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fixing XLM conversion tests with dummy input

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This commit is contained in:
thomwolf 2019-12-12 14:46:30 +01:00
parent fafd4c86ec
commit f19dad61c7
3 changed files with 17 additions and 3 deletions
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6
transformers/modeling_tf_pytorch_utils.py
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@ -78,6 +78,7 @@ def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_i
logger.info("Loading PyTorch weights from {}".format(pt_path))
pt_state_dict = torch.load(pt_path, map_location='cpu')
logger.info("PyTorch checkpoint contains {:,} parameters".format(sum(t.numel() for t in pt_state_dict.values())))
return load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys)
@ -134,7 +135,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
start_prefix_to_remove = tf_model.base_model_prefix + '.'
symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights
tf_loaded_numel = 0
weight_value_tuples = []
all_pytorch_weights = set(list(pt_state_dict.keys()))
for symbolic_weight in symbolic_weights:
@ -159,6 +160,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
e.args += (symbolic_weight.shape, array.shape)
raise e
tf_loaded_numel += array.size
# logger.warning("Initialize TF weight {}".format(symbolic_weight.name))
weight_value_tuples.append((symbolic_weight, array))
@ -169,6 +171,8 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
if tf_inputs is not None:
tfo = tf_model(tf_inputs, training=False) # Make sure restore ops are run
logger.info("Loaded {:,} parameters in the TF 2.0 model.".format(tf_loaded_numel))
logger.info("Weights or buffers not loaded from PyTorch model: {}".format(all_pytorch_weights))
return tf_model

2
transformers/modeling_tf_xlm.py
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@ -460,7 +460,7 @@ class TFXLMPreTrainedModel(TFPreTrainedModel):
langs_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
else:
langs_list = None
return [inputs_list, attns_list, langs_list]
return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list}
XLM_START_DOCSTRING = r""" The XLM model was proposed in

12
transformers/modeling_xlm.py
View File

@ -227,6 +227,16 @@ class XLMPreTrainedModel(PreTrainedModel):
def __init__(self, *inputs, **kwargs):
super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs)
@property
def dummy_inputs(self):
inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
if self.config.use_lang_emb and self.config.n_langs > 1:
langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
else:
langs_list = None
return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list}
def _init_weights(self, module):
""" Initialize the weights. """
if isinstance(module, nn.Embedding):
@ -646,7 +656,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds)