| import pytest |
| from ragen.llm_agent.ctx_manager import ContextManager |
| from omegaconf import OmegaConf |
| from verl.verl.protocol import DataProto |
|
|
| class DummyTokenizer: |
| name_or_path = "qwen" |
|
|
| def apply_chat_template(self, messages, add_generation_prompt, tokenize): |
| return " ".join([msg["content"] for msg in messages]) |
|
|
| def __call__(self, texts, return_tensors, padding, padding_side, truncation): |
| import torch |
| class DummyOutput: |
| input_ids = torch.tensor([[1, 2, 3]]) |
| attention_mask = torch.tensor([[1, 1, 1]]) |
| return DummyOutput() |
|
|
| def encode(self, text): |
| |
| return [42, 43] |
|
|
| @pytest.fixture |
| def dummy_config(): |
| cfg = OmegaConf.create({ |
| "agent_proxy": { |
| "max_context_window": 2, |
| "enable_think": False, |
| "use_turn_scores": False, |
| "action_sep": "|", |
| "reward_normalization": { |
| "grouping": "batch", |
| "method": "identity" |
| } |
| }, |
| "enable_response_mask": False, |
| "es_manager": { |
| "train": { |
| "env_configs": { |
| "n_groups": [1], |
| "tags": ["sokoban"] |
| }, |
| "group_size": 1 |
| } |
| }, |
| "custom_envs": { |
| "sokoban": { |
| "env_type": "sokoban", |
| "max_actions_per_traj": 10 |
| } |
| }, |
| "actor_rollout_ref": { |
| "rollout": { |
| "response_length": 128 |
| } |
| } |
| }) |
| return cfg |
|
|
| def test_context_window_truncation(dummy_config): |
| tokenizer = DummyTokenizer() |
| ctx = ContextManager(config=dummy_config, tokenizer=tokenizer, mode="train") |
| ctx.prefix_lookup = {0: "Initial prompt"} |
| ctx.env_config_lookup = {0: {"max_tokens": 128}} |
| ctx.env_nums = {"": 1} |
|
|
| env_outputs = [{ |
| "env_id": 0, |
| "group_id": 0, |
| "history": [ |
| {"state": "S1", "llm_response": "R1", "reward": 0.1, "actions_left": 5}, |
| {"state": "S2", "llm_response": "R2", "reward": 0.2, "actions_left": 4}, |
| {"state": "S3", "llm_response": "R3", "reward": 0.3, "actions_left": 3}, |
| ], |
| "metrics": {}, |
| }] |
|
|
| lm_inputs: DataProto = ctx.get_lm_inputs(env_outputs, prepare_for_update=True) |
| messages = lm_inputs.non_tensor_batch["messages_list"][0] |
|
|
| |
| assert "S1" not in str(messages) |
| assert "S2" in str(messages) |
| assert "S3" in str(messages) |
|
|
