GithubHelp home page GithubHelp logo

longmbart's People

Contributors

a-rios avatar ajkl avatar armancohan avatar dominikmartinez avatar fischerl92 avatar ibeltagy avatar ptark avatar riklopfer avatar schmmd avatar separius avatar tannonk avatar trellixvulnteam avatar

Stargazers

avatar avatar avatar avatar

Watchers

avatar avatar avatar avatar

Forkers

gizmondd saeub tschomacker omarmomen14 trellixvulnteam

longmbart's Issues

float_mask.repeat(1, 1, repeat_size, 1) causes RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor

I am trying to fine-tune my own longmbart on text simplification. But I am little stucked. Conversion worked but I got an Error when starting to fine-tune. I would really appreciate any hints on how to fix the problem.

What I did previously:

  1. pip install -q -r requirements.txt
  2. converted the model:
python ./scripts/convert_mbart_to_longformerencoderdecoder.py \
   --save_model_to ./output/converted-longmbart \
   --attention_window 512 \
   --cache_dir ./output/mbart-large-cc25 \
   --base_model facebook/mbart-large-cc25 \
   --tokenizer_name_or_path facebook/mbart-large-cc25\
   --add_language_tags de_OR de_SI \
   --initialize_tags de_DE de_DE \
   --max_pos 1024 \
   --verbose 1
  1. started the fine-tuning:
python -m longformer.simplification \
--from_pretrained ./output/converted-longmbart \
--tokenizer ./output/converted-longmbart \
--save_dir ./output/longmbart-fine-tuned \
--save_prefix "w512" \
--train_source ./data/train-source.txt \
--train_target ./data/train-target.txt \
--val_source ./data/val-source.txt \
--val_target ./data/val-target.txt \
--test_source ./data/test-source.txt \
--test_target ./data/test-target.txt \
--max_output_len 1024 \
--max_input_len 1024 \
--batch_size 1 \
--grad_accum 60 \
--num_workers 5 \
--gpus 1 \
--seed 222 \
--attention_dropout 0.1 \
--dropout 0.3 \
--attention_mode sliding_chunks \
--attention_window 512 \
--label_smoothing 0.2 \
--lr 0.00003 \
--val_every 1.0 \
--val_percent_check 1.0 \
--test_percent_check 1.0 \
--early_stopping_metric 'rougeL' \
--patience 10 \
--lr_reduce_patience 8 \
--lr_reduce_factor 0.5 \
--grad_ckpt \
--progress_bar_refresh_rate 10 \
--tags_included

This threw the following RuntimeError:

Current Bevior: RuntimeError

Epoch 0:   0%|                                            | 0/2 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/opt/conda/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/jovyan/git/longmbart/longformer/simplification.py", line 527, in <module>
    main(args)
  File "/home/jovyan/git/longmbart/longformer/simplification.py", line 518, in main
    trainer.fit(model)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 510, in fit
    results = self.accelerator_backend.train()
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 158, in train
    results = self.ddp_train(process_idx=self.task_idx, model=model)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 307, in ddp_train
    results = self.train_or_test()
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py", line 74, in train_or_test
    results = self.trainer.train()
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 561, in train
    self.train_loop.run_training_epoch()
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/training_loop.py", line 549, in run_training_epoch
    batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/training_loop.py", line 704, in run_training_batch
    self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/training_loop.py", line 482, in optimizer_step
    model_ref.optimizer_step(
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/core/lightning.py", line 1296, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py", line 286, in step
    self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py", line 140, in __optimizer_step
    trainer.precision_connector.backend.optimizer_step(trainer, optimizer, closure)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/plugins/native_amp.py", line 75, in optimizer_step
    closure()
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/training_loop.py", line 694, in train_step_and_backward_closure
    result = self.training_step_and_backward(
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/training_loop.py", line 792, in training_step_and_backward
    result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/trainer/training_loop.py", line 316, in training_step
    training_step_output = self.trainer.accelerator_backend.training_step(args)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 164, in training_step
    return self._step(args)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 176, in _step
    output = self.trainer.model(*args)
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/pytorch_lightning/overrides/data_parallel.py", line 179, in forward
    output = self.module.training_step(*inputs[0], **kwargs[0])
  File "/home/jovyan/git/longmbart/longformer/simplification.py", line 251, in training_step
    output = self.forward(*batch)
  File "/home/jovyan/git/longmbart/longformer/simplification.py", line 231, in forward
    outputs = self.model(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/transformers/models/mbart/modeling_mbart.py", line 1346, in forward
    outputs = self.model(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/transformers/models/mbart/modeling_mbart.py", line 1211, in forward
    encoder_outputs = self.encoder(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/transformers/models/mbart/modeling_mbart.py", line 840, in forward
    layer_outputs = encoder_layer(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/transformers/models/mbart/modeling_mbart.py", line 331, in forward
    hidden_states, attn_weights, _ = self.self_attn(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/jovyan/git/longmbart/longformer/longformer_encoder_decoder.py", line 66, in forward
    outputs = self.longformer_self_attn(
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/jovyan/git/longmbart/longformer/longformer.py", line 184, in forward
    float_mask = float_mask.repeat(1, 1, repeat_size, 1)
RuntimeError: Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor
​

I have checked float_mask and its size: torch.Size([1, 1, 1024, 1024, 1, 1]). Which looks odd to me

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.