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4 changes: 3 additions & 1 deletion nemo_rl/experience/rollouts.py
Original file line number Diff line number Diff line change
Expand Up @@ -311,7 +311,9 @@ def run_multi_turn_rollout(
>= max_seq_len
):
# truncate
tokenized_obs = tokenized_obs[: max_seq_len - active_input_lengths[i]]
tokenized_obs = tokenized_obs[
: max_seq_len - (len(generated_ids[i]) + active_input_lengths[i])
]
truncation_mask[i] = True
# Record truncation
sample_truncated[active_indices[i]] = True
Expand Down
40 changes: 40 additions & 0 deletions tests/unit/experience/test_rollouts.py
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
import torch
from transformers import AutoTokenizer

from nemo_rl.data.llm_message_utils import batched_message_log_to_flat_message
from nemo_rl.distributed.batched_data_dict import BatchedDataDict
from nemo_rl.distributed.virtual_cluster import RayVirtualCluster
from nemo_rl.environments.games.sliding_puzzle import (
Expand Down Expand Up @@ -440,6 +441,45 @@ def test_run_multi_step_calculator_vllm(multi_step_setup_vllm):
print("\nMulti-Step Calculator VLLM Test assertions passed.")


@pytest.mark.skipif(
not torch.cuda.is_available() or torch.cuda.device_count() < 1,
reason="VLLM test requires at least 1 GPU",
)
def test_max_seqlen_respected(multi_step_setup_vllm):
"""Tests multi-step calculator rollout with VllmGeneration."""
vllm_generation, rollout_tokenizer, task_to_env, initial_batch, rollout_cluster = (
multi_step_setup_vllm
)
max_rollout_turns = initial_batch["extra_env_info"][0]["max_steps"] + 1
max_seq_len = 290

print("\nRunning multi-step calculator rollout (VLLM)...")
vllm_generation.prepare_for_generation()
final_batch, rollout_metrics = run_multi_turn_rollout(
policy_generation=vllm_generation,
input_batch=initial_batch,
tokenizer=rollout_tokenizer,
task_to_env=task_to_env,
max_seq_len=max_seq_len,
max_rollout_turns=max_rollout_turns,
)
vllm_generation.finish_generation()
print("Multi-step calculator rollout complete (VLLM).")

# --- Assertions ---
assert isinstance(final_batch, BatchedDataDict)
assert "message_log" in final_batch
assert "total_reward" in final_batch
assert len(final_batch["message_log"]) == len(initial_batch["message_log"])
flattened_message_log, _ = batched_message_log_to_flat_message(
final_batch["message_log"]
)
# Check that the sequence length is respected by flattening the message log and checking the length
assert len(flattened_message_log["token_ids"][0]) == max_seq_len, (
f"Sequence length {len(flattened_message_log['token_ids'][0])} is not equal to max_seq_len {max_seq_len}"
)


# --- Fixture for Sliding Puzzle Environment ---
@pytest.fixture(scope="function")
def sliding_puzzle_environment(rollout_cluster):
Expand Down