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test_postprocs.py
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test_postprocs.py
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# 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.
import pytest
import torch
from _utils_internal import get_available_devices
from torchrl.collectors.utils import split_trajectories
from torchrl.data.postprocs.postprocs import MultiStep
from torchrl.data.tensordict.tensordict import TensorDict, assert_allclose_td
@pytest.mark.parametrize("n", range(13))
@pytest.mark.parametrize("device", get_available_devices())
@pytest.mark.parametrize("key", ["observation", "pixels", "observation_whatever"])
def test_multistep(n, key, device, T=11):
torch.manual_seed(0)
# mock data
b = 5
done = torch.zeros(b, T, 1, dtype=torch.bool, device=device)
done[0, -1] = True
done[1, -2] = True
done[2, -3] = True
done[3, -4] = True
terminal = done.clone()
terminal[:, -1] = done.sum(1) != 1
mask = done.clone().cumsum(1).cumsum(1) >= 2
mask = ~mask
total_obs = torch.randn(1, T + 1, 1, device=device).expand(b, T + 1, 1)
tensordict = TensorDict(
source={
key: total_obs[:, :T] * mask.to(torch.float),
"next_" + key: total_obs[:, 1:] * mask.to(torch.float),
"done": done,
"reward": torch.randn(1, T, 1, device=device).expand(b, T, 1)
* mask.to(torch.float),
"mask": mask,
},
batch_size=(b, T),
).to(device)
ms = MultiStep(
0.9,
n,
).to(device)
ms_tensordict = ms(tensordict.clone())
assert ms_tensordict.get("done").max() == 1
if n == 0:
assert_allclose_td(tensordict, ms_tensordict.select(*list(tensordict.keys())))
# assert that done at last step is similar to unterminated traj
assert (ms_tensordict.get("gamma")[4] == ms_tensordict.get("gamma")[0]).all()
assert (
ms_tensordict.get("next_" + key)[4] == ms_tensordict.get("next_" + key)[0]
).all()
assert (
ms_tensordict.get("steps_to_next_obs")[4]
== ms_tensordict.get("steps_to_next_obs")[0]
).all()
# check that next obs is properly replaced, or that it is terminated
next_obs = ms_tensordict.get(key)[:, (1 + ms.n_steps_max) :]
true_next_obs = ms_tensordict.get("next_" + key)[:, : -(1 + ms.n_steps_max)]
terminated = ~ms_tensordict.get("nonterminal")
assert ((next_obs == true_next_obs) | terminated[:, (1 + ms.n_steps_max) :]).all()
# test gamma computation
torch.testing.assert_allclose(
ms_tensordict.get("gamma"), ms.gamma ** ms_tensordict.get("steps_to_next_obs")
)
# test reward
if n > 0:
assert (
ms_tensordict.get("reward") != ms_tensordict.get("original_reward")
).any()
else:
assert (
ms_tensordict.get("reward") == ms_tensordict.get("original_reward")
).all()
class TestSplits:
@staticmethod
def create_fake_trajs(
num_workers=32,
traj_len=200,
):
traj_ids = torch.arange(num_workers).unsqueeze(-1)
steps_count = torch.zeros(num_workers).unsqueeze(-1)
workers = torch.arange(num_workers)
out = []
for i in range(traj_len):
done = steps_count == traj_ids # traj_id 0 has 0 steps, 1 has 1 step etc.
td = TensorDict(
source={
"traj_ids": traj_ids,
"a": traj_ids.clone(),
"steps_count": steps_count,
"workers": workers,
"done": done,
},
batch_size=[num_workers],
)
out.append(td.clone())
steps_count += 1
traj_ids[done] = traj_ids.max() + torch.arange(1, done.sum() + 1)
steps_count[done] = 0
out = torch.stack(out, 1)
return out
@pytest.mark.parametrize("num_workers", range(4, 35))
@pytest.mark.parametrize("traj_len", [10, 17, 50, 97, 200])
def test_splits(self, num_workers, traj_len):
trajs = TestSplits.create_fake_trajs(num_workers, traj_len)
assert trajs.shape[0] == num_workers
assert trajs.shape[1] == traj_len
split_trajs = split_trajectories(trajs)
assert split_trajs.shape[0] == split_trajs.get("traj_ids").max() + 1
assert split_trajs.shape[1] == split_trajs.get("steps_count").max() + 1
split_trajs.get("mask").sum() == num_workers * traj_len
assert split_trajs.get("done").sum(1).max() == 1
out_mask = split_trajs[split_trajs.get("mask")]
for i in range(split_trajs.shape[0]):
traj_id_split = split_trajs[i].get("traj_ids")[split_trajs[i].get("mask")]
assert 1 == len(traj_id_split.unique())
for w in range(num_workers):
assert (out_mask.get("workers") == w).sum() == traj_len
# Assert that either the chain is not done XOR if it is it must have the desired length (equal to traj id by design)
for i in range(split_trajs.get("traj_ids").max()):
idx_traj_id = out_mask.get("traj_ids") == i
# (!=) == (xor)
c1 = (idx_traj_id.sum() - 1 == i) and (
out_mask.get("done")[idx_traj_id].sum() == 1
) # option 1: trajectory is complete
c2 = out_mask.get("done")[idx_traj_id].sum() == 0
assert c1 != c2, (
f"traj_len={traj_len}, "
f"num_workers={num_workers}, "
f"traj_id={i}, "
f"idx_traj_id.sum()={idx_traj_id.sum()}, "
f"done={out_mask.get('done')[idx_traj_id].sum()}"
)
assert (
split_trajs.get("traj_ids").unique().numel()
== split_trajs.get("traj_ids").max() + 1
)
if __name__ == "__main__":
pytest.main([__file__, "--capture", "no"])