Enable CPU training

[ronaldtse]

Currently fails with no GPU:

 File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 852, in to
 return self._apply(convert)
 File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 530, in _apply
 module._apply(fn)
 File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 552, in _apply
 param_applied = fn(param)
 File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 850, in convert
 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
 File "/usr/local/lib/python3.9/site-packages/torch/cuda/__init__.py", line 166, in _lazy_init
 raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enabled

[gilgameshjw]

done

[ronaldtse]

I'm facing this double-invocation error:

The model has 15413521 trainable parameters parameters
/usr/local/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
 warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")
Length of training iterator = 86962
Length of valid iterator = 4676
data loaded
----------------------------------------------------------
Eval: 0%| | 0/4676 [00:00<?, ?it/sTraceback (most recent call last): | 0/4676 [00:00<?, ?it/s]
 File "<string>", line 1, in <module> | 0/2000000 [00:00<?, ?it/s]
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 116, in spawn_main
 exitcode = _main(fd, parent_sentinel)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
 prepare(preparation_data)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 236, in prepare
 _fixup_main_from_path(data['init_main_from_path'])
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
 main_content = runpy.run_path(main_path,
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 268, in run_path
 return _run_module_code(code, init_globals, run_name,
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 97, in _run_module_code
 _run_code(code, mod_globals, init_globals,
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
 exec(code, run_globals)
 File "/Users/me/src/interscript/rababa/python/train.py", line 43, in <module>
 trainer.run()
 File "/Users/me/src/interscript/rababa/python/trainer.py", line 209, in run
 for batch_inputs in repeater(train_iterator):
 File "/Users/me/src/interscript/rababa/python/util/utils.py", line 64, in repeater
 for data in loader:
 File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
 return self._get_iterator()
 File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
 return _MultiProcessingDataLoaderIter(self)
 File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
 w.start()
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/process.py", line 121, in start
 self._popen = self._Popen(self)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
 return _default_context.get_context().Process._Popen(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
 return Popen(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
 super().__init__(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
 self._launch(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 42, in _launch
 prep_data = spawn.get_preparation_data(process_obj._name)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 154, in get_preparation_data
 _check_not_importing_main()
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 134, in _check_not_importing_main
 raise RuntimeError('''
RuntimeError: 
 An attempt has been made to start a new process before the
 current process has finished its bootstrapping phase.

 This probably means that you are not using fork to start your
 child processes and you have forgotten to use the proper idiom
 in the main module:

 if __name__ == '__main__':
 freeze_support()
 ...

 The "freeze_support()" line can be omitted if the program
 is not going to be frozen to produce an executable.

[gilgameshjw]

should be solved.

[ronaldtse]

@gilgameshjw I'm still seeing the same issue... double invocation, and no progress:

me:~/src/interscript/rababa/python (main *): python3.9 train.py --model "cbhg" --config config/cbhg.yml

CONFIGURATION CA_MSA.base.cbhg
- session_name : base
- data_directory : data
- data_type : CA_MSA
- log_directory : log_dir
- load_training_data : True
- load_test_data : False
- load_validation_data : True
- n_training_examples : None
- n_test_examples : None
- n_validation_examples : None
- test_file_name : test.csv
- is_data_preprocessed : False
- data_separator : |
- diacritics_separator : *
- text_encoder : ArabicEncoderWithStartSymbol
- text_cleaner : valid_arabic_cleaners
- max_len : 600
- reconcile : True
- max_steps : 2000000
- learning_rate : 0.001
- batch_size : 32
- adam_beta1 : 0.9
- adam_beta2 : 0.999
- use_decay : True
- weight_decay : 0.0
- embedding_dim : 256
- use_prenet : False
- prenet_sizes : [512, 256]
- cbhg_projections : [128, 256]
- cbhg_filters : 16
- cbhg_gru_units : 256
- post_cbhg_layers_units : [256, 256]
- post_cbhg_use_batch_norm : True
- use_mixed_precision : False
- optimizer_type : Adam
- device : cuda
- evaluate_frequency : 5000
- evaluate_with_error_rates_frequency : 5000
- n_predicted_text_tensorboard : 10
- model_save_frequency : 5000
- train_plotting_frequency : 50000000
- n_steps_avg_losses : [100, 500, 1000, 5000]
- error_rates_n_batches : 10000
- test_model_path : None
- train_resume_model_path : None
- len_input_symbols : 44
- len_target_symbols : 17
- optimizer : OptimizerType.Adam
- git_hash : v0.1.0-34-g4e8ffa9
The model has 15413521 trainable parameters parameters
/usr/local/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
 warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")
Length of training iterator = 86962
Length of valid iterator = 4676
data loaded
----------------------------------------------------------
Eval: 0%| | 0/4676 [00:00<?, ?it/s]
CONFIGURATION CA_MSA.base.cbhg | 0/4676 [00:00<?, ?it/s]
- session_name : base | 0/2000000 [00:00<?, ?it/s]
- data_directory : data
- data_type : CA_MSA
- log_directory : log_dir
- load_training_data : True
- load_test_data : False
- load_validation_data : True
- n_training_examples : None
- n_test_examples : None
- n_validation_examples : None
- test_file_name : test.csv
- is_data_preprocessed : False
- data_separator : |
- diacritics_separator : *
- text_encoder : ArabicEncoderWithStartSymbol
- text_cleaner : valid_arabic_cleaners
- max_len : 600
- reconcile : True
- max_steps : 2000000
- learning_rate : 0.001
- batch_size : 32
- adam_beta1 : 0.9
- adam_beta2 : 0.999
- use_decay : True
- weight_decay : 0.0
- embedding_dim : 256
- use_prenet : False
- prenet_sizes : [512, 256]
- cbhg_projections : [128, 256]
- cbhg_filters : 16
- cbhg_gru_units : 256
- post_cbhg_layers_units : [256, 256]
- post_cbhg_use_batch_norm : True
- use_mixed_precision : False
- optimizer_type : Adam
- device : cuda
- evaluate_frequency : 5000
- evaluate_with_error_rates_frequency : 5000
- n_predicted_text_tensorboard : 10
- model_save_frequency : 5000
- train_plotting_frequency : 50000000
- n_steps_avg_losses : [100, 500, 1000, 5000]
- error_rates_n_batches : 10000
- test_model_path : None
- train_resume_model_path : None
- len_input_symbols : 44
- len_target_symbols : 17
- optimizer : OptimizerType.Adam
- git_hash : v0.1.0-34-g4e8ffa9
The model has 15413521 trainable parameters parameters
/usr/local/lib/python3.9/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
 warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")
Length of training iterator = 86962
Length of valid iterator = 4676
data loaded
----------------------------------------------------------
Eval: 0%| | 0/4676 [00:00<?, ?it/sTraceback (most recent call last): | 0/4676 [00:00<?, ?it/s]
 File "<string>", line 1, in <module> | 0/2000000 [00:00<?, ?it/s]
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 116, in spawn_main
 exitcode = _main(fd, parent_sentinel)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 125, in _main
 prepare(preparation_data)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 236, in prepare
 _fixup_main_from_path(data['init_main_from_path'])
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
 main_content = runpy.run_path(main_path,
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 268, in run_path
 return _run_module_code(code, init_globals, run_name,
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 97, in _run_module_code
 _run_code(code, mod_globals, init_globals,
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/runpy.py", line 87, in _run_code
 exec(code, run_globals)
 File "/Users/me/src/interscript/rababa/python/train.py", line 43, in <module>
 trainer.run()
 File "/Users/me/src/interscript/rababa/python/trainer.py", line 210, in run
 for batch_inputs in repeater(train_iterator):
 File "/Users/me/src/interscript/rababa/python/util/utils.py", line 64, in repeater
 for data in loader:
 File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
 return self._get_iterator()
 File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
 return _MultiProcessingDataLoaderIter(self)
 File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
 w.start()
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/process.py", line 121, in start
 self._popen = self._Popen(self)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 224, in _Popen
 return _default_context.get_context().Process._Popen(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/context.py", line 284, in _Popen
 return Popen(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 32, in __init__
 super().__init__(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_fork.py", line 19, in __init__
 self._launch(process_obj)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/popen_spawn_posix.py", line 42, in _launch
 prep_data = spawn.get_preparation_data(process_obj._name)
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 154, in get_preparation_data
 _check_not_importing_main()
 File "/usr/local/Cellar/python@3.9/3.9.6/Frameworks/Python.framework/Versions/3.9/lib/python3.9/multiprocessing/spawn.py", line 134, in _check_not_importing_main
 raise RuntimeError('''
RuntimeError: 
 An attempt has been made to start a new process before the
 current process has finished its bootstrapping phase.

 This probably means that you are not using fork to start your
 child processes and you have forgotten to use the proper idiom
 in the main module:

 if __name__ == '__main__':
 freeze_support()
 ...

 The "freeze_support()" line can be omitted if the program
 is not going to be frozen to produce an executable.
Eval: 0%| | 0/4676 [00:00<?, ?it/s]
WER/DER : : 0%| | 0/4676 [00:00<?, ?it/s]
 0%| | 0/2000000 [00:00<?, ?it/s]

[ronaldtse]

Works on GitHub Actions, but still failing locally... there seems to be a double invocation still in the logs.

[gilgameshjw]

i think i fixed it yesterday

[gilgameshjw]

Never seen that pbm that I dont understand.

Was python installed with conda, 3.6?
That might be the issue!

[ronaldtse]

I just tried Pyenv with 3.7, upgraded pip, and updated setup.py. It seems to work! (Just extremely slow...)

CONFIGURATION CA_MSA.base.cbhg
- session_name : base
- data_directory : data
- data_type : CA_MSA
- log_directory : log_dir
- load_training_data : True
- load_test_data : False
- load_validation_data : True
- n_training_examples : None
- n_test_examples : None
- n_validation_examples : None
- test_file_name : test.csv
- is_data_preprocessed : False
- data_separator : |
- diacritics_separator : *
- text_encoder : ArabicEncoderWithStartSymbol
- text_cleaner : valid_arabic_cleaners
- max_len : 600
- reconcile : True
- max_steps : 2000000
- learning_rate : 0.001
- batch_size : 32
- adam_beta1 : 0.9
- adam_beta2 : 0.999
- use_decay : True
- weight_decay : 0.0
- embedding_dim : 256
- use_prenet : False
- prenet_sizes : [512, 256]
- cbhg_projections : [128, 256]
- cbhg_filters : 16
- cbhg_gru_units : 256
- post_cbhg_layers_units : [256, 256]
- post_cbhg_use_batch_norm : True
- use_mixed_precision : False
- optimizer_type : Adam
- device : cuda
- evaluate_frequency : 5000
- evaluate_with_error_rates_frequency : 5000
- n_predicted_text_tensorboard : 10
- model_save_frequency : 5000
- train_plotting_frequency : 50000000
- n_steps_avg_losses : [100, 500, 1000, 5000]
- error_rates_n_batches : 10000
- test_model_path : None
- train_resume_model_path : None
- len_input_symbols : 44
- len_target_symbols : 17
- optimizer : OptimizerType.Adam
- git_hash : v0.1.0-61-g46102ec
The model has 15413521 trainable parameters parameters
/Users/me/.pyenv/versions/3.7.11/lib/python3.7/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.
 warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.")
Length of training iterator = 86962
Length of valid iterator = 4676
data loaded
----------------------------------------------------------
Eval: 0%| | 0/4676 [00:00<?, ?it/s[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads) | 0/2000000 [00:00<?, ?it/s]
[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
 /Users/me/.pyenv/versions/3.7.11/lib/python3.7/site-packages/torch/nn/functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1156.)
 return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)


Global Step 4: 0%| | 3/2000000 [00:26<4821:01:28, 8.68s/it]
loss: 3.081960916519165 

[ronaldtse]

There are some warnings, but doesn't seem to affect training:

[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native 

[W ParallelNative.cpp:212] Warning: Cannot set number of intraop threads after parallel work has started or after set_num_threads call when using native parallel backend (function set_num_threads)
 /Users/me/.pyenv/versions/3.7.11/lib/python3.7/site-packages/torch/nn/functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at ../c10/core/TensorImpl.h:1156.)

 return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)

[ronaldtse]

Interestingly it works with Python 3.6, 3.7 but not 3.9 (haven't tried 3.8).

[ronaldtse]

Closing this since 3.7 works.