Source code for ax.benchmark.problems.hpo.torchvision

# 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.

# pyre-strict

from collections.abc import Mapping
from dataclasses import dataclass, field, InitVar
from functools import lru_cache

import torch
from ax.benchmark.benchmark_problem import BenchmarkProblem, get_soo_opt_config
from ax.benchmark.benchmark_test_function import BenchmarkTestFunction
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace
from ax.exceptions.core import UserInputError
from torch import nn, optim, Tensor
from torch.nn import functional as F
from torch.utils.data import DataLoader

try:  # We don't require TorchVision by default.
    from torchvision import datasets, transforms

    _REGISTRY = {
        "MNIST": datasets.MNIST,
        "FashionMNIST": datasets.FashionMNIST,
    }


except ModuleNotFoundError:
    transforms = None
    datasets = None
    _REGISTRY = {}


CLASSIFICATION_OPTIMAL_VALUE = 1.0


[docs] class CNN(nn.Module): def __init__(self) -> None: 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)
[docs] def forward(self, x: Tensor) -> Tensor: 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] @lru_cache(maxsize=64) def train_and_evaluate( lr: float, momentum: float, weight_decay: float, step_size: int, gamma: float, device: torch.device, train_loader: DataLoader, test_loader: DataLoader, ) -> float: """Return the fraction of correctly classified test examples.""" net = CNN() net.to(device=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 train_loader: inputs = inputs.to(device=device) labels = labels.to(device=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 test_loader: outputs = net(inputs) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() return correct / total
[docs] @dataclass(kw_only=True) class PyTorchCNNTorchvisionBenchmarkTestFunction(BenchmarkTestFunction): name: str # The name of the dataset to load -- MNIST or FashionMNIST device: torch.device = field( default_factory=lambda: torch.device( "cuda" if torch.cuda.is_available() else "cpu" ) ) # Using `InitVar` prevents the DataLoaders from being serialized; instead # they are reconstructed upon deserialization. # Pyre doesn't understand InitVars. # pyre-ignore: Undefined attribute [16]: `typing.Type` has no attribute # `train_loader` train_loader: InitVar[DataLoader | None] = None # pyre-ignore test_loader: InitVar[DataLoader | None] = None outcome_names: list[str] = field(default_factory=lambda: ["accuracy"]) def __post_init__(self, train_loader: None, test_loader: None) -> None: if self.name not in _REGISTRY: raise UserInputError( f"Unrecognized torchvision dataset {self.name}. Please ensure it " "is listed in ax/benchmark/problems/hop/torchvision.py registry." ) dataset_fn = _REGISTRY[self.name] train_set = dataset_fn( root="./data", train=True, download=True, transform=transforms.ToTensor(), ) test_set = dataset_fn( root="./data", train=False, download=True, transform=transforms.ToTensor(), ) # pyre-fixme: Undefined attribute [16]: # `PyTorchCNNTorchvisionBenchmarkTestFunction` has no attribute # `train_loader`. self.train_loader = DataLoader(train_set, num_workers=1) # pyre-fixme self.test_loader = DataLoader(test_set, num_workers=1) # pyre-fixme[14]: Inconsistent override (super class takes a more general # type, TParameterization)
[docs] def evaluate_true(self, params: Mapping[str, int | float]) -> Tensor: frac_correct = train_and_evaluate( **params, device=self.device, # pyre-fixme[16]: `PyTorchCNNTorchvisionBenchmarkTestFunction` has no # attribute `train_loader`. train_loader=self.train_loader, # pyre-fixme[16]: `PyTorchCNNTorchvisionBenchmarkTestFunction` has no # attribute `test_loader`. test_loader=self.test_loader, ) return torch.tensor(frac_correct, dtype=torch.double)
[docs] def get_pytorch_cnn_torchvision_benchmark_problem( name: str, num_trials: int, ) -> BenchmarkProblem: 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, ), ] ) test_function = PyTorchCNNTorchvisionBenchmarkTestFunction(name=name) optimization_config = get_soo_opt_config( outcome_names=test_function.outcome_names, lower_is_better=False ) return BenchmarkProblem( name=f"HPO_PyTorchCNN_Torchvision::{name}", search_space=search_space, optimization_config=optimization_config, num_trials=num_trials, optimal_value=CLASSIFICATION_OPTIMAL_VALUE, test_function=test_function, )