Datasets:
jblitzar
/
github-python

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if tb_logger is not None:
_iter = epoch * len(train_loader) + i
tb_logger.add_scalar('train_acc', top1.avg, _iter)
tb_logger.add_scalar('train_loss', losses.avg, _iter)
def validate(val_loader,
model,
criterion,
print_freq,
rank,
logger,
sampled=None):
n = len(val_loader)
batch_time = AverageMeter(10)
losses = AverageMeter(n)
top1 = AverageMeter(n)
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
target = target.cuda(non_blocking=True)
if not sampled:
output = model(input, target)
else:
output, target = model(input, target)
loss = criterion(output, target)
prec1, = accuracy(output, target, topk=(1, ))
losses.update(loss.item())
top1.update(prec1[0])
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0 and rank == 0 and logger is not None:
logger.info(
'Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
i,
len(val_loader),
batch_time=batch_time,
loss=losses,
top1=top1))
if rank == 0:
logger.info(' * Prec@1 {top1.avg:.3f}'.format(top1=top1))
return top1.avg, losses.avg
if __name__ == '__main__':
main()
# <FILESEP>
"""GPT-like model in Mesh-Tensorflow"""
from functools import partial
import mesh_tensorflow as mtf
import tensorflow.compat.v1 as tf
from tensorflow.python.tpu import tpu_config, tpu_estimator
from tensorflow_estimator.python.estimator import estimator as estimator_lib
from utils import save_config, expand_attention_types_params, yes_or_no, remove_gs_or_filepath, setup_logging, \
check_dataset
from inputs import sequential_input, pred_input, handle_pred_output, mlm_sample_text, generic_text
from export import export_model
from model_fns import model_fn
from data.encoders import fetch_encoder
from configs import fetch_model_params
from tasks import task_descriptors
import argparse
import json
import numpy
def parse_args():
# Parse command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--tpu", type=str, help="Name of TPU to train on, if any.")
parser.add_argument("--gpu_ids", nargs="+", type=str, default=["device:GPU:0"],
help="If training on GPU, can specify your GPU names in a list - i.e 'device:GPU:0 device:GPU:1'")
parser.add_argument("--model", type=str, default=None, help="JSON file that contains model parameters.")
parser.add_argument("--steps_per_checkpoint", type=int, default=5000, help="Save a model checkpoint every X steps.")
parser.add_argument("--auto_layout", action="store_true", help="If set, generates and prints the most memory "
"efficient layout according to MTF auto layout.")
parser.add_argument("--auto_layout_and_mesh_shape", action="store_true",
help="If set, generates and prints the most memory efficient layout and mesh shape according to"
" MTF auto layout.")
parser.add_argument("--new", action="store_true", help="If set, deletes previous checkpoint, if it exists, and "
"starts a new training run")
parser.add_argument("--predict", action="store_true", help="If set, uses the model to predict rather than train.")
parser.add_argument("--eval", action="store_true", help="If set, run model in evaluation mode.")
parser.add_argument("--prompt", type=str, help="path to .txt file containing a prompt for prediction. If empty, "
"defaults to unicorns.",
default="")
parser.add_argument("--check_dataset", action="store_true",