File size: 2,706 Bytes
70a7f67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | 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" # or "llama-3" or any string your code expects
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 a dummy list of token ids; must be at least length 1 for [0] indexing
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} # For metrics
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]
# Ensure only last 2 turns are present
assert "S1" not in str(messages)
assert "S2" in str(messages)
assert "S3" in str(messages)
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