[BUG] Collectors of batched environemnts return more frames than requested

[matteobettini]

Describe the bug

The collectors currently force the actual collected frames_per_batch to be divisible by the number of batched environments (which can be collector workers or parallel workers)(if looking at #828 this could also be vectorized dimensions in the batch size).

This leads to the user feeding a desired frames_per_batch at collector creation, and actually getting more frames than requested. As you can see in the following example:

gym_env = lambda: GymEnv("Pendulum-v1", device="cpu")
gym_parallel_env = lambda: ParallelEnv(10, gym_env)

pendulum_policy = TensorDictModule(
    nn.Linear(3, 1), in_keys=["observation"], out_keys=["action"]
)

coll = SyncDataCollector(
    gym_parallel_env,
    pendulum_policy,
    total_frames=20000,
    max_frames_per_traj=5,
    frames_per_batch=145,
    split_trajs=False,
)

for data in coll:
    print("Ending", data) # batch_size=torch.Size([10, 15]) aka 150 frames
    break

Which is caused for example by code like this:

self.frames_per_batch = -(-frames_per_batch // self.n_env)

This behavior might be dangerous for some users which might think that at each iteration they are training on x frames and instead they are training on x+y frames.

Solutions

1. Throw an error if the frames_per_batch is not divisible by the number of batched envs
2. Throw a warning if the frames_per_batch is not divisible by the number of batched envs
3. Find a way to return only the requested amount of frames through discarding some of the collected data

[vmoens]

Makes sense, I'll add that to my collector refactoring

[matteobettini]

#828 introduces warnings for this, but we would like to turn those into errors to resolve this issue