# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict, Iterable, Set
import pandas as pd
import torch
from ax.benchmark.benchmark_problem import SingleObjectiveBenchmarkProblem
from ax.core.base_trial import BaseTrial, TrialStatus
from ax.core.data import Data
from ax.core.metric import Metric
from ax.core.objective import Objective
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.runner import Runner
from ax.core.search_space import SearchSpace
from ax.utils.common.base import Base
from ax.utils.common.equality import equality_typechecker
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
[docs]class PyTorchCNNBenchmarkProblem(SingleObjectiveBenchmarkProblem):
@equality_typechecker
def __eq__(self, other: Base) -> bool:
if not isinstance(other, PyTorchCNNBenchmarkProblem):
return False
# Checking the whole datasets' equality here would be too expensive to be
# worth it; just check names instead
return self.name == other.name
[docs] @classmethod
def from_datasets(
cls, name: str, num_trials: int, train_set: Dataset, test_set: Dataset
) -> "PyTorchCNNBenchmarkProblem":
optimal_value = 1
search_space = SearchSpace(
parameters=[
RangeParameter(
name="lr", parameter_type=ParameterType.FLOAT, lower=1e-6, upper=0.4
),
RangeParameter(
name="momentum",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=1,
),
RangeParameter(
name="weight_decay",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=1,
),
RangeParameter(
name="step_size",
parameter_type=ParameterType.INT,
lower=1,
upper=100,
),
RangeParameter(
name="gamma",
parameter_type=ParameterType.FLOAT,
lower=0,
upper=1,
),
]
)
optimization_config = OptimizationConfig(
objective=Objective(
metric=PyTorchCNNMetric(),
minimize=False,
)
)
runner = PyTorchCNNRunner(name=name, train_set=train_set, test_set=test_set)
return cls(
name=f"HPO_PyTorchCNN_{name}",
optimal_value=optimal_value,
search_space=search_space,
optimization_config=optimization_config,
runner=runner,
num_trials=num_trials,
)
[docs]class PyTorchCNNMetric(Metric):
def __init__(self) -> None:
super().__init__(name="accuracy")
# pyre-fixme[2]: Parameter must be annotated.
[docs] def fetch_trial_data(self, trial: BaseTrial, **kwargs) -> Data:
accuracy = [
trial.run_metadata["accuracy"][name]
for name, arm in trial.arms_by_name.items()
]
df = pd.DataFrame(
{
"arm_name": [name for name, _ in trial.arms_by_name.items()],
"metric_name": self.name,
"mean": accuracy,
"sem": 0,
"trial_index": trial.index,
}
)
return Data(df=df)
[docs]class PyTorchCNNRunner(Runner):
def __init__(self, name: str, train_set: Dataset, test_set: Dataset) -> None:
self.name = name
# pyre-fixme[4]: Attribute must be annotated.
self.train_loader = DataLoader(train_set)
# pyre-fixme[4]: Attribute must be annotated.
self.test_loader = DataLoader(test_set)
self.results: Dict[int, float] = {}
self.statuses: Dict[int, TrialStatus] = {}
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
[docs] class CNN(nn.Module):
# pyre-fixme[3]: Return type must be annotated.
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 20, kernel_size=5, stride=1)
self.fc1 = nn.Linear(8 * 8 * 20, 64)
self.fc2 = nn.Linear(64, 10)
# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[2]: Parameter must be annotated.
[docs] def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 3, 3)
x = x.view(-1, 8 * 8 * 20)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=-1)
[docs] def train_and_evaluate(
self,
lr: float,
momentum: float,
weight_decay: float,
step_size: int,
gamma: float,
) -> float:
net = self.CNN()
net.to(device=self.device)
# Train
net.train()
criterion = nn.NLLLoss(reduction="sum")
optimizer = optim.SGD(
net.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay,
)
scheduler = optim.lr_scheduler.StepLR(
optimizer, step_size=step_size, gamma=gamma
)
for inputs, labels in self.train_loader:
inputs = inputs.to(device=self.device)
labels = labels.to(device=self.device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
scheduler.step()
# Evaluate
net.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in self.test_loader:
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
return correct / total
[docs] def run(self, trial: BaseTrial) -> Dict[str, Any]:
self.statuses[trial.index] = TrialStatus.RUNNING
self.statuses[trial.index] = TrialStatus.COMPLETED
return {
"accuracy": {
arm.name: self.train_and_evaluate(
lr=arm.parameters["lr"], # pyre-ignore[6]
momentum=arm.parameters["momentum"], # pyre-ignore[6]
weight_decay=arm.parameters["weight_decay"], # pyre-ignore[6]
step_size=arm.parameters["step_size"], # pyre-ignore[6]
gamma=arm.parameters["gamma"], # pyre-ignore[6]
)
for arm in trial.arms
}
}
[docs] def poll_trial_status(
self, trials: Iterable[BaseTrial]
) -> Dict[TrialStatus, Set[int]]:
return {TrialStatus.COMPLETED: {t.index for t in trials}}
