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skip finetuned checkpoints

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This commit is contained in:
thomwolf 2019-09-13 16:43:49 +02:00
parent 65c49bb27e
commit b97af8cce9
3 changed files with 17 additions and 108 deletions
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5
pytorch_transformers/__init__.py
View File

@ -113,6 +113,11 @@ if _tf_available:
load_gpt2_pt_weights_in_tf2,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_transfo_xl import (TFTransfoXLPreTrainedModel, TFTransfoXLMainLayer,
TFTransfoXLModel, TFTransfoXLLMHeadModel,
load_transfo_xl_pt_weights_in_tf2,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,

14
pytorch_transformers/convert_pytorch_checkpoint_to_tf2.py
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@ -27,7 +27,8 @@ from pytorch_transformers import is_torch_available, cached_path
from pytorch_transformers import (BertConfig, TFBertForPreTraining, load_bert_pt_weights_in_tf2,
GPT2Config, TFGPT2LMHeadModel, load_gpt2_pt_weights_in_tf2,
XLNetConfig, TFXLNetLMHeadModel, load_xlnet_pt_weights_in_tf2,
XLMConfig, TFXLMWithLMHeadModel, load_xlm_pt_weights_in_tf2,)
XLMConfig, TFXLMWithLMHeadModel, load_xlm_pt_weights_in_tf2,
TransfoXLConfig, TFTransfoXLLMHeadModel, load_transfo_xl_pt_weights_in_tf2,)
if is_torch_available():
import torch
@ -35,12 +36,15 @@ if is_torch_available():
from pytorch_transformers import (BertForPreTraining, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,)
XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,)
else:
(BertForPreTraining, BERT_PRETRAINED_MODEL_ARCHIVE_MAP, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,) = (
XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,) = (
None, None, None,
None, None, None,
None, None, None,
None, None, None,
@ -55,6 +59,7 @@ MODEL_CLASSES = {
'gpt2': (GPT2Config, TFGPT2LMHeadModel, load_gpt2_pt_weights_in_tf2, GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP),
'xlnet': (XLNetConfig, TFXLNetLMHeadModel, load_xlnet_pt_weights_in_tf2, XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP),
'xlm': (XLMConfig, TFXLMWithLMHeadModel, load_xlm_pt_weights_in_tf2, XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP),
'transfo-xl': (TransfoXLConfig, TFTransfoXLLMHeadModel, load_transfo_xl_pt_weights_in_tf2, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP),
}
def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False):
@ -118,6 +123,9 @@ def convert_all_pt_checkpoints_to_tf(args_model_type, tf_dump_path, compare_with
print("-" * 100)
print(" Converting checkpoint {}/{}: {}".format(i, len(aws_config_map), shortcut_name))
print("-" * 100)
if 'finetuned' in shortcut_name:
print(" Skipping fintenued checkpoint ")
continue
config_file = cached_path(aws_config_map[shortcut_name], force_download=True)
model_file = cached_path(aws_model_maps[shortcut_name], force_download=True)

106
pytorch_transformers/modeling_tf_transfo_xl_utilities.py
View File

@ -13,8 +13,7 @@
# 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 PyTorch Transformer XL model.
Directly adapted from https://github.com/kimiyoung/transformer-xl.
""" A TF 2.0 Adaptive Softmax for Transformer XL model.
"""
from collections import defaultdict
@ -174,106 +173,3 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
self.add_metric(loss, name=self.name, aggregation='mean' if return_mean else '')
return out
def mul_adaptive_logsoftmax(hidden, target, n_token, d_embed, d_proj, cutoffs,
params, tie_projs,
initializer=None, proj_initializer=None,
div_val=1, perms=None, proj_same_dim=True,
scope='adaptive_softmax',
**kwargs):
def _logit(x, W, b, proj):
y = x
if x.shape.ndims == 3:
if proj is not None:
y = tf.einsum('ibd,ed->ibe', y, proj)
return tf.einsum('ibd,nd->ibn', y, W) + b
else:
if proj is not None:
y = tf.einsum('id,ed->ie', y, proj)
return tf.einsum('id,nd->in', y, W) + b
params_W, params_projs = params[0], params[1]
with tf.variable_scope(scope):
if len(cutoffs) == 0:
softmax_b = tf.get_variable('bias', [n_token],
initializer=tf.zeros_initializer())
output = _logit(hidden, params_W, softmax_b, params_projs)
nll = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target,
logits=output)
nll = tf.reduce_mean(nll)
else:
total_loss, total_cnt = 0, 0
cutoff_ends = [0] + cutoffs + [n_token]
for i in range(len(cutoff_ends) - 1):
with tf.variable_scope('cutoff_{}'.format(i)):
l_idx, r_idx = cutoff_ends[i], cutoff_ends[i + 1]
cur_d_embed = d_embed // (div_val ** i)
if div_val == 1:
cur_W = params_W[l_idx: r_idx]
else:
cur_W = params_W[i]
cur_b = tf.get_variable('b', [r_idx - l_idx],
initializer=tf.zeros_initializer())
if tie_projs[i]:
if div_val == 1:
cur_proj = params_projs
else:
cur_proj = params_projs[i]
else:
if (div_val == 1 or not proj_same_dim) and d_proj == cur_d_embed:
cur_proj = None
else:
cur_proj = tf.get_variable('proj', [cur_d_embed, d_proj],
initializer=proj_initializer)
if i == 0:
cluster_W = tf.get_variable('cluster_W', [len(cutoffs), d_embed],
initializer=tf.zeros_initializer())
cluster_b = tf.get_variable('cluster_b', [len(cutoffs)],
initializer=tf.zeros_initializer())
cur_W = tf.concat([cur_W, cluster_W], 0)
cur_b = tf.concat([cur_b, cluster_b], 0)
head_logit = _logit(hidden, cur_W, cur_b, cur_proj)
head_target = kwargs.get("head_target")
head_nll = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=head_target,
logits=head_logit)
masked_loss = head_nll * perms[i]
total_loss += tf.reduce_sum(masked_loss)
total_cnt += tf.reduce_sum(perms[i])
# head_logprob = tf.nn.log_softmax(head_logit)
# final_logprob = head_logprob * perms[i][:, :, None]
# final_target = tf.one_hot(target, tf.shape(head_logprob)[2])
# total_loss -= tf.einsum('ibn,ibn->', final_logprob, final_target)
# total_cnt += tf.reduce_sum(perms[i])
else:
cur_head_nll = tf.einsum('ib,ibk->k', head_nll, perms[i])
cur_hidden = tf.einsum('ibd,ibk->kd', hidden, perms[i])
tail_logit = _logit(cur_hidden, cur_W, cur_b, cur_proj)
tail_target = tf.einsum('ib,ibk->k', tf.to_float(target - l_idx),
perms[i])
tail_nll = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=tf.to_int32(tail_target),
logits=tail_logit)
sum_nll = cur_head_nll + tail_nll
mask = tf.reduce_sum(perms[i], [0, 1])
masked_loss = sum_nll * mask
total_loss += tf.reduce_sum(masked_loss)
total_cnt += tf.reduce_sum(mask)
nll = total_loss / total_cnt
return nll