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Fix importing unofficial TF models with extra optimizer weights

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monologg 2020-01-27 23:39:44 +09:00 committed by Lysandre Debut
parent d7dabfeff5
commit 73368963b2
4 changed files with 19 additions and 4 deletions
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8
src/transformers/modeling_albert.py
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@ -117,7 +117,13 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
name = name.split("/") name = name.split("/")
# Ignore the gradients applied by the LAMB/ADAM optimizers. # Ignore the gradients applied by the LAMB/ADAM optimizers.
if "adam_m" in name or "adam_v" in name or "global_step" in name: if (
"adam_m" in name
or "adam_v" in name
or "AdamWeightDecayOptimizer" in name
or "AdamWeightDecayOptimizer_1" in name
or "global_step" in name
):
logger.info("Skipping {}".format("/".join(name))) logger.info("Skipping {}".format("/".join(name)))
continue continue

5
src/transformers/modeling_bert.py
View File

@ -86,7 +86,10 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
name = name.split("/") name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model # which are not required for using pretrained model
if any(n in ["adam_v", "adam_m", "global_step"] for n in name): if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info("Skipping {}".format("/".join(name))) logger.info("Skipping {}".format("/".join(name)))
continue continue
pointer = model pointer = model

5
src/transformers/modeling_t5.py
View File

@ -79,7 +79,10 @@ def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
name = txt_name.split("/") name = txt_name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model # which are not required for using pretrained model
if any(n in ["adam_v", "adam_m", "global_step"] for n in name): if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info("Skipping {}".format("/".join(name))) logger.info("Skipping {}".format("/".join(name)))
tf_weights.pop(txt_name, None) tf_weights.pop(txt_name, None)
continue continue

5
templates/adding_a_new_model/modeling_xxx.py
View File

@ -76,7 +76,10 @@ def load_tf_weights_in_xxx(model, config, tf_checkpoint_path):
name = name.split("/") name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model # which are not required for using pretrained model
if any(n in ["adam_v", "adam_m", "global_step"] for n in name): if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info("Skipping {}".format("/".join(name))) logger.info("Skipping {}".format("/".join(name)))
continue continue
pointer = model pointer = model