-
Notifications
You must be signed in to change notification settings - Fork 28
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #159 from GFNOrg/device_handling_fix
device handling fix
- Loading branch information
Showing
3 changed files
with
68 additions
and
8 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,66 @@ | ||
#!/usr/bin/env python | ||
import torch | ||
from tqdm import tqdm | ||
|
||
from gfn.gflownet import TBGFlowNet | ||
from gfn.gym import HyperGrid | ||
from gfn.modules import DiscretePolicyEstimator | ||
from gfn.samplers import Sampler | ||
from gfn.utils import NeuralNet | ||
|
||
torch.manual_seed(0) | ||
exploration_rate = 0.5 | ||
learning_rate = 0.0005 | ||
|
||
# Setup the Environment. | ||
env = HyperGrid( | ||
ndim=5, | ||
height=2, | ||
device_str="cuda" if torch.cuda.is_available() else "cpu", | ||
) | ||
|
||
# Build the GFlowNet. | ||
module_PF = NeuralNet( | ||
input_dim=env.preprocessor.output_dim, | ||
output_dim=env.n_actions, | ||
) | ||
module_PB = NeuralNet( | ||
input_dim=env.preprocessor.output_dim, | ||
output_dim=env.n_actions - 1, | ||
torso=module_PF.torso, | ||
) | ||
pf_estimator = DiscretePolicyEstimator( | ||
module_PF, env.n_actions, is_backward=False, preprocessor=env.preprocessor | ||
) | ||
pb_estimator = DiscretePolicyEstimator( | ||
module_PB, env.n_actions, is_backward=True, preprocessor=env.preprocessor | ||
) | ||
gflownet = TBGFlowNet(init_logZ=0.0, pf=pf_estimator, pb=pb_estimator, off_policy=True) | ||
|
||
# Feed pf to the sampler. | ||
sampler = Sampler(estimator=pf_estimator) | ||
|
||
# Move the gflownet to the GPU. | ||
if torch.cuda.is_available(): | ||
gflownet = gflownet.to("cuda") | ||
|
||
# Policy parameters have their own LR. Log Z gets dedicated learning rate | ||
# (typically higher). | ||
optimizer = torch.optim.Adam(gflownet.pf_pb_parameters(), lr=1e-3) | ||
optimizer.add_param_group({"params": gflownet.logz_parameters(), "lr": 1e-1}) | ||
|
||
n_iterations = int(1e4) | ||
batch_size = int(1e5) | ||
|
||
for i in (pbar := tqdm(range(n_iterations))): | ||
trajectories = sampler.sample_trajectories( | ||
env, | ||
n_trajectories=batch_size, | ||
off_policy=True, | ||
epsilon=exploration_rate, | ||
) | ||
optimizer.zero_grad() | ||
loss = gflownet.loss(env, trajectories) | ||
loss.backward() | ||
optimizer.step() | ||
pbar.set_postfix({"loss": loss.item()}) |