Dear authors, thanks for sharing the code for this wonderful work!

I am currently trying to run the naive gflownet training code in molecular docking setting by running python gflownet.py under the mols directory. I have unzipped the datasets and have all requirements installed. And I have successfully run the model in the toy grid environment.

However, I got this error when I run in the mols environment:

Exception while sampling: tensors used as indices must be long, byte or bool tensors

And when I further look up, it seems like the problem occurs around the line 70 in model_block.py. I tried to print out the stem_block_batch_idx but it doesn't seems like could be transfered to long type directly, which is required by an index:

tensor([[-8.4156e-02, -4.2767e-02, -7.2483e-02, -3.3011e-02, -1.1865e-02, 2.0981e-03, 1.3293e-02, -7.3515e-03, -4.1853e-02, 2.1048e-02, 3.8597e-02, -1.5558e-02, 2.1581e-02, 4.9257e-03, 9.5167e-02, 4.0965e-02, 2.0146e-02, -5.5610e-02, -3.5318e-02, -3.1394e-02, 7.2078e-02, 1.8894e-02, -3.0249e-02, 2.9740e-02, 5.6950e-02, -3.8425e-02, 2.8620e-02, 9.2052e-02, -8.5357e-03, 1.6788e-02, 7.7801e-02, -4.2119e-02, 1.3606e-02, 7.5316e-02, 4.7131e-02, -4.3429e-03, 1.4157e-04, 2.0939e-02, -2.3499e-02, -6.5888e-02, -2.8960e-02, 3.1548e-02, -9.2680e-03, 5.4192e-02, -9.6579e-03, 2.0602e-02, 1.8935e-02, 4.1228e-03, -6.3467e-02, 3.6747e-02, 1.4168e-02, -6.1473e-03, -1.9472e-02, -3.3970e-02, -5.7308e-03, -4.6021e-02, -3.8956e-02, 4.7375e-02, -8.4562e-02, -1.0087e-02, 2.0478e-02, -6.8286e-02, 5.4663e-02, -5.1468e-02, 1.2617e-02, 2.4625e-02, 5.2167e-02, 5.7779e-02, -5.7788e-02, -1.3323e-02, 1.3913e-02, -7.4439e-02, -4.0981e-02, 5.0797e-02, -5.6230e-02, -5.0963e-02, -5.5488e-02, -2.7339e-02, 1.0469e-02, 3.4695e-02, -3.2623e-02, 7.6694e-03, -5.8748e-03, 7.0495e-02, -2.2805e-02, -5.4334e-03, -2.1636e-02, 1.9597e-02, 6.2370e-02, -2.4995e-02, 1.6165e-02, -4.6878e-03, 2.9743e-02, 1.2653e-02, -5.4271e-02, 1.1247e-02, -3.8340e-03, -4.7489e-02, 1.5719e-02, 3.2552e-02, 6.0665e-02, -1.2330e-02, 2.6115e-02, -2.7376e-02, 3.4152e-02, -1.0086e-02, -2.4257e-02, 3.2202e-02, -3.2659e-02, 8.6094e-02, -3.1996e-02, 7.8751e-02, 4.5367e-02, -3.8693e-02, -3.6531e-02, 6.7311e-03, 3.2884e-02, -3.2774e-02, -3.8855e-02, 2.8814e-02, 4.3942e-02, -1.3374e-02, 3.0905e-02, -7.0064e-02, -5.7230e-03, 4.5093e-02, 3.8167e-02, -3.0602e-02, -4.0387e-02, -1.5985e-02, -9.5962e-02, -1.1354e-02, 2.0879e-02, 1.4092e-02, -3.8405e-02, 1.4337e-02, -6.0682e-02, -9.0190e-03, -5.0898e-02, -4.7344e-02, 4.1045e-02, -6.7031e-02, 8.8112e-02, 3.2149e-02, 3.7748e-02, -4.0757e-02, 1.4378e-02, -1.0749e-01, 6.1679e-02, -6.7268e-03, -2.7889e-02, -5.9315e-02, -5.5883e-02, -2.6489e-02, 7.3640e-02, 1.8273e-02, -5.2330e-02, -7.7003e-05, 6.8413e-04, -1.4364e-01, -1.9389e-02, 4.5649e-02, -4.0468e-02, -4.2819e-02, 4.5874e-02, -1.6481e-02, 1.2627e-02, -8.4941e-02, -3.7458e-02, 2.1359e-02, -9.2863e-02, -3.4932e-03, 7.1990e-02, 6.2144e-02, 8.1462e-02, -2.0569e-02, 5.9194e-02, 1.6996e-03, 8.0618e-03, 6.1753e-02, 4.1602e-02, 1.0910e-02, 2.0523e-02, -9.9781e-04, 1.9131e-02, -1.0267e-02, -9.4474e-02, -3.5725e-02, 9.9953e-03, -4.3195e-02, -7.9051e-02, -3.1881e-02, 9.2158e-03, -9.6167e-04, -2.7508e-02, 7.1478e-02, -5.4107e-02, 8.0026e-02, -1.8887e-02, 4.6941e-02, 6.5166e-02, 1.2000e-02, 3.9906e-02, -2.8206e-02, 3.7483e-02, 3.5408e-02, -2.5863e-02, 2.3528e-02, 7.1814e-03, 8.0863e-02, -1.3736e-02, -8.5978e-02, -4.1238e-02, -1.2545e-02, 5.5479e-02, 7.3487e-03, 8.9125e-02, -3.4814e-02, -4.5358e-02, 4.9893e-02, 3.5286e-02, 3.2084e-02, 5.0868e-02, 2.3549e-02, -9.2907e-02, -6.9315e-03, -1.3088e-02, 8.7066e-02, 1.1554e-02, 1.3771e-02, -1.7489e-02, -5.2921e-02, 9.2110e-03, 1.6766e-02, 4.8030e-02, 1.4481e-02, 2.9254e-03, 3.5795e-02, 1.0397e-01, -2.0675e-03, -2.9916e-02, -5.3299e-02, -2.1396e-02, -5.3189e-02, 3.2805e-02, -2.6538e-03, -2.6352e-02, -1.2823e-02, 6.1972e-02, 5.4822e-02, 4.5579e-02, -3.6638e-02, 8.1013e-03, -5.6014e-02, 1.5187e-02, -6.5561e-02]], device='cuda:0', dtype=torch.float64, grad_fn=)

I wonder if I am running the code in the correct way. Is this index correct and if so, do you know what's happening?

Hi there,

Thank you very much for sharing the source codes.

For reproducibility, I modified the codes as follows,

https://github.com/GFNOrg/gflownet/blob/831a6989d1abd5c05123ec84654fb08629d9bc38/mols/gflownet.py#L84

---> self.train_rng = np.random.RandomState(142857)

as well as to add

torch.manual_seed(142857)
torch.cuda.manual_seed(142857)
torch.cuda.manual_seed_all(142857)

However, I encountered an issue. I ran it more than 3 times with the same random seed, but the results are totally different (although they are close). I didn't modify other parts, except for addressing package compatibility issues.

0 [1152.62, 112.939, 23.232] 100 [460.257, 44.253, 17.728] 200 [68.114, 6.007, 8.045]

0 [1151.024, 112.603, 24.993] 100 [471.219, 45.525, 15.964] 200 [66.349, 6.174, 4.607]

0 [1263.066, 124.094, 22.128] 100 [467.747, 44.899, 18.76] 200 [61.992, 5.715, 4.841]

I am wondering whether you encountered such an issue before.

Best,

Dong

Hello, I'm a bit confused where this reward function comes from: https://github.com/GFNOrg/gflownet/blob/831a6989d1abd5c05123ec84654fb08629d9bc38/grid/toy_grid_dag.py#L97

My understanding is that the reward should be as defined in the paper (https://i.samkg.dev/2233/firefox_xGnEaZVBlN.png) - are these two equivalent in some way?

Hi there!

Thanks for sharing the code and just wanted to say I've enjoyed your paper. I was reading your code and noticed that there might be a subtle bug in the grid-env dag script. I might also have read it wrong...

https://github.com/bengioe/gflownet/blob/dddfbc522255faa5d6a76249633c94a54962cbcb/grid/toy_grid_dag.py#L316-L320

On line 316, we zip two things: zip([e for d, e in zip(done, self.envs) if not d], acts)

Here done is a vector of bools of length batch-size, self.envs is a list of GridEnv of length n-envs or buffer-size, and acts is a vector of ints of length (n-envs or buffer-size,).

By default, all the lengths of the above objects should be 16.

I was reading through the code, and noticed that if any of the elements in done are True, then on line 316 we filter them out with if not d. If env[0] was "done", then we would have a list of 15 envs, basically self.envs[1:]. Then when you zip up the actions and the shorter list envs, the actions will be aligned incorrectly... We will basically end up with self.envs[1:] being aligned to actions act[:-1]. As a result, step is now length 15, and on the next line, we again line up the incorrect actions of length 16 with our step list of length 16.

Perhaps we need to filter act based on the done vector? E.g act = act[done] after line 316?

Maybe I've got this wrong, so apologies for the noise if that's the case, but thought I'd leave a note in case what I'm suggesting is the case.

All the best!

Hello,

First, I really enjoyed reading the paper. Amazing work!

I have a question regarding the number of building blocks used for generating small molecules. Appendix A.3 of the paper states that there are a total of 105 unique building blocks (after accounting for different attachment points) and that they were obtained by the process suggested by the JT-VAE paper. (Jin et al. (2020)). However, in the JT-VAE paper, the total vocabulary size is $|\chi|=780$ obtained from the same ZINC dataset. My understanding is they are both the same. If that is correct, why are the number of building blocks different here? What am I missing? If they are not the same, can you please explain the difference?

Thank you so much for your help