text stringlengths 1 93.6k |
|---|
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])
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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'
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'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
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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
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from inputs import sequential_input, pred_input, handle_pred_output, mlm_sample_text, generic_text
|
from export import export_model
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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
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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",
|
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