def test_visualizing(dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
builder_helpers.remove_dirs()
data_loader_spec_args = {'batch_size': 1, 'dev_strs': [dev_str]}
trainer_spec_args = {
'total_iterations': 10,
'ld_chkpt': False,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoaderMin,
ExampleNetworkMin,
ExampleTrainerMin,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args)
trainer.setup()
try:
trainer.visualize()
except OSError:
pass
trainer.close()
builder_helpers.remove_dirs()
def test_simple_multi_dev_trainers(dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
if call is not helpers.torch_call:
# ToDo: add multi-dev support for all backends, not just torch
pytest.skip()
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
# devices
dev_strs = list()
dev_str0 = dev_str
dev_strs.append(dev_str0)
if 'gpu' in dev_str:
idx = ivy.num_gpus() - 1
dev_str1 = dev_str[:-1] + str(idx)
dev_strs.append(dev_str1)
# test
builder_helpers.remove_dirs()
simple_example.main(compile_mode=compile_mode, dev_strs=dev_strs)
builder_helpers.remove_dirs()
def test_simple_trainers(dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
# test
builder_helpers.remove_dirs()
simple_example.main(compile_mode=compile_mode)
builder_helpers.remove_dirs()
def test_checkpoint_save_and_restore_via_public_trainer_methods(
dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
builder_helpers.remove_dirs()
data_loader_spec_args = {'batch_size': 1, 'dev_strs': [dev_str]}
trainer_spec_args = {
'total_iterations': 0,
'ld_chkpt': False,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoaderMin,
ExampleNetworkMin,
ExampleTrainerMin,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args)
trainer.setup()
chkpt0_path = os.path.join('chkpt/', 'test_chkpt0.hdf5')
trainer.save(chkpt0_path)
assert os.path.exists(chkpt0_path)
trainer.train()
chkpt1_path = os.path.join('chkpt/', 'test_chkpt1.hdf5')
trainer.save(chkpt1_path)
trainer.close()
assert os.path.exists(chkpt1_path)
data_loader_spec_args = {'batch_size': 1, 'dev_strs': [dev_str]}
trainer_spec_args = {
'total_iterations': 10,
'ld_chkpt': False,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoaderMin,
ExampleNetworkMin,
ExampleTrainerMin,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args)
trainer.setup()
trainer.restore(chkpt0_path)
trainer.train()
chkpt3_path = os.path.join('chkpt/', 'test_chkpt3.hdf5')
trainer.save(chkpt3_path)
trainer.close()
assert os.path.exists(chkpt3_path)
builder_helpers.remove_dirs()
def test_full_trainers(dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
if call is helpers.jnp_call and ivy.wrapped_mode():
# Jax does not support ivy.Array instances when calling _jax.grad()
pytest.skip()
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
# test
builder_helpers.remove_dirs()
full_example.main(compile_mode=compile_mode)
builder_helpers.remove_dirs()
def test_checkpoint_loading(dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
builder_helpers.remove_dirs()
data_loader_spec_args = {'batch_size': 1, 'dev_strs': [dev_str]}
trainer_spec_args = {
'total_iterations': 10,
'ld_chkpt': False,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoaderMin,
ExampleNetworkMin,
ExampleTrainerMin,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args)
trainer.setup()
trainer.train()
trainer.close()
trainer_spec_args = {
'total_iterations': 20,
'ld_chkpt': True,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoaderMin,
ExampleNetworkMin,
ExampleTrainerMin,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args)
trainer.setup()
trainer.train()
trainer.close()
checkpoint_nums = [
int(fname.split('-')[-1].split('.')[0])
for fname in os.listdir('log/chkpts')
]
assert max(checkpoint_nums) == 19
builder_helpers.remove_dirs()
def test_tune_numeric_spec(dev_str, call):
if call is not helpers.torch_call:
# ToDo: work out why the backend framework is fixed for tune after the first call,
# and include other frameworks in test once this is fixed
pytest.skip()
builder_helpers.remove_dirs()
data_loader_spec_args = {'batch_size': 1}
trainer_spec_args = {
'total_iterations': 10,
'ld_chkpt': False,
'log_freq': 1,
'log_dir': os.path.join(THIS_DIR, 'log')
}
tuner_spec_args = {
'framework': ivy.current_framework_str(),
'train_steps_per_tune_step': 2,
'trainer_spec': {
'initial_learning_rate': {
'min': 10**-6,
'max': 10**-3,
'exponent': 10
}
},
'name': 'tune',
'num_samples': 5,
'parallel_trials': 1,
'grace_period': 1,
'checkpoint_freq': 0
}
tuner = builder.build_tuner(ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args,
tuner_spec_args=tuner_spec_args)
tuner.tune()
builder_helpers.remove_dirs()
def test_reduced_cost_after_checkpoint_load(dev_str, call, compile_mode):
if call is helpers.np_call:
# numpy does not support gradients, required for training
pytest.skip()
if call is helpers.jnp_call and ivy.wrapped_mode():
# Jax does not support ivy.Array instances when calling _jax.grad()
pytest.skip()
example_dir = os.path.relpath(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../ivy_builder_demos'))
# currently only PyTorch supports graph compilation
compile_mode = compile_mode if ivy.current_framework_str(
) == 'torch' else False
# dataset dirs specification
dataset_dirs_args = dict()
# dataset specification
dataset_spec_filepath = os.path.join(example_dir, 'json_specs',
'dataset_spec.json.example')
dataset_spec_args = builder.parse_json_to_cont(dataset_spec_filepath)
# data loader specification
data_loader_spec_filepath = os.path.join(example_dir, 'json_specs',
'data_loader_spec.json.example')
data_loader_spec_args = builder.parse_json_to_cont(
data_loader_spec_filepath)
# network specification
network_spec_filepath = os.path.join(example_dir, 'json_specs',
'network_spec.json.example')
network_spec_args = builder.parse_json_to_cont(network_spec_filepath)
builder_helpers.remove_dirs()
ivy.seed(0)
trainer_spec_args = {
'total_iterations': 1,
'ld_chkpt': False,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
dataset_dirs_args=dataset_dirs_args,
dataset_dirs_class=ExampleDatasetDirs,
dataset_spec_args=dataset_spec_args,
dataset_spec_class=ExampleDatasetSpec,
data_loader_spec_args=data_loader_spec_args,
data_loader_spec_class=ExampleDataLoaderSpec,
network_spec_args=network_spec_args,
network_spec_class=ExampleNetworkSpec,
trainer_spec_args=trainer_spec_args)
trainer.setup()
trainer.train()
initial_cost = trainer._total_cost
assert trainer._global_step == 1
trainer.close()
ivy.seed(0)
steps_to_take_first = 10
trainer_spec_args = {
'total_iterations': steps_to_take_first,
'ld_chkpt': False,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
dataset_dirs_args=dataset_dirs_args,
dataset_dirs_class=ExampleDatasetDirs,
dataset_spec_args=dataset_spec_args,
dataset_spec_class=ExampleDatasetSpec,
data_loader_spec_args=data_loader_spec_args,
data_loader_spec_class=ExampleDataLoaderSpec,
network_spec_args=network_spec_args,
network_spec_class=ExampleNetworkSpec,
trainer_spec_args=trainer_spec_args)
trainer.setup()
trainer.train()
ten_step_cost = trainer._total_cost
assert trainer._global_step == steps_to_take_first
trainer.close()
assert initial_cost > ten_step_cost
steps_to_take_second = 20
trainer_spec_args = {
'total_iterations': steps_to_take_second,
'ld_chkpt': True,
'save_freq': 1,
'compile_mode': compile_mode
}
trainer = builder.build_trainer(
ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
dataset_dirs_args=dataset_dirs_args,
dataset_dirs_class=ExampleDatasetDirs,
dataset_spec_args=dataset_spec_args,
dataset_spec_class=ExampleDatasetSpec,
data_loader_spec_args=data_loader_spec_args,
data_loader_spec_class=ExampleDataLoaderSpec,
network_spec_args=network_spec_args,
network_spec_class=ExampleNetworkSpec,
trainer_spec_args=trainer_spec_args)
trainer.setup()
trainer.train()
twenty_step_cost = trainer._total_cost
assert trainer._global_step == steps_to_take_second
trainer.close()
assert ten_step_cost > twenty_step_cost
builder_helpers.remove_dirs()
def test_tune_general_spec(dev_str, call):
if call is not helpers.torch_call:
# ToDo: work out why the backend framework is fixed for tune after the first call,
# and include other frameworks in test once this is fixed
pytest.skip()
builder_helpers.remove_dirs()
data_loader_spec_args = {'batch_size': 1}
trainer_spec_args = {
'total_iterations': 2,
'ld_chkpt': False,
'log_freq': 1,
'log_dir': os.path.join(THIS_DIR, 'log')
}
tuner_spec_args = {
'framework': ivy.current_framework_str(),
'train_steps_per_tune_step': 1,
'network_spec': {
'spec_a': {
"configs": [{
'param_0': True
}, {
'param_1': False
}],
"grid": True
},
'spec_b': {
"configs": [{
'param_0': True
}, {
'param_0': False
}]
},
'spec_c': {
"spec_c_a": {
"configs": [1, 2],
"grid": True
},
"spec_c_b": {
"configs": ['100', '200']
},
},
'spec_d_AND_spec_e_AND_spec_f': {
"configs": [(False, False, False), (False, True, False),
(True, False, True), (False, True, True)],
"grid":
True
}
},
'name': 'tune',
'num_samples': 1,
'parallel_trials': 1,
'grace_period': 1,
'checkpoint_freq': 0
}
tuner = builder.build_tuner(ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args,
tuner_spec_args=tuner_spec_args)
tuner.tune()
builder_helpers.remove_dirs()
def test_tune_resume_training(dev_str, call):
if call is not helpers.torch_call:
# ToDo: work out why the backend framework is fixed for tune after the first call,
# and include other frameworks in test once this is fixed
pytest.skip()
if ivy.wrapped_mode():
# this test fails when running all tests for some reason, need to further investigate
pytest.skip()
builder_helpers.remove_dirs()
# tuner spec args
train_steps_per_tune_step = 2
data_loader_spec_args = {'batch_size': 1}
tuner_spec_args = {
'framework': ivy.current_framework_str(),
'train_steps_per_tune_step': train_steps_per_tune_step,
'trainer_spec': {
'initial_learning_rate': {
'min': 10**-5,
'max': 10**-4,
'num_grid_samples': 2,
'grid': True
}
},
'name': 'tune',
'num_samples': 1,
'parallel_trials': 1,
'grace_period': -1,
'checkpoint_freq': 0
}
# first run
total_iterations = 5
trainer_spec_args = {
'total_iterations': total_iterations,
'ld_chkpt': False,
'log_freq': 1,
'log_dir': os.path.join(THIS_DIR, 'log'),
'save_freq': 1
}
tuner = builder.build_tuner(ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args,
tuner_spec_args=tuner_spec_args)
first_results = ivy.Container(tuner.tune().results)
first_losses = first_results.at_keys('cost').to_flat_list()
# second run
trainer_spec_args = {
'total_iterations': total_iterations * 2,
'ld_chkpt': True,
'log_freq': 1,
'log_dir': os.path.join(THIS_DIR, 'log'),
'save_freq': 1
}
tuner = builder.build_tuner(ExampleDataLoader,
ExampleNetwork,
ExampleTrainer,
data_loader_spec_args=data_loader_spec_args,
trainer_spec_args=trainer_spec_args,
tuner_spec_args=tuner_spec_args)
second_results = ivy.Container(tuner.tune().results)
second_losses = second_results.at_keys('cost').to_flat_list()
# assertion
# first session ends training at ceil(5/2)=3 timesteps
first_timestep = int(
math.ceil(total_iterations / train_steps_per_tune_step))
assert min([
fts == first_timestep
for fts in first_results.at_keys('timestep').to_flat_list()
])
# second session ends training at ceil(10/2)=5 timesteps
second_timestep = int(
math.ceil(total_iterations * 2 / train_steps_per_tune_step))
assert min([
sts == second_timestep
for sts in second_results.at_keys('timestep').to_flat_list()
])
# both sessions trained for ceil(5/2)=3 training iterations
training_iteration = int(
math.ceil(total_iterations / train_steps_per_tune_step))
assert min([
fti == sti == training_iteration for fti, sti in zip(
first_results.at_keys('training_iteration').to_flat_list(),
second_results.at_keys('training_iteration').to_flat_list())
])
# the loss is lower for the second session, after the checkpoint load from the first
assert min([
second_loss < first_loss
for first_loss, second_loss in zip(first_losses, second_losses)
])
# end
builder_helpers.remove_dirs()