Skip to content

Commit

Permalink
fix yapf
Browse files Browse the repository at this point in the history
  • Loading branch information
@Niccolo-Ajroldi
Niccolo-Ajroldi committed Oct 25, 2024
1 parent 86114ef commit 1965241
Show file tree
Hide file tree
Showing 30 changed files with 390 additions and 390 deletions.
26 changes: 13 additions & 13 deletions prize_qualification_baselines/external_tuning/jax_nadamw_full_budget.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -252,19 +252,19 @@ def _loss_fn(params):
return new_optimizer_state, updated_params, new_model_state, loss, grad_norm


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/external_tuning/jax_nadamw_target_setting.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -252,19 +252,19 @@ def _loss_fn(params):
return new_optimizer_state, updated_params, new_model_state, loss, grad_norm


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/external_tuning/pytorch_nadamw_full_budget.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -224,19 +224,19 @@ def pytorch_cosine_warmup(step_hint: int, hyperparameters, optimizer):
return optimizer_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/external_tuning/pytorch_nadamw_target_setting.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -224,19 +224,19 @@ def pytorch_cosine_warmup(step_hint: int, hyperparameters, optimizer):
return optimizer_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/self_tuning/jax_nadamw_full_budget.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -264,19 +264,19 @@ def _loss_fn(params):
return new_optimizer_state, updated_params, new_model_state, loss, grad_norm


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/self_tuning/jax_nadamw_target_setting.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -264,19 +264,19 @@ def _loss_fn(params):
return new_optimizer_state, updated_params, new_model_state, loss, grad_norm


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/self_tuning/pytorch_nadamw_full_budget.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -236,19 +236,19 @@ def pytorch_cosine_warmup(step_hint: int, hyperparameters, optimizer):
return optimizer_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions prize_qualification_baselines/self_tuning/pytorch_nadamw_target_setting.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -236,19 +236,19 @@ def pytorch_cosine_warmup(step_hint: int, hyperparameters, optimizer):
return optimizer_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions reference_algorithms/development_algorithms/cifar/cifar_jax/submission.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -110,19 +110,19 @@ def _loss_fn(params):

# Not allowed to update the model parameters, hyperparameters, global step, or
# optimzier state.
def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions reference_algorithms/development_algorithms/cifar/cifar_pytorch/submission.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -53,19 +53,19 @@ def init_optimizer_state(workload: spec.Workload,
return optimizer_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params)."""
del current_params_types
del hyperparameters
Expand Down
26 changes: 13 additions & 13 deletions reference_algorithms/development_algorithms/mnist/mnist_jax/submission.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -75,19 +75,19 @@ def loss_fn(params):
return new_optimizer_state, updated_params, new_model_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
Expand Down
26 changes: 13 additions & 13 deletions reference_algorithms/development_algorithms/mnist/mnist_pytorch/submission.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -32,19 +32,19 @@ def init_optimizer_state(workload: spec.Workload,
return optimizer_state


def update_params(workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None
) -> spec.UpdateReturn:
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparameters,
batch: Dict[str, spec.Tensor],
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState,
train_state: Optional[Dict[str, Any]] = None) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params)."""
del hyperparameters
del loss_type
Expand Down
Loading

0 comments on commit 1965241

Please sign in to comment.