def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file,
pytorch_dump_path):
# Initialise PyTorch model
config = FunnelConfig.from_json_file(config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = FunnelForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_funnel(model, config, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))