizer, BartConfig
import torch
config = BartConfig.from_json_file('output_model/hate/config.json')
model = BartForConditionalGeneration.from_pretrained('output_model/hate/')
tok = BartTokenizer.from_pretrained('output_model/hate/')
app = Flask(__name__)
app.debug = True
@app.route("/", methods=['GET', 'POST'])
def index():
if request.method == "POST":
name = request.form["name"]
hate = " "
if (len(name) > 0):
if name.split(" ")[-1] == '':
a, b, c = tokenize_new([name], tokenizer_new)
out = np.round(model1.predict([a, b])[0][0])
if out <= 3:
hate = "No Hate detected"
elif out > 3 and out <= 5:
hate = "LOW"
elif out > 5 and out <= 7:
hate = "MEDIUM"
else:
hate = "HIGH"
def build_graph(self):
"""构建模型"""
if self.config.bart_pre_training:
self.model = CustomBartGeneration.from_pretrained(
self.config.bart_pre_training)
if self.model.config.vocab_size != self.config.vocab_size:
# 使用预训练模型时词汇表发生变化, 重置embedding表的大小
self.model.resize_token_embeddings(self.config.vocab_size)
else:
bart_config = BartConfig()
bart_config.activation_function = self.config.activate_func
bart_config.vocab_size = self.config.vocab_size
bart_config.d_model = self.config.embed_size
bart_config.max_position_embeddings = self.config.embed_size
bart_config.max_length = self.config.max_generate_length
bart_config.num_labels = self.config.num_labels
bart_config.image_para_freeze = self.config.image_para_freeze
bart_config.encoder_layers = self.config.n_layers
bart_config.decoder_layers = self.config.n_layers
bart_config.encoder_attention_heads = self.config.n_head
bart_config.decoder_attention_heads = self.config.n_head
bart_config.encoder_ffn_dim = self.config.ffn_dim
bart_config.decoder_ffn_dim = self.config.ffn_dim
bart_config.pad_token_id = PAD_ID
bart_config.bos_token_id = BOS_ID
bart_config.eos_token_id = EOS_ID
self.model = CustomBartGeneration(config=bart_config)
# multi-task
# bart_config.summary_use_proj = True
# bart_config.summary_activation = None
# bart_config.summary_first_dropout = True
# bart_config.summary_proj_to_labels = 0.1
# bart_config.summary_type = "cls_index"
# self.model = CustomBartGenerationDoubleHeads(config=bart_config)
if torch.cuda.is_available():
self.model.to(self.config.device)
if self.config.checkpoint:
self.checkpoint_dict = self.load_model(self.config.checkpoint)
if self.is_train:
no_decay = ['bias', 'layer_norm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in self.model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
self.config.weight_decay
}, {
'params': [
p for n, p in self.model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
self.optimizer = AdamW(optimizer_grouped_parameters,
lr=self.config.learning_rate,
eps=self.config.adam_epsilon)
self.scheduler = get_linear_schedule_with_warmup(
self.optimizer,
num_warmup_steps=self.config.num_warmup_steps,
num_training_steps=self.config.num_training_steps)
if self.config.checkpoint and self.checkpoint_dict:
self.optimizer.load_state_dict(
self.checkpoint_dict["optimizer"]) # 加载优化器参数
self.scheduler.load_state_dict(
self.checkpoint_dict["lr_scheduler"]) # 加载lr_scheduler
from transformers import BertModel, BertTokenizer, BertConfig, BartConfig
rubert_ckpt_name = 'DeepPavlov/rubert-base-cased'
tokenizer = BertTokenizer.from_pretrained(
rubert_ckpt_name, do_lower_case=False) # do_lower_case=False is crucial
assert tokenizer.padding_side == 'right'
test_text_sample = 'Ай да Пушкин! синхрофазотрон'
assert tokenizer.get_vocab().get('Пушкин') is not None
assert tokenizer.tokenize(test_text_sample) == [
'Ай', 'да', 'Пушкин', '!', 'синх', '##роф', '##аз', '##отрон'
]
enc_txt = encode_text(tokenizer, test_text_sample, max_len=32)
assert decode_text(tokenizer, enc_txt) == test_text_sample
config = BartConfig.from_pretrained('bart-large-cnn')
rubert_config = BertConfig.from_pretrained(rubert_ckpt_name)
config.model_type = 'rubart'
config.task_specific_params = None
config.vocab_size = rubert_config.vocab_size
config.pad_token_id = rubert_config.pad_token_id
config.bos_token_id = tokenizer.convert_tokens_to_ids('[CLS]')
config.eos_token_id = tokenizer.convert_tokens_to_ids('[SEP]')
config.prefix = None
config.decoder_start_token_id = config.bos_token_id
config.max_position_embeddings = rubert_config.max_position_embeddings
# TODO choose CLS/<S>
print(tokenizer.convert_ids_to_tokens([100, 101, 102, 103, 104, 105, 106,
107]))
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# n_sample for evluating the models during training
training_args.k_out = data_args.k_out
training_args.data_dir = data_args.data_dir
# Ensure output dir is not existed
if (
os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir)
and training_args.do_train and not training_args.overwrite_output_dir
):
raise ValueError(f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.")
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
config = BartConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(training_args, p, None):
assert hasattr(config, p), f"({config.__class__.__name__}) doesn't have a `{p}` attribute"
setattr(config, p, getattr(training_args, p))
tokenizer = BartTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
## TODO special token format: <E1>, <E2>, ... <P1>, <P2> ...
special_tokens = ['<E{}>'.format(i) for i in range(data_args.n_special_tokens)] + ['<P{}>'.format(i) for i in range(10)]
tokenizer.add_tokens(special_tokens)
model = BartForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
from_tf=".ckpt" in model_args.model_name_or_path,
config=config,
cache_dir=model_args.cache_dir,
)
model.resize_token_embeddings(len(tokenizer))
# use task specific params, e.g., data_args.task = 'summarization'
use_task_specific_params(model, data_args.task)
# set num_beams for evaluation
if data_args.eval_beams is None:
data_args.eval_beams = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(tokenizer, MBartTokenizer):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.tgt_lang]
if model_args.freeze_embeds:
freeze_embeds(model)
if model_args.freeze_encoder:
freeze_params(model.get_encoder())
assert_all_frozen(model.get_encoder())
# Get datasets
train_dataset = (
LegacySeq2SeqDataset(
tokenizer=tokenizer,
type_path="train",
data_dir=data_args.data_dir,
n_obs=data_args.n_train,
max_target_length=data_args.max_target_length,
max_source_length=data_args.max_source_length,
prefix=model.config.prefix or "",
)
if training_args.do_train
else None
)
eval_dataset = (
LegacySeq2SeqDataset(
tokenizer=tokenizer,
type_path="val",
data_dir=data_args.data_dir,
n_obs=data_args.n_val,
max_target_length=data_args.val_max_target_length,
max_source_length=data_args.max_source_length,
prefix=model.config.prefix or "",
)
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
test_dataset = (
LegacySeq2SeqDataset(
tokenizer=tokenizer,
type_path="test",
data_dir=data_args.data_dir,
n_obs=data_args.n_test,
max_target_length=data_args.test_max_target_length,
max_source_length=data_args.max_source_length,
prefix=model.config.prefix or "",
)
if training_args.do_predict
else None
)
trainer = Seq2SeqTrainer(
model=model,
config=config,
tokenizer=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=Seq2SeqDataCollator(tokenizer, data_args, training_args.tpu_num_cores),
data_args=data_args,
)
# Training
if training_args.do_train:
trainer.train(model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
# Evaluation (on dev set)
eval_results = {}
if training_args.do_eval:
output = trainer.evaluate()
predictions = output.predictions.tolist()
out_pred_path = training_args.output_dir + '/output_pred_dev.txt'
out_pred_metric = training_args.output_dir + '/output_metric_dev.json'
out_pred_ref = data_args.data_dir + '/val.target'
with open(out_pred_path, 'w') as eval_out:
for pred in predictions:
output_line = tokenizer.decode(pred,
skip_special_tokens=True, clean_up_tokenization_spaces=False)
eval_out.write(output_line + '\n')
metrics = {'epoch': 'eval_mode'}
metrics.update(eval_top1_acc(out_pred_path, out_pred_ref, data_args.k_out)) ## top1_metrics
metrics.update(eval_topk_acc(out_pred_path, out_pred_ref, data_args.k_out)) ## topk_metrics
metrics.update(eval_diversity(out_pred_path, data_args.k_out)) ## diversity_metrics
with open(out_pred_metric, 'w') as metric_out:
json.dump(metrics, metric_out, indent=1)
# Prediction (on test set)
if training_args.do_predict:
logging.info("*** Test ***")
test_output = trainer.predict(test_dataset=test_dataset)
predictions = test_output.predictions.tolist()
out_pred_path = training_args.output_dir + '/output_pred_test.txt'
out_pred_metric = training_args.output_dir + '/output_metric_test.json'
out_pred_ref = data_args.data_dir + '/test.target'
with open(out_pred_path, 'w') as eval_out:
for pred in predictions:
output_line = tokenizer.decode(pred,
skip_special_tokens=True, clean_up_tokenization_spaces=False)
eval_out.write(output_line + '\n')
metrics = {'epoch': 'test_mode'}
metrics.update(eval_top1_acc(out_pred_path, out_pred_ref, data_args.k_out)) ## top1_metrics
metrics.update(eval_topk_acc(out_pred_path, out_pred_ref, data_args.k_out)) ## topk_metrics
metrics.update(eval_diversity(out_pred_path, data_args.k_out)) ## diversity_metrics
with open(out_pred_metric, 'w') as metric_out:
json.dump(metrics, metric_out, indent=1)
def __init__(
self,
model_name_or_path, # teacher
tokenizer_name,
model_cache_dir,
input_max_length,
target_max_length,
summary_column_name,
document_column_name,
wandb_project,
wandb_run_name,
student_encoder_layers,
student_decoder_layers,
**kwargs,
):
super().__init__(
input_max_length,
target_max_length,
summary_column_name,
document_column_name,
wandb_project,
wandb_run_name,
)
self.tokenizer = BartTokenizer.from_pretrained(
tokenizer_name if tokenizer_name else model_name_or_path,
cache_dir=model_cache_dir,
)
teacher = BartForConditionalGeneration.from_pretrained(
model_name_or_path, cache_dir=model_cache_dir,
).eval()
student_updates = {
"decoder_layers": student_decoder_layers,
"encoder_layers": student_encoder_layers,
}
d_layers_to_copy = self._get_layers_to_copy(student_updates["decoder_layers"], teacher.config.decoder_layers)
e_layers_to_copy: List = self._get_layers_to_copy(student_updates["encoder_layers"], teacher.config.encoder_layers)
kw = teacher.config.to_diff_dict()
kw.update(student_updates)
# Copy weights
student_cfg = BartConfig(**kw)
student = BartForConditionalGeneration(student_cfg)
student, _ = self._init_student(student, teacher)
self._copy_to_student(d_layers_to_copy, e_layers_to_copy, student_encoder_layers, student_decoder_layers, student, teacher)
self.model = student
print(student)
inputs = self.tokenizer.encode_plus("TEXT TO SUMMARIZE", max_length=1024, return_tensors="pt")
# Summarize
outputs = self.model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=400,
min_length=150,
length_penalty=2.0,
num_beams=4,
early_stopping=True
)
# Decode
summary = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(summary)
if __name__ == "__main__":
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
# LOADING MODEL & DATA
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Device:", device)
model_created = False
if args.checkpoint != None:
model_created = True
if args.bart:
config = BartConfig.from_json_file(args.checkpoint +
"/config.json")
model = BartForConditionalGeneration.from_pretrained(
args.checkpoint + "/pytorch_model.bin", config=config)
else:
config = EncoderDecoderConfig.from_json_file(args.checkpoint +
"/config.json")
model = EncoderDecoderModel.from_pretrained(args.checkpoint +
"/pytorch_model.bin",
config=config)
if args.language == 'fr':
if args.bart:
model_name = "WikinewsSum/bart-large-multi-fr-wiki-news"
#config = BartConfig.from_pretrained(model_name)
tokenizer = BartTokenizer.from_pretrained(model_name)
if not model_created:
def __init__(self):
self.config = BartConfig(vocab_size=50264, output_past=True)
self.bart = load_hf_model(config=self.config, pretrained=True)
'batch_size': 64,
'tenacity': 5,
'epoch_size': 4
}
# Set up logger
logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model',
default='bart-large',
help='model name or path')
args = parser.parse_args()
config = BartConfig.from_pretrained(args.model)
model = BartModel.from_pretrained(args.model, config=config)
tokenizer = BartTokenizer.from_pretrained(args.model)
params_senteval['model'] = model.cuda().eval()
params_senteval['tokenizer'] = tokenizer
params_senteval['config'] = config
se = senteval.engine.SE(params_senteval, batcher, prepare)
transfer_tasks = [
'STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'MR', 'CR', 'MPQA',
'SUBJ', 'SST2', 'SST5', 'TREC', 'MRPC', 'SICKEntailment',
'SICKRelatedness', 'STSBenchmark', 'Length', 'WordContent', 'Depth',
'TopConstituents', 'BigramShift', 'Tense', 'SubjNumber', 'ObjNumber',
'OddManOut', 'CoordinationInversion', 'ImageCaptionRetrieval', 'SNLI'
]
def create_model(encoder_name="bert-base-uncased",
fl_vocab=None,
abs_fl_vocab=None,
dec_layers=6,
dec_dim=640,
dec_heads=8,
dropout=0.,
maxlen=20,
smoothing=0.,
numbeam=1,
abs_id=-100,
entropycontrib=1.):
if encoder_name != "bert-base-uncased":
raise NotImplementedError(
f"encoder '{encoder_name}' not supported yet.")
pretrained = AutoModel.from_pretrained(encoder_name)
encoder = pretrained
class BertEncoderWrapper(torch.nn.Module):
def __init__(self, model, dropout=0., **kw):
super(BertEncoderWrapper, self).__init__(**kw)
self.model = model
self.proj = torch.nn.Linear(pretrained.config.hidden_size,
dec_dim,
bias=False)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, input_ids, attention_mask=None):
ret, _ = self.model(input_ids, attention_mask=attention_mask)
if pretrained.config.hidden_size != dec_dim:
ret = self.proj(ret)
ret = self.dropout(ret)
ret = (ret, None, None)
return ret
class DummyEncoder(torch.nn.Module):
def __init__(self, dim, **kw):
super(DummyEncoder, self).__init__(**kw)
self.dim = dim
def forward(self, input_ids, attention_mask=None):
return torch.zeros(input_ids.size(0),
1,
self.dim,
device=input_ids.device)
encoder = BertEncoderWrapper(encoder, dropout=dropout)
abs_dec_vocabsize = abs_fl_vocab.number_of_ids()
dec_vocabsize = fl_vocab.number_of_ids()
decoder_config = BartConfig(
d_model=dec_dim,
pad_token_id=0,
bos_token_id=1,
vocab_size=abs_dec_vocabsize,
decoder_attention_heads=dec_heads // 2,
decoder_layers=dec_layers,
dropout=dropout,
attention_dropout=min(0.1, dropout / 2),
decoder_ffn_dim=dec_dim * 4,
encoder_attention_heads=dec_heads,
encoder_layers=dec_layers,
encoder_ffn_dim=dec_dim * 4,
)
adv_decoder_config = BartConfig(
d_model=dec_dim,
pad_token_id=0,
bos_token_id=1,
vocab_size=dec_vocabsize,
decoder_attention_heads=dec_heads // 2,
decoder_layers=dec_layers,
dropout=dropout,
attention_dropout=min(0.1, dropout / 2),
decoder_ffn_dim=dec_dim * 4,
encoder_attention_heads=dec_heads,
encoder_layers=dec_layers,
encoder_ffn_dim=dec_dim * 4,
)
decoder_lm_config = BartConfig(
d_model=dec_dim,
pad_token_id=0,
bos_token_id=1,
vocab_size=dec_vocabsize,
decoder_attention_heads=dec_heads // 2,
decoder_layers=dec_layers,
dropout=dropout,
attention_dropout=min(0.1, dropout / 2),
decoder_ffn_dim=dec_dim * 4,
encoder_attention_heads=dec_heads,
encoder_layers=dec_layers,
encoder_ffn_dim=dec_dim * 4,
)
model = BartGenerator(decoder_config)
model.model.encoder = encoder
advmodel = BartGenerator(adv_decoder_config)
advmodel.model.encoder = encoder
dummy_probs = torch.zeros(dec_vocabsize, dec_vocabsize)
for k, v in fl_vocab.D.items():
if k in abs_fl_vocab.D:
dummy_probs[v, :] = 1
else:
for j, w in fl_vocab.D.items():
if j not in abs_fl_vocab.D and not re.match("@.+@", j):
dummy_probs[v, w] = 1
dummy_probs = dummy_probs / dummy_probs.sum(1)
decoder_lm = DummyLM(dummy_probs) # TODO
orderless = {"op:and", "SW:concat"}
tensor2tree = partial(_tensor2tree, D=fl_vocab)
abstensor2tree = partial(_tensor2tree, D=abs_fl_vocab)
trainmodel = GeneratorTrain(model,
advmodel,
decoder_lm,
smoothing=smoothing,
tensor2tree=abstensor2tree,
orderless=orderless,
abs_id=abs_id,
entropycontrib=entropycontrib)
advtrainmodel = AdversaryTrain(advmodel,
smoothing=smoothing,
tensor2tree=tensor2tree,
orderless=orderless)
testmodel = GeneratorTest(model,
maxlen=maxlen,
numbeam=numbeam,
tensor2tree=abstensor2tree,
orderless=orderless)
return trainmodel, advtrainmodel, testmodel
def __init__(self, config):
BartConfig.__init__(self, config)
torch.nn.Module.__init__(self)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_data_aug",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=16,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=64,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=1e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
processors = {"rte": RteProcessor}
output_modes = {"rte": "classification"}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
# label_list = processor.get_labels() #["entailment", "neutral", "contradiction"]
# label_list = ['How_do_I_create_a_profile_v4', 'Profile_Switch_v4', 'Deactivate_Active_Devices_v4', 'Ads_on_Hulu_v4', 'Watching_Hulu_with_Live_TV_v4', 'Hulu_Costs_and_Commitments_v4', 'offline_downloads_v4', 'womens_world_cup_v5', 'forgot_username_v4', 'confirm_account_cancellation_v4', 'Devices_to_Watch_HBO_on_v4', 'remove_add_on_v4', 'Internet_Speed_for_HD_and_4K_v4', 'roku_related_questions_v4', 'amazon_related_questions_v4', 'Clear_Browser_Cache_v4', 'ads_on_ad_free_plan_v4', 'inappropriate_ads_v4', 'itunes_related_questions_v4', 'Internet_Speed_Recommendations_v4', 'NBA_Basketball_v5', 'unexpected_charges_v4', 'change_billing_date_v4', 'NFL_on_Hulu_v5', 'How_to_delete_a_profile_v4', 'Devices_to_Watch_Hulu_on_v4', 'Manage_your_Hulu_subscription_v4', 'cancel_hulu_account_v4', 'disney_bundle_v4', 'payment_issues_v4', 'home_network_location_v4', 'Main_Menu_v4', 'Resetting_Hulu_Password_v4', 'Update_Payment_v4', 'I_need_general_troubleshooting_help_v4', 'What_is_Hulu_v4', 'sprint_related_questions_v4', 'Log_into_TV_with_activation_code_v4', 'Game_of_Thrones_v4', 'video_playback_issues_v4', 'How_to_edit_a_profile_v4', 'Watchlist_Remove_Video_v4', 'spotify_related_questions_v4', 'Deactivate_Login_Sessions_v4', 'Transfer_to_Agent_v4', 'Use_Hulu_Internationally_v4']
train_examples, dev_examples, eval_examples, label_list = load_CLINC150_with_specific_domain(
'banking', 1, augment=args.do_data_aug)
num_labels = len(label_list)
# train_examples = None
num_train_optimization_steps = None
if args.do_train:
# train_examples = processor.get_RTE_as_train('/export/home/Dataset/glue_data/RTE/train.tsv') #train_pu_half_v1.txt
# train_examples = get_data_hulu_fewshot('train', 5)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
# cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_TRANSFORMERS_CACHE), 'distributed_{}'.format(args.local_rank))
# pretrain_model_dir = 'roberta-large-mnli' #'roberta-large' , 'roberta-large-mnli'
# pretrain_model_dir = '/export/home/Dataset/BERT_pretrained_mine/crossdataentail/trainMNLItestRTE/0.8772563176895307'
model_config = BartConfig.from_pretrained(pretrain_model_dir)
model_config.num_labels = num_labels
model = BartForSequenceClassification.from_pretrained(pretrain_model_dir,
config=model_config)
# print('after:', model.classification_head.out_proj.out_features)
# exit(0)
# tokenizer = RobertaTokenizer.from_pretrained(pretrain_model_dir, do_lower_case=args.do_lower_case)
tokenizer = BartTokenizer.from_pretrained(pretrain_model_dir,
do_lower_case=args.do_lower_case)
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
max_test_acc = 0.0
max_dev_acc = 0.0
if args.do_train:
train_features = convert_examples_to_features(
train_examples,
label_list,
args.max_seq_length,
tokenizer,
output_mode,
cls_token_at_end=
False, #bool(args.model_type in ['xlnet']), # xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
cls_token_segment_id=0, #2 if args.model_type in ['xlnet'] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=
True, #bool(args.model_type in ['roberta']), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=
False, #bool(args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token
])[0],
pad_token_segment_id=0
) #4 if args.model_type in ['xlnet'] else 0,)
'''load dev set'''
# dev_examples = processor.get_RTE_as_dev('/export/home/Dataset/glue_data/RTE/dev.tsv')
# dev_examples = get_data_hulu('dev')
dev_features = convert_examples_to_features(
dev_examples,
label_list,
args.max_seq_length,
tokenizer,
output_mode,
cls_token_at_end=
False, #bool(args.model_type in ['xlnet']), # xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
cls_token_segment_id=0, #2 if args.model_type in ['xlnet'] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=
True, #bool(args.model_type in ['roberta']), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=
False, #bool(args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token
])[0],
pad_token_segment_id=0
) #4 if args.model_type in ['xlnet'] else 0,)
dev_all_input_ids = torch.tensor([f.input_ids for f in dev_features],
dtype=torch.long)
dev_all_input_mask = torch.tensor([f.input_mask for f in dev_features],
dtype=torch.long)
dev_all_segment_ids = torch.tensor(
[f.segment_ids for f in dev_features], dtype=torch.long)
dev_all_label_ids = torch.tensor([f.label_id for f in dev_features],
dtype=torch.long)
dev_data = TensorDataset(dev_all_input_ids, dev_all_input_mask,
dev_all_segment_ids, dev_all_label_ids)
dev_sampler = SequentialSampler(dev_data)
dev_dataloader = DataLoader(dev_data,
sampler=dev_sampler,
batch_size=args.eval_batch_size)
'''load test set'''
# eval_examples = processor.get_RTE_as_test('/export/home/Dataset/RTE/test_RTE_1235.txt')
# eval_examples = get_data_hulu('test')
eval_features = convert_examples_to_features(
eval_examples,
label_list,
args.max_seq_length,
tokenizer,
output_mode,
cls_token_at_end=
False, #bool(args.model_type in ['xlnet']), # xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
cls_token_segment_id=0, #2 if args.model_type in ['xlnet'] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=
True, #bool(args.model_type in ['roberta']), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=
False, #bool(args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token
])[0],
pad_token_segment_id=0
) #4 if args.model_type in ['xlnet'] else 0,)
eval_all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
eval_all_input_mask = torch.tensor(
[f.input_mask for f in eval_features], dtype=torch.long)
eval_all_segment_ids = torch.tensor(
[f.segment_ids for f in eval_features], dtype=torch.long)
eval_all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(eval_all_input_ids, eval_all_input_mask,
eval_all_segment_ids, eval_all_label_ids)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
iter_co = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
model.train()
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
outputs = model(input_ids, input_mask, labels=label_ids)
# loss_fct = CrossEntropyLoss()
loss = outputs[
0] #loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
optimizer.step()
optimizer.zero_grad()
global_step += 1
iter_co += 1
# if iter_co %20==0:
if iter_co % len(train_dataloader) == 0:
'''
start evaluate on dev set after this epoch
'''
model.eval()
for idd, dev_or_test_dataloader in enumerate(
[dev_dataloader, eval_dataloader]):
if idd == 0:
logger.info("***** Running dev *****")
logger.info(" Num examples = %d",
len(dev_examples))
else:
logger.info("***** Running test *****")
logger.info(" Num examples = %d",
len(eval_examples))
# logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0
nb_eval_steps = 0
preds = []
gold_label_ids = []
# print('Evaluating...')
for input_ids, input_mask, segment_ids, label_ids in dev_or_test_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
gold_label_ids += list(
label_ids.detach().cpu().numpy())
with torch.no_grad():
logits = model(input_ids,
input_mask,
labels=None)
# print('logits:', logits)
logits = logits[0]
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(
logits.view(-1, num_labels),
label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(
preds[0],
logits.detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
'''
preds: size*3 ["entailment", "neutral", "contradiction"]
wenpeng added a softxmax so that each row is a prob vec
'''
pred_probs = softmax(preds, axis=1)
pred_label_ids = list(np.argmax(pred_probs, axis=1))
# pred_indices = np.argmax(pred_probs, axis=1)
# pred_label_ids = []
# for p in pred_indices:
# pred_label_ids.append(0 if p == 0 else 1)
gold_label_ids = gold_label_ids
assert len(pred_label_ids) == len(gold_label_ids)
hit_co = 0
for k in range(len(pred_label_ids)):
if pred_label_ids[k] == gold_label_ids[k]:
hit_co += 1
test_acc = hit_co / len(gold_label_ids)
if idd == 0: # this is dev
if test_acc > max_dev_acc:
max_dev_acc = test_acc
print('\ndev acc:', test_acc, ' max_dev_acc:',
max_dev_acc, '\n')
else:
print('\ndev acc:', test_acc, ' max_dev_acc:',
max_dev_acc, '\n')
break
else: # this is test
if test_acc > max_test_acc:
max_test_acc = test_acc
print('\ntest acc:', test_acc, ' max_test_acc:',
max_test_acc, '\n')
