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Fine-tuning wav2vec 2.0 with torch.compile #22849

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Fine-tuning wav2vec 2.0 with torch.compile#22849

Description

@w11wo
w11wo
opened on Apr 19, 2023

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

  • The official example scripts
  • My own modified scripts

Tasks

  • 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<?, ?it/s]/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/feature_extraction_utils.py:165: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  tensor = as_tensor(value)
/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/feature_extraction_utils.py:165: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  tensor = as_tensor(value)
/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/feature_extraction_utils.py:165: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  tensor = as_tensor(value)
/opt/conda/envs/torch/lib/python3.9/site-packages/transformers/feature_extraction_utils.py:165: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  tensor = as_tensor(value)
[2023-04-19 05:28:54,741] torch._inductor.utils: [WARNING] using triton random, expect difference from eager
Traceback (most recent call last):
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 670, in call_user_compiler
    compiled_fn = compiler_fn(gm, self.fake_example_inputs())
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/debug_utils.py", line 1055, in debug_wrapper
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/__init__.py", line 1390, in __call__
    return compile_fx(model_, inputs_, config_patches=self.config)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_inductor/compile_fx.py", line 455, in compile_fx
    return aot_autograd(
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_dynamo/backends/common.py", line 48, in compiler_fn
    cg = aot_module_simplified(gm, example_inputs, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 2805, in aot_module_simplified
    compiled_fn = create_aot_dispatcher_function(
  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/_functorch/aot_autograd.py", line 2498, in create_aot_dispatcher_function
    compiled_fn = compiler_fn(flat_fn, fake_flat_args, aot_config)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1713, in aot_wrapper_dedupe
    return compiler_fn(flat_fn, leaf_flat_args, aot_config)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 2087, in aot_dispatch_autograd
    fx_g = make_fx(joint_forward_backward, aot_config.decompositions)(
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/fx/experimental/proxy_tensor.py", line 714, in wrapped
    t = dispatch_trace(wrap_key(func, args, fx_tracer), tracer=fx_tracer, concrete_args=tuple(phs))
  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/torch/fx/experimental/proxy_tensor.py", line 443, in dispatch_trace
    graph = tracer.trace(root, concrete_args)
  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/torch/fx/_symbolic_trace.py", line 778, in trace
    (self.create_arg(fn(*args)),),
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/fx/_symbolic_trace.py", line 652, in flatten_fn
    tree_out = root_fn(*tree_args)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/fx/experimental/proxy_tensor.py", line 459, in wrapped
    out = f(*tensors)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1156, in traced_joint
    return functionalized_f_helper(primals, tangents)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1108, in functionalized_f_helper
    f_outs = flat_fn_no_input_mutations(fn, f_primals, f_tangents, meta, keep_input_mutations)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1076, in flat_fn_no_input_mutations
    outs = flat_fn_with_synthetic_bases_expanded(fn, primals, primals_after_cloning, maybe_tangents, meta, keep_input_mutations)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1048, in flat_fn_with_synthetic_bases_expanded
    outs = forward_or_joint(fn, primals_before_cloning, primals, maybe_tangents, meta, keep_input_mutations)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1017, in forward_or_joint
    backward_out = torch.autograd.grad(
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/autograd/__init__.py", line 269, in grad
    return handle_torch_function(
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/overrides.py", line 1534, in handle_torch_function
    result = mode.__torch_function__(public_api, types, args, kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_inductor/overrides.py", line 38, in __torch_function__
    return func(*args, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/autograd/__init__.py", line 303, in grad
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/fx/experimental/proxy_tensor.py", line 487, in __torch_dispatch__
    return self.inner_torch_dispatch(func, types, args, kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/fx/experimental/proxy_tensor.py", line 512, in inner_torch_dispatch
    out = proxy_call(self, func, args, kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/fx/experimental/proxy_tensor.py", line 345, in proxy_call
    out = func(*args, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_ops.py", line 287, in __call__
    return self._op(*args, **kwargs or {})
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 987, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1162, in dispatch
    op_impl_out = op_impl(self, func, *args, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 453, in index_tensor
    check_no_bool_index_tensors(func, *args, **kwargs)
  File "/opt/conda/envs/torch/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 432, in check_no_bool_index_tensors
    raise DynamicOutputShapeException(func)
torch._subclasses.fake_tensor.DynamicOutputShapeException: aten.index.Tensor

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/wilson_bookbotkids_com/run_audio_classification.py", line 418, in <module>
    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 <graph break in _mask_hidden_states>
    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 <graph break in _mask_hidden_states>
    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.

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