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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import horovod.tensorflow as hvd
import tensorflow as tf
class Dice(tf.keras.metrics.Metric):
def __init__(self, n_class, **kwargs):
super().__init__(**kwargs)
self.n_class = n_class
self.steps = self.add_weight(name="steps", initializer="zeros", aggregation=tf.VariableAggregation.SUM)
self.dice = self.add_weight(
name="dice",
shape=(n_class,),
initializer="zeros",
aggregation=tf.VariableAggregation.SUM,
)
def update_state(self, y_pred, y_true):
self.steps.assign_add(1)
self.dice.assign_add(self.compute_stats(y_pred, y_true))
def result(self):
dice_sum = hvd.allreduce(self.dice, op=hvd.mpi_ops.Sum)
steps_sum = hvd.allreduce(self.steps, op=hvd.mpi_ops.Sum)
return dice_sum / steps_sum
def compute_stats(self, y_pred, y_true):
scores = tf.TensorArray(tf.float32, size=self.n_class)
pred_classes = tf.argmax(y_pred, axis=-1)
if tf.rank(pred_classes) < tf.rank(y_true) and y_true.shape[-1] == 1:
y_true = tf.squeeze(y_true, axis=[-1])
for i in range(0, self.n_class):
if tf.math.count_nonzero(y_true == i) == 0:
scores = scores.write(i, 1 if tf.math.count_nonzero(pred_classes == i) == 0 else 0)
continue
true_pos, false_neg, false_pos = self.get_stats(pred_classes, y_true, i)
denom = tf.cast(2 * true_pos + false_pos + false_neg, dtype=tf.float32)
score_cls = tf.cast(2 * true_pos, tf.float32) / denom
scores = scores.write(i, score_cls)
return scores.stack()
@staticmethod
def get_stats(preds, target, class_idx):
true_pos = tf.math.count_nonzero(tf.logical_and(preds == class_idx, target == class_idx))
false_neg = tf.math.count_nonzero(tf.logical_and(preds != class_idx, target == class_idx))
false_pos = tf.math.count_nonzero(tf.logical_and(preds == class_idx, target != class_idx))
return true_pos, false_neg, false_pos
class Max(tf.keras.metrics.Metric):
def __init__(self, name="max", dtype=tf.float32):
super().__init__(name=name)
self.value = self.add_weight(
name=f"{name}_weight",
shape=(),
initializer="zeros",
dtype=dtype,
)
def update_state(self, new_value):
self.value.assign(tf.math.maximum(self.value, new_value))
def result(self):
return self.value
class MetricAggregator:
def __init__(self, name, dtype=tf.float32, improvement_metric="max"):
self.name = name
self.improvement_metric = improvement_metric
self.metrics = {
"value": tf.Variable(0, dtype=dtype),
"max": Max(name=f"{name}_max", dtype=dtype),
}
def logger_metrics(self):
return {
f"{self.name}_value": float(self.metrics["value"]),
f"{self.name}_max": float(self.metrics["max"].result()),
}
def checkpoint_metrics(self):
return {f"{self.name}_{k}": v for k, v in self.metrics.items()}
def update(self, value):
old_metric_value = self.metrics[self.improvement_metric].result()
self.metrics["value"] = value
self.metrics["max"].update_state(value)
new_metric_value = self.metrics[self.improvement_metric].result()
return old_metric_value < new_metric_value
def make_class_logger_metrics(scores, name="val_dice"):
metrics = {}
for i, v in enumerate(scores):
metrics[f"L{i}"] = float(v)
return metrics
if __name__ == "__main__":
n_class = 3
pred = tf.one_hot([0, 1, 1, 1, 2], depth=n_class, dtype=tf.float32)
target = tf.constant([0, 1, 1, 2, 2])
metric = Dice(n_class)
metric.update_state(pred, target)
# metric.result() == [1. 0.8 0.66]
# Check Dice computing with zero denominator
pred = tf.one_hot([0, 2], depth=n_class, dtype=tf.float32)
target = tf.constant([0, 2])
metric.update_state(pred, target)
# metric.result() == [1. 0.9 0.83]
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/runtime/metrics.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import horovod.tensorflow as hvd
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow.python.compiler.tensorrt import trt_convert as trt
from runtime.checkpoint import CheckpointManager
from runtime.losses import DiceCELoss, WeightDecay
from runtime.metrics import Dice, MetricAggregator, make_class_logger_metrics
from runtime.utils import is_main_process, make_empty_dir, progress_bar
def update_best_metrics(old, new, start_time, iteration, watch_metric=None):
did_change = False
for metric, value in new.items():
if metric not in old or old[metric]["value"] < value:
old[metric] = {"value": value, "timestamp": time.time() - start_time, "iter": int(iteration)}
if watch_metric == metric:
did_change = True
return did_change
def get_scheduler(args, total_steps):
scheduler = {
"poly": tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=args.learning_rate,
end_learning_rate=args.end_learning_rate,
decay_steps=total_steps,
power=0.9,
),
"cosine": tf.keras.optimizers.schedules.CosineDecay(
initial_learning_rate=args.learning_rate, decay_steps=total_steps
),
"cosine_annealing": tf.keras.optimizers.schedules.CosineDecayRestarts(
initial_learning_rate=args.learning_rate,
first_decay_steps=args.cosine_annealing_first_cycle_steps,
alpha=0.1,
),
"none": args.learning_rate,
}[args.scheduler.lower()]
return scheduler
def get_optimizer(args, scheduler):
optimizer = {
"sgd": tf.keras.optimizers.SGD(learning_rate=scheduler, momentum=args.momentum),
"adam": tf.keras.optimizers.Adam(learning_rate=scheduler),
"radam": tfa.optimizers.RectifiedAdam(learning_rate=scheduler),
}[args.optimizer.lower()]
if args.lookahead:
optimizer = tfa.optimizers.Lookahead(optimizer)
if args.amp:
optimizer = tf.keras.mixed_precision.LossScaleOptimizer(optimizer, dynamic=True)
return optimizer
def get_epoch_size(args, batch_size, dataset_size):
if args.steps_per_epoch:
return args.steps_per_epoch
div = args.gpus * (batch_size if args.dim == 3 else args.nvol)
return (dataset_size + div - 1) // div
def process_performance_stats(deltas, batch_size, mode):
deltas_ms = 1000 * np.array(deltas)
throughput_imgps = 1000.0 * batch_size / deltas_ms.mean()
stats = {f"throughput_{mode}": throughput_imgps, f"latency_{mode}_mean": deltas_ms.mean()}
for level in [90, 95, 99]:
stats.update({f"latency_{mode}_{level}": np.percentile(deltas_ms, level)})
return stats
def benchmark(args, step_fn, data, steps, warmup_steps, logger, mode="train"):
assert steps > warmup_steps, "Number of benchmarked steps has to be greater then number of warmup steps"
deltas = []
wrapped_data = progress_bar(
enumerate(data),
quiet=args.quiet,
desc=f"Benchmark ({mode})",
unit="step",
postfix={"phase": "warmup"},
total=steps,
)
start = time.perf_counter()
for step, (images, labels) in wrapped_data:
output_map = step_fn(images, labels, warmup_batch=step == 0)
if step >= warmup_steps:
deltas.append(time.perf_counter() - start)
if step == warmup_steps and is_main_process() and not args.quiet:
wrapped_data.set_postfix(phase="benchmark")
start = time.perf_counter()
if step >= steps:
break
stats = process_performance_stats(deltas, args.gpus * args.batch_size, mode=mode)
logger.log_metrics(stats)
def train(args, model, dataset, logger):
train_data = dataset.train_dataset()
epochs = args.epochs
batch_size = args.batch_size if args.dim == 3 else args.nvol
steps_per_epoch = get_epoch_size(args, batch_size, dataset.train_size())
total_steps = epochs * steps_per_epoch
scheduler = get_scheduler(args, total_steps)
optimizer = get_optimizer(args, scheduler)
loss_fn = DiceCELoss(
y_one_hot=True,
reduce_batch=args.reduce_batch,
include_background=args.include_background,
)
wdecay = WeightDecay(factor=args.weight_decay)
tstep = tf.Variable(0)
@tf.function
def train_step_fn(features, labels, warmup_batch=False):
features, labels = model.adjust_batch(features, labels)
with tf.GradientTape() as tape:
output_map = model(features)
dice_loss = model.compute_loss(loss_fn, labels, output_map)
loss = dice_loss + wdecay(model)
if args.amp:
loss = optimizer.get_scaled_loss(loss)
tape = hvd.DistributedGradientTape(tape)
gradients = tape.gradient(loss, model.trainable_variables)
if args.amp:
gradients = optimizer.get_unscaled_gradients(gradients)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# Note: broadcast should be done after the first gradient step to ensure optimizer initialization.
if warmup_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
return dice_loss
dice_metrics = MetricAggregator(name="dice")
checkpoint = CheckpointManager(
args.ckpt_dir,
strategy=args.ckpt_strategy,
resume_training=args.resume_training,
variables={"model": model, "optimizer": optimizer, "step": tstep, **dice_metrics.checkpoint_metrics()},
)
if args.benchmark:
benchmark(args, train_step_fn, train_data, args.bench_steps, args.warmup_steps, logger)
else:
wrapped_data = progress_bar(
train_data,
quiet=args.quiet,
desc="Train",
postfix={"epoch": 1},
unit="step",
total=total_steps - int(tstep),
)
start_time = time.time()
total_train_loss, dice_score = 0.0, 0.0
for images, labels in wrapped_data:
if tstep >= total_steps:
break
tstep.assign_add(1)
loss = train_step_fn(images, labels, warmup_batch=tstep == 1)
total_train_loss += float(loss)
lr = scheduler(tstep) if callable(scheduler) else scheduler
metrics = {"loss": float(loss), "learning_rate": float(lr)}
if tstep % steps_per_epoch == 0:
epoch = int(tstep // steps_per_epoch)
if epoch > args.skip_eval:
dice = evaluate(args, model, dataset, logger)
dice_score = tf.reduce_mean(dice[1:])
did_improve = dice_metrics.update(dice_score)
metrics = dice_metrics.logger_metrics()
metrics.update(make_class_logger_metrics(dice))
if did_improve:
metrics["time_to_train"] = time.time() - start_time
logger.log_metrics(metrics=metrics, step=int(tstep))
checkpoint.update(float(dice_score))
logger.flush()
else:
checkpoint.update(None)
if is_main_process() and not args.quiet:
wrapped_data.set_postfix(epoch=epoch + 1)
elif tstep % steps_per_epoch == 0:
total_train_loss = 0.0
metrics = {
"train_loss": round(total_train_loss / steps_per_epoch, 5),
"val_loss": round(1 - float(dice_score), 5),
"dice": round(float(dice_metrics.metrics["max"].result()), 5),
}
logger.log_metrics(metrics=metrics)
logger.flush()
def evaluate(args, model, dataset, logger):
dice = Dice(n_class=model.n_class)
data_size = dataset.val_size()
wrapped_data = progress_bar(
enumerate(dataset.val_dataset()),
quiet=args.quiet,
desc="Validation",
unit="step",
total=data_size,
)
for i, (features, labels) in wrapped_data:
if args.dim == 2:
features, labels = features[0], labels[0]
output_map = model.inference(features)
dice.update_state(output_map, labels)
if i + 1 == data_size:
break
result = dice.result()
if args.exec_mode == "evaluate":
metrics = {
"eval_dice": float(tf.reduce_mean(result)),
"eval_dice_nobg": float(tf.reduce_mean(result[1:])),
}
logger.log_metrics(metrics)
return result
def predict(args, model, dataset, logger):
if args.benchmark:
@tf.function
def predict_bench_fn(features, labels, warmup_batch):
if args.dim == 2:
features = features[0]
output_map = model(features, training=False)
return output_map
benchmark(
args,
predict_bench_fn,
dataset.test_dataset(),
args.bench_steps,
args.warmup_steps,
logger,
mode="predict",
)
else:
if args.save_preds:
prec = "amp" if args.amp else "fp32"
dir_name = f"preds_task_{args.task}_dim_{args.dim}_fold_{args.fold}_{prec}"
if args.tta:
dir_name += "_tta"
save_dir = args.results / dir_name
make_empty_dir(save_dir)
data_size = dataset.test_size()
wrapped_data = progress_bar(
enumerate(dataset.test_dataset()),
quiet=args.quiet,
desc="Predict",
unit="step",
total=data_size,
)
for i, (images, meta) in wrapped_data:
features, _ = model.adjust_batch(images, None)
pred = model.inference(features, training=False)
if args.save_preds:
model.save_pred(pred, meta, idx=i, data_module=dataset, save_dir=save_dir)
if i + 1 == data_size:
break
def export_model(args, model):
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(tf.train.latest_checkpoint(args.ckpt_dir)).expect_partial()
input_shape = [1, *model.patch_size, model.n_class]
dummy_input = tf.constant(tf.zeros(input_shape, dtype=tf.float32))
_ = model(dummy_input, training=False)
prec = "amp" if args.amp else "fp32"
path = str(args.results / f"saved_model_task_{args.task}_dim_{args.dim}_{prec}")
tf.keras.models.save_model(model, str(path))
trt_prec = trt.TrtPrecisionMode.FP32 if prec == "fp32" else trt.TrtPrecisionMode.FP16
converter = trt.TrtGraphConverterV2(
input_saved_model_dir=path,
conversion_params=trt.TrtConversionParams(precision_mode=trt_prec),
)
converter.convert()
trt_path = str(args.results / f"trt_saved_model_task_{args.task}_dim_{args.dim}_{prec}")
converter.save(trt_path)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/runtime/run.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from time import time
import tensorflow as tf
from models.nn_unet import NNUnet
from runtime.utils import rank_zero_only
class CheckpointManager:
def __init__(self, ckpt_dir, strategy, variables, step_counter=None, resume_training=False):
self.dir = Path(ckpt_dir)
self.strategy = strategy
self.vars = variables
self.ckpt = tf.train.Checkpoint(**variables)
self.creation_time = time()
self.latest_save_time = time()
if "last" in strategy:
self.last_manager = tf.train.CheckpointManager(
self.ckpt, self.dir, max_to_keep=1, checkpoint_name="ckpt-last", step_counter=step_counter
)
if resume_training:
self.ckpt.restore(self.last_manager.latest_checkpoint)
if "best" in strategy:
self.best_manager = tf.train.CheckpointManager(
self.ckpt, self.dir / "best", max_to_keep=1, checkpoint_name="ckpt-best", step_counter=step_counter
)
self.best_metric = None
@rank_zero_only
def update(self, metric_value=None):
if "last" in self.strategy:
self.last_manager.save()
if (
metric_value is not None
and "best" in self.strategy
and (self.best_metric is None or self.best_metric < metric_value)
):
self.latest_save_time = time()
if self.best_metric is not None:
print(
f"({int(self.latest_save_time - self.creation_time)}s)",
f"New best metric value achieved ({float(metric_value):.4f} > {float(self.best_metric):.4f}).",
)
print("Saving new checkpoint.")
self.best_metric = metric_value
self.best_manager.save()
def load_best(self):
self.ckpt.restore(self.best_manager.latest_checkpoint)
return self.best_metric, int(self.latest_save_time - self.creation_time)
def load_model(args):
if args.saved_model_dir is not None:
print(f"Loading SavedModel from {str(args.saved_model_dir)}")
model = tf.saved_model.load(str(args.saved_model_dir))
model = NNUnet(args, loaded_model=model)
else:
if not (Path(args.ckpt_dir).is_dir() and (Path(args.ckpt_dir) / "checkpoint").exists()):
raise ValueError(f"Could not find checkpoint directory {args.ckpt_dir}")
model = NNUnet(args)
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(tf.train.latest_checkpoint(args.ckpt_dir)).expect_partial()
return model
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/runtime/checkpoint.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import multiprocessing
import os
import pickle
import shutil
import sys
from functools import wraps
from pathlib import Path
import horovod.tensorflow as hvd
import numpy as np
import tensorflow as tf
from tqdm import tqdm
def hvd_init():
hvd.init()
gpus = tf.config.experimental.list_physical_devices("GPU")
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU")
def set_tf_flags(args):
os.environ["CUDA_CACHE_DISABLE"] = "0"
os.environ["HOROVOD_GPU_ALLREDUCE"] = "NCCL"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["TF_GPU_THREAD_MODE"] = "gpu_private"
os.environ["TF_GPU_THREAD_COUNT"] = str(hvd.size())
os.environ["TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT"] = "1"
os.environ["TF_ADJUST_HUE_FUSED"] = "1"
os.environ["TF_ADJUST_SATURATION_FUSED"] = "1"
os.environ["TF_ENABLE_WINOGRAD_NONFUSED"] = "1"
os.environ["TF_SYNC_ON_FINISH"] = "0"
os.environ["TF_AUTOTUNE_THRESHOLD"] = "2"
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "0"
os.environ["TF_ENABLE_LAYOUT_NHWC"] = "1"
os.environ["TF_CPP_VMODULE"] = "4"
if args.xla:
os.environ["TF_XLA_ENABLE_GPU_GRAPH_CAPTURE"] = "1"
if args.amp:
os.environ["XLA_FLAGS"] = "--xla_gpu_force_conv_nhwc"
tf.config.optimizer.set_jit(True)
if hvd.size() > 1:
tf.config.threading.set_inter_op_parallelism_threads(max(2, (multiprocessing.cpu_count() // hvd.size()) - 2))
else:
tf.config.threading.set_inter_op_parallelism_threads(8)
if args.amp:
tf.keras.mixed_precision.set_global_policy("mixed_float16")
def is_main_process():
return hvd.rank() == 0
def progress_bar(iterable, *args, quiet, **kwargs):
if quiet or not is_main_process():
return iterable
return tqdm(iterable, *args, **kwargs)
def rank_zero_only(fn):
@wraps(fn)
def wrapped_fn(*args, **kwargs):
if is_main_process():
return fn(*args, **kwargs)
return wrapped_fn
def set_seed(seed):
np.random.seed(seed)
tf.random.set_seed(seed)
def get_task_code(args):
return f"{args.task}_{args.dim}d_tf2"
def get_config_file(args):
task_code = get_task_code(args)
path = os.path.join(args.data, "config.pkl")
if not os.path.exists(path):
path = os.path.join(args.data, task_code, "config.pkl")
return pickle.load(open(path, "rb"))
def get_tta_flips(dim):
if dim == 2:
return [[1], [2], [1, 2]]
return [[1], [2], [3], [1, 2], [1, 3], [2, 3], [1, 2, 3]]
def make_empty_dir(path, force=False):
path = Path(path)
if path.exists():
if not path.is_dir():
print(f"Output path {path} exists and is not a directory." "Please remove it and try again.")
sys.exit(1)
else:
if not force:
decision = input(f"Output path {path} exists. Continue and replace it? [Y/n]: ")
if decision.strip().lower() not in ["", "y"]:
sys.exit(1)
shutil.rmtree(path, ignore_errors=True)
path.mkdir(parents=True)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/runtime/utils.py |
import argparse
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from pathlib import Path
def positive_int(value):
ivalue = int(value)
if ivalue <= 0:
raise argparse.ArgumentTypeError(f"Argparse error. Expected a positive integer but got {value}")
return ivalue
def non_negative_int(value):
ivalue = int(value)
if ivalue < 0:
raise argparse.ArgumentTypeError(f"Argparse error. Expected a non-negative integer but got {value}")
return ivalue
def float_0_1(value):
fvalue = float(value)
if not (0 <= fvalue <= 1):
raise argparse.ArgumentTypeError(f"Argparse error. Expected a float from range (0, 1), but got {value}")
return fvalue
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
class ArgParser(ArgumentParser):
def arg(self, *args, **kwargs):
return super().add_argument(*args, **kwargs)
def flag(self, *args, **kwargs):
return super().add_argument(*args, action="store_true", **kwargs)
def boolean_flag(self, *args, **kwargs):
return super().add_argument(*args, type=str2bool, nargs="?", const=True, metavar="BOOLEAN", **kwargs)
def get_main_args():
p = ArgParser(formatter_class=ArgumentDefaultsHelpFormatter)
# Runtime
p.arg(
"--exec-mode",
"--exec_mode",
type=str,
choices=["train", "evaluate", "predict", "export"],
default="train",
help="Execution mode to run the model",
)
p.arg("--gpus", type=non_negative_int, default=1)
p.arg("--data", type=Path, default=Path("/data"), help="Path to data directory")
p.arg("--task", type=str, default="01", help="Task number, MSD uses numbers 01-10")
p.arg("--dim", type=int, choices=[2, 3], default=3, help="UNet dimension")
p.arg("--seed", type=non_negative_int, default=None, help="Random seed")
p.flag("--benchmark", help="Run model benchmarking")
p.boolean_flag("--tta", default=False, help="Enable test time augmentation")
p.boolean_flag("--save-preds", "--save_preds", default=False, help="Save predictions")
# Logging
p.arg("--results", type=Path, default=Path("/results"), help="Path to results directory")
p.arg("--logname", type=str, default="dllogger.json", help="DLLogger output filename")
p.flag("--quiet", help="Minimalize stdout/stderr output")
p.boolean_flag("--use-dllogger", "--use_dllogger", default=True, help="Use DLLogger logging")
# Performance optimization
p.boolean_flag("--amp", default=False, help="Enable automatic mixed precision")
p.boolean_flag("--xla", default=False, help="Enable XLA compiling")
# Training hyperparameters and loss fn customization
p.arg("--batch-size", "--batch_size", type=positive_int, default=2, help="Batch size")
p.arg("--learning-rate", "--learning_rate", type=float, default=0.0003, help="Learning rate")
p.arg("--momentum", type=float, default=0.99, help="Momentum factor (SGD only)")
p.arg(
"--scheduler",
type=str,
default="cosine_annealing",
choices=["none", "poly", "cosine", "cosine_annealing"],
help="Learning rate scheduler",
)
p.arg("--end-learning-rate", type=float, default=0.00001, help="End learning rate for poly scheduler")
p.arg(
"--cosine-annealing-first-cycle-steps",
type=positive_int,
default=4096,
help="Length of a cosine decay cycle in steps, only with 'cosine_annealing' scheduler",
)
p.arg(
"--cosine-annealing-peak-decay", type=float_0_1, default=0.95, help="Multiplier reducing initial learning rate"
)
p.arg("--optimizer", type=str, default="adam", choices=["sgd", "adam", "radam"], help="Optimizer")
p.boolean_flag("--deep-supervision", "--deep_supervision", default=False, help="Use deep supervision.")
p.boolean_flag("--lookahead", default=False, help="Use Lookahead with the optimizer")
p.arg("--weight-decay", "--weight_decay", type=float, default=0.0001, help="Weight decay (L2 penalty)")
p.boolean_flag(
"--loss-batch-reduction",
dest="reduce_batch",
default=True,
help="Reduce batch dimension first during loss calculation",
)
p.boolean_flag(
"--loss-include-background",
dest="include_background",
default=False,
help="Include background class to loss calculation",
)
# UNet architecture
p.arg("--negative-slope", type=float, default=0.01, help="Negative slope for LeakyReLU")
p.arg(
"--norm",
type=str,
choices=["instance", "batch", "group", "none"],
default="instance",
help="Type of normalization layers",
)
# Checkpoints
p.arg(
"--ckpt-strategy",
type=str,
default="last_and_best",
choices=["last_and_best", "last_only", "none"],
help="Strategy how to save checkpoints",
)
p.arg("--ckpt-dir", type=Path, default=Path("/results/ckpt/"), help="Path to checkpoint directory")
p.arg("--saved-model-dir", type=Path, help="Path to saved model directory (for evaluation and prediction)")
p.flag("--resume-training", "--resume_training", help="Resume training from the last checkpoint")
p.boolean_flag("--load_sm", default=False, help="Load exported savedmodel")
p.boolean_flag("--validate", default=False, help="Validate exported savedmodel")
# Data loading and processing
p.arg(
"--nvol",
type=positive_int,
default=2,
help="Number of volumes which come into single batch size for 2D model",
)
p.arg(
"--oversampling",
type=float_0_1,
default=0.33,
help="Probability of crop to have some region with positive label",
)
p.arg(
"--num-workers",
type=non_negative_int,
default=8,
help="Number of subprocesses to use for data loading",
)
# Sliding window inference
p.arg(
"--overlap",
type=float_0_1,
default=0.25,
help="Amount of overlap between scans during sliding window inference",
)
p.arg(
"--blend",
"--blend-mode",
dest="blend_mode",
type=str,
choices=["gaussian", "constant"],
default="constant",
help="How to blend output of overlapping windows",
)
# Validation
p.arg("--nfolds", type=positive_int, default=5, help="Number of cross-validation folds")
p.arg("--fold", type=non_negative_int, default=0, help="Fold number")
p.arg("--epochs", type=positive_int, default=1000, help="Number of epochs")
p.arg("--skip-eval", type=non_negative_int, default=0, help="Skip evaluation for the first N epochs.")
p.arg(
"--steps-per-epoch",
type=positive_int,
help="Steps per epoch. By default ceil(training_dataset_size / batch_size / gpus)",
)
# Benchmarking
p.arg(
"--bench-steps",
type=non_negative_int,
default=200,
help="Number of benchmarked steps in total",
)
p.arg(
"--warmup-steps",
type=non_negative_int,
default=100,
help="Number of warmup steps before collecting benchmarking statistics",
)
args = p.parse_args()
return args
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/runtime/args.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
class DiceLoss(tf.keras.losses.Loss):
def __init__(self, y_one_hot=True, reduce_batch=False, eps=1e-6, include_background=False):
super().__init__()
self.y_one_hot = y_one_hot
self.reduce_batch = reduce_batch
self.eps = eps
self.include_background = include_background
def dice_coef(self, y_true, y_pred):
intersection = tf.reduce_sum(y_true * y_pred, axis=1)
pred_sum = tf.reduce_sum(y_pred, axis=1)
true_sum = tf.reduce_sum(y_true, axis=1)
dice = (2.0 * intersection + self.eps) / (pred_sum + true_sum + self.eps)
return tf.reduce_mean(dice, axis=0)
@tf.function
def call(self, y_true, y_pred):
n_class = y_pred.shape[-1]
if self.reduce_batch:
flat_shape = (1, -1, n_class)
else:
flat_shape = (y_pred.shape[0], -1, n_class)
if self.y_one_hot:
y_true = tf.one_hot(y_true, n_class)
flat_pred = tf.reshape(tf.cast(y_pred, tf.float32), flat_shape)
flat_true = tf.reshape(y_true, flat_shape)
dice_coefs = self.dice_coef(flat_true, tf.keras.activations.softmax(flat_pred, axis=-1))
if not self.include_background:
dice_coefs = dice_coefs[1:]
dice_loss = tf.reduce_mean(1 - dice_coefs)
return dice_loss
class DiceCELoss(tf.keras.losses.Loss):
def __init__(self, y_one_hot=True, **dice_kwargs):
super().__init__()
self.y_one_hot = y_one_hot
self.dice_loss = DiceLoss(y_one_hot=False, **dice_kwargs)
@tf.function
def call(self, y_true, y_pred):
y_pred = tf.cast(y_pred, tf.float32)
n_class = y_pred.shape[-1]
if self.y_one_hot:
y_true = tf.one_hot(y_true, n_class)
dice_loss = self.dice_loss(y_true, y_pred)
ce_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
labels=y_true,
logits=y_pred,
)
)
return dice_loss + ce_loss
class WeightDecay:
def __init__(self, factor):
self.factor = factor
@tf.function
def __call__(self, model):
# TODO: add_n -> accumulate_n ?
return self.factor * tf.add_n([tf.nn.l2_loss(v) for v in model.trainable_variables if "norm" not in v.name])
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/runtime/losses.py |
import itertools
import numpy as np
import tensorflow as tf
from scipy import signal
def get_window_slices(image_size, roi_size, overlap, strategy):
dim_starts = []
for image_x, roi_x in zip(image_size, roi_size):
interval = roi_x if roi_x == image_x else int(roi_x * (1 - overlap))
starts = list(range(0, image_x - roi_x + 1, interval))
if strategy == "overlap_inside" and starts[-1] + roi_x < image_x:
starts.append(image_x - roi_x)
dim_starts.append(starts)
slices = [(starts + (0,), roi_size + (-1,)) for starts in itertools.product(*dim_starts)]
batched_window_slices = [((0,) + start, (1,) + roi_size) for start, roi_size in slices]
return batched_window_slices
@tf.function
def gaussian_kernel(roi_size, sigma):
gauss = signal.windows.gaussian(roi_size[0], std=sigma * roi_size[0])
for s in roi_size[1:]:
gauss = np.outer(gauss, signal.windows.gaussian(s, std=sigma * s))
gauss = np.reshape(gauss, roi_size)
gauss = np.power(gauss, 1 / len(roi_size))
gauss /= gauss.max()
return tf.convert_to_tensor(gauss, dtype=tf.float32)
def get_importance_kernel(roi_size, blend_mode, sigma):
if blend_mode == "constant":
return tf.ones(roi_size, dtype=tf.float32)
elif blend_mode == "gaussian":
return gaussian_kernel(roi_size, sigma)
else:
raise ValueError(f'Invalid blend mode: {blend_mode}. Use either "constant" or "gaussian".')
@tf.function
def run_model(x, model, importance_map, **kwargs):
return tf.cast(model(x, **kwargs), dtype=tf.float32) * importance_map
def sliding_window_inference(
inputs,
roi_size,
model,
overlap,
n_class,
importance_map,
strategy="overlap_inside",
**kwargs,
):
image_size = tuple(inputs.shape[1:-1])
roi_size = tuple(roi_size)
# Padding to make sure that the image size is at least roi size
padded_image_size = tuple(max(image_size[i], roi_size[i]) for i in range(3))
padding_size = [image_x - input_x for image_x, input_x in zip(image_size, padded_image_size)]
paddings = [[0, 0]] + [[x // 2, x - x // 2] for x in padding_size] + [[0, 0]]
input_padded = tf.pad(inputs, paddings)
output_shape = (1, *padded_image_size, n_class)
output_sum = tf.zeros(output_shape, dtype=tf.float32)
output_weight_sum = tf.ones(output_shape, dtype=tf.float32)
window_slices = get_window_slices(padded_image_size, roi_size, overlap, strategy)
for window_slice in window_slices:
window = tf.slice(input_padded, begin=window_slice[0], size=window_slice[1])
pred = run_model(window, model, importance_map, **kwargs)
padding = [
[start, output_size - (start + size)] for start, size, output_size in zip(*window_slice, output_shape)
]
padding = padding[:-1] + [[0, 0]]
output_sum = output_sum + tf.pad(pred, padding)
output_weight_sum = output_weight_sum + tf.pad(importance_map, padding)
output = output_sum / output_weight_sum
crop_slice = [slice(pad[0], pad[0] + input_x) for pad, input_x in zip(paddings, inputs.shape[:-1])]
return output[crop_slice]
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/models/sliding_window.py |
import tensorflow as tf
from models import layers
class UNet(tf.keras.Model):
def __init__(
self,
input_shape,
n_class,
kernels,
strides,
normalization_layer,
negative_slope,
dimension,
deep_supervision,
):
super().__init__()
self.dim = dimension
self.n_class = n_class
self.negative_slope = negative_slope
self.norm = normalization_layer
self.deep_supervision = deep_supervision
filters = [min(2 ** (5 + i), 320 if dimension == 3 else 512) for i in range(len(strides))]
self.filters = filters
self.kernels = kernels
self.strides = strides
down_block = layers.ConvBlock
self.input_block = self.get_conv_block(
conv_block=down_block,
filters=filters[0],
kernel_size=kernels[0],
stride=strides[0],
input_shape=input_shape,
)
self.downsamples = self.get_block_list(
conv_block=down_block, filters=filters[1:], kernels=kernels[1:-1], strides=strides[1:-1]
)
self.bottleneck = self.get_conv_block(
conv_block=down_block, filters=filters[-1], kernel_size=kernels[-1], stride=strides[-1]
)
self.upsamples = self.get_block_list(
conv_block=layers.UpsampleBlock,
filters=filters[:-1][::-1],
kernels=kernels[1:][::-1],
strides=strides[1:][::-1],
)
self.output_block = self.get_output_block()
if self.deep_supervision:
self.deep_supervision_heads = [self.get_output_block(), self.get_output_block()]
self.n_layers = len(self.upsamples) - 1
def call(self, x, training=True):
skip_connections = []
out = self.input_block(x)
skip_connections.append(out)
for down_block in self.downsamples:
out = down_block(out)
skip_connections.append(out)
out = self.bottleneck(out)
decoder_outputs = []
for up_block in self.upsamples:
out = up_block(out, skip_connections.pop())
decoder_outputs.append(out)
out = self.output_block(out)
if training and self.deep_supervision:
out = [
out,
self.deep_supervision_heads[0](decoder_outputs[-2]),
self.deep_supervision_heads[1](decoder_outputs[-3]),
]
return out
def get_output_block(self):
return layers.OutputBlock(filters=self.n_class, dim=self.dim, negative_slope=self.negative_slope)
def get_conv_block(self, conv_block, filters, kernel_size, stride, **kwargs):
return conv_block(
dim=self.dim,
stride=stride,
norm=self.norm,
kernel_size=kernel_size,
filters=filters,
negative_slope=self.negative_slope,
**kwargs,
)
def get_block_list(self, conv_block, filters, kernels, strides):
layers = []
for filter, kernel, stride in zip(filters, kernels, strides):
conv_layer = self.get_conv_block(conv_block, filter, kernel, stride)
layers.append(conv_layer)
return layers
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/models/unet.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import tensorflow as tf
from runtime.utils import get_config_file, get_tta_flips, is_main_process
from skimage.transform import resize
from models.sliding_window import get_importance_kernel, sliding_window_inference
from models.unet import UNet
class NNUnet(tf.keras.Model):
def __init__(self, args, loaded_model=None):
super(NNUnet, self).__init__()
self.args = args
in_channels, n_class, kernels, strides, self.patch_size = self.get_unet_params(self.args)
self.n_class = n_class
input_shape = (None, None, None, in_channels)
if self.args.dim == 3:
input_shape = (None,) + input_shape
if loaded_model is not None:
input_dtype = tf.float16 if args.amp else tf.float32
@tf.function
def wrapped_model(inputs, *args, **kwargs):
return loaded_model(tf.cast(inputs, dtype=input_dtype), *args, **kwargs)
self.model = wrapped_model
else:
if not self.args.xla and self.args.norm == "instance":
self.args.norm = "atex_instance"
self.model = UNet(
input_shape=input_shape,
n_class=n_class,
kernels=kernels,
strides=strides,
dimension=self.args.dim,
normalization_layer=self.args.norm,
negative_slope=self.args.negative_slope,
deep_supervision=self.args.deep_supervision,
)
if is_main_process():
print(f"Filters: {self.model.filters},\nKernels: {kernels}\nStrides: {strides}")
self.tta_flips = get_tta_flips(self.args.dim)
if self.args.dim == 3:
self.predictor = self.sw_inference
elif self.args.benchmark:
self.predictor = self.call
else:
self.predictor = self.call_2d
if args.dim == 3:
importance_kernel = get_importance_kernel(self.patch_size, args.blend_mode, 0.125)
self.importance_map = tf.tile(
tf.reshape(importance_kernel, shape=[1, *self.patch_size, 1]),
multiples=[1, 1, 1, 1, n_class],
)
@tf.function
def call(self, *args, **kwargs):
return self.model(*args, **kwargs)
@tf.function(reduce_retracing=True)
def call_2d(self, *args, **kwargs):
return self.model(*args, **kwargs)
@tf.function
def compute_loss(self, loss_fn, label, preds):
if self.args.deep_supervision:
upsample_layer = tf.keras.layers.UpSampling3D if self.args.dim == 3 else tf.keras.layers.UpSampling2D
loss = loss_fn(label, preds[0])
upsample_factor = np.ones(self.args.dim, dtype=np.uint8)
for i, pred in enumerate(preds[1:]):
upsample_factor = upsample_factor * self.model.strides[i + 1]
upsampled_pred = upsample_layer(upsample_factor)(pred)
loss += 0.5 ** (i + 1) * loss_fn(label, upsampled_pred)
c_norm = 1 / (2 - 2 ** (-len(preds)))
return c_norm * loss
return loss_fn(label, preds)
def sw_inference(self, img, **kwargs):
return sliding_window_inference(
inputs=img,
roi_size=self.patch_size,
model=self.model,
overlap=self.args.overlap,
n_class=self.n_class,
importance_map=self.importance_map,
**kwargs,
)
def inference(self, img):
pred = self.predictor(img, training=False)
if self.args.tta:
for flip_axes in self.tta_flips:
flipped_img = tf.reverse(img, axis=flip_axes)
flipped_pred = self.predictor(flipped_img, training=False)
pred = pred + tf.reverse(flipped_pred, axis=flip_axes)
pred = pred / (len(self.tta_flips) + 1)
return pred
@staticmethod
def get_unet_params(args):
config = get_config_file(args)
patch_size, spacings = config["patch_size"], config["spacings"]
strides, kernels, sizes = [], [], patch_size[:]
while True:
spacing_ratio = [spacing / min(spacings) for spacing in spacings]
stride = [2 if ratio <= 2 and size >= 8 else 1 for (ratio, size) in zip(spacing_ratio, sizes)]
kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio]
if all(s == 1 for s in stride):
break
sizes = [i / j for i, j in zip(sizes, stride)]
spacings = [i * j for i, j in zip(spacings, stride)]
kernels.append(kernel)
strides.append(stride)
if len(strides) == 5:
break
strides.insert(0, len(spacings) * [1])
kernels.append(len(spacings) * [3])
return config["in_channels"], config["n_class"], kernels, strides, patch_size
@staticmethod
def layout_2d(x):
if x is None:
return None
batch_size, depth, height, width, channels = x.shape
return tf.reshape(x, (batch_size * depth, height, width, channels))
def adjust_batch(self, features, labels):
if self.args.dim == 2:
features, labels = self.layout_2d(features), self.layout_2d(labels)
return features, labels
def save_pred(self, pred, meta, idx, data_module, save_dir):
meta = meta[0].numpy()
original_shape = meta[2]
min_d, max_d = meta[0, 0], meta[1, 0]
min_h, max_h = meta[0, 1], meta[1, 1]
min_w, max_w = meta[0, 2], meta[1, 2]
if len(pred.shape) == 5 and pred.shape[0] == 1:
pred = tf.squeeze(pred, 0)
if not all(original_shape == pred.shape[:-1]):
paddings = [
[min_d, original_shape[0] - max_d],
[min_h, original_shape[1] - max_h],
[min_w, original_shape[2] - max_w],
[0, 0],
]
final_pred = tf.pad(pred, paddings=paddings)
else:
final_pred = pred
final_pred = tf.nn.softmax(final_pred, axis=-1)
final_pred = final_pred.numpy()
final_pred = np.moveaxis(final_pred, -1, 0)
if not all(original_shape == final_pred.shape[1:]):
class_ = final_pred.shape[0]
resized_pred = np.zeros((class_, *original_shape))
for i in range(class_):
resized_pred[i] = resize(
final_pred[i], original_shape, order=3, mode="edge", cval=0, clip=True, anti_aliasing=False
)
final_pred = resized_pred
fname = data_module.test_fname(idx)
output_fname = os.path.basename(fname).replace("_x", "")
np.save(os.path.join(save_dir, output_fname), final_pred, allow_pickle=False)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/models/nn_unet.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import nv_norms
import tensorflow as tf
import tensorflow_addons as tfa
convolutions = {
"Conv2d": tf.keras.layers.Conv2D,
"Conv3d": tf.keras.layers.Conv3D,
"ConvTranspose2d": tf.keras.layers.Conv2DTranspose,
"ConvTranspose3d": tf.keras.layers.Conv3DTranspose,
}
class KaimingNormal(tf.keras.initializers.VarianceScaling):
def __init__(self, negative_slope, seed=None):
super().__init__(
scale=2.0 / (1 + negative_slope**2), mode="fan_in", distribution="untruncated_normal", seed=seed
)
def get_config(self):
return {"seed": self.seed}
def get_norm(name):
if "group" in name:
return tfa.layers.GroupNormalization(32, axis=-1, center=True, scale=True)
elif "batch" in name:
return tf.keras.layers.BatchNormalization(axis=-1, center=True, scale=True)
elif "atex_instance" in name:
return nv_norms.InstanceNormalization(axis=-1)
elif "instance" in name:
return tfa.layers.InstanceNormalization(axis=-1, center=True, scale=True)
elif "none" in name:
return tf.identity
else:
raise ValueError("Invalid normalization layer")
def extract_args(kwargs):
args = {}
if "input_shape" in kwargs:
args["input_shape"] = kwargs["input_shape"]
return args
def get_conv(filters, kernel_size, stride, dim, use_bias=False, **kwargs):
conv = convolutions[f"Conv{dim}d"]
return conv(
filters=filters,
kernel_size=kernel_size,
strides=stride,
padding="same",
use_bias=use_bias,
kernel_initializer=KaimingNormal(kwargs["negative_slope"]),
data_format="channels_last",
**extract_args(kwargs),
)
def get_transp_conv(filters, kernel_size, stride, dim, **kwargs):
conv = convolutions[f"ConvTranspose{dim}d"]
return conv(
filters=filters,
kernel_size=kernel_size,
strides=stride,
padding="same",
use_bias=True,
data_format="channels_last",
**extract_args(kwargs),
)
class ConvLayer(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, stride, **kwargs):
super().__init__()
self.conv = get_conv(filters, kernel_size, stride, **kwargs)
self.norm = get_norm(kwargs["norm"])
self.lrelu = tf.keras.layers.LeakyReLU(alpha=kwargs["negative_slope"])
def call(self, data):
out = self.conv(data)
out = self.norm(out)
out = self.lrelu(out)
return out
class ConvBlock(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, stride, **kwargs):
super().__init__()
self.conv1 = ConvLayer(filters, kernel_size, stride, **kwargs)
kwargs.pop("input_shape", None)
self.conv2 = ConvLayer(filters, kernel_size, 1, **kwargs)
def call(self, input_data):
out = self.conv1(input_data)
out = self.conv2(out)
return out
class UpsampleBlock(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, stride, **kwargs):
super().__init__()
self.transp_conv = get_transp_conv(filters, stride, stride, **kwargs)
self.conv_block = ConvBlock(filters, kernel_size, 1, **kwargs)
def call(self, input_data, skip_data):
out = self.transp_conv(input_data)
out = tf.concat((out, skip_data), axis=-1)
out = self.conv_block(out)
return out
class OutputBlock(tf.keras.layers.Layer):
def __init__(self, filters, dim, negative_slope):
super().__init__()
self.conv = get_conv(
filters,
kernel_size=1,
stride=1,
dim=dim,
use_bias=True,
negative_slope=negative_slope,
)
def call(self, data):
return self.conv(data)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/models/layers.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
from argparse import ArgumentParser
from pathlib import Path
parser = ArgumentParser()
parser.add_argument("--mode", type=str, required=True, choices=["train", "predict"], help="Benchmarking mode")
parser.add_argument("--task", type=str, default="01", help="Task code")
parser.add_argument("--dim", type=int, required=True, choices=[2, 3], help="Dimension of UNet")
parser.add_argument("--gpus", type=int, default=1, help="Number of gpus")
parser.add_argument("--batch-size", "--batch_size", type=int, required=True)
parser.add_argument("--amp", action="store_true", help="Enable automatic mixed precision")
parser.add_argument("--bind", action="store_true", help="Bind CPUs for each GPU. Improves throughput for multi-GPU.")
parser.add_argument("--horovod", action="store_true")
parser.add_argument("--xla", action="store_true", help="Enable XLA compiling")
parser.add_argument(
"--bench-steps", "--bench_steps", type=int, default=200, help="Number of benchmarked steps in total"
)
parser.add_argument(
"--warmup-steps", "--warmup_steps", type=int, default=100, help="Warmup iterations before collecting statistics"
)
parser.add_argument("--results", type=Path, default=Path("/results"), help="Path to results directory")
parser.add_argument("--logname", type=str, default="perf.json", help="Name of the dlloger output")
if __name__ == "__main__":
args = parser.parse_args()
path_to_main = Path(__file__).resolve().parent.parent / "main.py"
cmd = f"horovodrun -np {args.gpus} " if args.horovod else ""
if args.bind:
cmd += "bindpcie --cpu=exclusive,nosmt "
cmd += f"python {path_to_main} --benchmark --ckpt-strategy none --seed 0 "
cmd += f"--exec-mode {args.mode} "
cmd += f"--task {args.task} "
cmd += f"--dim {args.dim} "
cmd += f"--batch-size {args.batch_size} "
cmd += f"--amp {args.amp} "
cmd += f"--xla {args.xla} "
cmd += f"--bench-steps {args.bench_steps} "
cmd += f"--warmup-steps {args.warmup_steps} "
cmd += f"--results {args.results} "
cmd += f"--logname {args.logname} "
cmd += f"--gpus {args.gpus} "
subprocess.run(cmd, shell=True)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/scripts/benchmark.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from pathlib import Path
from subprocess import call
parser = ArgumentParser()
parser.add_argument("--task", type=str, default="01", help="Task code")
parser.add_argument("--dim", type=int, required=True, choices=[2, 3], help="Dimension of UNet")
parser.add_argument("--gpus", type=int, default=1, help="Number of gpus")
parser.add_argument("--seed", type=int, default=1, help="Random seed")
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--fold", type=int, required=True, choices=[0, 1, 2, 3, 4], help="Fold number")
parser.add_argument("--amp", action="store_true", help="Enable automatic mixed precision")
parser.add_argument("--tta", action="store_true", help="Enable test time augmentation")
parser.add_argument("--horovod", action="store_true", help="Launch horovod within script")
parser.add_argument("--bind", action="store_true", help="Bind CPUs for each GPU. Improves throughput for multi-GPU.")
parser.add_argument("--results", type=Path, default=Path("/results"), help="Path to results directory")
parser.add_argument("--logname", type=str, default="train_log.json", help="Name of the dlloger output")
if __name__ == "__main__":
args = parser.parse_args()
skip = 100 if args.gpus == 1 else 150
path_to_main = Path(__file__).resolve().parent.parent / "main.py"
cmd = f"horovodrun -np {args.gpus} " if args.horovod else ""
if args.bind:
cmd += "bindpcie --cpu=exclusive,nosmt "
cmd += f"python {path_to_main} --exec-mode train --deep_supervision --xla --skip-eval {skip} "
cmd += f"--task {args.task} "
cmd += f"--dim {args.dim} "
cmd += f"--epochs {300 if args.gpus == 1 else 600} "
cmd += f"--batch-size {2 if args.dim == 3 else 64} "
cmd += f"--learning_rate {args.learning_rate} "
cmd += f"--fold {args.fold} "
cmd += f"--amp {args.amp} "
cmd += f"--tta {args.tta} "
cmd += f"--results {args.results} "
cmd += f"--logname {args.logname} "
cmd += f"--gpus {args.gpus} "
cmd += f"--seed {args.seed} "
call(cmd, shell=True)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/scripts/train.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from pathlib import Path
from subprocess import call
parser = ArgumentParser()
parser.add_argument("--data", type=Path, required=True, help="Path to data")
parser.add_argument("--task", type=str, default="01", help="Task code")
parser.add_argument("--dim", type=int, required=True, choices=[2, 3], help="Dimension of UNet")
parser.add_argument("--batch-size", "--batch_size", type=int, default=2, help="Batch size")
parser.add_argument("--fold", type=int, required=True, choices=[0, 1, 2, 3, 4], help="Fold number")
parser.add_argument("--amp", action="store_true", help="Enable automatic mixed precision")
parser.add_argument("--tta", action="store_true", help="Enable test time augmentation")
parser.add_argument("--save-preds", "--save_preds", action="store_true", help="Save predicted masks")
parser.add_argument(
"--results", type=Path, default=Path("/results"), help="Path to results directory, output for the predicted masks"
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--ckpt-dir", "--ckpt_dir", type=Path, help="Path to checkpoint directory")
group.add_argument("--saved-model-dir", "--saved_model_dir", type=Path, help="Path to saved model directory")
if __name__ == "__main__":
args = parser.parse_args()
path_to_main = Path(__file__).resolve().parent.parent / "main.py"
cmd = ""
cmd += f"python {path_to_main} --exec-mode predict "
cmd += f"--data {args.data} "
cmd += f"--task {args.task} "
cmd += f"--dim {args.dim} "
cmd += f"--batch-size {args.batch_size} "
cmd += f"--fold {args.fold} "
cmd += f"--amp {args.amp} "
cmd += f"--tta {args.tta} "
cmd += f"--save-preds {args.save_preds} "
cmd += f"--results {args.results} "
if args.ckpt_dir:
cmd += f"--ckpt-dir {args.ckpt_dir} "
elif args.saved_model_dir:
cmd += f"--saved-model-dir {args.saved_model_dir} "
cmd += "--use-wandb false"
call(cmd, shell=True)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/scripts/inference.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
def generate_foreground_bounding_box(img):
"""
Generate the spatial bounding box of foreground in the image with start-end positions.
Foreground is defined by positive intensity across channels.
The output format of the coordinates is:
[1st_spatial_dim_start, 2nd_spatial_dim_start, ..., Nth_spatial_dim_start],
[1st_spatial_dim_end, 2nd_spatial_dim_end, ..., Nth_spatial_dim_end]
The bounding boxes edges are aligned with the input image edges.
This function returns [0, 0, ...], [0, 0, ...] if there's no positive intensity.
Args:
img: source image to generate bounding box from.
"""
data = np.any(img > 0, axis=0)
ndim = len(data.shape)
if not data.any():
return [0] * ndim, [0] * ndim
else:
indices = np.where(data)
box_start = [ax.min() for ax in indices]
box_end = [ax.max() + 1 for ax in indices]
return box_start, box_end
def spatial_crop(img, box_start, box_end):
slices = [slice(s, e) for s, e in zip(box_start, box_end)]
sd = min(len(slices), len(img.shape[1:]))
slices = [slice(None)] + slices[:sd]
return img[tuple(slices)]
def crop_foreground(image, label=None):
box_start, box_end = generate_foreground_bounding_box(image)
box_start = np.asarray(box_start, dtype=np.int16)
box_end = np.asarray(box_end, dtype=np.int16)
image_cropped = spatial_crop(image, box_start, box_end)
label_cropped = spatial_crop(label, box_start, box_end) if label is not None else None
return image_cropped, label_cropped, (box_start, box_end)
def _normalize(img, nonzero, eps=1e-7):
slices = (img != 0) if nonzero else np.ones(img.shape, dtype=bool)
if not np.any(slices):
return img
sub = np.mean(img[slices])
div = np.std(img[slices])
if div == 0.0:
div = eps
img[slices] = (img[slices] - sub) / div
return img
def normalize_intensity(img, nonzero=True, channel_wise=True):
if channel_wise:
for i, d in enumerate(img):
img[i] = _normalize(d, nonzero=nonzero)
else:
img = _normalize(img, nonzero=nonzero)
return img.astype(np.float32)
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/data_preprocessing/transforms.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
task = {
"01": "Task01_BrainTumour",
"02": "Task02_Heart",
"03": "Task03_Liver",
"04": "Task04_Hippocampus",
"05": "Task05_Prostate",
"06": "Task06_Lung",
"07": "Task07_Pancreas",
"08": "Task08_HepaticVessel",
"09": "Task09_Spleen",
"10": "Task10_Colon",
}
patch_size = {
"01_3d_tf2": [128, 128, 128],
"02_3d_tf2": [80, 192, 160],
"03_3d_tf2": [128, 128, 128],
"04_3d_tf2": [40, 56, 40],
"05_3d_tf2": [20, 320, 256],
"06_3d_tf2": [80, 192, 160],
"07_3d_tf2": [40, 224, 224],
"08_3d_tf2": [64, 192, 192],
"09_3d_tf2": [64, 192, 160],
"10_3d_tf2": [56, 192, 160],
"01_2d_tf2": [192, 160],
"02_2d_tf2": [320, 256],
"03_2d_tf2": [512, 512],
"04_2d_tf2": [56, 40],
"05_2d_tf2": [320, 320],
"06_2d_tf2": [512, 512],
"07_2d_tf2": [512, 512],
"08_2d_tf2": [512, 512],
"09_2d_tf2": [512, 512],
"10_2d_tf2": [512, 512],
}
spacings = {
"01_3d_tf2": [1.0, 1.0, 1.0],
"02_3d_tf2": [1.37, 1.25, 1.25],
"03_3d_tf2": [1, 0.7676, 0.7676],
"04_3d_tf2": [1.0, 1.0, 1.0],
"05_3d_tf2": [3.6, 0.62, 0.62],
"06_3d_tf2": [1.24, 0.79, 0.79],
"07_3d_tf2": [2.5, 0.8, 0.8],
"08_3d_tf2": [1.5, 0.8, 0.8],
"09_3d_tf2": [1.6, 0.79, 0.79],
"10_3d_tf2": [3, 0.78, 0.78],
"11_3d_tf2": [5, 0.741, 0.741],
"01_2d_tf2": [1.0, 1.0],
"02_2d_tf2": [1.25, 1.25],
"03_2d_tf2": [0.7676, 0.7676],
"04_2d_tf2": [1.0, 1.0],
"05_2d_tf2": [0.62, 0.62],
"06_2d_tf2": [0.79, 0.79],
"07_2d_tf2": [0.8, 0.8],
"08_2d_tf2": [0.8, 0.8],
"09_2d_tf2": [0.79, 0.79],
"10_2d_tf2": [0.78, 0.78],
}
ct_min = {
"03": -17,
"06": -1024,
"07": -96,
"08": -3,
"09": -41,
"10": -30,
"11": -958,
}
ct_max = {
"03": 201,
"06": 325,
"07": 215,
"08": 243,
"09": 176,
"10": 165.82,
"11": 93,
}
ct_mean = {"03": 99.4, "06": -158.58, "07": 77.9, "08": 104.37, "09": 99.29, "10": 62.18, "11": -547.7}
ct_std = {"03": 39.36, "06": 324.7, "07": 75.4, "08": 52.62, "09": 39.47, "10": 32.65, "11": 281.08}
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/data_preprocessing/configs.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import json
import math
import os
import pickle
from pathlib import Path
import nibabel
import numpy as np
from joblib import Parallel, delayed
from runtime.utils import get_task_code, make_empty_dir
from skimage.transform import resize
from data_preprocessing import configs, transforms
class Preprocessor:
def __init__(self, args):
self.args = args
self.ct_min = 0
self.ct_max = 0
self.ct_mean = 0
self.ct_std = 0
self.target_spacing = None
self.task = args.task
self.task_code = get_task_code(args)
self.patch_size = configs.patch_size[self.task_code]
self.training = args.exec_mode == "training"
self.data_path = args.data / configs.task[args.task]
self.results = args.results / self.task_code
if not self.training:
self.results /= self.args.exec_mode
metadata_path = self.data_path / "dataset.json"
if self.args.exec_mode == "val":
dataset_json = json.load(open(metadata_path, "r"))
dataset_json["val"] = dataset_json["training"]
with open(metadata_path, "w") as outfile:
json.dump(dataset_json, outfile)
self.metadata = json.load(open(metadata_path, "r"))
self.modality = self.metadata["modality"]["0"]
def run(self):
print(f"Preprocessing {self.data_path}")
make_empty_dir(self.results, force=self.args.force)
if self.task_code in configs.spacings:
self.target_spacing = configs.spacings[self.task_code]
else:
self.collect_spacings()
print(f"Target spacing {self.target_spacing}")
if self.modality == "CT":
try:
self.ct_min = configs.ct_min[self.task]
self.ct_max = configs.ct_max[self.task]
self.ct_mean = configs.ct_mean[self.task]
self.ct_std = configs.ct_std[self.task]
except KeyError:
self.collect_intensities()
_mean = round(self.ct_mean, 2)
_std = round(self.ct_std, 2)
print(f"[CT] min: {self.ct_min}, max: {self.ct_max}, mean: {_mean}, std: {_std}")
self.run_parallel(self.preprocess_pair, self.args.exec_mode)
pickle.dump(
{
"patch_size": self.patch_size,
"spacings": self.target_spacing,
"n_class": len(self.metadata["labels"]),
"in_channels": len(self.metadata["modality"]),
},
open(os.path.join(self.results, "config.pkl"), "wb"),
)
def preprocess_pair(self, pair):
fname = os.path.basename(pair["image"] if isinstance(pair, dict) else pair)
image, label, image_spacings = self.load_pair(pair)
original_size = image.shape[1:]
image, label, bbox = transforms.crop_foreground(image, label)
test_metadata = np.vstack([bbox, original_size]) if not self.training else None
if self.args.dim == 3:
image, label = self.resample(image, label, image_spacings)
if self.modality == "CT":
image = np.clip(image, self.ct_min, self.ct_max)
image = self.normalize(image)
if self.training:
image, label = self.standardize(image, label)
image, label = np.transpose(image, (1, 2, 3, 0)), np.transpose(label, (1, 2, 3, 0))
self.save(image, label, fname, test_metadata)
def resample(self, image, label, image_spacings):
if self.target_spacing != image_spacings:
image, label = self.resample_pair(image, label, image_spacings)
return image, label
def standardize(self, image, label):
pad_shape = self.calculate_pad_shape(image)
image_shape = image.shape[1:]
if pad_shape != image_shape:
paddings = [(pad_sh - image_sh) / 2 for (pad_sh, image_sh) in zip(pad_shape, image_shape)]
image = self.pad(image, paddings)
label = self.pad(label, paddings)
if self.args.dim == 2: # Center cropping 2D images.
_, _, height, weight = image.shape
start_h = (height - self.patch_size[0]) // 2
start_w = (weight - self.patch_size[1]) // 2
image = image[:, :, start_h : start_h + self.patch_size[0], start_w : start_w + self.patch_size[1]]
label = label[:, :, start_h : start_h + self.patch_size[0], start_w : start_w + self.patch_size[1]]
return image, label
def normalize(self, image):
if self.modality == "CT":
return (image - self.ct_mean) / self.ct_std
return transforms.normalize_intensity(image, nonzero=True, channel_wise=True)
def save(self, image, label, fname, test_metadata):
mean, std = np.round(np.mean(image, (0, 1, 2)), 2), np.round(np.std(image, (0, 1, 2)), 2)
print(f"Saving {fname} shape {image.shape} mean {mean} std {std}")
self.save_npy(image, fname, "_x.npy")
if label is not None:
self.save_npy(label, fname, "_y.npy")
if test_metadata is not None:
self.save_npy(test_metadata, fname, "_meta.npy")
def load_pair(self, pair):
image = self.load_nifty(pair["image"] if isinstance(pair, dict) else pair)
image_spacing = self.load_spacing(image)
image = image.get_fdata().astype(np.float32)
image = self.standardize_layout(image)
if self.training:
label = self.load_nifty(pair["label"]).get_fdata().astype(np.uint8)
label = self.standardize_layout(label)
else:
label = None
return image, label, image_spacing
def resample_pair(self, image, label, spacing):
shape = self.calculate_new_shape(spacing, image.shape[1:])
if self.check_anisotrophy(spacing):
image = self.resample_anisotrophic_image(image, shape)
if label is not None:
label = self.resample_anisotrophic_label(label, shape)
else:
image = self.resample_regular_image(image, shape)
if label is not None:
label = self.resample_regular_label(label, shape)
image = image.astype(np.float32)
if label is not None:
label = label.astype(np.uint8)
return image, label
def calculate_pad_shape(self, image):
min_shape = self.patch_size[:]
image_shape = image.shape[1:]
if len(min_shape) == 2: # In 2D case we don't want to pad depth axis.
min_shape.insert(0, image_shape[0])
pad_shape = [max(mshape, ishape) for mshape, ishape in zip(min_shape, image_shape)]
return pad_shape
def get_intensities(self, pair):
image = self.load_nifty(pair["image"]).get_fdata().astype(np.float32)
label = self.load_nifty(pair["label"]).get_fdata().astype(np.uint8)
foreground_idx = np.where(label > 0)
intensities = image[foreground_idx].tolist()
return intensities
def collect_intensities(self):
intensities = self.run_parallel(self.get_intensities, "training")
intensities = list(itertools.chain.from_iterable(intensities))
self.ct_min, self.ct_max = np.percentile(intensities, [0.5, 99.5])
self.ct_mean, self.ct_std = np.mean(intensities), np.std(intensities)
def get_spacing(self, pair):
image = nibabel.load(self.data_path / pair["image"])
spacing = self.load_spacing(image)
return spacing
def collect_spacings(self):
spacing = self.run_parallel(self.get_spacing, "training")
spacing = np.array(spacing)
target_spacing = np.median(spacing, axis=0)
if max(target_spacing) / min(target_spacing) >= 3:
lowres_axis = np.argmin(target_spacing)
target_spacing[lowres_axis] = np.percentile(spacing[:, lowres_axis], 10)
self.target_spacing = list(target_spacing)
def check_anisotrophy(self, spacing):
def check(spacing):
return np.max(spacing) / np.min(spacing) >= 3
return check(spacing) or check(self.target_spacing)
def calculate_new_shape(self, spacing, shape):
spacing_ratio = np.array(spacing) / np.array(self.target_spacing)
new_shape = (spacing_ratio * np.array(shape)).astype(int).tolist()
return new_shape
def save_npy(self, image, fname, suffix):
np.save(os.path.join(self.results, fname.replace(".nii.gz", suffix)), image, allow_pickle=False)
def run_parallel(self, func, exec_mode):
return Parallel(n_jobs=self.args.n_jobs)(delayed(func)(pair) for pair in self.metadata[exec_mode])
def load_nifty(self, fname):
return nibabel.load(os.path.join(self.data_path, fname))
@staticmethod
def load_spacing(image):
return image.header["pixdim"][1:4].tolist()[::-1]
@staticmethod
def pad(image, padding):
pad_d, pad_w, pad_h = padding
return np.pad(
image,
(
(0, 0),
(math.floor(pad_d), math.ceil(pad_d)),
(math.floor(pad_w), math.ceil(pad_w)),
(math.floor(pad_h), math.ceil(pad_h)),
),
)
@staticmethod
def standardize_layout(data):
if len(data.shape) == 3:
data = np.expand_dims(data, 3)
return np.transpose(data, (3, 2, 1, 0))
@staticmethod
def resize_fn(image, shape, order, mode):
return resize(image, shape, order=order, mode=mode, cval=0, clip=True, anti_aliasing=False)
def resample_anisotrophic_image(self, image, shape):
resized_channels = []
for image_c in image:
resized = [self.resize_fn(i, shape[1:], 3, "edge") for i in image_c]
resized = np.stack(resized, axis=0)
resized = self.resize_fn(resized, shape, 0, "constant")
resized_channels.append(resized)
resized = np.stack(resized_channels, axis=0)
return resized
def resample_regular_image(self, image, shape):
resized_channels = []
for image_c in image:
resized_channels.append(self.resize_fn(image_c, shape, 3, "edge"))
resized = np.stack(resized_channels, axis=0)
return resized
def resample_anisotrophic_label(self, label, shape):
depth = label.shape[1]
reshaped = np.zeros(shape, dtype=np.uint8)
shape_2d = shape[1:]
reshaped_2d = np.zeros((depth, *shape_2d), dtype=np.uint8)
n_class = np.max(label)
for class_ in range(1, n_class + 1):
for depth_ in range(depth):
mask = label[0, depth_] == class_
resized_2d = self.resize_fn(mask.astype(float), shape_2d, 1, "edge")
reshaped_2d[depth_][resized_2d >= 0.5] = class_
for class_ in range(1, n_class + 1):
mask = reshaped_2d == class_
resized = self.resize_fn(mask.astype(float), shape, 0, "constant")
reshaped[resized >= 0.5] = class_
reshaped = np.expand_dims(reshaped, 0)
return reshaped
def resample_regular_label(self, label, shape):
reshaped = np.zeros(shape, dtype=np.uint8)
n_class = np.max(label)
for class_ in range(1, n_class + 1):
mask = label[0] == class_
resized = self.resize_fn(mask.astype(float), shape, 1, "edge")
reshaped[resized >= 0.5] = class_
reshaped = np.expand_dims(reshaped, 0)
return reshaped
| DeepLearningExamples-master | TensorFlow2/Segmentation/nnUNet/data_preprocessing/preprocessor.py |
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import collections
import json
import os
import tensorflow as tf
from utils import log, heading
from run_pretraining import PretrainingConfig
from modeling import PretrainingModel
def from_pretrained_ckpt(args):
config = PretrainingConfig(
model_name='postprocessing',
data_dir='postprocessing',
generator_hidden_size=0.3333333,
)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
if args.amp:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
print('Compute dtype: %s' % policy.compute_dtype) # Compute dtype: float16
print('Variable dtype: %s' % policy.variable_dtype) # Variable dtype: float32
# Set up model
model = PretrainingModel(config)
# Load checkpoint
checkpoint = tf.train.Checkpoint(step=tf.Variable(1), model=model)
checkpoint.restore(args.pretrained_checkpoint).expect_partial()
log(" ** Restored from {} at step {}".format(args.pretrained_checkpoint, int(checkpoint.step) - 1))
disc_dir = os.path.join(args.output_dir, 'discriminator')
gen_dir = os.path.join(args.output_dir, 'generator')
heading(" ** Saving discriminator")
model.discriminator(model.discriminator.dummy_inputs)
model.discriminator.save_pretrained(disc_dir)
heading(" ** Saving generator")
model.generator(model.generator.dummy_inputs)
model.generator.save_pretrained(gen_dir)
if __name__ == '__main__':
# Parse essential args
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained_checkpoint')
parser.add_argument('--output_dir')
parser.add_argument('--amp', action='store_true', default=False)
args = parser.parse_args()
from_pretrained_ckpt(args)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/postprocess_pretrained_ckpt.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Configuration base class and utilities."""
import copy
import json
import logging
import os
from typing import Dict, Optional, Tuple
from utils import log
from file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
logger = logging.getLogger(__name__)
class PretrainedConfig(object):
r""" Base class for all configuration classes.
Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving configurations.
Note:
A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to initialize a model does **not** load the model weights.
It only affects the model's configuration.
Class attributes (overridden by derived classes):
- ``pretrained_config_archive_map``: a python ``dict`` with `shortcut names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
- ``model_type``: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in :class:`~transformers.AutoConfig`.
Args:
finetuning_task (:obj:`string` or :obj:`None`, `optional`, defaults to :obj:`None`):
Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
num_labels (:obj:`int`, `optional`, defaults to `2`):
Number of classes to use when the model is a classification model (sequences/tokens)
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
Should the model returns attentions weights.
output_hidden_states (:obj:`string`, `optional`, defaults to :obj:`False`):
Should the model returns all hidden-states.
torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
Is the model used with Torchscript (for PyTorch models).
"""
pretrained_config_archive_map = {} # type: Dict[str, str]
model_type = "" # type: str
def __init__(self, **kwargs):
# Attributes with defaults
self.output_attentions = kwargs.pop("output_attentions", False)
self.output_hidden_states = kwargs.pop("output_hidden_states", False)
self.output_past = kwargs.pop("output_past", True) # Not used by all models
self.torchscript = kwargs.pop("torchscript", False) # Only used by PyTorch models
self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
self.pruned_heads = kwargs.pop("pruned_heads", {})
# Is decoder is used in encoder-decoder models to differentiate encoder from decoder
self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
self.is_decoder = kwargs.pop("is_decoder", False)
# Parameters for sequence generation
self.max_length = kwargs.pop("max_length", 20)
self.min_length = kwargs.pop("min_length", 0)
self.do_sample = kwargs.pop("do_sample", False)
self.early_stopping = kwargs.pop("early_stopping", False)
self.num_beams = kwargs.pop("num_beams", 1)
self.temperature = kwargs.pop("temperature", 1.0)
self.top_k = kwargs.pop("top_k", 50)
self.top_p = kwargs.pop("top_p", 1.0)
self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
self.length_penalty = kwargs.pop("length_penalty", 1.0)
self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
self.bad_words_ids = kwargs.pop("bad_words_ids", None)
self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
# Fine-tuning task arguments
self.architectures = kwargs.pop("architectures", None)
self.finetuning_task = kwargs.pop("finetuning_task", None)
self.num_labels = kwargs.pop("num_labels", 2)
self.id2label = kwargs.pop("id2label", {i: "LABEL_{}".format(i) for i in range(self.num_labels)})
self.id2label = dict((int(key), value) for key, value in self.id2label.items())
self.label2id = kwargs.pop("label2id", dict(zip(self.id2label.values(), self.id2label.keys())))
self.label2id = dict((key, int(value)) for key, value in self.label2id.items())
# Tokenizer arguments TODO: eventually tokenizer and models should share the same config
self.prefix = kwargs.pop("prefix", None)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
# task specific arguments
self.task_specific_params = kwargs.pop("task_specific_params", None)
# TPU arguments
self.xla_device = kwargs.pop("xla_device", None)
# Additional attributes without default values
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
log("Can't set {} with value {} for {}".format(key, value, self))
raise err
@property
def num_labels(self):
return self._num_labels
@num_labels.setter
def num_labels(self, num_labels):
self._num_labels = num_labels
self.id2label = {i: "LABEL_{}".format(i) for i in range(self.num_labels)}
self.id2label = dict((int(key), value) for key, value in self.id2label.items())
self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
self.label2id = dict((key, int(value)) for key, value in self.label2id.items())
def save_pretrained(self, save_directory):
"""
Save a configuration object to the directory `save_directory`, so that it
can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
Args:
save_directory (:obj:`string`):
Directory where the configuration JSON file will be saved.
"""
assert os.path.isdir(
save_directory
), "Saving path should be a directory where the model and configuration can be saved"
# If we save using the predefined names, we can load using `from_pretrained`
output_config_file = os.path.join(save_directory, CONFIG_NAME)
self.to_json_file(output_config_file)
log("Configuration saved in {}".format(output_config_file))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> "PretrainedConfig":
r"""
Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
Args:
pretrained_model_name_or_path (:obj:`string`):
either:
- a string with the `shortcut name` of a pre-trained model configuration to load from cache or
download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to
our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a configuration file saved using the
:func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved configuration JSON `file`, e.g.:
``./my_model_directory/configuration.json``.
cache_dir (:obj:`string`, `optional`):
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
kwargs (:obj:`Dict[str, any]`, `optional`):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is
controlled by the `return_unused_kwargs` keyword parameter.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Force to (re-)download the model weights and configuration files and override the cached versions if they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies (:obj:`Dict`, `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g.:
:obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.`
The proxies are used on each request.
return_unused_kwargs: (`optional`) bool:
If False, then this function returns just the final configuration object.
If True, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs` is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part
of kwargs which has not been used to update `config` and is otherwise ignored.
Returns:
:class:`PretrainedConfig`: An instance of a configuration object
Examples::
# We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
# derived class: BertConfig
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
config = BertConfig.from_pretrained('./test/saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True
config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
foo=False, return_unused_kwargs=True)
assert config.output_attention == True
assert unused_kwargs == {'foo': False}
"""
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
return cls.from_dict(config_dict, **kwargs)
@classmethod
def get_config_dict(
cls, pretrained_model_name_or_path: str, pretrained_config_archive_map: Optional[Dict] = None, **kwargs
) -> Tuple[Dict, Dict]:
"""
From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used
for instantiating a Config using `from_dict`.
Parameters:
pretrained_model_name_or_path (:obj:`string`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
pretrained_config_archive_map: (:obj:`Dict[str, str]`, `optional`) Dict:
A map of `shortcut names` to `url`. By default, will use the current class attribute.
Returns:
:obj:`Tuple[Dict, Dict]`: The dictionary that will be used to instantiate the configuration object.
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
if pretrained_config_archive_map is None:
pretrained_config_archive_map = cls.pretrained_config_archive_map
if pretrained_model_name_or_path in pretrained_config_archive_map:
config_file = pretrained_config_archive_map[pretrained_model_name_or_path]
elif os.path.isdir(pretrained_model_name_or_path):
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
else:
config_file = hf_bucket_url(pretrained_model_name_or_path, postfix=CONFIG_NAME)
try:
# Load from URL or cache if already cached
resolved_config_file = cached_path(
config_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
config_dict = cls._dict_from_json_file(resolved_config_file)
except EnvironmentError:
if pretrained_model_name_or_path in pretrained_config_archive_map:
msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
config_file
)
else:
msg = (
"Can't load '{}'. Make sure that:\n\n"
"- '{}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
"- or '{}' is the correct path to a directory containing a '{}' file\n\n".format(
pretrained_model_name_or_path,
pretrained_model_name_or_path,
pretrained_model_name_or_path,
CONFIG_NAME,
)
)
raise EnvironmentError(msg)
except json.JSONDecodeError:
msg = (
"Couldn't reach server at '{}' to download configuration file or "
"configuration file is not a valid JSON file. "
"Please check network or file content here: {}.".format(config_file, resolved_config_file)
)
raise EnvironmentError(msg)
if resolved_config_file == config_file:
log("loading configuration file {}".format(config_file))
else:
log("loading configuration file {} from cache at {}".format(config_file, resolved_config_file))
return config_dict, kwargs
@classmethod
def from_dict(cls, config_dict: Dict, **kwargs) -> "PretrainedConfig":
"""
Constructs a `Config` from a Python dictionary of parameters.
Args:
config_dict (:obj:`Dict[str, any]`):
Dictionary that will be used to instantiate the configuration object. Such a dictionary can be retrieved
from a pre-trained checkpoint by leveraging the :func:`~transformers.PretrainedConfig.get_config_dict`
method.
kwargs (:obj:`Dict[str, any]`):
Additional parameters from which to initialize the configuration object.
Returns:
:class:`PretrainedConfig`: An instance of a configuration object
"""
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
config = cls(**config_dict)
if hasattr(config, "pruned_heads"):
config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())
# Update config with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
setattr(config, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
# log("Model config {}".format(str(config)))
if return_unused_kwargs:
return config, kwargs
else:
return config
@classmethod
def from_json_file(cls, json_file: str) -> "PretrainedConfig":
"""
Constructs a `Config` from the path to a json file of parameters.
Args:
json_file (:obj:`string`):
Path to the JSON file containing the parameters.
Returns:
:class:`PretrainedConfig`: An instance of a configuration object
"""
config_dict = cls._dict_from_json_file(json_file)
return cls(**config_dict)
@classmethod
def _dict_from_json_file(cls, json_file: str):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def __eq__(self, other):
return self.__dict__ == other.__dict__
def __repr__(self):
return "{} {}".format(self.__class__.__name__, self.to_json_string())
def to_dict(self):
"""
Serializes this instance to a Python dictionary.
Returns:
:obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
if hasattr(self.__class__, "model_type"):
output["model_type"] = self.__class__.model_type
return output
def to_json_string(self):
"""
Serializes this instance to a JSON string.
Returns:
:obj:`string`: String containing all the attributes that make up this configuration instance in JSON format.
"""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path):
"""
Save this instance to a json file.
Args:
json_file_path (:obj:`string`):
Path to the JSON file in which this configuration instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string())
def update(self, config_dict: Dict):
"""
Updates attributes of this class
with attributes from `config_dict`.
Args:
:obj:`Dict[str, any]`: Dictionary of attributes that shall be updated for this class.
"""
for key, value in config_dict.items():
setattr(self, key, value)
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
"bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
"bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
"bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
"bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
"bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
"bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
"bert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
"bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
"bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
"bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
"bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
"bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
"bert-base-german-dbmdz-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json",
"bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json",
"bert-base-japanese": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-config.json",
"bert-base-japanese-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-config.json",
"bert-base-japanese-char": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-config.json",
"bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-config.json",
"bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/config.json",
"bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/config.json",
"bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/config.json",
}
class BertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.BertModel`.
It is used to instantiate an BERT model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the BERT `bert-base-uncased <https://huggingface.co/bert-base-uncased>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Args:
vocab_size (:obj:`int`, optional, defaults to 30522):
Vocabulary size of the BERT model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
hidden_size (:obj:`int`, optional, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, optional, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, optional, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the encoder and pooler.
If string, "gelu", "relu", "swish" and "gelu_new" are supported.
hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, optional, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, optional, defaults to 2):
The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
initializer_range (:obj:`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers.
Example::
from transformers import BertModel, BertConfig
# Initializing a BERT bert-base-uncased style configuration
configuration = BertConfig()
# Initializing a model from the bert-base-uncased style configuration
model = BertModel(configuration)
# Accessing the model configuration
configuration = model.config
Attributes:
pretrained_config_archive_map (Dict[str, str]):
A dictionary containing all the available pre-trained checkpoints.
"""
pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "bert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps | DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/configuration_utils.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ELECTRA model configuration """
import logging
from configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/electra-small-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-generator/config.json",
"google/electra-base-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-generator/config.json",
"google/electra-large-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-generator/config.json",
"google/electra-small-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-discriminator/config.json",
"google/electra-base-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-discriminator/config.json",
"google/electra-large-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-discriminator/config.json",
}
class ElectraConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.ElectraModel`.
It is used to instantiate an ELECTRA model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the ELECTRA `google/electra-small-discriminator <https://huggingface.co/google/electra-small-discriminator>`__
architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
for more information.
Args:
vocab_size (:obj:`int`, optional, defaults to 30522):
Vocabulary size of the ELECTRA model. Defines the different tokens that
can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.ElectraModel`.
embedding_size (:obj:`int`, optional, defaults to 128):
Dimensionality of the encoder layers and the pooler layer.
hidden_size (:obj:`int`, optional, defaults to 256):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, optional, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, optional, defaults to 4):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, optional, defaults to 1024):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
The non-linear activation function (function or string) in the encoder and pooler.
If string, "gelu", "relu", "swish" and "gelu_new" are supported.
hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, optional, defaults to 512):
The maximum sequence length that this model might ever be used with.
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, optional, defaults to 2):
The vocabulary size of the `token_type_ids` passed into :class:`~transformers.ElectraModel`.
initializer_range (:obj:`float`, optional, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
The epsilon used by the layer normalization layers.
Example::
from transformers import ElectraModel, ElectraConfig
# Initializing a ELECTRA electra-base-uncased style configuration
configuration = ElectraConfig()
# Initializing a model from the electra-base-uncased style configuration
model = ElectraModel(configuration)
# Accessing the model configuration
configuration = model.config
Attributes:
pretrained_config_archive_map (Dict[str, str]):
A dictionary containing all the available pre-trained checkpoints.
"""
pretrained_config_archive_map = ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "electra"
def __init__(
self,
vocab_size=30522,
embedding_size=128,
hidden_size=256,
num_hidden_layers=12,
num_attention_heads=4,
intermediate_size=1024,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/configuration.py |
# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for OpenAI GPT."""
import copy
import functools
import itertools
import json
import logging
import operator
import os
import re
import collections
import unicodedata
from collections import UserDict, defaultdict
from contextlib import contextmanager
from typing import List, Optional, Sequence, Tuple, Union
from tokenizers import AddedToken, Encoding
from tokenizers.implementations import BaseTokenizer
from file_utils import cached_path, hf_bucket_url, is_remote_url, is_tf_available, is_torch_available
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
logger = logging.getLogger(__name__)
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
ADDED_TOKENS_FILE = "added_tokens.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
# Define type aliases
TextInput = str
TextPairInput = Tuple[str, str]
PreTokenizedInput = List[str]
PreTokenizedInputPair = Tuple[List[str], List[str]]
def flatten(x: Sequence):
"""
Flatten the provided (potentially nested) sequence
Args:
x (Sequence): Potentially nested sequence to flatten
Returns:
list: Flattened sequence
"""
return functools.reduce(operator.iconcat, x, [])
@contextmanager
def truncate_and_pad(
tokenizer: BaseTokenizer,
max_length: int,
stride: int,
strategy: str,
pad_to_max_length: bool,
padding_side: str,
pad_token_id: int,
pad_token_type_id: int,
pad_token: str,
):
"""
This contextmanager is in charge of defining the truncation and the padding strategies and then
restore the tokenizer settings afterwards.
This contextmanager assumes the provider tokenizer has no padding / truncation strategy
before the managed section. If your tokenizer set a padding / truncation strategy before,
then it will be reset to no padding/truncation when exiting the managed section.
Args:
tokenizer (BaseTokenizer): The tokenizer which will be used
max_length (int): The maximum size of the sequence
stride (int): The stride to use when handling overflow
strategy (str): Overflowing logic to use
pad_to_max_length (bool): Boolean indicating if the output needs to be padded up to max_length
padding_side (str): "left" or "right" indicating the direction the output sequence will be padded
pad_token_id (int): The integer representation of the padding token to use
pad_token_type_id (int): The integer representation of the padding token type to use
pad_token (str): The string representation of the padding token to use
Returns:
"""
# Handle all the truncation and padding stuff
if max_length is not None:
tokenizer.enable_truncation(max_length, stride=stride, strategy=strategy)
if pad_to_max_length and (pad_token and pad_token_id >= 0):
tokenizer.enable_padding(
max_length=max_length,
direction=padding_side,
pad_id=pad_token_id,
pad_type_id=pad_token_type_id,
pad_token=pad_token,
)
elif pad_to_max_length:
logger.warning(
"Disabled padding because no padding token set (pad_token: {}, pad_token_id: {}).\n"
"To remove this error, you can add a new pad token and then resize model embedding:\n"
"\ttokenizer.pad_token = '<PAD>'\n\tmodel.resize_token_embeddings(len(tokenizer))".format(
pad_token, pad_token_id
)
)
yield
if max_length is not None:
tokenizer.no_truncation()
if pad_to_max_length and (pad_token and pad_token_id >= 0):
tokenizer.no_padding()
class BatchEncoding(UserDict):
"""
Data structure derived from Dictionary holding all the required information to forward through
a model.
In addition, this structure expose utility methods to map from word/char space to token space.
"""
def __init__(self, data: dict, encoding: Optional[Union[Encoding, Sequence[Encoding]]] = None):
super().__init__(data)
if isinstance(encoding, Encoding):
encoding = [encoding]
self._encodings = encoding
def __getitem__(self, item: Union[int, str]) -> Encoding:
if isinstance(item, str):
return self.data[item]
elif self._encodings is not None:
return self._encodings[item]
else:
raise KeyError("int index is supported only on {} from a Rust tokenizer".format(type(self).__name__))
def __getattr__(self, item: str):
return self.data[item]
@property
def encodings(self) -> Optional[List[Encoding]]:
"""
Return the list all encoding from the tokenization process
Returns: List[Encoding] or None if input was tokenized through Python tokenizer
"""
return self._encodings
def keys(self):
return self.data.keys()
def values(self):
return self.data.values()
def items(self):
return self.data.items()
def char_to_token_offsets(self, sentence: int, char: int) -> Tuple[int, int]:
"""
Find the Offsets of the token containing the character at the specified position
Args:
sentence: Index of the sentence relative to the batch provided to the tokenizer
char: Char index to get the relative token offsets
Returns:
tuple: (token start, token end)
"""
if not self._encodings:
raise ValueError("char_to_token_offsets() is not available when using Python based tokenizers")
return self[sentence].char_to_token_offsets(char)
def char_to_token(self, sentence: int, char: int) -> int:
"""
Return the index of the token at position of the given char.
Args:
sentence (int): Index of the sentence relative to the batch provided to the tokenizer
char (int): Char index to get the relative token offsets
Returns:
int: Integer referring to the position of the token in the returned set of tokens for the sentence
"""
if not self._encodings:
raise ValueError("char_to_token() is not available when using Python based tokenizers")
return self[sentence].char_to_token(char)
def char_to_word_offsets(self, sentence: int, char: int) -> Tuple[int, int]:
"""
Find the Offsets of the word containing the character at the specified position
Args:
sentence (int): Index of the sentence relative to the batch provided to the tokenizer
char (int): Char index to get the relative token offsets
Returns:
tuple: (word start, word end) representing the first and last characters of the word
"""
if not self._encodings:
raise ValueError("char_to_word_offsets() is not available when using Python based tokenizers")
return self[sentence].char_to_word_offsets(char)
def token_to_word_offsets(self, sentence: int, index: int) -> Optional[Tuple[int, int]]:
"""
Find the Offsets of the word containing the token at the given index
Args:
sentence (int): Index of the sentence relative to the batch provided to the tokenizer
index (int): Index of the token to map to the original word offsets
Returns:
Optional[tuple]: (word start, word end) or None
"""
if not self._encodings:
raise ValueError("token_to_word_offsets() is not available when using Python based tokenizers")
return self[sentence].token_to_word_offsets(index)
class SpecialTokensMixin:
SPECIAL_TOKENS_ATTRIBUTES = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
"additional_special_tokens",
]
def __init__(self, **kwargs):
self._bos_token = None
self._eos_token = None
self._unk_token = None
self._sep_token = None
self._pad_token = None
self._cls_token = None
self._mask_token = None
self._pad_token_type_id = 0
self._additional_special_tokens = []
for key, value in kwargs.items():
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
elif isinstance(value, AddedToken):
setattr(self, key, str(value))
elif isinstance(value, str):
setattr(self, key, value)
else:
raise TypeError(
"special token {} has to be either str or AddedToken but got: {}".format(key, type(value))
)
@property
def bos_token(self):
""" Beginning of sentence token (string). Log an error if used while not having been set. """
if self._bos_token is None:
logger.error("Using bos_token, but it is not set yet.")
return self._bos_token
@property
def eos_token(self):
""" End of sentence token (string). Log an error if used while not having been set. """
if self._eos_token is None:
logger.error("Using eos_token, but it is not set yet.")
return self._eos_token
@property
def unk_token(self):
""" Unknown token (string). Log an error if used while not having been set. """
if self._unk_token is None:
logger.error("Using unk_token, but it is not set yet.")
return self._unk_token
@property
def sep_token(self):
""" Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
if self._sep_token is None:
logger.error("Using sep_token, but it is not set yet.")
return self._sep_token
@property
def pad_token(self):
""" Padding token (string). Log an error if used while not having been set. """
if self._pad_token is None:
logger.error("Using pad_token, but it is not set yet.")
return self._pad_token
@property
def cls_token(self):
""" Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
if self._cls_token is None:
logger.error("Using cls_token, but it is not set yet.")
return self._cls_token
@property
def mask_token(self):
""" Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
if self._mask_token is None:
logger.error("Using mask_token, but it is not set yet.")
return self._mask_token
@property
def additional_special_tokens(self):
""" All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. """
if self._additional_special_tokens is None:
logger.error("Using additional_special_tokens, but it is not set yet.")
return self._additional_special_tokens
@bos_token.setter
def bos_token(self, value):
self._bos_token = value
@eos_token.setter
def eos_token(self, value):
self._eos_token = value
@unk_token.setter
def unk_token(self, value):
self._unk_token = value
@sep_token.setter
def sep_token(self, value):
self._sep_token = value
@pad_token.setter
def pad_token(self, value):
self._pad_token = value
@cls_token.setter
def cls_token(self, value):
self._cls_token = value
@mask_token.setter
def mask_token(self, value):
self._mask_token = value
@property
def bos_token_id(self):
""" Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.bos_token)
@property
def eos_token_id(self):
""" Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.eos_token)
@property
def unk_token_id(self):
""" Id of the unknown token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.unk_token)
@property
def sep_token_id(self):
""" Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.sep_token)
@property
def pad_token_id(self):
""" Id of the padding token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.pad_token)
@property
def pad_token_type_id(self):
""" Id of the padding token type in the vocabulary."""
return self._pad_token_type_id
@property
def cls_token_id(self):
""" Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.cls_token)
@property
def mask_token_id(self):
""" Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.mask_token)
@property
def additional_special_tokens_ids(self):
""" Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.additional_special_tokens)
@property
def special_tokens_map(self):
""" A dictionary mapping special token class attribute (cls_token, unk_token...) to their
values ('<unk>', '<cls>'...)
"""
set_attr = {}
for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
attr_value = getattr(self, "_" + attr)
if attr_value:
set_attr[attr] = attr_value
return set_attr
@property
def all_special_tokens(self):
""" List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
(cls_token, unk_token...).
"""
all_toks = []
set_attr = self.special_tokens_map
for attr_value in set_attr.values():
all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = list(set(all_toks))
return all_toks
@property
def all_special_ids(self):
""" List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
class attributes (cls_token, unk_token...).
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids
@additional_special_tokens.setter
def additional_special_tokens(self, value):
self._additional_special_tokens = value
class PreTrainedTokenizer(SpecialTokensMixin):
""" Base class for all tokenizers.
Handle all the shared methods for tokenization and special tokens as well as methods downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
This class also contain the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
Class attributes (overridden by derived classes):
- ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file required by the model, and as associated values, the filename for saving the associated file (string).
- ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the associated pretrained vocabulary file.
- ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model, or None if the model has no maximum input size.
- ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, a dictionnary of specific arguments to pass to the ``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the ``from_pretrained()`` method.
Parameters:
- ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token`` and ``self.bos_token_id``
- ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token`` and ``self.eos_token_id``
- ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token`` and ``self.unk_token_id``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token`` and ``self.sep_token_id``
- ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token`` and ``self.pad_token_id``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token`` and ``self.cls_token_id``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
"""
vocab_files_names = {}
pretrained_vocab_files_map = {}
pretrained_init_configuration = {}
max_model_input_sizes = {}
model_input_names = ["token_type_ids", "attention_mask"]
padding_side = "right"
NO_PAD_TOKEN_FOR_BATCH_MSG = (
"No padding token is set for this model, therefore no batch can be made with uneven "
"sequences. Set a padding token or adjust the lengths of the sequences building the "
"batch so that every sequence is of the same length."
)
UNEVEN_SEQUENCES_FOR_BATCH_MSG = (
"The sequences building the batch are not of the same size, no tensor "
"can be built. Set `pad_to_max_length=True` to pad the smaller sequences"
"up to the larger sequence's length."
)
@property
def vocab_size(self) -> int:
""" Size of the base vocabulary (without the added tokens) """
raise NotImplementedError
@property
def is_fast(self):
return False
def get_vocab(self):
""" Returns the vocabulary as a dict of {token: index} pairs. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. """
raise NotImplementedError()
def __init__(self, max_len=None, **kwargs):
super().__init__(**kwargs)
self.max_len = max_len if max_len is not None else int(1e12)
# Padding side is right by default and over-riden in subclasses. If specified in the kwargs, it is changed.
self.padding_side = kwargs.pop("padding_side", self.padding_side)
self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
# Added tokens
self.added_tokens_encoder = {}
self.unique_added_tokens_encoder = set()
self.added_tokens_decoder = {}
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
self.init_inputs = ()
self.init_kwargs = {}
def __len__(self):
""" Size of the full vocabulary with the added tokens """
return self.vocab_size + len(self.added_tokens_encoder)
@classmethod
def from_pretrained(cls, *inputs, **kwargs):
r"""
Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
Args:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
- (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
Examples::
# We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from S3 and cache.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Download vocabulary from S3 (user-uploaded) and cache.
tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
# If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == '<unk>'
"""
return cls._from_pretrained(*inputs, **kwargs)
@classmethod
def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
s3_models = list(cls.max_model_input_sizes.keys())
vocab_files = {}
init_configuration = {}
if pretrained_model_name_or_path in s3_models:
# Get the vocabulary from AWS S3 bucket
for file_id, map_list in cls.pretrained_vocab_files_map.items():
vocab_files[file_id] = map_list[pretrained_model_name_or_path]
if (
cls.pretrained_init_configuration
and pretrained_model_name_or_path in cls.pretrained_init_configuration
):
init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()
else:
# Get the vocabulary from local files
logger.info(
"Model name '{}' not found in model shortcut name list ({}). "
"Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.".format(
pretrained_model_name_or_path, ", ".join(s3_models), pretrained_model_name_or_path
)
)
if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
if len(cls.vocab_files_names) > 1:
raise ValueError(
"Calling {}.from_pretrained() with the path to a single file or url is not supported."
"Use a model identifier or the path to a directory instead.".format(cls.__name__)
)
logger.warning(
"Calling {}.from_pretrained() with the path to a single file or url is deprecated".format(
cls.__name__
)
)
file_id = list(cls.vocab_files_names.keys())[0]
vocab_files[file_id] = pretrained_model_name_or_path
else:
# At this point pretrained_model_name_or_path is either a directory or a model identifier name
additional_files_names = {
"added_tokens_file": ADDED_TOKENS_FILE,
"special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE,
"tokenizer_config_file": TOKENIZER_CONFIG_FILE,
}
# Look for the tokenizer main vocabulary files + the additional tokens files
for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items():
if os.path.isdir(pretrained_model_name_or_path):
full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
if not os.path.exists(full_file_name):
logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
full_file_name = None
else:
full_file_name = hf_bucket_url(pretrained_model_name_or_path, postfix=file_name)
vocab_files[file_id] = full_file_name
# Get files from url, cache, or disk depending on the case
try:
resolved_vocab_files = {}
for file_id, file_path in vocab_files.items():
if file_path is None:
resolved_vocab_files[file_id] = None
else:
resolved_vocab_files[file_id] = cached_path(
file_path,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
except EnvironmentError:
if pretrained_model_name_or_path in s3_models:
msg = "Couldn't reach server at '{}' to download vocabulary files."
else:
msg = (
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path or url to a directory containing vocabulary files "
"named {}, but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path,
", ".join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()),
)
)
raise EnvironmentError(msg)
if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):
raise EnvironmentError(
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path, a model identifier, or url to a directory containing vocabulary files "
"named {} but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path,
", ".join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()),
)
)
for file_id, file_path in vocab_files.items():
if file_path == resolved_vocab_files[file_id]:
logger.info("loading file {}".format(file_path))
else:
logger.info("loading file {} from cache at {}".format(file_path, resolved_vocab_files[file_id]))
# Prepare tokenizer initialization kwargs
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None)
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
saved_init_inputs = init_kwargs.pop("init_inputs", ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
init_kwargs = init_configuration
# Update with newly provided kwargs
init_kwargs.update(kwargs)
# Set max length if needed
if pretrained_model_name_or_path in cls.max_model_input_sizes:
# if we're using a pretrained model, ensure the tokenizer
# wont index sequences longer than the number of positional embeddings
max_len = cls.max_model_input_sizes[pretrained_model_name_or_path]
if max_len is not None and isinstance(max_len, (int, float)):
init_kwargs["max_len"] = min(init_kwargs.get("max_len", int(1e12)), max_len)
# Merge resolved_vocab_files arguments in init_kwargs.
added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None)
special_tokens_map_file = resolved_vocab_files.pop("special_tokens_map_file", None)
for args_name, file_path in resolved_vocab_files.items():
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if special_tokens_map_file is not None:
with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
special_tokens_map = json.load(special_tokens_map_handle)
for key, value in special_tokens_map.items():
if key not in init_kwargs:
init_kwargs[key] = value
# Instantiate tokenizer.
try:
tokenizer = cls(*init_inputs, **init_kwargs)
except OSError:
raise OSError(
"Unable to load vocabulary from file. "
"Please check that the provided vocabulary is accessible and not corrupted."
)
# Save inputs and kwargs for saving and re-loading with ``save_pretrained``
tokenizer.init_inputs = init_inputs
tokenizer.init_kwargs = init_kwargs
# update unique_added_tokens_encoder with special tokens for correct tokenization
tokenizer.unique_added_tokens_encoder.update(set(tokenizer.all_special_tokens))
# Add supplementary tokens.
if added_tokens_file is not None:
with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
added_tok_encoder = json.load(added_tokens_handle)
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
tokenizer.added_tokens_encoder.update(added_tok_encoder)
tokenizer.added_tokens_decoder.update(added_tok_decoder)
tokenizer.unique_added_tokens_encoder.update(set(tokenizer.added_tokens_encoder.keys()))
return tokenizer
def save_pretrained(self, save_directory):
""" Save the tokenizer vocabulary files together with:
- added tokens,
- special-tokens-to-class-attributes-mapping,
- tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
This won't save modifications other than (added tokens and special token mapping) you may have
applied to the tokenizer after the instantiation (e.g. modifying tokenizer.do_lower_case after creation).
This method make sure the full tokenizer can then be re-loaded using the :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
if not os.path.isdir(save_directory):
logger.error("Saving directory ({}) should be a directory".format(save_directory))
return
special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)
tokenizer_config = copy.deepcopy(self.init_kwargs)
if len(self.init_inputs) > 0:
tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs)
for file_id in self.vocab_files_names.keys():
tokenizer_config.pop(file_id, None)
with open(tokenizer_config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tokenizer_config, ensure_ascii=False))
with open(special_tokens_map_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.special_tokens_map, ensure_ascii=False))
if len(self.added_tokens_encoder) > 0:
with open(added_tokens_file, "w", encoding="utf-8") as f:
out_str = json.dumps(self.added_tokens_encoder, ensure_ascii=False)
f.write(out_str)
vocab_files = self.save_vocabulary(save_directory)
return vocab_files + (special_tokens_map_file, added_tokens_file)
def save_vocabulary(self, save_directory):
""" Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
and special token mappings.
Please use :func:`~transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full Tokenizer state if you want to reload it using the :func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
raise NotImplementedError
def add_tokens(self, new_tokens):
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the
vocabulary, they are added to it with indices starting from length of the current vocabulary.
Args:
new_tokens: string or list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
"""
if not new_tokens:
return 0
if not isinstance(new_tokens, list):
new_tokens = [new_tokens]
to_add_tokens = []
for token in new_tokens:
assert isinstance(token, str)
if self.init_kwargs.get("do_lower_case", False) and token not in self.all_special_tokens:
token = token.lower()
if (
token != self.unk_token
and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)
and token not in to_add_tokens
):
to_add_tokens.append(token)
logger.info("Adding %s to the vocabulary", token)
added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(to_add_tokens))
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
self.added_tokens_encoder.update(added_tok_encoder)
self.unique_added_tokens_encoder = set(self.added_tokens_encoder.keys()).union(set(self.all_special_tokens))
self.added_tokens_decoder.update(added_tok_decoder)
return len(to_add_tokens)
def num_special_tokens_to_add(self, pair=False):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Note:
This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
inside your training loop.
Args:
pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the
number of added tokens in the case of a single sequence if set to False.
Returns:
Number of tokens added to sequences
"""
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
def add_special_tokens(self, special_tokens_dict):
"""
Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
to class attributes. If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Using `add_special_tokens` will ensure your special tokens can be used in several ways:
- special tokens are carefully handled by the tokenizer (they are never split)
- you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
``additional_special_tokens``].
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
special_tokens_dict = {'cls_token': '<CLS>'}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
assert tokenizer.cls_token == '<CLS>'
"""
if not special_tokens_dict:
return 0
added_tokens = 0
for key, value in special_tokens_dict.items():
assert key in self.SPECIAL_TOKENS_ATTRIBUTES
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
added_tokens += self.add_tokens(value)
else:
assert isinstance(value, str)
added_tokens += self.add_tokens([value])
logger.info("Assigning %s to the %s key of the tokenizer", value, key)
setattr(self, key, value)
return added_tokens
def tokenize(self, text: TextInput, **kwargs):
""" Converts a string in a sequence of tokens (string), using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based
vocabularies (BPE/SentencePieces/WordPieces).
Take care of added tokens.
text: The sequence to be encoded.
add_prefix_space: Only applies to GPT-2 and RoBERTa tokenizers. When `True`, this ensures that the sequence
begins with an empty space. False by default except for when using RoBERTa with `add_special_tokens=True`.
**kwargs: passed to the `prepare_for_tokenization` preprocessing method.
"""
all_special_tokens = self.all_special_tokens
text = self.prepare_for_tokenization(text, **kwargs)
def lowercase_text(t):
# convert non-special tokens to lowercase
escaped_special_toks = [re.escape(s_tok) for s_tok in all_special_tokens]
pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)"
return re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), t)
if self.init_kwargs.get("do_lower_case", False):
text = lowercase_text(text)
def split_on_token(tok, text):
result = []
split_text = text.split(tok)
for i, sub_text in enumerate(split_text):
sub_text = sub_text.rstrip()
if i == 0 and not sub_text:
result += [tok]
elif i == len(split_text) - 1:
if sub_text:
result += [sub_text]
else:
pass
else:
if sub_text:
result += [sub_text]
result += [tok]
return result
def split_on_tokens(tok_list, text):
if not text.strip():
return []
if not tok_list:
return self._tokenize(text)
tokenized_text = []
text_list = [text]
for tok in tok_list:
tokenized_text = []
for sub_text in text_list:
if sub_text not in self.unique_added_tokens_encoder:
tokenized_text += split_on_token(tok, sub_text)
else:
tokenized_text += [sub_text]
text_list = tokenized_text
return list(
itertools.chain.from_iterable(
(
self._tokenize(token) if token not in self.unique_added_tokens_encoder else [token]
for token in tokenized_text
)
)
)
added_tokens = self.unique_added_tokens_encoder
tokenized_text = split_on_tokens(added_tokens, text)
return tokenized_text
def _tokenize(self, text, **kwargs):
""" Converts a string in a sequence of tokens (string), using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based
vocabularies (BPE/SentencePieces/WordPieces).
Do NOT take care of added tokens.
"""
raise NotImplementedError
def convert_tokens_to_ids(self, tokens):
""" Converts a single token, or a sequence of tokens, (str) in a single integer id
(resp. a sequence of ids), using the vocabulary.
"""
if tokens is None:
return None
if isinstance(tokens, str):
return self._convert_token_to_id_with_added_voc(tokens)
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
return ids
def _convert_token_to_id_with_added_voc(self, token):
if token is None:
return None
if token in self.added_tokens_encoder:
return self.added_tokens_encoder[token]
return self._convert_token_to_id(token)
def _convert_token_to_id(self, token):
raise NotImplementedError
def encode(
self,
text: TextInput,
text_pair: Optional[TextInput] = None,
add_special_tokens: bool = True,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
return_tensors: Optional[str] = None,
**kwargs
):
"""
Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
Args:
text (:obj:`str` or :obj:`List[str]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair (:obj:`str` or :obj:`List[str]`, `optional`, defaults to :obj:`None`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
If set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the
model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
which can be set to the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
**kwargs: passed to the `self.tokenize()` method
"""
encoded_inputs = self.encode_plus(
text,
text_pair=text_pair,
max_length=max_length,
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
pad_to_max_length=pad_to_max_length,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs["input_ids"]
def encode_plus(
self,
text: TextInput,
text_pair: Optional[TextInput] = None,
add_special_tokens: bool = True,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
is_pretokenized: bool = False,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
text (:obj:`str` or :obj:`List[str]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair (:obj:`str` or :obj:`List[str]`, `optional`, defaults to :obj:`None`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
If set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the
model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
which can be set to the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
Set to True to indicate the input is already tokenized
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
Whether to return token type IDs. If left to the default, will return the token type IDs according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`_
return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`none`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return overflowing token information (default False).
return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return special tokens mask information (default False).
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return (char_start, char_end) for each token (default False).
If using Python's tokenizer, this method will raise NotImplementedError. This one is only available on
Rust-based tokenizers inheriting from PreTrainedTokenizerFast.
**kwargs: passed to the `self.tokenize()` method
Return:
A Dictionary of shape::
{
input_ids: list[int],
token_type_ids: list[int] if return_token_type_ids is True (default)
attention_mask: list[int] if return_attention_mask is True (default)
overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
}
With the fields:
- ``input_ids``: list of token ids to be fed to a model
- ``token_type_ids``: list of token type ids to be fed to a model
- ``attention_mask``: list of indices specifying which tokens should be attended to by the model
- ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
- ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
- ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, add_special_tokens=add_special_tokens, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers."
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
)
# Throw an error if we can pad because there is no padding token
if pad_to_max_length and self.pad_token_id is None:
raise ValueError(
"Unable to set proper padding strategy as the tokenizer does not have a padding token. In this case please set the `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via the function add_special_tokens if you want to use a padding strategy"
)
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
return self.prepare_for_model(
first_ids,
pair_ids=second_ids,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextPairInput], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
is_pretokenized: bool = False,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_masks: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_masks: bool = False,
return_offsets_mapping: bool = False,
return_input_lengths: bool = False,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
batch_text_or_text_pairs (:obj:`List[str]` or :obj:`List[List[str]]`):
Batch of sequences or pair of sequences to be encoded.
This can be a list of string/string-sequences/int-sequences or a list of pair of
string/string-sequences/int-sequence (see details in encode_plus)
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
If set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the
model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
which can be set to the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
Set to True to indicate the input is already tokenized
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
Whether to return token type IDs. If left to the default, will return the token type IDs according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`_
return_attention_masks (:obj:`bool`, `optional`, defaults to :obj:`none`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return overflowing token information (default False).
return_special_tokens_masks (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return special tokens mask information (default False).
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return (char_start, char_end) for each token (default False).
If using Python's tokenizer, this method will raise NotImplementedError. This one is only available on
Rust-based tokenizers inheriting from PreTrainedTokenizerFast.
return_input_lengths (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set the resulting dictionary will include the length of each sample
**kwargs: passed to the `self.tokenize()` method
Return:
A Dictionary of shape::
{
input_ids: list[List[int]],
token_type_ids: list[List[int]] if return_token_type_ids is True (default)
attention_mask: list[List[int]] if return_attention_mask is True (default)
overflowing_tokens: list[List[int]] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: List[int] if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[List[int]] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
}
With the fields:
- ``input_ids``: list of token ids to be fed to a model
- ``token_type_ids``: list of token type ids to be fed to a model
- ``attention_mask``: list of indices specifying which tokens should be attended to by the model
- ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
- ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
- ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, add_special_tokens=add_special_tokens, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
# Throw an error if we can pad because there is no padding token
if pad_to_max_length and self.pad_token_id is None:
raise ValueError(
"Unable to set proper padding strategy as the tokenizer does not have a padding token. In this case please set the `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via the function add_special_tokens if you want to use a padding strategy"
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers."
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
)
input_ids = []
for ids_or_pair_ids in batch_text_or_text_pairs:
if isinstance(ids_or_pair_ids, (list, tuple)) and len(ids_or_pair_ids) == 2 and not is_pretokenized:
ids, pair_ids = ids_or_pair_ids
else:
ids, pair_ids = ids_or_pair_ids, None
first_ids = get_input_ids(ids)
second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
input_ids.append((first_ids, second_ids))
if max_length is None and pad_to_max_length:
def total_sequence_length(input_pairs):
first_ids, second_ids = input_pairs
return len(first_ids) + (
self.num_special_tokens_to_add()
if second_ids is None
else (len(second_ids) + self.num_special_tokens_to_add(pair=True))
)
max_length = max([total_sequence_length(ids) for ids in input_ids])
batch_outputs = {}
for first_ids, second_ids in input_ids:
# Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by
# the model. It adds special tokens, truncates sequences if overflowing while taking into account
# the special tokens and manages a window stride for overflowing tokens
outputs = self.prepare_for_model(
first_ids,
pair_ids=second_ids,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
return_attention_mask=return_attention_masks,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_masks,
)
# Append the non-padded length to the output
if return_input_lengths:
outputs["input_len"] = len(outputs["input_ids"])
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
if return_tensors is not None:
# Do the tensor conversion in batch
for key, value in batch_outputs.items():
if return_tensors == "tf" and is_tf_available():
try:
batch_outputs[key] = tf.constant(value)
except ValueError:
if None in [item for sequence in value for item in sequence]:
raise ValueError(self.NO_PAD_TOKEN_FOR_BATCH_MSG)
else:
raise ValueError(self.UNEVEN_SEQUENCES_FOR_BATCH_MSG)
elif return_tensors == "pt" and is_torch_available():
try:
batch_outputs[key] = torch.tensor(value)
except ValueError:
raise ValueError(self.UNEVEN_SEQUENCES_FOR_BATCH_MSG)
except RuntimeError:
if None in [item for sequence in value for item in sequence]:
raise ValueError(self.NO_PAD_TOKEN_FOR_BATCH_MSG)
else:
raise
elif return_tensors is not None:
logger.warning(
"Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
return_tensors
)
)
return BatchEncoding(batch_outputs)
def prepare_for_model(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
max_length: Optional[int] = None,
add_special_tokens: bool = True,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
):
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
It adds special tokens, truncates
sequences if overflowing while taking into account the special tokens and manages a window stride for
overflowing tokens
Args:
ids: list of tokenized input ids. Can be obtained from a string by chaining the
`tokenize` and `convert_tokens_to_ids` methods.
pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the
`tokenize` and `convert_tokens_to_ids` methods.
max_length: maximum length of the returned list. Will truncate by taking into account the special tokens.
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
stride: window stride for overflowing tokens. Can be useful for edge effect removal when using sequential
list of inputs.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length: if set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the model's max length.
The tokenizer padding sides are handled by the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True).
return_attention_mask: (optional) Set to False to avoid returning attention mask (default True)
return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
Return:
A Dictionary of shape::
{
input_ids: list[int],
token_type_ids: list[int] if return_token_type_ids is True (default)
overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
}
With the fields:
``input_ids``: list of token ids to be fed to a model
``token_type_ids``: list of token type ids to be fed to a model
``overflowing_tokens``: list of overflowing tokens if a max length is specified.
``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
pair = bool(pair_ids is not None)
len_ids = len(ids)
len_pair_ids = len(pair_ids) if pair else 0
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
encoded_inputs = {}
# Handle max sequence length
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
if max_length and total_len > max_length:
ids, pair_ids, overflowing_tokens = self.truncate_sequences(
ids,
pair_ids=pair_ids,
num_tokens_to_remove=total_len - max_length,
truncation_strategy=truncation_strategy,
stride=stride,
)
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = total_len - max_length
# Handle special_tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else [])
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if max_length and len(encoded_inputs["input_ids"]) > max_length:
encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length]
if return_token_type_ids:
encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length]
if max_length is None and len(encoded_inputs["input_ids"]) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
"indexing errors".format(len(ids), self.max_len)
)
needs_to_be_padded = pad_to_max_length and (
max_length
and len(encoded_inputs["input_ids"]) < max_length
or max_length is None
and len(encoded_inputs["input_ids"]) < self.max_len
and self.max_len <= 10000
)
if pad_to_max_length and max_length is None and self.max_len > 10000:
logger.warning(
"Sequence can't be padded as no maximum length is specified and the model maximum length is too high."
)
if needs_to_be_padded:
difference = (max_length if max_length is not None else self.max_len) - len(encoded_inputs["input_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [0] * difference
if return_token_type_ids:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [1] * len(encoded_inputs["input_ids"])
if return_token_type_ids:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
elif return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
# Prepare inputs as tensors if asked
if return_tensors == "tf" and is_tf_available():
encoded_inputs["input_ids"] = tf.constant([encoded_inputs["input_ids"]])
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = tf.constant([encoded_inputs["token_type_ids"]])
if "attention_mask" in encoded_inputs:
encoded_inputs["attention_mask"] = tf.constant([encoded_inputs["attention_mask"]])
elif return_tensors == "pt" and is_torch_available():
encoded_inputs["input_ids"] = torch.tensor([encoded_inputs["input_ids"]])
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = torch.tensor([encoded_inputs["token_type_ids"]])
if "attention_mask" in encoded_inputs:
encoded_inputs["attention_mask"] = torch.tensor([encoded_inputs["attention_mask"]])
elif return_tensors is not None:
logger.warning(
"Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
return_tensors
)
)
return BatchEncoding(encoded_inputs)
def prepare_for_tokenization(self, text, **kwargs):
""" Performs any necessary transformations before tokenization """
return text
def truncate_sequences(
self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy="longest_first", stride=0
):
"""Truncates a sequence pair in place to the maximum length.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences).
Overflowing tokens only contains overflow from the first sequence.
- 'only_first': Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove.
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
"""
if num_tokens_to_remove <= 0:
return ids, pair_ids, []
if truncation_strategy == "longest_first":
overflowing_tokens = []
for _ in range(num_tokens_to_remove):
if pair_ids is None or len(ids) > len(pair_ids):
overflowing_tokens = [ids[-1]] + overflowing_tokens
ids = ids[:-1]
else:
pair_ids = pair_ids[:-1]
window_len = min(len(ids), stride)
if window_len > 0:
overflowing_tokens = ids[-window_len:] + overflowing_tokens
elif truncation_strategy == "only_first":
assert len(ids) > num_tokens_to_remove
window_len = min(len(ids), stride + num_tokens_to_remove)
overflowing_tokens = ids[-window_len:]
ids = ids[:-num_tokens_to_remove]
elif truncation_strategy == "only_second":
assert pair_ids is not None and len(pair_ids) > num_tokens_to_remove
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
overflowing_tokens = pair_ids[-window_len:]
pair_ids = pair_ids[:-num_tokens_to_remove]
elif truncation_strategy == "do_not_truncate":
raise ValueError("Input sequence are too long for max_length. Please select a truncation strategy.")
else:
raise ValueError(
"Truncation_strategy should be selected in ['longest_first', 'only_first', 'only_second', 'do_not_truncate']"
)
return (ids, pair_ids, overflowing_tokens)
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
if token_ids_1 is None:
return len(token_ids_0) * [0]
return [0] * len(token_ids_0) + [1] * len(token_ids_1)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A RoBERTa sequence has the following format:
single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>
"""
if token_ids_1 is None:
return token_ids_0
return token_ids_0 + token_ids_1
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
""" Converts a single index or a sequence of indices (integers) in a token "
(resp.) a sequence of tokens (str), using the vocabulary and added tokens.
Args:
skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
"""
if isinstance(ids, int):
if ids in self.added_tokens_decoder:
return self.added_tokens_decoder[ids]
else:
return self._convert_id_to_token(ids)
tokens = []
for index in ids:
index = int(index)
if skip_special_tokens and index in self.all_special_ids:
continue
if index in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[index])
else:
tokens.append(self._convert_id_to_token(index))
return tokens
def _convert_id_to_token(self, index):
raise NotImplementedError
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string.
The most simple way to do it is ' '.join(self.convert_ids_to_tokens(token_ids))
but we often want to remove sub-word tokenization artifacts at the same time.
"""
return " ".join(self.convert_ids_to_tokens(tokens))
def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
"""
Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
with options to remove special tokens and clean up tokenization spaces.
Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
Args:
token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.
skip_special_tokens: if set to True, will replace special tokens.
clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.
"""
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separatly for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
text = " ".join(sub_texts)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
@staticmethod
def clean_up_tokenization(out_string):
""" Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
"""
out_string = (
out_string.replace(" .", ".")
.replace(" ?", "?")
.replace(" !", "!")
.replace(" ,", ",")
.replace(" ' ", "'")
.replace(" n't", "n't")
.replace(" 'm", "'m")
.replace(" do not", " don't")
.replace(" 's", "'s")
.replace(" 've", "'ve")
.replace(" 're", "'re")
)
return out_string
def trim_batch(
input_ids, pad_token_id, attention_mask=None,
):
"""Remove columns that are populated exclusively by pad_token_id"""
keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = index
return vocab
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
"bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
"bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
"bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
"bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
"bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
"bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
"bert-base-german-cased": "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
"bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
"bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
"bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
"bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
"bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
"bert-base-german-dbmdz-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt",
"bert-base-german-dbmdz-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt",
"bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/vocab.txt",
"bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/vocab.txt",
"bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"bert-base-uncased": 512,
"bert-large-uncased": 512,
"bert-base-cased": 512,
"bert-large-cased": 512,
"bert-base-multilingual-uncased": 512,
"bert-base-multilingual-cased": 512,
"bert-base-chinese": 512,
"bert-base-german-cased": 512,
"bert-large-uncased-whole-word-masking": 512,
"bert-large-cased-whole-word-masking": 512,
"bert-large-uncased-whole-word-masking-finetuned-squad": 512,
"bert-large-cased-whole-word-masking-finetuned-squad": 512,
"bert-base-cased-finetuned-mrpc": 512,
"bert-base-german-dbmdz-cased": 512,
"bert-base-german-dbmdz-uncased": 512,
"bert-base-finnish-cased-v1": 512,
"bert-base-finnish-uncased-v1": 512,
"bert-base-dutch-cased": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"bert-base-uncased": {"do_lower_case": True},
"bert-large-uncased": {"do_lower_case": True},
"bert-base-cased": {"do_lower_case": False},
"bert-large-cased": {"do_lower_case": False},
"bert-base-multilingual-uncased": {"do_lower_case": True},
"bert-base-multilingual-cased": {"do_lower_case": False},
"bert-base-chinese": {"do_lower_case": False},
"bert-base-german-cased": {"do_lower_case": False},
"bert-large-uncased-whole-word-masking": {"do_lower_case": True},
"bert-large-cased-whole-word-masking": {"do_lower_case": False},
"bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
"bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
"bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
"bert-base-german-dbmdz-cased": {"do_lower_case": False},
"bert-base-german-dbmdz-uncased": {"do_lower_case": True},
"bert-base-finnish-cased-v1": {"do_lower_case": False},
"bert-base-finnish-uncased-v1": {"do_lower_case": True},
"bert-base-dutch-cased": {"do_lower_case": False},
}
# Bert Classes
class BertTokenizer(PreTrainedTokenizer):
r"""
Constructs a BERT tokenizer. Based on WordPiece.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users
should refer to the superclass for more information regarding methods.
Args:
vocab_file (:obj:`string`):
File containing the vocabulary.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to lowercase the input when tokenizing.
do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to do basic tokenization before WordPiece.
never_split (:obj:`bool`, `optional`, defaults to :obj:`True`):
List of tokens which will never be split during tokenization. Only has an effect when
:obj:`do_basic_tokenize=True`
unk_token (:obj:`string`, `optional`, defaults to "[UNK]"):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (:obj:`string`, `optional`, defaults to "[SEP]"):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences
for sequence classification or for a text and a question for question answering.
It is also used as the last token of a sequence built with special tokens.
pad_token (:obj:`string`, `optional`, defaults to "[PAD]"):
The token used for padding, for example when batching sequences of different lengths.
cls_token (:obj:`string`, `optional`, defaults to "[CLS]"):
The classifier token which is used when doing sequence classification (classification of the whole
sequence instead of per-token classification). It is the first token of the sequence when built with
special tokens.
mask_token (:obj:`string`, `optional`, defaults to "[MASK]"):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether to tokenize Chinese characters.
This should likely be deactivated for Japanese:
see: https://github.com/huggingface/transformers/issues/328
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
**kwargs
):
super().__init__(
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A BERT sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
A BERT sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
if token_ids_1 is None, only returns the first portion of the mask (0's).
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, vocab_path):
"""
Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory.
Args:
vocab_path (:obj:`str`):
The directory in which to save the vocabulary.
Returns:
:obj:`Tuple(str)`: Paths to the files saved.
"""
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"])
else:
vocab_file = vocab_path
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
"Saving vocabulary to {}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!".format(vocab_file)
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True):
""" Constructs a BasicTokenizer.
Args:
**do_lower_case**: Whether to lower case the input.
**never_split**: (`optional`) list of str
Kept for backward compatibility purposes.
Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
List of token not to split.
**tokenize_chinese_chars**: (`optional`) boolean (default True)
Whether to tokenize Chinese characters.
This should likely be deactivated for Japanese:
see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
"""
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = never_split
self.tokenize_chinese_chars = tokenize_chinese_chars
def tokenize(self, text, never_split=None):
""" Basic Tokenization of a piece of text.
Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer.
Args:
**never_split**: (`optional`) list of str
Kept for backward compatibility purposes.
Now implemented directly at the base class level (see :func:`PreTrainedTokenizer.tokenize`)
List of token not to split.
"""
never_split = self.never_split + (never_split if never_split is not None else [])
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case and token not in never_split:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if never_split is not None and text in never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer`.
Returns:
A list of wordpiece tokens.
"""
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/tokenization_utils.py |
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import os
import pynvml
pynvml.nvmlInit()
def systemGetDriverVersion():
return pynvml.nvmlSystemGetDriverVersion()
def deviceGetCount():
return pynvml.nvmlDeviceGetCount()
class device:
# assume nvml returns list of 64 bit ints
_nvml_affinity_elements = math.ceil(os.cpu_count() / 64)
def __init__(self, device_idx):
super().__init__()
self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_idx)
def getName(self):
return pynvml.nvmlDeviceGetName(self.handle)
def getCpuAffinity(self):
affinity_string = ''
for j in pynvml.nvmlDeviceGetCpuAffinity(
self.handle, device._nvml_affinity_elements
):
# assume nvml returns list of 64 bit ints
affinity_string = '{:064b}'.format(j) + affinity_string
affinity_list = [int(x) for x in affinity_string]
affinity_list.reverse() # so core 0 is in 0th element of list
return [i for i, e in enumerate(affinity_list) if e != 0]
def set_affinity(gpu_id=None):
if gpu_id is None:
gpu_id = int(os.getenv('LOCAL_RANK', 0))
dev = device(gpu_id)
os.sched_setaffinity(0, dev.getCpuAffinity())
# list of ints representing the logical cores this process is now affinitied with
return os.sched_getaffinity(0)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/gpu_affinity.py |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helpers for preparing pre-training data and supplying them to the model."""
import collections
import numpy as np
import tensorflow as tf
import utils
import tokenization
def get_dataset(config, batch_size, num_cpu_threads=4, world_size=1, rank=0):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"input_ids": tf.io.FixedLenFeature([config.max_seq_length], tf.int64),
"input_mask": tf.io.FixedLenFeature([config.max_seq_length], tf.int64),
"segment_ids": tf.io.FixedLenFeature([config.max_seq_length], tf.int64),
}
input_files = []
for input_pattern in config.pretrain_tfrecords.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
d = d.shard(num_shards=world_size, index=rank)
d = d.repeat()
d = d.shuffle(buffer_size=len(input_files), seed=config.seed, reshuffle_each_iteration=False)
cycle_length = min(num_cpu_threads, len(input_files))
d = d.interleave(
tf.data.TFRecordDataset,
cycle_length=cycle_length,
deterministic=True)
d = d.shuffle(buffer_size=100, seed=config.seed, reshuffle_each_iteration=False)
d = d.map(lambda record: _decode_record(record, name_to_features))
d = d.batch(batch_size)
return d
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.io.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
# model inputs - it's a bit nicer to use a namedtuple rather than keep the
# features as a dict
Inputs = collections.namedtuple(
"Inputs", ["input_ids", "input_mask", "segment_ids", "masked_lm_positions",
"masked_lm_ids", "masked_lm_weights"])
def features_to_inputs(features):
return Inputs(
input_ids=features["input_ids"],
input_mask=features["input_mask"],
segment_ids=features["segment_ids"],
masked_lm_positions=(features["masked_lm_positions"]
if "masked_lm_positions" in features else None),
masked_lm_ids=(features["masked_lm_ids"]
if "masked_lm_ids" in features else None),
masked_lm_weights=(features["masked_lm_weights"]
if "masked_lm_weights" in features else None),
)
def get_updated_inputs(inputs, **kwargs):
features = inputs._asdict()
for k, v in kwargs.items():
features[k] = v
return features_to_inputs(features)
def get_shape_list(tensor, expected_rank=None, name=None):
"""Returns a list of the shape of tensor, preferring static dimensions.
Args:
tensor: A tf.Tensor object to find the shape of.
expected_rank: (optional) int. The expected rank of `tensor`. If this is
specified and the `tensor` has a different rank, and exception will be
thrown.
name: Optional name of the tensor for the error message.
Returns:
A list of dimensions of the shape of tensor. All static dimensions will
be returned as python integers, and dynamic dimensions will be returned
as tf.Tensor scalars.
"""
if isinstance(tensor, np.ndarray) or isinstance(tensor, list):
shape = np.array(tensor).shape
if isinstance(expected_rank, six.integer_types):
assert len(shape) == expected_rank
elif expected_rank is not None:
assert len(shape) in expected_rank
return shape
#
# if name is None:
# name = tensor.name
#
# if expected_rank is not None:
# assert_rank(tensor, expected_rank, name)
shape = tensor.shape.as_list()
non_static_indexes = []
for (index, dim) in enumerate(shape):
if dim is None:
non_static_indexes.append(index)
if not non_static_indexes:
return shape
dyn_shape = tf.shape(tensor)
for index in non_static_indexes:
shape[index] = dyn_shape[index]
return shape
def gather_positions(sequence, positions):
"""Gathers the vectors at the specific positions over a minibatch.
Args:
sequence: A [batch_size, seq_length] or
[batch_size, seq_length, depth] tensor of values
positions: A [batch_size, n_positions] tensor of indices
Returns: A [batch_size, n_positions] or
[batch_size, n_positions, depth] tensor of the values at the indices
"""
shape = get_shape_list(sequence, expected_rank=[2, 3])
depth_dimension = (len(shape) == 3)
if depth_dimension:
B, L, D = shape
else:
B, L = shape
D = 1
sequence = tf.expand_dims(sequence, -1)
position_shift = tf.expand_dims(L * tf.range(B), -1)
flat_positions = tf.reshape(positions + position_shift, [-1])
flat_sequence = tf.reshape(sequence, [B * L, D])
gathered = tf.gather(flat_sequence, flat_positions)
if depth_dimension:
return tf.reshape(gathered, [B, -1, D])
else:
return tf.reshape(gathered, [B, -1])
def scatter_update(sequence, updates, positions):
"""Scatter-update a sequence.
Args:
sequence: A [batch_size, seq_len] or [batch_size, seq_len, depth] tensor
updates: A tensor of size batch_size*seq_len(*depth)
positions: A [batch_size, n_positions] tensor
Returns: A tuple of two tensors. First is a [batch_size, seq_len] or
[batch_size, seq_len, depth] tensor of "sequence" with elements at
"positions" replaced by the values at "updates." Updates to index 0 are
ignored. If there are duplicated positions the update is only applied once.
Second is a [batch_size, seq_len] mask tensor of which inputs were updated.
"""
shape = get_shape_list(sequence, expected_rank=[2, 3])
depth_dimension = (len(shape) == 3)
if depth_dimension:
B, L, D = shape
else:
B, L = shape
D = 1
sequence = tf.expand_dims(sequence, -1)
N = get_shape_list(positions)[1]
shift = tf.expand_dims(L * tf.range(B), -1)
flat_positions = tf.reshape(positions + shift, [-1, 1])
flat_updates = tf.reshape(updates, [-1, D])
updates = tf.scatter_nd(flat_positions, flat_updates, [B * L, D])
updates = tf.reshape(updates, [B, L, D])
flat_updates_mask = tf.ones([B * N], tf.int32)
updates_mask = tf.scatter_nd(flat_positions, flat_updates_mask, [B * L])
updates_mask = tf.reshape(updates_mask, [B, L])
not_first_token = tf.concat([tf.zeros((B, 1), tf.int32),
tf.ones((B, L - 1), tf.int32)], -1)
updates_mask *= not_first_token
updates_mask_3d = tf.expand_dims(updates_mask, -1)
# account for duplicate positions
if sequence.dtype == tf.float32:
updates_mask_3d = tf.cast(updates_mask_3d, tf.float32)
updates /= tf.maximum(1.0, updates_mask_3d)
else:
assert sequence.dtype == tf.int32
updates = tf.math.floordiv(updates, tf.maximum(1, updates_mask_3d))
updates_mask = tf.minimum(updates_mask, 1)
updates_mask_3d = tf.minimum(updates_mask_3d, 1)
updated_sequence = (((1 - updates_mask_3d) * sequence) +
(updates_mask_3d * updates))
if not depth_dimension:
updated_sequence = tf.squeeze(updated_sequence, -1)
return updated_sequence, updates_mask
def _get_candidates_mask(inputs: Inputs, vocab,
disallow_from_mask=None):
"""Returns a mask tensor of positions in the input that can be masked out."""
ignore_ids = [vocab["[SEP]"], vocab["[CLS]"], vocab["[MASK]"]]
candidates_mask = tf.ones_like(inputs.input_ids, tf.bool)
for ignore_id in ignore_ids:
candidates_mask &= tf.not_equal(inputs.input_ids, ignore_id)
candidates_mask &= tf.cast(inputs.input_mask, tf.bool)
if disallow_from_mask is not None:
candidates_mask &= ~disallow_from_mask
return candidates_mask
def mask(config, inputs, mask_prob, proposal_distribution=1.0,
disallow_from_mask=None, already_masked=None):
"""Implementation of dynamic masking. The optional arguments aren't needed for
BERT/ELECTRA and are from early experiments in "strategically" masking out
tokens instead of uniformly at random.
Args:
config: configure_pretraining.PretrainingConfig
inputs: pretrain_data.Inputs containing input input_ids/input_mask
mask_prob: percent of tokens to mask
proposal_distribution: for non-uniform masking can be a [B, L] tensor
of scores for masking each position.
disallow_from_mask: a boolean tensor of [B, L] of positions that should
not be masked out
already_masked: a boolean tensor of [B, N] of already masked-out tokens
for multiple rounds of masking
Returns: a pretrain_data.Inputs with masking added
"""
# Get the batch size, sequence length, and max masked-out tokens
N = config.max_predictions_per_seq
B, L = get_shape_list(inputs.input_ids)
# Find indices where masking out a token is allowed
vocab = tokenization.ElectraTokenizer(
config.vocab_file, do_lower_case=config.do_lower_case).get_vocab()
candidates_mask = _get_candidates_mask(inputs, vocab, disallow_from_mask)
# Set the number of tokens to mask out per example
num_tokens = tf.cast(tf.reduce_sum(inputs.input_mask, -1), tf.float32)
num_to_predict = tf.maximum(1, tf.minimum(
N, tf.cast(tf.round(num_tokens * mask_prob), tf.int32)))
masked_lm_weights = tf.cast(tf.sequence_mask(num_to_predict, N), tf.float32)
if already_masked is not None:
masked_lm_weights *= (1 - already_masked)
# Get a probability of masking each position in the sequence
candidate_mask_float = tf.cast(candidates_mask, tf.float32)
sample_prob = (proposal_distribution * candidate_mask_float)
sample_prob /= tf.reduce_sum(sample_prob, axis=-1, keepdims=True)
# Sample the positions to mask out
sample_prob = tf.stop_gradient(sample_prob)
sample_logits = tf.math.log(sample_prob)
masked_lm_positions = tf.random.categorical(
sample_logits, N, dtype=tf.int32)
masked_lm_positions *= tf.cast(masked_lm_weights, tf.int32)
# Get the ids of the masked-out tokens
shift = tf.expand_dims(L * tf.range(B), -1)
flat_positions = tf.reshape(masked_lm_positions + shift, [-1, 1])
masked_lm_ids = tf.gather_nd(tf.reshape(inputs.input_ids, [-1]),
flat_positions)
masked_lm_ids = tf.reshape(masked_lm_ids, [B, -1])
masked_lm_ids *= tf.cast(masked_lm_weights, tf.int32)
# Update the input ids
replace_with_mask_positions = masked_lm_positions * tf.cast(
tf.less(tf.random.uniform([B, N]), 0.85), tf.int32)
inputs_ids, _ = scatter_update(
inputs.input_ids, tf.fill([B, N], vocab["[MASK]"]),
replace_with_mask_positions)
return get_updated_inputs(
inputs,
input_ids=tf.stop_gradient(inputs_ids),
masked_lm_positions=masked_lm_positions,
masked_lm_ids=masked_lm_ids,
masked_lm_weights=masked_lm_weights
)
def unmask(inputs: Inputs):
unmasked_input_ids, _ = scatter_update(
inputs.input_ids, inputs.masked_lm_ids, inputs.masked_lm_positions)
return get_updated_inputs(inputs, input_ids=unmasked_input_ids)
def sample_from_softmax(logits, disallow=None):
if disallow is not None:
logits -= 1000.0 * disallow
uniform_noise = tf.random.uniform(
get_shape_list(logits), minval=0, maxval=1)
gumbel_noise = tf.cast(-tf.math.log(-tf.math.log(uniform_noise + 1e-9) + 1e-9), logits.dtype)
return tf.one_hot(tf.argmax(tf.nn.softmax(logits + gumbel_noise), -1,
output_type=tf.int32), logits.shape[-1])
ENDC = "\033[0m"
COLORS = ["\033[" + str(n) + "m" for n in list(range(91, 97)) + [90]]
RED = COLORS[0]
BLUE = COLORS[3]
CYAN = COLORS[5]
GREEN = COLORS[1]
def print_tokens(inputs: Inputs, inv_vocab, updates_mask=None):
"""Pretty-print model inputs."""
pos_to_tokid = {}
for tokid, pos, weight in zip(
inputs.masked_lm_ids[0], inputs.masked_lm_positions[0],
inputs.masked_lm_weights[0]):
if weight == 0:
pass
else:
pos_to_tokid[pos] = tokid
text = ""
provided_update_mask = (updates_mask is not None)
if not provided_update_mask:
updates_mask = np.zeros_like(inputs.input_ids)
for pos, (tokid, um) in enumerate(
zip(inputs.input_ids[0], updates_mask[0])):
token = inv_vocab[tokid]
if token == "[PAD]":
break
if pos in pos_to_tokid:
token = RED + token + " (" + inv_vocab[pos_to_tokid[pos]] + ")" + ENDC
if provided_update_mask:
assert um == 1
else:
if provided_update_mask:
assert um == 0
text += token + " "
utils.log(utils.printable_text(text))
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/pretrain_utils.py |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Writes out text data as tfrecords that ELECTRA can be pre-trained on."""
import argparse
import multiprocessing
import os
import random
import time
import tensorflow as tf
import utils
from tokenization import ElectraTokenizer
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return feature
class ExampleBuilder(object):
"""Given a stream of input text, creates pretraining examples."""
def __init__(self, tokenizer, max_length):
self._tokenizer = tokenizer
self._current_sentences = []
self._current_length = 0
self._max_length = max_length
self._target_length = max_length
def add_line(self, line):
"""Adds a line of text to the current example being built."""
line = line.strip().replace("\n", " ")
if (not line) and self._current_length != 0: # empty lines separate docs
return self._create_example()
bert_tokens = self._tokenizer.tokenize(line)
bert_tokids = self._tokenizer.convert_tokens_to_ids(bert_tokens)
self._current_sentences.append(bert_tokids)
self._current_length += len(bert_tokids)
if self._current_length >= self._target_length:
return self._create_example()
return None
def _create_example(self):
"""Creates a pre-training example from the current list of sentences."""
# small chance to only have one segment as in classification tasks
if random.random() < 0.1:
first_segment_target_length = 100000
else:
# -3 due to not yet having [CLS]/[SEP] tokens in the input text
first_segment_target_length = (self._target_length - 3) // 2
first_segment = []
second_segment = []
for sentence in self._current_sentences:
# the sentence goes to the first segment if (1) the first segment is
# empty, (2) the sentence doesn't put the first segment over length or
# (3) 50% of the time when it does put the first segment over length
if (len(first_segment) == 0 or
len(first_segment) + len(sentence) < first_segment_target_length or
(len(second_segment) == 0 and
len(first_segment) < first_segment_target_length and
random.random() < 0.5)):
first_segment += sentence
else:
second_segment += sentence
# trim to max_length while accounting for not-yet-added [CLS]/[SEP] tokens
first_segment = first_segment[:self._max_length - 2]
second_segment = second_segment[:max(0, self._max_length -
len(first_segment) - 3)]
# prepare to start building the next example
self._current_sentences = []
self._current_length = 0
# small chance for random-length instead of max_length-length example
if random.random() < 0.05:
self._target_length = random.randint(5, self._max_length)
else:
self._target_length = self._max_length
return self._make_tf_example(first_segment, second_segment)
def _make_tf_example(self, first_segment, second_segment):
"""Converts two "segments" of text into a tf.train.Example."""
vocab = self._tokenizer.vocab
input_ids = [vocab["[CLS]"]] + first_segment + [vocab["[SEP]"]]
segment_ids = [0] * len(input_ids)
if second_segment:
input_ids += second_segment + [vocab["[SEP]"]]
segment_ids += [1] * (len(second_segment) + 1)
input_mask = [1] * len(input_ids)
input_ids += [0] * (self._max_length - len(input_ids))
input_mask += [0] * (self._max_length - len(input_mask))
segment_ids += [0] * (self._max_length - len(segment_ids))
tf_example = tf.train.Example(features=tf.train.Features(feature={
"input_ids": create_int_feature(input_ids),
"input_mask": create_int_feature(input_mask),
"segment_ids": create_int_feature(segment_ids)
}))
return tf_example
class ExampleWriter(object):
"""Writes pre-training examples to disk."""
def __init__(self, job_id, vocab_file, output_dir, max_seq_length,
num_jobs, blanks_separate_docs, do_lower_case,
num_out_files=1000):
self._blanks_separate_docs = blanks_separate_docs
tokenizer = ElectraTokenizer(
vocab_file=vocab_file,
do_lower_case=do_lower_case)
self._example_builder = ExampleBuilder(tokenizer, max_seq_length)
self._writers = []
for i in range(num_out_files):
if i % num_jobs == job_id:
output_fname = os.path.join(
output_dir, "pretrain_data.tfrecord-{:}-of-{:}".format(
i, num_out_files))
self._writers.append(tf.io.TFRecordWriter(output_fname))
self.n_written = 0
def write_examples(self, input_file):
"""Writes out examples from the provided input file."""
with tf.io.gfile.GFile(input_file) as f:
for line in f:
line = line.strip()
if line or self._blanks_separate_docs:
example = self._example_builder.add_line(line)
if example:
self._writers[self.n_written % len(self._writers)].write(
example.SerializeToString())
self.n_written += 1
example = self._example_builder.add_line("")
if example:
self._writers[self.n_written % len(self._writers)].write(
example.SerializeToString())
self.n_written += 1
def finish(self):
for writer in self._writers:
writer.close()
def write_examples(job_id, args):
"""A single process creating and writing out pre-processed examples."""
def log(*args):
msg = " ".join(map(str, args))
print("Job {}:".format(job_id), msg)
log("Creating example writer")
example_writer = ExampleWriter(
job_id=job_id,
vocab_file=args.vocab_file,
output_dir=args.output_dir,
max_seq_length=args.max_seq_length,
num_jobs=args.num_processes,
blanks_separate_docs=args.blanks_separate_docs,
do_lower_case=args.do_lower_case,
num_out_files=args.num_out_files,
)
log("Writing tf examples")
fnames = sorted(tf.io.gfile.listdir(args.corpus_dir))
fnames = [f for (i, f) in enumerate(fnames)
if i % args.num_processes == job_id]
random.shuffle(fnames)
start_time = time.time()
for file_no, fname in enumerate(fnames):
if file_no > 0:
elapsed = time.time() - start_time
log("processed {:}/{:} files ({:.1f}%), ELAPSED: {:}s, ETA: {:}s, "
"{:} examples written".format(
file_no, len(fnames), 100.0 * file_no / len(fnames), int(elapsed),
int((len(fnames) - file_no) / (file_no / elapsed)),
example_writer.n_written))
example_writer.write_examples(os.path.join(args.corpus_dir, fname))
example_writer.finish()
log("Done!")
# python build_pretraining_dataset --corpus-dir
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--corpus-dir", required=True,
help="Location of pre-training text files.")
parser.add_argument("--vocab-file", required=True,
help="Location of vocabulary file.")
parser.add_argument("--output-dir", required=True,
help="Where to write out the tfrecords.")
parser.add_argument("--max-seq-length", default=128, type=int,
help="Number of tokens per example.")
parser.add_argument("--num-processes", default=1, type=int,
help="Parallelize across multiple processes.")
parser.add_argument("--blanks-separate-docs", default=True, type=bool,
help="Whether blank lines indicate document boundaries.")
parser.add_argument("--do-lower-case", dest='do_lower_case',
action='store_true', help="Lower case input text.")
parser.add_argument("--no-lower-case", dest='do_lower_case',
action='store_false', help="Don't lower case input text.")
parser.add_argument("--num-out-files", default=1000, type=int,
help="Number of output files.")
parser.add_argument("--seed", default=1314, type=int)
args = parser.parse_args()
random.seed(args.seed)
utils.rmkdir(args.output_dir)
if args.num_processes == 1:
write_examples(0, args)
else:
jobs = []
for i in range(args.num_processes):
job = multiprocessing.Process(target=write_examples, args=(i, args))
jobs.append(job)
job.start()
for job in jobs:
job.join()
if __name__ == "__main__":
main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/build_pretraining_dataset.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions and classes related to optimization (weight updates)."""
import re
import collections
import tensorflow as tf
import tensorflow_addons.optimizers as tfa_optimizers
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.training import training_ops
from utils import log
class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
"""Applys a warmup schedule on a given learning rate decay schedule."""
def __init__(self, initial_learning_rate, decay_schedule_fn, warmup_steps, power=1.0, name=None):
super().__init__()
self.initial_learning_rate = initial_learning_rate
self.warmup_steps = warmup_steps
self.power = power
self.decay_schedule_fn = decay_schedule_fn
self.name = name
def __call__(self, step):
with tf.name_scope(self.name or "WarmUp") as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
global_step_float = tf.cast(step, tf.float32)
warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
warmup_percent_done = global_step_float / warmup_steps_float
warmup_learning_rate = self.initial_learning_rate * tf.math.pow(warmup_percent_done, self.power)
return tf.cond(
global_step_float < warmup_steps_float,
lambda: warmup_learning_rate,
lambda: self.decay_schedule_fn(step - self.warmup_steps),
name=name,
)
def get_config(self):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def create_optimizer(init_lr, num_train_steps, num_warmup_steps, weight_decay_rate=0.01,
layerwise_lr_decay=-1, n_transformer_layers=None, clip_norm=1.0,
optimizer="adam", skip_adaptive=False, power=1.0, beta_1=0.9, beta_2=0.999, end_lr=0.0):
"""Creates an optimizer with learning rate schedule."""
# Implements linear decay of the learning rate.
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=init_lr, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=end_lr, power=power
)
if num_warmup_steps:
learning_rate_fn = WarmUp(
initial_learning_rate=init_lr, decay_schedule_fn=learning_rate_fn, warmup_steps=num_warmup_steps
)
layer_decay = None
if layerwise_lr_decay > 0 and n_transformer_layers is not None:
layer_decay = _get_layer_decay(layerwise_lr_decay, n_transformer_layers)
if optimizer == "adam":
optimizer = AdamWeightDecay(
learning_rate=learning_rate_fn,
weight_decay_rate=weight_decay_rate,
layer_decay=layer_decay,
beta_1=beta_1,
beta_2=beta_2,
epsilon=1e-6,
exclude_from_weight_decay=["layer_norm", "bias", "LayerNorm"],
clip_norm=clip_norm,
)
else:
if skip_adaptive:
skip_list = ["layer_norm", "bias", "LayerNorm"]
else:
skip_list = ["None"]
log("Skip list for LAMB {}".format(skip_list))
optimizer = tfa_optimizers.LAMB(
learning_rate=learning_rate_fn,
weight_decay_rate=weight_decay_rate,
beta_1=beta_1,
beta_2=beta_2,
epsilon=1e-6,
exclude_from_weight_decay=["layer_norm", "bias", "LayerNorm"],
exclude_from_layer_adaptation=skip_list,
)
return optimizer
class AdamWeightDecay(tf.keras.optimizers.Adam):
"""Adam enables L2 weight decay and clip_by_global_norm on gradients.
Just adding the square of the weights to the loss function is *not* the
correct way of using L2 regularization/weight decay with Adam, since that will
interact with the m and v parameters in strange ways.
Instead we want ot decay the weights in a manner that doesn't interact with
the m/v parameters. This is equivalent to adding the square of the weights to
the loss with plain (non-momentum) SGD.
"""
def __init__(
self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-7,
amsgrad=False,
weight_decay_rate=0.0,
include_in_weight_decay=None,
exclude_from_weight_decay=None,
layer_decay=None,
clip_norm=1.0,
name="AdamWeightDecay",
**kwargs
):
super().__init__(learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs)
self.weight_decay_rate = weight_decay_rate
self._include_in_weight_decay = include_in_weight_decay
self._exclude_from_weight_decay = exclude_from_weight_decay
self.layer_decay = layer_decay
self.clip_norm = clip_norm
@classmethod
def from_config(cls, config):
"""Creates an optimizer from its config with WarmUp custom object."""
custom_objects = {"WarmUp": WarmUp}
return super().from_config(config, custom_objects=custom_objects)
def _prepare_local(self, var_device, var_dtype, apply_state):
super()._prepare_local(var_device, var_dtype, apply_state)
apply_state["weight_decay_rate"] = tf.constant(self.weight_decay_rate, name="adam_weight_decay_rate")
def _decay_weights_op(self, var, learning_rate, apply_state):
do_decay = self._do_use_weight_decay(var.name)
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state["weight_decay_rate"], use_locking=self._use_locking
)
return tf.no_op()
def apply_gradients(self, grads_and_vars, name=None, experimental_aggregate_gradients=True):
grads, tvars = list(zip(*grads_and_vars))
# Being done in train_step
##(grads, _) = tf.clip_by_global_norm(grads, clip_norm=self.clip_norm)
return super().apply_gradients(zip(grads, tvars), name=name,
experimental_aggregate_gradients=experimental_aggregate_gradients)
def _get_lr(self, var, apply_state):
"""Retrieves the learning rate with the given state."""
# if apply_state is None:
# return self._decayed_lr_t[var_dtype], {}
var_name, var_device, var_dtype = var.name, var.device, var.dtype.base_dtype
apply_state = apply_state or {}
coefficients = apply_state.get((var_device, var_dtype))
if coefficients is None:
coefficients = self._fallback_apply_state(var_device, var_dtype)
apply_state[(var_device, var_dtype)] = coefficients
lr_t = coefficients["lr_t"]
lr = coefficients["lr"]
if self.layer_decay is not None:
update_for_var = False
for key in self.layer_decay:
if key in var_name:
update_for_var = True
lr_t *= self.layer_decay[key]
lr *= self.layer_decay[key]
break
if not update_for_var:
raise ValueError("No learning rate specified for variable", var)
return lr_t, lr, coefficients, dict(apply_state=apply_state)
def _resource_apply_dense(self, grad, var, apply_state=None):
# print("Dense: {} {} {}".format(var.name, var.device, var.dtype.base_dtype))
lr_t, _, coefficients, kwargs = self._get_lr(var, apply_state)
decay = self._decay_weights_op(var, lr_t, apply_state)
with tf.control_dependencies([decay]):
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
if not self.amsgrad:
return training_ops.resource_apply_adam(
var.handle,
m.handle,
v.handle,
coefficients['beta_1_power'],
coefficients['beta_2_power'],
lr_t,
coefficients['beta_1_t'],
coefficients['beta_2_t'],
coefficients['epsilon'],
grad,
use_locking=self._use_locking)
else:
vhat = self.get_slot(var, 'vhat')
return training_ops.resource_apply_adam_with_amsgrad(
var.handle,
m.handle,
v.handle,
vhat.handle,
coefficients['beta_1_power'],
coefficients['beta_2_power'],
lr_t,
coefficients['beta_1_t'],
coefficients['beta_2_t'],
coefficients['epsilon'],
grad,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
# print("Sparse: {} {} {}".format(var.name, var.device, var.dtype.base_dtype))
lr_t, lr, coefficients, kwargs = self._get_lr(var, apply_state)
decay = self._decay_weights_op(var, lr_t, apply_state)
with tf.control_dependencies([decay]):
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, 'm')
m_scaled_g_values = grad * coefficients['one_minus_beta_1_t']
m_t = state_ops.assign(m, m * coefficients['beta_1_t'],
use_locking=self._use_locking)
with tf.control_dependencies([m_t]):
m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, 'v')
v_scaled_g_values = (grad * grad) * coefficients['one_minus_beta_2_t']
v_t = state_ops.assign(v, v * coefficients['beta_2_t'],
use_locking=self._use_locking)
with tf.control_dependencies([v_t]):
v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
if not self.amsgrad:
v_sqrt = math_ops.sqrt(v_t)
var_update = state_ops.assign_sub(
var, lr * m_t / (v_sqrt + coefficients['epsilon']),
use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t])
else:
v_hat = self.get_slot(var, 'vhat')
v_hat_t = math_ops.maximum(v_hat, v_t)
with tf.control_dependencies([v_hat_t]):
v_hat_t = state_ops.assign(
v_hat, v_hat_t, use_locking=self._use_locking)
v_hat_sqrt = math_ops.sqrt(v_hat_t)
var_update = state_ops.assign_sub(
var,
lr * m_t / (v_hat_sqrt + coefficients['epsilon']),
use_locking=self._use_locking)
return control_flow_ops.group(*[var_update, m_t, v_t, v_hat_t])
def get_config(self):
config = super().get_config()
config.update({"weight_decay_rate": self.weight_decay_rate})
return config
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(r, param_name) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
# Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
class GradientAccumulator(object):
"""Distribution strategies-aware gradient accumulation utility."""
def __init__(self):
"""Initializes the accumulator."""
self._gradients = []
self._accum_steps = tf.Variable(
initial_value=0, dtype=tf.int64, trainable=False, aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA
)
@property
def step(self):
"""Number of accumulated steps."""
return self._accum_steps.value()
@property
def gradients(self):
"""The accumulated gradients."""
return list(
gradient.value() if gradient is not None else gradient for gradient in self._get_replica_gradients()
)
def __call__(self, gradients):
"""Accumulates :obj:`gradients`."""
if not self._gradients:
self._gradients.extend(
[
tf.Variable(tf.zeros_like(gradient), trainable=False) if gradient is not None else gradient
for gradient in gradients
]
)
if len(gradients) != len(self._gradients):
raise ValueError("Expected %s gradients, but got %d" % (len(self._gradients), len(gradients)))
for accum_gradient, gradient in zip(self._get_replica_gradients(), gradients):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(gradient)
self._accum_steps.assign_add(1)
def reset(self):
"""Resets the accumulated gradients."""
if self._gradients:
self._accum_steps.assign(0)
for gradient in self._get_replica_gradients():
if gradient is not None:
gradient.assign(tf.zeros_like(gradient))
def _get_replica_gradients(self):
if tf.distribute.has_strategy():
# In a replica context, we want to accumulate gradients on each replica
# without synchronization, so we directly assign the value of the
# current replica.
replica_context = tf.distribute.get_replica_context()
if replica_context is None or tf.distribute.get_strategy().num_replicas_in_sync == 1:
return self._gradients
return (
gradient.device_map.select_for_current_replica(gradient.values, replica_context)
for gradient in self._gradients
if gradient is not None
)
else:
return self._gradients
def _get_layer_decay(layer_decay, n_layers):
"""Have lower learning rates for layers closer to the input."""
key_to_depths = collections.OrderedDict({
"/embeddings/": 0,
"/embeddings_project/": 0,
"/start_logits/": n_layers + 2,
"/end_logits/": n_layers + 2,
"/answer_class/": n_layers + 2,
"/qa_outputs/": n_layers + 2,
})
for layer in range(n_layers):
key_to_depths["encoder/layer_._" + str(layer) + "/"] = layer + 1
return {
key: layer_decay ** (n_layers + 2 - depth)
for key, depth in key_to_depths.items()
}
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/optimization.py |
# Copyright 2020 The Google AI Team, Stanford University and The HuggingFace Inc. team.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from tokenization_utils import BertTokenizer
from tokenization_utils import BertTokenizer
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"google/electra-small-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-generator/vocab.txt",
"google/electra-base-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-generator/vocab.txt",
"google/electra-large-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-generator/vocab.txt",
"google/electra-small-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-discriminator/vocab.txt",
"google/electra-base-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-discriminator/vocab.txt",
"google/electra-large-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-discriminator/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"google/electra-small-generator": 512,
"google/electra-base-generator": 512,
"google/electra-large-generator": 512,
"google/electra-small-discriminator": 512,
"google/electra-base-discriminator": 512,
"google/electra-large-discriminator": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class ElectraTokenizer(BertTokenizer):
r"""
Constructs an Electra tokenizer.
:class:`~transformers.ElectraTokenizer` is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
tokenization: punctuation splitting + wordpiece.
Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/tokenization.py |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pre-trains an ELECTRA model."""
import argparse
import collections
import json
import time
import datetime
import os
import tensorflow as tf
import horovod.tensorflow as hvd
from horovod.tensorflow.compression import Compression
from gpu_affinity import set_affinity
import utils
import sys
import pretrain_utils
from utils import get_rank, get_world_size, is_main_process, log, log_config, setup_logger, postprocess_dllog
from tokenization import ElectraTokenizer
from modeling import PretrainingModel
from optimization import create_optimizer, GradientAccumulator
import dllogger
class PretrainingConfig(object):
"""Defines pre-training hyperparameters."""
def __init__(self, model_name, **kwargs):
self.model_name = model_name
self.seed = 42
self.debug = False # debug mode for quickly running things
self.do_train = True # pre-train ELECTRA
self.do_eval = False # evaluate generator/discriminator on unlabeled data
self.phase2 = False
# amp
self.amp = True
self.xla = True
self.fp16_compression = False
# optimizer type
self.optimizer = 'adam'
self.gradient_accumulation_steps = 1
# lamb whitelisting for LN and biases
self.skip_adaptive = False
# loss functions
self.electra_objective = True # if False, use the BERT objective instead
self.gen_weight = 1.0 # masked language modeling / generator loss
self.disc_weight = 50.0 # discriminator loss
self.mask_prob = 0.15 # percent of input tokens to mask out / replace
# optimization
self.learning_rate = 5e-4
self.lr_decay_power = 0.5
self.weight_decay_rate = 0.01
self.num_warmup_steps = 10000
self.opt_beta_1 = 0.878
self.opt_beta_2 = 0.974
self.end_lr = 0.0
# training settings
self.log_freq = 10
self.skip_checkpoint = False
self.save_checkpoints_steps = 1000
self.num_train_steps = 1000000
self.num_eval_steps = 100
self.keep_checkpoint_max = 5 # maximum number of recent checkpoint files to keep; change to 0 or None to keep all checkpoints
self.restore_checkpoint = None
self.load_weights = False
self.steps_this_run = -1
# model settings
self.model_size = "base" # one of "small", "base", or "large"
# override the default transformer hparams for the provided model size; see
# modeling.BertConfig for the possible hparams and util.training_utils for
# the defaults
self.model_hparam_overrides = (
kwargs["model_hparam_overrides"]
if "model_hparam_overrides" in kwargs else {})
self.embedding_size = None # bert hidden size by default
self.vocab_size = 30522 # number of tokens in the vocabulary
self.do_lower_case = True # lowercase the input?
# generator settings
self.uniform_generator = False # generator is uniform at random
self.shared_embeddings = True # share generator/discriminator token embeddings?
# self.untied_generator = True # tie all generator/discriminator weights?
self.generator_layers = 1.0 # frac of discriminator layers for generator
self.generator_hidden_size = 0.25 # frac of discrim hidden size for gen
self.disallow_correct = False # force the generator to sample incorrect
# tokens (so 15% of tokens are always
# fake)
self.temperature = 1.0 # temperature for sampling from generator
# batch sizes
self.max_seq_length = 128
self.train_batch_size = 128
self.eval_batch_size = 128
self.results_dir = "results"
self.json_summary = None
self.update(kwargs)
# default locations of data files
self.pretrain_tfrecords = os.path.join(
"data", "pretrain_tfrecords/pretrain_data.tfrecord*")
self.vocab_file = os.path.join("vocab", "vocab.txt")
self.model_dir = os.path.join(self.results_dir, "models", model_name)
self.checkpoints_dir = os.path.join(self.model_dir, "checkpoints")
self.weights_dir = os.path.join(self.model_dir, "weights")
self.results_txt = os.path.join(self.results_dir, "unsup_results.txt")
self.results_pkl = os.path.join(self.results_dir, "unsup_results.pkl")
self.log_dir = os.path.join(self.model_dir, "logs")
self.max_predictions_per_seq = int((self.mask_prob + 0.005) *
self.max_seq_length)
# defaults for different-sized model
if self.model_size == "base":
self.embedding_size = 768
self.hidden_size = 768
self.num_hidden_layers = 12
if self.hidden_size % 64 != 0:
raise ValueError("Hidden size {} should be divisible by 64. Number of attention heads is hidden size {} / 64 ".format(self.hidden_size, self.hidden_size))
self.num_attention_heads = int(self.hidden_size / 64.)
elif self.model_size == "large":
self.embedding_size = 1024
self.hidden_size = 1024
self.num_hidden_layers = 24
if self.hidden_size % 64 != 0:
raise ValueError("Hidden size {} should be divisible by 64. Number of attention heads is hidden size {} / 64 ".format(self.hidden_size, self.hidden_size))
self.num_attention_heads = int(self.hidden_size / 64.)
else:
raise ValueError("--model_size : 'base' and 'large supported only.")
self.act_func = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.update(kwargs)
def update(self, kwargs):
for k, v in kwargs.items():
if v is not None:
self.__dict__[k] = v
def metric_fn(config, metrics, eval_fn_inputs):
"""Computes the loss and accuracy of the model."""
d = eval_fn_inputs
metrics["masked_lm_accuracy"].update_state(
y_true=tf.reshape(d["masked_lm_ids"], [-1]),
y_pred=tf.reshape(d["masked_lm_preds"], [-1]),
sample_weight=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"].update_state(
values=tf.reshape(d["mlm_loss"], [-1]),
sample_weight=tf.reshape(d["masked_lm_weights"], [-1]))
if config.electra_objective:
metrics["sampled_masked_lm_accuracy"].update_state(
y_true=tf.reshape(d["masked_lm_ids"], [-1]),
y_pred=tf.reshape(d["sampled_tokids"], [-1]),
sample_weight=tf.reshape(d["masked_lm_weights"], [-1]))
if config.disc_weight > 0:
metrics["disc_loss"].update_state(d["disc_loss"])
#metrics["disc_auc"].update_state(
# d["disc_labels"] * d["input_mask"],
# d["disc_probs"] * tf.cast(d["input_mask"], tf.float32))
metrics["disc_accuracy"].update_state(
y_true=d["disc_labels"], y_pred=d["disc_preds"],
sample_weight=d["input_mask"])
metrics["disc_precision"].update_state(
y_true=d["disc_labels"], y_pred=d["disc_preds"],
sample_weight=d["disc_preds"] * d["input_mask"])
metrics["disc_recall"].update_state(
y_true=d["disc_labels"], y_pred=d["disc_preds"],
sample_weight=d["disc_labels"] * d["input_mask"])
return metrics
@tf.function
def train_one_step(config, model, optimizer, features, accumulator, first_step, take_step, clip_norm=1.0):
#Forward and Backward pass
with tf.GradientTape() as tape:
total_loss, eval_fn_inputs = model(features, is_training=True)
unscaled_loss = tf.stop_gradient(total_loss)
if config.amp:
total_loss = optimizer.get_scaled_loss(total_loss)
#Backpropogate gradients
#tape = hvd.DistributedGradientTape(
# tape, sparse_as_dense=True,
# compression=Compression.fp16 if config.amp and config.fp16_compression else Compression.none)
gradients = tape.gradient(total_loss, model.trainable_variables)
#Get unscaled gradients if AMP
if config.amp:
gradients = optimizer.get_unscaled_gradients(gradients)
#Accumulate gradients
accumulator(gradients)
#Need to call apply_gradients on very first step irrespective of gradient accumulation
#This is required for the optimizer to build it's states
if first_step or take_step:
#All reduce and Clip the accumulated gradients
allreduced_accumulated_gradients = [None if g is None else hvd.allreduce(g / tf.cast(config.gradient_accumulation_steps, g.dtype),
compression=Compression.fp16 if config.amp and config.fp16_compression else Compression.none)
for g in accumulator.gradients]
(clipped_accumulated_gradients, _) = tf.clip_by_global_norm(allreduced_accumulated_gradients, clip_norm=clip_norm)
#Weight update
optimizer.apply_gradients(zip(clipped_accumulated_gradients, model.trainable_variables))
accumulator.reset()
#brodcast model weights after first train step
if first_step:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
return unscaled_loss, eval_fn_inputs
def main(e2e_start_time):
# Parse essential argumentss
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", required=True)
parser.add_argument("--model_size", default="base", type=str, help="base or large")
parser.add_argument("--pretrain_tfrecords", type=str)
parser.add_argument("--phase2", action='store_true')
parser.add_argument("--fp16_compression", action='store_true')
parser.add_argument("--amp", action='store_true',
help="Whether to use fp16.")
parser.add_argument("--xla", action='store_true',
help="Whether to use xla.")
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--num_train_steps", type=int)
parser.add_argument("--num_warmup_steps", type=int)
parser.add_argument("--learning_rate", type=float)
parser.add_argument("--train_batch_size", type=int)
parser.add_argument("--max_seq_length", type=int)
parser.add_argument("--mask_prob", type=float)
parser.add_argument("--disc_weight", type=float)
parser.add_argument("--generator_hidden_size", type=float)
parser.add_argument("--log_freq", type=int, default=10, help="Training metrics logging frequency")
parser.add_argument("--save_checkpoints_steps", type=int)
parser.add_argument("--steps_this_run", type=int, default=-1, help="run a fixed number of steps only")
parser.add_argument("--keep_checkpoint_max", type=int)
parser.add_argument("--restore_checkpoint", default=None, type=str)
parser.add_argument("--load_weights", action='store_true')
parser.add_argument("--weights_dir")
parser.add_argument("--optimizer", default="adam", type=str, help="adam or lamb")
parser.add_argument("--skip_adaptive", action='store_true', help="Whether to apply adaptive LR on LayerNorm and biases")
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Number of Gradient Accumulation steps")
parser.add_argument("--lr_decay_power", type=float, default=0.5, help="LR decay power")
parser.add_argument("--opt_beta_1", type=float, default=0.878, help="Optimizer beta1")
parser.add_argument("--opt_beta_2", type=float, default=0.974, help="Optimizer beta2")
parser.add_argument("--end_lr", type=float, default=0.0, help="Ending LR")
parser.add_argument("--log_dir", type=str, default=None, help="Path to store logs")
parser.add_argument("--results_dir", type=str, default=None, help="Path to store all model results")
parser.add_argument("--skip_checkpoint", action='store_true', default=False, help="Path to store logs")
parser.add_argument('--json-summary', type=str, default=None,
help='If provided, the json summary will be written to the specified file.')
args = parser.parse_args()
config = PretrainingConfig(**args.__dict__)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
# Set up tensorflow
hvd.init()
args.log_dir = config.log_dir
# DLLogger
setup_logger(args)
dllogger.metadata('training_sequences_per_second', {'unit': 'sequences/s'})
dllogger.metadata('final_loss', {'unit': None})
dllogger.metadata('e2e_train_time', {'unit': 's'})
set_affinity(hvd.local_rank())
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
tf.config.optimizer.set_jit(config.xla)
#tf.config.optimizer.set_experimental_options({"auto_mixed_precision": config.amp})
if config.amp:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
print('Compute dtype: %s' % policy.compute_dtype) # Compute dtype: float16
print('Variable dtype: %s' % policy.variable_dtype) # Variable dtype: float32
#tf.random.set_seed(config.seed)
# Set up config cont'
if config.load_weights and config.restore_checkpoint:
raise ValueError("`load_weights` and `restore_checkpoint` should not be on at the same time.")
if config.phase2 and not config.restore_checkpoint:
raise ValueError("`phase2` cannot be used without `restore_checkpoint`.")
utils.heading("Config:")
log_config(config)
# Save pretrain configs
pretrain_config_json = os.path.join(config.checkpoints_dir, 'pretrain_config.json')
if is_main_process():
utils.write_json(config.__dict__, pretrain_config_json)
log("Configuration saved in {}".format(pretrain_config_json))
# Set up model
model = PretrainingModel(config)
# Set up metrics
metrics = dict()
metrics["train_perf"] = tf.keras.metrics.Mean(name="train_perf")
metrics["total_loss"] = tf.keras.metrics.Mean(name="total_loss")
metrics["masked_lm_accuracy"] = tf.keras.metrics.Accuracy(name="masked_lm_accuracy")
metrics["masked_lm_loss"] = tf.keras.metrics.Mean(name="masked_lm_loss")
if config.electra_objective:
metrics["sampled_masked_lm_accuracy"] = tf.keras.metrics.Accuracy(name="sampled_masked_lm_accuracy")
if config.disc_weight > 0:
metrics["disc_loss"] = tf.keras.metrics.Mean(name="disc_loss")
metrics["disc_auc"] = tf.keras.metrics.AUC(name="disc_auc")
metrics["disc_accuracy"] = tf.keras.metrics.Accuracy(name="disc_accuracy")
metrics["disc_precision"] = tf.keras.metrics.Accuracy(name="disc_precision")
metrics["disc_recall"] = tf.keras.metrics.Accuracy(name="disc_recall")
# Set up tensorboard
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = os.path.join(config.log_dir, current_time,
'train_' + str(get_rank()) + '_of_' + str(get_world_size()))
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
# Set up dataset
dataset = pretrain_utils.get_dataset(
config, config.train_batch_size, world_size=get_world_size(), rank=get_rank())
train_iterator = iter(dataset)
# Set up optimizer
optimizer = create_optimizer(
init_lr=config.learning_rate,
num_train_steps=config.num_train_steps,
num_warmup_steps=config.num_warmup_steps,
weight_decay_rate=config.weight_decay_rate,
optimizer=config.optimizer,
skip_adaptive=config.skip_adaptive,
power=config.lr_decay_power,
beta_1=config.opt_beta_1,
beta_2=config.opt_beta_2,
end_lr=config.end_lr)
accumulator = GradientAccumulator()
if config.amp:
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(optimizer, "dynamic")
# Set up model checkpoint
checkpoint = tf.train.Checkpoint(
step=tf.Variable(0), phase2=tf.Variable(False), optimizer=optimizer, model=model)
manager = tf.train.CheckpointManager(checkpoint, config.checkpoints_dir, max_to_keep=config.keep_checkpoint_max)
if config.restore_checkpoint and config.restore_checkpoint != "latest":
checkpoint.restore(config.restore_checkpoint)
log(" ** Restored model checkpoint from {}".format(config.restore_checkpoint))
elif config.restore_checkpoint and config.restore_checkpoint == "latest" and manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
log(" ** Restored model checkpoint from {}".format(manager.latest_checkpoint))
elif config.load_weights:
model.generator(model.generator.dummy_inputs)
model.discriminator(model.discriminator.dummy_inputs)
model.generator.load_weights(os.path.join(config.weights_dir, 'generator', 'tf_model.h5'))
model.discriminator.load_weights(os.path.join(config.weights_dir, 'discriminator', 'tf_model.h5'))
else:
log(" ** Initializing from scratch.")
restore_iterator = bool(config.restore_checkpoint) and config.restore_checkpoint == "latest"
# Initialize global step for phase2
if config.phase2 and not bool(checkpoint.phase2):
optimizer.iterations.assign(0)
checkpoint.step.assign(0)
checkpoint.phase2.assign(True)
restore_iterator = False
if bool(checkpoint.phase2):
manager = tf.train.CheckpointManager(
checkpoint, config.checkpoints_dir,
checkpoint_name='ckpt-p2',
max_to_keep=config.keep_checkpoint_max)
# Set up iterator checkpoint
iter_checkpoint = tf.train.Checkpoint(
train_iterator=train_iterator, world_size=tf.Variable(get_world_size()), rank=tf.Variable(get_rank()))
iter_manager = tf.train.CheckpointManager(
iter_checkpoint,
os.path.join(config.checkpoints_dir, 'iter_ckpt_rank_' + '{:02}'.format(get_rank())),
checkpoint_name='iter_ckpt_rank_' + '{:02}'.format(get_rank()),
max_to_keep=config.keep_checkpoint_max)
if restore_iterator and iter_manager.latest_checkpoint:
ckpt_world_size = tf.train.load_variable(
iter_manager.latest_checkpoint, 'world_size/.ATTRIBUTES/VARIABLE_VALUE')
if ckpt_world_size == get_world_size():
iter_checkpoint.restore(iter_manager.latest_checkpoint)
log(" ** Restored iterator checkpoint from {}".format(iter_manager.latest_checkpoint), all_rank=True)
utils.heading("Running training")
accumulator.reset()
train_start, start_step = time.time(), int(checkpoint.step) - 1
local_step = 0
saved_ckpt = False
while int(checkpoint.step) <= config.num_train_steps:
saved_ckpt = False
step = int(checkpoint.step)
features = next(train_iterator)
iter_start = time.time()
# if step == 200: tf.profiler.experimental.start(logdir=train_log_dir)
total_loss, eval_fn_inputs = train_one_step(config, model, optimizer, features, accumulator,
local_step==1, take_step=local_step % args.gradient_accumulation_steps == 0)
# if step == 300: tf.profiler.experimental.stop()
metrics["train_perf"].update_state(
config.train_batch_size * get_world_size() / (time.time() - iter_start))
metrics["total_loss"].update_state(values=total_loss)
metric_fn(config, metrics, eval_fn_inputs)
if (step % args.log_freq == 0) and (local_step % args.gradient_accumulation_steps == 0):
log_info_dict = {k:float(v.result().numpy() * 100) if "accuracy" in k else float(v.result().numpy()) for k, v in metrics.items()}
dllogger.log(step=(step,), data=log_info_dict, verbosity=0)
log('Step:{step:6d}, Loss:{total_loss:10.6f}, Gen_loss:{masked_lm_loss:10.6f}, Disc_loss:{disc_loss:10.6f}, Gen_acc:{masked_lm_accuracy:6.2f}, '
'Disc_acc:{disc_accuracy:6.2f}, Perf:{train_perf:4.0f}, Loss Scaler: {loss_scale}, Elapsed: {elapsed}, ETA: {eta}, '.format(
step=step, **log_info_dict,
loss_scale=optimizer.loss_scale if config.amp else 1,
elapsed=utils.get_readable_time(time.time() - train_start),
eta=utils.get_readable_time(
(time.time() - train_start) / (step - start_step) * (config.num_train_steps - step))),
all_rank=True)
with train_summary_writer.as_default():
for key, m in metrics.items():
tf.summary.scalar(key, m.result(), step=step)
if int(checkpoint.step) < config.num_train_steps:
for m in metrics.values():
m.reset_states()
#Print allreduced metrics on the last step
if (int(checkpoint.step) == config.num_train_steps and (local_step % args.gradient_accumulation_steps == 0)) or ((local_step + 1) % (config.save_checkpoints_steps * args.gradient_accumulation_steps) == 0):
log_info_dict = {k:float(hvd.allreduce(v.result()).numpy() * 100) if "accuracy" in k else float(hvd.allreduce(v.result()).numpy()) for k, v in metrics.items()}
log_info_dict["training_sequences_per_second"] = log_info_dict["train_perf"]
log_info_dict["final_loss"] = log_info_dict["total_loss"]
log_info_dict["e2e_train_time"] = time.time() - e2e_start_time
dllogger.log(step=(), data=log_info_dict, verbosity=0)
log('<FINAL STEP METRICS> Step:{step:6d}, Loss:{total_loss:10.6f}, Gen_loss:{masked_lm_loss:10.6f}, Disc_loss:{disc_loss:10.6f}, Gen_acc:{masked_lm_accuracy:6.2f}, '
'Disc_acc:{disc_accuracy:6.2f}, Perf:{train_perf:4.0f},'.format(
step=step, **log_info_dict),
all_rank=False)
if local_step % args.gradient_accumulation_steps == 0:
checkpoint.step.assign(int(optimizer.iterations))
if not config.skip_checkpoint and (local_step % (config.save_checkpoints_steps * args.gradient_accumulation_steps) == 0):
saved_ckpt = True
if is_main_process():
save_path = manager.save(checkpoint_number=step)
log(" ** Saved model checkpoint for step {}: {}".format(step, save_path))
iter_save_path = iter_manager.save(checkpoint_number=step)
log(" ** Saved iterator checkpoint for step {}: {}".format(step, iter_save_path), all_rank=True)
local_step += 1
if config.steps_this_run != -1 and (local_step % (config.steps_this_run * args.gradient_accumulation_steps) == 0):
#terminating run sooner as steps_this_run has been reached
log("terminating as steps_this_run:{} has been reached".format(config.steps_this_run))
break
step = (int(checkpoint.step) - 1)
dllogger.flush()
if not config.skip_checkpoint and not saved_ckpt:
if is_main_process():
save_path = manager.save(checkpoint_number=step)
log(" ** Saved model checkpoint for step {}: {}".format(step, save_path))
iter_save_path = iter_manager.save(checkpoint_number=step)
log(" ** Saved iterator checkpoint for step {}: {}".format(step, iter_save_path), all_rank=True)
return args
if __name__ == "__main__":
start_time = time.time()
args = main(start_time)
log("Total Time:{:.4f}".format(time.time() - start_time))
if is_main_process():
postprocess_dllog(args)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/run_pretraining.py |
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import subprocess
import time
import argparse
import json
import logging
import collections
import tensorflow as tf
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
from configuration import ElectraConfig
from modeling import TFElectraForQuestionAnswering
from tokenization import ElectraTokenizer
from squad_utils import SquadResult, RawResult, _get_best_indices
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/electra-small-generator",
"google/electra-base-generator",
"google/electra-large-generator",
"google/electra-small-discriminator",
"google/electra-base-discriminator",
"google/electra-large-discriminator",
# See all ELECTRA models at https://huggingface.co/models?filter=electra
]
_PrelimPrediction = collections.namedtuple(
"PrelimPrediction",
["start_index", "end_index", "start_logit", "end_logit"])
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--electra_model", default=None, type=str, required=True,
help="Model selected in the list: " + ", ".join(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST))
parser.add_argument("--init_checkpoint",
default=None,
type=str,
required=True,
help="The checkpoint file from pretraining")
parser.add_argument("--question",
default=None,
type=str,
required=True,
help="Question")
parser.add_argument("--context",
default=None,
type=str,
required=True,
help="Context")
parser.add_argument(
"--joint_head",
default=True,
type=bool,
help="Jointly predict the start and end positions",
)
parser.add_argument(
"--beam_size",
default=4,
type=int,
help="Beam size when doing joint predictions",
)
parser.add_argument("--n_best_size", default=20, type=int,
help="The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument("--max_answer_length", default=30, type=int,
help="The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument('--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument('--null_score_diff_threshold',
type=float, default=0.0,
help="If null_score - best_non_null is greater than the threshold predict null.")
args = parser.parse_args()
return args
def get_predictions_joint_head(start_indices, end_indices, result, max_len, args):
predictions = []
for i in range(args.beam_size):
start_index = start_indices[i]
for j in range(args.beam_size):
# for end_index in end_indices:
end_index = end_indices[i * args.beam_size + j]
if start_index >= max_len:
continue
if end_index >= max_len:
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > args.max_answer_length:
continue
predictions.append(
_PrelimPrediction(
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[i],
end_logit=result.end_logits[i * args.beam_size + j]))
return predictions
def get_predictions(start_indices, end_indices, result, max_len, args):
predictions = []
for start_index in start_indices:
for end_index in end_indices:
if start_index >= max_len:
continue
if end_index >= max_len:
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > args.max_answer_length:
continue
predictions.append(
_PrelimPrediction(
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
return predictions
def main():
args = parse_args()
print("***** Loading tokenizer and model *****")
electra_model = args.electra_model
config = ElectraConfig.from_pretrained(electra_model)
tokenizer = ElectraTokenizer.from_pretrained(electra_model)
model = TFElectraForQuestionAnswering.from_pretrained(electra_model, config=config, args=args)
print("***** Loading fine-tuned checkpoint: {} *****".format(args.init_checkpoint))
model.load_weights(args.init_checkpoint, by_name=False, skip_mismatch=False).expect_partial()
question, text = args.question, args.context
encoding = tokenizer.encode_plus(question, text, return_tensors='tf')
input_ids, token_type_ids, attention_mask = encoding["input_ids"], encoding["token_type_ids"], \
encoding["attention_mask"]
all_tokens = tokenizer.convert_ids_to_tokens(input_ids.numpy()[0])
if not args.joint_head:
start_logits, end_logits = model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
)[:2]
start_logits = start_logits[0].numpy().tolist()
end_logits = end_logits[0].numpy().tolist()
result = RawResult(unique_id=0,
start_logits=start_logits,
end_logits=end_logits)
start_indices = _get_best_indices(result.start_logits, args.n_best_size)
end_indices = _get_best_indices(result.end_logits, args.n_best_size)
predictions = get_predictions(start_indices, end_indices, result, len(all_tokens), args)
null_score = result.start_logits[0] + result.end_logits[0]
else:
outputs = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
output = [output[0].numpy().tolist() for output in outputs]
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
0,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
predictions = get_predictions_joint_head(result.start_top_index, result.end_top_index, result, len(all_tokens), args)
null_score = result.cls_logits
predictions = sorted(predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
answer = predictions[0]
answer = ' '.join(all_tokens[answer.start_index: answer.end_index + 1])
if args.null_score_diff_threshold > null_score and args.version_2_with_negative:
answer = ''
print(answer)
return answer
if __name__ == "__main__":
main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/run_inference.py |
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json, pickle, sys, unicodedata, six, time, os
import horovod.tensorflow as hvd
import tensorflow as tf
import dllogger
def get_rank():
try:
return hvd.rank()
except:
return 0
def get_world_size():
try:
return hvd.size()
except:
return 1
def is_main_process():
return get_rank() == 0
def format_step(step):
if isinstance(step, str):
return step
s = ""
if len(step) == 1:
s += "Training Iteration: {} ".format(step[0])
return s
if len(step) > 0:
s += "Training Epoch: {} ".format(step[0])
if len(step) > 1:
s += "Training Iteration: {} ".format(step[1])
return s
def load_json(path):
with tf.io.gfile.GFile(path, "r") as f:
return json.load(f)
def write_json(o, path):
if "/" in path:
tf.io.gfile.makedirs(path.rsplit("/", 1)[0])
with tf.io.gfile.GFile(path, "w") as f:
json.dump(o, f)
def load_pickle(path):
with tf.io.gfile.GFile(path, "rb") as f:
return pickle.load(f)
def write_pickle(o, path):
if "/" in path:
tf.io.gfile.makedirs(path.rsplit("/", 1)[0])
with tf.io.gfile.GFile(path, "wb") as f:
pickle.dump(o, f, -1)
def mkdir(path):
if not tf.io.gfile.exists(path):
tf.io.gfile.makedirs(path)
def rmrf(path):
if tf.io.gfile.exists(path):
tf.io.gfile.rmtree(path)
def rmkdir(path):
rmrf(path)
mkdir(path)
def log(*args, **kwargs):
all_rank = kwargs.pop("all_rank", False)
if not all_rank and not is_main_process():
return
msg = " ".join(map(str, args))
sys.stdout.write(msg + "\n")
sys.stdout.flush()
def log_config(config):
for key, value in sorted(config.__dict__.items()):
log(key, value)
log()
def heading(*args):
log(80 * "=")
log(*args)
log(80 * "=")
def nest_dict(d, prefixes, delim="_"):
"""Go from {prefix_key: value} to {prefix: {key: value}}."""
nested = {}
for k, v in d.items():
for prefix in prefixes:
if k.startswith(prefix + delim):
if prefix not in nested:
nested[prefix] = {}
nested[prefix][k.split(delim, 1)[1]] = v
else:
nested[k] = v
return nested
def flatten_dict(d, delim="_"):
"""Go from {prefix: {key: value}} to {prefix_key: value}."""
flattened = {}
for k, v in d.items():
if isinstance(v, dict):
for k2, v2 in v.items():
flattened[k + delim + k2] = v2
else:
flattened[k] = v
return flattened
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def get_readable_time(elapsed):
d, h, m, s = [int(x) for x in time.strftime("%d:%H:%M:%S", time.gmtime(elapsed)).split(':')]
d -= 1
return '{:2d}h{:2d}m{:2d}s'.format(24*d + h, m, s)
def setup_logger(args):
os.makedirs(args.log_dir, exist_ok=True)
if not args.json_summary:
log_path = os.path.join(args.log_dir, 'dllogger_rank{}.log'.format(get_rank()))
else:
log_path = "{}_rank{}".format(args.json_summary, get_rank())
if is_main_process():
dllogger.init(backends = [dllogger.JSONStreamBackend(verbosity=1, filename=log_path),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
else:
dllogger.init(backends = [dllogger.JSONStreamBackend(verbosity=1, filename=log_path)])
for k,v in vars(args).items():
dllogger.log(step='PARAMETER', data={k:v}, verbosity=0)
container_setup_info = {
'NVIDIA_TENSORFLOW_VERSION': os.environ.get('NVIDIA_TENSORFLOW_VERSION'),
'TENSORFLOW_VERSION': os.environ.get('TENSORFLOW_VERSION'),
'CUBLAS_VERSION': os.environ.get('CUBLAS_VERSION'),
'NCCL_VERSION': os.environ.get('NCCL_VERSION'),
'CUDA_DRIVER_VERSION': os.environ.get('CUDA_DRIVER_VERSION'),
'CUDNN_VERSION': os.environ.get('CUDNN_VERSION'),
'CUDA_VERSION': os.environ.get('CUDA_VERSION'),
'NVIDIA_PIPELINE_ID': os.environ.get('NVIDIA_PIPELINE_ID'),
'NVIDIA_BUILD_ID': os.environ.get('NVIDIA_BUILD_ID'),
'NVIDIA_TF32_OVERRIDE': os.environ.get('NVIDIA_TF32_OVERRIDE'),
}
dllogger.log(step='PARAMETER', data=container_setup_info, verbosity=0)
def postprocess_dllog(args):
if not args.json_summary:
log_path = os.path.join(args.log_dir, 'dllogger_rank{}.log')
else:
log_path = str(args.json_summary) + "_rank{}"
logfiles = [open(log_path.format(i), 'r') for i in range(get_world_size())]
if not args.json_summary:
log_path = os.path.join(args.log_dir, 'dllogger.log')
else:
log_path = str(args.json_summary)
with open(log_path, 'w') as dest_file:
for lines in zip(*[f.readlines() for f in logfiles]):
json_lines = [json.loads(l[5:]) for l in lines]
assert all(x['type'] == json_lines[0]['type'] for x in json_lines)
if json_lines[0]['type'] != 'LOG':
dest_file.write(lines[0])
continue
assert all(x['step'] == json_lines[0]['step'] for x in json_lines)
if json_lines[0]['step'] == 'PARAMETER':
dest_file.write(lines[0])
else:
d = dict.fromkeys(json_lines[0]['data'])
for k in d.keys():
vs = [line['data'][k] for line in json_lines]
d[k] = sum(vs)/len(vs)
json_lines[0]['data'] = d
dest_file.write('DLLL ')
dest_file.write(json.dumps(json_lines[0]))
dest_file.write('\n')
for l in logfiles:
l.close()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/utils.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF general model utils."""
import functools
import logging
import os
import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.saving import hdf5_format
from configuration_utils import PretrainedConfig, BertConfig
from file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
from file_utils import MULTIPLE_CHOICE_DUMMY_INPUTS, add_start_docstrings, add_start_docstrings_to_callable
from tokenization_utils import BatchEncoding
from utils import log
class TFModelUtilsMixin:
"""
A few utilities for `tf.keras.Model`s, to be used as a mixin.
"""
def num_parameters(self, only_trainable: bool = False) -> int:
"""
Get number of (optionally, trainable) parameters in the model.
"""
if only_trainable:
return int(sum(np.prod(w.shape.as_list()) for w in self.trainable_variables))
else:
return self.count_params()
def keras_serializable(cls):
"""
Decorate a Keras Layer class to support Keras serialization.
This is done by:
1. adding a `transformers_config` dict to the Keras config dictionary in `get_config` (called by Keras at
serialization time
2. wrapping `__init__` to accept that `transformers_config` dict (passed by Keras at deserialization time) and
convert it to a config object for the actual layer initializer
3. registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does
not need to be supplied in `custom_objects` in the call to `tf.keras.models.load_model`
:param cls: a tf.keras.layers.Layers subclass that accepts a `config` argument to its initializer (typically a
`TF*MainLayer` class in this project)
:return: the same class object, with modifications for Keras deserialization.
"""
initializer = cls.__init__
config_class = getattr(cls, "config_class", None)
if config_class is None:
raise AttributeError("Must set `config_class` to use @keras_serializable")
@functools.wraps(initializer)
def wrapped_init(self, *args, **kwargs):
transformers_config = kwargs.pop("transformers_config", None)
config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.get("config", None)
if config is not None and transformers_config is not None:
raise ValueError("Must pass either `config` or `transformers_config`, not both")
elif config is not None:
# normal layer construction, call with unchanged args (config is already in there)
initializer(self, *args, **kwargs)
elif transformers_config is not None:
# Keras deserialization, convert dict to config
config = config_class.from_dict(transformers_config)
initializer(self, config, *args, **kwargs)
else:
raise ValueError("Must pass either `config` (PretrainedConfig) or `transformers_config` (dict)")
self._transformers_config = config
cls.__init__ = wrapped_init
if not hasattr(cls, "get_config"):
raise TypeError("Only use @keras_serializable on tf.keras.layers.Layer subclasses")
if hasattr(cls.get_config, "_is_default"):
def get_config(self):
cfg = super(cls, self).get_config()
cfg["transformers_config"] = self._transformers_config.to_dict()
return cfg
cls.get_config = get_config
cls._keras_serializable = True
if hasattr(tf.keras.utils, "register_keras_serializable"):
cls = tf.keras.utils.register_keras_serializable()(cls)
return cls
class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
r""" Base class for all TF models.
:class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
- ``pretrained_model_archive_map``: a python ``dict`` of with `short-cut-names` (string) as keys and `url` (string) of associated pretrained weights as values.
- ``load_tf_weights``: a python ``method`` for loading a TensorFlow checkpoint in a PyTorch model, taking as arguments:
- ``model``: an instance of the relevant subclass of :class:`~transformers.PreTrainedModel`,
- ``config``: an instance of the relevant subclass of :class:`~transformers.PretrainedConfig`,
- ``path``: a path (string) to the TensorFlow checkpoint.
- ``base_model_prefix``: a string indicating the attribute associated to the base model in derived classes of the same architecture adding modules on top of the base model.
"""
config_class = None
pretrained_model_archive_map = {}
base_model_prefix = ""
@property
def dummy_inputs(self):
""" Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(DUMMY_INPUTS)}
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
if not isinstance(config, PretrainedConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
"To create a model from a pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
)
)
# Save config in model
self.config = config
def get_input_embeddings(self):
"""
Returns the model's input embeddings.
Returns:
:obj:`tf.keras.layers.Layer`:
A torch module mapping vocabulary to hidden states.
"""
base_model = getattr(self, self.base_model_prefix, self)
if base_model is not self:
return base_model.get_input_embeddings()
else:
raise NotImplementedError
def get_output_embeddings(self):
"""
Returns the model's output embeddings.
Returns:
:obj:`tf.keras.layers.Layer`:
A torch module mapping hidden states to vocabulary.
"""
return None # Overwrite for models with output embeddings
def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
""" Build a resized Embedding Variable from a provided token Embedding Module.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
Args:
new_num_tokens: (`optional`) int
New number of tokens in the embedding matrix.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
If not provided or None: return the provided token Embedding Module.
Return: ``tf.Variable``
Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
"""
# if new_num_tokens is None:
# return old_embeddings
# old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
# if old_num_tokens == new_num_tokens:
# return old_embeddings
# # Build new embeddings
# new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
# new_embeddings.to(old_embeddings.weight.device)
# # initialize all new embeddings (in particular added tokens)
# self._init_weights(new_embeddings)
# # Copy token embeddings from the previous weights
# num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
# new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]
# return new_embeddings
def resize_token_embeddings(self, new_num_tokens=None):
""" Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
Take care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
Arguments:
new_num_tokens: (`optional`) int:
New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end.
If not provided or None: does nothing and just returns a pointer to the input tokens ``tf.Variable`` Module of the model.
Return: ``tf.Variable``
Pointer to the input tokens Embeddings Module of the model
"""
raise NotImplementedError
def prune_heads(self, heads_to_prune):
""" Prunes heads of the base model.
Arguments:
heads_to_prune: dict with keys being selected layer indices (`int`) and associated values being the list of heads to prune in said layer (list of `int`).
"""
raise NotImplementedError
def save_pretrained(self, save_directory):
""" Save a model and its configuration file to a directory, so that it
can be re-loaded using the :func:`~transformers.PreTrainedModel.from_pretrained` class method.
"""
if os.path.isfile(save_directory):
log("Provided path ({}) should be a directory, not a file".format(save_directory))
return
os.makedirs(save_directory, exist_ok=True)
# Save configuration file
self.config.save_pretrained(save_directory)
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(save_directory, TF2_WEIGHTS_NAME)
self.save_weights(output_model_file)
with h5py.File(output_model_file, "r") as f:
if "layer_names" not in f.attrs and "model_weights" in f:
f = f["model_weights"]
hdf5_layer_names = set(hdf5_format.load_attributes_from_hdf5_group(f, "layer_names"))
log(f"Model weights saved in {output_model_file}: {hdf5_layer_names}")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.
The warning ``Weights from XXX not initialized from pretrained model`` means that the weights of XXX do not come pre-trained with the rest of the model.
It is up to you to train those weights with a downstream fine-tuning task.
The warning ``Weights from XXX not used in YYY`` means that the layer XXX is not used by YYY, therefore those weights are discarded.
Parameters:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
- a path or url to a `PyTorch state_dict save file` (e.g. `./pt_model/pytorch_model.bin`). In this case, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
model_args: (`optional`) Sequence of positional arguments:
All remaning positional arguments will be passed to the underlying model's ``__init__`` method
config: (`optional`) one of:
- an instance of a class derived from :class:`~transformers.PretrainedConfig`, or
- a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()`
Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
- the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
- the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
- the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
from_pt: (`optional`) boolean, default False:
Load the model weights from a PyTorch state_dict save file (see docstring of pretrained_model_name_or_path argument).
cache_dir: (`optional`) string:
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
Examples::
# For example purposes. Not runnable.
model = BertModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = BertModel.from_pretrained('./test/saved_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = BertModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = BertConfig.from_json_file('./tf_model/my_tf_model_config.json')
model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_pt=True, config=config)
"""
config = kwargs.pop("config", None)
cache_dir = kwargs.pop("cache_dir", None)
from_pt = kwargs.pop("from_pt", False)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
# Load config if we don't provide a configuration
if not isinstance(config, PretrainedConfig):
config_path = config if config is not None else pretrained_model_name_or_path
config, model_kwargs = cls.config_class.from_pretrained(
config_path,
*model_args,
cache_dir=cache_dir,
return_unused_kwargs=True,
force_download=force_download,
resume_download=resume_download,
**kwargs,
)
else:
model_kwargs = kwargs
# Load model
if pretrained_model_name_or_path is not None:
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
elif os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
# Load from a TF 2.0 checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
elif from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
# Load from a PyTorch checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
else:
raise EnvironmentError(
"Error no file named {} found in directory {} or `from_pt` set to False".format(
[WEIGHTS_NAME, TF2_WEIGHTS_NAME], pretrained_model_name_or_path
)
)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
archive_file = pretrained_model_name_or_path
elif os.path.isfile(pretrained_model_name_or_path + ".index"):
archive_file = pretrained_model_name_or_path + ".index"
else:
archive_file = hf_bucket_url(
pretrained_model_name_or_path, postfix=(WEIGHTS_NAME if from_pt else TF2_WEIGHTS_NAME)
)
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(
archive_file,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
)
except EnvironmentError as e:
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
log("Couldn't reach server at '{}' to download pretrained weights.".format(archive_file))
else:
log(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
", ".join(cls.pretrained_model_archive_map.keys()),
archive_file,
)
)
raise e
if resolved_archive_file == archive_file:
log("loading weights file {}".format(archive_file))
else:
log("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
else:
resolved_archive_file = None
# Instantiate model.
model = cls(config, *model_args, **model_kwargs)
if from_pt:
# Load from a PyTorch checkpoint
raise NotImplementedError
# return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
model(model.dummy_inputs, training=False) # build the network with dummy inputs
assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)
# 'by_name' allow us to do transfer learning by skipping/adding layers
# see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
try:
model.load_weights(resolved_archive_file, by_name=True)
except OSError:
raise OSError(
"Unable to load weights from h5 file. "
"If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. "
)
model(model.dummy_inputs, training=False) # Make sure restore ops are run
# Check if the models are the same to output loading information
with h5py.File(resolved_archive_file, "r") as f:
if "layer_names" not in f.attrs and "model_weights" in f:
f = f["model_weights"]
hdf5_layer_names = set(hdf5_format.load_attributes_from_hdf5_group(f, "layer_names"))
model_layer_names = set(layer.name for layer in model.layers)
missing_keys = list(model_layer_names - hdf5_layer_names)
unexpected_keys = list(hdf5_layer_names - model_layer_names)
error_msgs = []
if len(unexpected_keys) > 0:
log(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
)
else:
log(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
log(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
f"and are newly initialized: {missing_keys}\n"
)
else:
log(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
f"If your task is similar to the task the model of the ckeckpoint was trained on, "
f"you can already use {model.__class__.__name__} for predictions without further training."
)
if len(error_msgs) > 0:
raise RuntimeError(
"Error(s) in loading weights for {}:\n\t{}".format(model.__class__.__name__, "\n\t".join(error_msgs))
)
if output_loading_info:
loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs}
return model, loading_info
return model
def prepare_inputs_for_generation(self, inputs, **kwargs):
return {"inputs": inputs}
def _do_output_past(self, outputs):
has_output_past = hasattr(self.config, "output_past") and self.config.output_past
has_mem_len = hasattr(self.config, "mem_len") and self.config.mem_len
if has_output_past and not has_mem_len and len(outputs) > 1:
return True
elif has_mem_len and self.config.mem_len > 0 and len(outputs) > 1:
return True
return False
def generate(
self,
input_ids=None,
max_length=None,
min_length=None,
do_sample=None,
early_stopping=None,
num_beams=None,
temperature=None,
top_k=None,
top_p=None,
repetition_penalty=None,
bad_words_ids=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
length_penalty=None,
no_repeat_ngram_size=None,
num_return_sequences=None,
attention_mask=None,
decoder_start_token_id=None,
):
r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling
and beam-search.
Adapted in part from `Facebook's XLM beam search code`_.
.. _`Facebook's XLM beam search code`:
https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529
Parameters:
input_ids: (`optional`) `tf.Tensor` of `dtype=tf.int32` of shape `(batch_size, sequence_length)`
The sequence used as a prompt for the generation. If `None` the method initializes
it as an empty `torch.LongTensor` of shape `(1,)`.
max_length: (`optional`) int
The max length of the sequence to be generated. Between 1 and infinity. Default to 20.
min_length: (`optional`) int
The min length of the sequence to be generated. Between 0 and infinity. Default to 0.
do_sample: (`optional`) bool
If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.
early_stopping: (`optional`) bool
if set to `True` beam search is stopped when at least `num_beams` sentences finished per batch. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.
num_beams: (`optional`) int
Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.
temperature: (`optional`) float
The value used to module the next token probabilities. Must be strictely positive. Default to 1.0.
top_k: (`optional`) int
The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
top_p: (`optional`) float
The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
repetition_penalty: (`optional`) float
The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.
bos_token_id: (`optional`) int
Beginning of sentence token if no prompt is provided. Default to specicic model bos_token_id or None if it does not exist.
pad_token_id: (`optional`) int
Pad token. Defaults to pad_token_id as defined in the models config.
eos_token_id: (`optional`) int
EOS token. Defaults to eos_token_id as defined in the models config.
length_penalty: (`optional`) float
Exponential penalty to the length. Default to 1.
no_repeat_ngram_size: (`optional`) int
If set to int > 0, all ngrams of size `no_repeat_ngram_size` can only occur once.
bad_words_ids: (`optional`) list of lists of int
`bad_words_ids` contains tokens that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.
num_return_sequences: (`optional`) int
The number of independently computed returned sequences for each element in the batch. Default to 1.
attention_mask (`optional`) obj: `tf.Tensor` with `dtype=tf.int32` of same shape as `input_ids`
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
Defaults to `None`.
`What are attention masks? <../glossary.html#attention-mask>`__
decoder_start_token_id=None: (`optional`) int
If an encoder-decoder model starts decoding with a different token than BOS.
Defaults to `None` and is changed to `BOS` later.
Return:
output: `tf.Tensor` of `dtype=tf.int32` shape `(batch_size * num_return_sequences, sequence_length)`
sequence_length is either equal to max_length or shorter if all batches finished early due to the `eos_token_id`
Examples::
tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache.
outputs = model.generate(max_length=40) # do greedy decoding
print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
tokenizer = AutoTokenizer.from_pretrained('openai-gpt') # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained('openai-gpt') # Download model and configuration from S3 and cache.
input_context = 'The dog'
input_ids = tokenizer.encode(input_context, return_tensors='tf') # encode input context
outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
for i in range(3): # 3 output sequences were generated
print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache.
input_context = 'The dog'
input_ids = tokenizer.encode(input_context, return_tensors='tf') # encode input context
outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3) # 3 generate sequences using by sampling
for i in range(3): # 3 output sequences were generated
print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
tokenizer = AutoTokenizer.from_pretrained('ctrl') # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained('ctrl') # Download model and configuration from S3 and cache.
input_context = 'Legal My neighbor is' # "Legal" is one of the control codes for ctrl
input_ids = tokenizer.encode(input_context, return_tensors='tf') # encode input context
outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2) # generate sequences
print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
tokenizer = AutoTokenizer.from_pretrained('gpt2') # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained('gpt2') # Download model and configuration from S3 and cache.
input_context = 'My cute dog' # "Legal" is one of the control codes for ctrl
bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
input_ids = tokenizer.encode(input_context, return_tensors='tf') # encode input context
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids) # generate sequences without allowing bad_words to be generated
"""
# We cannot generate if the model does not have a LM head
if self.get_output_embeddings() is None:
raise AttributeError(
"You tried to generate sequences with a model that does not have a LM Head."
"Please use another model class (e.g. `TFOpenAIGPTLMHeadModel`, `TFXLNetLMHeadModel`, `TFGPT2LMHeadModel`, `TFCTRLLMHeadModel`, `TFT5ForConditionalGeneration`, `TFTransfoXLLMHeadModel`)"
)
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
do_sample = do_sample if do_sample is not None else self.config.do_sample
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
num_beams = num_beams if num_beams is not None else self.config.num_beams
temperature = temperature if temperature is not None else self.config.temperature
top_k = top_k if top_k is not None else self.config.top_k
top_p = top_p if top_p is not None else self.config.top_p
repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
no_repeat_ngram_size = (
no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
)
bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
)
decoder_start_token_id = (
decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
)
if input_ids is not None:
batch_size = shape_list(input_ids)[0] # overriden by the input batch_size
else:
batch_size = 1
assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictely positive integer."
assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictely positive integer."
assert temperature > 0, "`temperature` should be strictely positive."
assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
assert input_ids is not None or (
isinstance(bos_token_id, int) and bos_token_id >= 0
), "If input_ids is not defined, `bos_token_id` should be a positive integer."
assert pad_token_id is None or (
isinstance(pad_token_id, int) and (pad_token_id >= 0)
), "`pad_token_id` should be a positive integer."
assert (eos_token_id is None) or (
isinstance(eos_token_id, int) and (eos_token_id >= 0)
), "`eos_token_id` should be a positive integer."
assert length_penalty > 0, "`length_penalty` should be strictely positive."
assert (
isinstance(num_return_sequences, int) and num_return_sequences > 0
), "`num_return_sequences` should be a strictely positive integer."
assert (
bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"
if input_ids is None:
assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
"you should either supply a context to complete as `input_ids` input "
"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
)
input_ids = tf.fill((batch_size, 1), bos_token_id)
else:
assert len(shape_list(input_ids)) == 2, "Input prompt should be of shape (batch_size, sequence length)."
# not allow to duplicate outputs when greedy decoding
if do_sample is False:
if num_beams == 1:
# no_beam_search greedy generation conditions
assert (
num_return_sequences == 1
), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1"
else:
# beam_search greedy generation conditions
assert (
num_beams >= num_return_sequences
), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"
# create attention mask if necessary
# TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids.numpy()):
attention_mask = tf.cast(tf.math.not_equal(input_ids, pad_token_id), dtype=tf.int32)
elif attention_mask is None:
attention_mask = tf.ones_like(input_ids)
if pad_token_id is None and eos_token_id is not None:
log(
"Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
)
pad_token_id = eos_token_id
# current position and vocab size
cur_len = shape_list(input_ids)[1]
vocab_size = self.config.vocab_size
# set effective batch size and effective batch multiplier according to do_sample
if do_sample:
effective_batch_size = batch_size * num_return_sequences
effective_batch_mult = num_return_sequences
else:
effective_batch_size = batch_size
effective_batch_mult = 1
# Expand input ids if num_beams > 1 or num_return_sequences > 1
if num_return_sequences > 1 or num_beams > 1:
input_ids_len = shape_list(input_ids)[-1]
input_ids = tf.broadcast_to(
tf.expand_dims(input_ids, 1), (batch_size, effective_batch_mult * num_beams, input_ids_len)
)
attention_mask = tf.broadcast_to(
tf.expand_dims(attention_mask, 1), (batch_size, effective_batch_mult * num_beams, input_ids_len)
)
input_ids = tf.reshape(
input_ids, (effective_batch_size * num_beams, input_ids_len)
) # shape: (batch_size * num_return_sequences * num_beams, cur_len)
attention_mask = tf.reshape(
attention_mask, (effective_batch_size * num_beams, input_ids_len)
) # shape: (batch_size * num_return_sequences * num_beams, cur_len)
if self.config.is_encoder_decoder:
if decoder_start_token_id is None:
decoder_start_token_id = bos_token_id
assert (
decoder_start_token_id is not None
), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)
# get encoder and store encoder outputs
encoder = self.get_encoder()
encoder_outputs = encoder(input_ids, attention_mask=attention_mask)
# create empty decoder_input_ids
input_ids = tf.ones((effective_batch_size * num_beams, 1), dtype=tf.int32,) * decoder_start_token_id
cur_len = 1
else:
encoder_outputs = None
cur_len = shape_list(input_ids)[-1]
if num_beams > 1:
output = self._generate_beam_search(
input_ids,
cur_len=cur_len,
max_length=max_length,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
bad_words_ids=bad_words_ids,
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
batch_size=effective_batch_size,
num_return_sequences=num_return_sequences,
length_penalty=length_penalty,
num_beams=num_beams,
vocab_size=vocab_size,
encoder_outputs=encoder_outputs,
attention_mask=attention_mask,
)
else:
output = self._generate_no_beam_search(
input_ids,
cur_len=cur_len,
max_length=max_length,
min_length=min_length,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
bad_words_ids=bad_words_ids,
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
batch_size=effective_batch_size,
vocab_size=vocab_size,
encoder_outputs=encoder_outputs,
attention_mask=attention_mask,
)
return output
def _generate_no_beam_search(
self,
input_ids,
cur_len,
max_length,
min_length,
do_sample,
temperature,
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
bad_words_ids,
bos_token_id,
pad_token_id,
eos_token_id,
decoder_start_token_id,
batch_size,
vocab_size,
encoder_outputs,
attention_mask,
):
""" Generate sequences for each example without beam search (num_beams == 1).
All returned sequence are generated independantly.
"""
# length of generated sentences / unfinished sentences
unfinished_sents = tf.ones_like(input_ids[:, 0])
sent_lengths = tf.ones_like(input_ids[:, 0]) * max_length
past = encoder_outputs # defined for encoder-decoder models, None for decoder-only models
while cur_len < max_length:
model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, attention_mask=attention_mask)
outputs = self(**model_inputs)
next_token_logits = outputs[0][:, -1, :]
# if model has past, then set the past variable to speed up decoding
if self._do_output_past(outputs):
past = outputs[1]
# repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0:
next_token_logits_penalties = _create_next_token_logits_penalties(
input_ids, next_token_logits, repetition_penalty
)
next_token_logits = tf.math.multiply(next_token_logits, next_token_logits_penalties)
if no_repeat_ngram_size > 0:
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
banned_tokens = calc_banned_ngram_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len)
# create banned_tokens boolean mask
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
next_token_logits = set_tensor_by_indices_to_value(
next_token_logits, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
)
if bad_words_ids is not None:
# calculate a list of banned tokens according to bad words
banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
next_token_logits = set_tensor_by_indices_to_value(
next_token_logits, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
)
# set eos token prob to zero if min_length is not reached
if eos_token_id is not None and cur_len < min_length:
# create eos_token_id boolean mask
is_token_logit_eos_token = tf.convert_to_tensor(
[True if token is eos_token_id else False for token in range(vocab_size)], dtype=tf.bool
)
eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [batch_size, vocab_size])
next_token_logits = set_tensor_by_indices_to_value(
next_token_logits, eos_token_indices_mask, -float("inf")
)
if do_sample:
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
next_token_logits = next_token_logits / temperature
# Top-p/top-k filtering
next_token_logits = tf_top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
# Sample
next_token = tf.squeeze(
tf.random.categorical(next_token_logits, dtype=tf.int32, num_samples=1), axis=1
)
else:
# Greedy decoding
next_token = tf.math.argmax(next_token_logits, axis=-1, output_type=tf.int32)
# update generations and finished sentences
if eos_token_id is not None:
# pad finished sentences if eos_token_id exist
tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)
else:
tokens_to_add = next_token
input_ids = tf.concat([input_ids, tf.expand_dims(tokens_to_add, -1)], 1)
if eos_token_id is not None:
eos_in_sents = tokens_to_add == eos_token_id
# if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
is_sents_unfinished_and_token_to_add_is_eos = tf.math.multiply(
unfinished_sents, tf.cast(eos_in_sents, tf.int32)
)
sent_lengths = (
sent_lengths * (1 - is_sents_unfinished_and_token_to_add_is_eos)
+ cur_len * is_sents_unfinished_and_token_to_add_is_eos
)
# unfinished_sents is set to zero if eos in sentence
unfinished_sents -= is_sents_unfinished_and_token_to_add_is_eos
# stop when there is a </s> in each sentence, or if we exceed the maximul length
if tf.math.reduce_max(unfinished_sents) == 0:
break
# extend attention_mask for new generated input if only decoder
if self.config.is_encoder_decoder is False:
attention_mask = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
cur_len = cur_len + 1
# if there are different sentences lengths in the batch, some batches have to be padded
min_sent_length = tf.math.reduce_min(sent_lengths)
max_sent_length = tf.math.reduce_max(sent_lengths)
if min_sent_length != max_sent_length:
assert pad_token_id is not None, "`Pad_token_id` has to be defined if batches have different lengths"
# finished sents are filled with pad_token
padding = tf.ones([batch_size, max_sent_length.numpy()], dtype=tf.int32) * pad_token_id
# create length masks for tf.where operation
broad_casted_sent_lengths = tf.broadcast_to(
tf.expand_dims(sent_lengths, -1), [batch_size, max_sent_length]
)
broad_casted_range = tf.transpose(
tf.broadcast_to(tf.expand_dims(tf.range(max_length), -1), [max_length, batch_size])
)
decoded = tf.where(broad_casted_range < broad_casted_sent_lengths, input_ids, padding)
else:
decoded = input_ids
return decoded
def _generate_beam_search(
self,
input_ids,
cur_len,
max_length,
min_length,
do_sample,
early_stopping,
temperature,
top_k,
top_p,
repetition_penalty,
no_repeat_ngram_size,
bad_words_ids,
bos_token_id,
pad_token_id,
decoder_start_token_id,
eos_token_id,
batch_size,
num_return_sequences,
length_penalty,
num_beams,
vocab_size,
encoder_outputs,
attention_mask,
):
""" Generate sequences for each example with beam search.
"""
# generated hypotheses
generated_hyps = [
BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
for _ in range(batch_size)
]
# for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
if do_sample is False:
beam_scores_begin = tf.zeros((batch_size, 1), dtype=tf.float32)
beam_scores_end = tf.ones((batch_size, num_beams - 1), dtype=tf.float32) * (-1e9)
beam_scores = tf.concat([beam_scores_begin, beam_scores_end], -1)
else:
beam_scores = tf.zeros((batch_size, num_beams), dtype=tf.float32)
beam_scores = tf.reshape(beam_scores, (batch_size * num_beams,))
# cache compute states
past = encoder_outputs
# done sentences
done = [False for _ in range(batch_size)]
while cur_len < max_length:
model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, attention_mask=attention_mask)
outputs = self(**model_inputs) # (batch_size * num_beams, cur_len, vocab_size)
next_token_logits = outputs[0][:, -1, :] # (batch_size * num_beams, vocab_size)
# if model has past, then set the past variable to speed up decoding
if self._do_output_past(outputs):
past = outputs[1]
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0:
next_token_logits_penalties = _create_next_token_logits_penalties(
input_ids, next_token_logits, repetition_penalty
)
next_token_logits = tf.math.multiply(next_token_logits, next_token_logits_penalties)
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
next_token_logits = next_token_logits / temperature
# calculate log softmax score
scores = tf.nn.log_softmax(next_token_logits, axis=-1) # (batch_size * num_beams, vocab_size)
# set eos token prob to zero if min_length is not reached
if eos_token_id is not None and cur_len < min_length:
# create eos_token_id boolean mask
num_batch_hypotheses = batch_size * num_beams
is_token_logit_eos_token = tf.convert_to_tensor(
[True if token is eos_token_id else False for token in range(vocab_size)], dtype=tf.bool
)
eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [num_batch_hypotheses, vocab_size])
scores = set_tensor_by_indices_to_value(scores, eos_token_indices_mask, -float("inf"))
if no_repeat_ngram_size > 0:
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
num_batch_hypotheses = batch_size * num_beams
banned_tokens = calc_banned_ngram_tokens(
input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
)
# create banned_tokens boolean mask
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = set_tensor_by_indices_to_value(
scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
)
if bad_words_ids is not None:
# calculate a list of banned tokens according to bad words
banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)
banned_tokens_indices_mask = []
for banned_tokens_slice in banned_tokens:
banned_tokens_indices_mask.append(
[True if token in banned_tokens_slice else False for token in range(vocab_size)]
)
scores = set_tensor_by_indices_to_value(
scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
)
assert shape_list(scores) == [batch_size * num_beams, vocab_size]
if do_sample:
_scores = scores + tf.broadcast_to(
beam_scores[:, None], (batch_size * num_beams, vocab_size)
) # (batch_size * num_beams, vocab_size)
# Top-p/top-k filtering
_scores = tf_top_k_top_p_filtering(
_scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
) # (batch_size * num_beams, vocab_size)
# Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
_scores = tf.reshape(_scores, (batch_size, num_beams * vocab_size))
next_tokens = tf.random.categorical(
_scores, dtype=tf.int32, num_samples=2 * num_beams
) # (batch_size, 2 * num_beams)
# Compute next scores
next_scores = tf.gather(_scores, next_tokens, batch_dims=1) # (batch_size, 2 * num_beams)
# sort the sampled vector to make sure that the first num_beams samples are the best
next_scores_indices = tf.argsort(next_scores, direction="DESCENDING", axis=1)
next_scores = tf.gather(next_scores, next_scores_indices, batch_dims=1) # (batch_size, num_beams * 2)
next_tokens = tf.gather(next_tokens, next_scores_indices, batch_dims=1) # (batch_size, num_beams * 2)
else:
# Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
next_scores = scores + tf.broadcast_to(
beam_scores[:, None], (batch_size * num_beams, vocab_size)
) # (batch_size * num_beams, vocab_size)
# re-organize to group the beam together (we are keeping top hypothesis accross beams)
next_scores = tf.reshape(
next_scores, (batch_size, num_beams * vocab_size)
) # (batch_size, num_beams * vocab_size)
next_scores, next_tokens = tf.math.top_k(next_scores, k=2 * num_beams, sorted=True)
assert shape_list(next_scores) == shape_list(next_tokens) == [batch_size, 2 * num_beams]
# next batch beam content
next_batch_beam = []
# for each sentence
for batch_idx in range(batch_size):
# if we are done with this sentence
if done[batch_idx]:
assert (
len(generated_hyps[batch_idx]) >= num_beams
), "Batch can only be done if at least {} beams have been generated".format(num_beams)
assert (
eos_token_id is not None and pad_token_id is not None
), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch
continue
# next sentence beam content
next_sent_beam = []
# next tokens for this sentence
for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
zip(next_tokens[batch_idx], next_scores[batch_idx])
):
# get beam and token IDs
beam_id = beam_token_id // vocab_size
token_id = beam_token_id % vocab_size
effective_beam_id = batch_idx * num_beams + beam_id
# add to generated hypotheses if end of sentence or last iteration
if (eos_token_id is not None) and (token_id.numpy() == eos_token_id):
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
if is_beam_token_worse_than_top_num_beams:
continue
generated_hyps[batch_idx].add(
tf.identity(input_ids[effective_beam_id]), beam_token_score.numpy()
)
else:
# add next predicted token if it is not eos_token
next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
# the beam for next step is full
if len(next_sent_beam) == num_beams:
break
# Check if were done so that we can save a pad step if all(done)
done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
tf.reduce_max(next_scores[batch_idx]).numpy(), cur_len=cur_len
)
# update next beam content
assert len(next_sent_beam) == num_beams, "Beam should always be full"
next_batch_beam.extend(next_sent_beam)
assert len(next_batch_beam) == num_beams * (batch_idx + 1)
# stop when we are done with each sentence
if all(done):
break
# sanity check / prepare next batch
assert len(next_batch_beam) == batch_size * num_beams
beam_scores = tf.convert_to_tensor([x[0] for x in next_batch_beam], dtype=tf.float32)
beam_tokens = tf.convert_to_tensor([x[1] for x in next_batch_beam], dtype=tf.int32)
beam_idx = tf.convert_to_tensor([x[2] for x in next_batch_beam], dtype=tf.int32)
# re-order batch
input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx])
input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1)
# re-order internal states
if past is not None:
past = self._reorder_cache(past, beam_idx)
# extend attention_mask for new generated input if only decoder
if self.config.is_encoder_decoder is False:
attention_mask = tf.concat(
[attention_mask, tf.ones((shape_list(attention_mask)[0], 1), dtype=tf.int32)], axis=-1
)
# update current length
cur_len = cur_len + 1
# finalize all open beam hypotheses and end to generated hypotheses
for batch_idx in range(batch_size):
# Add all open beam hypothesis to generated_hyps
if done[batch_idx]:
continue
# test that beam scores match previously calculated scores if not eos and batch_idx not done
if eos_token_id is not None and all(
(token_id % vocab_size).numpy().item() is not eos_token_id for token_id in next_tokens[batch_idx]
):
assert tf.reduce_all(
next_scores[batch_idx, :num_beams] == tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]
), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
next_scores[:, :num_beams][batch_idx], tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]
)
# need to add best num_beams hypotheses to generated hyps
for beam_id in range(num_beams):
effective_beam_id = batch_idx * num_beams + beam_id
final_score = beam_scores[effective_beam_id].numpy().item()
final_tokens = input_ids[effective_beam_id]
generated_hyps[batch_idx].add(final_tokens, final_score)
# depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences
# select the best hypotheses
sent_lengths_list = []
best = []
# retrieve best hypotheses
for i, hypotheses in enumerate(generated_hyps):
sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
for j in range(output_num_return_sequences_per_batch):
best_hyp = sorted_hyps.pop()[1]
sent_lengths_list.append(len(best_hyp))
best.append(best_hyp)
assert output_batch_size == len(best), "Output batch size {} must match output beam hypotheses {}".format(
output_batch_size, len(best)
)
sent_lengths = tf.convert_to_tensor(sent_lengths_list, dtype=tf.int32)
# shorter batches are filled with pad_token
if tf.reduce_min(sent_lengths).numpy() != tf.reduce_max(sent_lengths).numpy():
assert pad_token_id is not None, "`Pad_token_id` has to be defined"
sent_max_len = min(tf.reduce_max(sent_lengths).numpy() + 1, max_length)
decoded_list = []
# fill with hypothesis and eos_token_id if necessary
for i, hypo in enumerate(best):
assert sent_lengths[i] == shape_list(hypo)[0]
# if sent_length is max_len do not pad
if sent_lengths[i] == sent_max_len:
decoded_slice = hypo
else:
# else pad to sent_max_len
num_pad_tokens = sent_max_len - sent_lengths[i]
padding = pad_token_id * tf.ones((num_pad_tokens,), dtype=tf.int32)
decoded_slice = tf.concat([hypo, padding], axis=-1)
# finish sentence with EOS token
if sent_lengths[i] < max_length:
decoded_slice = tf.where(
tf.range(sent_max_len, dtype=tf.int32) == sent_lengths[i],
eos_token_id * tf.ones((sent_max_len,), dtype=tf.int32),
decoded_slice,
)
# add to list
decoded_list.append(decoded_slice)
decoded = tf.stack(decoded_list)
else:
# none of the hypotheses have an eos_token
assert (len(hypo) == max_length for hypo in best)
decoded = tf.stack(best)
return decoded
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = []
for layer_past in past:
# get the correct batch idx from layer past batch dim
# batch dim of `past` and `mems` is at 2nd position
reordered_layer_past = [tf.identity(tf.expand_dims(layer_past[:, i], 1)) for i in beam_idx]
reordered_layer_past = tf.concat(reordered_layer_past, axis=1)
# check that shape matches
assert shape_list(reordered_layer_past) == shape_list(layer_past)
reordered_past.append(reordered_layer_past)
past = tuple(reordered_past)
return past
def _create_next_token_logits_penalties(input_ids, logits, repetition_penalty):
# create logit penalties for already seen input_ids
token_penalties = np.ones(shape_list(logits))
prev_input_ids = [np.unique(input_id) for input_id in input_ids.numpy()]
for i, prev_input_id in enumerate(prev_input_ids):
logit_penalized = logits[i].numpy()[prev_input_id]
logit_penalties = np.zeros(logit_penalized.shape)
# if previous logit score is < 0 then multiply repetition penalty else divide
logit_penalties[logit_penalized < 0] = repetition_penalty
logit_penalties[logit_penalized > 0] = 1 / repetition_penalty
np.put(token_penalties[i], prev_input_id, logit_penalties)
return tf.convert_to_tensor(token_penalties, dtype=tf.float32)
def calc_banned_ngram_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, cur_len):
# Copied from fairseq for no_repeat_ngram in beam_search"""
if cur_len + 1 < no_repeat_ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].numpy().tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - no_repeat_ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].numpy().tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def calc_banned_bad_words_ids(prev_input_ids, bad_words_ids):
banned_tokens = []
def _tokens_match(prev_tokens, tokens):
if len(tokens) == 0:
# if bad word tokens is just one token always ban it
return True
if len(tokens) > len(prev_input_ids):
# if bad word tokens are longer then prev input_ids they can't be equal
return False
if prev_tokens[-len(tokens) :] == tokens:
# if tokens match
return True
else:
return False
for prev_input_ids_slice in prev_input_ids:
banned_tokens_slice = []
for banned_token_seq in bad_words_ids:
assert len(banned_token_seq) > 0, "Banned words token sequences {} cannot have an empty list".format(
bad_words_ids
)
if _tokens_match(prev_input_ids_slice.numpy().tolist(), banned_token_seq[:-1]) is False:
# if tokens do not match continue
continue
banned_tokens_slice.append(banned_token_seq[-1])
banned_tokens.append(banned_tokens_slice)
return banned_tokens
def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
Args:
logits: logits distribution shape (batch size, vocabulary size)
if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
Make sure we keep at least min_tokens_to_keep per batch example in the output
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
"""
logits_shape = shape_list(logits)
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep), logits_shape[-1]) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = logits < tf.math.top_k(logits, k=top_k)[0][..., -1, None]
logits = set_tensor_by_indices_to_value(logits, indices_to_remove, filter_value)
if top_p < 1.0:
sorted_indices = tf.argsort(logits, direction="DESCENDING")
sorted_logits = tf.gather(
logits, sorted_indices, axis=-1, batch_dims=1
) # expects logits to be of dim (batch_size, vocab_size)
cumulative_probs = tf.math.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1)
# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
sorted_indices_to_remove = cumulative_probs > top_p
if min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
sorted_indices_to_remove = tf.concat(
[
tf.zeros_like(sorted_indices_to_remove[:, :min_tokens_to_keep]),
sorted_indices_to_remove[:, min_tokens_to_keep:],
],
-1,
)
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove = tf.roll(sorted_indices_to_remove, 1, axis=-1)
sorted_indices_to_remove = tf.concat(
[tf.zeros_like(sorted_indices_to_remove[:, :1]), sorted_indices_to_remove[:, 1:]], -1,
)
# scatter sorted tensors to original indexing
indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)
logits = set_tensor_by_indices_to_value(logits, indices_to_remove, filter_value)
return logits
def scatter_values_on_batch_indices(values, batch_indices):
shape = shape_list(batch_indices)
# broadcast batch dim to shape
broad_casted_batch_dims = tf.reshape(tf.broadcast_to(tf.expand_dims(tf.range(shape[0]), axis=-1), shape), [1, -1])
# transform batch_indices to pair_indices
pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
# scatter values to pair indices
return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), shape)
def set_tensor_by_indices_to_value(tensor, indices, value):
# create value_tensor since tensor value assignment is not possible in TF
value_tensor = tf.zeros_like(tensor) + value
return tf.where(indices, value_tensor, tensor)
class BeamHypotheses(object):
def __init__(self, num_beams, max_length, length_penalty, early_stopping):
"""
Initialize n-best list of hypotheses.
"""
self.max_length = max_length - 1 # ignoring bos_token
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.num_beams = num_beams
self.beams = []
self.worst_score = 1e9
def __len__(self):
"""
Number of hypotheses in the list.
"""
return len(self.beams)
def add(self, hyp, sum_logprobs):
"""
Add a new hypothesis to the list.
"""
score = sum_logprobs / len(hyp) ** self.length_penalty
if len(self) < self.num_beams or score > self.worst_score:
self.beams.append((score, hyp))
if len(self) > self.num_beams:
sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
del self.beams[sorted_scores[0][1]]
self.worst_score = sorted_scores[1][0]
else:
self.worst_score = min(score, self.worst_score)
def is_done(self, best_sum_logprobs, cur_len=None):
"""
If there are enough hypotheses and that none of the hypotheses being generated
can become better than the worst one in the heap, then we are done with this sentence.
"""
if len(self) < self.num_beams:
return False
elif self.early_stopping:
return True
else:
if cur_len is None:
cur_len = self.max_length
cur_score = best_sum_logprobs / cur_len ** self.length_penalty
ret = self.worst_score >= cur_score
return ret
class TFConv1D(tf.keras.layers.Layer):
def __init__(self, nf, nx, initializer_range=0.02, **kwargs):
""" TFConv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)
Basically works like a Linear layer but the weights are transposed
"""
super().__init__(**kwargs)
self.nf = nf
self.nx = nx
self.initializer_range = initializer_range
def build(self, input_shape):
self.weight = self.add_weight(
"weight", shape=[self.nx, self.nf], initializer=get_initializer(self.initializer_range)
)
self.bias = self.add_weight("bias", shape=[1, self.nf], initializer=tf.zeros_initializer())
def call(self, x):
bz, sl = shape_list(x)[:2]
x = tf.reshape(x, [-1, self.nx])
x = tf.matmul(x, self.weight) + self.bias
x = tf.reshape(x, [bz, sl, self.nf])
return x
class TFSharedEmbeddings(tf.keras.layers.Layer):
"""Construct shared token embeddings.
"""
def __init__(self, vocab_size, hidden_size, initializer_range=None, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.initializer_range = hidden_size ** -0.5 if initializer_range is None else initializer_range
def build(self, input_shape):
"""Build shared token embedding layer
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
self.weight = self.add_weight(
"weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range)
)
super().build(input_shape)
def call(self, inputs, mode="embedding"):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, input_ids):
"""Applies embedding based on inputs tensor."""
return tf.gather(self.weight, input_ids)
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [..., hidden_size]
Returns:
float32 tensor with shape [..., vocab_size].
"""
first_dims = shape_list(inputs)[:-1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.weight, transpose_b=True)
return tf.reshape(logits, first_dims + [self.vocab_size])
class TFSequenceSummary(tf.keras.layers.Layer):
r""" Compute a single vector summary of a sequence hidden states according to various possibilities:
Args of the config class:
summary_type:
- 'last' => [default] take the last token hidden state (like XLNet)
- 'first' => take the first token hidden state (like Bert)
- 'mean' => take the mean of all tokens hidden states
- 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
- 'attn' => Not implemented now, use multi-head attention
summary_use_proj: Add a projection after the vector extraction
summary_proj_to_labels: If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
summary_activation: 'tanh' => add a tanh activation to the output, Other => no activation. Default
summary_first_dropout: Add a dropout before the projection and activation
summary_last_dropout: Add a dropout after the projection and activation
"""
def __init__(self, config, initializer_range=0.02, **kwargs):
super().__init__(**kwargs)
self.summary_type = config.summary_type if hasattr(config, "summary_use_proj") else "last"
if self.summary_type == "attn":
# We should use a standard multi-head attention module with absolute positional embedding for that.
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
raise NotImplementedError
self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj
if self.has_summary:
if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
num_classes = config.num_labels
else:
num_classes = config.hidden_size
self.summary = tf.keras.layers.Dense(
num_classes, kernel_initializer=get_initializer(initializer_range), name="summary"
)
self.has_activation = hasattr(config, "summary_activation") and config.summary_activation == "tanh"
if self.has_activation:
self.activation = tf.keras.activations.tanh
self.has_first_dropout = hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0
if self.has_first_dropout:
self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout)
self.has_last_dropout = hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0
if self.has_last_dropout:
self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout)
def call(self, inputs, training=False):
""" hidden_states: float Tensor in shape [bsz, seq_len, hidden_size], the hidden-states of the last layer.
cls_index: [optional] position of the classification token if summary_type == 'cls_index',
shape (bsz,) or more generally (bsz, ...) where ... are optional leading dimensions of hidden_states.
if summary_type == 'cls_index' and cls_index is None:
we take the last token of the sequence as classification token
"""
if not isinstance(inputs, (dict, tuple, list)):
hidden_states = inputs
cls_index = None
elif isinstance(inputs, (tuple, list)):
hidden_states = inputs[0]
cls_index = inputs[1] if len(inputs) > 1 else None
assert len(inputs) <= 2, "Too many inputs."
else:
hidden_states = inputs.get("hidden_states")
cls_index = inputs.get("cls_index", None)
if self.summary_type == "last":
output = hidden_states[:, -1]
elif self.summary_type == "first":
output = hidden_states[:, 0]
elif self.summary_type == "mean":
output = tf.reduce_mean(hidden_states, axis=1)
elif self.summary_type == "cls_index":
hidden_shape = shape_list(hidden_states) # e.g. [batch, num choices, seq length, hidden dims]
if cls_index is None:
cls_index = tf.fill(
hidden_shape[:-2], hidden_shape[-2] - 1
) # A tensor full of shape [batch] or [batch, num choices] full of sequence length
cls_shape = shape_list(cls_index)
if len(cls_shape) <= len(hidden_shape) - 2:
cls_index = cls_index[..., tf.newaxis]
# else:
# cls_index = cls_index[..., tf.newaxis]
# cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2)
output = tf.squeeze(
output, axis=len(hidden_shape) - 2
) # shape of output: (batch, num choices, hidden_size)
elif self.summary_type == "attn":
raise NotImplementedError
if self.has_first_dropout:
output = self.first_dropout(output, training=training)
if self.has_summary:
output = self.summary(output)
if self.has_activation:
output = self.activation(output)
if self.has_last_dropout:
output = self.last_dropout(output, training=training)
return output
def shape_list(x):
"""Deal with dynamic shape in tensorflow cleanly."""
static = x.shape.as_list()
dynamic = tf.shape(x)
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
def get_initializer(initializer_range=0.02):
"""Creates a `tf.initializers.truncated_normal` with the given range.
Args:
initializer_range: float, initializer range for stddev.
Returns:
TruncatedNormal initializer with stddev = `initializer_range`.
"""
return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
"bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-tf_model.h5",
"bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-tf_model.h5",
"bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-tf_model.h5",
"bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-tf_model.h5",
"bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-tf_model.h5",
"bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-tf_model.h5",
"bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-tf_model.h5",
"bert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-tf_model.h5",
"bert-large-uncased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-tf_model.h5",
"bert-large-cased-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-tf_model.h5",
"bert-large-uncased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-tf_model.h5",
"bert-large-cased-whole-word-masking-finetuned-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-tf_model.h5",
"bert-base-cased-finetuned-mrpc": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-tf_model.h5",
"bert-base-japanese": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-tf_model.h5",
"bert-base-japanese-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-tf_model.h5",
"bert-base-japanese-char": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-tf_model.h5",
"bert-base-japanese-char-whole-word-masking": "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-tf_model.h5",
"bert-base-finnish-cased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-cased-v1/tf_model.h5",
"bert-base-finnish-uncased-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/TurkuNLP/bert-base-finnish-uncased-v1/tf_model.h5",
"bert-base-dutch-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/wietsedv/bert-base-dutch-cased/tf_model.h5",
}
def gelu(x):
""" Gaussian Error Linear Unit.
Original Implementation of the gelu activation function in Google Bert repo when initially created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
return x * cdf
def gelu_new(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf
def swish(x):
return x * tf.sigmoid(x)
ACT2FN = {
"gelu": tf.keras.layers.Activation(gelu),
"relu": tf.keras.activations.relu,
"swish": tf.keras.layers.Activation(swish),
"gelu_new": tf.keras.layers.Activation(gelu_new),
}
class TFBertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.initializer_range = config.initializer_range
self.position_embeddings = tf.keras.layers.Embedding(
config.max_position_embeddings,
config.hidden_size,
embeddings_initializer=get_initializer(self.initializer_range),
name="position_embeddings",
)
self.token_type_embeddings = tf.keras.layers.Embedding(
config.type_vocab_size,
config.hidden_size,
embeddings_initializer=get_initializer(self.initializer_range),
name="token_type_embeddings",
)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
def call(self, inputs, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
if input_ids is not None:
input_shape = shape_list(input_ids)
else:
input_shape = shape_list(inputs_embeds)[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
batch_size = shape_list(inputs)[0]
length = shape_list(inputs)[1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size])
class TFBertSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
)
self.output_attentions = config.output_attentions
self.num_attention_heads = config.num_attention_heads
assert config.hidden_size % config.num_attention_heads == 0
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.amp = config.amp
self.query = tf.keras.layers.Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = tf.matmul(
query_layer, key_layer, transpose_b=True
) # (batch size, num_heads, seq_len_q, seq_len_k)
dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
attention_scores = attention_scores / tf.cast(tf.math.sqrt(dk), tf.float16 if self.amp else tf.float32)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
context_layer = tf.reshape(
context_layer, (batch_size, -1, self.all_head_size)
) # (batch_size, seq_len_q, all_head_size)
outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
return outputs
class TFBertSelfOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, inputs, training=False):
hidden_states, input_tensor = inputs
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFBertAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFBertSelfAttention(config, name="self")
self.dense_output = TFBertSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(self, inputs, training=False):
input_tensor, attention_mask, head_mask = inputs
self_outputs = self.self_attention([input_tensor, attention_mask, head_mask], training=training)
attention_output = self.dense_output([self_outputs[0], input_tensor], training=training)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class TFBertIntermediate(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class TFBertOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, inputs, training=False):
hidden_states, input_tensor = inputs
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFBertLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.attention = TFBertAttention(config, name="attention")
self.intermediate = TFBertIntermediate(config, name="intermediate")
self.bert_output = TFBertOutput(config, name="output")
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
attention_output = attention_outputs[0]
intermediate_output = self.intermediate(attention_output)
layer_output = self.bert_output([intermediate_output, attention_output], training=training)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs
class TFBertEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module([hidden_states, attention_mask, head_mask[i]], training=training)
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # outputs, (hidden states), (attentions)
class TFBertPooler(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
def call(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
return pooled_output
class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class TFBertLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.transform = TFBertPredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def call(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
class TFBertMLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.predictions = TFBertLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class TFBertNSPHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.seq_relationship = tf.keras.layers.Dense(
2, kernel_initializer=get_initializer(config.initializer_range), name="seq_relationship"
)
def call(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
@keras_serializable
class TFBertMainLayer(tf.keras.layers.Layer):
config_class = BertConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.embeddings = TFBertEmbeddings(config, name="embeddings")
self.encoder = TFBertEncoder(config, name="encoder")
self.pooler = TFBertPooler(config, name="pooler")
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
raise NotImplementedError
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
token_type_ids = inputs.get("token_type_ids", token_type_ids)
position_ids = inputs.get("position_ids", position_ids)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
outputs = (sequence_output, pooled_output,) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
class TFBertPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = BertConfig
pretrained_model_archive_map = TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "bert"
BERT_START_DOCSTRING = r"""
This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
Use it as a regular TF 2.0 Keras Model and
refer to the TF 2.0 documentation for all matter related to general usage and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors
in the first positional argument :
- a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
BERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.BertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
`What are token type IDs? <../glossary.html#token-type-ids>`__
position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`__
head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`, defaults to :obj:`None`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
(if set to :obj:`False`) for evaluation.
"""
@add_start_docstrings(
"The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
BERT_START_DOCSTRING,
)
class TFBertModel(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
outputs = self.bert(inputs, **kwargs)
return outputs
@add_start_docstrings(
"""Bert Model with two heads on top as done during the pre-training:
a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
BERT_START_DOCSTRING,
)
class TFBertForPreTraining(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
def get_output_embeddings(self):
return self.bert.embeddings
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
prediction_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_scores (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForPreTraining
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
prediction_scores, seq_relationship_scores = outputs[:2]
"""
outputs = self.bert(inputs, **kwargs)
sequence_output, pooled_output = outputs[:2]
prediction_scores = self.mlm(sequence_output, training=kwargs.get("training", False))
seq_relationship_score = self.nsp(pooled_output)
outputs = (prediction_scores, seq_relationship_score,) + outputs[
2:
] # add hidden states and attention if they are here
return outputs # prediction_scores, seq_relationship_score, (hidden_states), (attentions)
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
class TFBertForMaskedLM(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
def get_output_embeddings(self):
return self.bert.embeddings
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForMaskedLM
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForMaskedLM.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
prediction_scores = outputs[0]
"""
outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0]
prediction_scores = self.mlm(sequence_output, training=kwargs.get("training", False))
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
return outputs # prediction_scores, (hidden_states), (attentions)
@add_start_docstrings(
"""Bert Model with a `next sentence prediction (classification)` head on top. """, BERT_START_DOCSTRING,
)
class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.nsp = TFBertNSPHead(config, name="nsp___cls")
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
seq_relationship_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, 2)`)
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForNextSentencePrediction
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
seq_relationship_scores = outputs[0]
"""
outputs = self.bert(inputs, **kwargs)
pooled_output = outputs[1]
seq_relationship_score = self.nsp(pooled_output)
outputs = (seq_relationship_score,) + outputs[2:] # add hidden states and attention if they are here
return outputs # seq_relationship_score, (hidden_states), (attentions)
@add_start_docstrings(
"""Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
BERT_START_DOCSTRING,
)
class TFBertForSequenceClassification(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
outputs = self.bert(inputs, **kwargs)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=kwargs.get("training", False))
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings(
"""Bert Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
BERT_START_DOCSTRING,
)
class TFBertForMultipleChoice(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self):
""" Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
classification_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, num_choices)`:
`num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above).
Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForMultipleChoice
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForMultipleChoice.from_pretrained('bert-base-uncased')
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = tf.constant([tokenizer.encode(s) for s in choices])[None, :] # Batch size 1, 2 choices
outputs = model(input_ids)
classification_scores = outputs[0]
"""
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
token_type_ids = inputs.get("token_type_ids", token_type_ids)
position_ids = inputs.get("position_ids", position_ids)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]
else:
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_inputs = [
flat_input_ids,
flat_attention_mask,
flat_token_type_ids,
flat_position_ids,
head_mask,
inputs_embeds,
]
outputs = self.bert(flat_inputs, training=training)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=training)
logits = self.classifier(pooled_output)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
outputs = (reshaped_logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # reshaped_logits, (hidden_states), (attentions)
@add_start_docstrings(
"""Bert Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
BERT_START_DOCSTRING,
)
class TFBertForTokenClassification(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForTokenClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForTokenClassification.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
scores = outputs[0]
"""
outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=kwargs.get("training", False))
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # scores, (hidden_states), (attentions)
@add_start_docstrings(
"""Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
the hidden-states output to compute `span start logits` and `span end logits`). """,
BERT_START_DOCSTRING,
)
class TFBertForQuestionAnswering(TFBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.bert = TFBertMainLayer(config, name="bert")
self.qa_outputs = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@add_start_docstrings_to_callable(BERT_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-start scores (before SoftMax).
end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
Span-end scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import BertTokenizer, TFBertForQuestionAnswering
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForQuestionAnswering.from_pretrained('bert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
start_scores, end_scores = outputs[:2]
"""
outputs = self.bert(inputs, **kwargs)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
outputs = (start_logits, end_logits,) + outputs[2:]
return outputs # start_logits, end_logits, (hidden_states), (attentions)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/modeling_utils.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import boto3
import requests
from botocore.config import Config
from botocore.exceptions import ClientError
from filelock import FileLock
from tqdm.auto import tqdm
# from examples import __version__
__version__ = "0.1"
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
try:
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
if USE_TORCH in ("1", "ON", "YES", "AUTO") and USE_TF not in ("1", "ON", "YES"):
import torch
_torch_available = True # pylint: disable=invalid-name
logger.info("PyTorch version {} available.".format(torch.__version__))
else:
logger.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
except ImportError:
_torch_available = False # pylint: disable=invalid-name
try:
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
if USE_TF in ("1", "ON", "YES", "AUTO") and USE_TORCH not in ("1", "ON", "YES"):
import tensorflow as tf
assert hasattr(tf, "__version__") and int(tf.__version__[0]) >= 2
_tf_available = True # pylint: disable=invalid-name
logger.info("TensorFlow version {} available.".format(tf.__version__))
else:
logger.info("Disabling Tensorflow because USE_TORCH is set")
_tf_available = False
except (ImportError, AssertionError):
_tf_available = False # pylint: disable=invalid-name
try:
from torch.hub import _get_torch_home
torch_cache_home = _get_torch_home()
except ImportError:
torch_cache_home = os.path.expanduser(
os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
)
default_cache_path = os.path.join(torch_cache_home, "transformers")
try:
from pathlib import Path
PYTORCH_PRETRAINED_BERT_CACHE = Path(
os.getenv("PYTORCH_TRANSFORMERS_CACHE", os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path))
)
except (AttributeError, ImportError):
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv(
"PYTORCH_TRANSFORMERS_CACHE", os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
)
PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE # Kept for backward compatibility
TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE # Kept for backward compatibility
WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = "tf_model.h5"
TF_WEIGHTS_NAME = "model.ckpt"
CONFIG_NAME = "config.json"
MODEL_CARD_NAME = "modelcard.json"
MULTIPLE_CHOICE_DUMMY_INPUTS = [[[0], [1]], [[0], [1]]]
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
CLOUDFRONT_DISTRIB_PREFIX = "https://d2ws9o8vfrpkyk.cloudfront.net"
def is_torch_available():
return _torch_available
def is_tf_available():
return _tf_available
def add_start_docstrings(*docstr):
def docstring_decorator(fn):
fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
return fn
return docstring_decorator
def add_start_docstrings_to_callable(*docstr):
def docstring_decorator(fn):
class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
intro = " The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
note = r"""
.. note::
Although the recipe for forward pass needs to be defined within
this function, one should call the :class:`Module` instance afterwards
instead of this since the former takes care of running the
pre and post processing steps while the latter silently ignores them.
"""
fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
return fn
return docstring_decorator
def add_end_docstrings(*docstr):
def docstring_decorator(fn):
fn.__doc__ = fn.__doc__ + "".join(docstr)
return fn
return docstring_decorator
def is_remote_url(url_or_filename):
parsed = urlparse(url_or_filename)
return parsed.scheme in ("http", "https", "s3")
def hf_bucket_url(identifier, postfix=None, cdn=False) -> str:
endpoint = CLOUDFRONT_DISTRIB_PREFIX if cdn else S3_BUCKET_PREFIX
if postfix is None:
return "/".join((endpoint, identifier))
else:
return "/".join((endpoint, identifier, postfix))
def url_to_filename(url, etag=None):
"""
Convert `url` into a hashed filename in a repeatable way.
If `etag` is specified, append its hash to the url's, delimited
by a period.
If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name
so that TF 2.0 can identify it as a HDF5 file
(see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
"""
url_bytes = url.encode("utf-8")
url_hash = sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode("utf-8")
etag_hash = sha256(etag_bytes)
filename += "." + etag_hash.hexdigest()
if url.endswith(".h5"):
filename += ".h5"
return filename
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + ".json"
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata["url"]
etag = metadata["etag"]
return url, etag
def cached_path(
url_or_filename,
cache_dir=None,
force_download=False,
proxies=None,
resume_download=False,
user_agent=None,
extract_compressed_file=False,
force_extract=False,
local_files_only=False,
) -> Optional[str]:
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
Args:
cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
force_download: if True, re-dowload the file even if it's already cached in the cache dir.
resume_download: if True, resume the download if incompletly recieved file is found.
user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
file in a folder along the archive.
force_extract: if True when extract_compressed_file is True and the archive was already extracted,
re-extract the archive and overide the folder where it was extracted.
Return:
None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
Local path (string) otherwise
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if is_remote_url(url_or_filename):
# URL, so get it from the cache (downloading if necessary)
output_path = get_from_cache(
url_or_filename,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
user_agent=user_agent,
local_files_only=local_files_only,
)
elif os.path.exists(url_or_filename):
# File, and it exists.
output_path = url_or_filename
elif urlparse(url_or_filename).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
if extract_compressed_file:
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
output_dir, output_file = os.path.split(output_path)
output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
output_path_extracted = os.path.join(output_dir, output_extract_dir_name)
if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
lock_path = output_path + ".lock"
with FileLock(lock_path):
shutil.rmtree(output_path_extracted, ignore_errors=True)
os.makedirs(output_path_extracted)
if is_zipfile(output_path):
with ZipFile(output_path, "r") as zip_file:
zip_file.extractall(output_path_extracted)
zip_file.close()
elif tarfile.is_tarfile(output_path):
tar_file = tarfile.open(output_path)
tar_file.extractall(output_path_extracted)
tar_file.close()
else:
raise EnvironmentError("Archive format of {} could not be identified".format(output_path))
return output_path_extracted
return output_path
def split_s3_path(url):
"""Split a full s3 path into the bucket name and path."""
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
raise ValueError("bad s3 path {}".format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
# Remove '/' at beginning of path.
if s3_path.startswith("/"):
s3_path = s3_path[1:]
return bucket_name, s3_path
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise EnvironmentError("file {} not found".format(url))
else:
raise
return wrapper
@s3_request
def s3_etag(url, proxies=None):
"""Check ETag on S3 object."""
s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
bucket_name, s3_path = split_s3_path(url)
s3_object = s3_resource.Object(bucket_name, s3_path)
return s3_object.e_tag
@s3_request
def s3_get(url, temp_file, proxies=None):
"""Pull a file directly from S3."""
s3_resource = boto3.resource("s3", config=Config(proxies=proxies))
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
def http_get(url, temp_file, proxies=None, resume_size=0, user_agent=None):
ua = "transformers/{}; python/{}".format(__version__, sys.version.split()[0])
if is_torch_available():
ua += "; torch/{}".format(torch.__version__)
if is_tf_available():
ua += "; tensorflow/{}".format(tf.__version__)
if isinstance(user_agent, dict):
ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
elif isinstance(user_agent, str):
ua += "; " + user_agent
headers = {"user-agent": ua}
if resume_size > 0:
headers["Range"] = "bytes=%d-" % (resume_size,)
response = requests.get(url, stream=True, proxies=proxies, headers=headers)
if response.status_code == 416: # Range not satisfiable
return
content_length = response.headers.get("Content-Length")
total = resume_size + int(content_length) if content_length is not None else None
progress = tqdm(
unit="B",
unit_scale=True,
total=total,
initial=resume_size,
desc="Downloading",
disable=bool(logger.getEffectiveLevel() == logging.NOTSET),
)
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(
url,
cache_dir=None,
force_download=False,
proxies=None,
etag_timeout=10,
resume_download=False,
user_agent=None,
local_files_only=False,
) -> Optional[str]:
"""
Given a URL, look for the corresponding file in the local cache.
If it's not there, download it. Then return the path to the cached file.
Return:
None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
Local path (string) otherwise
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
etag = None
if not local_files_only:
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url, proxies=proxies)
else:
try:
response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
if response.status_code == 200:
etag = response.headers.get("ETag")
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(cache_path):
return cache_path
else:
matching_files = [
file
for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
if not file.endswith(".json") and not file.endswith(".lock")
]
if len(matching_files) > 0:
return os.path.join(cache_dir, matching_files[-1])
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
" to False."
)
return None
# From now on, etag is not None.
if os.path.exists(cache_path) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
lock_path = cache_path + ".lock"
with FileLock(lock_path):
if resume_download:
incomplete_path = cache_path + ".incomplete"
@contextmanager
def _resumable_file_manager():
with open(incomplete_path, "a+b") as f:
yield f
temp_file_manager = _resumable_file_manager
if os.path.exists(incomplete_path):
resume_size = os.stat(incomplete_path).st_size
else:
resume_size = 0
else:
temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
resume_size = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
# GET file object
if url.startswith("s3://"):
if resume_download:
logger.warn('Warning: resumable downloads are not implemented for "s3://" urls')
s3_get(url, temp_file, proxies=proxies)
else:
http_get(url, temp_file, proxies=proxies, resume_size=resume_size, user_agent=user_agent)
logger.info("storing %s in cache at %s", url, cache_path)
os.replace(temp_file.name, cache_path)
logger.info("creating metadata file for %s", cache_path)
meta = {"url": url, "etag": etag}
meta_path = cache_path + ".json"
with open(meta_path, "w") as meta_file:
json.dump(meta, meta_file)
return cache_path
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/file_utils.py |
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import subprocess
import time
import argparse
import json
import logging
import tensorflow as tf
import horovod.tensorflow as hvd
from horovod.tensorflow.compression import Compression
from gpu_affinity import set_affinity
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
from tqdm import tqdm
import dllogger
from utils import is_main_process, format_step, get_rank, get_world_size, log
from configuration import ElectraConfig
from modeling import TFElectraForQuestionAnswering
from tokenization import ElectraTokenizer
from optimization import create_optimizer
from squad_utils import SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features, \
SquadResult, RawResult, get_answers
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/electra-small-generator",
"google/electra-base-generator",
"google/electra-large-generator",
"google/electra-small-discriminator",
"google/electra-base-discriminator",
"google/electra-large-discriminator",
# See all ELECTRA models at https://huggingface.co/models?filter=electra
]
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--electra_model", default=None, type=str, required=True,
help="Model selected in the list: " + ", ".join(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST))
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="Path to dataset.")
parser.add_argument("--output_dir", default=".", type=str, required=True,
help="The output directory where the model checkpoints and predictions will be written.")
parser.add_argument("--init_checkpoint",
default=None,
type=str,
help="The checkpoint file from pretraining")
# Other parameters
parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to use evaluate accuracy of predictions")
parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument("--predict_file", default=None, type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
parser.add_argument("--learning_rate", default=1e-4, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay_rate", default=0.01, type=float, help="Weight decay if we apply some.")
parser.add_argument("--layerwise_lr_decay", default=0.8, type=float,
help="The layerwise learning rate decay. Shallower layers have lower learning rates.")
parser.add_argument("--num_train_epochs", default=3, type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1.0, type=float,
help="Total number of training steps 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("--max_seq_length", default=384, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--doc_stride", default=128, type=int,
help="When splitting up a long document into chunks, how much stride to take between chunks.")
parser.add_argument("--max_query_length", default=64, type=int,
help="The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--vocab_file", default=None, type=str,
help="Path to vocabulary file use for tokenization")
parser.add_argument("--ci", action="store_true", help="true if running on CI")
parser.add_argument(
"--joint_head",
default=True,
type=bool,
help="Jointly predict the start and end positions",
)
parser.add_argument(
"--beam_size",
default=4,
type=int,
help="Beam size when doing joint predictions",
)
parser.add_argument("--n_best_size", default=20, type=int,
help="The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument("--max_answer_length", default=30, type=int,
help="The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument("--verbose_logging", action='store_true',
help="If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
)
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("--do_lower_case",
action='store_true',
help="Whether to lower case the input text. True for uncased models, False for cased models.")
parser.add_argument("--local_rank",
type=int,
default=os.getenv('LOCAL_RANK', -1),
help="local_rank for distributed training on gpus")
parser.add_argument('--amp',
action='store_true',
help="Automatic mixed precision training")
parser.add_argument('--fp16_all_reduce',
action='store_true',
help="Whether to use 16-bit all reduce")
parser.add_argument('--xla',
action='store_true',
help="Whether to use XLA")
parser.add_argument('--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument('--null_score_diff_threshold',
type=float, default=0.0,
help="If null_score - best_non_null is greater than the threshold predict null.")
parser.add_argument('--log_freq',
type=int, default=50,
help='frequency of logging loss.')
parser.add_argument('--json-summary', type=str, default="results/dllogger.json",
help='If provided, the json summary will be written to the specified file.')
parser.add_argument("--eval_script",
help="Script to evaluate squad predictions",
default="evaluate.py",
type=str)
parser.add_argument("--use_env",
action='store_true',
help="Whether to read local rank from ENVVAR")
parser.add_argument('--skip_checkpoint',
default=False,
action='store_true',
help="Whether to save checkpoints")
parser.add_argument('--disable-progress-bar',
default=False,
action='store_true',
help='Disable tqdm progress bar')
parser.add_argument("--skip_cache",
default=False,
action='store_true',
help="Whether to cache train features")
parser.add_argument("--cache_dir",
default=None,
type=str,
help="Location to cache train feaures. Will default to the dataset direct")
args = parser.parse_args()
if not args.do_train and (not args.init_checkpoint or args.init_checkpoint == 'None'):
raise ValueError("Checkpoint is required if do_train is not set")
return args
def get_dataset_from_features(features, batch_size, drop_remainder=True, ngpu=8, mode="train", v2=False):
"""Input function for training"""
all_input_ids = tf.convert_to_tensor([f.input_ids for f in features], dtype=tf.int64)
all_input_mask = tf.convert_to_tensor([f.attention_mask for f in features], dtype=tf.int64)
all_segment_ids = tf.convert_to_tensor([f.token_type_ids for f in features], dtype=tf.int64)
all_start_pos = tf.convert_to_tensor([f.start_position for f in features], dtype=tf.int64)
all_end_pos = tf.convert_to_tensor([f.end_position for f in features], dtype=tf.int64)
# if v2 else None:
all_cls_index = tf.convert_to_tensor([f.cls_index for f in features], dtype=tf.int64)
all_p_mask = tf.convert_to_tensor([f.p_mask for f in features], dtype=tf.float32)
all_is_impossible = tf.convert_to_tensor([f.is_impossible for f in features], dtype=tf.float32)
dataset = tf.data.Dataset.from_tensor_slices(
(all_input_ids, all_input_mask, all_segment_ids, all_start_pos, all_end_pos)
+ (all_cls_index, all_p_mask, all_is_impossible))
if ngpu > 1:
dataset = dataset.shard(get_world_size(), get_rank())
if mode == "train":
dataset = dataset.shuffle(batch_size * 3)
# dataset = dataset.map(self._preproc_samples,
# num_parallel_calls=multiprocessing.cpu_count()//self._num_gpus)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
dataset = dataset.prefetch(batch_size)
return dataset
@tf.function
def train_step(model, inputs, loss, amp, opt, init, v2=False, loss_class=None, fp16=False, clip_norm=1.0):
with tf.GradientTape() as tape:
[input_ids, input_mask, segment_ids, start_positions, end_positions, cls_index, p_mask, is_impossible] = inputs
if not v2:
is_impossible = None
start_logits, end_logits, cls_logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
start_positions=start_positions,
end_positions=end_positions,
cls_index=cls_index,
p_mask=p_mask,
is_impossible=is_impossible,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=True,
)[0:3]
# If we are on multi-GPU, split add a dimension
if len(start_positions.shape) > 1:
start_positions = tf.squeeze(start_positions, axis=-1, name="squeeze_start_positions")
if len(end_positions.shape) > 1:
end_positions = tf.squeeze(end_positions, axis=-1, name="squeeze_end_positions")
if is_impossible is not None and len(is_impossible.shape) > 1 and v2 and cls_logits is not None:
is_impossible = tf.squeeze(is_impossible, axis=-1, name="squeeze_is_impossible")
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.shape[1]
start_positions = tf.clip_by_value(start_positions, 0, ignored_index, name="clip_start_positions")
end_positions = tf.clip_by_value(end_positions, 0, ignored_index, name="clip_end_positions")
start_loss = loss(y_true=start_positions, y_pred=tf.cast(start_logits, tf.float32))
end_loss = loss(y_true=end_positions, y_pred=tf.cast(end_logits, tf.float32))
loss_value = (start_loss + end_loss) / 2
if v2:
cls_loss_value = loss_class(y_true=is_impossible, y_pred=tf.cast(cls_logits, tf.float32))
loss_value += cls_loss_value * 0.5
unscaled_loss = tf.stop_gradient(loss_value)
if amp:
loss_value = opt.get_scaled_loss(loss_value)
tape = hvd.DistributedGradientTape(tape, sparse_as_dense=True,
compression=Compression.fp16 if fp16 else Compression.none)
gradients = tape.gradient(loss_value, model.trainable_variables)
if amp:
gradients = opt.get_unscaled_gradients(gradients)
(gradients, _) = tf.clip_by_global_norm(gradients, clip_norm=clip_norm)
opt.apply_gradients(zip(gradients, model.trainable_variables)) # , clip_norm=1.0)
if init:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
return unscaled_loss # , outputs#, tape.gradient(loss_value, model.trainable_variables)
@tf.function
def infer_step(model, input_ids,
attention_mask=None,
token_type_ids=None,
cls_index=None,
p_mask=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
return model(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
cls_index=cls_index,
p_mask=p_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
training=training,
)
def main():
args = parse_args()
hvd.init()
set_affinity(hvd.local_rank())
if is_main_process():
log("Running total processes: {}".format(get_world_size()))
log("Starting process: {}".format(get_rank()))
if is_main_process():
dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
filename=args.json_summary),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
else:
dllogger.init(backends=[])
dllogger.metadata("exact_match", {"unit": None})
dllogger.metadata("F1", {"unit": None})
dllogger.metadata("inference_sequences_per_second", {"unit": "sequences/s"})
dllogger.metadata("training_sequences_per_second", {"unit": "sequences/s"})
tf.random.set_seed(args.seed)
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
# script parameters
BATCH_SIZE = args.train_batch_size
EVAL_BATCH_SIZE = args.predict_batch_size
USE_XLA = args.xla
USE_AMP = args.amp
EPOCHS = args.num_train_epochs
if not args.do_train:
EPOCHS = args.num_train_epochs = 1
log("Since running inference only, setting args.num_train_epochs to 1")
if not os.path.exists(args.output_dir) and is_main_process():
os.makedirs(args.output_dir)
# TensorFlow configuration
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
tf.config.optimizer.set_jit(USE_XLA)
#tf.config.optimizer.set_experimental_options({"auto_mixed_precision": USE_AMP})
if args.amp:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
print('Compute dtype: %s' % policy.compute_dtype) # Compute dtype: float16
print('Variable dtype: %s' % policy.variable_dtype) # Variable dtype: float32
if is_main_process():
log("***** Loading tokenizer and model *****")
# Load tokenizer and model from pretrained model/vocabulary. Specify the number of labels to classify (2+: classification, 1: regression)
electra_model = args.electra_model
config = ElectraConfig.from_pretrained(electra_model, cache_dir=args.cache_dir)
config.update({"amp": args.amp})
if args.vocab_file is None:
tokenizer = ElectraTokenizer.from_pretrained(electra_model, cache_dir=args.cache_dir)
else:
tokenizer = ElectraTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case)
model = TFElectraForQuestionAnswering.from_pretrained(electra_model, config=config, cache_dir=args.cache_dir, args=args)
if is_main_process():
log("***** Loading dataset *****")
# Load data
processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
train_examples = processor.get_train_examples(args.data_dir) if args.do_train else None
dev_examples = processor.get_dev_examples(args.data_dir) if args.do_predict else None
if is_main_process():
log("***** Loading features *****")
# Load cached features
squad_version = '2.0' if args.version_2_with_negative else '1.1'
if args.cache_dir is None:
args.cache_dir = args.data_dir
cached_train_features_file = args.cache_dir.rstrip('/') + '/' + 'TF2_train-v{4}.json_{1}_{2}_{3}'.format(
electra_model.split("/")[1], str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length), squad_version)
cached_dev_features_file = args.cache_dir.rstrip('/') + '/' + 'TF2_dev-v{4}.json_{1}_{2}_{3}'.format(
electra_model.split("/")[1], str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length), squad_version)
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader) if args.do_train else []
with open(cached_dev_features_file, "rb") as reader:
dev_features = pickle.load(reader) if args.do_predict else []
except:
train_features = ( # TODO: (yy) do on rank 0?
squad_convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True,
return_dataset="",
)
if args.do_train
else []
)
dev_features = (
squad_convert_examples_to_features(
examples=dev_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False,
return_dataset="",
)
if args.do_predict
else []
)
# Dump Cached features
if not args.skip_cache and is_main_process():
if args.do_train:
log("***** Building Cache Files: {} *****".format(cached_train_features_file))
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
if args.do_predict:
log("***** Building Cache Files: {} *****".format(cached_dev_features_file))
with open(cached_dev_features_file, "wb") as writer:
pickle.dump(dev_features, writer)
len_train_features = len(train_features)
total_train_steps = int((len_train_features * EPOCHS / BATCH_SIZE) / get_world_size()) + 1
train_steps_per_epoch = int((len_train_features / BATCH_SIZE) / get_world_size()) + 1
len_dev_features = len(dev_features)
total_dev_steps = int((len_dev_features / EVAL_BATCH_SIZE)) + 1
train_dataset = get_dataset_from_features(train_features, BATCH_SIZE,
v2=args.version_2_with_negative) if args.do_train else []
dev_dataset = get_dataset_from_features(dev_features, EVAL_BATCH_SIZE, drop_remainder=False, ngpu=1, mode="dev",
v2=args.version_2_with_negative) if args.do_predict else []
opt = create_optimizer(init_lr=args.learning_rate, num_train_steps=total_train_steps,
num_warmup_steps=int(args.warmup_proportion * total_train_steps),
weight_decay_rate=args.weight_decay_rate,
layerwise_lr_decay=args.layerwise_lr_decay,
n_transformer_layers=model.num_hidden_layers)
if USE_AMP:
# loss scaling is currently required when using mixed precision
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")
# Define loss function
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_class = tf.keras.losses.BinaryCrossentropy(
from_logits=True,
name='binary_crossentropy'
)
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
model.compile(optimizer=opt, loss=loss, metrics=[metric])
train_loss_results = []
if args.do_train and is_main_process():
log("***** Running training *****")
log(" Num examples = ", len_train_features)
log(" Num Epochs = ", args.num_train_epochs)
log(" Instantaneous batch size per GPU = ", args.train_batch_size)
log(
" Total train batch size (w. parallel, distributed & accumulation) = ",
args.train_batch_size
* get_world_size(),
)
log(" Total optimization steps =", total_train_steps)
total_train_time = 0
latency = []
for epoch in range(EPOCHS):
if args.do_train:
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_perf_avg = tf.keras.metrics.Mean()
epoch_start = time.time()
epoch_iterator = tqdm(train_dataset, total=train_steps_per_epoch, desc="Iteration", mininterval=5,
disable=not is_main_process())
for iter, inputs in enumerate(epoch_iterator):
# breaking criterion if max_steps if > 1
if args.max_steps > 0 and (epoch * train_steps_per_epoch + iter) > args.max_steps:
break
iter_start = time.time()
# Optimize the model
loss_value = train_step(model, inputs, loss, USE_AMP, opt, (iter == 0 and epoch == 0),
v2=args.version_2_with_negative, loss_class=loss_class, fp16=USE_AMP)
#introduce CPU-GPU sync for training perf computation
loss_numpy = loss_value.numpy()
epoch_perf_avg.update_state(1. * BATCH_SIZE / (time.time() - iter_start))
if iter % args.log_freq == 0:
if is_main_process():
log("\nEpoch: {:03d}, Step:{:6d}, Loss:{:12.8f}, Perf:{:5.0f}, loss_scale:{}, opt_step:{}".format(epoch, iter, loss_value,
epoch_perf_avg.result() * get_world_size(), opt.loss_scale if config.amp else 1,
int(opt.iterations)))
dllogger.log(step=(epoch, iter,), data={"step_loss": float(loss_value.numpy()),
"train_perf": float( epoch_perf_avg.result().numpy() * get_world_size())})
# Track progress
epoch_loss_avg.update_state(loss_value) # Add current batch loss
# End epoch
train_loss_results.append(epoch_loss_avg.result())
total_train_time += float(time.time() - epoch_start)
# Summarize and save checkpoint at the end of each epoch
if is_main_process():
dllogger.log(step=tuple(), data={"e2e_train_time": total_train_time,
"training_sequences_per_second": float(
epoch_perf_avg.result().numpy() * get_world_size()),
"final_loss": float(epoch_loss_avg.result().numpy())})
if not args.skip_checkpoint:
if args.ci:
checkpoint_name = "{}/electra_base_qa_v2_{}_epoch_{}_ckpt".format(args.output_dir, args.version_2_with_negative, epoch + 1)
else:
checkpoint_name = "checkpoints/electra_base_qa_v2_{}_epoch_{}_ckpt".format(args.version_2_with_negative, epoch + 1)
if is_main_process():
model.save_weights(checkpoint_name)
if args.do_predict and (args.evaluate_during_training or epoch == args.num_train_epochs - 1):
if not args.do_train:
log("***** Loading checkpoint: {} *****".format(args.init_checkpoint))
model.load_weights(args.init_checkpoint).expect_partial()
current_feature_id = 0
all_results = []
if is_main_process():
log("***** Running evaluation *****")
log(" Num Batches = ", total_dev_steps)
log(" Batch size = ", args.predict_batch_size)
raw_infer_start = time.time()
if is_main_process():
infer_perf_avg = tf.keras.metrics.Mean()
dev_iterator = tqdm(dev_dataset, total=total_dev_steps, desc="Iteration", mininterval=5,
disable=not is_main_process())
for input_ids, input_mask, segment_ids, start_positions, end_positions, cls_index, p_mask, is_impossible in dev_iterator:
# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
iter_start = time.time()
if not args.joint_head:
batch_start_logits, batch_end_logits = infer_step(model, input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
)[:2]
#Synchronize with GPU to compute time
_ = batch_start_logits.numpy()
else:
outputs = infer_step(model, input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
cls_index=cls_index,
p_mask=p_mask,
)
#Synchronize with GPU to compute time
_ = outputs[0].numpy()
infer_time = (time.time() - iter_start)
infer_perf_avg.update_state(1. * EVAL_BATCH_SIZE / infer_time)
latency.append(infer_time)
for iter_ in range(input_ids.shape[0]):
if not args.joint_head:
start_logits = batch_start_logits[iter_].numpy().tolist()
end_logits = batch_end_logits[iter_].numpy().tolist()
dev_feature = dev_features[current_feature_id]
current_feature_id += 1
unique_id = int(dev_feature.unique_id)
all_results.append(RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
else:
dev_feature = dev_features[current_feature_id]
current_feature_id += 1
unique_id = int(dev_feature.unique_id)
output = [output[iter_].numpy().tolist() for output in outputs]
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
all_results.append(result)
# Compute and save predictions
answers, nbest_answers = get_answers(dev_examples, dev_features, all_results, args)
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
e2e_infer_time = time.time() - raw_infer_start
# if args.version_2_with_negative:
# output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json")
# else:
# output_null_log_odds_file = None
with open(output_prediction_file, "w") as f:
f.write(json.dumps(answers, indent=4) + "\n")
with open(output_nbest_file, "w") as f:
f.write(json.dumps(nbest_answers, indent=4) + "\n")
if args.do_eval:
if args.version_2_with_negative:
dev_file = "dev-v2.0.json"
else:
dev_file = "dev-v1.1.json"
eval_out = subprocess.check_output([sys.executable, args.eval_script,
args.data_dir + "/" + dev_file, output_prediction_file])
log(eval_out.decode('UTF-8'))
scores = str(eval_out).strip()
exact_match = float(scores.split(":")[1].split(",")[0])
if args.version_2_with_negative:
f1 = float(scores.split(":")[2].split(",")[0])
else:
f1 = float(scores.split(":")[2].split("}")[0])
log("Epoch: {:03d} Results: {}".format(epoch, eval_out.decode('UTF-8')))
log("**EVAL SUMMARY** - Epoch: {:03d}, EM: {:6.3f}, F1: {:6.3f}, Infer_Perf: {:4.0f} seq/s"
.format(epoch, exact_match, f1, infer_perf_avg.result()))
latency_all = sorted(latency)[:-2]
log(
"**LATENCY SUMMARY** - Epoch: {:03d}, Ave: {:6.3f} ms, 90%: {:6.3f} ms, 95%: {:6.3f} ms, 99%: {:6.3f} ms"
.format(epoch, sum(latency_all) / len(latency_all) * 1000,
sum(latency_all[:int(len(latency_all) * 0.9)]) / int(len(latency_all) * 0.9) * 1000,
sum(latency_all[:int(len(latency_all) * 0.95)]) / int(len(latency_all) * 0.95) * 1000,
sum(latency_all[:int(len(latency_all) * 0.99)]) / int(len(latency_all) * 0.99) * 1000,
))
dllogger.log(step=tuple(),
data={"inference_sequences_per_second": float(infer_perf_avg.result().numpy()),
"e2e_inference_time": e2e_infer_time})
if is_main_process() and args.do_train and args.do_eval:
log(
"**RESULTS SUMMARY** - EM: {:6.3f}, F1: {:6.3f}, Train_Time: {:4.0f} s, Train_Perf: {:4.0f} seq/s, Infer_Perf: {:4.0f} seq/s"
.format(exact_match, f1, total_train_time, epoch_perf_avg.result() * get_world_size(),
infer_perf_avg.result()))
dllogger.log(step=tuple(), data={"exact_match": exact_match, "F1": f1})
if __name__ == "__main__":
main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/run_tf_squad.py |
# Copyright 2020 The Google AI Team, Stanford University and The HuggingFace Inc. team.
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import tensorflow as tf
from configuration import ElectraConfig
from file_utils import add_start_docstrings, add_start_docstrings_to_callable
from modeling_utils import ACT2FN, TFBertEncoder, TFBertPreTrainedModel
from modeling_utils import get_initializer, shape_list
from tokenization_utils import BatchEncoding
import pretrain_utils, collections
logger = logging.getLogger(__name__)
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP = {
"google/electra-small-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-generator/tf_model.h5",
"google/electra-base-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-generator/tf_model.h5",
"google/electra-large-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-generator/tf_model.h5",
"google/electra-small-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-discriminator/tf_model.h5",
"google/electra-base-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-base-discriminator/tf_model.h5",
"google/electra-large-discriminator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-large-discriminator/tf_model.h5",
}
class TFElectraEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.embedding_size = config.embedding_size
self.initializer_range = config.initializer_range
self.position_embeddings = tf.keras.layers.Embedding(
config.max_position_embeddings,
config.embedding_size,
embeddings_initializer=get_initializer(self.initializer_range),
name="position_embeddings",
)
self.token_type_embeddings = tf.keras.layers.Embedding(
config.type_vocab_size,
config.embedding_size,
embeddings_initializer=get_initializer(self.initializer_range),
name="token_type_embeddings",
)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.amp = config.amp
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
def call(self, inputs, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
if input_ids is not None:
input_shape = shape_list(input_ids)
else:
input_shape = shape_list(inputs_embeds)[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
if self.amp:
embeddings = inputs_embeds + tf.cast(position_embeddings, tf.float16) + tf.cast(token_type_embeddings, tf.float16)
else:
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
batch_size = shape_list(inputs)[0]
length = shape_list(inputs)[1]
x = tf.reshape(inputs, [-1, self.embedding_size])
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size])
class TFElectraDiscriminatorPredictions(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
self.dense_prediction = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="dense_prediction")
self.config = config
def call(self, discriminator_hidden_states, training=False):
hidden_states = self.dense(discriminator_hidden_states)
hidden_states = ACT2FN[self.config.hidden_act](hidden_states)
logits = tf.squeeze(self.dense_prediction(hidden_states), axis=-1)
return logits
class TFElectraGeneratorPredictions(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dense = tf.keras.layers.Dense(
config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense")
def call(self, generator_hidden_states, training=False):
hidden_states = self.dense(generator_hidden_states)
hidden_states = ACT2FN["gelu"](hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class TFElectraPreTrainedModel(TFBertPreTrainedModel):
config_class = ElectraConfig
pretrained_model_archive_map = TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "electra"
def get_extended_attention_mask(self, attention_mask, input_shape):
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def get_head_mask(self, head_mask):
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
return head_mask
class TFElectraMainLayer(TFElectraPreTrainedModel):
config_class = ElectraConfig
def __init__(self, config, shared_embeddings=False, input_embeddings=None, **kwargs):
super().__init__(config, **kwargs)
if shared_embeddings and input_embeddings is not None:
self.embeddings = input_embeddings
else:
self.embeddings = TFElectraEmbeddings(config, name="embeddings")
if config.embedding_size != config.hidden_size:
self.embeddings_project = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="embeddings_project")
self.encoder = TFBertEncoder(config, name="encoder")
self.config = config
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
raise NotImplementedError
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
token_type_ids = inputs.get("token_type_ids", token_type_ids)
position_ids = inputs.get("position_ids", position_ids)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
head_mask = self.get_head_mask(head_mask)
hidden_states = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
if hasattr(self, "embeddings_project"):
hidden_states = self.embeddings_project(hidden_states, training=training)
hidden_states = self.encoder([hidden_states, extended_attention_mask, head_mask], training=training)
return hidden_states
ELECTRA_START_DOCSTRING = r"""
This model is a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ sub-class.
Use it as a regular TF 2.0 Keras Model and
refer to the TF 2.0 documentation for all matter related to general usage and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors
in the first positional argument :
- a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.ElectraConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ELECTRA_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.ElectraTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
`What are attention masks? <../glossary.html#attention-mask>`__
head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`, defaults to :obj:`None`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
(if set to :obj:`False`) for evaluation.
"""
@add_start_docstrings(
"The bare Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to "
"the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the "
"hidden size and embedding size are different."
""
"Both the generator and discriminator checkpoints may be loaded into this model.",
ELECTRA_START_DOCSTRING,
)
class TFElectraModel(TFElectraPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
def get_input_embeddings(self):
return self.electra.embeddings
@add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
def call(self, inputs, **kwargs):
r"""
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import ElectraTokenizer, TFElectraModel
tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
model = TFElectraModel.from_pretrained('google/electra-small-discriminator')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
outputs = self.electra(inputs, **kwargs)
return outputs
@add_start_docstrings(
"""
Electra model with a binary classification head on top as used during pre-training for identifying generated
tokens.
Even though both the discriminator and generator may be loaded into this model, the discriminator is
the only model of the two to have the correct classification head to be used for this model.""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForPreTraining(TFElectraPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predictions")
def get_input_embeddings(self):
return self.electra.embeddings
@add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
r"""
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Prediction scores of the head (scores for each token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import ElectraTokenizer, TFElectraForPreTraining
tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
model = TFElectraForPreTraining.from_pretrained('google/electra-small-discriminator')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
scores = outputs[0]
"""
discriminator_hidden_states = self.electra(
input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training=training
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.discriminator_predictions(discriminator_sequence_output)
output = (logits,)
output += discriminator_hidden_states[1:]
return output # (loss), scores, (hidden_states), (attentions)
class TFElectraMaskedLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def call(self, hidden_states, training=False):
hidden_states = self.input_embeddings(hidden_states, mode="linear")
hidden_states = hidden_states + self.bias
return hidden_states
@add_start_docstrings(
"""
Electra model with a language modeling head on top.
Even though both the discriminator and generator may be loaded into this model, the generator is
the only model of the two to have been trained for the masked language modeling task.""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForMaskedLM(TFElectraPreTrainedModel):
def __init__(self, config, shared_embeddings=False, input_embeddings=None, **kwargs):
super().__init__(config, **kwargs)
self.vocab_size = config.vocab_size
self.electra = TFElectraMainLayer(config,
shared_embeddings=shared_embeddings,
input_embeddings=input_embeddings,
name="electra")
self.generator_predictions = TFElectraGeneratorPredictions(config, name="generator_predictions")
if isinstance(config.hidden_act, str):
self.activation = ACT2FN[config.hidden_act]
else:
self.activation = config.hidden_act
self.generator_lm_head = TFElectraMaskedLMHead(config, self.electra.embeddings, name="generator_lm_head")
def get_input_embeddings(self):
return self.electra.embeddings
def get_output_embeddings(self):
return self.generator_lm_head
@add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
r"""
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import ElectraTokenizer, TFElectraForMaskedLM
tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-generator')
model = TFElectraForMaskedLM.from_pretrained('google/electra-small-generator')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
prediction_scores = outputs[0]
"""
generator_hidden_states = self.electra(
input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training=training
)
generator_sequence_output = generator_hidden_states[0]
prediction_scores = self.generator_predictions(generator_sequence_output, training=training)
prediction_scores = self.generator_lm_head(prediction_scores, training=training)
output = (prediction_scores,)
output += generator_hidden_states[1:]
return output # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
def get_generator_config(config, bert_config):
"""Get model config for the generator network."""
gen_config = ElectraConfig.from_dict(bert_config.to_dict())
gen_config.hidden_size = int(round(
bert_config.hidden_size * config.generator_hidden_size))
#To keep hidden size divisble by 64 - attention head size
if gen_config.hidden_size % 64 != 0:
gen_config.hidden_size += 64 - (gen_config.hidden_size % 64)
gen_config.num_hidden_layers = int(round(
bert_config.num_hidden_layers * config.generator_layers))
gen_config.intermediate_size = 4 * gen_config.hidden_size
gen_config.num_attention_heads = max(1, gen_config.hidden_size // 64)
return gen_config
class PretrainingModel(tf.keras.Model):
"""Transformer pre-training using the replaced-token-detection task."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
# Set up model config
self._config = config
self.disc_config = ElectraConfig(vocab_size=config.vocab_size,
embedding_size=config.embedding_size,
hidden_size=config.hidden_size,
num_hidden_layers=config.num_hidden_layers,
num_attention_heads=config.num_attention_heads,
intermediate_size=4*config.hidden_size,
hidden_act=config.act_func,
hidden_dropout_prob=config.hidden_dropout_prob,
attention_probs_dropout_prob=config.attention_probs_dropout_prob, )
self.disc_config.update({"amp": config.amp})
# Set up discriminator
self.discriminator = TFElectraForPreTraining(self.disc_config)
# Set up generator
gen_config = get_generator_config(config, self.disc_config)
gen_config.update({"amp": config.amp})
if config.electra_objective:
if config.shared_embeddings:
self.generator = TFElectraForMaskedLM(
gen_config, shared_embeddings=True,
input_embeddings=self.discriminator.get_input_embeddings())
else:
self.generator = TFElectraForMaskedLM(gen_config)
else:
self.generator = TFElectraForMaskedLM(self.disc_config)
def call(self, features, is_training):
config = self._config
# Mask the input
masked_inputs = pretrain_utils.mask(
config, pretrain_utils.features_to_inputs(features), config.mask_prob)
# Generator
if config.uniform_generator:
mlm_output = self._get_masked_lm_output(masked_inputs, None, is_training=is_training)
else:
mlm_output = self._get_masked_lm_output(
masked_inputs, self.generator, is_training=is_training)
fake_data = self._get_fake_data(masked_inputs, mlm_output.logits)
total_loss = config.gen_weight * mlm_output.loss
# Discriminator
disc_output = None
if config.electra_objective:
disc_output = self._get_discriminator_output(
fake_data.inputs, self.discriminator, fake_data.is_fake_tokens,
is_training=is_training)
total_loss += config.disc_weight * disc_output.loss
# Evaluation inputs
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
if config.electra_objective:
eval_fn_inputs.update({
"disc_loss": disc_output.per_example_loss,
"disc_labels": disc_output.labels,
"disc_probs": disc_output.probs,
"disc_preds": disc_output.preds,
"sampled_tokids": tf.argmax(fake_data.sampled_tokens, -1,
output_type=tf.int32)
})
return total_loss, eval_fn_inputs
def _get_masked_lm_output(self, inputs, generator, is_training=False):
"""Masked language modeling softmax layer."""
masked_lm_weights = inputs.masked_lm_weights
if self._config.uniform_generator:
logits = tf.zeros(self.disc_config.vocab_size)
logits_tiled = tf.zeros(
pretrain_utils.get_shape_list(inputs.masked_lm_ids) +
[self.disc_config.vocab_size])
logits_tiled += tf.reshape(logits, [1, 1, self.disc_config.vocab_size])
logits = logits_tiled
else:
outputs = generator(
input_ids=inputs.input_ids,
attention_mask=inputs.input_mask,
token_type_ids=inputs.segment_ids,
training=is_training)
logits = outputs[0]
logits = pretrain_utils.gather_positions(
logits, inputs.masked_lm_positions)
oh_labels = tf.one_hot(
inputs.masked_lm_ids, depth=self.disc_config.vocab_size,
dtype=tf.float32)
probs = tf.cast(tf.nn.softmax(logits), tf.float32)
log_probs = tf.cast(tf.nn.log_softmax(logits), tf.float32)
label_log_probs = -tf.reduce_sum(log_probs * oh_labels, axis=-1)
numerator = tf.reduce_sum(masked_lm_weights * label_log_probs)
denominator = tf.reduce_sum(masked_lm_weights) + 1e-6
loss = numerator / denominator
preds = tf.argmax(log_probs, axis=-1, output_type=tf.int32)
MLMOutput = collections.namedtuple(
"MLMOutput", ["logits", "probs", "loss", "per_example_loss", "preds"])
return MLMOutput(
logits=logits, probs=probs, per_example_loss=label_log_probs,
loss=loss, preds=preds)
def _get_discriminator_output(self, inputs, discriminator, labels, is_training=False):
"""Discriminator binary classifier."""
outputs = discriminator(
input_ids=inputs.input_ids,
attention_mask=inputs.input_mask,
token_type_ids=inputs.segment_ids,
training=is_training,
)
logits = outputs[0]
weights = tf.cast(inputs.input_mask, tf.float32)
labelsf = tf.cast(labels, tf.float32)
logits = tf.cast(logits, tf.float32)
losses = tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits, labels=labelsf) * weights
per_example_loss = (tf.reduce_sum(losses, axis=-1) /
(1e-6 + tf.reduce_sum(weights, axis=-1)))
loss = tf.reduce_sum(losses) / (1e-6 + tf.reduce_sum(weights))
probs = tf.nn.sigmoid(logits)
preds = tf.cast(tf.round((tf.sign(logits) + 1) / 2), tf.int32)
DiscOutput = collections.namedtuple(
"DiscOutput", ["loss", "per_example_loss", "probs", "preds",
"labels"])
return DiscOutput(
loss=loss, per_example_loss=per_example_loss, probs=probs,
preds=preds, labels=labels,
)
def _get_fake_data(self, inputs, mlm_logits):
"""Sample from the generator to create corrupted input."""
inputs = pretrain_utils.unmask(inputs)
disallow = tf.one_hot(
inputs.masked_lm_ids, depth=self.disc_config.vocab_size,
dtype=tf.float32) if self._config.disallow_correct else None
sampled_tokens = tf.stop_gradient(pretrain_utils.sample_from_softmax(
mlm_logits / self._config.temperature, disallow=disallow))
sampled_tokids = tf.argmax(sampled_tokens, -1, output_type=tf.int32)
updated_input_ids, masked = pretrain_utils.scatter_update(
inputs.input_ids, sampled_tokids, inputs.masked_lm_positions)
labels = masked * (1 - tf.cast(
tf.equal(updated_input_ids, inputs.input_ids), tf.int32))
updated_inputs = pretrain_utils.get_updated_inputs(
inputs, input_ids=updated_input_ids)
FakedData = collections.namedtuple("FakedData", [
"inputs", "is_fake_tokens", "sampled_tokens"])
return FakedData(inputs=updated_inputs, is_fake_tokens=labels,
sampled_tokens=sampled_tokens)
@add_start_docstrings(
"""
Electra model with a token classification head on top.
Both the discriminator and generator may be loaded into this model.""",
ELECTRA_START_DOCSTRING,
)
class TFElectraForTokenClassification(TFElectraPreTrainedModel):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier")
@add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING)
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
r"""
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`:
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples::
import tensorflow as tf
from transformers import ElectraTokenizer, TFElectraForTokenClassification
tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-discriminator')
model = TFElectraForTokenClassification.from_pretrained('google/electra-small-discriminator')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
scores = outputs[0]
"""
discriminator_hidden_states = self.electra(
input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training=training
)
discriminator_sequence_output = discriminator_hidden_states[0]
discriminator_sequence_output = self.dropout(discriminator_sequence_output)
logits = self.classifier(discriminator_sequence_output)
output = (logits,)
output += discriminator_hidden_states[1:]
return output # (loss), scores, (hidden_states), (attentions)
class TFPoolerStartLogits(tf.keras.Model):
""" Compute SQuAD start_logits from sequence hidden states. """
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.dense = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="start_logit_pooler_dense"
)
def call(self, hidden_states, p_mask=None, next_layer_dtype=tf.float32):
""" Args:
**p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`
invalid position mask such as query and special symbols (PAD, SEP, CLS)
1.0 means token should be masked.
"""
x = tf.squeeze(self.dense(hidden_states), axis=-1,
name="squeeze_start_logit_pooler")
if p_mask is not None:
x = tf.cast(x, tf.float32) * (1 - p_mask) - 1e30 * p_mask
return x
class TFPoolerEndLogits(tf.keras.Model):
""" Compute SQuAD end_logits from sequence hidden states and start token hidden state.
"""
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.dense_0 = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range),
name="end_logit_pooler_dense_0"
)
self.activation = tf.keras.layers.Activation('tanh') # nn.Tanh()
self.LayerNorm = tf.keras.layers.LayerNormalization(axis=-1, epsilon=config.layer_norm_eps,
name="end_logit_pooler_LayerNorm")
self.dense_1 = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="end_logit_pooler_dense_1"
)
def call(self, hidden_states, start_states=None, start_positions=None, p_mask=None, training=False,
next_layer_dtype=tf.float32):
""" Args:
One of ``start_states``, ``start_positions`` should be not None.
If both are set, ``start_positions`` overrides ``start_states``.
**start_states**: ``torch.LongTensor`` of shape identical to hidden_states
hidden states of the first tokens for the labeled span.
**start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
position of the first token for the labeled span:
**p_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, seq_len)``
Mask of invalid position such as query and special symbols (PAD, SEP, CLS)
1.0 means token should be masked.
"""
assert (
start_states is not None or start_positions is not None
), "One of start_states, start_positions should be not None"
if start_positions is not None and training:
bsz, slen, hsz = hidden_states.shape
start_states = tf.gather(hidden_states, start_positions[:, None], axis=1,
batch_dims=1) # shape (bsz, 1, hsz)
start_states = tf.broadcast_to(start_states, (bsz, slen, hsz)) # shape (bsz, slen, hsz)
x = self.dense_0(tf.concat([hidden_states, start_states], axis=-1))
x = self.activation(x)
if training:
# since we are not doing beam search, add dimension with value=1. corresponds to dimension with top_k during inference - if not layernorm crashes
x = tf.expand_dims(x, axis=2)
x = self.LayerNorm(x)
if training:
# undo the additional dimension added above
x = tf.squeeze(self.dense_1(x), axis=[-1, -2])
else:
x = tf.squeeze(self.dense_1(x), axis=-1)
if p_mask is not None:
x = tf.cast(x, tf.float32) * (1 - p_mask) - 1e30 * p_mask
return x
class TFPoolerAnswerClass(tf.keras.Model):
""" Compute SQuAD 2.0 answer class from classification and start tokens hidden states. """
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.dense_0 = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range),
name="pooler_answer_class_dense_0"
)
self.activation = tf.keras.layers.Activation('tanh')
self.dense_1 = tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer=get_initializer(config.initializer_range),
name="pooler_answer_class_dense_1"
)
def call(self, hidden_states, start_states=None, start_positions=None, cls_index=None):
"""
Args:
One of ``start_states``, ``start_positions`` should be not None.
If both are set, ``start_positions`` overrides ``start_states``.
**start_states**: ``torch.LongTensor`` of shape identical to ``hidden_states``.
hidden states of the first tokens for the labeled span.
**start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``
position of the first token for the labeled span.
**cls_index**: torch.LongTensor of shape ``(batch_size,)``
position of the CLS token. If None, take the last token.
note(Original repo):
no dependency on end_feature so that we can obtain one single `cls_logits`
for each sample
"""
assert (
start_states is not None or start_positions is not None
), "One of start_states, start_positions should be not None"
if start_positions is not None:
start_states = tf.gather(hidden_states, start_positions[:, None], axis=1,
batch_dims=1) # shape (bsz, 1, hsz)
start_states = tf.squeeze(start_states, axis=1) # shape (bsz, hsz)
if cls_index is not None:
cls_token_state = tf.gather(hidden_states, cls_index[:, None], axis=1, batch_dims=1) # shape (bsz, 1, hsz)
cls_token_state = tf.squeeze(cls_token_state, axis=1) # shape (bsz, hsz)
else:
cls_token_state = hidden_states[:, 0, :] # shape (bsz, hsz)
x = self.dense_0(tf.concat([start_states, cls_token_state], axis=-1))
x = self.activation(x)
x = tf.squeeze(self.dense_1(x), axis=-1)
return x
class TFElectraForQuestionAnswering(TFElectraPreTrainedModel):
def __init__(self, config, args):
super().__init__(config, args)
self.start_n_top = args.beam_size # config.start_n_top
self.end_n_top = args.beam_size # config.end_n_top
self.joint_head = args.joint_head
self.v2 = args.version_2_with_negative
self.electra = TFElectraMainLayer(config, name="electra")
self.num_hidden_layers = config.num_hidden_layers
self.amp = config.amp
##old head
if not self.joint_head:
self.qa_outputs = tf.keras.layers.Dense(
2, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs")
else:
self.start_logits = TFPoolerStartLogits(config, name='start_logits')
self.end_logits = TFPoolerEndLogits(config, name='end_logits')
if self.v2:
self.answer_class = TFPoolerAnswerClass(config, name='answer_class')
def call(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
start_positions=None,
end_positions=None,
cls_index=None,
p_mask=None,
is_impossible=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False,
):
outputs = self.electra(
input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, training=training
)
discriminator_sequence_output = outputs[0]
# Simple head model
if not self.joint_head:
logits = self.qa_outputs(discriminator_sequence_output)
[start_logits, end_logits] = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1, name="squeeze_start_logit")
end_logits = tf.squeeze(end_logits, axis=-1, name="squeeze_end_logit")
outputs = (start_logits, end_logits) + outputs
return outputs
start_logits = self.start_logits(discriminator_sequence_output, p_mask=p_mask,
next_layer_dtype=self.end_logits.dense_0.dtype)
if training: # start_positions is not None and end_positions is not None:
# during training, compute the end logits based on the ground truth of the start position
end_logits = self.end_logits(discriminator_sequence_output, start_positions=start_positions, p_mask=p_mask,
training=training,
next_layer_dtype=tf.float16 if self.amp else tf.float32)
if self.v2: # cls_index is not None:#cls_index is not None and is_impossible is not None:
# Predict answerability from the representation of CLS and START
cls_logits = self.answer_class(discriminator_sequence_output, start_positions=start_positions,
cls_index=cls_index)
else:
cls_logits = None
outputs = (start_logits, end_logits, cls_logits) + outputs
else:
# during inference, compute the end logits based on beam search
bsz, slen, hsz = discriminator_sequence_output.shape
start_n_top = min(self.start_n_top, slen)
end_n_top = min(self.end_n_top, slen)
start_log_probs = tf.nn.log_softmax(start_logits, axis=-1, name="start_logit_softmax") # shape (bsz, slen)
start_top_log_probs, start_top_index = tf.math.top_k(start_log_probs, k=start_n_top,
name="start_log_probs_top_k")
start_states = tf.gather(discriminator_sequence_output, start_top_index, axis=1,
batch_dims=1) # shape (bsz, start_n_top, hsz)
start_states = tf.broadcast_to(tf.expand_dims(start_states, axis=1),
[bsz, slen, start_n_top, hsz]) # shape (bsz, slen, start_n_top, hsz)
discriminator_sequence_output_expanded = tf.broadcast_to(
tf.expand_dims(discriminator_sequence_output, axis=2),
list(start_states.shape)) # shape (bsz, slen, start_n_top, hsz)
p_mask = tf.expand_dims(p_mask, axis=-1) if p_mask is not None else None
end_logits = self.end_logits(discriminator_sequence_output_expanded, start_states=start_states,
p_mask=p_mask, next_layer_dtype=tf.float16 if self.amp else tf.float32) # self.answer_class.dense_0.dtype)
end_log_probs = tf.nn.log_softmax(end_logits, axis=1,
name="end_logit_softmax") # shape (bsz, slen, start_n_top)
# need to transpose because tf.math.top_k works on default axis=-1
end_log_probs = tf.transpose(end_log_probs, perm=[0, 2, 1])
end_top_log_probs, end_top_index = tf.math.top_k(
end_log_probs, k=end_n_top) # shape (bsz, end_n_top, start_n_top).perm(0,2,1)
end_top_log_probs = tf.reshape(end_top_log_probs, (
-1, start_n_top * end_n_top)) # shape (bsz, self.start_n_top * self.end_n_top)
end_top_index = tf.reshape(end_top_index,
(-1, start_n_top * end_n_top)) # shape (bsz, self.start_n_top * self.end_n_top)
if self.v2: # cls_index is not None:
start_p = tf.nn.softmax(start_logits, axis=-1, name="start_softmax")
start_states = tf.einsum(
"blh,bl->bh", discriminator_sequence_output, tf.cast(start_p, tf.float16) if self.amp else start_p
) # get the representation of START as weighted sum of hidden states
# explicitly setting cls_index to None
cls_logits = self.answer_class(
discriminator_sequence_output, start_states=start_states, cls_index=None)
# one single `cls_logits` for each sample
else:
cls_logits = tf.fill([bsz], 0.0)
outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs
# return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
return outputs
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/modeling.py |
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import csv
import logging
import os
import math
import collections
from functools import partial
from multiprocessing import Pool, cpu_count
import horovod.tensorflow as hvd
import numpy as np
from tqdm import tqdm
from file_utils import is_tf_available, is_torch_available
from tokenization_utils import BasicTokenizer, whitespace_tokenize
if is_torch_available():
import torch
from torch.utils.data import TensorDataset
if is_tf_available():
import tensorflow as tf
logger = logging.getLogger(__name__)
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _new_check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# if len(doc_spans) == 1:
# return True
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span["start"] + doc_span["length"] - 1
if position < doc_span["start"]:
continue
if position > end:
continue
num_left_context = position - doc_span["start"]
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
def squad_convert_example_to_features(example, max_seq_length, doc_stride, max_query_length, is_training):
features = []
if is_training and not example.is_impossible:
# Get start and end position
start_position = example.start_position
end_position = example.end_position
# If the answer cannot be found in the text, then skip this example.
actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)])
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
return []
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
)
spans = []
truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
sequence_added_tokens = (
tokenizer.max_len - tokenizer.max_len_single_sentence + 1
if "roberta" in str(type(tokenizer)) or "camembert" in str(type(tokenizer))
else tokenizer.max_len - tokenizer.max_len_single_sentence
)
sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):
encoded_dict = tokenizer.encode_plus(
truncated_query if tokenizer.padding_side == "right" else span_doc_tokens,
span_doc_tokens if tokenizer.padding_side == "right" else truncated_query,
max_length=max_seq_length,
return_overflowing_tokens=True,
pad_to_max_length=True,
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
truncation_strategy="only_second" if tokenizer.padding_side == "right" else "only_first",
return_token_type_ids=True,
)
paragraph_len = min(
len(all_doc_tokens) - len(spans) * doc_stride,
max_seq_length - len(truncated_query) - sequence_pair_added_tokens,
)
if tokenizer.pad_token_id in encoded_dict["input_ids"]:
if tokenizer.padding_side == "right":
non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)]
else:
last_padding_id_position = (
len(encoded_dict["input_ids"]) - 1 - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id)
)
non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :]
else:
non_padded_ids = encoded_dict["input_ids"]
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
token_to_orig_map = {}
for i in range(paragraph_len):
index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
encoded_dict["paragraph_len"] = paragraph_len
encoded_dict["tokens"] = tokens
encoded_dict["token_to_orig_map"] = token_to_orig_map
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
encoded_dict["token_is_max_context"] = {}
encoded_dict["start"] = len(spans) * doc_stride
encoded_dict["length"] = paragraph_len
spans.append(encoded_dict)
if "overflowing_tokens" not in encoded_dict:
break
span_doc_tokens = encoded_dict["overflowing_tokens"]
for doc_span_index in range(len(spans)):
for j in range(spans[doc_span_index]["paragraph_len"]):
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
index = (
j
if tokenizer.padding_side == "left"
else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
)
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
for span in spans:
# Identify the position of the CLS token
cls_index = span["input_ids"].index(tokenizer.cls_token_id)
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# Original TF implem also keep the classification token (set to 0) (not sure why...)
p_mask = np.array(span["token_type_ids"])
p_mask = np.minimum(p_mask, 1)
if tokenizer.padding_side == "right":
# Limit positive values to one
p_mask = 1 - p_mask
p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 1
# Set the CLS index to '0'
p_mask[cls_index] = 0
span_is_impossible = example.is_impossible
start_position = 0
end_position = 0
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = span["start"]
doc_end = span["start"] + span["length"] - 1
out_of_span = False
if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = cls_index
end_position = cls_index
span_is_impossible = True
else:
if tokenizer.padding_side == "left":
doc_offset = 0
else:
doc_offset = len(truncated_query) + sequence_added_tokens
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
features.append(
SquadFeatures(
span["input_ids"],
span["attention_mask"],
span["token_type_ids"],
cls_index,
p_mask.tolist(),
example_index=0, # Can not set unique_id and example_index here. They will be set after multiple processing.
unique_id=0,
paragraph_len=span["paragraph_len"],
token_is_max_context=span["token_is_max_context"],
tokens=span["tokens"],
token_to_orig_map=span["token_to_orig_map"],
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
)
)
return features
def squad_convert_example_to_features_init(tokenizer_for_convert):
global tokenizer
tokenizer = tokenizer_for_convert
def squad_convert_examples_to_features(
examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, return_dataset=False, threads=1
):
"""
Converts a list of examples into a list of features that can be directly given as input to a model.
It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args:
examples: list of :class:`~transformers.data.processors.squad.SquadExample`
tokenizer: an instance of a child of :class:`~transformers.PreTrainedTokenizer`
max_seq_length: The maximum sequence length of the inputs.
doc_stride: The stride used when the context is too large and is split across several features.
max_query_length: The maximum length of the query.
is_training: whether to create features for model evaluation or model training.
return_dataset: Default False. Either 'pt' or 'tf'.
if 'pt': returns a torch.data.TensorDataset,
if 'tf': returns a tf.data.Dataset
threads: multiple processing threadsa-smi
Returns:
list of :class:`~transformers.data.processors.squad.SquadFeatures`
Example::
processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
)
"""
# Defining helper methods
features = []
threads = min(threads, cpu_count())
with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
annotate_ = partial(
squad_convert_example_to_features,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=is_training,
)
features = list(
tqdm(
p.imap(annotate_, examples, chunksize=32),
total=len(examples),
desc="convert squad examples to features",
mininterval=5,
disable=hvd.rank() not in [-1, 0]
)
)
new_features = []
unique_id = 1000000000
example_index = 0
for example_features in tqdm(features, total=len(features), desc="add example index and unique id",
mininterval=5, disable=hvd.rank() not in [-1, 0]):
if not example_features:
continue
for example_feature in example_features:
example_feature.example_index = example_index
example_feature.unique_id = unique_id
new_features.append(example_feature)
unique_id += 1
example_index += 1
features = new_features
del new_features
if return_dataset == "pt":
if not is_torch_available():
raise RuntimeError("PyTorch must be installed to return a PyTorch dataset.")
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_masks = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
all_is_impossible = torch.tensor([f.is_impossible for f in features], dtype=torch.float)
if not is_training:
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(
all_input_ids, all_attention_masks, all_token_type_ids, all_example_index, all_cls_index, all_p_mask
)
else:
all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
dataset = TensorDataset(
all_input_ids,
all_attention_masks,
all_token_type_ids,
all_start_positions,
all_end_positions,
all_cls_index,
all_p_mask,
all_is_impossible,
)
return features, dataset
elif return_dataset == "tf":
if not is_tf_available():
raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
def gen():
for ex in features:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
{
"start_position": ex.start_position,
"end_position": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
return tf.data.Dataset.from_generator(
gen,
(
{"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32},
{
"start_position": tf.int64,
"end_position": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
),
(
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
},
{
"start_position": tf.TensorShape([]),
"end_position": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
),
)
return features
class DataProcessor(object): # TODO can be removed
"""Base class for data converters for sequence classification data sets."""
def get_example_from_tensor_dict(self, tensor_dict):
"""Gets an example from a dict with tensorflow tensors
Args:
tensor_dict: Keys and values should match the corresponding Glue
tensorflow_dataset examples.
"""
raise NotImplementedError()
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
def tfds_map(self, example):
"""Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are.
This method converts examples to the correct format."""
if len(self.get_labels()) > 1:
example.label = self.get_labels()[int(example.label)]
return example
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8-sig") as f:
return list(csv.reader(f, delimiter="\t", quotechar=quotechar))
class SquadProcessor(DataProcessor):
"""
Processor for the SQuAD data set.
Overriden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and version 2.0 of SQuAD, respectively.
"""
train_file = None
dev_file = None
def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False):
if not evaluate:
answer = tensor_dict["answers"]["text"][0].numpy().decode("utf-8")
answer_start = tensor_dict["answers"]["answer_start"][0].numpy()
answers = []
else:
answers = [
{"answer_start": start.numpy(), "text": text.numpy().decode("utf-8")}
for start, text in zip(tensor_dict["answers"]["answer_start"], tensor_dict["answers"]["text"])
]
answer = None
answer_start = None
return SquadExample(
qas_id=tensor_dict["id"].numpy().decode("utf-8"),
question_text=tensor_dict["question"].numpy().decode("utf-8"),
context_text=tensor_dict["context"].numpy().decode("utf-8"),
answer_text=answer,
start_position_character=answer_start,
title=tensor_dict["title"].numpy().decode("utf-8"),
answers=answers,
)
def get_examples_from_dataset(self, dataset, evaluate=False):
"""
Creates a list of :class:`~transformers.data.processors.squad.SquadExample` using a TFDS dataset.
Args:
dataset: The tfds dataset loaded from `tensorflow_datasets.load("squad")`
evaluate: boolean specifying if in evaluation mode or in training mode
Returns:
List of SquadExample
Examples::
import tensorflow_datasets as tfds
dataset = tfds.load("squad")
training_examples = get_examples_from_dataset(dataset, evaluate=False)
evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
"""
if evaluate:
dataset = dataset["validation"]
else:
dataset = dataset["train"]
examples = []
for tensor_dict in tqdm(dataset, mininterval=5, disable=hvd.rank() not in [-1, 0]):
examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate))
return examples
def get_train_examples(self, data_dir, filename=None):
"""
Returns the training examples from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the training file has a different name than the original one
which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
"""
if data_dir is None:
data_dir = ""
if self.train_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "train")
def get_dev_examples(self, data_dir, filename=None):
"""
Returns the evaluation example from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the evaluation file has a different name than the original one
which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
"""
if data_dir is None:
data_dir = ""
if self.dev_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "dev")
def _create_examples(self, input_data, set_type):
is_training = set_type == "train"
examples = []
for entry in tqdm(input_data, mininterval=5, disable=hvd.rank() not in [-1, 0]):
title = entry["title"]
for paragraph in entry["paragraphs"]:
context_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position_character = None
answer_text = None
answers = []
if "is_impossible" in qa:
is_impossible = qa["is_impossible"]
else:
is_impossible = False
if not is_impossible:
if is_training:
answer = qa["answers"][0]
answer_text = answer["text"]
start_position_character = answer["answer_start"]
else:
answers = qa["answers"]
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
context_text=context_text,
answer_text=answer_text,
start_position_character=start_position_character,
title=title,
is_impossible=is_impossible,
answers=answers,
)
examples.append(example)
return examples
class SquadV1Processor(SquadProcessor):
train_file = "train-v1.1.json"
dev_file = "dev-v1.1.json"
class SquadV2Processor(SquadProcessor):
train_file = "train-v2.0.json"
dev_file = "dev-v2.0.json"
class SquadExample(object):
"""
A single training/test example for the Squad dataset, as loaded from disk.
Args:
qas_id: The example's unique identifier
question_text: The question string
context_text: The context string
answer_text: The answer string
start_position_character: The character position of the start of the answer
title: The title of the example
answers: None by default, this is used during evaluation. Holds answers as well as their start positions.
is_impossible: False by default, set to True if the example has no possible answer.
"""
def __init__(
self,
qas_id,
question_text,
context_text,
answer_text,
start_position_character,
title,
answers=[],
is_impossible=False,
):
self.qas_id = qas_id
self.question_text = question_text
self.context_text = context_text
self.answer_text = answer_text
self.title = title
self.is_impossible = is_impossible
self.answers = answers
self.start_position, self.end_position = 0, 0
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
# Split on whitespace so that different tokens may be attributed to their original position.
for c in self.context_text:
if _is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
self.doc_tokens = doc_tokens
self.char_to_word_offset = char_to_word_offset
# Start and end positions only has a value during evaluation.
if start_position_character is not None and not is_impossible:
self.start_position = char_to_word_offset[start_position_character]
self.end_position = char_to_word_offset[
min(start_position_character + len(answer_text) - 1, len(char_to_word_offset) - 1)
]
class SquadFeatures(object):
"""
Single squad example features to be fed to a model.
Those features are model-specific and can be crafted from :class:`~transformers.data.processors.squad.SquadExample`
using the :method:`~transformers.data.processors.squad.squad_convert_examples_to_features` method.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
token_type_ids: Segment token indices to indicate first and second portions of the inputs.
cls_index: the index of the CLS token.
p_mask: Mask identifying tokens that can be answers vs. tokens that cannot.
Mask with 1 for tokens than cannot be in the answer and 0 for token that can be in an answer
example_index: the index of the example
unique_id: The unique Feature identifier
paragraph_len: The length of the context
token_is_max_context: List of booleans identifying which tokens have their maximum context in this feature object.
If a token does not have their maximum context in this feature object, it means that another feature object
has more information related to that token and should be prioritized over this feature for that token.
tokens: list of tokens corresponding to the input ids
token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
start_position: start of the answer token index
end_position: end of the answer token index
"""
def __init__(
self,
input_ids,
attention_mask,
token_type_ids,
cls_index,
p_mask,
example_index,
unique_id,
paragraph_len,
token_is_max_context,
tokens,
token_to_orig_map,
start_position,
end_position,
is_impossible,
):
self.input_ids = input_ids
self.attention_mask = attention_mask
self.token_type_ids = token_type_ids
self.cls_index = cls_index
self.p_mask = p_mask
self.example_index = example_index
self.unique_id = unique_id
self.paragraph_len = paragraph_len
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
class SquadResult(object):
"""
Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
Args:
unique_id: The unique identifier corresponding to that example.
start_logits: The logits corresponding to the start of the answer
end_logits: The logits corresponding to the end of the answer
"""
def __init__(self, unique_id, start_logits, end_logits, start_top_index=None, end_top_index=None, cls_logits=None):
self.start_logits = start_logits
self.end_logits = end_logits
self.unique_id = unique_id
if start_top_index:
self.start_top_index = start_top_index
self.end_top_index = end_top_index
self.cls_logits = cls_logits
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
def get_answers(examples, features, results, args):
predictions = collections.defaultdict(list) # it is possible that one example corresponds to multiple features
_Prediction = collections.namedtuple('_Prediction', ['text', 'start_logit', 'end_logit'])
if args.version_2_with_negative:
null_vals = collections.defaultdict(lambda: (float("inf"), 0, 0))
for ex, feat, result in match_results(examples, features, results):
if not args.joint_head:
start_indices = _get_best_indices(result.start_logits, args.n_best_size)
end_indices = _get_best_indices(result.end_logits, args.n_best_size)
prelim_predictions = get_valid_prelim_predictions(start_indices, end_indices, feat, result, args)
feature_null_score = result.start_logits[0] + result.end_logits[0]
else:
prelim_predictions = get_valid_prelim_predictions_joint_head(result.start_top_index, result.end_top_index,
feat, result, args)
# start_indices = result.start_top_index
# end_indices = result.end_top_index
feature_null_score = result.cls_logits
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
if args.version_2_with_negative and feature_null_score < null_vals[ex.qas_id][0]:
null_vals[ex.qas_id] = (feature_null_score, result.start_logits[0], result.end_logits[0])
curr_predictions = []
seen_predictions = set()
for pred in prelim_predictions:
if len(curr_predictions) == args.n_best_size:
break
if pred.start_index > 0: # this is a non-null prediction TODO: this probably is irrelevant
final_text = get_answer_text(ex, feat, pred, args)
else:
final_text = ''
if final_text in seen_predictions:
continue
seen_predictions.add(final_text)
curr_predictions.append(_Prediction(final_text, pred.start_logit, pred.end_logit))
predictions[ex.qas_id] += curr_predictions
# Add empty prediction
if args.version_2_with_negative:
for qas_id in predictions.keys():
predictions[qas_id].append(_Prediction('',
null_vals[qas_id][1],
null_vals[qas_id][2]))
nbest_answers = collections.defaultdict(list)
answers = {}
for qas_id, preds in predictions.items():
# nbest = sorted(
# preds,
# key=lambda x: (x.start_logit + x.end_logit),
# reverse=True)[:args.n_best_size]
seen_predictions = set()
nbest = []
for pred in sorted(predictions[qas_id], key=lambda x: (x.start_logit + x.end_logit), reverse=True):
if len(nbest) >= args.n_best_size:
break
if pred.text in seen_predictions:
continue
seen_predictions.add(pred.text)
nbest.append(pred)
# In very rare edge cases we could only have single null prediction.
# So we just create a nonce prediction in this case to avoid failure.
if not nbest or (args.version_2_with_negative and len(nbest) == 1):
nbest.append(_Prediction(text="empty", start_logit=0.0, end_logit=0.0))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry and entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_answers[qas_id].append(output)
if args.version_2_with_negative:
if not args.joint_head:
score_diff = null_vals[qas_id][0] - best_non_null_entry.start_logit - best_non_null_entry.end_logit
else:
score_diff = null_vals[qas_id][0]
if score_diff > args.null_score_diff_threshold:
answers[qas_id] = ""
else:
answers[qas_id] = best_non_null_entry.text
else:
answers[qas_id] = nbest_answers[qas_id][0]['text']
return answers, nbest_answers
def get_answer_text(example, feature, pred, args):
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, args.do_lower_case, args.verbose_logging)
return final_text
def get_valid_prelim_predictions_joint_head(start_indices, end_indices, feature, result, args):
_PrelimPrediction = collections.namedtuple(
"PrelimPrediction",
["start_index", "end_index", "start_logit", "end_logit"])
prelim_predictions = []
# for start_index in start_indices:
for i in range(args.beam_size):
start_index = start_indices[i]
for j in range(args.beam_size):
# for end_index in end_indices:
end_index = end_indices[i * args.beam_size + j]
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > args.max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[i], # start_index],
end_logit=result.end_logits[i * args.beam_size + j])) # end_index]))
return prelim_predictions
def get_valid_prelim_predictions(start_indices, end_indices, feature, result, args):
_PrelimPrediction = collections.namedtuple(
"PrelimPrediction",
["start_index", "end_index", "start_logit", "end_logit"])
prelim_predictions = []
for start_index in start_indices:
for end_index in end_indices:
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > args.max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
return prelim_predictions
def match_results(examples, features, results):
unique_f_ids = set([f.unique_id for f in features])
unique_r_ids = set([r.unique_id for r in results])
matching_ids = unique_f_ids & unique_r_ids
features = [f for f in features if f.unique_id in matching_ids]
results = [r for r in results if r.unique_id in matching_ids]
features.sort(key=lambda x: x.unique_id)
results.sort(key=lambda x: x.unique_id)
for f, r in zip(features, results): # original code assumes strict ordering of examples. TODO: rewrite this
yield examples[f.example_index], f, r
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info(
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def _get_best_indices(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indices = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indices.append(index_and_score[i][0])
return best_indices
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/squad_utils.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bz2
import os
import urllib.request
import sys
class SquadDownloader:
def __init__(self, save_path):
self.save_path = save_path + '/squad'
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
if not os.path.exists(self.save_path + '/v1.1'):
os.makedirs(self.save_path + '/v1.1')
if not os.path.exists(self.save_path + '/v2.0'):
os.makedirs(self.save_path + '/v2.0')
self.download_urls = {
'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json' : 'v1.1/train-v1.1.json',
'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json' : 'v1.1/dev-v1.1.json',
'https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/' : 'v1.1/evaluate-v1.1.py',
'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json' : 'v2.0/train-v2.0.json',
'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json' : 'v2.0/dev-v2.0.json',
'https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/' : 'v2.0/evaluate-v2.0.py',
}
def download(self):
for item in self.download_urls:
url = item
file = self.download_urls[item]
print('Downloading:', url)
if os.path.isfile(self.save_path + '/' + file):
print('** Download file already exists, skipping download')
else:
response = urllib.request.urlopen(url)
with open(self.save_path + '/' + file, "wb") as handle:
handle.write(response.read())
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/SquadDownloader.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import BookscorpusTextFormatting
import Downloader
import TextSharding
import WikicorpusTextFormatting
import argparse
import itertools
import multiprocessing
import os
import pprint
import subprocess
def main(args):
working_dir = os.environ['DATA_PREP_WORKING_DIR']
print('Working Directory:', working_dir)
print('Action:', args.action)
print('Dataset Name:', args.dataset)
if args.input_files:
args.input_files = args.input_files.split(',')
hdf5_tfrecord_folder_prefix = "_lower_case_" + str(args.do_lower_case) + "_seq_len_" + str(args.max_seq_length) \
+ "_random_seed_" + str(args.random_seed)
directory_structure = {
'download' : working_dir + '/download', # Downloaded and decompressed
'extracted' : working_dir +'/extracted', # Extracted from whatever the initial format is (e.g., wikiextractor)
'formatted' : working_dir + '/formatted_one_article_per_line', # This is the level where all sources should look the same
'sharded' : working_dir + '/sharded_' + "training_shards_" + str(args.n_training_shards) + "_test_shards_" + str(args.n_test_shards) + "_fraction_" + str(args.fraction_test_set),
'tfrecord' : working_dir + '/tfrecord'+ hdf5_tfrecord_folder_prefix,
'hdf5': working_dir + '/hdf5' + hdf5_tfrecord_folder_prefix
}
print('\nDirectory Structure:')
pp = pprint.PrettyPrinter(indent=2)
pp.pprint(directory_structure)
print('')
if args.action == 'download':
if not os.path.exists(directory_structure['download']):
os.makedirs(directory_structure['download'])
downloader = Downloader.Downloader(args.dataset, directory_structure['download'])
downloader.download()
elif args.action == 'text_formatting':
assert args.dataset != 'google_pretrained_weights' and args.dataset != 'nvidia_pretrained_weights' and args.dataset != 'squad' and args.dataset != 'mrpc', 'Cannot perform text_formatting on pretrained weights'
if not os.path.exists(directory_structure['extracted']):
os.makedirs(directory_structure['extracted'])
if not os.path.exists(directory_structure['formatted']):
os.makedirs(directory_structure['formatted'])
if args.dataset == 'bookscorpus':
books_path = directory_structure['download'] + '/bookscorpus'
#books_path = directory_structure['download']
output_filename = directory_structure['formatted'] + '/bookscorpus_one_book_per_line.txt'
books_formatter = BookscorpusTextFormatting.BookscorpusTextFormatting(books_path, output_filename, recursive=True)
books_formatter.merge()
elif args.dataset == 'wikicorpus_en':
if args.skip_wikiextractor == 0:
path_to_wikiextractor_in_container = '/workspace/wikiextractor/WikiExtractor.py'
wikiextractor_command = path_to_wikiextractor_in_container + ' ' + directory_structure['download'] + '/' + args.dataset + '/wikicorpus_en.xml ' + '-b 100M --processes ' + str(args.n_processes) + ' -o ' + directory_structure['extracted'] + '/' + args.dataset
print('WikiExtractor Command:', wikiextractor_command)
wikiextractor_process = subprocess.run(wikiextractor_command, shell=True, check=True)
#wikiextractor_process.communicate()
wiki_path = directory_structure['extracted'] + '/wikicorpus_en'
output_filename = directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt'
wiki_formatter = WikicorpusTextFormatting.WikicorpusTextFormatting(wiki_path, output_filename, recursive=True)
wiki_formatter.merge()
elif args.dataset == 'wikicorpus_zh':
raise NotImplementedError(
'wikicorpus_zh not fully supported at this time. The simplified/tradition Chinese data needs to be '
'translated and properly segmented still, and should work once this step is added.')
# if args.skip_wikiextractor == 0:
# path_to_wikiextractor_in_container = '/workspace/wikiextractor/WikiExtractor.py'
# wikiextractor_command = path_to_wikiextractor_in_container + ' ' + directory_structure['download'] + '/' + args.dataset + '/wikicorpus_zh.xml ' + '-b 100M --processes ' + str(args.n_processes) + ' -o ' + directory_structure['extracted'] + '/' + args.dataset
# print('WikiExtractor Command:', wikiextractor_command)
# wikiextractor_process = subprocess.run(wikiextractor_command, shell=True, check=True)
# #wikiextractor_process.communicate()
#
# wiki_path = directory_structure['extracted'] + '/wikicorpus_zh'
# output_filename = directory_structure['formatted'] + '/wikicorpus_zh_one_article_per_line.txt'
# wiki_formatter = WikicorpusTextFormatting.WikicorpusTextFormatting(wiki_path, output_filename, recursive=True)
# wiki_formatter.merge()
#
# assert os.stat(output_filename).st_size > 0, 'File glob did not pick up extracted wiki files from WikiExtractor.'
elif args.action == 'sharding':
# Note: books+wiki requires user to provide list of input_files (comma-separated with no spaces)
if args.dataset == 'bookscorpus' or 'wikicorpus' in args.dataset or 'books_wiki' in args.dataset:
if args.input_files is None:
if args.dataset == 'bookscorpus':
args.input_files = [directory_structure['formatted'] + '/bookscorpus_one_book_per_line.txt']
elif args.dataset == 'wikicorpus_en':
args.input_files = [directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt']
elif args.dataset == 'wikicorpus_zh':
args.input_files = [directory_structure['formatted'] + '/wikicorpus_zh_one_article_per_line.txt']
elif args.dataset == 'books_wiki_en_corpus':
args.input_files = [directory_structure['formatted'] + '/bookscorpus_one_book_per_line.txt', directory_structure['formatted'] + '/wikicorpus_en_one_article_per_line.txt']
output_file_prefix = directory_structure['sharded'] + '/' + args.dataset + '/' + args.dataset
if not os.path.exists(directory_structure['sharded']):
os.makedirs(directory_structure['sharded'])
if not os.path.exists(directory_structure['sharded'] + '/' + args.dataset):
os.makedirs(directory_structure['sharded'] + '/' + args.dataset)
# Segmentation is here because all datasets look the same in one article/book/whatever per line format, and
# it seemed unnecessarily complicated to add an additional preprocessing step to call just for this.
# Different languages (e.g., Chinese simplified/traditional) may require translation and
# other packages to be called from here -- just add a conditional branch for those extra steps
segmenter = TextSharding.NLTKSegmenter()
sharding = TextSharding.Sharding(args.input_files, output_file_prefix, args.n_training_shards, args.n_test_shards, args.fraction_test_set)
sharding.load_articles()
sharding.segment_articles_into_sentences(segmenter)
sharding.distribute_articles_over_shards()
sharding.write_shards_to_disk()
for _dir in ['train', 'test']:
if not os.path.exists(directory_structure['sharded'] + '/' + args.dataset + '/' + _dir):
os.makedirs(directory_structure['sharded'] + '/' + args.dataset + '/' + _dir)
absolute_dir = directory_structure['sharded'] + '/' + args.dataset
command = 'mv ' + absolute_dir + '/*' + _dir + '*.txt' + ' ' + absolute_dir + '/' + _dir
mv_process = subprocess.Popen(command, shell=True)
mv_process.wait()
else:
assert False, 'Unsupported dataset for sharding'
elif args.action == 'create_tfrecord_files':
if not os.path.exists(directory_structure['tfrecord'] + "/" + args.dataset):
os.makedirs(directory_structure['tfrecord'] + "/" + args.dataset)
if args.vocab_file is None:
args.vocab_file = os.path.join(working_dir, "vocab.txt")
for _dir in ['train', 'test']:
electra_preprocessing_command = 'python /workspace/electra/build_pretraining_dataset.py'
electra_preprocessing_command += ' --corpus-dir=' + directory_structure['sharded'] + '/' + args.dataset + '/' + _dir
electra_preprocessing_command += ' --output-dir=' + directory_structure['tfrecord'] + '/' + args.dataset + '/' + _dir
electra_preprocessing_command += ' --vocab-file=' + args.vocab_file
electra_preprocessing_command += ' --do-lower-case' if args.do_lower_case else ' --no-lower-case'
electra_preprocessing_command += ' --max-seq-length=' + str(args.max_seq_length)
electra_preprocessing_command += ' --num-processes=8'
electra_preprocessing_command += ' --num-out-files=' + str(args.n_training_shards) if _dir == 'train' \
else ' --num-out-files=' + str(args.n_test_shards)
electra_preprocessing_process = subprocess.Popen(electra_preprocessing_command, shell=True)
electra_preprocessing_process.wait()
elif args.action == 'create_hdf5_files':
raise NotImplementedError
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Preprocessing Application for Everything BERT-related'
)
parser.add_argument(
'--action',
type=str,
help='Specify the action you want the app to take. e.g., generate vocab, segment, create tfrecords',
choices={
'download', # Download and verify mdf5/sha sums
'text_formatting', # Convert into a file that contains one article/book per line
'sharding', # Convert previous formatted text into shards containing one sentence per line
'create_tfrecord_files', # Turn each shard into a TFrecord with masking and next sentence prediction info
'create_hdf5_files' # Turn each shard into a HDF5 file with masking and next sentence prediction info
}
)
parser.add_argument(
'--dataset',
type=str,
help='Specify the dataset to perform --action on',
choices={
'bookscorpus',
'wikicorpus_en',
'wikicorpus_zh',
'books_wiki_en_corpus',
'google_pretrained_weights',
'nvidia_pretrained_weights',
'mrpc',
'squad',
'all'
}
)
parser.add_argument(
'--input_files',
type=str,
help='Specify the input files in a comma-separated list (no spaces)'
)
parser.add_argument(
'--n_training_shards',
type=int,
help='Specify the number of training shards to generate',
default=2048
)
parser.add_argument(
'--n_test_shards',
type=int,
help='Specify the number of test shards to generate',
default=2048
)
parser.add_argument(
'--fraction_test_set',
type=float,
help='Specify the fraction (0..1) of the data to withhold for the test data split (based on number of sequences)',
default=0.1
)
parser.add_argument(
'--segmentation_method',
type=str,
help='Specify your choice of sentence segmentation',
choices={
'nltk'
},
default='nltk'
)
parser.add_argument(
'--n_processes',
type=int,
help='Specify the max number of processes to allow at one time',
default=4
)
parser.add_argument(
'--random_seed',
type=int,
help='Specify the base seed to use for any random number generation',
default=12345
)
parser.add_argument(
'--dupe_factor',
type=int,
help='Specify the duplication factor',
default=5
)
parser.add_argument(
'--masked_lm_prob',
type=float,
help='Specify the probability for masked lm',
default=0.15
)
parser.add_argument(
'--max_seq_length',
type=int,
help='Specify the maximum sequence length',
default=512
)
parser.add_argument(
'--do_lower_case',
type=int,
help='Specify whether it is cased (0) or uncased (1) (any number greater than 0 will be treated as uncased)',
default=0
)
parser.add_argument(
'--vocab_file',
type=str,
help='Specify absolute path to vocab file to use)'
)
parser.add_argument(
'--skip_wikiextractor',
type=int,
help='Specify whether to skip wikiextractor step 0=False, 1=True',
default=0
)
parser.add_argument(
'--interactive_json_config_generator',
type=str,
help='Specify the action you want the app to take. e.g., generate vocab, segment, create tfrecords'
)
args = parser.parse_args()
main(args)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/dataPrep.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from GooglePretrainedWeightDownloader import GooglePretrainedWeightDownloader
from NVIDIAPretrainedWeightDownloader import NVIDIAPretrainedWeightDownloader
from WikiDownloader import WikiDownloader
from BooksDownloader import BooksDownloader
from MRPCDownloader import MRPCDownloader
from SquadDownloader import SquadDownloader
class Downloader:
def __init__(self, dataset_name, save_path):
self.dataset_name = dataset_name
self.save_path = save_path
def download(self):
if self.dataset_name == 'bookscorpus':
self.download_bookscorpus()
elif self.dataset_name == 'wikicorpus_en':
self.download_wikicorpus('en')
elif self.dataset_name == 'wikicorpus_zh':
self.download_wikicorpus('zh')
elif self.dataset_name == 'google_pretrained_weights':
self.download_google_pretrained_weights()
elif self.dataset_name == 'nvidia_pretrained_weights':
self.download_nvidia_pretrained_weights()
elif self.dataset_name == 'mrpc':
self.download_mrpc()
elif self.dataset_name == 'squad':
self.download_squad()
elif self.dataset_name == 'all':
self.download_bookscorpus(self.save_path)
self.download_wikicorpus('en', self.save_path)
self.download_wikicorpus('zh', self.save_path)
self.download_google_pretrained_weights(self.save_path)
self.download_nvidia_pretrained_weights(self.save_path)
self.download_mrpc(self.save_path)
self.download_squad(self.save_path)
else:
print(self.dataset_name)
assert False, 'Unknown dataset_name provided to downloader'
def download_bookscorpus(self):
downloader = BooksDownloader(self.save_path)
downloader.download()
def download_wikicorpus(self, language):
downloader = WikiDownloader(language, self.save_path)
downloader.download()
def download_google_pretrained_weights(self):
downloader = GooglePretrainedWeightDownloader(self.save_path)
downloader.download()
def download_nvidia_pretrained_weights(self):
downloader = NVIDIAPretrainedWeightDownloader(self.save_path)
downloader.download()
def download_mrpc(self):
downloader = MRPCDownloader(self.save_path)
downloader.download()
def download_squad(self):
downloader = SquadDownloader(self.save_path)
downloader.download()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/Downloader.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import os
class BookscorpusTextFormatting:
def __init__(self, books_path, output_filename, recursive = False):
self.books_path = books_path
self.recursive = recursive
self.output_filename = output_filename
# This puts one book per line
def merge(self):
with open(self.output_filename, mode='w', newline='\n') as ofile:
for filename in glob.glob(self.books_path + '/' + '*.txt', recursive=True):
with open(filename, mode='r', encoding='utf-8-sig', newline='\n') as file:
for line in file:
if line.strip() != '':
ofile.write(line.strip() + ' ')
ofile.write("\n\n") | DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/BookscorpusTextFormatting.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
class NVIDIAPretrainedWeightDownloader:
def __init__(self, save_path):
self.save_path = save_path + '/nvidia_pretrained_weights'
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
pass
def download(self):
assert False, 'NVIDIAPretrainedWeightDownloader not implemented yet.' | DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/NVIDIAPretrainedWeightDownloader.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/__init__.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bz2
import os
import urllib.request
import sys
class MRPCDownloader:
def __init__(self, save_path):
self.save_path = save_path + '/mrpc'
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
# Documentation - Download link obtained from here: https://github.com/nyu-mll/GLUE-baselines/blob/master/download_glue_data.py
self.download_urls = {
'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc' : 'mrpc_dev_ids.tsv'
}
def download(self):
for item in self.download_urls:
url = item
file = self.download_urls[item]
print('Downloading:', url)
if os.path.isfile(self.save_path + '/' + file):
print('** Download file already exists, skipping download')
else:
response = urllib.request.urlopen(url)
with open(self.save_path + '/' + file, "wb") as handle:
handle.write(response.read())
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/MRPCDownloader.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bz2
import os
import urllib.request
import subprocess
import sys
class WikiDownloader:
def __init__(self, language, save_path):
self.save_path = save_path + '/wikicorpus_' + language
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
self.language = language
self.download_urls = {
'en' : 'https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2',
'zh' : 'https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2'
}
self.output_files = {
'en' : 'wikicorpus_en.xml.bz2',
'zh' : 'wikicorpus_zh.xml.bz2'
}
def download(self):
if self.language in self.download_urls:
url = self.download_urls[self.language]
filename = self.output_files[self.language]
print('Downloading:', url)
if os.path.isfile(self.save_path + '/' + filename):
print('** Download file already exists, skipping download')
else:
response = urllib.request.urlopen(url)
with open(self.save_path + '/' + filename, "wb") as handle:
handle.write(response.read())
# Always unzipping since this is relatively fast and will overwrite
print('Unzipping:', self.output_files[self.language])
subprocess.run('bzip2 -dk ' + self.save_path + '/' + filename, shell=True, check=True)
else:
assert False, 'WikiDownloader not implemented for this language yet.' | DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/WikiDownloader.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import os
class WikicorpusTextFormatting:
def __init__(self, wiki_path, output_filename, recursive = False):
self.wiki_path = wiki_path
self.recursive = recursive
self.output_filename = output_filename
# This puts one article per line
def merge(self):
with open(self.output_filename, mode='w', newline='\n') as ofile:
for dirname in glob.glob(self.wiki_path + '/*/', recursive=False):
for filename in glob.glob(dirname + 'wiki_*', recursive=self.recursive):
print(filename)
article_lines = []
article_open = False
with open(filename, mode='r', newline='\n') as file:
for line in file:
if '<doc id=' in line:
article_open = True
elif '</doc>' in line:
article_open = False
for oline in article_lines[1:]:
if oline != '\n':
ofile.write(oline.rstrip() + " ")
ofile.write("\n\n")
article_lines = []
else:
if article_open:
article_lines.append(line) | DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/WikicorpusTextFormatting.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
class BooksDownloader:
def __init__(self, save_path):
self.save_path = save_path
pass
def download(self):
bookscorpus_download_command = 'python3 /workspace/bookcorpus/download_files.py --list /workspace/bookcorpus/url_list.jsonl --out'
bookscorpus_download_command += ' ' + self.save_path + '/bookscorpus'
bookscorpus_download_command += ' --trash-bad-count'
bookscorpus_download_process = subprocess.run(bookscorpus_download_command, shell=True, check=True)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/BooksDownloader.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from itertools import islice
import multiprocessing
import statistics
class Sharding:
def __init__(self, input_files, output_name_prefix, n_training_shards, n_test_shards, fraction_test_set):
assert len(input_files) > 0, 'The input file list must contain at least one file.'
assert n_training_shards > 0, 'There must be at least one output shard.'
assert n_test_shards > 0, 'There must be at least one output shard.'
self.n_training_shards = n_training_shards
self.n_test_shards = n_test_shards
self.fraction_test_set = fraction_test_set
self.input_files = input_files
self.output_name_prefix = output_name_prefix
self.output_training_identifier = '_training'
self.output_test_identifier = '_test'
self.output_file_extension = '.txt'
self.articles = {} # key: integer identifier, value: list of articles
self.sentences = {} # key: integer identifier, value: list of sentences
self.output_training_files = {} # key: filename, value: list of articles to go into file
self.output_test_files = {} # key: filename, value: list of articles to go into file
self.init_output_files()
# Remember, the input files contain one article per line (the whitespace check is to skip extraneous blank lines)
def load_articles(self):
print('Start: Loading Articles')
global_article_count = 0
for input_file in self.input_files:
print('input file:', input_file)
with open(input_file, mode='r', newline='\n') as f:
for i, line in enumerate(f):
if line.strip():
self.articles[global_article_count] = line.rstrip()
global_article_count += 1
print('End: Loading Articles: There are', len(self.articles), 'articles.')
def segment_articles_into_sentences(self, segmenter):
print('Start: Sentence Segmentation')
if len(self.articles) is 0:
self.load_articles()
assert len(self.articles) is not 0, 'Please check that input files are present and contain data.'
# TODO: WIP: multiprocessing (create independent ranges and spawn processes)
use_multiprocessing = 'serial'
def chunks(data, size=len(self.articles)):
it = iter(data)
for i in range(0, len(data), size):
yield {k: data[k] for k in islice(it, size)}
if use_multiprocessing == 'manager':
manager = multiprocessing.Manager()
return_dict = manager.dict()
jobs = []
n_processes = 7 # in addition to the main process, total = n_proc+1
def work(articles, return_dict):
sentences = {}
for i, article in enumerate(articles):
sentences[i] = segmenter.segment_string(articles[article])
if i % 5000 == 0:
print('Segmenting article', i)
return_dict.update(sentences)
for item in chunks(self.articles, len(self.articles)):
p = multiprocessing.Process(target=work, args=(item, return_dict))
# Busy wait
while len(jobs) >= n_processes:
pass
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
elif use_multiprocessing == 'queue':
work_queue = multiprocessing.Queue()
jobs = []
for item in chunks(self.articles, len(self.articles)):
pass
else: # serial option
for i, article in enumerate(self.articles):
self.sentences[i] = segmenter.segment_string(self.articles[article])
if i % 5000 == 0:
print('Segmenting article', i)
print('End: Sentence Segmentation')
def init_output_files(self):
print('Start: Init Output Files')
assert len(self.output_training_files) is 0, 'Internal storage self.output_files already contains data. This function is intended to be used by the constructor only.'
assert len(self.output_test_files) is 0, 'Internal storage self.output_files already contains data. This function is intended to be used by the constructor only.'
for i in range(self.n_training_shards):
name = self.output_name_prefix + self.output_training_identifier + '_' + str(i) + self.output_file_extension
self.output_training_files[name] = []
for i in range(self.n_test_shards):
name = self.output_name_prefix + self.output_test_identifier + '_' + str(i) + self.output_file_extension
self.output_test_files[name] = []
print('End: Init Output Files')
def get_sentences_per_shard(self, shard):
result = 0
for article_id in shard:
result += len(self.sentences[article_id])
return result
def distribute_articles_over_shards(self):
print('Start: Distribute Articles Over Shards')
assert len(self.articles) >= self.n_training_shards + self.n_test_shards, 'There are fewer articles than shards. Please add more data or reduce the number of shards requested.'
# Create dictionary with - key: sentence count per article, value: article id number
sentence_counts = defaultdict(lambda: [])
max_sentences = 0
total_sentences = 0
for article_id in self.sentences:
current_length = len(self.sentences[article_id])
sentence_counts[current_length].append(article_id)
max_sentences = max(max_sentences, current_length)
total_sentences += current_length
n_sentences_assigned_to_training = int((1 - self.fraction_test_set) * total_sentences)
nominal_sentences_per_training_shard = n_sentences_assigned_to_training // self.n_training_shards
nominal_sentences_per_test_shard = (total_sentences - n_sentences_assigned_to_training) // self.n_test_shards
consumed_article_set = set({})
unused_article_set = set(self.articles.keys())
# Make first pass and add one article worth of lines per file
for file in self.output_training_files:
current_article_id = sentence_counts[max_sentences][-1]
sentence_counts[max_sentences].pop(-1)
self.output_training_files[file].append(current_article_id)
consumed_article_set.add(current_article_id)
unused_article_set.remove(current_article_id)
# Maintain the max sentence count
while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0:
max_sentences -= 1
if len(self.sentences[current_article_id]) > nominal_sentences_per_training_shard:
nominal_sentences_per_training_shard = len(self.sentences[current_article_id])
print('Warning: A single article contains more than the nominal number of sentences per training shard.')
for file in self.output_test_files:
current_article_id = sentence_counts[max_sentences][-1]
sentence_counts[max_sentences].pop(-1)
self.output_test_files[file].append(current_article_id)
consumed_article_set.add(current_article_id)
unused_article_set.remove(current_article_id)
# Maintain the max sentence count
while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0:
max_sentences -= 1
if len(self.sentences[current_article_id]) > nominal_sentences_per_test_shard:
nominal_sentences_per_test_shard = len(self.sentences[current_article_id])
print('Warning: A single article contains more than the nominal number of sentences per test shard.')
training_counts = []
test_counts = []
for shard in self.output_training_files:
training_counts.append(self.get_sentences_per_shard(self.output_training_files[shard]))
for shard in self.output_test_files:
test_counts.append(self.get_sentences_per_shard(self.output_test_files[shard]))
training_median = statistics.median(training_counts)
test_median = statistics.median(test_counts)
# Make subsequent passes over files to find articles to add without going over limit
history_remaining = []
n_history_remaining = 4
while len(consumed_article_set) < len(self.articles):
for fidx, file in enumerate(self.output_training_files):
nominal_next_article_size = min(nominal_sentences_per_training_shard - training_counts[fidx], max_sentences)
# Maintain the max sentence count
while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0:
max_sentences -= 1
while len(sentence_counts[nominal_next_article_size]) == 0 and nominal_next_article_size > 0:
nominal_next_article_size -= 1
if nominal_next_article_size not in sentence_counts or nominal_next_article_size is 0 or training_counts[fidx] > training_median:
continue # skip adding to this file, will come back later if no file can accept unused articles
current_article_id = sentence_counts[nominal_next_article_size][-1]
sentence_counts[nominal_next_article_size].pop(-1)
self.output_training_files[file].append(current_article_id)
consumed_article_set.add(current_article_id)
unused_article_set.remove(current_article_id)
for fidx, file in enumerate(self.output_test_files):
nominal_next_article_size = min(nominal_sentences_per_test_shard - test_counts[fidx], max_sentences)
# Maintain the max sentence count
while len(sentence_counts[max_sentences]) == 0 and max_sentences > 0:
max_sentences -= 1
while len(sentence_counts[nominal_next_article_size]) == 0 and nominal_next_article_size > 0:
nominal_next_article_size -= 1
if nominal_next_article_size not in sentence_counts or nominal_next_article_size is 0 or test_counts[fidx] > test_median:
continue # skip adding to this file, will come back later if no file can accept unused articles
current_article_id = sentence_counts[nominal_next_article_size][-1]
sentence_counts[nominal_next_article_size].pop(-1)
self.output_test_files[file].append(current_article_id)
consumed_article_set.add(current_article_id)
unused_article_set.remove(current_article_id)
# If unable to place articles a few times, bump up nominal sizes by fraction until articles get placed
if len(history_remaining) == n_history_remaining:
history_remaining.pop(0)
history_remaining.append(len(unused_article_set))
history_same = True
for i in range(1, len(history_remaining)):
history_same = history_same and (history_remaining[i-1] == history_remaining[i])
if history_same:
nominal_sentences_per_training_shard += 1
# nominal_sentences_per_test_shard += 1
training_counts = []
test_counts = []
for shard in self.output_training_files:
training_counts.append(self.get_sentences_per_shard(self.output_training_files[shard]))
for shard in self.output_test_files:
test_counts.append(self.get_sentences_per_shard(self.output_test_files[shard]))
training_median = statistics.median(training_counts)
test_median = statistics.median(test_counts)
print('Distributing data over shards:', len(unused_article_set), 'articles remaining.')
if len(unused_article_set) != 0:
print('Warning: Some articles did not make it into output files.')
for shard in self.output_training_files:
print('Training shard:', self.get_sentences_per_shard(self.output_training_files[shard]))
for shard in self.output_test_files:
print('Test shard:', self.get_sentences_per_shard(self.output_test_files[shard]))
print('End: Distribute Articles Over Shards')
def write_shards_to_disk(self):
print('Start: Write Shards to Disk')
for shard in self.output_training_files:
self.write_single_shard(shard, self.output_training_files[shard])
for shard in self.output_test_files:
self.write_single_shard(shard, self.output_test_files[shard])
print('End: Write Shards to Disk')
def write_single_shard(self, shard_name, shard):
with open(shard_name, mode='w', newline='\n') as f:
for article_id in shard:
for line in self.sentences[article_id]:
f.write(line + '\n')
f.write('\n') # Line break between articles
import nltk
nltk.download('punkt')
class NLTKSegmenter:
def __init(self):
pass
def segment_string(self, article):
return nltk.tokenize.sent_tokenize(article)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/TextSharding.py |
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import hashlib
import os
import urllib.request
import zipfile
class GooglePretrainedWeightDownloader:
def __init__(self, save_path):
self.save_path = save_path + '/google_pretrained_weights'
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
# Download urls
self.model_urls = {
'bert_base_uncased': ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip', 'uncased_L-12_H-768_A-12.zip'),
'bert_large_uncased': ('https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-24_H-1024_A-16.zip', 'uncased_L-24_H-1024_A-16.zip'),
'bert_base_cased': ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip', 'cased_L-12_H-768_A-12.zip'),
'bert_large_cased': ('https://storage.googleapis.com/bert_models/2018_10_18/cased_L-24_H-1024_A-16.zip', 'cased_L-24_H-1024_A-16.zip'),
'bert_base_multilingual_cased': ('https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip', 'multi_cased_L-12_H-768_A-12.zip'),
'bert_large_multilingual_uncased': ('https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip', 'multilingual_L-12_H-768_A-12.zip'),
'bert_base_chinese': ('https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip', 'chinese_L-12_H-768_A-12.zip')
}
# SHA256sum verification for file download integrity (and checking for changes from the download source over time)
self.bert_base_uncased_sha = {
'bert_config.json': '7b4e5f53efbd058c67cda0aacfafb340113ea1b5797d9ce6ee411704ba21fcbc',
'bert_model.ckpt.data-00000-of-00001': '58580dc5e0bf0ae0d2efd51d0e8272b2f808857f0a43a88aaf7549da6d7a8a84',
'bert_model.ckpt.index': '04c1323086e2f1c5b7c0759d8d3e484afbb0ab45f51793daab9f647113a0117b',
'bert_model.ckpt.meta': 'dd5682170a10c3ea0280c2e9b9a45fee894eb62da649bbdea37b38b0ded5f60e',
'vocab.txt': '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3',
}
self.bert_large_uncased_sha = {
'bert_config.json': 'bfa42236d269e2aeb3a6d30412a33d15dbe8ea597e2b01dc9518c63cc6efafcb',
'bert_model.ckpt.data-00000-of-00001': 'bc6b3363e3be458c99ecf64b7f472d2b7c67534fd8f564c0556a678f90f4eea1',
'bert_model.ckpt.index': '68b52f2205ffc64dc627d1120cf399c1ef1cbc35ea5021d1afc889ffe2ce2093',
'bert_model.ckpt.meta': '6fcce8ff7628f229a885a593625e3d5ff9687542d5ef128d9beb1b0c05edc4a1',
'vocab.txt': '07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3',
}
self.bert_base_cased_sha = {
'bert_config.json': 'f11dfb757bea16339a33e1bf327b0aade6e57fd9c29dc6b84f7ddb20682f48bc',
'bert_model.ckpt.data-00000-of-00001': '734d5a1b68bf98d4e9cb6b6692725d00842a1937af73902e51776905d8f760ea',
'bert_model.ckpt.index': '517d6ef5c41fc2ca1f595276d6fccf5521810d57f5a74e32616151557790f7b1',
'bert_model.ckpt.meta': '5f8a9771ff25dadd61582abb4e3a748215a10a6b55947cbb66d0f0ba1694be98',
'vocab.txt': 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02',
}
self.bert_large_cased_sha = {
'bert_config.json': '7adb2125c8225da495656c982fd1c5f64ba8f20ad020838571a3f8a954c2df57',
'bert_model.ckpt.data-00000-of-00001': '6ff33640f40d472f7a16af0c17b1179ca9dcc0373155fb05335b6a4dd1657ef0',
'bert_model.ckpt.index': 'ef42a53f577fbe07381f4161b13c7cab4f4fc3b167cec6a9ae382c53d18049cf',
'bert_model.ckpt.meta': 'd2ddff3ed33b80091eac95171e94149736ea74eb645e575d942ec4a5e01a40a1',
'vocab.txt': 'eeaa9875b23b04b4c54ef759d03db9d1ba1554838f8fb26c5d96fa551df93d02',
}
self.bert_base_multilingual_cased_sha = {
'bert_config.json': 'e76c3964bc14a8bb37a5530cdc802699d2f4a6fddfab0611e153aa2528f234f0',
'bert_model.ckpt.data-00000-of-00001': '55b8a2df41f69c60c5180e50a7c31b7cdf6238909390c4ddf05fbc0d37aa1ac5',
'bert_model.ckpt.index': '7d8509c2a62b4e300feb55f8e5f1eef41638f4998dd4d887736f42d4f6a34b37',
'bert_model.ckpt.meta': '95e5f1997e8831f1c31e5cf530f1a2e99f121e9cd20887f2dce6fe9e3343e3fa',
'vocab.txt': 'fe0fda7c425b48c516fc8f160d594c8022a0808447475c1a7c6d6479763f310c',
}
self.bert_large_multilingual_uncased_sha = {
'bert_config.json': '49063bb061390211d2fdd108cada1ed86faa5f90b80c8f6fdddf406afa4c4624',
'bert_model.ckpt.data-00000-of-00001': '3cd83912ebeb0efe2abf35c9f1d5a515d8e80295e61c49b75c8853f756658429',
'bert_model.ckpt.index': '87c372c1a3b1dc7effaaa9103c80a81b3cbab04c7933ced224eec3b8ad2cc8e7',
'bert_model.ckpt.meta': '27f504f34f02acaa6b0f60d65195ec3e3f9505ac14601c6a32b421d0c8413a29',
'vocab.txt': '87b44292b452f6c05afa49b2e488e7eedf79ea4f4c39db6f2f4b37764228ef3f',
}
self.bert_base_chinese_sha = {
'bert_config.json': '7aaad0335058e2640bcb2c2e9a932b1cd9da200c46ea7b8957d54431f201c015',
'bert_model.ckpt.data-00000-of-00001': '756699356b78ad0ef1ca9ba6528297bcb3dd1aef5feadd31f4775d7c7fc989ba',
'bert_model.ckpt.index': '46315546e05ce62327b3e2cd1bed22836adcb2ff29735ec87721396edb21b82e',
'bert_model.ckpt.meta': 'c0f8d51e1ab986604bc2b25d6ec0af7fd21ff94cf67081996ec3f3bf5d823047',
'vocab.txt': '45bbac6b341c319adc98a532532882e91a9cefc0329aa57bac9ae761c27b291c',
}
# Relate SHA to urls for loop below
self.model_sha = {
'bert_base_uncased': self.bert_base_uncased_sha,
'bert_large_uncased': self.bert_large_uncased_sha,
'bert_base_cased': self.bert_base_cased_sha,
'bert_large_cased': self.bert_large_cased_sha,
'bert_base_multilingual_cased': self.bert_base_multilingual_cased_sha,
'bert_large_multilingual_uncased': self.bert_large_multilingual_uncased_sha,
'bert_base_chinese': self.bert_base_chinese_sha
}
# Helper to get sha256sum of a file
def sha256sum(self, filename):
h = hashlib.sha256()
b = bytearray(128*1024)
mv = memoryview(b)
with open(filename, 'rb', buffering=0) as f:
for n in iter(lambda : f.readinto(mv), 0):
h.update(mv[:n])
return h.hexdigest()
def download(self):
# Iterate over urls: download, unzip, verify sha256sum
found_mismatch_sha = False
for model in self.model_urls:
url = self.model_urls[model][0]
file = self.save_path + '/' + self.model_urls[model][1]
print('Downloading', url)
response = urllib.request.urlopen(url)
with open(file, 'wb') as handle:
handle.write(response.read())
print('Unzipping', file)
zip = zipfile.ZipFile(file, 'r')
zip.extractall(self.save_path)
zip.close()
sha_dict = self.model_sha[model]
for extracted_file in sha_dict:
sha = sha_dict[extracted_file]
if sha != self.sha256sum(file[:-4] + '/' + extracted_file):
found_mismatch_sha = True
print('SHA256sum does not match on file:', extracted_file, 'from download url:', url)
else:
print(file[:-4] + '/' + extracted_file, '\t', 'verified')
if not found_mismatch_sha:
print("All downloads pass sha256sum verification.")
def serialize(self):
pass
def deserialize(self):
pass
def listAvailableWeights(self):
print("Available Weight Datasets")
for item in self.model_urls:
print(item)
def listLocallyStoredWeights(self):
pass
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/ELECTRA/data/GooglePretrainedWeightDownloader.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Defining common flags used across all BERT models/applications."""
from absl import flags
import tensorflow as tf
from official.utils.flags import core as flags_core
def define_common_bert_flags():
"""Define common flags for BERT tasks."""
flags_core.define_base(
data_dir=False,
model_dir=True,
clean=False,
train_epochs=False,
epochs_between_evals=False,
stop_threshold=False,
batch_size=False,
num_gpu=True,
hooks=False,
export_dir=False,
distribution_strategy=True,
run_eagerly=True)
flags.DEFINE_string('bert_config_file', None,
'Bert configuration file to define core bert layers.')
flags.DEFINE_string(
'model_export_path', None,
'Path to the directory, where trainined model will be '
'exported.')
flags.DEFINE_string('tpu', '', 'TPU address to connect to.')
flags.DEFINE_string(
'init_checkpoint', None,
'Initial checkpoint (usually from a pre-trained BERT model).')
flags.DEFINE_bool('use_horovod', False, 'Whether to use horovod.')
flags.DEFINE_integer('num_accumulation_steps', 1,
'Number of accumulation steps before gradient update.')
flags.DEFINE_integer('num_train_epochs', 3,
'Total number of training epochs to perform.')
flags.DEFINE_integer(
'steps_per_loop', 200,
'Number of steps per graph-mode loop. Only training step '
'happens inside the loop. Callbacks will not be called '
'inside.')
flags.DEFINE_float('learning_rate', 5e-5,
'The initial learning rate for Adam.')
flags.DEFINE_boolean(
'scale_loss', False,
'Whether to divide the loss by number of replica inside the per-replica '
'loss function.')
flags.DEFINE_boolean(
'use_keras_compile_fit', False,
'If True, uses Keras compile/fit() API for training logic. Otherwise '
'use custom training loop.')
flags.DEFINE_string(
'hub_module_url', None, 'TF-Hub path/url to Bert module. '
'If specified, init_checkpoint flag should not be used.')
flags.DEFINE_enum(
'model_type', 'bert', ['bert', 'albert'],
'Specifies the type of the model. '
'If "bert", will use canonical BERT; if "albert", will use ALBERT model.')
flags.DEFINE_boolean(
'use_fp16', False,
'Whether to use fp32 or fp16 arithmetic on GPU.')
flags.DEFINE_string("optimizer_type", "adam",
"Optimizer used for training - LAMB or ADAM")
flags.DEFINE_integer(
'save_checkpoint_steps', 1000,
'save checkpoint for every n steps')
flags.DEFINE_string(
'dllog_path', 'bert_dllogger.json', 'filename where dllogger writes to')
flags.DEFINE_boolean(
'benchmark', False,
'Benchmark mode.')
# Adds flags for mixed precision training.
flags_core.define_performance(
num_parallel_calls=False,
inter_op=False,
intra_op=False,
synthetic_data=False,
max_train_steps=False,
dtype=True,
dynamic_loss_scale=True,
loss_scale=True,
all_reduce_alg=False,
num_packs=False,
enable_xla=True,
fp16_implementation=True,
)
def use_float16():
return flags_core.get_tf_dtype(flags.FLAGS) == tf.float16
def get_loss_scale():
return flags_core.get_loss_scale(flags.FLAGS, default_for_fp16='dynamic')
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/common_flags.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""BERT finetuning task dataset generator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import json
from absl import app
from absl import flags
import tensorflow as tf
import classifier_data_lib
# word-piece tokenizer based squad_lib
import squad_lib as squad_lib_wp
# sentence-piece tokenizer based squad_lib
import squad_lib_sp
import tokenization
FLAGS = flags.FLAGS
flags.DEFINE_enum(
"fine_tuning_task_type", "classification", ["classification", "squad"],
"The name of the BERT fine tuning task for which data "
"will be generated..")
# BERT classification specific flags.
flags.DEFINE_string(
"input_data_dir", None,
"The input data dir. Should contain the .tsv files (or other data files) "
"for the task.")
flags.DEFINE_enum("classification_task_name", "MNLI",
["COLA", "MNLI", "MRPC", "QNLI", "SST-2", "XNLI"],
"The name of the task to train BERT classifier.")
# BERT Squad task specific flags.
flags.DEFINE_string(
"squad_data_file", None,
"The input data file in for generating training data for BERT squad task.")
flags.DEFINE_integer(
"doc_stride", 128,
"When splitting up a long document into chunks, how much stride to "
"take between chunks.")
flags.DEFINE_integer(
"max_query_length", 64,
"The maximum number of tokens for the question. Questions longer than "
"this will be truncated to this length.")
flags.DEFINE_bool(
"version_2_with_negative", False,
"If true, the SQuAD examples contain some that do not have an answer.")
# Shared flags across BERT fine-tuning tasks.
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string(
"train_data_output_path", None,
"The path in which generated training input data will be written as tf"
" records.")
flags.DEFINE_string(
"eval_data_output_path", None,
"The path in which generated training input data will be written as tf"
" records.")
flags.DEFINE_string("meta_data_file_path", None,
"The path in which input meta data will be written.")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_string("sp_model_file", "",
"The path to the model used by sentence piece tokenizer.")
flags.DEFINE_enum(
"tokenizer_impl", "word_piece", ["word_piece", "sentence_piece"],
"Specifies the tokenizer implementation, i.e., whehter to use word_piece "
"or sentence_piece tokenizer. Canonical BERT uses word_piece tokenizer, "
"while ALBERT uses sentence_piece tokenizer.")
def generate_classifier_dataset():
"""Generates classifier dataset and returns input meta data."""
assert FLAGS.input_data_dir and FLAGS.classification_task_name
processors = {
"cola": classifier_data_lib.ColaProcessor,
"mnli": classifier_data_lib.MnliProcessor,
"mrpc": classifier_data_lib.MrpcProcessor,
"qnli": classifier_data_lib.QnliProcessor,
"sst-2": classifier_data_lib.SstProcessor,
"xnli": classifier_data_lib.XnliProcessor,
}
task_name = FLAGS.classification_task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
if FLAGS.tokenizer_impl == "word_piece":
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
processor_text_fn = tokenization.convert_to_unicode
else:
assert FLAGS.tokenizer_impl == "sentence_piece"
tokenizer = tokenization.FullSentencePieceTokenizer(FLAGS.sp_model_file)
processor_text_fn = functools.partial(
tokenization.preprocess_text, lower=FLAGS.do_lower_case)
processor = processors[task_name](processor_text_fn)
return classifier_data_lib.generate_tf_record_from_data_file(
processor,
FLAGS.input_data_dir,
tokenizer,
train_data_output_path=FLAGS.train_data_output_path,
eval_data_output_path=FLAGS.eval_data_output_path,
max_seq_length=FLAGS.max_seq_length)
def generate_squad_dataset():
"""Generates squad training dataset and returns input meta data."""
assert FLAGS.squad_data_file
if FLAGS.tokenizer_impl == "word_piece":
return squad_lib_wp.generate_tf_record_from_json_file(
FLAGS.squad_data_file, FLAGS.vocab_file, FLAGS.train_data_output_path,
FLAGS.max_seq_length, FLAGS.do_lower_case, FLAGS.max_query_length,
FLAGS.doc_stride, FLAGS.version_2_with_negative)
else:
assert FLAGS.tokenizer_impl == "sentence_piece"
return squad_lib_sp.generate_tf_record_from_json_file(
FLAGS.squad_data_file, FLAGS.sp_model_file,
FLAGS.train_data_output_path, FLAGS.max_seq_length, FLAGS.do_lower_case,
FLAGS.max_query_length, FLAGS.doc_stride, FLAGS.version_2_with_negative)
def main(_):
if FLAGS.tokenizer_impl == "word_piece":
if not FLAGS.vocab_file:
raise ValueError(
"FLAG vocab_file for word-piece tokenizer is not specified.")
else:
assert FLAGS.tokenizer_impl == "sentence_piece"
if not FLAGS.sp_model_file:
raise ValueError(
"FLAG sp_model_file for sentence-piece tokenizer is not specified.")
if FLAGS.fine_tuning_task_type == "classification":
input_meta_data = generate_classifier_dataset()
else:
input_meta_data = generate_squad_dataset()
with tf.io.gfile.GFile(FLAGS.meta_data_file_path, "w") as writer:
writer.write(json.dumps(input_meta_data, indent=4) + "\n")
if __name__ == "__main__":
flags.mark_flag_as_required("train_data_output_path")
flags.mark_flag_as_required("meta_data_file_path")
app.run(main)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/create_finetuning_data.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import math
import os
import pynvml
pynvml.nvmlInit()
def systemGetDriverVersion():
return pynvml.nvmlSystemGetDriverVersion()
def deviceGetCount():
return pynvml.nvmlDeviceGetCount()
class device:
# assume nvml returns list of 64 bit ints
_nvml_affinity_elements = math.ceil(os.cpu_count() / 64)
def __init__(self, device_idx):
super().__init__()
self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_idx)
def getName(self):
return pynvml.nvmlDeviceGetName(self.handle)
def getCpuAffinity(self):
affinity_string = ''
for j in pynvml.nvmlDeviceGetCpuAffinity(
self.handle, device._nvml_affinity_elements
):
# assume nvml returns list of 64 bit ints
affinity_string = '{:064b}'.format(j) + affinity_string
affinity_list = [int(x) for x in affinity_string]
affinity_list.reverse() # so core 0 is in 0th element of list
return [i for i, e in enumerate(affinity_list) if e != 0]
def set_affinity(gpu_id=None):
if gpu_id is None:
gpu_id = int(os.getenv('LOCAL_RANK', 0))
dev = device(gpu_id)
os.sched_setaffinity(0, dev.getCpuAffinity())
# list of ints representing the logical cores this process is now affinitied with
return os.sched_getaffinity(0)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/gpu_affinity.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""BERT model input pipelines."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import horovod.tensorflow as hvd
def decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.io.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def single_file_dataset(input_file, name_to_features, use_horovod=False):
"""Creates a single-file dataset to be passed for BERT custom training."""
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if use_horovod:
d = d.shard(hvd.size(), hvd.rank())
d = d.map(lambda record: decode_record(record, name_to_features))
# When `input_file` is a path to a single file or a list
# containing a single path, disable auto sharding so that
# same input file is sent to all workers.
if isinstance(input_file, str) or len(input_file) == 1:
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = (
tf.data.experimental.AutoShardPolicy.OFF)
d = d.with_options(options)
return d
def create_pretrain_dataset(input_patterns,
seq_length,
max_predictions_per_seq,
batch_size,
is_training=True,
input_pipeline_context=None,
use_horovod=False):
"""Creates input dataset from (tf)records files for pretraining."""
name_to_features = {
'input_ids':
tf.io.FixedLenFeature([seq_length], tf.int64),
'input_mask':
tf.io.FixedLenFeature([seq_length], tf.int64),
'segment_ids':
tf.io.FixedLenFeature([seq_length], tf.int64),
'masked_lm_positions':
tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64),
'masked_lm_ids':
tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64),
'masked_lm_weights':
tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32),
'next_sentence_labels':
tf.io.FixedLenFeature([1], tf.int64),
}
dataset = tf.data.Dataset.list_files(input_patterns, shuffle=is_training)
if use_horovod:
dataset = dataset.shard(hvd.size(), hvd.rank())
if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
input_pipeline_context.input_pipeline_id)
dataset = dataset.repeat()
# We set shuffle buffer to exactly match total number of
# training files to ensure that training data is well shuffled.
input_files = []
for input_pattern in input_patterns:
input_files.extend(tf.io.gfile.glob(input_pattern))
dataset = dataset.shuffle(len(input_files))
# In parallel, create tf record dataset for each train files.
# cycle_length = 8 means that up to 8 files will be read and deserialized in
# parallel. You may want to increase this number if you have a large number of
# CPU cores.
dataset = dataset.interleave(
tf.data.TFRecordDataset, cycle_length=8,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
decode_fn = lambda record: decode_record(record, name_to_features)
dataset = dataset.map(
decode_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
def _select_data_from_record(record):
"""Filter out features to use for pretraining."""
x = {
'input_word_ids': record['input_ids'],
'input_mask': record['input_mask'],
'input_type_ids': record['segment_ids'],
'masked_lm_positions': record['masked_lm_positions'],
'masked_lm_ids': record['masked_lm_ids'],
'masked_lm_weights': record['masked_lm_weights'],
'next_sentence_labels': record['next_sentence_labels'],
}
y = record['masked_lm_weights']
return (x, y)
dataset = dataset.map(
_select_data_from_record,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
if is_training:
dataset = dataset.shuffle(100)
dataset = dataset.batch(batch_size, drop_remainder=True)
dataset = dataset.prefetch(1024)
return dataset
def create_classifier_dataset(file_path,
seq_length,
batch_size,
is_training=True,
input_pipeline_context=None,
use_horovod=False):
"""Creates input dataset from (tf)records files for train/eval."""
name_to_features = {
'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64),
'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
'label_ids': tf.io.FixedLenFeature([], tf.int64),
'is_real_example': tf.io.FixedLenFeature([], tf.int64),
}
dataset = single_file_dataset(file_path, name_to_features, use_horovod)
# The dataset is always sharded by number of hosts.
# num_input_pipelines is the number of hosts rather than number of cores.
if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
input_pipeline_context.input_pipeline_id)
def _select_data_from_record(record):
x = {
'input_word_ids': record['input_ids'],
'input_mask': record['input_mask'],
'input_type_ids': record['segment_ids']
}
y = record['label_ids']
return (x, y)
dataset = dataset.map(_select_data_from_record)
if is_training:
dataset = dataset.shuffle(100)
dataset = dataset.repeat()
dataset = dataset.batch(batch_size, drop_remainder=is_training)
dataset = dataset.prefetch(1024)
return dataset
def create_squad_dataset(file_path,
seq_length,
batch_size,
is_training=True,
input_pipeline_context=None,
use_horovod=False):
"""Creates input dataset from (tf)records files for train/eval."""
name_to_features = {
'input_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
'input_mask': tf.io.FixedLenFeature([seq_length], tf.int64),
'segment_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
}
if is_training:
name_to_features['start_positions'] = tf.io.FixedLenFeature([], tf.int64)
name_to_features['end_positions'] = tf.io.FixedLenFeature([], tf.int64)
else:
name_to_features['unique_ids'] = tf.io.FixedLenFeature([], tf.int64)
dataset = single_file_dataset(file_path, name_to_features, use_horovod)
# The dataset is always sharded by number of hosts.
# num_input_pipelines is the number of hosts rather than number of cores.
if input_pipeline_context and input_pipeline_context.num_input_pipelines > 1:
dataset = dataset.shard(input_pipeline_context.num_input_pipelines,
input_pipeline_context.input_pipeline_id)
def _select_data_from_record(record):
"""Dispatches record to features and labels."""
x, y = {}, {}
for name, tensor in record.items():
if name in ('start_positions', 'end_positions'):
y[name] = tensor
elif name == 'input_ids':
x['input_word_ids'] = tensor
elif name == 'segment_ids':
x['input_type_ids'] = tensor
else:
x[name] = tensor
return (x, y)
dataset = dataset.map(_select_data_from_record)
if is_training:
dataset = dataset.shuffle(100)
dataset = dataset.repeat()
dataset = dataset.batch(batch_size, drop_remainder=True)
dataset = dataset.prefetch(1024)
return dataset
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/input_pipeline.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Library to process data for SQuAD 1.1 and SQuAD 2.0."""
# pylint: disable=g-bad-import-order
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import six
from absl import logging
import tensorflow as tf
import tokenization
# pylint: enable=g-bad-import-order
class SquadExample(object):
"""A single training/test example for simple sequence classification.
For examples without an answer, the start and end position are -1.
"""
def __init__(self,
qas_id,
question_text,
doc_tokens,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=False):
self.qas_id = qas_id
self.question_text = question_text
self.doc_tokens = doc_tokens
self.orig_answer_text = orig_answer_text
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ""
s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tokens,
token_to_orig_map,
token_is_max_context,
input_ids,
input_mask,
segment_ids,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.token_is_max_context = token_is_max_context
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
class FeatureWriter(object):
"""Writes InputFeature to TF example file."""
def __init__(self, filename, is_training):
self.filename = filename
self.is_training = is_training
self.num_features = 0
self._writer = tf.io.TFRecordWriter(filename)
def process_feature(self, feature):
"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
self.num_features += 1
def create_int_feature(values):
feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=list(values)))
return feature
features = collections.OrderedDict()
features["unique_ids"] = create_int_feature([feature.unique_id])
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
if self.is_training:
features["start_positions"] = create_int_feature([feature.start_position])
features["end_positions"] = create_int_feature([feature.end_position])
impossible = 0
if feature.is_impossible:
impossible = 1
features["is_impossible"] = create_int_feature([impossible])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
self._writer.write(tf_example.SerializeToString())
def close(self):
self._writer.close()
def read_squad_examples(input_file, is_training, version_2_with_negative, input_data=None):
"""Read a SQuAD json file into a list of SquadExample."""
if input_data is None:
with tf.io.gfile.GFile(input_file, "r") as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
for c in paragraph_text:
if is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
end_position = None
orig_answer_text = None
is_impossible = False
if is_training:
if version_2_with_negative:
is_impossible = qa["is_impossible"]
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer.")
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
answer_offset = answer["answer_start"]
answer_length = len(orig_answer_text)
start_position = char_to_word_offset[answer_offset]
end_position = char_to_word_offset[answer_offset + answer_length -
1]
# Only add answers where the text can be exactly recovered from the
# document. If this CAN'T happen it's likely due to weird Unicode
# stuff so we will just skip the example.
#
# Note that this means for training mode, every example is NOT
# guaranteed to be preserved.
actual_text = " ".join(
doc_tokens[start_position:(end_position + 1)])
cleaned_answer_text = " ".join(
tokenization.whitespace_tokenize(orig_answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logging.warning("Could not find answer: '%s' vs. '%s'",
actual_text, cleaned_answer_text)
continue
else:
start_position = -1
end_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
doc_tokens=doc_tokens,
orig_answer_text=orig_answer_text,
start_position=start_position,
end_position=end_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def convert_examples_to_features(examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
output_fn,
batch_size=None):
"""Loads a data file into a list of `InputBatch`s."""
base_id = 1000000000
unique_id = base_id
feature = None
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
start_position = None
end_position = None
if is_training and not example.is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start and
tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = 0
end_position = 0
else:
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and example.is_impossible:
start_position = 0
end_position = 0
if example_index < 2:
logging.info("*** Example ***")
logging.info("unique_id: %s", (unique_id))
logging.info("example_index: %s", (example_index))
logging.info("doc_span_index: %s", (doc_span_index))
logging.info("tokens: %s",
" ".join([tokenization.printable_text(x) for x in tokens]))
logging.info(
"token_to_orig_map: %s", " ".join([
"%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)
]))
logging.info(
"token_is_max_context: %s", " ".join([
"%d:%s" % (x, y)
for (x, y) in six.iteritems(token_is_max_context)
]))
logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
if is_training and example.is_impossible:
logging.info("impossible example")
if is_training and not example.is_impossible:
answer_text = " ".join(tokens[start_position:(end_position + 1)])
logging.info("start_position: %d", (start_position))
logging.info("end_position: %d", (end_position))
logging.info("answer: %s", tokenization.printable_text(answer_text))
feature = InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
start_position=start_position,
end_position=end_position,
is_impossible=example.is_impossible)
# Run callback
if is_training:
output_fn(feature)
else:
output_fn(feature, is_padding=False)
unique_id += 1
if not is_training and feature:
assert batch_size
num_padding = 0
num_examples = unique_id - base_id
if unique_id % batch_size != 0:
num_padding = batch_size - (num_examples % batch_size)
logging.info("Adding padding examples to make sure no partial batch.")
logging.info("Adds %d padding examples for inference.", num_padding)
dummy_feature = copy.deepcopy(feature)
for _ in range(num_padding):
dummy_feature.unique_id = unique_id
# Run callback
output_fn(feature, is_padding=True)
unique_id += 1
return unique_id - base_id
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can
# often find a "better match". For example:
#
# Question: What year was John Smith born?
# Context: The leader was John Smith (1895-1943).
# Answer: 1895
#
# The original whitespace-tokenized answer will be "(1895-1943).". However
# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
# the exact answer, 1895.
#
# However, this is not always possible. Consider the following:
#
# Question: What country is the top exporter of electornics?
# Context: The Japanese electronics industry is the lagest in the world.
# Answer: Japan
#
# In this case, the annotator chose "Japan" as a character sub-span of
# the word "Japanese". Since our WordPiece tokenizer does not split
# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
# in SQuAD, but does happen.
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
def get_predictions(all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
version_2_with_negative=False,
null_score_diff_threshold=0.0,
verbose=False):
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(
tok_text, orig_text, do_lower_case, verbose=verbose)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't inlude the empty option in the n-best, inlcude it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(
text="", start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# pytype: disable=attribute-error
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (
best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
# pytype: enable=attribute-error
all_nbest_json[example.qas_id] = nbest_json
return all_predictions, all_nbest_json, scores_diff_json
def write_predictions(all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
version_2_with_negative=False,
null_score_diff_threshold=0.0,
verbose=False):
"""Write final predictions to the json file and log-odds of null if needed."""
logging.info("Writing predictions to: %s", (output_prediction_file))
logging.info("Writing nbest to: %s", (output_nbest_file))
all_predictions, all_nbest_json, scores_diff_json = get_predictions(
all_examples, all_features, all_results, n_best_size,
max_answer_length, do_lower_case, version_2_with_negative,
null_score_diff_threshold, verbose)
with tf.io.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with tf.io.gfile.GFile(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with tf.io.gfile.GFile(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
def get_final_text(pred_text, orig_text, do_lower_case, verbose=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heruistic between
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose:
logging.info("Unable to find text: '%s' in '%s'", pred_text, orig_text)
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose:
logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose:
logging.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose:
logging.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)): # pylint: disable=consider-using-enumerate
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
def generate_tf_record_from_json_file(input_file_path,
vocab_file_path,
output_path,
max_seq_length=384,
do_lower_case=True,
max_query_length=64,
doc_stride=128,
version_2_with_negative=False):
"""Generates and saves training data into a tf record file."""
train_examples = read_squad_examples(
input_file=input_file_path,
is_training=True,
version_2_with_negative=version_2_with_negative)
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file_path, do_lower_case=do_lower_case)
train_writer = FeatureWriter(filename=output_path, is_training=True)
number_of_examples = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=True,
output_fn=train_writer.process_feature)
train_writer.close()
meta_data = {
"task_type": "bert_squad",
"train_data_size": number_of_examples,
"max_seq_length": max_seq_length,
"max_query_length": max_query_length,
"doc_stride": doc_stride,
"version_2_with_negative": version_2_with_negative,
}
return meta_data
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/squad_lib.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions and classes related to optimization (weight updates)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import tensorflow as tf
import tensorflow_addons.optimizers as tfa_optimizers
class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
"""Applys a warmup schedule on a given learning rate decay schedule."""
def __init__(
self,
initial_learning_rate,
decay_schedule_fn,
warmup_steps,
power=1.0,
name=None):
super(WarmUp, self).__init__()
self.initial_learning_rate = initial_learning_rate
self.warmup_steps = warmup_steps
self.power = power
self.decay_schedule_fn = decay_schedule_fn
self.name = name
def __call__(self, step):
with tf.name_scope(self.name or 'WarmUp') as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
global_step_float = tf.cast(step, tf.float32)
warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
warmup_percent_done = global_step_float / warmup_steps_float
warmup_learning_rate = (
self.initial_learning_rate *
tf.math.pow(warmup_percent_done, self.power))
return tf.cond(global_step_float < warmup_steps_float,
lambda: warmup_learning_rate,
lambda: self.decay_schedule_fn(step),
name=name)
def get_config(self):
return {
'initial_learning_rate': self.initial_learning_rate,
'decay_schedule_fn': self.decay_schedule_fn,
'warmup_steps': self.warmup_steps,
'power': self.power,
'name': self.name
}
def create_optimizer(init_lr, num_train_steps, num_warmup_steps, optimizer_type="adam"):
"""Creates an optimizer with learning rate schedule."""
# Implements linear decay of the learning rate.
if optimizer_type == "adam":
power = 1.0
decayed_learning_rate_at_crossover_point = init_lr * (
(1.0 - float(num_warmup_steps) / float(num_train_steps)) ** power)
else:
power = 0.5
decayed_learning_rate_at_crossover_point = init_lr
init_lr = init_lr * (init_lr / decayed_learning_rate_at_crossover_point)
print('decayed_learning_rate_at_crossover_point = %e, adjusted_init_lr = %e' % (decayed_learning_rate_at_crossover_point, init_lr))
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=init_lr,
decay_steps=num_train_steps,
end_learning_rate=0.0,
power=power)
if num_warmup_steps:
learning_rate_fn = WarmUp(initial_learning_rate=init_lr,
decay_schedule_fn=learning_rate_fn,
warmup_steps=num_warmup_steps)
if optimizer_type == 'adam':
optimizer = AdamWeightDecay(
learning_rate=learning_rate_fn,
weight_decay_rate=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'])
else:
skip_list = ['None'] # to avoid exclude_from_layer_adaptation set to exclude_from_weight_decay if the arg is None
optimizer = tfa_optimizers.LAMB(
learning_rate=learning_rate_fn,
weight_decay_rate=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'],
exclude_from_layer_adaptation=skip_list)
return optimizer
class AdamWeightDecay(tf.keras.optimizers.Adam):
"""Adam enables L2 weight decay and clip_by_global_norm on gradients.
Just adding the square of the weights to the loss function is *not* the
correct way of using L2 regularization/weight decay with Adam, since that will
interact with the m and v parameters in strange ways.
Instead we want ot decay the weights in a manner that doesn't interact with
the m/v parameters. This is equivalent to adding the square of the weights to
the loss with plain (non-momentum) SGD.
"""
def __init__(self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-7,
amsgrad=False,
weight_decay_rate=0.0,
include_in_weight_decay=None,
exclude_from_weight_decay=None,
name='AdamWeightDecay',
**kwargs):
super(AdamWeightDecay, self).__init__(learning_rate, beta_1, beta_2,
epsilon, amsgrad, name, **kwargs)
self.weight_decay_rate = weight_decay_rate
self._include_in_weight_decay = include_in_weight_decay
self._exclude_from_weight_decay = exclude_from_weight_decay
@classmethod
def from_config(cls, config):
"""Creates an optimizer from its config with WarmUp custom object."""
custom_objects = {'WarmUp': WarmUp}
return super(AdamWeightDecay, cls).from_config(
config, custom_objects=custom_objects)
def _prepare_local(self, var_device, var_dtype, apply_state):
super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype,
apply_state)
apply_state[(var_device, var_dtype)]['weight_decay_rate'] = tf.constant(
self.weight_decay_rate, name='adam_weight_decay_rate')
def _decay_weights_op(self, var, learning_rate, apply_state):
do_decay = self._do_use_weight_decay(var.name)
if do_decay:
return var.assign_sub(
learning_rate * var *
apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'],
use_locking=self._use_locking)
return tf.no_op()
def _get_lr(self, var_device, var_dtype, apply_state):
"""Retrieves the learning rate with the given state."""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
apply_state = apply_state or {}
coefficients = apply_state.get((var_device, var_dtype))
if coefficients is None:
coefficients = self._fallback_apply_state(var_device, var_dtype)
apply_state[(var_device, var_dtype)] = coefficients
return coefficients['lr_t'], dict(apply_state=apply_state)
def _resource_apply_dense(self, grad, var, apply_state=None):
lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
decay = self._decay_weights_op(var, lr_t, apply_state)
with tf.control_dependencies([decay]):
return super(AdamWeightDecay,
self)._resource_apply_dense(grad, var, **kwargs)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
decay = self._decay_weights_op(var, lr_t, apply_state)
with tf.control_dependencies([decay]):
return super(AdamWeightDecay,
self)._resource_apply_sparse(grad, var, indices, **kwargs)
def get_config(self):
config = super(AdamWeightDecay, self).get_config()
config.update({
'weight_decay_rate': self.weight_decay_rate,
})
return config
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(r, param_name) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
# Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
class GradientAccumulator():
def __init__(self):
self._gradients = []
self._accum_steps = None
def zero(self, dtype):
return tf.Variable(
tf.constant(0, dtype=dtype),
trainable=False,
synchronization=tf.VariableSynchronization.ON_READ,
aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
@property
def step(self):
if self._accum_steps is None:
self._accum_steps = self.zero(tf.int64)
return self._accum_steps.value()
@property
def gradients(self):
if not self._gradients:
raise ValueError("The accumulator should be called first to initialize the gradients")
return list(gradient.value() if gradient is not None else None for gradient in self._gradients)
def reset(self):
if not self._gradients:
return
self._accum_steps.assign(0)
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros(tf.shape(gradient), dtype=gradient.dtype))
def add_gradients(self, grads):
if not self._gradients:
_ = self.step
self._gradients.extend([
tf.Variable(
tf.zeros_like(g),
trainable=False,
synchronization=tf.VariableSynchronization.ON_READ
) if g is not None else None
for g in grads
])
if len(grads) != len(self._gradients):
raise ValueError("Expected %s gradients, but got %d" % (
len(self._gradients), len(grads)))
for accum_grad, grad in zip(self._gradients, grads):
if accum_grad is not None:
accum_grad.assign_add(grad)
self._accum_steps.assign_add(1)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/optimization.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Run BERT on SQuAD 1.1 and SQuAD 2.0 in tf2.0."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import time
import shutil
import sys
import subprocess
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
import horovod.tensorflow as hvd
import numpy as np
from dllogger import Verbosity
# Import BERT model libraries.
from official.nlp import bert_models
import common_flags
import input_pipeline
from official.modeling import model_training_utils
import model_saving_utils
from official.nlp import bert_modeling as modeling
import optimization
# word-piece tokenizer based squad_lib
import squad_lib as squad_lib_wp
# sentence-piece tokenizer based squad_lib
import squad_lib_sp
import tokenization
import gpu_affinity
import tf_trt
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.utils.misc import tpu_lib
import dllogger_class
flags.DEFINE_enum(
'mode', 'train_and_predict',
['train_and_predict', 'train', 'predict', 'export_only', 'sm_predict', 'trt_predict'],
'One of {"train_and_predict", "train", "predict", "export_only", "sm_predict", "trt_predict"}. '
'`train_and_predict`: both train and predict to a json file. '
'`train`: only trains the model. '
'trains the model and evaluates in the meantime. '
'`predict`: predict answers from the squad json file. '
'`export_only`: will take the latest checkpoint inside '
'model_dir and export a `SavedModel`.'
'`sm_predict`: will load SavedModel from savedmodel_dir and predict answers'
'`trt_predict`: will load SavedModel from savedmodel_dir, convert and predict answers with TF-TRT')
flags.DEFINE_string('train_data_path', '',
'Training data path with train tfrecords.')
flags.DEFINE_string(
'input_meta_data_path', None,
'Path to file that contains meta data about input '
'to be used for training and evaluation.')
flags.DEFINE_string(
"eval_script", None,
"SQuAD evaluate.py file to compute f1 and exact_match E.g., evaluate-v1.1.py")
# Model training specific flags.
flags.DEFINE_integer('train_batch_size', 8, 'Total batch size for training.')
# Predict processing related.
flags.DEFINE_string('predict_file', None,
'Prediction data path with train tfrecords.')
flags.DEFINE_string('vocab_file', None,
'The vocabulary file that the BERT model was trained on.')
flags.DEFINE_bool(
'do_lower_case', True,
'Whether to lower case the input text. Should be True for uncased '
'models and False for cased models.')
flags.DEFINE_bool(
'verbose_logging', False,
'If true, all of the warnings related to data processing will be printed. '
'A number of warnings are expected for a normal SQuAD evaluation.')
flags.DEFINE_integer('predict_batch_size', 8,
'Total batch size for prediction.')
flags.DEFINE_integer(
'n_best_size', 20,
'The total number of n-best predictions to generate in the '
'nbest_predictions.json output file.')
flags.DEFINE_integer(
'max_answer_length', 30,
'The maximum length of an answer that can be generated. This is needed '
'because the start and end predictions are not conditioned on one another.')
flags.DEFINE_string(
'sp_model_file', None,
'The path to the sentence piece model. Used by sentence piece tokenizer '
'employed by ALBERT.')
flags.DEFINE_string(
'savedmodel_dir', None,
'The path of SavedModel for Savedmodel and TF-TRT prediction.')
common_flags.define_common_bert_flags()
FLAGS = flags.FLAGS
MODEL_CLASSES = {
'bert': (modeling.BertConfig, squad_lib_wp, tokenization.FullTokenizer),
'albert': (modeling.AlbertConfig, squad_lib_sp,
tokenization.FullSentencePieceTokenizer),
}
def squad_loss_fn(start_positions,
end_positions,
start_logits,
end_logits,
loss_factor=1.0):
"""Returns sparse categorical crossentropy for start/end logits."""
start_loss = tf.keras.backend.sparse_categorical_crossentropy(
start_positions, start_logits, from_logits=True)
end_loss = tf.keras.backend.sparse_categorical_crossentropy(
end_positions, end_logits, from_logits=True)
total_loss = (tf.reduce_mean(start_loss) + tf.reduce_mean(end_loss)) / 2
total_loss *= loss_factor
return total_loss
def get_loss_fn(loss_factor=1.0):
"""Gets a loss function for squad task."""
def _loss_fn(labels, model_outputs):
start_positions = labels['start_positions']
end_positions = labels['end_positions']
start_logits, end_logits = model_outputs
return squad_loss_fn(
start_positions,
end_positions,
start_logits,
end_logits,
loss_factor=loss_factor)
return _loss_fn
def get_raw_results(predictions):
"""Converts multi-replica predictions to RawResult."""
squad_lib = MODEL_CLASSES[FLAGS.model_type][1]
for unique_ids, start_logits, end_logits in zip(predictions['unique_ids'],
predictions['start_logits'],
predictions['end_logits']):
for values in zip(unique_ids.numpy(), start_logits.numpy(),
end_logits.numpy()):
yield squad_lib.RawResult(
unique_id=values[0],
start_logits=values[1].tolist(),
end_logits=values[2].tolist())
def get_dataset_fn(input_file_pattern, max_seq_length, global_batch_size,
is_training, use_horovod):
"""Gets a closure to create a dataset.."""
def _dataset_fn(ctx=None):
"""Returns tf.data.Dataset for distributed BERT pretraining."""
batch_size = ctx.get_per_replica_batch_size(
global_batch_size) if ctx else global_batch_size
dataset = input_pipeline.create_squad_dataset(
input_file_pattern,
max_seq_length,
batch_size,
is_training=is_training,
input_pipeline_context=ctx,
use_horovod=use_horovod)
return dataset
return _dataset_fn
def predict_squad_customized(strategy, input_meta_data, bert_config,
predict_tfrecord_path, num_steps):
"""Make predictions using a Bert-based squad model."""
predict_dataset_fn = get_dataset_fn(
predict_tfrecord_path,
input_meta_data['max_seq_length'],
FLAGS.predict_batch_size,
is_training=False,
use_horovod=False)
if strategy:
predict_iterator = iter(
strategy.experimental_distribute_datasets_from_function(
predict_dataset_fn))
else:
predict_iterator = iter(predict_dataset_fn())
if FLAGS.mode == 'trt_predict':
squad_model = tf_trt.TFTRTModel(FLAGS.savedmodel_dir, "amp" if FLAGS.use_fp16 else "fp32")
elif FLAGS.mode == 'sm_predict':
squad_model = tf_trt.SavedModel(FLAGS.savedmodel_dir, "amp" if FLAGS.use_fp16 else "fp32")
else:
with distribution_utils.get_strategy_scope(strategy):
squad_model, _ = bert_models.squad_model(
bert_config, input_meta_data['max_seq_length'], float_type=tf.float16 if FLAGS.use_fp16 else tf.float32)
if FLAGS.init_checkpoint:
checkpoint = tf.train.Checkpoint(model=squad_model)
checkpoint.restore(FLAGS.init_checkpoint).expect_partial()
checkpoint_path = tf.train.latest_checkpoint(FLAGS.model_dir)
logging.info('Restoring checkpoints from %s', checkpoint_path)
checkpoint = tf.train.Checkpoint(model=squad_model)
checkpoint.restore(checkpoint_path).expect_partial()
@tf.function
def predict_step(iterator):
"""Predicts on distributed devices."""
def _replicated_step(inputs):
"""Replicated prediction calculation."""
x, _ = inputs
unique_ids = x.pop('unique_ids')
if FLAGS.benchmark:
t0 = tf.timestamp()
unique_ids = t0
start_logits, end_logits = squad_model(x, training=False)
return dict(
unique_ids=unique_ids,
start_logits=start_logits,
end_logits=end_logits)
def tuple_fun(x):
return (x,)
if strategy:
outputs = strategy.experimental_run_v2(
_replicated_step, args=(next(iterator),))
map_func = strategy.experimental_local_results
else:
outputs = _replicated_step(next(iterator),)
map_func = tuple_fun
return tf.nest.map_structure(map_func, outputs)
all_results = []
time_list = []
eval_start_time = time.time()
elapsed_secs = 0
for _ in range(num_steps):
predictions = predict_step(predict_iterator)
if FLAGS.benchmark:
# transfer tensor to CPU for synchronization
t0 = predictions['unique_ids'][0]
start_logits = predictions['start_logits'][0]
start_logits.numpy()
elapsed_secs = time.time() - t0.numpy()
# Removing first 4 (arbitrary) number of startup iterations from perf evaluations
if _ > 3:
time_list.append(elapsed_secs)
continue
for result in get_raw_results(predictions):
all_results.append(result)
if len(all_results) % 100 == 0:
logging.info('Made predictions for %d records.', len(all_results))
eval_time_elapsed = time.time() - eval_start_time
logging.info("-----------------------------")
logging.info("Summary Inference Statistics")
logging.info("Batch size = %d", FLAGS.predict_batch_size)
logging.info("Sequence Length = %d", input_meta_data['max_seq_length'])
logging.info("Precision = %s", "fp16" if FLAGS.use_fp16 else "fp32")
logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed,
num_steps * FLAGS.predict_batch_size)
if FLAGS.benchmark:
eval_time_wo_overhead = sum(time_list)
time_list.sort()
num_sentences = (num_steps - 4) * FLAGS.predict_batch_size
avg = np.mean(time_list)
cf_50 = max(time_list[:int(len(time_list) * 0.50)])
cf_90 = max(time_list[:int(len(time_list) * 0.90)])
cf_95 = max(time_list[:int(len(time_list) * 0.95)])
cf_99 = max(time_list[:int(len(time_list) * 0.99)])
cf_100 = max(time_list[:int(len(time_list) * 1)])
ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead
logging.info("Total Inference Time W/O Overhead = %0.2f for Sequences = %d", eval_time_wo_overhead,
(num_steps - 4) * FLAGS.predict_batch_size)
logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000)
logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000)
logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000)
logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000)
logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000)
logging.info("Latency Average (ms) = %0.2f", avg * 1000)
logging.info("Throughput Average (sequences/sec) = %0.2f", ss_sentences_per_second)
dllogging = input_meta_data['dllogging']
dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
logging.info("-----------------------------")
return all_results
def train_squad(strategy,
input_meta_data,
custom_callbacks=None,
run_eagerly=False):
"""Run bert squad training."""
if strategy:
logging.info('Training using customized training loop with distribution'
' strategy.')
# Enables XLA in Session Config. Should not be set for TPU.
keras_utils.set_config_v2(FLAGS.enable_xla)
use_float16 = common_flags.use_float16()
if use_float16:
tf.keras.mixed_precision.experimental.set_policy('mixed_float16')
bert_config = MODEL_CLASSES[FLAGS.model_type][0].from_json_file(
FLAGS.bert_config_file)
epochs = FLAGS.num_train_epochs
num_train_examples = input_meta_data['train_data_size']
max_seq_length = input_meta_data['max_seq_length']
global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps
if FLAGS.use_horovod:
global_batch_size *= hvd.size()
steps_per_epoch = int(num_train_examples / global_batch_size)
warmup_steps = int(epochs * num_train_examples * 0.1 / global_batch_size)
train_input_fn = get_dataset_fn(
FLAGS.train_data_path,
max_seq_length,
FLAGS.train_batch_size,
is_training=True,
use_horovod=FLAGS.use_horovod)
if FLAGS.benchmark:
steps_per_epoch = 800
epochs = 1
def _get_squad_model():
"""Get Squad model and optimizer."""
squad_model, core_model = bert_models.squad_model(
bert_config,
max_seq_length,
float_type=tf.float16 if FLAGS.use_fp16 else tf.float32,
hub_module_url=FLAGS.hub_module_url)
learning_rate = FLAGS.learning_rate * hvd.size() if FLAGS.use_horovod else FLAGS.learning_rate
squad_model.optimizer = optimization.create_optimizer(
learning_rate, steps_per_epoch * epochs, warmup_steps, FLAGS.optimizer_type)
if FLAGS.use_fp16:
squad_model.optimizer = tf.keras.mixed_precision.LossScaleOptimizer(squad_model.optimizer,
dynamic=True)
return squad_model, core_model
# The original BERT model does not scale the loss by
# 1/num_replicas_in_sync. It could be an accident. So, in order to use
# the same hyper parameter, we do the same thing here by keeping each
# replica loss as it is.
loss_fn = get_loss_fn(
loss_factor=1.0 /
strategy.num_replicas_in_sync if FLAGS.scale_loss and strategy else 1.0)
params = {'dllogging' : input_meta_data['dllogging'],
'FLAGS' : FLAGS}
model_training_utils.run_customized_training_loop(
strategy=strategy,
model_fn=_get_squad_model,
loss_fn=loss_fn,
model_dir=FLAGS.model_dir,
steps_per_epoch=steps_per_epoch,
num_accumulative_step=FLAGS.num_accumulation_steps,
steps_per_loop=FLAGS.steps_per_loop,
epochs=epochs,
train_input_fn=train_input_fn,
init_checkpoint=FLAGS.init_checkpoint,
hvd=hvd if FLAGS.use_horovod else None,
run_eagerly=run_eagerly,
custom_callbacks=custom_callbacks,
params=params)
def predict_squad(strategy, input_meta_data):
"""Makes predictions for a squad dataset."""
keras_utils.set_config_v2(FLAGS.enable_xla)
config_cls, squad_lib, tokenizer_cls = MODEL_CLASSES[FLAGS.model_type]
bert_config = config_cls.from_json_file(FLAGS.bert_config_file)
if tokenizer_cls == tokenization.FullTokenizer:
tokenizer = tokenizer_cls(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
else:
assert tokenizer_cls == tokenization.FullSentencePieceTokenizer
tokenizer = tokenizer_cls(sp_model_file=FLAGS.sp_model_file)
doc_stride = input_meta_data['doc_stride']
max_query_length = input_meta_data['max_query_length']
# Whether data should be in Ver 2.0 format.
version_2_with_negative = input_meta_data.get('version_2_with_negative',
False)
eval_examples = squad_lib.read_squad_examples(
input_file=FLAGS.predict_file,
is_training=False,
version_2_with_negative=version_2_with_negative)
eval_writer = squad_lib.FeatureWriter(
filename=os.path.join(FLAGS.model_dir, 'eval.tf_record'),
is_training=False)
eval_features = []
def _append_feature(feature, is_padding):
if not is_padding:
eval_features.append(feature)
eval_writer.process_feature(feature)
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
kwargs = dict(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=input_meta_data['max_seq_length'],
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False,
output_fn=_append_feature,
batch_size=FLAGS.predict_batch_size)
# squad_lib_sp requires one more argument 'do_lower_case'.
if squad_lib == squad_lib_sp:
kwargs['do_lower_case'] = FLAGS.do_lower_case
dataset_size = squad_lib.convert_examples_to_features(**kwargs)
eval_writer.close()
logging.info('***** Running predictions *****')
logging.info(' Num orig examples = %d', len(eval_examples))
logging.info(' Num split examples = %d', len(eval_features))
logging.info(' Batch size = %d', FLAGS.predict_batch_size)
num_steps = int(dataset_size / FLAGS.predict_batch_size)
if FLAGS.benchmark and num_steps > 1000:
num_steps = 1000
all_results = predict_squad_customized(strategy, input_meta_data, bert_config,
eval_writer.filename, num_steps)
if FLAGS.benchmark:
return
output_prediction_file = os.path.join(FLAGS.model_dir, 'predictions.json')
output_nbest_file = os.path.join(FLAGS.model_dir, 'nbest_predictions.json')
output_null_log_odds_file = os.path.join(FLAGS.model_dir, 'null_odds.json')
squad_lib.write_predictions(
eval_examples,
eval_features,
all_results,
FLAGS.n_best_size,
FLAGS.max_answer_length,
FLAGS.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose=FLAGS.verbose_logging)
if FLAGS.eval_script:
eval_out = subprocess.check_output([sys.executable, FLAGS.eval_script,
FLAGS.predict_file, output_prediction_file])
scores = str(eval_out).strip()
exact_match = float(scores.split(":")[1].split(",")[0])
if version_2_with_negative:
f1 = float(scores.split(":")[2].split(",")[0])
else:
f1 = float(scores.split(":")[2].split("}")[0])
dllogging = input_meta_data['dllogging']
dllogging.logger.log(step=(), data={"f1": f1}, verbosity=Verbosity.DEFAULT)
dllogging.logger.log(step=(), data={"exact_match": exact_match}, verbosity=Verbosity.DEFAULT)
print(str(eval_out))
def export_squad(model_export_path, input_meta_data):
"""Exports a trained model as a `SavedModel` for inference.
Args:
model_export_path: a string specifying the path to the SavedModel directory.
input_meta_data: dictionary containing meta data about input and model.
Raises:
Export path is not specified, got an empty string or None.
"""
if not model_export_path:
raise ValueError('Export path is not specified: %s' % model_export_path)
bert_config = MODEL_CLASSES[FLAGS.model_type][0].from_json_file(
FLAGS.bert_config_file)
squad_model, _ = bert_models.squad_model(
bert_config, input_meta_data['max_seq_length'], float_type=tf.float32)
model_saving_utils.export_bert_model(
model_export_path + '/savedmodel', model=squad_model, checkpoint_dir=FLAGS.model_dir)
model_name = FLAGS.triton_model_name
model_folder = model_export_path + "/triton_models/" + model_name
version_folder = model_folder + "/" + str(FLAGS.triton_model_version)
final_model_folder = version_folder + "/model.savedmodel"
if not os.path.exists(version_folder):
os.makedirs(version_folder)
if (not os.path.exists(final_model_folder)):
os.rename(model_export_path + '/savedmodel', final_model_folder)
print("Model saved to dir", final_model_folder)
else:
if (FLAGS.triton_model_overwrite):
shutil.rmtree(final_model_folder)
os.rename(model_export_path + '/savedmodel', final_model_folder)
print("WARNING: Existing model was overwritten. Model dir: {}".format(final_model_folder))
else:
print("ERROR: Could not save Triton model. Folder already exists. Use '--triton_model_overwrite=True' if you would like to overwrite an existing model. Model dir: {}".format(final_model_folder))
return
config_filename = os.path.join(model_folder, "config.pbtxt")
if (os.path.exists(config_filename) and not FLAGS.triton_model_overwrite):
print("ERROR: Could not save Triton model config. Config file already exists. Use '--triton_model_overwrite=True' if you would like to overwrite an existing model config. Model config: {}".format(config_filename))
return
config_template = r"""
name: "{model_name}"
platform: "tensorflow_savedmodel"
max_batch_size: {max_batch_size}
input [
{{
name: "input_mask"
data_type: TYPE_INT32
dims: {seq_length}
}},
{{
name: "input_type_ids"
data_type: TYPE_INT32
dims: {seq_length}
}},
{{
name: "input_word_ids"
data_type: TYPE_INT32
dims: {seq_length}
}}
]
output [
{{
name: "end_positions"
data_type: TYPE_FP32
dims: {seq_length}
}},
{{
name: "start_positions"
data_type: TYPE_FP32
dims: {seq_length}
}}
]
{dynamic_batching}
instance_group [
{{
count: {engine_count}
kind: KIND_GPU
gpus: [{gpu_list}]
}}
]"""
batching_str = ""
max_batch_size = FLAGS.triton_max_batch_size
if (FLAGS.triton_dyn_batching_delay > 0):
# Use only full and half full batches
pref_batch_size = [int(max_batch_size / 2.0), max_batch_size]
batching_str = r"""
dynamic_batching {{
preferred_batch_size: [{0}]
max_queue_delay_microseconds: {1}
}}""".format(", ".join([str(x) for x in pref_batch_size]), int(FLAGS.triton_dyn_batching_delay * 1000.0))
config_values = {
"model_name": model_name,
"max_batch_size": max_batch_size,
"seq_length": input_meta_data['max_seq_length'],
"dynamic_batching": batching_str,
"gpu_list": ", ".join([x.name.split(":")[-1] for x in tf.config.list_physical_devices('GPU')]),
"engine_count": FLAGS.triton_engine_count
}
with open(model_folder + "/config.pbtxt", "w") as file:
final_config_str = config_template.format_map(config_values)
file.write(final_config_str)
def main(_):
# Users should always run this script under TF 2.x
# The container haven't changed version number yet, skip the check.
assert tf.version.VERSION.startswith('2.')
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
if FLAGS.mode == 'export_only':
export_squad(FLAGS.model_export_path, input_meta_data)
return
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
if FLAGS.use_horovod:
if strategy:
raise ValueError('Should not run horovod with distribution strategy')
hvd.init()
if gpus:
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
gpu_affinity.set_affinity(hvd.local_rank())
if FLAGS.use_fp16:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16")
tf.keras.mixed_precision.experimental.set_policy(policy)
os.makedirs(FLAGS.model_dir, exist_ok=True)
dllogging = dllogger_class.dllogger_class(FLAGS.dllog_path)
input_meta_data['dllogging'] = dllogging
if FLAGS.mode in ('train', 'train_and_predict'):
train_squad(strategy, input_meta_data)
if FLAGS.mode in ('predict', 'sm_predict', 'trt_predict', 'train_and_predict') and (not FLAGS.use_horovod or hvd.rank() == 0):
predict_squad(strategy, input_meta_data)
if __name__ == '__main__':
flags.mark_flag_as_required('bert_config_file')
flags.mark_flag_as_required('model_dir')
app.run(main)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/run_squad.py |
# coding=utf-8
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tokenization classes implementation.
The file is forked from:
https://github.com/google-research/bert/blob/master/tokenization.py.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
import sentencepiece as spm
SPIECE_UNDERLINE = "▁"
def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
"""Checks whether the casing config is consistent with the checkpoint name."""
# The casing has to be passed in by the user and there is no explicit check
# as to whether it matches the checkpoint. The casing information probably
# should have been stored in the bert_config.json file, but it's not, so
# we have to heuristically detect it to validate.
if not init_checkpoint:
return
m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
if m is None:
return
model_name = m.group(1)
lower_models = [
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
]
cased_models = [
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
"multi_cased_L-12_H-768_A-12"
]
is_bad_config = False
if model_name in lower_models and not do_lower_case:
is_bad_config = True
actual_flag = "False"
case_name = "lowercased"
opposite_flag = "True"
if model_name in cased_models and do_lower_case:
is_bad_config = True
actual_flag = "True"
case_name = "cased"
opposite_flag = "False"
if is_bad_config:
raise ValueError(
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
"However, `%s` seems to be a %s model, so you "
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
"how the model was pre-training. If this error is wrong, please "
"just comment out this check." %
(actual_flag, init_checkpoint, model_name, case_name, opposite_flag))
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with tf.io.gfile.GFile(vocab_file, "r") as reader:
while True:
token = convert_to_unicode(reader.readline())
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class FullTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = convert_to_unicode(text)
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer.
Returns:
A list of wordpiece tokens.
"""
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically control characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat in ("Cc", "Cf"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
def preprocess_text(inputs, remove_space=True, lower=False):
"""Preprocesses data by removing extra space and normalize data.
This method is used together with sentence piece tokenizer and is forked from:
https://github.com/google-research/google-research/blob/master/albert/tokenization.py
Args:
inputs: The input text.
remove_space: Whether to remove the extra space.
lower: Whether to lowercase the text.
Returns:
The preprocessed text.
"""
outputs = inputs
if remove_space:
outputs = " ".join(inputs.strip().split())
if six.PY2 and isinstance(outputs, str):
try:
outputs = six.ensure_text(outputs, "utf-8")
except UnicodeDecodeError:
outputs = six.ensure_text(outputs, "latin-1")
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if lower:
outputs = outputs.lower()
return outputs
def encode_pieces(sp_model, text, sample=False):
"""Segements text into pieces.
This method is used together with sentence piece tokenizer and is forked from:
https://github.com/google-research/google-research/blob/master/albert/tokenization.py
Args:
sp_model: A spm.SentencePieceProcessor object.
text: The input text to be segemented.
sample: Whether to randomly sample a segmentation output or return a
deterministic one.
Returns:
A list of token pieces.
"""
if six.PY2 and isinstance(text, six.text_type):
text = six.ensure_binary(text, "utf-8")
if not sample:
pieces = sp_model.EncodeAsPieces(text)
else:
pieces = sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
piece = printable_text(piece)
if len(piece) > 1 and piece[-1] == "," and piece[-2].isdigit():
cur_pieces = sp_model.EncodeAsPieces(piece[:-1].replace(
SPIECE_UNDERLINE, ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
return new_pieces
def encode_ids(sp_model, text, sample=False):
"""Segments text and return token ids.
This method is used together with sentence piece tokenizer and is forked from:
https://github.com/google-research/google-research/blob/master/albert/tokenization.py
Args:
sp_model: A spm.SentencePieceProcessor object.
text: The input text to be segemented.
sample: Whether to randomly sample a segmentation output or return a
deterministic one.
Returns:
A list of token ids.
"""
pieces = encode_pieces(sp_model, text, sample=sample)
ids = [sp_model.PieceToId(piece) for piece in pieces]
return ids
class FullSentencePieceTokenizer(object):
"""Runs end-to-end sentence piece tokenization.
The interface of this class is intended to keep the same as above
`FullTokenizer` class for easier usage.
"""
def __init__(self, sp_model_file):
"""Inits FullSentencePieceTokenizer.
Args:
sp_model_file: The path to the sentence piece model file.
"""
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(sp_model_file)
self.vocab = {
self.sp_model.IdToPiece(i): i
for i in six.moves.range(self.sp_model.GetPieceSize())
}
def tokenize(self, text):
"""Tokenizes text into pieces."""
return encode_pieces(self.sp_model, text)
def convert_tokens_to_ids(self, tokens):
"""Converts a list of tokens to a list of ids."""
return [self.sp_model.PieceToId(printable_text(token)) for token in tokens]
def convert_ids_to_tokens(self, ids):
"""Converts a list of ids ot a list of tokens."""
return [self.sp_model.IdToPiece(id_) for id_ in ids]
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/tokenization.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Run masked LM/next sentence masked_lm pre-training for BERT in tf2.0."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
import horovod.tensorflow as hvd
import os
# Import BERT model libraries.
from official.nlp import bert_models
import common_flags
import input_pipeline
import model_saving_utils
from official.modeling import model_training_utils
from official.nlp import bert_modeling as modeling
import optimization
import gpu_affinity
import dllogger_class
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.utils.misc import tpu_lib
flags.DEFINE_string('input_files', None,
'File path to retrieve training data for pre-training.')
# Model training specific flags.
flags.DEFINE_integer(
'max_seq_length', 128,
'The maximum total input sequence length after WordPiece tokenization. '
'Sequences longer than this will be truncated, and sequences shorter '
'than this will be padded.')
flags.DEFINE_integer('max_predictions_per_seq', 20,
'Maximum predictions per sequence_output.')
flags.DEFINE_integer('train_batch_size', 32, 'Total batch size for training.')
flags.DEFINE_integer('num_steps_per_epoch', 1000,
'Total number of training steps to run per epoch.')
flags.DEFINE_float('warmup_steps', 10000,
'Warmup steps for Adam weight decay optimizer.')
common_flags.define_common_bert_flags()
FLAGS = flags.FLAGS
def get_pretrain_dataset_fn(input_file_pattern, seq_length,
max_predictions_per_seq, global_batch_size):
"""Returns input dataset from input file string."""
def _dataset_fn(ctx=None):
"""Returns tf.data.Dataset for distributed BERT pretraining."""
input_patterns = input_file_pattern.split(',')
batch_size = ctx.get_per_replica_batch_size(
global_batch_size) if ctx else global_batch_size
train_dataset = input_pipeline.create_pretrain_dataset(
input_patterns,
seq_length,
max_predictions_per_seq,
batch_size,
is_training=True,
input_pipeline_context=ctx,
use_horovod=FLAGS.use_horovod)
return train_dataset
return _dataset_fn
def get_loss_fn(loss_factor=1.0):
"""Returns loss function for BERT pretraining."""
def _bert_pretrain_loss_fn(unused_labels, losses, **unused_args):
return tf.keras.backend.mean(losses) * loss_factor
return _bert_pretrain_loss_fn
def run_customized_training(strategy,
bert_config,
max_seq_length,
max_predictions_per_seq,
model_dir,
steps_per_epoch,
steps_per_loop,
epochs,
initial_lr,
warmup_steps,
input_files,
train_batch_size):
"""Run BERT pretrain model training using low-level API."""
train_input_fn = get_pretrain_dataset_fn(input_files, max_seq_length,
max_predictions_per_seq,
train_batch_size)
def _get_pretrain_model():
"""Gets a pretraining model."""
pretrain_model, core_model = bert_models.pretrain_model(
bert_config, max_seq_length, max_predictions_per_seq, float_type=tf.float16 if FLAGS.use_fp16 else tf.float32)
pretrain_model.optimizer = optimization.create_optimizer(
initial_lr, steps_per_epoch * epochs, warmup_steps, FLAGS.optimizer_type)
if FLAGS.use_fp16:
pretrain_model.optimizer = tf.keras.mixed_precision.LossScaleOptimizer(pretrain_model.optimizer,
dynamic=True)
return pretrain_model, core_model
dllogging = dllogger_class.dllogger_class(FLAGS.dllog_path)
params = {'dllogging' : dllogging, 'FLAGS' : FLAGS}
logging.info("init_lr = %f", initial_lr)
trained_model = model_training_utils.run_customized_training_loop(
strategy=strategy,
model_fn=_get_pretrain_model,
loss_fn=get_loss_fn(
loss_factor=1.0 /
strategy.num_replicas_in_sync if FLAGS.scale_loss and strategy else 1.0),
model_dir=model_dir,
train_input_fn=train_input_fn,
steps_per_epoch=steps_per_epoch,
num_accumulative_step=FLAGS.num_accumulation_steps,
steps_per_loop=steps_per_loop,
epochs=epochs,
sub_model_export_name='pretrained/bert_model',
init_checkpoint=FLAGS.init_checkpoint,
hvd=hvd if FLAGS.use_horovod else None,
params=params)
return trained_model
def run_bert_pretrain(strategy):
"""Runs BERT pre-training."""
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
# Padding for divisibility by 8
# if bert_config.vocab_size % 8 != 0:
# bert_config.vocab_size += 8 - bert_config.vocab_size % 8
if strategy:
logging.info('Training using customized training loop TF 2.0 with distrubuted'
'strategy.')
keras_utils.set_config_v2(FLAGS.enable_xla)
# Runs customized training loop.
return run_customized_training(
strategy,
bert_config,
FLAGS.max_seq_length,
FLAGS.max_predictions_per_seq,
FLAGS.model_dir,
FLAGS.num_steps_per_epoch,
FLAGS.steps_per_loop,
FLAGS.num_train_epochs,
FLAGS.learning_rate * hvd.size() if FLAGS.use_horovod else FLAGS.learning_rate,
FLAGS.warmup_steps,
FLAGS.input_files,
FLAGS.train_batch_size)
def main(_):
# Users should always run this script under TF 2.x
assert tf.version.VERSION.startswith('2.')
if not FLAGS.model_dir:
FLAGS.model_dir = '/tmp/bert20/'
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
if strategy:
print('***** Number of cores used : ', strategy.num_replicas_in_sync)
if FLAGS.use_horovod:
if strategy:
raise ValueError('Should not run horovod with distribution strategy')
hvd.init()
if gpus:
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
gpu_affinity.set_affinity(hvd.local_rank())
if FLAGS.use_fp16:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16")
tf.keras.mixed_precision.experimental.set_policy(policy)
run_bert_pretrain(strategy)
if __name__ == '__main__':
app.run(main)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/run_pretraining.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Run ALBERT on SQuAD 1.1 and SQuAD 2.0 using sentence piece tokenization.
The file is forked from:
https://github.com/google-research/ALBERT/blob/master/run_squad_sp.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
from absl import logging
import numpy as np
import tensorflow as tf
import tokenization
class SquadExample(object):
"""A single training/test example for simple sequence classification.
For examples without an answer, the start and end position are -1.
"""
def __init__(self,
qas_id,
question_text,
paragraph_text,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=False):
self.qas_id = qas_id
self.question_text = question_text
self.paragraph_text = paragraph_text
self.orig_answer_text = orig_answer_text
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ""
s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tok_start_to_orig_index,
tok_end_to_orig_index,
token_is_max_context,
tokens,
input_ids,
input_mask,
segment_ids,
paragraph_len,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tok_start_to_orig_index = tok_start_to_orig_index
self.tok_end_to_orig_index = tok_end_to_orig_index
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.paragraph_len = paragraph_len
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def read_squad_examples(input_file, is_training, version_2_with_negative):
"""Read a SQuAD json file into a list of SquadExample."""
del version_2_with_negative
with tf.io.gfile.GFile(input_file, "r") as reader:
input_data = json.load(reader)["data"]
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
orig_answer_text = None
is_impossible = False
if is_training:
is_impossible = qa.get("is_impossible", False)
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer.")
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
start_position = answer["answer_start"]
else:
start_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
paragraph_text=paragraph_text,
orig_answer_text=orig_answer_text,
start_position=start_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def _convert_index(index, pos, m=None, is_start=True):
"""Converts index."""
if index[pos] is not None:
return index[pos]
n = len(index)
rear = pos
while rear < n - 1 and index[rear] is None:
rear += 1
front = pos
while front > 0 and index[front] is None:
front -= 1
assert index[front] is not None or index[rear] is not None
if index[front] is None:
if index[rear] >= 1:
if is_start:
return 0
else:
return index[rear] - 1
return index[rear]
if index[rear] is None:
if m is not None and index[front] < m - 1:
if is_start:
return index[front] + 1
else:
return m - 1
return index[front]
if is_start:
if index[rear] > index[front] + 1:
return index[front] + 1
else:
return index[rear]
else:
if index[rear] > index[front] + 1:
return index[rear] - 1
else:
return index[front]
def convert_examples_to_features(examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
output_fn,
do_lower_case,
batch_size=None):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
base_id = 1000000000
unique_id = base_id
max_n, max_m = 1024, 1024
f = np.zeros((max_n, max_m), dtype=np.float32)
for (example_index, example) in enumerate(examples):
if example_index % 100 == 0:
logging.info("Converting %d/%d pos %d neg %d", example_index,
len(examples), cnt_pos, cnt_neg)
query_tokens = tokenization.encode_ids(
tokenizer.sp_model,
tokenization.preprocess_text(
example.question_text, lower=do_lower_case))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = tokenization.encode_pieces(
tokenizer.sp_model,
tokenization.preprocess_text(
example.paragraph_text, lower=do_lower_case))
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
for i, token in enumerate(para_tokens):
new_token = token.replace(tokenization.SPIECE_UNDERLINE, " ")
chartok_to_tok_index.extend([i] * len(new_token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(new_token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = "".join(para_tokens).replace(tokenization.SPIECE_UNDERLINE,
" ")
n, m = len(paragraph_text), len(tok_cat_text)
if n > max_n or m > max_m:
max_n = max(n, max_n)
max_m = max(m, max_m)
f = np.zeros((max_n, max_m), dtype=np.float32)
g = {}
# pylint: disable=cell-var-from-loop
def _lcs_match(max_dist, n=n, m=m):
"""Longest-common-substring algorithm."""
f.fill(0)
g.clear()
### longest common sub sequence
# f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
for i in range(n):
# unlike standard LCS, this is specifically optimized for the setting
# because the mismatch between sentence pieces and original text will
# be small
for j in range(i - max_dist, i + max_dist):
if j >= m or j < 0:
continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (tokenization.preprocess_text(
paragraph_text[i], lower=do_lower_case,
remove_space=False) == tok_cat_text[j] and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
# pylint: enable=cell-var-from-loop
max_dist = abs(n - m) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[n - 1, m - 1] > 0.8 * n:
break
max_dist *= 2
orig_to_chartok_index = [None] * n
chartok_to_orig_index = [None] * m
i, j = n - 1, m - 1
while i >= 0 and j >= 0:
if (i, j) not in g:
break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if (all(v is None for v in orig_to_chartok_index) or
f[n - 1, m - 1] < 0.8 * n):
logging.info("MISMATCH DETECTED!")
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(
chartok_to_orig_index, start_chartok_pos, n, is_start=True)
end_orig_pos = _convert_index(
chartok_to_orig_index, end_chartok_pos, n, is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = 0
tok_end_position = 0
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(
orig_to_chartok_index, start_position, is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(
orig_to_chartok_index, end_position, is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
return tokenizer.sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_is_max_context = {}
segment_ids = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
tokens.append(tokenizer.sp_model.PieceToId("[CLS]"))
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_to_orig_index[split_token_index])
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
segment_ids.append(1)
tokens.append(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(1)
paragraph_len = len(tokens)
input_ids = tokens
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start and
tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
# continue
start_position = 0
end_position = 0
span_is_impossible = True
else:
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and span_is_impossible:
start_position = 0
end_position = 0
if example_index < 20:
logging.info("*** Example ***")
logging.info("unique_id: %s", (unique_id))
logging.info("example_index: %s", (example_index))
logging.info("doc_span_index: %s", (doc_span_index))
logging.info("tok_start_to_orig_index: %s",
" ".join([str(x) for x in cur_tok_start_to_orig_index]))
logging.info("tok_end_to_orig_index: %s",
" ".join([str(x) for x in cur_tok_end_to_orig_index]))
logging.info(
"token_is_max_context: %s", " ".join(
["%d:%s" % (x, y) for (x, y) in token_is_max_context.items()]))
logging.info(
"input_pieces: %s",
" ".join([tokenizer.sp_model.IdToPiece(x) for x in tokens]))
logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
if is_training and span_is_impossible:
logging.info("impossible example span")
if is_training and not span_is_impossible:
pieces = [
tokenizer.sp_model.IdToPiece(token)
for token in tokens[start_position:(end_position + 1)]
]
answer_text = tokenizer.sp_model.DecodePieces(pieces)
logging.info("start_position: %d", (start_position))
logging.info("end_position: %d", (end_position))
logging.info("answer: %s", (tokenization.printable_text(answer_text)))
# With multi processing, the example_index is actually the index
# within the current process therefore we use example_index=None
# to avoid being used in the future.
# The current code does not use example_index of training data.
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible)
# Run callback
if is_training:
output_fn(feature)
else:
output_fn(feature, is_padding=False)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
if not is_training and feature:
assert batch_size
num_padding = 0
num_examples = unique_id - base_id
if unique_id % batch_size != 0:
num_padding = batch_size - (num_examples % batch_size)
dummy_feature = copy.deepcopy(feature)
for _ in range(num_padding):
dummy_feature.unique_id = unique_id
# Run callback
output_fn(feature, is_padding=True)
unique_id += 1
logging.info("Total number of instances: %d = pos %d neg %d",
cnt_pos + cnt_neg, cnt_pos, cnt_neg)
return unique_id - base_id
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
def write_predictions(all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
version_2_with_negative=False,
null_score_diff_threshold=0.0,
verbose=False):
"""Write final predictions to the json file and log-odds of null if needed."""
del do_lower_case, verbose
logging.info("Writing predictions to: %s", (output_prediction_file))
logging.info("Writing nbest to: %s", (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
doc_offset = feature.tokens.index("[SEP]") + 1
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index - doc_offset >= len(feature.tok_start_to_orig_index):
continue
if end_index - doc_offset >= len(feature.tok_end_to_orig_index):
continue
# if start_index not in feature.tok_start_to_orig_index:
# continue
# if end_index not in feature.tok_end_to_orig_index:
# continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index - doc_offset,
end_index=end_index - doc_offset,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=-1,
end_index=-1,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index >= 0: # this is a non-null prediction
tok_start_to_orig_index = feature.tok_start_to_orig_index
tok_end_to_orig_index = feature.tok_end_to_orig_index
start_orig_pos = tok_start_to_orig_index[pred.start_index]
end_orig_pos = tok_end_to_orig_index[pred.end_index]
paragraph_text = example.paragraph_text
final_text = paragraph_text[start_orig_pos:end_orig_pos + 1].strip()
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't inlude the empty option in the n-best, inlcude it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(
text="", start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
assert best_non_null_entry is not None
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (
best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with tf.io.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with tf.io.gfile.GFile(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with tf.io.gfile.GFile(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
class FeatureWriter(object):
"""Writes InputFeature to TF example file."""
def __init__(self, filename, is_training):
self.filename = filename
self.is_training = is_training
self.num_features = 0
self._writer = tf.io.TFRecordWriter(filename)
def process_feature(self, feature):
"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
self.num_features += 1
def create_int_feature(values):
feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=list(values)))
return feature
features = collections.OrderedDict()
features["unique_ids"] = create_int_feature([feature.unique_id])
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
if self.is_training:
features["start_positions"] = create_int_feature([feature.start_position])
features["end_positions"] = create_int_feature([feature.end_position])
impossible = 0
if feature.is_impossible:
impossible = 1
features["is_impossible"] = create_int_feature([impossible])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
self._writer.write(tf_example.SerializeToString())
def close(self):
self._writer.close()
def generate_tf_record_from_json_file(input_file_path,
sp_model_file,
output_path,
max_seq_length=384,
do_lower_case=True,
max_query_length=64,
doc_stride=128,
version_2_with_negative=False):
"""Generates and saves training data into a tf record file."""
train_examples = read_squad_examples(
input_file=input_file_path,
is_training=True,
version_2_with_negative=version_2_with_negative)
tokenizer = tokenization.FullSentencePieceTokenizer(
sp_model_file=sp_model_file)
train_writer = FeatureWriter(filename=output_path, is_training=True)
number_of_examples = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
do_lower_case=do_lower_case)
train_writer.close()
meta_data = {
"task_type": "bert_squad",
"train_data_size": number_of_examples,
"max_seq_length": max_seq_length,
"max_query_length": max_query_length,
"doc_stride": doc_stride,
"version_2_with_negative": version_2_with_negative,
}
return meta_data
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/squad_lib_sp.py |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from dllogger import Logger, StdOutBackend, JSONStreamBackend, Verbosity
import numpy
class dllogger_class():
def format_step(self, step):
if isinstance(step, str):
return step
elif isinstance(step, int):
return "Iteration: {} ".format(step)
elif len(step) > 0:
return "Iteration: {} ".format(step[0])
else:
return ""
def __init__(self, log_path="bert_dllog.json"):
self.logger = Logger([
StdOutBackend(Verbosity.DEFAULT, step_format=self.format_step),
JSONStreamBackend(Verbosity.VERBOSE, log_path),
])
self.logger.metadata("mlm_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("nsp_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("avg_loss_step", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("total_loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("loss", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
self.logger.metadata("f1", {"unit": None, "format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("precision", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("recall", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("mcc", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata("exact_match", {"format": ":.4f", "GOAL": "MINIMIZE", "STAGE": "VAL"})
self.logger.metadata(
"throughput_train",
{"unit": "sequences/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "TRAIN"},
)
self.logger.metadata(
"throughput_inf",
{"unit": "sequences/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "VAL"},
)
self.logger.metadata(
"throughput_val",
{"unit": "sequences/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "VAL"},
)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/dllogger_class.py |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""BERT classification finetuning runner in tf2.0."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import math
import os
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
import horovod.tensorflow as hvd
# Import BERT model libraries.
from official.nlp import bert_models
import common_flags
import input_pipeline
import model_saving_utils
from official.modeling import model_training_utils
from official.nlp import bert_modeling as modeling
import optimization
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
flags.DEFINE_enum(
'mode', 'train_and_eval', ['train_and_eval', 'export_only'],
'One of {"train_and_eval", "export_only"}. `train_and_eval`: '
'trains the model and evaluates in the meantime. '
'`export_only`: will take the latest checkpoint inside '
'model_dir and export a `SavedModel`.')
flags.DEFINE_string('train_data_path', None,
'Path to training data for BERT classifier.')
flags.DEFINE_string('eval_data_path', None,
'Path to evaluation data for BERT classifier.')
# Model training specific flags.
flags.DEFINE_string(
'input_meta_data_path', None,
'Path to file that contains meta data about input '
'to be used for training and evaluation.')
flags.DEFINE_integer('train_batch_size', 32, 'Batch size for training.')
flags.DEFINE_integer('eval_batch_size', 32, 'Batch size for evaluation.')
common_flags.define_common_bert_flags()
FLAGS = flags.FLAGS
def get_loss_fn(num_classes, loss_factor=1.0):
"""Gets the classification loss function."""
def classification_loss_fn(labels, logits):
"""Classification loss."""
labels = tf.squeeze(labels)
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(
tf.cast(labels, dtype=tf.int32), depth=num_classes, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(
tf.cast(one_hot_labels, dtype=tf.float32) * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
loss *= loss_factor
return loss
return classification_loss_fn
def get_dataset_fn(input_file_pattern, max_seq_length, global_batch_size,
is_training, use_horovod):
"""Gets a closure to create a dataset."""
def _dataset_fn(ctx=None):
"""Returns tf.data.Dataset for distributed BERT pretraining."""
batch_size = ctx.get_per_replica_batch_size(
global_batch_size) if ctx else global_batch_size
dataset = input_pipeline.create_classifier_dataset(
input_file_pattern,
max_seq_length,
batch_size,
is_training=is_training,
input_pipeline_context=ctx,
use_horovod=use_horovod)
return dataset
return _dataset_fn
def run_bert_classifier(strategy,
bert_config,
input_meta_data,
model_dir,
epochs,
steps_per_epoch,
steps_per_loop,
eval_steps,
warmup_steps,
initial_lr,
init_checkpoint,
train_input_fn,
eval_input_fn,
custom_callbacks=None,
run_eagerly=False,
use_keras_compile_fit=False):
"""Run BERT classifier training using low-level API."""
max_seq_length = input_meta_data['max_seq_length']
num_classes = input_meta_data['num_labels']
def _get_classifier_model():
"""Gets a classifier model."""
classifier_model, core_model = (
bert_models.classifier_model(
bert_config,
tf.float32,
num_classes,
max_seq_length,
hub_module_url=FLAGS.hub_module_url))
classifier_model.optimizer = optimization.create_optimizer(
initial_lr, steps_per_epoch * epochs, warmup_steps)
if FLAGS.fp16_implementation == 'graph_rewrite':
# Note: when flags_obj.fp16_implementation == "graph_rewrite", dtype as
# determined by flags_core.get_tf_dtype(flags_obj) would be 'float32'
# which will ensure tf.compat.v2.keras.mixed_precision and
# tf.train.experimental.enable_mixed_precision_graph_rewrite do not double
# up.
classifier_model.optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
classifier_model.optimizer)
return classifier_model, core_model
# During distributed training, loss used for gradient computation is
# summed over from all replicas. When Keras compile/fit() API is used,
# the fit() API internally normalizes the loss by dividing the loss by
# the number of replicas used for computation. However, when custom
# training loop is used this is not done automatically and should be
# done manually by the end user.
loss_multiplier = 1.0
if FLAGS.scale_loss and strategy and not use_keras_compile_fit:
loss_multiplier = 1.0 / strategy.num_replicas_in_sync
loss_fn = get_loss_fn(num_classes, loss_factor=loss_multiplier)
# Defines evaluation metrics function, which will create metrics in the
# correct device and strategy scope.
def metric_fn():
return tf.keras.metrics.SparseCategoricalAccuracy(
'test_accuracy', dtype=tf.float32)
if use_keras_compile_fit:
# Start training using Keras compile/fit API.
logging.info('Training using TF 2.0 Keras compile/fit API with '
'distribution strategy.')
return run_keras_compile_fit(
model_dir,
strategy,
_get_classifier_model,
train_input_fn,
eval_input_fn,
loss_fn,
metric_fn,
init_checkpoint,
epochs,
steps_per_epoch,
eval_steps,
custom_callbacks=None)
# Use user-defined loop to start training.
logging.info('Training using customized training loop TF 2.0 with '
'distribution strategy.')
return model_training_utils.run_customized_training_loop(
strategy=strategy,
model_fn=_get_classifier_model,
loss_fn=loss_fn,
model_dir=model_dir,
steps_per_epoch=steps_per_epoch,
steps_per_loop=steps_per_loop,
epochs=epochs,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
eval_steps=eval_steps,
init_checkpoint=init_checkpoint,
metric_fn=metric_fn,
hvd=hvd if FLAGS.use_horovod else None,
custom_callbacks=custom_callbacks,
run_eagerly=run_eagerly)
def run_keras_compile_fit(model_dir,
strategy,
model_fn,
train_input_fn,
eval_input_fn,
loss_fn,
metric_fn,
init_checkpoint,
epochs,
steps_per_epoch,
eval_steps,
custom_callbacks=None):
"""Runs BERT classifier model using Keras compile/fit API."""
with distribution_utils.get_strategy_scope(strategy):
training_dataset = train_input_fn()
evaluation_dataset = eval_input_fn()
bert_model, sub_model = model_fn()
optimizer = bert_model.optimizer
if init_checkpoint:
checkpoint = tf.train.Checkpoint(model=sub_model)
checkpoint.restore(init_checkpoint).assert_existing_objects_matched()
bert_model.compile(optimizer=optimizer, loss=loss_fn, metrics=[metric_fn()])
summary_dir = os.path.join(model_dir, 'summaries')
summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
checkpoint_path = os.path.join(model_dir, 'checkpoint')
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
checkpoint_path, save_weights_only=True)
if custom_callbacks is not None:
custom_callbacks += [summary_callback, checkpoint_callback]
else:
custom_callbacks = [summary_callback, checkpoint_callback]
bert_model.fit(
x=training_dataset,
validation_data=evaluation_dataset,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_steps=eval_steps,
callbacks=custom_callbacks)
return bert_model
def export_classifier(model_export_path, input_meta_data,
restore_model_using_load_weights,
bert_config, model_dir):
"""Exports a trained model as a `SavedModel` for inference.
Args:
model_export_path: a string specifying the path to the SavedModel directory.
input_meta_data: dictionary containing meta data about input and model.
restore_model_using_load_weights: Whether to use checkpoint.restore() API
for custom checkpoint or to use model.load_weights() API.
There are 2 different ways to save checkpoints. One is using
tf.train.Checkpoint and another is using Keras model.save_weights().
Custom training loop implementation uses tf.train.Checkpoint API
and Keras ModelCheckpoint callback internally uses model.save_weights()
API. Since these two API's cannot be used together, model loading logic
must be take into account how model checkpoint was saved.
bert_config: Bert configuration file to define core bert layers.
model_dir: The directory where the model weights and training/evaluation
summaries are stored.
Raises:
Export path is not specified, got an empty string or None.
"""
if not model_export_path:
raise ValueError('Export path is not specified: %s' % model_export_path)
if not model_dir:
raise ValueError('Export path is not specified: %s' % model_dir)
classifier_model = bert_models.classifier_model(
bert_config, tf.float32, input_meta_data['num_labels'],
input_meta_data['max_seq_length'])[0]
model_saving_utils.export_bert_model(
model_export_path,
model=classifier_model,
checkpoint_dir=model_dir,
restore_model_using_load_weights=restore_model_using_load_weights)
def run_bert(strategy,
input_meta_data,
train_input_fn=None,
eval_input_fn=None):
"""Run BERT training."""
if FLAGS.model_type == 'bert':
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
else:
assert FLAGS.model_type == 'albert'
bert_config = modeling.AlbertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.mode == 'export_only':
# As Keras ModelCheckpoint callback used with Keras compile/fit() API
# internally uses model.save_weights() to save checkpoints, we must
# use model.load_weights() when Keras compile/fit() is used.
export_classifier(FLAGS.model_export_path, input_meta_data,
FLAGS.use_keras_compile_fit,
bert_config, FLAGS.model_dir)
return
if FLAGS.mode != 'train_and_eval':
raise ValueError('Unsupported mode is specified: %s' % FLAGS.mode)
# Enables XLA in Session Config. Should not be set for TPU.
keras_utils.set_config_v2(FLAGS.enable_xla)
epochs = FLAGS.num_train_epochs
train_data_size = input_meta_data['train_data_size']
global_batch_size = FLAGS.train_batch_size
learning_rate = FLAGS.learning_rate
if FLAGS.use_horovod:
global_batch_size *= hvd.size()
learning_rate *= hvd.size()
steps_per_epoch = int(train_data_size / global_batch_size)
warmup_steps = int(epochs * train_data_size * 0.1 / global_batch_size)
eval_steps = int(
math.ceil(input_meta_data['eval_data_size'] / FLAGS.eval_batch_size))
if strategy:
# Runs customized training loop.
logging.info('Training using customized training loop TF 2.0 with distrubuted'
'strategy.')
trained_model = run_bert_classifier(
strategy,
bert_config,
input_meta_data,
FLAGS.model_dir,
epochs,
steps_per_epoch,
FLAGS.steps_per_loop,
eval_steps,
warmup_steps,
learning_rate,
FLAGS.init_checkpoint,
train_input_fn,
eval_input_fn,
run_eagerly=FLAGS.run_eagerly,
use_keras_compile_fit=FLAGS.use_keras_compile_fit)
if FLAGS.model_export_path:
model_saving_utils.export_bert_model(
FLAGS.model_export_path,
model=trained_model,
restore_model_using_load_weights=FLAGS.use_keras_compile_fit)
return trained_model
def main(_):
# Users should always run this script under TF 2.x
assert tf.version.VERSION.startswith('2.')
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
if FLAGS.use_fp16:
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
if FLAGS.use_horovod:
if strategy:
raise ValueError('Should not run horovod with distribution strategy')
hvd.init()
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
if not FLAGS.model_dir:
FLAGS.model_dir = '/tmp/bert20/'
max_seq_length = input_meta_data['max_seq_length']
train_input_fn = get_dataset_fn(
FLAGS.train_data_path,
max_seq_length,
FLAGS.train_batch_size,
is_training=True,
use_horovod=FLAGS.use_horovod)
eval_input_fn = get_dataset_fn(
FLAGS.eval_data_path,
max_seq_length,
FLAGS.eval_batch_size,
is_training=False,
use_horovod=FLAGS.use_horovod)
run_bert(strategy, input_meta_data, train_input_fn, eval_input_fn)
if __name__ == '__main__':
flags.mark_flag_as_required('bert_config_file')
flags.mark_flag_as_required('input_meta_data_path')
flags.mark_flag_as_required('model_dir')
app.run(main)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/run_classifier.py |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
from tensorflow.python.compiler.tensorrt import trt_convert as trt
from tensorflow.compat.v1.saved_model import tag_constants, signature_constants
def export_model(model_dir, prec, tf_trt_model_dir=None):
model = tf.saved_model.load(model_dir)
input_shape = [1, 384]
dummy_input = tf.constant(tf.zeros(input_shape, dtype=tf.int32))
x = [
tf.constant(dummy_input, name='input_word_ids'),
tf.constant(dummy_input, name='input_mask'),
tf.constant(dummy_input, name='input_type_ids'),
]
_ = model(x)
trt_prec = trt.TrtPrecisionMode.FP32 if prec == "fp32" else trt.TrtPrecisionMode.FP16
converter = trt.TrtGraphConverterV2(
input_saved_model_dir=model_dir,
conversion_params=trt.TrtConversionParams(precision_mode=trt_prec),
)
converter.convert()
tf_trt_model_dir = tf_trt_model_dir or f'/tmp/tf-trt_model_{prec}'
converter.save(tf_trt_model_dir)
print(f"TF-TRT model saved at {tf_trt_model_dir}")
class SavedModel:
def __init__(self, model_dir, precision):
self.saved_model_loaded = tf.saved_model.load(model_dir, tags=[tag_constants.SERVING])
self.graph_func = self.saved_model_loaded.signatures[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
self.precision = tf.float16 if precision == "amp" else tf.float32
def __call__(self, x, **kwargs):
return self.infer_step(x)
@tf.function
def infer_step(self, x):
output = self.graph_func(**x)
return output['start_positions'], output['end_positions']
class TFTRTModel:
def __init__(self, model_dir, precision):
temp_tftrt_dir = f"/tmp/tf-trt_model_{precision}"
export_model(model_dir, precision, temp_tftrt_dir)
saved_model_loaded = tf.saved_model.load(temp_tftrt_dir, tags=[tag_constants.SERVING])
print(f"TF-TRT model loaded from {temp_tftrt_dir}")
self.graph_func = saved_model_loaded.signatures[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
self.precision = tf.float16 if precision == "amp" else tf.float32
def __call__(self, x, **kwargs):
return self.infer_step(x)
@tf.function
def infer_step(self, x):
output = self.graph_func(**x)
return output['start_positions'], output['end_positions']
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/tf_trt.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Create masked LM/next sentence masked_lm TF examples for BERT."""
import collections
import itertools
import random
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
import tokenization
FLAGS = flags.FLAGS
flags.DEFINE_string("input_file", None,
"Input raw text file (or comma-separated list of files).")
flags.DEFINE_string(
"output_file", None,
"Output TF example file (or comma-separated list of files).")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_bool(
"do_whole_word_mask", False,
"Whether to use whole word masking rather than per-WordPiece masking.")
flags.DEFINE_integer(
"max_ngram_size", None,
"Mask contiguous whole words (n-grams) of up to `max_ngram_size` using a "
"weighting scheme to favor shorter n-grams. "
"Note: `--do_whole_word_mask=True` must also be set when n-gram masking.")
flags.DEFINE_bool(
"gzip_compress", False,
"Whether to use `GZIP` compress option to get compressed TFRecord files.")
flags.DEFINE_bool(
"use_v2_feature_names", False,
"Whether to use the feature names consistent with the models.")
flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")
flags.DEFINE_integer("max_predictions_per_seq", 20,
"Maximum number of masked LM predictions per sequence.")
flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
flags.DEFINE_integer(
"dupe_factor", 10,
"Number of times to duplicate the input data (with different masks).")
flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
flags.DEFINE_float(
"short_seq_prob", 0.1,
"Probability of creating sequences which are shorter than the "
"maximum length.")
class TrainingInstance(object):
"""A single training instance (sentence pair)."""
def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
is_random_next):
self.tokens = tokens
self.segment_ids = segment_ids
self.is_random_next = is_random_next
self.masked_lm_positions = masked_lm_positions
self.masked_lm_labels = masked_lm_labels
def __str__(self):
s = ""
s += "tokens: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens]))
s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
s += "is_random_next: %s\n" % self.is_random_next
s += "masked_lm_positions: %s\n" % (" ".join(
[str(x) for x in self.masked_lm_positions]))
s += "masked_lm_labels: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.masked_lm_labels]))
s += "\n"
return s
def __repr__(self):
return self.__str__()
def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files,
gzip_compress, use_v2_feature_names):
"""Creates TF example files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(
tf.io.TFRecordWriter(
output_file, options="GZIP" if gzip_compress else ""))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
next_sentence_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
if use_v2_feature_names:
features["input_word_ids"] = create_int_feature(input_ids)
features["input_type_ids"] = create_int_feature(segment_ids)
else:
features["input_ids"] = create_int_feature(input_ids)
features["segment_ids"] = create_int_feature(segment_ids)
features["input_mask"] = create_int_feature(input_mask)
features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
features["next_sentence_labels"] = create_int_feature([next_sentence_label])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
if inst_index < 20:
logging.info("*** Example ***")
logging.info("tokens: %s", " ".join(
[tokenization.printable_text(x) for x in instance.tokens]))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
logging.info("%s: %s", feature_name, " ".join([str(x) for x in values]))
for writer in writers:
writer.close()
logging.info("Wrote %d total instances", total_written)
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return feature
def create_float_feature(values):
feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return feature
def create_training_instances(input_files,
tokenizer,
max_seq_length,
dupe_factor,
short_seq_prob,
masked_lm_prob,
max_predictions_per_seq,
rng,
do_whole_word_mask=False,
max_ngram_size=None):
"""Create `TrainingInstance`s from raw text."""
all_documents = [[]]
# Input file format:
# (1) One sentence per line. These should ideally be actual sentences, not
# entire paragraphs or arbitrary spans of text. (Because we use the
# sentence boundaries for the "next sentence prediction" task).
# (2) Blank lines between documents. Document boundaries are needed so
# that the "next sentence prediction" task doesn't span between documents.
for input_file in input_files:
with tf.io.gfile.GFile(input_file, "rb") as reader:
while True:
line = tokenization.convert_to_unicode(reader.readline())
if not line:
break
line = line.strip()
# Empty lines are used as document delimiters
if not line:
all_documents.append([])
tokens = tokenizer.tokenize(line)
if tokens:
all_documents[-1].append(tokens)
# Remove empty documents
all_documents = [x for x in all_documents if x]
rng.shuffle(all_documents)
vocab_words = list(tokenizer.vocab.keys())
instances = []
for _ in range(dupe_factor):
for document_index in range(len(all_documents)):
instances.extend(
create_instances_from_document(
all_documents, document_index, max_seq_length, short_seq_prob,
masked_lm_prob, max_predictions_per_seq, vocab_words, rng,
do_whole_word_mask, max_ngram_size))
rng.shuffle(instances)
return instances
def create_instances_from_document(
all_documents, document_index, max_seq_length, short_seq_prob,
masked_lm_prob, max_predictions_per_seq, vocab_words, rng,
do_whole_word_mask=False,
max_ngram_size=None):
"""Creates `TrainingInstance`s for a single document."""
document = all_documents[document_index]
# Account for [CLS], [SEP], [SEP]
max_num_tokens = max_seq_length - 3
# We *usually* want to fill up the entire sequence since we are padding
# to `max_seq_length` anyways, so short sequences are generally wasted
# computation. However, we *sometimes*
# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
# sequences to minimize the mismatch between pre-training and fine-tuning.
# The `target_seq_length` is just a rough target however, whereas
# `max_seq_length` is a hard limit.
target_seq_length = max_num_tokens
if rng.random() < short_seq_prob:
target_seq_length = rng.randint(2, max_num_tokens)
# We DON'T just concatenate all of the tokens from a document into a long
# sequence and choose an arbitrary split point because this would make the
# next sentence prediction task too easy. Instead, we split the input into
# segments "A" and "B" based on the actual "sentences" provided by the user
# input.
instances = []
current_chunk = []
current_length = 0
i = 0
while i < len(document):
segment = document[i]
current_chunk.append(segment)
current_length += len(segment)
if i == len(document) - 1 or current_length >= target_seq_length:
if current_chunk:
# `a_end` is how many segments from `current_chunk` go into the `A`
# (first) sentence.
a_end = 1
if len(current_chunk) >= 2:
a_end = rng.randint(1, len(current_chunk) - 1)
tokens_a = []
for j in range(a_end):
tokens_a.extend(current_chunk[j])
tokens_b = []
# Random next
is_random_next = False
if len(current_chunk) == 1 or rng.random() < 0.5:
is_random_next = True
target_b_length = target_seq_length - len(tokens_a)
# This should rarely go for more than one iteration for large
# corpora. However, just to be careful, we try to make sure that
# the random document is not the same as the document
# we're processing.
for _ in range(10):
random_document_index = rng.randint(0, len(all_documents) - 1)
if random_document_index != document_index:
break
random_document = all_documents[random_document_index]
random_start = rng.randint(0, len(random_document) - 1)
for j in range(random_start, len(random_document)):
tokens_b.extend(random_document[j])
if len(tokens_b) >= target_b_length:
break
# We didn't actually use these segments so we "put them back" so
# they don't go to waste.
num_unused_segments = len(current_chunk) - a_end
i -= num_unused_segments
# Actual next
else:
is_random_next = False
for j in range(a_end, len(current_chunk)):
tokens_b.extend(current_chunk[j])
truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)
assert len(tokens_a) >= 1
assert len(tokens_b) >= 1
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
(tokens, masked_lm_positions,
masked_lm_labels) = create_masked_lm_predictions(
tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng,
do_whole_word_mask, max_ngram_size)
instance = TrainingInstance(
tokens=tokens,
segment_ids=segment_ids,
is_random_next=is_random_next,
masked_lm_positions=masked_lm_positions,
masked_lm_labels=masked_lm_labels)
instances.append(instance)
current_chunk = []
current_length = 0
i += 1
return instances
MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
["index", "label"])
# A _Gram is a [half-open) interval of token indices which form a word.
# E.g.,
# words: ["The", "doghouse"]
# tokens: ["The", "dog", "##house"]
# grams: [(0,1), (1,3)]
_Gram = collections.namedtuple("_Gram", ["begin", "end"])
def _window(iterable, size):
"""Helper to create a sliding window iterator with a given size.
E.g.,
input = [1, 2, 3, 4]
_window(input, 1) => [1], [2], [3], [4]
_window(input, 2) => [1, 2], [2, 3], [3, 4]
_window(input, 3) => [1, 2, 3], [2, 3, 4]
_window(input, 4) => [1, 2, 3, 4]
_window(input, 5) => None
Args:
iterable: elements to iterate over.
size: size of the window.
Yields:
Elements of `iterable` batched into a sliding window of length `size`.
"""
i = iter(iterable)
window = []
try:
for e in range(0, size):
window.append(next(i))
yield window
except StopIteration:
# handle the case where iterable's length is less than the window size.
return
for e in i:
window = window[1:] + [e]
yield window
def _contiguous(sorted_grams):
"""Test whether a sequence of grams is contiguous.
Args:
sorted_grams: _Grams which are sorted in increasing order.
Returns:
True if `sorted_grams` are touching each other.
E.g.,
_contiguous([(1, 4), (4, 5), (5, 10)]) == True
_contiguous([(1, 2), (4, 5)]) == False
"""
for a, b in _window(sorted_grams, 2):
if a.end != b.begin:
return False
return True
def _masking_ngrams(grams, max_ngram_size, max_masked_tokens, rng):
"""Create a list of masking {1, ..., n}-grams from a list of one-grams.
This is an extention of 'whole word masking' to mask multiple, contiguous
words such as (e.g., "the red boat").
Each input gram represents the token indices of a single word,
words: ["the", "red", "boat"]
tokens: ["the", "red", "boa", "##t"]
grams: [(0,1), (1,2), (2,4)]
For a `max_ngram_size` of three, possible outputs masks include:
1-grams: (0,1), (1,2), (2,4)
2-grams: (0,2), (1,4)
3-grams; (0,4)
Output masks will not overlap and contain less than `max_masked_tokens` total
tokens. E.g., for the example above with `max_masked_tokens` as three,
valid outputs are,
[(0,1), (1,2)] # "the", "red" covering two tokens
[(1,2), (2,4)] # "red", "boa", "##t" covering three tokens
The length of the selected n-gram follows a zipf weighting to
favor shorter n-gram sizes (weight(1)=1, weight(2)=1/2, weight(3)=1/3, ...).
Args:
grams: List of one-grams.
max_ngram_size: Maximum number of contiguous one-grams combined to create
an n-gram.
max_masked_tokens: Maximum total number of tokens to be masked.
rng: `random.Random` generator.
Returns:
A list of n-grams to be used as masks.
"""
if not grams:
return None
grams = sorted(grams)
num_tokens = grams[-1].end
# Ensure our grams are valid (i.e., they don't overlap).
for a, b in _window(grams, 2):
if a.end > b.begin:
raise ValueError("overlapping grams: {}".format(grams))
# Build map from n-gram length to list of n-grams.
ngrams = {i: [] for i in range(1, max_ngram_size+1)}
for gram_size in range(1, max_ngram_size+1):
for g in _window(grams, gram_size):
if _contiguous(g):
# Add an n-gram which spans these one-grams.
ngrams[gram_size].append(_Gram(g[0].begin, g[-1].end))
# Shuffle each list of n-grams.
for v in ngrams.values():
rng.shuffle(v)
# Create the weighting for n-gram length selection.
# Stored cummulatively for `random.choices` below.
cummulative_weights = list(
itertools.accumulate([1./n for n in range(1, max_ngram_size+1)]))
output_ngrams = []
# Keep a bitmask of which tokens have been masked.
masked_tokens = [False] * num_tokens
# Loop until we have enough masked tokens or there are no more candidate
# n-grams of any length.
# Each code path should ensure one or more elements from `ngrams` are removed
# to guarentee this loop terminates.
while (sum(masked_tokens) < max_masked_tokens and
sum(len(s) for s in ngrams.values())):
# Pick an n-gram size based on our weights.
sz = random.choices(range(1, max_ngram_size+1),
cum_weights=cummulative_weights)[0]
# Ensure this size doesn't result in too many masked tokens.
# E.g., a two-gram contains _at least_ two tokens.
if sum(masked_tokens) + sz > max_masked_tokens:
# All n-grams of this length are too long and can be removed from
# consideration.
ngrams[sz].clear()
continue
# All of the n-grams of this size have been used.
if not ngrams[sz]:
continue
# Choose a random n-gram of the given size.
gram = ngrams[sz].pop()
num_gram_tokens = gram.end-gram.begin
# Check if this would add too many tokens.
if num_gram_tokens + sum(masked_tokens) > max_masked_tokens:
continue
# Check if any of the tokens in this gram have already been masked.
if sum(masked_tokens[gram.begin:gram.end]):
continue
# Found a usable n-gram! Mark its tokens as masked and add it to return.
masked_tokens[gram.begin:gram.end] = [True] * (gram.end-gram.begin)
output_ngrams.append(gram)
return output_ngrams
def _wordpieces_to_grams(tokens):
"""Reconstitue grams (words) from `tokens`.
E.g.,
tokens: ['[CLS]', 'That', 'lit', '##tle', 'blue', 'tru', '##ck', '[SEP]']
grams: [ [1,2), [2, 4), [4,5) , [5, 6)]
Args:
tokens: list of wordpieces
Returns:
List of _Grams representing spans of whole words
(without "[CLS]" and "[SEP]").
"""
grams = []
gram_start_pos = None
for i, token in enumerate(tokens):
if gram_start_pos is not None and token.startswith("##"):
continue
if gram_start_pos is not None:
grams.append(_Gram(gram_start_pos, i))
if token not in ["[CLS]", "[SEP]"]:
gram_start_pos = i
else:
gram_start_pos = None
if gram_start_pos is not None:
grams.append(_Gram(gram_start_pos, len(tokens)))
return grams
def create_masked_lm_predictions(tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng,
do_whole_word_mask,
max_ngram_size=None):
"""Creates the predictions for the masked LM objective."""
if do_whole_word_mask:
grams = _wordpieces_to_grams(tokens)
else:
# Here we consider each token to be a word to allow for sub-word masking.
if max_ngram_size:
raise ValueError("cannot use ngram masking without whole word masking")
grams = [_Gram(i, i+1) for i in range(0, len(tokens))
if tokens[i] not in ["[CLS]", "[SEP]"]]
num_to_predict = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
# Generate masks. If `max_ngram_size` in [0, None] it means we're doing
# whole word masking or token level masking. Both of these can be treated
# as the `max_ngram_size=1` case.
masked_grams = _masking_ngrams(grams, max_ngram_size or 1,
num_to_predict, rng)
masked_lms = []
output_tokens = list(tokens)
for gram in masked_grams:
# 80% of the time, replace all n-gram tokens with [MASK]
if rng.random() < 0.8:
replacement_action = lambda idx: "[MASK]"
else:
# 10% of the time, keep all the original n-gram tokens.
if rng.random() < 0.5:
replacement_action = lambda idx: tokens[idx]
# 10% of the time, replace each n-gram token with a random word.
else:
replacement_action = lambda idx: rng.choice(vocab_words)
for idx in range(gram.begin, gram.end):
output_tokens[idx] = replacement_action(idx)
masked_lms.append(MaskedLmInstance(index=idx, label=tokens[idx]))
assert len(masked_lms) <= num_to_predict
masked_lms = sorted(masked_lms, key=lambda x: x.index)
masked_lm_positions = []
masked_lm_labels = []
for p in masked_lms:
masked_lm_positions.append(p.index)
masked_lm_labels.append(p.label)
return (output_tokens, masked_lm_positions, masked_lm_labels)
def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
"""Truncates a pair of sequences to a maximum sequence length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_num_tokens:
break
trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
assert len(trunc_tokens) >= 1
# We want to sometimes truncate from the front and sometimes from the
# back to add more randomness and avoid biases.
if rng.random() < 0.5:
del trunc_tokens[0]
else:
trunc_tokens.pop()
def main(_):
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
logging.info("*** Reading from input files ***")
for input_file in input_files:
logging.info(" %s", input_file)
rng = random.Random(FLAGS.random_seed)
instances = create_training_instances(
input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor,
FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq,
rng, FLAGS.do_whole_word_mask, FLAGS.max_ngram_size)
output_files = FLAGS.output_file.split(",")
logging.info("*** Writing to output files ***")
for output_file in output_files:
logging.info(" %s", output_file)
write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
FLAGS.max_predictions_per_seq, output_files,
FLAGS.gzip_compress,
FLAGS.use_v2_feature_names)
if __name__ == "__main__":
flags.mark_flag_as_required("input_file")
flags.mark_flag_as_required("output_file")
flags.mark_flag_as_required("vocab_file")
app.run(main) | DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/create_pretraining_data.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities to save models."""
from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function
import os
from absl import logging
import tensorflow as tf
import typing
def export_bert_model(model_export_path: typing.Text,
model: tf.keras.Model,
checkpoint_dir: typing.Optional[typing.Text] = None,
restore_model_using_load_weights: bool = False) -> None:
"""Export BERT model for serving which does not include the optimizer.
Arguments:
model_export_path: Path to which exported model will be saved.
model: Keras model object to export.
checkpoint_dir: Path from which model weights will be loaded, if
specified.
restore_model_using_load_weights: Whether to use checkpoint.restore() API
for custom checkpoint or to use model.load_weights() API.
There are 2 different ways to save checkpoints. One is using
tf.train.Checkpoint and another is using Keras model.save_weights().
Custom training loop implementation uses tf.train.Checkpoint API
and Keras ModelCheckpoint callback internally uses model.save_weights()
API. Since these two API's cannot be used toghether, model loading logic
must be take into account how model checkpoint was saved.
Raises:
ValueError when either model_export_path or model is not specified.
"""
if not model_export_path:
raise ValueError('model_export_path must be specified.')
if not isinstance(model, tf.keras.Model):
raise ValueError('model must be a tf.keras.Model object.')
if checkpoint_dir:
# Keras compile/fit() was used to save checkpoint using
# model.save_weights().
if restore_model_using_load_weights:
model_weight_path = os.path.join(checkpoint_dir, 'checkpoint')
assert tf.io.gfile.exists(model_weight_path)
model.load_weights(model_weight_path)
# tf.train.Checkpoint API was used via custom training loop logic.
else:
checkpoint = tf.train.Checkpoint(model=model)
# Restores the model from latest checkpoint.
latest_checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
assert latest_checkpoint_file
logging.info('Checkpoint file %s found and restoring from '
'checkpoint', latest_checkpoint_file)
checkpoint.restore(
latest_checkpoint_file).assert_existing_objects_matched()
model.save(model_export_path, include_optimizer=False, save_format='tf')
class BertModelCheckpoint(tf.keras.callbacks.Callback):
"""Keras callback that saves model at the end of every epoch."""
def __init__(self, checkpoint_dir, checkpoint):
"""Initializes BertModelCheckpoint.
Arguments:
checkpoint_dir: Directory of the to be saved checkpoint file.
checkpoint: tf.train.Checkpoint object.
"""
super(BertModelCheckpoint, self).__init__()
self.checkpoint_file_name = os.path.join(
checkpoint_dir, 'bert_training_checkpoint_step_{global_step}.ckpt')
assert isinstance(checkpoint, tf.train.Checkpoint)
self.checkpoint = checkpoint
def on_epoch_end(self, epoch, logs=None):
global_step = tf.keras.backend.get_value(self.model.optimizer.iterations)
formatted_file_name = self.checkpoint_file_name.format(
global_step=global_step)
saved_path = self.checkpoint.save(formatted_file_name)
logging.info('Saving model TF checkpoint to : %s', saved_path)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/model_saving_utils.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""BERT library to process data for classification task."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from absl import logging
import tensorflow as tf
import tokenization
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def __init__(self, process_text_fn=tokenization.convert_to_unicode):
self.process_text_fn = process_text_fn
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of `InputExample`s for prediction."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@staticmethod
def get_processor_name():
"""Gets the string identifier of the processor."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with tf.io.gfile.GFile(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
lines.append(line)
return lines
class XnliProcessor(DataProcessor):
"""Processor for the XNLI data set."""
def __init__(self, process_text_fn=tokenization.convert_to_unicode):
super(XnliProcessor, self).__init__(process_text_fn)
self.language = "zh"
def get_train_examples(self, data_dir):
"""See base class."""
lines = self._read_tsv(
os.path.join(data_dir, "multinli",
"multinli.train.%s.tsv" % self.language))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "train-%d" % (i)
text_a = self.process_text_fn(line[0])
text_b = self.process_text_fn(line[1])
label = self.process_text_fn(line[2])
if label == self.process_text_fn("contradictory"):
label = self.process_text_fn("contradiction")
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_dev_examples(self, data_dir):
"""See base class."""
lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "dev-%d" % (i)
language = self.process_text_fn(line[0])
if language != self.process_text_fn(self.language):
continue
text_a = self.process_text_fn(line[6])
text_b = self.process_text_fn(line[7])
label = self.process_text_fn(line[1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
@staticmethod
def get_processor_name():
"""See base class."""
return "XNLI"
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
"dev_matched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
@staticmethod
def get_processor_name():
"""See base class."""
return "MNLI"
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, self.process_text_fn(line[0]))
text_a = self.process_text_fn(line[8])
text_b = self.process_text_fn(line[9])
if set_type == "test":
label = "contradiction"
else:
label = self.process_text_fn(line[-1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
@staticmethod
def get_processor_name():
"""See base class."""
return "MRPC"
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = self.process_text_fn(line[3])
text_b = self.process_text_fn(line[4])
if set_type == "test":
label = "0"
else:
label = self.process_text_fn(line[0])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
@staticmethod
def get_processor_name():
"""See base class."""
return "COLA"
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
# Only the test set has a header
if set_type == "test" and i == 0:
continue
guid = "%s-%s" % (set_type, i)
if set_type == "test":
text_a = self.process_text_fn(line[1])
label = "0"
else:
text_a = self.process_text_fn(line[3])
label = self.process_text_fn(line[1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class SstProcessor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
@staticmethod
def get_processor_name():
"""See base class."""
return "SST-2"
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
if set_type == "test":
text_a = tokenization.convert_to_unicode(line[1])
label = "0"
else:
text_a = tokenization.convert_to_unicode(line[0])
label = tokenization.convert_to_unicode(line[1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class QnliProcessor(DataProcessor):
"""Processor for the QNLI data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
@staticmethod
def get_processor_name():
"""See base class."""
return "QNLI"
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, 1)
if set_type == "test":
text_a = tokenization.convert_to_unicode(line[1])
text_b = tokenization.convert_to_unicode(line[2])
label = "entailment"
else:
text_a = tokenization.convert_to_unicode(line[1])
text_b = tokenization.convert_to_unicode(line[2])
label = tokenization.convert_to_unicode(line[-1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
logging.info("*** Example ***")
logging.info("guid: %s", (example.guid))
logging.info("tokens: %s",
" ".join([tokenization.printable_text(x) for x in tokens]))
logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
logging.info("label: %s (id = %d)", example.label, label_id)
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
is_real_example=True)
return feature
def file_based_convert_examples_to_features(examples, label_list,
max_seq_length, tokenizer,
output_file):
"""Convert a set of `InputExample`s to a TFRecord file."""
writer = tf.io.TFRecordWriter(output_file)
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
logging.info("Writing example %d of %d", ex_index, len(examples))
feature = convert_single_example(ex_index, example, label_list,
max_seq_length, tokenizer)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["label_ids"] = create_int_feature([feature.label_id])
features["is_real_example"] = create_int_feature(
[int(feature.is_real_example)])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def generate_tf_record_from_data_file(processor,
data_dir,
tokenizer,
train_data_output_path=None,
eval_data_output_path=None,
max_seq_length=128):
"""Generates and saves training data into a tf record file.
Arguments:
processor: Input processor object to be used for generating data. Subclass
of `DataProcessor`.
data_dir: Directory that contains train/eval data to process. Data files
should be in from "dev.tsv", "test.tsv", or "train.tsv".
tokenizer: The tokenizer to be applied on the data.
train_data_output_path: Output to which processed tf record for training
will be saved.
eval_data_output_path: Output to which processed tf record for evaluation
will be saved.
max_seq_length: Maximum sequence length of the to be generated
training/eval data.
Returns:
A dictionary containing input meta data.
"""
assert train_data_output_path or eval_data_output_path
label_list = processor.get_labels()
assert train_data_output_path
train_input_data_examples = processor.get_train_examples(data_dir)
file_based_convert_examples_to_features(train_input_data_examples, label_list,
max_seq_length, tokenizer,
train_data_output_path)
num_training_data = len(train_input_data_examples)
if eval_data_output_path:
eval_input_data_examples = processor.get_dev_examples(data_dir)
file_based_convert_examples_to_features(eval_input_data_examples,
label_list, max_seq_length,
tokenizer, eval_data_output_path)
meta_data = {
"task_type": "bert_classification",
"processor_type": processor.get_processor_name(),
"num_labels": len(processor.get_labels()),
"train_data_size": num_training_data,
"max_seq_length": max_seq_length,
}
if eval_data_output_path:
meta_data["eval_data_size"] = len(eval_input_data_examples)
return meta_data
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/classifier_data_lib.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helper functions for running models in a distributed setting."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
import string
import tensorflow as tf
from official.utils.misc import tpu_lib
def _collective_communication(all_reduce_alg):
"""Return a CollectiveCommunication based on all_reduce_alg.
Args:
all_reduce_alg: a string specifying which collective communication to pick,
or None.
Returns:
tf.distribute.experimental.CollectiveCommunication object
Raises:
ValueError: if `all_reduce_alg` not in [None, 'ring', 'nccl']
"""
collective_communication_options = {
None: tf.distribute.experimental.CollectiveCommunication.AUTO,
"ring": tf.distribute.experimental.CollectiveCommunication.RING,
"nccl": tf.distribute.experimental.CollectiveCommunication.NCCL
}
if all_reduce_alg not in collective_communication_options:
raise ValueError(
"When used with `multi_worker_mirrored`, valid values for "
"all_reduce_alg are ['ring', 'nccl']. Supplied value: {}".format(
all_reduce_alg))
return collective_communication_options[all_reduce_alg]
def _mirrored_cross_device_ops(all_reduce_alg, num_packs):
"""Return a CrossDeviceOps based on all_reduce_alg and num_packs.
Args:
all_reduce_alg: a string specifying which cross device op to pick, or None.
num_packs: an integer specifying number of packs for the cross device op.
Returns:
tf.distribute.CrossDeviceOps object or None.
Raises:
ValueError: if `all_reduce_alg` not in [None, 'nccl', 'hierarchical_copy'].
"""
if all_reduce_alg is None:
return None
mirrored_all_reduce_options = {
"nccl": tf.distribute.NcclAllReduce,
"hierarchical_copy": tf.distribute.HierarchicalCopyAllReduce
}
if all_reduce_alg not in mirrored_all_reduce_options:
raise ValueError(
"When used with `mirrored`, valid values for all_reduce_alg are "
"['nccl', 'hierarchical_copy']. Supplied value: {}".format(
all_reduce_alg))
cross_device_ops_class = mirrored_all_reduce_options[all_reduce_alg]
return cross_device_ops_class(num_packs=num_packs)
def get_distribution_strategy(distribution_strategy="mirrored",
num_gpus=0,
num_workers=1,
all_reduce_alg=None,
num_packs=1,
tpu_address=None):
"""Return a DistributionStrategy for running the model.
Args:
distribution_strategy: a string specifying which distribution strategy to
use. Accepted values are 'off', 'one_device', 'mirrored',
'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case insensitive.
'off' means not to use Distribution Strategy; 'tpu' means to use
TPUStrategy using `tpu_address`.
num_gpus: Number of GPUs to run this model.
num_workers: Number of workers to run this model.
all_reduce_alg: Optional. Specifies which algorithm to use when performing
all-reduce. For `MirroredStrategy`, valid values are "nccl" and
"hierarchical_copy". For `MultiWorkerMirroredStrategy`, valid values are
"ring" and "nccl". If None, DistributionStrategy will choose based on
device topology.
num_packs: Optional. Sets the `num_packs` in `tf.distribute.NcclAllReduce`
or `tf.distribute.HierarchicalCopyAllReduce` for `MirroredStrategy`.
tpu_address: Optional. String that represents TPU to connect to. Must not
be None if `distribution_strategy` is set to `tpu`.
Returns:
tf.distribute.DistibutionStrategy object.
Raises:
ValueError: if `distribution_strategy` is 'off' or 'one_device' and
`num_gpus` is larger than 1; or `num_gpus` is negative or if
`distribution_strategy` is `tpu` but `tpu_address` is not specified.
"""
if num_gpus < 0:
raise ValueError("`num_gpus` can not be negative.")
distribution_strategy = distribution_strategy.lower()
if distribution_strategy == "off":
return None
if distribution_strategy == "tpu":
# When tpu_address is an empty string, we communicate with local TPUs.
cluster_resolver = tpu_lib.tpu_initialize(tpu_address)
return tf.distribute.experimental.TPUStrategy(cluster_resolver)
if distribution_strategy == "multi_worker_mirrored":
return tf.distribute.experimental.MultiWorkerMirroredStrategy(
communication=_collective_communication(all_reduce_alg))
if distribution_strategy == "one_device":
if num_gpus == 0:
return tf.distribute.OneDeviceStrategy("device:CPU:0")
if num_gpus > 1:
raise ValueError("`OneDeviceStrategy` can not be used for more than "
"one device.")
return tf.distribute.OneDeviceStrategy("device:GPU:0")
if distribution_strategy == "mirrored":
if num_gpus == 0:
devices = ["device:CPU:0"]
else:
devices = ["device:GPU:%d" % i for i in range(num_gpus)]
return tf.distribute.MirroredStrategy(
devices=devices,
cross_device_ops=_mirrored_cross_device_ops(all_reduce_alg, num_packs))
if distribution_strategy == "parameter_server":
return tf.distribute.experimental.ParameterServerStrategy()
raise ValueError(
"Unrecognized Distribution Strategy: %r" % distribution_strategy)
def per_replica_batch_size(batch_size, num_gpus):
"""For multi-gpu, batch-size must be a multiple of the number of GPUs.
Note that distribution strategy handles this automatically when used with
Keras. For using with Estimator, we need to get per GPU batch.
Args:
batch_size: Global batch size to be divided among devices. This should be
equal to num_gpus times the single-GPU batch_size for multi-gpu training.
num_gpus: How many GPUs are used with DistributionStrategies.
Returns:
Batch size per device.
Raises:
ValueError: if batch_size is not divisible by number of devices
"""
if num_gpus <= 1:
return batch_size
remainder = batch_size % num_gpus
if remainder:
err = ('When running with multiple GPUs, batch size '
'must be a multiple of the number of available GPUs. Found {} '
'GPUs with a batch size of {}; try --batch_size={} instead.'
).format(num_gpus, batch_size, batch_size - remainder)
raise ValueError(err)
return int(batch_size / num_gpus)
# The `SyntheticDataset` is a temporary solution for generating synthetic data
# directly on devices. It is only useful for Keras with Distribution
# Strategies. We will have better support in `tf.data` or Distribution Strategy
# later.
class SyntheticDataset(object):
"""A dataset that generates synthetic data on each device."""
def __init__(self, dataset, split_by=1):
# dataset.take(1) doesn't have GPU kernel.
with tf.device('device:CPU:0'):
tensor = tf.data.experimental.get_single_element(dataset.take(1))
flat_tensor = tf.nest.flatten(tensor)
variable_data = []
initializers = []
for t in flat_tensor:
rebatched_t = tf.split(t, num_or_size_splits=split_by, axis=0)[0]
assert rebatched_t.shape.is_fully_defined(), rebatched_t.shape
v = tf.compat.v1.get_local_variable(self._random_name(),
initializer=rebatched_t)
variable_data.append(v)
initializers.append(v.initializer)
input_data = tf.nest.pack_sequence_as(tensor, variable_data)
self._iterator = SyntheticIterator(input_data, initializers)
def _random_name(self, size=10, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
def __iter__(self):
return self._iterator
def make_one_shot_iterator(self):
return self._iterator
def make_initializable_iterator(self):
return self._iterator
class SyntheticIterator(object):
"""A dataset that generates synthetic data on each device."""
def __init__(self, input_data, initializers):
self._input_data = input_data
self._initializers = initializers
def get_next(self):
return self._input_data
def next(self):
return self.__next__()
def __next__(self):
try:
return self.get_next()
except tf.errors.OutOfRangeError:
raise StopIteration
def initialize(self):
if tf.executing_eagerly():
return tf.no_op()
else:
return self._initializers
def _monkey_patch_dataset_method(strategy):
"""Monkey-patch `strategy`'s `make_dataset_iterator` method."""
def make_dataset(self, dataset):
tf.compat.v1.logging.info('Using pure synthetic data.')
with self.scope():
if self.extended._global_batch_size: # pylint: disable=protected-access
return SyntheticDataset(dataset, self.num_replicas_in_sync)
else:
return SyntheticDataset(dataset)
def make_iterator(self, dataset):
dist_dataset = make_dataset(self, dataset)
return iter(dist_dataset)
strategy.orig_make_dataset_iterator = strategy.make_dataset_iterator
strategy.make_dataset_iterator = make_iterator
strategy.orig_distribute_dataset = strategy.experimental_distribute_dataset
strategy.experimental_distribute_dataset = make_dataset
def _undo_monkey_patch_dataset_method(strategy):
if hasattr(strategy, 'orig_make_dataset_iterator'):
strategy.make_dataset_iterator = strategy.orig_make_dataset_iterator
if hasattr(strategy, 'orig_distribute_dataset'):
strategy.make_dataset_iterator = strategy.orig_distribute_dataset
def set_up_synthetic_data():
_monkey_patch_dataset_method(tf.distribute.OneDeviceStrategy)
_monkey_patch_dataset_method(tf.distribute.MirroredStrategy)
_monkey_patch_dataset_method(
tf.distribute.experimental.MultiWorkerMirroredStrategy)
def undo_set_up_synthetic_data():
_undo_monkey_patch_dataset_method(tf.distribute.OneDeviceStrategy)
_undo_monkey_patch_dataset_method(tf.distribute.MirroredStrategy)
_undo_monkey_patch_dataset_method(
tf.distribute.experimental.MultiWorkerMirroredStrategy)
def configure_cluster(worker_hosts=None, task_index=-1):
"""Set multi-worker cluster spec in TF_CONFIG environment variable.
Args:
worker_hosts: comma-separated list of worker ip:port pairs.
Returns:
Number of workers in the cluster.
"""
tf_config = json.loads(os.environ.get('TF_CONFIG', '{}'))
if tf_config:
num_workers = (len(tf_config['cluster'].get('chief', [])) +
len(tf_config['cluster'].get('worker', [])))
elif worker_hosts:
workers = worker_hosts.split(',')
num_workers = len(workers)
if num_workers > 1 and task_index < 0:
raise ValueError('Must specify task_index when number of workers > 1')
task_index = 0 if num_workers == 1 else task_index
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': workers
},
'task': {'type': 'worker', 'index': task_index}
})
else:
num_workers = 1
return num_workers
def get_strategy_scope(strategy):
if strategy:
strategy_scope = strategy.scope()
else:
strategy_scope = DummyContextManager()
return strategy_scope
class DummyContextManager(object):
def __enter__(self):
pass
def __exit__(self, *args):
pass
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/distribution_utils.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Model Helper functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.misc import keras_utils
from official.utils.misc import model_helpers
class PastStopThresholdTest(tf.test.TestCase):
"""Tests for past_stop_threshold."""
def setUp(self):
super(PastStopThresholdTest, self).setUp()
if keras_utils.is_v2_0:
tf.compat.v1.disable_eager_execution()
def test_past_stop_threshold(self):
"""Tests for normal operating conditions."""
self.assertTrue(model_helpers.past_stop_threshold(0.54, 1))
self.assertTrue(model_helpers.past_stop_threshold(54, 100))
self.assertFalse(model_helpers.past_stop_threshold(0.54, 0.1))
self.assertFalse(model_helpers.past_stop_threshold(-0.54, -1.5))
self.assertTrue(model_helpers.past_stop_threshold(-0.54, 0))
self.assertTrue(model_helpers.past_stop_threshold(0, 0))
self.assertTrue(model_helpers.past_stop_threshold(0.54, 0.54))
def test_past_stop_threshold_none_false(self):
"""Tests that check None returns false."""
self.assertFalse(model_helpers.past_stop_threshold(None, -1.5))
self.assertFalse(model_helpers.past_stop_threshold(None, None))
self.assertFalse(model_helpers.past_stop_threshold(None, 1.5))
# Zero should be okay, though.
self.assertTrue(model_helpers.past_stop_threshold(0, 1.5))
def test_past_stop_threshold_not_number(self):
"""Tests for error conditions."""
with self.assertRaises(ValueError):
model_helpers.past_stop_threshold("str", 1)
with self.assertRaises(ValueError):
model_helpers.past_stop_threshold("str", tf.constant(5))
with self.assertRaises(ValueError):
model_helpers.past_stop_threshold("str", "another")
with self.assertRaises(ValueError):
model_helpers.past_stop_threshold(0, None)
with self.assertRaises(ValueError):
model_helpers.past_stop_threshold(0.7, "str")
with self.assertRaises(ValueError):
model_helpers.past_stop_threshold(tf.constant(4), None)
class SyntheticDataTest(tf.test.TestCase):
"""Tests for generate_synthetic_data."""
def test_generate_synethetic_data(self):
input_element, label_element = tf.compat.v1.data.make_one_shot_iterator(
model_helpers.generate_synthetic_data(input_shape=tf.TensorShape([5]),
input_value=123,
input_dtype=tf.float32,
label_shape=tf.TensorShape([]),
label_value=456,
label_dtype=tf.int32)).get_next()
with self.session() as sess:
for n in range(5):
inp, lab = sess.run((input_element, label_element))
self.assertAllClose(inp, [123., 123., 123., 123., 123.])
self.assertEquals(lab, 456)
def test_generate_only_input_data(self):
d = model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([4]),
input_value=43.5,
input_dtype=tf.float32)
element = tf.compat.v1.data.make_one_shot_iterator(d).get_next()
self.assertFalse(isinstance(element, tuple))
with self.session() as sess:
inp = sess.run(element)
self.assertAllClose(inp, [43.5, 43.5, 43.5, 43.5])
def test_generate_nested_data(self):
d = model_helpers.generate_synthetic_data(
input_shape={'a': tf.TensorShape([2]),
'b': {'c': tf.TensorShape([3]), 'd': tf.TensorShape([])}},
input_value=1.1)
element = tf.compat.v1.data.make_one_shot_iterator(d).get_next()
self.assertIn('a', element)
self.assertIn('b', element)
self.assertEquals(len(element['b']), 2)
self.assertIn('c', element['b'])
self.assertIn('d', element['b'])
self.assertNotIn('c', element)
with self.session() as sess:
inp = sess.run(element)
self.assertAllClose(inp['a'], [1.1, 1.1])
self.assertAllClose(inp['b']['c'], [1.1, 1.1, 1.1])
self.assertAllClose(inp['b']['d'], 1.1)
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/model_helpers_test.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
""" Tests for distribution util functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.misc import distribution_utils
class GetDistributionStrategyTest(tf.test.TestCase):
"""Tests for get_distribution_strategy."""
def test_one_device_strategy_cpu(self):
ds = distribution_utils.get_distribution_strategy(num_gpus=0)
self.assertEquals(ds.num_replicas_in_sync, 1)
self.assertEquals(len(ds.extended.worker_devices), 1)
self.assertIn('CPU', ds.extended.worker_devices[0])
def test_one_device_strategy_gpu(self):
ds = distribution_utils.get_distribution_strategy(num_gpus=1)
self.assertEquals(ds.num_replicas_in_sync, 1)
self.assertEquals(len(ds.extended.worker_devices), 1)
self.assertIn('GPU', ds.extended.worker_devices[0])
def test_mirrored_strategy(self):
ds = distribution_utils.get_distribution_strategy(num_gpus=5)
self.assertEquals(ds.num_replicas_in_sync, 5)
self.assertEquals(len(ds.extended.worker_devices), 5)
for device in ds.extended.worker_devices:
self.assertIn('GPU', device)
class PerReplicaBatchSizeTest(tf.test.TestCase):
"""Tests for per_replica_batch_size."""
def test_batch_size(self):
self.assertEquals(
distribution_utils.per_replica_batch_size(147, num_gpus=0), 147)
self.assertEquals(
distribution_utils.per_replica_batch_size(147, num_gpus=1), 147)
self.assertEquals(
distribution_utils.per_replica_batch_size(147, num_gpus=7), 21)
def test_batch_size_with_remainder(self):
with self.assertRaises(ValueError):
distribution_utils.per_replica_batch_size(147, num_gpus=5)
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/distribution_utils_test.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Miscellaneous functions that can be called by models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numbers
import tensorflow as tf
from tensorflow.python.util import nest
def past_stop_threshold(stop_threshold, eval_metric):
"""Return a boolean representing whether a model should be stopped.
Args:
stop_threshold: float, the threshold above which a model should stop
training.
eval_metric: float, the current value of the relevant metric to check.
Returns:
True if training should stop, False otherwise.
Raises:
ValueError: if either stop_threshold or eval_metric is not a number
"""
if stop_threshold is None:
return False
if not isinstance(stop_threshold, numbers.Number):
raise ValueError("Threshold for checking stop conditions must be a number.")
if not isinstance(eval_metric, numbers.Number):
raise ValueError("Eval metric being checked against stop conditions "
"must be a number.")
if eval_metric >= stop_threshold:
tf.compat.v1.logging.info(
"Stop threshold of {} was passed with metric value {}.".format(
stop_threshold, eval_metric))
return True
return False
def generate_synthetic_data(
input_shape, input_value=0, input_dtype=None, label_shape=None,
label_value=0, label_dtype=None):
"""Create a repeating dataset with constant values.
Args:
input_shape: a tf.TensorShape object or nested tf.TensorShapes. The shape of
the input data.
input_value: Value of each input element.
input_dtype: Input dtype. If None, will be inferred by the input value.
label_shape: a tf.TensorShape object or nested tf.TensorShapes. The shape of
the label data.
label_value: Value of each input element.
label_dtype: Input dtype. If None, will be inferred by the target value.
Returns:
Dataset of tensors or tuples of tensors (if label_shape is set).
"""
# TODO(kathywu): Replace with SyntheticDataset once it is in contrib.
element = input_element = nest.map_structure(
lambda s: tf.constant(input_value, input_dtype, s), input_shape)
if label_shape:
label_element = nest.map_structure(
lambda s: tf.constant(label_value, label_dtype, s), label_shape)
element = (input_element, label_element)
return tf.data.Dataset.from_tensors(element).repeat()
def apply_clean(flags_obj):
if flags_obj.clean and tf.io.gfile.exists(flags_obj.model_dir):
tf.compat.v1.logging.info("--clean flag set. Removing existing model dir:"
" {}".format(flags_obj.model_dir))
tf.io.gfile.rmtree(flags_obj.model_dir)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/model_helpers.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Initializes TPU system for TF 2.0."""
import tensorflow as tf
def tpu_initialize(tpu_address):
"""Initializes TPU for TF 2.0 training.
Args:
tpu_address: string, bns address of master TPU worker.
Returns:
A TPUClusterResolver.
"""
cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=tpu_address)
if tpu_address not in ('', 'local'):
tf.config.experimental_connect_to_cluster(cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
return cluster_resolver
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/tpu_lib.py |
"""A simple Python callstack sampler."""
import contextlib
import datetime
import signal
import traceback
class CallstackSampler(object):
"""A simple signal-based Python callstack sampler.
"""
def __init__(self, interval=None):
self.stacks = []
self.interval = 0.001 if interval is None else interval
def _sample(self, signum, frame):
"""Samples the current stack."""
del signum
stack = traceback.extract_stack(frame)
formatted_stack = []
formatted_stack.append(datetime.datetime.utcnow())
for filename, lineno, function_name, text in stack:
formatted_frame = '{}:{}({})({})'.format(filename, lineno, function_name,
text)
formatted_stack.append(formatted_frame)
self.stacks.append(formatted_stack)
signal.setitimer(signal.ITIMER_VIRTUAL, self.interval, 0)
@contextlib.contextmanager
def profile(self):
signal.signal(signal.SIGVTALRM, self._sample)
signal.setitimer(signal.ITIMER_VIRTUAL, self.interval, 0)
try:
yield
finally:
signal.setitimer(signal.ITIMER_VIRTUAL, 0)
def save(self, fname):
with open(fname, 'w') as f:
for s in self.stacks:
for l in s:
f.write('%s\n' % l)
f.write('\n')
@contextlib.contextmanager
def callstack_sampling(filename, interval=None):
"""Periodically samples the Python callstack.
Args:
filename: the filename
interval: the sampling interval, in seconds. Defaults to 0.001.
Yields:
nothing
"""
sampler = CallstackSampler(interval=interval)
with sampler.profile():
yield
sampler.save(filename)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/callstack_sampler.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helper functions for the Keras implementations of models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
import os
import time
from absl import logging
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python import tf2
from tensorflow.python.eager import profiler
class BatchTimestamp(object):
"""A structure to store batch time stamp."""
def __init__(self, batch_index, timestamp):
self.batch_index = batch_index
self.timestamp = timestamp
def __repr__(self):
return "'BatchTimestamp<batch_index: {}, timestamp: {}>'".format(
self.batch_index, self.timestamp)
class TimeHistory(tf.keras.callbacks.Callback):
"""Callback for Keras models."""
def __init__(self, batch_size, log_steps):
"""Callback for logging performance.
Args:
batch_size: Total batch size.
log_steps: Interval of steps between logging of batch level stats.
"""
self.batch_size = batch_size
super(TimeHistory, self).__init__()
self.log_steps = log_steps
self.global_steps = 0
# Logs start of step 1 then end of each step based on log_steps interval.
self.timestamp_log = []
# Records the time each epoch takes to run from start to finish of epoch.
self.epoch_runtime_log = []
def on_train_end(self, logs=None):
self.train_finish_time = time.time()
def on_epoch_begin(self, epoch, logs=None):
self.epoch_start = time.time()
def on_batch_begin(self, batch, logs=None):
self.global_steps += 1
if self.global_steps == 1:
self.start_time = time.time()
self.timestamp_log.append(BatchTimestamp(self.global_steps,
self.start_time))
def on_batch_end(self, batch, logs=None):
"""Records elapse time of the batch and calculates examples per second."""
if self.global_steps % self.log_steps == 0:
timestamp = time.time()
elapsed_time = timestamp - self.start_time
examples_per_second = (self.batch_size * self.log_steps) / elapsed_time
self.timestamp_log.append(BatchTimestamp(self.global_steps, timestamp))
logging.info(
"BenchmarkMetric: {'global step':%d, 'time_taken': %f,"
"'examples_per_second': %f}",
self.global_steps, elapsed_time, examples_per_second)
self.start_time = timestamp
def on_epoch_end(self, epoch, logs=None):
epoch_run_time = time.time() - self.epoch_start
self.epoch_runtime_log.append(epoch_run_time)
logging.info(
"BenchmarkMetric: {'epoch':%d, 'time_taken': %f}",
epoch, epoch_run_time)
def get_profiler_callback(model_dir, profile_steps, enable_tensorboard,
steps_per_epoch):
"""Validate profile_steps flag value and return profiler callback."""
profile_steps_error_message = (
'profile_steps must be a comma separated pair of positive integers, '
'specifying the first and last steps to be profiled.'
)
try:
profile_steps = [int(i) for i in profile_steps.split(',')]
except ValueError:
raise ValueError(profile_steps_error_message)
if len(profile_steps) != 2:
raise ValueError(profile_steps_error_message)
start_step, stop_step = profile_steps
if start_step < 0 or start_step > stop_step:
raise ValueError(profile_steps_error_message)
if enable_tensorboard:
logging.warning(
'Both TensorBoard and profiler callbacks are used. Note that the '
'TensorBoard callback profiles the 2nd step (unless otherwise '
'specified). Please make sure the steps profiled by the two callbacks '
'do not overlap.')
return ProfilerCallback(model_dir, start_step, stop_step, steps_per_epoch)
class ProfilerCallback(tf.keras.callbacks.Callback):
"""Save profiles in specified step range to log directory."""
def __init__(self, log_dir, start_step, stop_step, steps_per_epoch):
super(ProfilerCallback, self).__init__()
self.log_dir = log_dir
self.start_step = start_step
self.stop_step = stop_step
self.start_epoch = start_step // steps_per_epoch
self.stop_epoch = stop_step // steps_per_epoch
self.start_step_in_epoch = start_step % steps_per_epoch
self.stop_step_in_epoch = stop_step % steps_per_epoch
self.should_start = False
self.should_stop = False
def on_epoch_begin(self, epoch, logs=None):
if epoch == self.start_epoch:
self.should_start = True
if epoch == self.stop_epoch:
self.should_stop = True
def on_batch_begin(self, batch, logs=None):
if batch == self.start_step_in_epoch and self.should_start:
self.should_start = False
profiler.start()
logging.info('Profiler started at Step %s', self.start_step)
def on_batch_end(self, batch, logs=None):
if batch == self.stop_step_in_epoch and self.should_stop:
self.should_stop = False
results = profiler.stop()
profiler.save(self.log_dir, results)
logging.info(
'Profiler saved profiles for steps between %s and %s to %s',
self.start_step, self.stop_step, self.log_dir)
def set_session_config(enable_eager=False,
enable_xla=False):
"""Sets the session config."""
if is_v2_0():
set_config_v2(enable_xla=enable_xla)
else:
config = get_config_proto_v1(enable_xla=enable_xla)
if enable_eager:
tf.compat.v1.enable_eager_execution(config=config)
else:
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
def get_config_proto_v1(enable_xla=False):
"""Return config proto according to flag settings, or None to use default."""
config = None
if enable_xla:
config = tf.compat.v1.ConfigProto()
config.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_2)
return config
def set_config_v2(enable_xla=False):
"""Config eager context according to flag values using TF 2.0 API."""
if enable_xla:
tf.config.optimizer.set_jit(True)
def is_v2_0():
"""Returns true if using tf 2.0."""
return tf2.enabled()
def set_gpu_thread_mode_and_count(gpu_thread_mode,
datasets_num_private_threads,
num_gpus, per_gpu_thread_count):
"""Set GPU thread mode and count, and adjust dataset threads count."""
cpu_count = multiprocessing.cpu_count()
logging.info('Logical CPU cores: %s', cpu_count)
# Allocate private thread pool for each GPU to schedule and launch kernels
per_gpu_thread_count = per_gpu_thread_count or 2
os.environ['TF_GPU_THREAD_MODE'] = gpu_thread_mode
os.environ['TF_GPU_THREAD_COUNT'] = str(per_gpu_thread_count)
logging.info('TF_GPU_THREAD_COUNT: %s',
os.environ['TF_GPU_THREAD_COUNT'])
logging.info('TF_GPU_THREAD_MODE: %s',
os.environ['TF_GPU_THREAD_MODE'])
# Limit data preprocessing threadpool to CPU cores minus number of total GPU
# private threads and memory copy threads.
total_gpu_thread_count = per_gpu_thread_count * num_gpus
num_runtime_threads = num_gpus
if not datasets_num_private_threads:
datasets_num_private_threads = min(
cpu_count - total_gpu_thread_count - num_runtime_threads,
num_gpus * 8)
logging.info('Set datasets_num_private_threads to %s',
datasets_num_private_threads)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/misc/keras_utils.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Flags which will be nearly universal across models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow as tf
from official.utils.flags._conventions import help_wrap
from official.utils.logs import hooks_helper
def define_base(data_dir=True, model_dir=True, clean=False, train_epochs=False,
epochs_between_evals=False, stop_threshold=False,
batch_size=True, num_gpu=False, hooks=False, export_dir=False,
distribution_strategy=False, run_eagerly=False):
"""Register base flags.
Args:
data_dir: Create a flag for specifying the input data directory.
model_dir: Create a flag for specifying the model file directory.
clean: Create a flag for removing the model_dir.
train_epochs: Create a flag to specify the number of training epochs.
epochs_between_evals: Create a flag to specify the frequency of testing.
stop_threshold: Create a flag to specify a threshold accuracy or other
eval metric which should trigger the end of training.
batch_size: Create a flag to specify the batch size.
num_gpu: Create a flag to specify the number of GPUs used.
hooks: Create a flag to specify hooks for logging.
export_dir: Create a flag to specify where a SavedModel should be exported.
distribution_strategy: Create a flag to specify which Distribution Strategy
to use.
run_eagerly: Create a flag to specify to run eagerly op by op.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if data_dir:
flags.DEFINE_string(
name="data_dir", short_name="dd", default="/tmp",
help=help_wrap("The location of the input data."))
key_flags.append("data_dir")
if model_dir:
flags.DEFINE_string(
name="model_dir", short_name="md", default="/tmp",
help=help_wrap("The location of the model checkpoint files."))
key_flags.append("model_dir")
if clean:
flags.DEFINE_boolean(
name="clean", default=False,
help=help_wrap("If set, model_dir will be removed if it exists."))
key_flags.append("clean")
if train_epochs:
flags.DEFINE_integer(
name="train_epochs", short_name="te", default=1,
help=help_wrap("The number of epochs used to train."))
key_flags.append("train_epochs")
if epochs_between_evals:
flags.DEFINE_integer(
name="epochs_between_evals", short_name="ebe", default=1,
help=help_wrap("The number of training epochs to run between "
"evaluations."))
key_flags.append("epochs_between_evals")
if stop_threshold:
flags.DEFINE_float(
name="stop_threshold", short_name="st",
default=None,
help=help_wrap("If passed, training will stop at the earlier of "
"train_epochs and when the evaluation metric is "
"greater than or equal to stop_threshold."))
if batch_size:
flags.DEFINE_integer(
name="batch_size", short_name="bs", default=32,
help=help_wrap("Batch size for training and evaluation. When using "
"multiple gpus, this is the global batch size for "
"all devices. For example, if the batch size is 32 "
"and there are 4 GPUs, each GPU will get 8 examples on "
"each step."))
key_flags.append("batch_size")
if num_gpu:
flags.DEFINE_integer(
name="num_gpus", short_name="ng",
default=1,
help=help_wrap(
"How many GPUs to use at each worker with the "
"DistributionStrategies API. The default is 1."))
if run_eagerly:
flags.DEFINE_boolean(
name="run_eagerly", default=False,
help="Run the model op by op without building a model function.")
if hooks:
# Construct a pretty summary of hooks.
hook_list_str = (
u"\ufeff Hook:\n" + u"\n".join([u"\ufeff {}".format(key) for key
in hooks_helper.HOOKS]))
flags.DEFINE_list(
name="hooks", short_name="hk", default="LoggingTensorHook",
help=help_wrap(
u"A list of (case insensitive) strings to specify the names of "
u"training hooks.\n{}\n\ufeff Example: `--hooks ProfilerHook,"
u"ExamplesPerSecondHook`\n See official.utils.logs.hooks_helper "
u"for details.".format(hook_list_str))
)
key_flags.append("hooks")
if export_dir:
flags.DEFINE_string(
name="export_dir", short_name="ed", default=None,
help=help_wrap("If set, a SavedModel serialization of the model will "
"be exported to this directory at the end of training. "
"See the README for more details and relevant links.")
)
key_flags.append("export_dir")
if distribution_strategy:
flags.DEFINE_string(
name="distribution_strategy", short_name="ds", default="off",
help=help_wrap("The Distribution Strategy to use for training. "
"Accepted values are 'off', 'one_device', "
"'mirrored', 'parameter_server', 'collective', "
"case insensitive. 'off' means not to use "
"Distribution Strategy; 'default' means to choose "
"from `MirroredStrategy` or `OneDeviceStrategy` "
"according to the number of GPUs.")
)
return key_flags
def get_num_gpus(flags_obj):
"""Treat num_gpus=-1 as 'use all'."""
if flags_obj.num_gpus != -1:
return flags_obj.num_gpus
from tensorflow.python.client import device_lib # pylint: disable=g-import-not-at-top
local_device_protos = device_lib.list_local_devices()
return sum([1 for d in local_device_protos if d.device_type == "GPU"])
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_base.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import unittest
from absl import flags
import tensorflow as tf
from official.utils.flags import core as flags_core # pylint: disable=g-bad-import-order
def define_flags():
flags_core.define_base(clean=True, num_gpu=False, stop_threshold=True,
hooks=True, train_epochs=True,
epochs_between_evals=True)
flags_core.define_performance(
num_parallel_calls=True, inter_op=True, intra_op=True,
dynamic_loss_scale=True, loss_scale=True, synthetic_data=True,
dtype=True)
flags_core.define_image()
flags_core.define_benchmark()
class BaseTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(BaseTester, cls).setUpClass()
define_flags()
def test_default_setting(self):
"""Test to ensure fields exist and defaults can be set.
"""
defaults = dict(
data_dir="dfgasf",
model_dir="dfsdkjgbs",
train_epochs=534,
epochs_between_evals=15,
batch_size=256,
hooks=["LoggingTensorHook"],
num_parallel_calls=18,
inter_op_parallelism_threads=5,
intra_op_parallelism_threads=10,
data_format="channels_first"
)
flags_core.set_defaults(**defaults)
flags_core.parse_flags()
for key, value in defaults.items():
assert flags.FLAGS.get_flag_value(name=key, default=None) == value
def test_benchmark_setting(self):
defaults = dict(
hooks=["LoggingMetricHook"],
benchmark_log_dir="/tmp/12345",
gcp_project="project_abc",
)
flags_core.set_defaults(**defaults)
flags_core.parse_flags()
for key, value in defaults.items():
assert flags.FLAGS.get_flag_value(name=key, default=None) == value
def test_booleans(self):
"""Test to ensure boolean flags trigger as expected.
"""
flags_core.parse_flags([__file__, "--use_synthetic_data"])
assert flags.FLAGS.use_synthetic_data
def test_parse_dtype_info(self):
flags_core.parse_flags([__file__, "--dtype", "fp16"])
self.assertEqual(flags_core.get_tf_dtype(flags.FLAGS), tf.float16)
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS,
default_for_fp16=2), 2)
flags_core.parse_flags(
[__file__, "--dtype", "fp16", "--loss_scale", "5"])
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS,
default_for_fp16=2), 5)
flags_core.parse_flags(
[__file__, "--dtype", "fp16", "--loss_scale", "dynamic"])
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS,
default_for_fp16=2), "dynamic")
flags_core.parse_flags([__file__, "--dtype", "fp32"])
self.assertEqual(flags_core.get_tf_dtype(flags.FLAGS), tf.float32)
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS,
default_for_fp16=2), 1)
flags_core.parse_flags([__file__, "--dtype", "fp32", "--loss_scale", "5"])
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS,
default_for_fp16=2), 5)
with self.assertRaises(SystemExit):
flags_core.parse_flags([__file__, "--dtype", "int8"])
with self.assertRaises(SystemExit):
flags_core.parse_flags([__file__, "--dtype", "fp16",
"--loss_scale", "abc"])
def test_get_nondefault_flags_as_str(self):
defaults = dict(
clean=True,
data_dir="abc",
hooks=["LoggingTensorHook"],
stop_threshold=1.5,
use_synthetic_data=False
)
flags_core.set_defaults(**defaults)
flags_core.parse_flags()
expected_flags = ""
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
flags.FLAGS.clean = False
expected_flags += "--noclean"
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
flags.FLAGS.data_dir = "xyz"
expected_flags += " --data_dir=xyz"
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
flags.FLAGS.hooks = ["aaa", "bbb", "ccc"]
expected_flags += " --hooks=aaa,bbb,ccc"
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
flags.FLAGS.stop_threshold = 3.
expected_flags += " --stop_threshold=3.0"
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
flags.FLAGS.use_synthetic_data = True
expected_flags += " --use_synthetic_data"
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
# Assert that explicit setting a flag to its default value does not cause it
# to appear in the string
flags.FLAGS.use_synthetic_data = False
expected_flags = expected_flags[:-len(" --use_synthetic_data")]
self.assertEqual(flags_core.get_nondefault_flags_as_str(), expected_flags)
if __name__ == "__main__":
unittest.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/flags_test.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Public interface for flag definition.
See _example.py for detailed instructions on defining flags.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
from six.moves import shlex_quote
from absl import app as absl_app
from absl import flags
from official.utils.flags import _base
from official.utils.flags import _benchmark
from official.utils.flags import _conventions
from official.utils.flags import _device
from official.utils.flags import _distribution
from official.utils.flags import _misc
from official.utils.flags import _performance
def set_defaults(**kwargs):
for key, value in kwargs.items():
flags.FLAGS.set_default(name=key, value=value)
def parse_flags(argv=None):
"""Reset flags and reparse. Currently only used in testing."""
flags.FLAGS.unparse_flags()
absl_app.parse_flags_with_usage(argv or sys.argv)
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
define_base = register_key_flags_in_core(_base.define_base)
# We have define_base_eager for compatibility, since it used to be a separate
# function from define_base.
define_base_eager = define_base
define_benchmark = register_key_flags_in_core(_benchmark.define_benchmark)
define_device = register_key_flags_in_core(_device.define_device)
define_image = register_key_flags_in_core(_misc.define_image)
define_performance = register_key_flags_in_core(_performance.define_performance)
define_distribution = register_key_flags_in_core(
_distribution.define_distribution)
help_wrap = _conventions.help_wrap
get_num_gpus = _base.get_num_gpus
get_tf_dtype = _performance.get_tf_dtype
get_loss_scale = _performance.get_loss_scale
DTYPE_MAP = _performance.DTYPE_MAP
require_cloud_storage = _device.require_cloud_storage
def _get_nondefault_flags_as_dict():
"""Returns the nondefault flags as a dict from flag name to value."""
nondefault_flags = {}
for flag_name in flags.FLAGS:
flag_value = getattr(flags.FLAGS, flag_name)
if (flag_name != flags.FLAGS[flag_name].short_name and
flag_value != flags.FLAGS[flag_name].default):
nondefault_flags[flag_name] = flag_value
return nondefault_flags
def get_nondefault_flags_as_str():
"""Returns flags as a string that can be passed as command line arguments.
E.g., returns: "--batch_size=256 --use_synthetic_data" for the following code
block:
```
flags.FLAGS.batch_size = 256
flags.FLAGS.use_synthetic_data = True
print(get_nondefault_flags_as_str())
```
Only flags with nondefault values are returned, as passing default flags as
command line arguments has no effect.
Returns:
A string with the flags, that can be passed as command line arguments to a
program to use the flags.
"""
nondefault_flags = _get_nondefault_flags_as_dict()
flag_strings = []
for name, value in sorted(nondefault_flags.items()):
if isinstance(value, bool):
flag_str = '--{}'.format(name) if value else '--no{}'.format(name)
elif isinstance(value, list):
flag_str = '--{}={}'.format(name, ','.join(value))
else:
flag_str = '--{}={}'.format(name, value)
flag_strings.append(flag_str)
return ' '.join(shlex_quote(flag_str) for flag_str in flag_strings)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/core.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Flags for benchmarking models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
from official.utils.flags._conventions import help_wrap
def define_benchmark(benchmark_log_dir=True, bigquery_uploader=True):
"""Register benchmarking flags.
Args:
benchmark_log_dir: Create a flag to specify location for benchmark logging.
bigquery_uploader: Create flags for uploading results to BigQuery.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
flags.DEFINE_enum(
name="benchmark_logger_type", default="BaseBenchmarkLogger",
enum_values=["BaseBenchmarkLogger", "BenchmarkFileLogger",
"BenchmarkBigQueryLogger"],
help=help_wrap("The type of benchmark logger to use. Defaults to using "
"BaseBenchmarkLogger which logs to STDOUT. Different "
"loggers will require other flags to be able to work."))
flags.DEFINE_string(
name="benchmark_test_id", short_name="bti", default=None,
help=help_wrap("The unique test ID of the benchmark run. It could be the "
"combination of key parameters. It is hardware "
"independent and could be used compare the performance "
"between different test runs. This flag is designed for "
"human consumption, and does not have any impact within "
"the system."))
flags.DEFINE_integer(
name='log_steps', default=100,
help='For every log_steps, we log the timing information such as '
'examples per second. Besides, for every log_steps, we store the '
'timestamp of a batch end.')
if benchmark_log_dir:
flags.DEFINE_string(
name="benchmark_log_dir", short_name="bld", default=None,
help=help_wrap("The location of the benchmark logging.")
)
if bigquery_uploader:
flags.DEFINE_string(
name="gcp_project", short_name="gp", default=None,
help=help_wrap(
"The GCP project name where the benchmark will be uploaded."))
flags.DEFINE_string(
name="bigquery_data_set", short_name="bds", default="test_benchmark",
help=help_wrap(
"The Bigquery dataset name where the benchmark will be uploaded."))
flags.DEFINE_string(
name="bigquery_run_table", short_name="brt", default="benchmark_run",
help=help_wrap("The Bigquery table name where the benchmark run "
"information will be uploaded."))
flags.DEFINE_string(
name="bigquery_run_status_table", short_name="brst",
default="benchmark_run_status",
help=help_wrap("The Bigquery table name where the benchmark run "
"status information will be uploaded."))
flags.DEFINE_string(
name="bigquery_metric_table", short_name="bmt",
default="benchmark_metric",
help=help_wrap("The Bigquery table name where the benchmark metric "
"information will be uploaded."))
@flags.multi_flags_validator(
["benchmark_logger_type", "benchmark_log_dir"],
message="--benchmark_logger_type=BenchmarkFileLogger will require "
"--benchmark_log_dir being set")
def _check_benchmark_log_dir(flags_dict):
benchmark_logger_type = flags_dict["benchmark_logger_type"]
if benchmark_logger_type == "BenchmarkFileLogger":
return flags_dict["benchmark_log_dir"]
return True
return key_flags
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_benchmark.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Central location for shared argparse convention definitions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import codecs
import functools
from absl import app as absl_app
from absl import flags
# This codifies help string conventions and makes it easy to update them if
# necessary. Currently the only major effect is that help bodies start on the
# line after flags are listed. All flag definitions should wrap the text bodies
# with help wrap when calling DEFINE_*.
_help_wrap = functools.partial(flags.text_wrap, length=80, indent="",
firstline_indent="\n")
# Pretty formatting causes issues when utf-8 is not installed on a system.
def _stdout_utf8():
try:
codecs.lookup("utf-8")
except LookupError:
return False
return sys.stdout.encoding == "UTF-8"
if _stdout_utf8():
help_wrap = _help_wrap
else:
def help_wrap(text, *args, **kwargs):
return _help_wrap(text, *args, **kwargs).replace(u"\ufeff", u"")
# Replace None with h to also allow -h
absl_app.HelpshortFlag.SHORT_NAME = "h"
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_conventions.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Flags for managing compute devices. Currently only contains TPU flags."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow as tf
from official.utils.flags._conventions import help_wrap
def require_cloud_storage(flag_names):
"""Register a validator to check directory flags.
Args:
flag_names: An iterable of strings containing the names of flags to be
checked.
"""
msg = "TPU requires GCS path for {}".format(", ".join(flag_names))
@flags.multi_flags_validator(["tpu"] + flag_names, message=msg)
def _path_check(flag_values): # pylint: disable=missing-docstring
if flag_values["tpu"] is None:
return True
valid_flags = True
for key in flag_names:
if not flag_values[key].startswith("gs://"):
tf.compat.v1.logging.error("{} must be a GCS path.".format(key))
valid_flags = False
return valid_flags
def define_device(tpu=True):
"""Register device specific flags.
Args:
tpu: Create flags to specify TPU operation.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if tpu:
flags.DEFINE_string(
name="tpu", default=None,
help=help_wrap(
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a "
"grpc://ip.address.of.tpu:8470 url. Passing `local` will use the"
"CPU of the local instance instead. (Good for debugging.)"))
key_flags.append("tpu")
flags.DEFINE_string(
name="tpu_zone", default=None,
help=help_wrap(
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_string(
name="tpu_gcp_project", default=None,
help=help_wrap(
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE "
"project from metadata."))
flags.DEFINE_integer(name="num_tpu_shards", default=8,
help=help_wrap("Number of shards (TPU chips)."))
return key_flags
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_device.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Register flags for optimizing performance."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
from absl import flags # pylint: disable=g-bad-import-order
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.flags._conventions import help_wrap
# Map string to TensorFlow dtype
DTYPE_MAP = {
"fp16": tf.float16,
"bf16": tf.bfloat16,
"fp32": tf.float32,
}
def get_tf_dtype(flags_obj):
if getattr(flags_obj, "fp16_implementation", None) == "graph_rewrite":
# If the graph_rewrite is used, we build the graph with fp32, and let the
# graph rewrite change ops to fp16.
return tf.float32
return DTYPE_MAP[flags_obj.dtype]
def get_loss_scale(flags_obj, default_for_fp16):
if flags_obj.loss_scale == "dynamic":
return flags_obj.loss_scale
elif flags_obj.loss_scale is not None:
return float(flags_obj.loss_scale)
elif flags_obj.dtype == "fp32":
return 1 # No loss scaling is needed for fp32
else:
assert flags_obj.dtype == "fp16"
return default_for_fp16
def define_performance(num_parallel_calls=False, inter_op=False, intra_op=False,
synthetic_data=False, max_train_steps=False, dtype=False,
all_reduce_alg=False, num_packs=False,
tf_gpu_thread_mode=False,
datasets_num_private_threads=False,
datasets_num_parallel_batches=False,
dynamic_loss_scale=False, fp16_implementation=False,
loss_scale=False,
tf_data_experimental_slack=False, enable_xla=False,
force_v2_in_keras_compile=False,
training_dataset_cache=False):
"""Register flags for specifying performance tuning arguments.
Args:
num_parallel_calls: Create a flag to specify parallelism of data loading.
inter_op: Create a flag to allow specification of inter op threads.
intra_op: Create a flag to allow specification of intra op threads.
synthetic_data: Create a flag to allow the use of synthetic data.
max_train_steps: Create a flags to allow specification of maximum number
of training steps
dtype: Create flags for specifying dtype.
all_reduce_alg: If set forces a specific algorithm for multi-gpu.
num_packs: If set provides number of packs for MirroredStrategy's cross
device ops.
tf_gpu_thread_mode: gpu_private triggers us of private thread pool.
datasets_num_private_threads: Number of private threads for datasets.
datasets_num_parallel_batches: Determines how many batches to process in
parallel when using map and batch from tf.data.
dynamic_loss_scale: Allow the "loss_scale" flag to take on the value
"dynamic". Only valid if `dtype` is True.
fp16_implementation: Create fp16_implementation flag.
loss_scale: Controls the loss scaling, normally for mixed-precision
training. Can only be turned on if dtype is also True.
tf_data_experimental_slack: Determines whether to enable tf.data's
`experimental_slack` option.
enable_xla: Determines if XLA (auto clustering) is turned on.
force_v2_in_keras_compile: Forces the use of run_distribued path even if not
using a `strategy`. This is not the same as
`tf.distribute.OneDeviceStrategy`
training_dataset_cache: Whether to cache the training dataset on workers.
Typically used to improve training performance when training data is in
remote storage and can fit into worker memory.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if num_parallel_calls:
flags.DEFINE_integer(
name="num_parallel_calls", short_name="npc",
default=multiprocessing.cpu_count(),
help=help_wrap("The number of records that are processed in parallel "
"during input processing. This can be optimized per "
"data set but for generally homogeneous data sets, "
"should be approximately the number of available CPU "
"cores. (default behavior)"))
if inter_op:
flags.DEFINE_integer(
name="inter_op_parallelism_threads", short_name="inter", default=0,
help=help_wrap("Number of inter_op_parallelism_threads to use for CPU. "
"See TensorFlow config.proto for details.")
)
if intra_op:
flags.DEFINE_integer(
name="intra_op_parallelism_threads", short_name="intra", default=0,
help=help_wrap("Number of intra_op_parallelism_threads to use for CPU. "
"See TensorFlow config.proto for details."))
if synthetic_data:
flags.DEFINE_bool(
name="use_synthetic_data", short_name="synth", default=False,
help=help_wrap(
"If set, use fake data (zeroes) instead of a real dataset. "
"This mode is useful for performance debugging, as it removes "
"input processing steps, but will not learn anything."))
if max_train_steps:
flags.DEFINE_integer(
name="max_train_steps", short_name="mts", default=None, help=help_wrap(
"The model will stop training if the global_step reaches this "
"value. If not set, training will run until the specified number "
"of epochs have run as usual. It is generally recommended to set "
"--train_epochs=1 when using this flag."
))
if dtype:
flags.DEFINE_enum(
name="dtype", short_name="dt", default="fp32",
enum_values=DTYPE_MAP.keys(),
help=help_wrap("The TensorFlow datatype used for calculations. "
"Variables may be cast to a higher precision on a "
"case-by-case basis for numerical stability."))
loss_scale_help_text = (
"The amount to scale the loss by when the model is run. {}. Before "
"gradients are computed, the loss is multiplied by the loss scale, "
"making all gradients loss_scale times larger. To adjust for this, "
"gradients are divided by the loss scale before being applied to "
"variables. This is mathematically equivalent to training without "
"a loss scale, but the loss scale helps avoid some intermediate "
"gradients from underflowing to zero. If not provided the default "
"for fp16 is 128 and 1 for all other dtypes.{}"
)
if dynamic_loss_scale:
loss_scale_help_text = loss_scale_help_text.format(
"This can be an int/float or the string 'dynamic'",
" The string 'dynamic' can be used to dynamically determine the "
"optimal loss scale during training, but currently this "
"significantly slows down performance")
loss_scale_validation_msg = ("loss_scale should be a positive int/float "
"or the string 'dynamic'.")
else:
loss_scale_help_text = loss_scale_help_text.format(
"This must be an int/float", "")
loss_scale_validation_msg = "loss_scale should be a positive int/float."
if loss_scale:
flags.DEFINE_string(
name="loss_scale", short_name="ls", default=None,
help=help_wrap(loss_scale_help_text))
@flags.validator(flag_name="loss_scale",
message=loss_scale_validation_msg)
def _check_loss_scale(loss_scale): # pylint: disable=unused-variable
"""Validator to check the loss scale flag is valid."""
if loss_scale is None:
return True # null case is handled in get_loss_scale()
if loss_scale == "dynamic" and dynamic_loss_scale:
return True
try:
loss_scale = float(loss_scale)
except ValueError:
return False
return loss_scale > 0
if fp16_implementation:
flags.DEFINE_enum(
name="fp16_implementation", default="keras",
enum_values=("keras', 'graph_rewrite"),
help=help_wrap(
"When --dtype=fp16, how fp16 should be implemented. This has no "
"impact on correctness. 'keras' uses the "
"tf.keras.mixed_precision API. 'graph_rewrite' uses the "
"tf.train.experimental.enable_mixed_precision_graph_rewrite "
"API."))
@flags.multi_flags_validator(["fp16_implementation", "dtype",
"loss_scale"])
def _check_fp16_implementation(flags_dict):
"""Validator to check fp16_implementation flag is valid."""
if (flags_dict["fp16_implementation"] == "graph_rewrite" and
flags_dict["dtype"] != "fp16"):
raise flags.ValidationError("--fp16_implementation should not be "
"specified unless --dtype=fp16")
return True
if all_reduce_alg:
flags.DEFINE_string(
name="all_reduce_alg", short_name="ara", default=None,
help=help_wrap("Defines the algorithm to use for performing all-reduce."
"When specified with MirroredStrategy for single "
"worker, this controls "
"tf.contrib.distribute.AllReduceCrossTowerOps. When "
"specified with MultiWorkerMirroredStrategy, this "
"controls "
"tf.distribute.experimental.CollectiveCommunication; "
"valid options are `ring` and `nccl`."))
if num_packs:
flags.DEFINE_integer(
name="num_packs", default=1,
help=help_wrap("Sets `num_packs` in the cross device ops used in "
"MirroredStrategy. For details, see "
"tf.distribute.NcclAllReduce."))
if tf_gpu_thread_mode:
flags.DEFINE_string(
name="tf_gpu_thread_mode", short_name="gt_mode", default=None,
help=help_wrap(
"Whether and how the GPU device uses its own threadpool.")
)
flags.DEFINE_integer(
name="per_gpu_thread_count", short_name="pgtc", default=0,
help=help_wrap(
"The number of threads to use for GPU. Only valid when "
"tf_gpu_thread_mode is not global.")
)
if datasets_num_private_threads:
flags.DEFINE_integer(
name="datasets_num_private_threads",
default=None,
help=help_wrap(
"Number of threads for a private threadpool created for all"
"datasets computation..")
)
if datasets_num_parallel_batches:
flags.DEFINE_integer(
name="datasets_num_parallel_batches",
default=None,
help=help_wrap(
"Determines how many batches to process in parallel when using "
"map and batch from tf.data.")
)
if training_dataset_cache:
flags.DEFINE_boolean(
name="training_dataset_cache",
default=False,
help=help_wrap(
"Determines whether to cache the training dataset on workers. "
"Typically used to improve training performance when training "
"data is in remote storage and can fit into worker memory.")
)
if tf_data_experimental_slack:
flags.DEFINE_boolean(
name="tf_data_experimental_slack",
default=False,
help=help_wrap(
"Whether to enable tf.data's `experimental_slack` option.")
)
if enable_xla:
flags.DEFINE_boolean(
name="enable_xla", default=False,
help="Whether to enable XLA auto jit compilation")
if force_v2_in_keras_compile:
flags.DEFINE_boolean(
name="force_v2_in_keras_compile", default=None,
help="Forces the use of run_distribued path even if not"
"using a `strategy`. This is not the same as"
"`tf.distribute.OneDeviceStrategy`")
return key_flags
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_performance.py |
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Flags related to distributed execution."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import tensorflow as tf
from official.utils.flags._conventions import help_wrap
def define_distribution(worker_hosts=True, task_index=True):
"""Register distributed execution flags.
Args:
worker_hosts: Create a flag for specifying comma-separated list of workers.
task_index: Create a flag for specifying index of task.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if worker_hosts:
flags.DEFINE_string(
name='worker_hosts', default=None,
help=help_wrap(
'Comma-separated list of worker ip:port pairs for running '
'multi-worker models with DistributionStrategy. The user would '
'start the program on each host with identical value for this '
'flag.'))
if task_index:
flags.DEFINE_integer(
name='task_index', default=-1,
help=help_wrap('If multi-worker training, the task_index of this '
'worker.'))
return key_flags
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_distribution.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Misc flags."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
from official.utils.flags._conventions import help_wrap
def define_image(data_format=True):
"""Register image specific flags.
Args:
data_format: Create a flag to specify image axis convention.
Returns:
A list of flags for core.py to marks as key flags.
"""
key_flags = []
if data_format:
flags.DEFINE_enum(
name="data_format", short_name="df", default=None,
enum_values=["channels_first", "channels_last"],
help=help_wrap(
"A flag to override the data format used in the model. "
"channels_first provides a performance boost on GPU but is not "
"always compatible with CPU. If left unspecified, the data format "
"will be chosen automatically based on whether TensorFlow was "
"built for CPU or GPU."))
key_flags.append("data_format")
return key_flags
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/flags/_misc.py |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Hooks helper to return a list of TensorFlow hooks for training by name.
More hooks can be added to this set. To add a new hook, 1) add the new hook to
the registry in HOOKS, 2) add a corresponding function that parses out necessary
parameters.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.logs import hooks
from official.utils.logs import logger
from official.utils.logs import metric_hook
_TENSORS_TO_LOG = dict((x, x) for x in ['learning_rate',
'cross_entropy',
'train_accuracy'])
def get_train_hooks(name_list, use_tpu=False, **kwargs):
"""Factory for getting a list of TensorFlow hooks for training by name.
Args:
name_list: a list of strings to name desired hook classes. Allowed:
LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
as keys in HOOKS
use_tpu: Boolean of whether computation occurs on a TPU. This will disable
hooks altogether.
**kwargs: a dictionary of arguments to the hooks.
Returns:
list of instantiated hooks, ready to be used in a classifier.train call.
Raises:
ValueError: if an unrecognized name is passed.
"""
if not name_list:
return []
if use_tpu:
tf.compat.v1.logging.warning('hooks_helper received name_list `{}`, but a '
'TPU is specified. No hooks will be used.'
.format(name_list))
return []
train_hooks = []
for name in name_list:
hook_name = HOOKS.get(name.strip().lower())
if hook_name is None:
raise ValueError('Unrecognized training hook requested: {}'.format(name))
else:
train_hooks.append(hook_name(**kwargs))
return train_hooks
def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs): # pylint: disable=unused-argument
"""Function to get LoggingTensorHook.
Args:
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
**kwargs: a dictionary of arguments to LoggingTensorHook.
Returns:
Returns a LoggingTensorHook with a standard set of tensors that will be
printed to stdout.
"""
if tensors_to_log is None:
tensors_to_log = _TENSORS_TO_LOG
return tf.estimator.LoggingTensorHook(
tensors=tensors_to_log,
every_n_iter=every_n_iter)
def get_profiler_hook(model_dir, save_steps=1000, **kwargs): # pylint: disable=unused-argument
"""Function to get ProfilerHook.
Args:
model_dir: The directory to save the profile traces to.
save_steps: `int`, print profile traces every N steps.
**kwargs: a dictionary of arguments to ProfilerHook.
Returns:
Returns a ProfilerHook that writes out timelines that can be loaded into
profiling tools like chrome://tracing.
"""
return tf.estimator.ProfilerHook(save_steps=save_steps, output_dir=model_dir)
def get_examples_per_second_hook(every_n_steps=100,
batch_size=128,
warm_steps=5,
**kwargs): # pylint: disable=unused-argument
"""Function to get ExamplesPerSecondHook.
Args:
every_n_steps: `int`, print current and average examples per second every
N steps.
batch_size: `int`, total batch size used to calculate examples/second from
global time.
warm_steps: skip this number of steps before logging and running average.
**kwargs: a dictionary of arguments to ExamplesPerSecondHook.
Returns:
Returns a ProfilerHook that writes out timelines that can be loaded into
profiling tools like chrome://tracing.
"""
return hooks.ExamplesPerSecondHook(
batch_size=batch_size, every_n_steps=every_n_steps,
warm_steps=warm_steps, metric_logger=logger.get_benchmark_logger())
def get_logging_metric_hook(tensors_to_log=None,
every_n_secs=600,
**kwargs): # pylint: disable=unused-argument
"""Function to get LoggingMetricHook.
Args:
tensors_to_log: List of tensor names or dictionary mapping labels to tensor
names. If not set, log _TENSORS_TO_LOG by default.
every_n_secs: `int`, the frequency for logging the metric. Default to every
10 mins.
**kwargs: a dictionary of arguments.
Returns:
Returns a LoggingMetricHook that saves tensor values in a JSON format.
"""
if tensors_to_log is None:
tensors_to_log = _TENSORS_TO_LOG
return metric_hook.LoggingMetricHook(
tensors=tensors_to_log,
metric_logger=logger.get_benchmark_logger(),
every_n_secs=every_n_secs)
def get_step_counter_hook(**kwargs):
"""Function to get StepCounterHook."""
del kwargs
return tf.estimator.StepCounterHook()
# A dictionary to map one hook name and its corresponding function
HOOKS = {
'loggingtensorhook': get_logging_tensor_hook,
'profilerhook': get_profiler_hook,
'examplespersecondhook': get_examples_per_second_hook,
'loggingmetrichook': get_logging_metric_hook,
'stepcounterhook': get_step_counter_hook
}
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/hooks_helper.py |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Hook that counts examples per second every N steps or seconds."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.logs import logger
class ExamplesPerSecondHook(tf.estimator.SessionRunHook):
"""Hook to print out examples per second.
Total time is tracked and then divided by the total number of steps
to get the average step time and then batch_size is used to determine
the running average of examples per second. The examples per second for the
most recent interval is also logged.
"""
def __init__(self,
batch_size,
every_n_steps=None,
every_n_secs=None,
warm_steps=0,
metric_logger=None):
"""Initializer for ExamplesPerSecondHook.
Args:
batch_size: Total batch size across all workers used to calculate
examples/second from global time.
every_n_steps: Log stats every n steps.
every_n_secs: Log stats every n seconds. Exactly one of the
`every_n_steps` or `every_n_secs` should be set.
warm_steps: The number of steps to be skipped before logging and running
average calculation. warm_steps steps refers to global steps across all
workers, not on each worker
metric_logger: instance of `BenchmarkLogger`, the benchmark logger that
hook should use to write the log. If None, BaseBenchmarkLogger will
be used.
Raises:
ValueError: if neither `every_n_steps` or `every_n_secs` is set, or
both are set.
"""
if (every_n_steps is None) == (every_n_secs is None):
raise ValueError("exactly one of every_n_steps"
" and every_n_secs should be provided.")
self._logger = metric_logger or logger.BaseBenchmarkLogger()
self._timer = tf.estimator.SecondOrStepTimer(
every_steps=every_n_steps, every_secs=every_n_secs)
self._step_train_time = 0
self._total_steps = 0
self._batch_size = batch_size
self._warm_steps = warm_steps
# List of examples per second logged every_n_steps.
self.current_examples_per_sec_list = []
def begin(self):
"""Called once before using the session to check global step."""
self._global_step_tensor = tf.compat.v1.train.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError(
"Global step should be created to use StepCounterHook.")
def before_run(self, run_context): # pylint: disable=unused-argument
"""Called before each call to run().
Args:
run_context: A SessionRunContext object.
Returns:
A SessionRunArgs object or None if never triggered.
"""
return tf.estimator.SessionRunArgs(self._global_step_tensor)
def after_run(self, run_context, run_values): # pylint: disable=unused-argument
"""Called after each call to run().
Args:
run_context: A SessionRunContext object.
run_values: A SessionRunValues object.
"""
global_step = run_values.results
if self._timer.should_trigger_for_step(
global_step) and global_step > self._warm_steps:
elapsed_time, elapsed_steps = self._timer.update_last_triggered_step(
global_step)
if elapsed_time is not None:
self._step_train_time += elapsed_time
self._total_steps += elapsed_steps
# average examples per second is based on the total (accumulative)
# training steps and training time so far
average_examples_per_sec = self._batch_size * (
self._total_steps / self._step_train_time)
# current examples per second is based on the elapsed training steps
# and training time per batch
current_examples_per_sec = self._batch_size * (
elapsed_steps / elapsed_time)
# Logs entries to be read from hook during or after run.
self.current_examples_per_sec_list.append(current_examples_per_sec)
self._logger.log_metric(
"average_examples_per_sec", average_examples_per_sec,
global_step=global_step)
self._logger.log_metric(
"current_examples_per_sec", current_examples_per_sec,
global_step=global_step)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/hooks.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for metric_hook."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tempfile
import time
import tensorflow as tf # pylint: disable=g-bad-import-order
from tensorflow.python.training import monitored_session # pylint: disable=g-bad-import-order
from official.utils.logs import metric_hook
from official.utils.testing import mock_lib
class LoggingMetricHookTest(tf.test.TestCase):
"""Tests for LoggingMetricHook."""
def setUp(self):
super(LoggingMetricHookTest, self).setUp()
self._log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
self._logger = mock_lib.MockBenchmarkLogger()
def tearDown(self):
super(LoggingMetricHookTest, self).tearDown()
tf.io.gfile.rmtree(self.get_temp_dir())
def test_illegal_args(self):
with self.assertRaisesRegexp(ValueError, "nvalid every_n_iter"):
metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=0)
with self.assertRaisesRegexp(ValueError, "nvalid every_n_iter"):
metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=-10)
with self.assertRaisesRegexp(ValueError, "xactly one of"):
metric_hook.LoggingMetricHook(
tensors=["t"], every_n_iter=5, every_n_secs=5)
with self.assertRaisesRegexp(ValueError, "xactly one of"):
metric_hook.LoggingMetricHook(tensors=["t"])
with self.assertRaisesRegexp(ValueError, "metric_logger"):
metric_hook.LoggingMetricHook(tensors=["t"], every_n_iter=5)
def test_print_at_end_only(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
t = tf.constant(42.0, name="foo")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], at_end=True, metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
for _ in range(3):
mon_sess.run(train_op)
self.assertEqual(self._logger.logged_metric, [])
hook.end(sess)
self.assertEqual(len(self._logger.logged_metric), 1)
metric = self._logger.logged_metric[0]
self.assertRegexpMatches(metric["name"], "foo")
self.assertEqual(metric["value"], 42.0)
self.assertEqual(metric["unit"], None)
self.assertEqual(metric["global_step"], 0)
def test_global_step_not_found(self):
with tf.Graph().as_default():
t = tf.constant(42.0, name="foo")
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], at_end=True, metric_logger=self._logger)
with self.assertRaisesRegexp(
RuntimeError, "should be created to use LoggingMetricHook."):
hook.begin()
def test_log_tensors(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
t1 = tf.constant(42.0, name="foo")
t2 = tf.constant(43.0, name="bar")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t1, t2], at_end=True, metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
for _ in range(3):
mon_sess.run(train_op)
self.assertEqual(self._logger.logged_metric, [])
hook.end(sess)
self.assertEqual(len(self._logger.logged_metric), 2)
metric1 = self._logger.logged_metric[0]
self.assertRegexpMatches(str(metric1["name"]), "foo")
self.assertEqual(metric1["value"], 42.0)
self.assertEqual(metric1["unit"], None)
self.assertEqual(metric1["global_step"], 0)
metric2 = self._logger.logged_metric[1]
self.assertRegexpMatches(str(metric2["name"]), "bar")
self.assertEqual(metric2["value"], 43.0)
self.assertEqual(metric2["unit"], None)
self.assertEqual(metric2["global_step"], 0)
def _validate_print_every_n_steps(self, sess, at_end):
t = tf.constant(42.0, name="foo")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], every_n_iter=10, at_end=at_end,
metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
for _ in range(3):
self._logger.logged_metric = []
for _ in range(9):
mon_sess.run(train_op)
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
# Add additional run to verify proper reset when called multiple times.
self._logger.logged_metric = []
mon_sess.run(train_op)
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
self._logger.logged_metric = []
hook.end(sess)
if at_end:
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
else:
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
def test_print_every_n_steps(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
self._validate_print_every_n_steps(sess, at_end=False)
# Verify proper reset.
self._validate_print_every_n_steps(sess, at_end=False)
def test_print_every_n_steps_and_end(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
self._validate_print_every_n_steps(sess, at_end=True)
# Verify proper reset.
self._validate_print_every_n_steps(sess, at_end=True)
def _validate_print_every_n_secs(self, sess, at_end):
t = tf.constant(42.0, name="foo")
train_op = tf.constant(3)
hook = metric_hook.LoggingMetricHook(
tensors=[t.name], every_n_secs=1.0, at_end=at_end,
metric_logger=self._logger)
hook.begin()
mon_sess = monitored_session._HookedSession(sess, [hook]) # pylint: disable=protected-access
sess.run(tf.compat.v1.global_variables_initializer())
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
# assertNotRegexpMatches is not supported by python 3.1 and later
self._logger.logged_metric = []
mon_sess.run(train_op)
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
time.sleep(1.0)
self._logger.logged_metric = []
mon_sess.run(train_op)
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
self._logger.logged_metric = []
hook.end(sess)
if at_end:
self.assertRegexpMatches(str(self._logger.logged_metric), t.name)
else:
# assertNotRegexpMatches is not supported by python 3.1 and later
self.assertEqual(str(self._logger.logged_metric).find(t.name), -1)
def test_print_every_n_secs(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
self._validate_print_every_n_secs(sess, at_end=False)
# Verify proper reset.
self._validate_print_every_n_secs(sess, at_end=False)
def test_print_every_n_secs_and_end(self):
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
tf.compat.v1.train.get_or_create_global_step()
self._validate_print_every_n_secs(sess, at_end=True)
# Verify proper reset.
self._validate_print_every_n_secs(sess, at_end=True)
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/metric_hook_test.py |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for hooks_helper."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.logs import hooks_helper
from official.utils.misc import keras_utils
class BaseTest(unittest.TestCase):
def setUp(self):
super(BaseTest, self).setUp()
if keras_utils.is_v2_0:
tf.compat.v1.disable_eager_execution()
def test_raise_in_non_list_names(self):
with self.assertRaises(ValueError):
hooks_helper.get_train_hooks(
'LoggingTensorHook, ProfilerHook', model_dir="", batch_size=256)
def test_raise_in_invalid_names(self):
invalid_names = ['StepCounterHook', 'StopAtStepHook']
with self.assertRaises(ValueError):
hooks_helper.get_train_hooks(invalid_names, model_dir="", batch_size=256)
def validate_train_hook_name(self,
test_hook_name,
expected_hook_name,
**kwargs):
returned_hook = hooks_helper.get_train_hooks(
[test_hook_name], model_dir="", **kwargs)
self.assertEqual(len(returned_hook), 1)
self.assertIsInstance(returned_hook[0], tf.estimator.SessionRunHook)
self.assertEqual(returned_hook[0].__class__.__name__.lower(),
expected_hook_name)
def test_get_train_hooks_logging_tensor_hook(self):
self.validate_train_hook_name('LoggingTensorHook', 'loggingtensorhook')
def test_get_train_hooks_profiler_hook(self):
self.validate_train_hook_name('ProfilerHook', 'profilerhook')
def test_get_train_hooks_examples_per_second_hook(self):
self.validate_train_hook_name('ExamplesPerSecondHook',
'examplespersecondhook')
def test_get_logging_metric_hook(self):
test_hook_name = 'LoggingMetricHook'
self.validate_train_hook_name(test_hook_name, 'loggingmetrichook')
if __name__ == '__main__':
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/hooks_helper_test.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Wrapper for the mlperf logging utils.
MLPerf compliance logging is only desired under a limited set of circumstances.
This module is intended to keep users from needing to consider logging (or
install the module) unless they are performing mlperf runs.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import json
import os
import re
import subprocess
import sys
import typing
import tensorflow as tf
_MIN_VERSION = (0, 0, 10)
_STACK_OFFSET = 2
SUDO = "sudo" if os.geteuid() else ""
# This indirection is used in docker.
DROP_CACHE_LOC = os.getenv("DROP_CACHE_LOC", "/proc/sys/vm/drop_caches")
_NCF_PREFIX = "NCF_RAW_"
# TODO(robieta): move line parsing to mlperf util
_PREFIX = r"(?:{})?:::MLPv([0-9]+).([0-9]+).([0-9]+)".format(_NCF_PREFIX)
_BENCHMARK = r"([a-zA-Z0-9_]+)"
_TIMESTAMP = r"([0-9]+\.[0-9]+)"
_CALLSITE = r"\((.+):([0-9]+)\)"
_TAG = r"([a-zA-Z0-9_]+)"
_VALUE = r"(.*)"
ParsedLine = namedtuple("ParsedLine", ["version", "benchmark", "timestamp",
"callsite", "tag", "value"])
LINE_PATTERN = re.compile(
"^{prefix} {benchmark} {timestamp} {callsite} {tag}(: |$){value}?$".format(
prefix=_PREFIX, benchmark=_BENCHMARK, timestamp=_TIMESTAMP,
callsite=_CALLSITE, tag=_TAG, value=_VALUE))
def parse_line(line): # type: (str) -> typing.Optional[ParsedLine]
match = LINE_PATTERN.match(line.strip())
if not match:
return
major, minor, micro, benchmark, timestamp = match.groups()[:5]
call_file, call_line, tag, _, value = match.groups()[5:]
return ParsedLine(version=(int(major), int(minor), int(micro)),
benchmark=benchmark, timestamp=timestamp,
callsite=(call_file, call_line), tag=tag, value=value)
def unparse_line(parsed_line): # type: (ParsedLine) -> str
version_str = "{}.{}.{}".format(*parsed_line.version)
callsite_str = "({}:{})".format(*parsed_line.callsite)
value_str = ": {}".format(parsed_line.value) if parsed_line.value else ""
return ":::MLPv{} {} {} {} {} {}".format(
version_str, parsed_line.benchmark, parsed_line.timestamp, callsite_str,
parsed_line.tag, value_str)
def get_mlperf_log():
"""Shielded import of mlperf_log module."""
try:
import mlperf_compliance
def test_mlperf_log_pip_version():
"""Check that mlperf_compliance is up to date."""
import pkg_resources
version = pkg_resources.get_distribution("mlperf_compliance")
version = tuple(int(i) for i in version.version.split("."))
if version < _MIN_VERSION:
tf.compat.v1.logging.warning(
"mlperf_compliance is version {}, must be >= {}".format(
".".join([str(i) for i in version]),
".".join([str(i) for i in _MIN_VERSION])))
raise ImportError
return mlperf_compliance.mlperf_log
mlperf_log = test_mlperf_log_pip_version()
except ImportError:
mlperf_log = None
return mlperf_log
class Logger(object):
"""MLPerf logger indirection class.
This logger only logs for MLPerf runs, and prevents various errors associated
with not having the mlperf_compliance package installed.
"""
class Tags(object):
def __init__(self, mlperf_log):
self._enabled = False
self._mlperf_log = mlperf_log
def __getattr__(self, item):
if self._mlperf_log is None or not self._enabled:
return
return getattr(self._mlperf_log, item)
def __init__(self):
self._enabled = False
self._mlperf_log = get_mlperf_log()
self.tags = self.Tags(self._mlperf_log)
def __call__(self, enable=False):
if enable and self._mlperf_log is None:
raise ImportError("MLPerf logging was requested, but mlperf_compliance "
"module could not be loaded.")
self._enabled = enable
self.tags._enabled = enable
return self
def __enter__(self):
pass
def __exit__(self, exc_type, exc_val, exc_tb):
self._enabled = False
self.tags._enabled = False
@property
def log_file(self):
if self._mlperf_log is None:
return
return self._mlperf_log.LOG_FILE
@property
def enabled(self):
return self._enabled
def ncf_print(self, key, value=None, stack_offset=_STACK_OFFSET,
deferred=False, extra_print=False, prefix=_NCF_PREFIX):
if self._mlperf_log is None or not self.enabled:
return
self._mlperf_log.ncf_print(key=key, value=value, stack_offset=stack_offset,
deferred=deferred, extra_print=extra_print,
prefix=prefix)
def set_ncf_root(self, path):
if self._mlperf_log is None:
return
self._mlperf_log.ROOT_DIR_NCF = path
LOGGER = Logger()
ncf_print, set_ncf_root = LOGGER.ncf_print, LOGGER.set_ncf_root
TAGS = LOGGER.tags
def clear_system_caches():
if not LOGGER.enabled:
return
ret_code = subprocess.call(
["sync && echo 3 | {} tee {}".format(SUDO, DROP_CACHE_LOC)],
shell=True)
if ret_code:
raise ValueError("Failed to clear caches")
if __name__ == "__main__":
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
with LOGGER(True):
ncf_print(key=TAGS.RUN_START)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/mlperf_helper.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Session hook for logging benchmark metric."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=g-bad-import-order
class LoggingMetricHook(tf.estimator.LoggingTensorHook):
"""Hook to log benchmark metric information.
This hook is very similar as tf.train.LoggingTensorHook, which logs given
tensors every N local steps, every N seconds, or at the end. The metric
information will be logged to given log_dir or via metric_logger in JSON
format, which can be consumed by data analysis pipeline later.
Note that if `at_end` is True, `tensors` should not include any tensor
whose evaluation produces a side effect such as consuming additional inputs.
"""
def __init__(self, tensors, metric_logger=None,
every_n_iter=None, every_n_secs=None, at_end=False):
"""Initializer for LoggingMetricHook.
Args:
tensors: `dict` that maps string-valued tags to tensors/tensor names,
or `iterable` of tensors/tensor names.
metric_logger: instance of `BenchmarkLogger`, the benchmark logger that
hook should use to write the log.
every_n_iter: `int`, print the values of `tensors` once every N local
steps taken on the current worker.
every_n_secs: `int` or `float`, print the values of `tensors` once every N
seconds. Exactly one of `every_n_iter` and `every_n_secs` should be
provided.
at_end: `bool` specifying whether to print the values of `tensors` at the
end of the run.
Raises:
ValueError:
1. `every_n_iter` is non-positive, or
2. Exactly one of every_n_iter and every_n_secs should be provided.
3. Exactly one of log_dir and metric_logger should be provided.
"""
super(LoggingMetricHook, self).__init__(
tensors=tensors,
every_n_iter=every_n_iter,
every_n_secs=every_n_secs,
at_end=at_end)
if metric_logger is None:
raise ValueError("metric_logger should be provided.")
self._logger = metric_logger
def begin(self):
super(LoggingMetricHook, self).begin()
self._global_step_tensor = tf.compat.v1.train.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError(
"Global step should be created to use LoggingMetricHook.")
if self._global_step_tensor.name not in self._current_tensors:
self._current_tensors[self._global_step_tensor.name] = (
self._global_step_tensor)
def after_run(self, unused_run_context, run_values):
# should_trigger is a internal state that populated at before_run, and it is
# using self_timer to determine whether it should trigger.
if self._should_trigger:
self._log_metric(run_values.results)
self._iter_count += 1
def end(self, session):
if self._log_at_end:
values = session.run(self._current_tensors)
self._log_metric(values)
def _log_metric(self, tensor_values):
self._timer.update_last_triggered_step(self._iter_count)
global_step = tensor_values[self._global_step_tensor.name]
# self._tag_order is populated during the init of LoggingTensorHook
for tag in self._tag_order:
self._logger.log_metric(tag, tensor_values[tag], global_step=global_step)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/metric_hook.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Logging utilities for benchmark.
For collecting local environment metrics like CPU and memory, certain python
packages need be installed. See README for details.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
import datetime
import json
import multiprocessing
import numbers
import os
import threading
import uuid
from six.moves import _thread as thread
from absl import flags
import tensorflow as tf
from tensorflow.python.client import device_lib
from official.utils.logs import cloud_lib
METRIC_LOG_FILE_NAME = "metric.log"
BENCHMARK_RUN_LOG_FILE_NAME = "benchmark_run.log"
_DATE_TIME_FORMAT_PATTERN = "%Y-%m-%dT%H:%M:%S.%fZ"
GCP_TEST_ENV = "GCP"
RUN_STATUS_SUCCESS = "success"
RUN_STATUS_FAILURE = "failure"
RUN_STATUS_RUNNING = "running"
FLAGS = flags.FLAGS
# Don't use it directly. Use get_benchmark_logger to access a logger.
_benchmark_logger = None
_logger_lock = threading.Lock()
def config_benchmark_logger(flag_obj=None):
"""Config the global benchmark logger."""
_logger_lock.acquire()
try:
global _benchmark_logger
if not flag_obj:
flag_obj = FLAGS
if (not hasattr(flag_obj, "benchmark_logger_type") or
flag_obj.benchmark_logger_type == "BaseBenchmarkLogger"):
_benchmark_logger = BaseBenchmarkLogger()
elif flag_obj.benchmark_logger_type == "BenchmarkFileLogger":
_benchmark_logger = BenchmarkFileLogger(flag_obj.benchmark_log_dir)
elif flag_obj.benchmark_logger_type == "BenchmarkBigQueryLogger":
from official.benchmark import benchmark_uploader as bu # pylint: disable=g-import-not-at-top
bq_uploader = bu.BigQueryUploader(gcp_project=flag_obj.gcp_project)
_benchmark_logger = BenchmarkBigQueryLogger(
bigquery_uploader=bq_uploader,
bigquery_data_set=flag_obj.bigquery_data_set,
bigquery_run_table=flag_obj.bigquery_run_table,
bigquery_run_status_table=flag_obj.bigquery_run_status_table,
bigquery_metric_table=flag_obj.bigquery_metric_table,
run_id=str(uuid.uuid4()))
else:
raise ValueError("Unrecognized benchmark_logger_type: %s"
% flag_obj.benchmark_logger_type)
finally:
_logger_lock.release()
return _benchmark_logger
def get_benchmark_logger():
if not _benchmark_logger:
config_benchmark_logger()
return _benchmark_logger
@contextlib.contextmanager
def benchmark_context(flag_obj):
"""Context of benchmark, which will update status of the run accordingly."""
benchmark_logger = config_benchmark_logger(flag_obj)
try:
yield
benchmark_logger.on_finish(RUN_STATUS_SUCCESS)
except Exception: # pylint: disable=broad-except
# Catch all the exception, update the run status to be failure, and re-raise
benchmark_logger.on_finish(RUN_STATUS_FAILURE)
raise
class BaseBenchmarkLogger(object):
"""Class to log the benchmark information to STDOUT."""
def log_evaluation_result(self, eval_results):
"""Log the evaluation result.
The evaluate result is a dictionary that contains metrics defined in
model_fn. It also contains a entry for global_step which contains the value
of the global step when evaluation was performed.
Args:
eval_results: dict, the result of evaluate.
"""
if not isinstance(eval_results, dict):
tf.compat.v1.logging.warning(
"eval_results should be dictionary for logging. Got %s",
type(eval_results))
return
global_step = eval_results[tf.compat.v1.GraphKeys.GLOBAL_STEP]
for key in sorted(eval_results):
if key != tf.compat.v1.GraphKeys.GLOBAL_STEP:
self.log_metric(key, eval_results[key], global_step=global_step)
def log_metric(self, name, value, unit=None, global_step=None, extras=None):
"""Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: number, the value of the metric. The value will not be logged if it
is not a number type.
unit: string, the unit of the metric, E.g "image per second".
global_step: int, the global_step when the metric is logged.
extras: map of string:string, the extra information about the metric.
"""
metric = _process_metric_to_json(name, value, unit, global_step, extras)
if metric:
tf.compat.v1.logging.info("Benchmark metric: %s", metric)
def log_run_info(self, model_name, dataset_name, run_params, test_id=None):
tf.compat.v1.logging.info(
"Benchmark run: %s", _gather_run_info(model_name, dataset_name,
run_params, test_id))
def on_finish(self, status):
pass
class BenchmarkFileLogger(BaseBenchmarkLogger):
"""Class to log the benchmark information to local disk."""
def __init__(self, logging_dir):
super(BenchmarkFileLogger, self).__init__()
self._logging_dir = logging_dir
if not tf.io.gfile.isdir(self._logging_dir):
tf.io.gfile.makedirs(self._logging_dir)
self._metric_file_handler = tf.io.gfile.GFile(
os.path.join(self._logging_dir, METRIC_LOG_FILE_NAME), "a")
def log_metric(self, name, value, unit=None, global_step=None, extras=None):
"""Log the benchmark metric information to local file.
Currently the logging is done in a synchronized way. This should be updated
to log asynchronously.
Args:
name: string, the name of the metric to log.
value: number, the value of the metric. The value will not be logged if it
is not a number type.
unit: string, the unit of the metric, E.g "image per second".
global_step: int, the global_step when the metric is logged.
extras: map of string:string, the extra information about the metric.
"""
metric = _process_metric_to_json(name, value, unit, global_step, extras)
if metric:
try:
json.dump(metric, self._metric_file_handler)
self._metric_file_handler.write("\n")
self._metric_file_handler.flush()
except (TypeError, ValueError) as e:
tf.compat.v1.logging.warning(
"Failed to dump metric to log file: name %s, value %s, error %s",
name, value, e)
def log_run_info(self, model_name, dataset_name, run_params, test_id=None):
"""Collect most of the TF runtime information for the local env.
The schema of the run info follows official/benchmark/datastore/schema.
Args:
model_name: string, the name of the model.
dataset_name: string, the name of dataset for training and evaluation.
run_params: dict, the dictionary of parameters for the run, it could
include hyperparameters or other params that are important for the run.
test_id: string, the unique name of the test run by the combination of key
parameters, eg batch size, num of GPU. It is hardware independent.
"""
run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)
with tf.io.gfile.GFile(os.path.join(
self._logging_dir, BENCHMARK_RUN_LOG_FILE_NAME), "w") as f:
try:
json.dump(run_info, f)
f.write("\n")
except (TypeError, ValueError) as e:
tf.compat.v1.logging.warning(
"Failed to dump benchmark run info to log file: %s", e)
def on_finish(self, status):
self._metric_file_handler.flush()
self._metric_file_handler.close()
class BenchmarkBigQueryLogger(BaseBenchmarkLogger):
"""Class to log the benchmark information to BigQuery data store."""
def __init__(self,
bigquery_uploader,
bigquery_data_set,
bigquery_run_table,
bigquery_run_status_table,
bigquery_metric_table,
run_id):
super(BenchmarkBigQueryLogger, self).__init__()
self._bigquery_uploader = bigquery_uploader
self._bigquery_data_set = bigquery_data_set
self._bigquery_run_table = bigquery_run_table
self._bigquery_run_status_table = bigquery_run_status_table
self._bigquery_metric_table = bigquery_metric_table
self._run_id = run_id
def log_metric(self, name, value, unit=None, global_step=None, extras=None):
"""Log the benchmark metric information to bigquery.
Args:
name: string, the name of the metric to log.
value: number, the value of the metric. The value will not be logged if it
is not a number type.
unit: string, the unit of the metric, E.g "image per second".
global_step: int, the global_step when the metric is logged.
extras: map of string:string, the extra information about the metric.
"""
metric = _process_metric_to_json(name, value, unit, global_step, extras)
if metric:
# Starting new thread for bigquery upload in case it might take long time
# and impact the benchmark and performance measurement. Starting a new
# thread might have potential performance impact for model that run on
# CPU.
thread.start_new_thread(
self._bigquery_uploader.upload_benchmark_metric_json,
(self._bigquery_data_set,
self._bigquery_metric_table,
self._run_id,
[metric]))
def log_run_info(self, model_name, dataset_name, run_params, test_id=None):
"""Collect most of the TF runtime information for the local env.
The schema of the run info follows official/benchmark/datastore/schema.
Args:
model_name: string, the name of the model.
dataset_name: string, the name of dataset for training and evaluation.
run_params: dict, the dictionary of parameters for the run, it could
include hyperparameters or other params that are important for the run.
test_id: string, the unique name of the test run by the combination of key
parameters, eg batch size, num of GPU. It is hardware independent.
"""
run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)
# Starting new thread for bigquery upload in case it might take long time
# and impact the benchmark and performance measurement. Starting a new
# thread might have potential performance impact for model that run on CPU.
thread.start_new_thread(
self._bigquery_uploader.upload_benchmark_run_json,
(self._bigquery_data_set,
self._bigquery_run_table,
self._run_id,
run_info))
thread.start_new_thread(
self._bigquery_uploader.insert_run_status,
(self._bigquery_data_set,
self._bigquery_run_status_table,
self._run_id,
RUN_STATUS_RUNNING))
def on_finish(self, status):
self._bigquery_uploader.update_run_status(
self._bigquery_data_set,
self._bigquery_run_status_table,
self._run_id,
status)
def _gather_run_info(model_name, dataset_name, run_params, test_id):
"""Collect the benchmark run information for the local environment."""
run_info = {
"model_name": model_name,
"dataset": {"name": dataset_name},
"machine_config": {},
"test_id": test_id,
"run_date": datetime.datetime.utcnow().strftime(
_DATE_TIME_FORMAT_PATTERN)}
_collect_tensorflow_info(run_info)
_collect_tensorflow_environment_variables(run_info)
_collect_run_params(run_info, run_params)
_collect_cpu_info(run_info)
_collect_memory_info(run_info)
_collect_test_environment(run_info)
return run_info
def _process_metric_to_json(
name, value, unit=None, global_step=None, extras=None):
"""Validate the metric data and generate JSON for insert."""
if not isinstance(value, numbers.Number):
tf.compat.v1.logging.warning(
"Metric value to log should be a number. Got %s", type(value))
return None
extras = _convert_to_json_dict(extras)
return {
"name": name,
"value": float(value),
"unit": unit,
"global_step": global_step,
"timestamp": datetime.datetime.utcnow().strftime(
_DATE_TIME_FORMAT_PATTERN),
"extras": extras}
def _collect_tensorflow_info(run_info):
run_info["tensorflow_version"] = {
"version": tf.version.VERSION, "git_hash": tf.version.GIT_VERSION}
def _collect_run_params(run_info, run_params):
"""Log the parameter information for the benchmark run."""
def process_param(name, value):
type_check = {
str: {"name": name, "string_value": value},
int: {"name": name, "long_value": value},
bool: {"name": name, "bool_value": str(value)},
float: {"name": name, "float_value": value},
}
return type_check.get(type(value),
{"name": name, "string_value": str(value)})
if run_params:
run_info["run_parameters"] = [
process_param(k, v) for k, v in sorted(run_params.items())]
def _collect_tensorflow_environment_variables(run_info):
run_info["tensorflow_environment_variables"] = [
{"name": k, "value": v}
for k, v in sorted(os.environ.items()) if k.startswith("TF_")]
# The following code is mirrored from tensorflow/tools/test/system_info_lib
# which is not exposed for import.
def _collect_cpu_info(run_info):
"""Collect the CPU information for the local environment."""
cpu_info = {}
cpu_info["num_cores"] = multiprocessing.cpu_count()
try:
# Note: cpuinfo is not installed in the TensorFlow OSS tree.
# It is installable via pip.
import cpuinfo # pylint: disable=g-import-not-at-top
info = cpuinfo.get_cpu_info()
cpu_info["cpu_info"] = info["brand"]
cpu_info["mhz_per_cpu"] = info["hz_advertised_raw"][0] / 1.0e6
run_info["machine_config"]["cpu_info"] = cpu_info
except ImportError:
tf.compat.v1.logging.warn(
"'cpuinfo' not imported. CPU info will not be logged.")
def _collect_memory_info(run_info):
try:
# Note: psutil is not installed in the TensorFlow OSS tree.
# It is installable via pip.
import psutil # pylint: disable=g-import-not-at-top
vmem = psutil.virtual_memory()
run_info["machine_config"]["memory_total"] = vmem.total
run_info["machine_config"]["memory_available"] = vmem.available
except ImportError:
tf.compat.v1.logging.warn(
"'psutil' not imported. Memory info will not be logged.")
def _collect_test_environment(run_info):
"""Detect the local environment, eg GCE, AWS or DGX, etc."""
if cloud_lib.on_gcp():
run_info["test_environment"] = GCP_TEST_ENV
# TODO(scottzhu): Add more testing env detection for other platform
def _parse_gpu_model(physical_device_desc):
# Assume all the GPU connected are same model
for kv in physical_device_desc.split(","):
k, _, v = kv.partition(":")
if k.strip() == "name":
return v.strip()
return None
def _convert_to_json_dict(input_dict):
if input_dict:
return [{"name": k, "value": v} for k, v in sorted(input_dict.items())]
else:
return []
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/logger.py |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for hooks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.logs import hooks
from official.utils.testing import mock_lib
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
class ExamplesPerSecondHookTest(tf.test.TestCase):
"""Tests for the ExamplesPerSecondHook.
In the test, we explicitly run global_step tensor after train_op in order to
keep the global_step value and the train_op (which increase the glboal_step
by 1) consistent. This is to correct the discrepancies in reported global_step
value when running on GPUs.
"""
def setUp(self):
"""Mock out logging calls to verify if correct info is being monitored."""
self._logger = mock_lib.MockBenchmarkLogger()
self.graph = tf.Graph()
with self.graph.as_default():
tf.compat.v1.train.create_global_step()
self.train_op = tf.compat.v1.assign_add(
tf.compat.v1.train.get_global_step(), 1)
self.global_step = tf.compat.v1.train.get_global_step()
def test_raise_in_both_secs_and_steps(self):
with self.assertRaises(ValueError):
hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=10,
every_n_secs=20,
metric_logger=self._logger)
def test_raise_in_none_secs_and_steps(self):
with self.assertRaises(ValueError):
hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=None,
every_n_secs=None,
metric_logger=self._logger)
def _validate_log_every_n_steps(self, every_n_steps, warm_steps):
hook = hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=every_n_steps,
warm_steps=warm_steps,
metric_logger=self._logger)
with tf.compat.v1.train.MonitoredSession(
tf.compat.v1.train.ChiefSessionCreator(), [hook]) as mon_sess:
for _ in range(every_n_steps):
# Explicitly run global_step after train_op to get the accurate
# global_step value
mon_sess.run(self.train_op)
mon_sess.run(self.global_step)
# Nothing should be in the list yet
self.assertFalse(self._logger.logged_metric)
mon_sess.run(self.train_op)
global_step_val = mon_sess.run(self.global_step)
if global_step_val > warm_steps:
self._assert_metrics()
else:
# Nothing should be in the list yet
self.assertFalse(self._logger.logged_metric)
# Add additional run to verify proper reset when called multiple times.
prev_log_len = len(self._logger.logged_metric)
mon_sess.run(self.train_op)
global_step_val = mon_sess.run(self.global_step)
if every_n_steps == 1 and global_step_val > warm_steps:
# Each time, we log two additional metrics. Did exactly 2 get added?
self.assertEqual(len(self._logger.logged_metric), prev_log_len + 2)
else:
# No change in the size of the metric list.
self.assertEqual(len(self._logger.logged_metric), prev_log_len)
def test_examples_per_sec_every_1_steps(self):
with self.graph.as_default():
self._validate_log_every_n_steps(1, 0)
def test_examples_per_sec_every_5_steps(self):
with self.graph.as_default():
self._validate_log_every_n_steps(5, 0)
def test_examples_per_sec_every_1_steps_with_warm_steps(self):
with self.graph.as_default():
self._validate_log_every_n_steps(1, 10)
def test_examples_per_sec_every_5_steps_with_warm_steps(self):
with self.graph.as_default():
self._validate_log_every_n_steps(5, 10)
def _validate_log_every_n_secs(self, every_n_secs):
hook = hooks.ExamplesPerSecondHook(
batch_size=256,
every_n_steps=None,
every_n_secs=every_n_secs,
metric_logger=self._logger)
with tf.compat.v1.train.MonitoredSession(
tf.compat.v1.train.ChiefSessionCreator(), [hook]) as mon_sess:
# Explicitly run global_step after train_op to get the accurate
# global_step value
mon_sess.run(self.train_op)
mon_sess.run(self.global_step)
# Nothing should be in the list yet
self.assertFalse(self._logger.logged_metric)
time.sleep(every_n_secs)
mon_sess.run(self.train_op)
mon_sess.run(self.global_step)
self._assert_metrics()
def test_examples_per_sec_every_1_secs(self):
with self.graph.as_default():
self._validate_log_every_n_secs(1)
def test_examples_per_sec_every_5_secs(self):
with self.graph.as_default():
self._validate_log_every_n_secs(5)
def _assert_metrics(self):
metrics = self._logger.logged_metric
self.assertEqual(metrics[-2]["name"], "average_examples_per_sec")
self.assertEqual(metrics[-1]["name"], "current_examples_per_sec")
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/hooks_test.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities that interact with cloud service.
"""
import requests
GCP_METADATA_URL = "http://metadata/computeMetadata/v1/instance/hostname"
GCP_METADATA_HEADER = {"Metadata-Flavor": "Google"}
def on_gcp():
"""Detect whether the current running environment is on GCP."""
try:
# Timeout in 5 seconds, in case the test environment has connectivity issue.
# There is not default timeout, which means it might block forever.
response = requests.get(
GCP_METADATA_URL, headers=GCP_METADATA_HEADER, timeout=5)
return response.status_code == 200
except requests.exceptions.RequestException:
return False
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/cloud_lib.py |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for cloud_lib."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import mock
import requests
from official.utils.logs import cloud_lib
class CloudLibTest(unittest.TestCase):
@mock.patch("requests.get")
def test_on_gcp(self, mock_requests_get):
mock_response = mock.MagicMock()
mock_requests_get.return_value = mock_response
mock_response.status_code = 200
self.assertEqual(cloud_lib.on_gcp(), True)
@mock.patch("requests.get")
def test_not_on_gcp(self, mock_requests_get):
mock_requests_get.side_effect = requests.exceptions.ConnectionError()
self.assertEqual(cloud_lib.on_gcp(), False)
if __name__ == "__main__":
unittest.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/cloud_lib_test.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for benchmark logger."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import tempfile
import time
import unittest
import mock
from absl.testing import flagsaver
import tensorflow as tf # pylint: disable=g-bad-import-order
try:
from google.cloud import bigquery
except ImportError:
bigquery = None
from official.utils.misc import keras_utils
from official.utils.flags import core as flags_core
from official.utils.logs import logger
class BenchmarkLoggerTest(tf.test.TestCase):
@classmethod
def setUpClass(cls): # pylint: disable=invalid-name
super(BenchmarkLoggerTest, cls).setUpClass()
flags_core.define_benchmark()
def test_get_default_benchmark_logger(self):
with flagsaver.flagsaver(benchmark_logger_type="foo"):
self.assertIsInstance(logger.get_benchmark_logger(),
logger.BaseBenchmarkLogger)
def test_config_base_benchmark_logger(self):
with flagsaver.flagsaver(benchmark_logger_type="BaseBenchmarkLogger"):
logger.config_benchmark_logger()
self.assertIsInstance(logger.get_benchmark_logger(),
logger.BaseBenchmarkLogger)
def test_config_benchmark_file_logger(self):
# Set the benchmark_log_dir first since the benchmark_logger_type will need
# the value to be set when it does the validation.
with flagsaver.flagsaver(benchmark_log_dir="/tmp"):
with flagsaver.flagsaver(benchmark_logger_type="BenchmarkFileLogger"):
logger.config_benchmark_logger()
self.assertIsInstance(logger.get_benchmark_logger(),
logger.BenchmarkFileLogger)
@unittest.skipIf(bigquery is None, "Bigquery dependency is not installed.")
@mock.patch.object(bigquery, "Client")
def test_config_benchmark_bigquery_logger(self, mock_bigquery_client):
with flagsaver.flagsaver(benchmark_logger_type="BenchmarkBigQueryLogger"):
logger.config_benchmark_logger()
self.assertIsInstance(logger.get_benchmark_logger(),
logger.BenchmarkBigQueryLogger)
@mock.patch("official.utils.logs.logger.config_benchmark_logger")
def test_benchmark_context(self, mock_config_benchmark_logger):
mock_logger = mock.MagicMock()
mock_config_benchmark_logger.return_value = mock_logger
with logger.benchmark_context(None):
tf.compat.v1.logging.info("start benchmarking")
mock_logger.on_finish.assert_called_once_with(logger.RUN_STATUS_SUCCESS)
@mock.patch("official.utils.logs.logger.config_benchmark_logger")
def test_benchmark_context_failure(self, mock_config_benchmark_logger):
mock_logger = mock.MagicMock()
mock_config_benchmark_logger.return_value = mock_logger
with self.assertRaises(RuntimeError):
with logger.benchmark_context(None):
raise RuntimeError("training error")
mock_logger.on_finish.assert_called_once_with(logger.RUN_STATUS_FAILURE)
class BaseBenchmarkLoggerTest(tf.test.TestCase):
def setUp(self):
super(BaseBenchmarkLoggerTest, self).setUp()
self._actual_log = tf.compat.v1.logging.info
self.logged_message = None
def mock_log(*args, **kwargs):
self.logged_message = args
self._actual_log(*args, **kwargs)
tf.compat.v1.logging.info = mock_log
def tearDown(self):
super(BaseBenchmarkLoggerTest, self).tearDown()
tf.compat.v1.logging.info = self._actual_log
def test_log_metric(self):
log = logger.BaseBenchmarkLogger()
log.log_metric("accuracy", 0.999, global_step=1e4, extras={"name": "value"})
expected_log_prefix = "Benchmark metric:"
self.assertRegexpMatches(str(self.logged_message), expected_log_prefix)
class BenchmarkFileLoggerTest(tf.test.TestCase):
def setUp(self):
super(BenchmarkFileLoggerTest, self).setUp()
# Avoid pulling extra env vars from test environment which affects the test
# result, eg. Kokoro test has a TF_PKG env which affect the test case
# test_collect_tensorflow_environment_variables()
self.original_environ = dict(os.environ)
os.environ.clear()
def tearDown(self):
super(BenchmarkFileLoggerTest, self).tearDown()
tf.io.gfile.rmtree(self.get_temp_dir())
os.environ.clear()
os.environ.update(self.original_environ)
def test_create_logging_dir(self):
non_exist_temp_dir = os.path.join(self.get_temp_dir(), "unknown_dir")
self.assertFalse(tf.io.gfile.isdir(non_exist_temp_dir))
logger.BenchmarkFileLogger(non_exist_temp_dir)
self.assertTrue(tf.io.gfile.isdir(non_exist_temp_dir))
def test_log_metric(self):
log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
log = logger.BenchmarkFileLogger(log_dir)
log.log_metric("accuracy", 0.999, global_step=1e4, extras={"name": "value"})
metric_log = os.path.join(log_dir, "metric.log")
self.assertTrue(tf.io.gfile.exists(metric_log))
with tf.io.gfile.GFile(metric_log) as f:
metric = json.loads(f.readline())
self.assertEqual(metric["name"], "accuracy")
self.assertEqual(metric["value"], 0.999)
self.assertEqual(metric["unit"], None)
self.assertEqual(metric["global_step"], 1e4)
self.assertEqual(metric["extras"], [{"name": "name", "value": "value"}])
def test_log_multiple_metrics(self):
log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
log = logger.BenchmarkFileLogger(log_dir)
log.log_metric("accuracy", 0.999, global_step=1e4, extras={"name": "value"})
log.log_metric("loss", 0.02, global_step=1e4)
metric_log = os.path.join(log_dir, "metric.log")
self.assertTrue(tf.io.gfile.exists(metric_log))
with tf.io.gfile.GFile(metric_log) as f:
accuracy = json.loads(f.readline())
self.assertEqual(accuracy["name"], "accuracy")
self.assertEqual(accuracy["value"], 0.999)
self.assertEqual(accuracy["unit"], None)
self.assertEqual(accuracy["global_step"], 1e4)
self.assertEqual(accuracy["extras"], [{"name": "name", "value": "value"}])
loss = json.loads(f.readline())
self.assertEqual(loss["name"], "loss")
self.assertEqual(loss["value"], 0.02)
self.assertEqual(loss["unit"], None)
self.assertEqual(loss["global_step"], 1e4)
self.assertEqual(loss["extras"], [])
def test_log_non_number_value(self):
log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
log = logger.BenchmarkFileLogger(log_dir)
const = tf.constant(1)
log.log_metric("accuracy", const)
metric_log = os.path.join(log_dir, "metric.log")
self.assertFalse(tf.io.gfile.exists(metric_log))
def test_log_evaluation_result(self):
eval_result = {"loss": 0.46237424,
"global_step": 207082,
"accuracy": 0.9285}
log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
log = logger.BenchmarkFileLogger(log_dir)
log.log_evaluation_result(eval_result)
metric_log = os.path.join(log_dir, "metric.log")
self.assertTrue(tf.io.gfile.exists(metric_log))
with tf.io.gfile.GFile(metric_log) as f:
accuracy = json.loads(f.readline())
self.assertEqual(accuracy["name"], "accuracy")
self.assertEqual(accuracy["value"], 0.9285)
self.assertEqual(accuracy["unit"], None)
self.assertEqual(accuracy["global_step"], 207082)
loss = json.loads(f.readline())
self.assertEqual(loss["name"], "loss")
self.assertEqual(loss["value"], 0.46237424)
self.assertEqual(loss["unit"], None)
self.assertEqual(loss["global_step"], 207082)
def test_log_evaluation_result_with_invalid_type(self):
eval_result = "{'loss': 0.46237424, 'global_step': 207082}"
log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
log = logger.BenchmarkFileLogger(log_dir)
log.log_evaluation_result(eval_result)
metric_log = os.path.join(log_dir, "metric.log")
self.assertFalse(tf.io.gfile.exists(metric_log))
@mock.patch("official.utils.logs.logger._gather_run_info")
def test_log_run_info(self, mock_gather_run_info):
log_dir = tempfile.mkdtemp(dir=self.get_temp_dir())
log = logger.BenchmarkFileLogger(log_dir)
run_info = {"model_name": "model_name",
"dataset": "dataset_name",
"run_info": "run_value"}
mock_gather_run_info.return_value = run_info
log.log_run_info("model_name", "dataset_name", {})
run_log = os.path.join(log_dir, "benchmark_run.log")
self.assertTrue(tf.io.gfile.exists(run_log))
with tf.io.gfile.GFile(run_log) as f:
run_info = json.loads(f.readline())
self.assertEqual(run_info["model_name"], "model_name")
self.assertEqual(run_info["dataset"], "dataset_name")
self.assertEqual(run_info["run_info"], "run_value")
def test_collect_tensorflow_info(self):
run_info = {}
logger._collect_tensorflow_info(run_info)
self.assertNotEqual(run_info["tensorflow_version"], {})
self.assertEqual(run_info["tensorflow_version"]["version"],
tf.version.VERSION)
self.assertEqual(run_info["tensorflow_version"]["git_hash"],
tf.version.GIT_VERSION)
def test_collect_run_params(self):
run_info = {}
run_parameters = {
"batch_size": 32,
"synthetic_data": True,
"train_epochs": 100.00,
"dtype": "fp16",
"resnet_size": 50,
"random_tensor": tf.constant(2.0)
}
logger._collect_run_params(run_info, run_parameters)
self.assertEqual(len(run_info["run_parameters"]), 6)
self.assertEqual(run_info["run_parameters"][0],
{"name": "batch_size", "long_value": 32})
self.assertEqual(run_info["run_parameters"][1],
{"name": "dtype", "string_value": "fp16"})
if keras_utils.is_v2_0():
self.assertEqual(run_info["run_parameters"][2],
{"name": "random_tensor", "string_value":
"tf.Tensor(2.0, shape=(), dtype=float32)"})
else:
self.assertEqual(run_info["run_parameters"][2],
{"name": "random_tensor", "string_value":
"Tensor(\"Const:0\", shape=(), dtype=float32)"})
self.assertEqual(run_info["run_parameters"][3],
{"name": "resnet_size", "long_value": 50})
self.assertEqual(run_info["run_parameters"][4],
{"name": "synthetic_data", "bool_value": "True"})
self.assertEqual(run_info["run_parameters"][5],
{"name": "train_epochs", "float_value": 100.00})
def test_collect_tensorflow_environment_variables(self):
os.environ["TF_ENABLE_WINOGRAD_NONFUSED"] = "1"
os.environ["TF_OTHER"] = "2"
os.environ["OTHER"] = "3"
run_info = {}
logger._collect_tensorflow_environment_variables(run_info)
self.assertIsNotNone(run_info["tensorflow_environment_variables"])
expected_tf_envs = [
{"name": "TF_ENABLE_WINOGRAD_NONFUSED", "value": "1"},
{"name": "TF_OTHER", "value": "2"},
]
self.assertEqual(run_info["tensorflow_environment_variables"],
expected_tf_envs)
def test_collect_memory_info(self):
run_info = {"machine_config": {}}
logger._collect_memory_info(run_info)
self.assertIsNotNone(run_info["machine_config"]["memory_total"])
self.assertIsNotNone(run_info["machine_config"]["memory_available"])
@unittest.skipIf(bigquery is None, "Bigquery dependency is not installed.")
class BenchmarkBigQueryLoggerTest(tf.test.TestCase):
def setUp(self):
super(BenchmarkBigQueryLoggerTest, self).setUp()
# Avoid pulling extra env vars from test environment which affects the test
# result, eg. Kokoro test has a TF_PKG env which affect the test case
# test_collect_tensorflow_environment_variables()
self.original_environ = dict(os.environ)
os.environ.clear()
self.mock_bq_uploader = mock.MagicMock()
self.logger = logger.BenchmarkBigQueryLogger(
self.mock_bq_uploader, "dataset", "run_table", "run_status_table",
"metric_table", "run_id")
def tearDown(self):
super(BenchmarkBigQueryLoggerTest, self).tearDown()
tf.io.gfile.rmtree(self.get_temp_dir())
os.environ.clear()
os.environ.update(self.original_environ)
def test_log_metric(self):
self.logger.log_metric(
"accuracy", 0.999, global_step=1e4, extras={"name": "value"})
expected_metric_json = [{
"name": "accuracy",
"value": 0.999,
"unit": None,
"global_step": 1e4,
"timestamp": mock.ANY,
"extras": [{"name": "name", "value": "value"}]
}]
# log_metric will call upload_benchmark_metric_json in a separate thread.
# Give it some grace period for the new thread before assert.
time.sleep(1)
self.mock_bq_uploader.upload_benchmark_metric_json.assert_called_once_with(
"dataset", "metric_table", "run_id", expected_metric_json)
@mock.patch("official.utils.logs.logger._gather_run_info")
def test_log_run_info(self, mock_gather_run_info):
run_info = {"model_name": "model_name",
"dataset": "dataset_name",
"run_info": "run_value"}
mock_gather_run_info.return_value = run_info
self.logger.log_run_info("model_name", "dataset_name", {})
# log_metric will call upload_benchmark_metric_json in a separate thread.
# Give it some grace period for the new thread before assert.
time.sleep(1)
self.mock_bq_uploader.upload_benchmark_run_json.assert_called_once_with(
"dataset", "run_table", "run_id", run_info)
self.mock_bq_uploader.insert_run_status.assert_called_once_with(
"dataset", "run_status_table", "run_id", "running")
def test_on_finish(self):
self.logger.on_finish(logger.RUN_STATUS_SUCCESS)
# log_metric will call upload_benchmark_metric_json in a separate thread.
# Give it some grace period for the new thread before assert.
time.sleep(1)
self.mock_bq_uploader.update_run_status.assert_called_once_with(
"dataset", "run_status_table", "run_id", logger.RUN_STATUS_SUCCESS)
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/logs/logger_test.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/accelerator/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions specific to running TensorFlow on TPUs."""
import tensorflow as tf
# "local" is a magic word in the TPU cluster resolver; it informs the resolver
# to use the local CPU as the compute device. This is useful for testing and
# debugging; the code flow is ostensibly identical, but without the need to
# actually have a TPU on the other end.
LOCAL = "local"
def construct_scalar_host_call(metric_dict, model_dir, prefix=""):
"""Construct a host call to log scalars when training on TPU.
Args:
metric_dict: A dict of the tensors to be logged.
model_dir: The location to write the summary.
prefix: The prefix (if any) to prepend to the metric names.
Returns:
A tuple of (function, args_to_be_passed_to_said_function)
"""
# type: (dict, str) -> (function, list)
metric_names = list(metric_dict.keys())
def host_call_fn(global_step, *args):
"""Training host call. Creates scalar summaries for training metrics.
This function is executed on the CPU and should not directly reference
any Tensors in the rest of the `model_fn`. To pass Tensors from the
model to the `metric_fn`, provide as part of the `host_call`. See
https://www.tensorflow.org/api_docs/python/tf/contrib/tpu/TPUEstimatorSpec
for more information.
Arguments should match the list of `Tensor` objects passed as the second
element in the tuple passed to `host_call`.
Args:
global_step: `Tensor with shape `[batch]` for the global_step
*args: Remaining tensors to log.
Returns:
List of summary ops to run on the CPU host.
"""
step = global_step[0]
with tf.contrib.summary.create_file_writer(
logdir=model_dir, filename_suffix=".host_call").as_default():
with tf.contrib.summary.always_record_summaries():
for i, name in enumerate(metric_names):
tf.contrib.summary.scalar(prefix + name, args[i][0], step=step)
return tf.contrib.summary.all_summary_ops()
# To log the current learning rate, and gradient norm for Tensorboard, the
# summary op needs to be run on the host CPU via host_call. host_call
# expects [batch_size, ...] Tensors, thus reshape to introduce a batch
# dimension. These Tensors are implicitly concatenated to
# [params['batch_size']].
global_step_tensor = tf.reshape(
tf.compat.v1.train.get_or_create_global_step(), [1])
other_tensors = [tf.reshape(metric_dict[key], [1]) for key in metric_names]
return host_call_fn, [global_step_tensor] + other_tensors
def embedding_matmul(embedding_table, values, mask, name="embedding_matmul"):
"""Performs embedding lookup via a matmul.
The matrix to be multiplied by the embedding table Tensor is constructed
via an implementation of scatter based on broadcasting embedding indices
and performing an equality comparison against a broadcasted
range(num_embedding_table_rows). All masked positions will produce an
embedding vector of zeros.
Args:
embedding_table: Tensor of embedding table.
Rank 2 (table_size x embedding dim)
values: Tensor of embedding indices. Rank 2 (batch x n_indices)
mask: Tensor of mask / weights. Rank 2 (batch x n_indices)
name: Optional name scope for created ops
Returns:
Rank 3 tensor of embedding vectors.
"""
with tf.name_scope(name):
n_embeddings = embedding_table.get_shape().as_list()[0]
batch_size, padded_size = values.shape.as_list()
emb_idcs = tf.tile(
tf.reshape(values, (batch_size, padded_size, 1)), (1, 1, n_embeddings))
emb_weights = tf.tile(
tf.reshape(mask, (batch_size, padded_size, 1)), (1, 1, n_embeddings))
col_idcs = tf.tile(
tf.reshape(tf.range(n_embeddings), (1, 1, n_embeddings)),
(batch_size, padded_size, 1))
one_hot = tf.where(
tf.equal(emb_idcs, col_idcs), emb_weights,
tf.zeros((batch_size, padded_size, n_embeddings)))
return tf.tensordot(one_hot, embedding_table, 1)
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/accelerator/tpu.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test TPU optimized matmul embedding."""
import numpy as np
import tensorflow as tf
from official.utils.accelerator import tpu as tpu_utils
TEST_CASES = [
dict(embedding_dim=256, vocab_size=1000, sequence_length=64,
batch_size=32, seed=54131),
dict(embedding_dim=8, vocab_size=15, sequence_length=12,
batch_size=256, seed=536413),
dict(embedding_dim=2048, vocab_size=512, sequence_length=50,
batch_size=8, seed=35124)
]
class TPUBaseTester(tf.test.TestCase):
def construct_embedding_and_values(self, embedding_dim, vocab_size,
sequence_length, batch_size, seed):
np.random.seed(seed)
embeddings = np.random.random(size=(vocab_size, embedding_dim))
embedding_table = tf.convert_to_tensor(value=embeddings, dtype=tf.float32)
tokens = np.random.randint(low=1, high=vocab_size-1,
size=(batch_size, sequence_length))
for i in range(batch_size):
tokens[i, np.random.randint(low=0, high=sequence_length-1):] = 0
values = tf.convert_to_tensor(value=tokens, dtype=tf.int32)
mask = tf.cast(tf.not_equal(values, 0), dtype=tf.float32)
return embedding_table, values, mask
def _test_embedding(self, embedding_dim, vocab_size,
sequence_length, batch_size, seed):
"""Test that matmul embedding matches embedding lookup (gather)."""
with self.test_session():
embedding_table, values, mask = self.construct_embedding_and_values(
embedding_dim=embedding_dim,
vocab_size=vocab_size,
sequence_length=sequence_length,
batch_size=batch_size,
seed=seed
)
embedding = (tf.nn.embedding_lookup(params=embedding_table, ids=values) *
tf.expand_dims(mask, -1))
matmul_embedding = tpu_utils.embedding_matmul(
embedding_table=embedding_table, values=values, mask=mask)
self.assertAllClose(embedding, matmul_embedding)
def _test_masking(self, embedding_dim, vocab_size,
sequence_length, batch_size, seed):
"""Test that matmul embedding properly zeros masked positions."""
with self.test_session():
embedding_table, values, mask = self.construct_embedding_and_values(
embedding_dim=embedding_dim,
vocab_size=vocab_size,
sequence_length=sequence_length,
batch_size=batch_size,
seed=seed
)
matmul_embedding = tpu_utils.embedding_matmul(
embedding_table=embedding_table, values=values, mask=mask)
self.assertAllClose(matmul_embedding,
matmul_embedding * tf.expand_dims(mask, -1))
def test_embedding_0(self):
self._test_embedding(**TEST_CASES[0])
def test_embedding_1(self):
self._test_embedding(**TEST_CASES[1])
def test_embedding_2(self):
self._test_embedding(**TEST_CASES[2])
def test_masking_0(self):
self._test_masking(**TEST_CASES[0])
def test_masking_1(self):
self._test_masking(**TEST_CASES[1])
def test_masking_2(self):
self._test_masking(**TEST_CASES[2])
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/accelerator/tpu_test.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/export/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Convenience functions for exporting models as SavedModels or other types."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def build_tensor_serving_input_receiver_fn(shape, dtype=tf.float32,
batch_size=1):
"""Returns a input_receiver_fn that can be used during serving.
This expects examples to come through as float tensors, and simply
wraps them as TensorServingInputReceivers.
Arguably, this should live in tf.estimator.export. Testing here first.
Args:
shape: list representing target size of a single example.
dtype: the expected datatype for the input example
batch_size: number of input tensors that will be passed for prediction
Returns:
A function that itself returns a TensorServingInputReceiver.
"""
def serving_input_receiver_fn():
# Prep a placeholder where the input example will be fed in
features = tf.compat.v1.placeholder(
dtype=dtype, shape=[batch_size] + shape, name='input_tensor')
return tf.estimator.export.TensorServingInputReceiver(
features=features, receiver_tensors=features)
return serving_input_receiver_fn
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/export/export.py |
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for exporting utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.utils.export import export
class ExportUtilsTest(tf.test.TestCase):
"""Tests for the ExportUtils."""
def test_build_tensor_serving_input_receiver_fn(self):
receiver_fn = export.build_tensor_serving_input_receiver_fn(shape=[4, 5])
with tf.Graph().as_default():
receiver = receiver_fn()
self.assertIsInstance(
receiver, tf.estimator.export.TensorServingInputReceiver)
self.assertIsInstance(receiver.features, tf.Tensor)
self.assertEqual(receiver.features.shape, tf.TensorShape([1, 4, 5]))
self.assertEqual(receiver.features.dtype, tf.float32)
self.assertIsInstance(receiver.receiver_tensors, dict)
# Note that Python 3 can no longer index .values() directly; cast to list.
self.assertEqual(list(receiver.receiver_tensors.values())[0].shape,
tf.TensorShape([1, 4, 5]))
def test_build_tensor_serving_input_receiver_fn_batch_dtype(self):
receiver_fn = export.build_tensor_serving_input_receiver_fn(
shape=[4, 5], dtype=tf.int8, batch_size=10)
with tf.Graph().as_default():
receiver = receiver_fn()
self.assertIsInstance(
receiver, tf.estimator.export.TensorServingInputReceiver)
self.assertIsInstance(receiver.features, tf.Tensor)
self.assertEqual(receiver.features.shape, tf.TensorShape([10, 4, 5]))
self.assertEqual(receiver.features.dtype, tf.int8)
self.assertIsInstance(receiver.receiver_tensors, dict)
# Note that Python 3 can no longer index .values() directly; cast to list.
self.assertEqual(list(receiver.receiver_tensors.values())[0].shape,
tf.TensorShape([10, 4, 5]))
if __name__ == "__main__":
tf.test.main()
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/export/export_test.py |
DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/data/__init__.py |
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Convenience functions for managing dataset file buffers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import atexit
import multiprocessing
import os
import tempfile
import uuid
import numpy as np
import six
import tensorflow as tf
class _GarbageCollector(object):
"""Deletes temporary buffer files at exit.
Certain tasks (such as NCF Recommendation) require writing buffers to
temporary files. (Which may be local or distributed.) It is not generally safe
to delete these files during operation, but they should be cleaned up. This
class keeps track of temporary files created, and deletes them at exit.
"""
def __init__(self):
self.temp_buffers = []
def register(self, filepath):
self.temp_buffers.append(filepath)
def purge(self):
try:
for i in self.temp_buffers:
if tf.io.gfile.exists(i):
tf.io.gfile.remove(i)
tf.compat.v1.logging.info("Buffer file {} removed".format(i))
except Exception as e:
tf.compat.v1.logging.error("Failed to cleanup buffer files: {}".format(e))
_GARBAGE_COLLECTOR = _GarbageCollector()
atexit.register(_GARBAGE_COLLECTOR.purge)
_ROWS_PER_CORE = 50000
def write_to_temp_buffer(dataframe, buffer_folder, columns):
if buffer_folder is None:
_, buffer_path = tempfile.mkstemp()
else:
tf.io.gfile.makedirs(buffer_folder)
buffer_path = os.path.join(buffer_folder, str(uuid.uuid4()))
_GARBAGE_COLLECTOR.register(buffer_path)
return write_to_buffer(dataframe, buffer_path, columns)
def iter_shard_dataframe(df, rows_per_core=1000):
"""Two way shard of a dataframe.
This function evenly shards a dataframe so that it can be mapped efficiently.
It yields a list of dataframes with length equal to the number of CPU cores,
with each dataframe having rows_per_core rows. (Except for the last batch
which may have fewer rows in the dataframes.) Passing vectorized inputs to
a multiprocessing pool is much more effecient than iterating through a
dataframe in serial and passing a list of inputs to the pool.
Args:
df: Pandas dataframe to be sharded.
rows_per_core: Number of rows in each shard.
Returns:
A list of dataframe shards.
"""
n = len(df)
num_cores = min([multiprocessing.cpu_count(), n])
num_blocks = int(np.ceil(n / num_cores / rows_per_core))
max_batch_size = num_cores * rows_per_core
for i in range(num_blocks):
min_index = i * max_batch_size
max_index = min([(i + 1) * max_batch_size, n])
df_shard = df[min_index:max_index]
n_shard = len(df_shard)
boundaries = np.linspace(0, n_shard, num_cores + 1, dtype=np.int64)
yield [df_shard[boundaries[j]:boundaries[j+1]] for j in range(num_cores)]
def _shard_dict_to_examples(shard_dict):
"""Converts a dict of arrays into a list of example bytes."""
n = [i for i in shard_dict.values()][0].shape[0]
feature_list = [{} for _ in range(n)]
for column, values in shard_dict.items():
if len(values.shape) == 1:
values = np.reshape(values, values.shape + (1,))
if values.dtype.kind == "i":
feature_map = lambda x: tf.train.Feature(
int64_list=tf.train.Int64List(value=x))
elif values.dtype.kind == "f":
feature_map = lambda x: tf.train.Feature(
float_list=tf.train.FloatList(value=x))
else:
raise ValueError("Invalid dtype")
for i in range(n):
feature_list[i][column] = feature_map(values[i])
examples = [
tf.train.Example(features=tf.train.Features(feature=example_features))
for example_features in feature_list
]
return [e.SerializeToString() for e in examples]
def _serialize_shards(df_shards, columns, pool, writer):
"""Map sharded dataframes to bytes, and write them to a buffer.
Args:
df_shards: A list of pandas dataframes. (Should be of similar size)
columns: The dataframe columns to be serialized.
pool: A multiprocessing pool to serialize in parallel.
writer: A TFRecordWriter to write the serialized shards.
"""
# Pandas does not store columns of arrays as nd arrays. stack remedies this.
map_inputs = [{c: np.stack(shard[c].values, axis=0) for c in columns}
for shard in df_shards]
# Failure within pools is very irksome. Thus, it is better to thoroughly check
# inputs in the main process.
for inp in map_inputs:
# Check that all fields have the same number of rows.
assert len(set([v.shape[0] for v in inp.values()])) == 1
for val in inp.values():
assert hasattr(val, "dtype")
assert hasattr(val.dtype, "kind")
assert val.dtype.kind in ("i", "f")
assert len(val.shape) in (1, 2)
shard_bytes = pool.map(_shard_dict_to_examples, map_inputs)
for s in shard_bytes:
for example in s:
writer.write(example)
def write_to_buffer(dataframe, buffer_path, columns, expected_size=None):
"""Write a dataframe to a binary file for a dataset to consume.
Args:
dataframe: The pandas dataframe to be serialized.
buffer_path: The path where the serialized results will be written.
columns: The dataframe columns to be serialized.
expected_size: The size in bytes of the serialized results. This is used to
lazily construct the buffer.
Returns:
The path of the buffer.
"""
if (tf.io.gfile.exists(buffer_path) and
tf.io.gfile.stat(buffer_path).length > 0):
actual_size = tf.io.gfile.stat(buffer_path).length
if expected_size == actual_size:
return buffer_path
tf.compat.v1.logging.warning(
"Existing buffer {} has size {}. Expected size {}. Deleting and "
"rebuilding buffer.".format(buffer_path, actual_size, expected_size))
tf.io.gfile.remove(buffer_path)
if dataframe is None:
raise ValueError(
"dataframe was None but a valid existing buffer was not found.")
tf.io.gfile.makedirs(os.path.split(buffer_path)[0])
tf.compat.v1.logging.info("Constructing TFRecordDataset buffer: {}"
.format(buffer_path))
count = 0
pool = multiprocessing.Pool(multiprocessing.cpu_count())
try:
with tf.io.TFRecordWriter(buffer_path) as writer:
for df_shards in iter_shard_dataframe(df=dataframe,
rows_per_core=_ROWS_PER_CORE):
_serialize_shards(df_shards, columns, pool, writer)
count += sum([len(s) for s in df_shards])
tf.compat.v1.logging.info("{}/{} examples written."
.format(str(count).ljust(8), len(dataframe)))
finally:
pool.terminate()
tf.compat.v1.logging.info("Buffer write complete.")
return buffer_path
| DeepLearningExamples-master | TensorFlow2/LanguageModeling/BERT/official/utils/data/file_io.py |
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