Fine-tuning wav2vec 2.0 with `torch.compile`

[w11wo]

System Info

• transformers version: 4.28.1
• Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
• Python version: 3.9.0
• Huggingface_hub version: 0.13.3
• Safetensors version: not installed
• PyTorch version (GPU?): 2.0.0+cu117 (True)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using GPU in script?: Yes
• Using distributed or parallel set-up in script?: No

Who can help?

No response

Information

• [x] The official example scripts
• [ ] My own modified scripts

Tasks

• [X] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• [ ] My own task or dataset (give details below)

Reproduction

python run_audio_classification.py \
    --model_name_or_path facebook/wav2vec2-base \
    --dataset_name superb \
    --dataset_config_name ks \
    --output_dir wav2vec2-base-ft-keyword-spotting \
    --overwrite_output_dir \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --fp16 \
    --learning_rate 3e-5 \
    --max_length_seconds 1 \
    --attention_mask False \
    --warmup_ratio 0.1 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 32 \
    --gradient_accumulation_steps 4 \
    --per_device_eval_batch_size 32 \
    --dataloader_num_workers 4 \
    --logging_strategy steps \
    --logging_steps 10 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --metric_for_best_model accuracy \
    --save_total_limit 3 \
    --seed 0 \
+   --torch_compile True

Expected behavior

I followed the example to fine-tune wav2vec 2.0 for audio classification, with the exception of using torch.compile, aiming to get faster training. However, I ran to an issue as follows

Error Log

``` [INFO|trainer.py:1769] 2023-04-19 05:28:50,832 >> ***** Running training ***** [INFO|trainer.py:1770] 2023-04-19 05:28:50,832 >> Num examples = 51,094 [INFO|trainer.py:1771] 2023-04-19 05:28:50,832 >> Num Epochs = 5 [INFO|trainer.py:1772] 2023-04-19 05:28:50,832 >> Instantaneous batch size per device = 32 [INFO|trainer.py:1773] 2023-04-19 05:28:50,832 >> Total train batch size (w. parallel, distributed & accumulation) = 128 [INFO|trainer.py:1774] 2023-04-19 05:28:50,833 >> Gradient Accumulation steps = 4 [INFO|trainer.py:1775] 2023-04-19 05:28:50,833 >> Total optimization steps = 1,995 [INFO|trainer.py:1776] 2023-04-19 05:28:50,834 >> Number of trainable parameters = 90,371,212 0%| | 0/1995 [00:00 main() File "/home/wilson_bookbotkids_com/run_audio_classification.py", line 392, in main train_result = trainer.train(resume_from_checkpoint=checkpoint) File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/trainer.py", line 1662, in train return inner_training_loop( File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/trainer.py", line 1929, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/trainer.py", line 2699, in training_step loss = self.compute_loss(model, inputs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/trainer.py", line 2731, in compute_loss outputs = model(**inputs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 82, in forward return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 209, in _fn return fn(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1817, in forward outputs = self.wav2vec2( File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1316, in forward hidden_states = self._mask_hidden_states( File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1249, in _mask_hidden_states if not getattr(self.config, "apply_spec_augment", True): File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1259, in mask_time_indices = _compute_mask_indices( File "/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py", line 1266, in mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 337, in catch_errors return callback(frame, cache_size, hooks) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 404, in _convert_frame result = inner_convert(frame, cache_size, hooks) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 104, in _fn return fn(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 262, in _convert_frame_assert return _compile( File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 163, in time_wrapper r = func(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 324, in _compile out_code = transform_code_object(code, transform) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/bytecode_transformation.py", line 445, in transform_code_object transformations(instructions, code_options) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 311, in transform tracer.run() File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1726, in run super().run() File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 576, in run and self.step() File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 540, in step getattr(self, inst.opname)(inst) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1792, in RETURN_VALUE self.output.compile_subgraph( File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 517, in compile_subgraph self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 588, in compile_and_call_fx_graph compiled_fn = self.call_user_compiler(gm) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 163, in time_wrapper r = func(*args, **kwargs) File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 675, in call_user_compiler raise BackendCompilerFailed(self.compiler_fn, e) from e torch._dynamo.exc.BackendCompilerFailed: debug_wrapper raised DynamicOutputShapeException: aten.index.Tensor Set torch._dynamo.config.verbose=True for more information You can suppress this exception and fall back to eager by setting: torch._dynamo.config.suppress_errors = True ```

I suspect that wav2vec 2.0 is not yet supported in PyTorch 2.0 and needs some modification to ensure compatibility when running torch.compile. The same error occurred when fine-tuning for automatic speech recognition.

[amyeroberts]

cc @sanchit-gandhi

[amyeroberts]

Hi @w11wo, thanks for raising this issue!

Please note that whilst we aim to support a wide variety of use cases with our examples, torch_compile is an experimental flag and not one we guarantee will work for for all of our models as the support is progressively rolled in in PyTorch.

[w11wo]

Hi @amyeroberts, no worries and thanks for the heads up. Looking forward to seeing wav2vec 2.0 supported. Cheers.

[sanchit-gandhi]

Hey @w11wo! Sorry for the late reply here and thanks for the detailed issue description! I had a quick look, and the issue seems to reside with the _compute_mask_indices function: https://github.com/huggingface/transformers/blob/4baa34c18f18274fe028ad5a5511ea3fba9eeece/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L132 The function is both dynamic and in NumPy - we'd need to make the function static (fixed shapes) for it to be compatible with torch compile. I sadly won't have time to look into this myself, but feel free to open a PR if you want to take a stab at updating this!

In the meantime, you can set SpecAug to 0 to avoid calling this dynamic function - you'll loose regularisation in the feature encoder outputs, but you should be able to torch compile the model. To do this, you simply need to set apply_spec_augment to False in the config: https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self/blob/54074b1c16f4de6a5ad59affb4caa8f2ea03a119/config.json#L4

[sanchit-gandhi]

cc @hollance

[sanchit-gandhi]

Hey @w11wo - any luck here? Did it work with specaug set to 0?

[w11wo]

Hi @sanchit-gandhi, unfortunately I haven't been able to test it out without SpecAugment, since my use case requires it to be used. I will try and test it out when I can.

[sanchit-gandhi]

Hey @w11wo - sure, sounds good! The offer still stands for opening a PR to fix this if you feel like having a go at re-working the SpecAug logic in the modelling file, think this could make for a nice PR :)

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[sanchit-gandhi]

Extending the offer of opening a PR to fix the SpecAug logic in the modelling file to the community! Would be a nice PR addition to re-work the SpecAug function so that it's compatible with torch compile (note that torch.compile is not guaranteed for the transformers library, but is a nice feature if it can be done without backwards breaking changes)

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.