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clean up apex integration

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This commit is contained in:
thomwolf 2018-12-13 13:02:17 +01:00
parent 4946c2c500
commit 52c53f39d0
3 changed files with 5 additions and 5 deletions
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4
pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py
View File

@ -59,9 +59,9 @@ def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytor
l = re.split(r'_(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'kernel':
if l[0] == 'kernel' or l[0] == 'gamma':
pointer = getattr(pointer, 'weight')
elif l[0] == 'output_bias':
elif l[0] == 'output_bias' or l[0] == 'beta':
pointer = getattr(pointer, 'bias')
elif l[0] == 'output_weights':
pointer = getattr(pointer, 'weight')

4
pytorch_pretrained_bert/modeling.py
View File

@ -516,9 +516,9 @@ class PreTrainedBertModel(nn.Module):
for key in state_dict.keys():
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma','weight')
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta','bias')
new_key = key.replace('beta', 'bias')
if new_key:
old_keys.append(key)
new_keys.append(new_key)

2
tests/optimization_test.py
View File

@ -35,7 +35,7 @@ class OptimizationTest(unittest.TestCase):
criterion = torch.nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
optimizer = BertAdam(params=[w], lr=2e-1,
weight_decay=0.0,
weight_decay_rate=0.0,
max_grad_norm=-1)
for _ in range(100):
loss = criterion(w, target)