[BUG] RuntimeError when passing dialogue data to LLMEnv

[albertbou92]

Describe the bug

I see very cool advancements in the direction of LLM RL training in the repo, awesome work! :)

After playing a bit with the LLMEnv I got the following error when passing dialogue data to the env.

RuntimeError: modifying the batch size of a lazy representation of a tensordict is not permitted. Consider instantiating the tensordict first by calling td = td.to_tensordict() before resetting the batch size.

Dialogue data is a pretty common format when using LLMs to allow the model to see inputs from the system and the user, and you can easily format the data into a single string by using different chat templates. Not sure if the intention is to support dialogue data in the format I am passing it to the env, but I feel like it would be convenient.

I detected that the bug goes away if I comment out this line, but I do not think this is the solution.
https://github.com/pytorch/rl/blob/main/torchrl/envs/transforms/llm.py#L534

To Reproduce

from datasets import Dataset
from torch.utils.data import DataLoader
from torchrl.envs.custom.llm import LLMEnv


def collate_fn(batch: list[dict]) -> dict[str, list]:
    return {k: [el[k] for el in batch] for k in batch[0]}

# Dummy data
sample = {
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the capital of France?"}
    ]
}


# Repeat the sample 1000 times
data = [sample] * 1000

# Create the dataset
dataset = Dataset.from_list(data)

# Create a PyTorch DataLoader
batch_size = 16
dataloader = DataLoader(
    dataset,
    batch_size=batch_size,
    shuffle=True,
    collate_fn=collate_fn
)

env = LLMEnv.from_dataloader(
    dataloader=dataloader,
    str2str=True,
    batch_size=4,
    str_key='messages'
)

obs = env.reset()

Additional context

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Reason and Possible fixes

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[vmoens]

Hello @albertbou92
Glad you like it!
I'm going to work on a version of this env that works fine with these kinds of data structures, something along the line of this:

@tensorclass
class History:
    role: str
    content: str

Then we need an env that stacks histories together and can call the parser from the HF tokenizer.

I already have a prototype working with MLGym, I'll ping you once I merge it in torchrl (should be pretty soon!)