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8c9ba62 | 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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | # -*- coding: utf-8 -*-
"""Test cases for Config modules."""
import datetime
import math
import os
import shutil
import unittest
import torch
from tests.tools import get_template_config, get_unittest_dataset_config
from trinity.common.config import InferenceModelConfig, load_config
CHECKPOINT_ROOT_DIR = os.path.join(os.path.dirname(__file__), "temp_checkpoint_dir")
class TestConfig(unittest.TestCase):
def test_load_default_config(self):
config = get_template_config()
config.buffer.batch_size = 8
config.algorithm.repeat_times = 10
config.model.model_path = "Qwen/Qwen3-1.7B"
config.cluster.gpu_per_node = 8
config.cluster.node_num = 2
config.explorer.rollout_model.engine_num = 2
config.explorer.rollout_model.tensor_parallel_size = 2
config.explorer.auxiliary_models.append(
InferenceModelConfig(model_path="Qwen/Qwen3-32B", tensor_parallel_size=4, engine_num=1),
)
config.check_and_update()
self.assertIsNotNone(config.trainer.trainer_config)
self.assertEqual(config.trainer.trainer_config.trainer.n_gpus_per_node, 8)
self.assertEqual(config.trainer.trainer_config.trainer.nnodes, 1)
self.assertEqual(config.trainer.trainer_config.trainer.project_name, config.project)
self.assertEqual(config.trainer.trainer_config.trainer.experiment_name, config.name)
self.assertEqual(
config.buffer.explorer_input.tasksets[0].repeat_times, config.algorithm.repeat_times
)
self.assertEqual(config.model.model_path, config.model.critic_model_path)
self.assertEqual(config.model.model_path, config.explorer.rollout_model.model_path)
def test_all_examples_are_valid(self):
example_dir = os.path.join(os.path.dirname(__file__), "..", "..", "examples")
for example_name in os.listdir(example_dir):
for filename in os.listdir(os.path.join(example_dir, example_name)):
if filename.endswith(".yaml") and not (
filename.startswith("train_")
or filename.startswith("verl_")
or filename.startswith("dj_")
or filename.startswith("tinker")
):
print(f"Checking config: {filename}")
config_path = os.path.join(example_dir, example_name, filename)
try:
config = load_config(config_path)
config.checkpoint_root_dir = "./.cache/"
config.check_and_update()
except Exception as e:
print(f"Error loading config {config_path}: {e}")
raise e
def test_continue_from_checkpoint_is_valid(self):
config = get_template_config()
config.name = "test"
config.project = "unittest"
config.checkpoint_root_dir = CHECKPOINT_ROOT_DIR
dir_path = os.path.join(config.checkpoint_root_dir, config.project, config.name)
os.makedirs(os.path.join(dir_path, "global_step_1"))
config.continue_from_checkpoint = True
config.check_and_update()
self.assertEqual(config.name, "test")
config.continue_from_checkpoint = False
config.check_and_update()
self.assertTrue(config.name.startswith("test_"))
timestamp = config.name.split("_")[-1]
self.assertTrue(datetime.datetime.strptime(timestamp, "%Y%m%d%H%M%S"))
def test_config_flatten(self):
config = get_template_config()
flat_config = config.flatten()
self.assertIsInstance(flat_config, dict)
for key, value in flat_config.items():
self.assertIsInstance(key, str)
self.assertNotIsInstance(value, dict)
def test_update_config_from_ray_cluster(self):
config = get_template_config()
config.cluster.node_num = None
config.cluster.gpu_per_node = None
config.check_and_update()
self.assertEqual(config.cluster.node_num, 2)
self.assertEqual(config.cluster.gpu_per_node, 2)
def test_default_workflow(self):
config = get_template_config()
config.buffer.explorer_input.default_workflow_type = "simple_workflow"
config.buffer.explorer_input.default_eval_workflow_type = "math_boxed_workflow"
config.buffer.explorer_input.eval_tasksets.append(get_unittest_dataset_config("gsm8k"))
st = get_unittest_dataset_config("countdown")
st.default_workflow_type = None
config.buffer.explorer_input.eval_tasksets.append(st)
config.check_and_update()
self.assertEqual(
config.buffer.explorer_input.eval_tasksets[0].default_workflow_type,
"math_workflow",
)
self.assertEqual(
config.buffer.explorer_input.eval_tasksets[1].default_workflow_type,
"math_boxed_workflow",
)
self.assertEqual(
config.buffer.explorer_input.tasksets[0].default_workflow_type,
"simple_workflow",
)
def test_max_token_len_per_gpu_set_correctly(self):
config = get_template_config()
config.model.max_model_len = 8192
config.trainer.ulysses_sequence_parallel_size = 2
config.trainer.max_token_len_per_gpu = None
config.check_and_update()
self.assertIsNotNone(config.trainer.trainer_config)
expected_max_token_len = math.ceil(
(2 * config.model.max_model_len) / config.trainer.ulysses_sequence_parallel_size
)
self.assertEqual(
config.trainer.trainer_config.actor_rollout_ref.actor.ppo_max_token_len_per_gpu,
expected_max_token_len,
)
self.assertEqual(
config.trainer.trainer_config.actor_rollout_ref.ref.log_prob_max_token_len_per_gpu,
expected_max_token_len,
)
self.assertEqual(
config.trainer.trainer_config.critic.ppo_max_token_len_per_gpu,
expected_max_token_len,
)
def test_optimizer_config_propagation(self):
config = get_template_config()
config.algorithm.optimizer.lr = 1e-4
config.algorithm.optimizer.weight_decay = 0.05
config.algorithm.optimizer.clip_grad = 2.0
config.trainer.total_steps = 1000
config.algorithm.optimizer.lr_scheduler_type = "cosine"
config.algorithm.optimizer.min_lr_ratio = 1e-2
config.check_and_update()
self.assertEqual(config.trainer.trainer_config.actor_rollout_ref.actor.optim.lr, 1e-4)
self.assertEqual(
config.trainer.trainer_config.actor_rollout_ref.actor.optim.weight_decay, 0.05
)
self.assertEqual(config.trainer.trainer_config.actor_rollout_ref.actor.optim.clip_grad, 2.0)
self.assertEqual(
config.trainer.trainer_config.actor_rollout_ref.actor.optim.lr_decay_steps, 1000
)
self.assertEqual(
config.trainer.trainer_config.actor_rollout_ref.actor.optim.lr_decay_style, "cosine"
)
self.assertTrue(
torch.allclose(
torch.tensor(
config.trainer.trainer_config.actor_rollout_ref.actor.optim.lr_warmup_init
),
torch.tensor(1e-6),
)
)
self.assertTrue(
torch.allclose(
torch.tensor(config.trainer.trainer_config.actor_rollout_ref.actor.optim.min_lr),
torch.tensor(1e-6),
)
)
# critic optimizer should not be affected
self.assertEqual(config.trainer.trainer_config.critic.optim.lr, 1e-5)
self.assertEqual(config.trainer.trainer_config.critic.optim.weight_decay, 0.01)
self.assertEqual(config.trainer.trainer_config.critic.optim.lr_decay_style, "constant")
self.assertEqual(config.trainer.trainer_config.critic.optim.clip_grad, 1.0)
def test_chat_template_path(self):
config = get_template_config()
config.model.chat_template_path = "tests/template/custom_chat_template.j2"
config.check_and_update()
self.assertIsNotNone(config.model.custom_chat_template)
self.assertEqual(
config.model.custom_chat_template,
config.buffer.explorer_input.tasksets[0].format.chat_template,
)
self.assertEqual(
config.model.custom_chat_template, config.explorer.rollout_model.chat_template
)
def tearDown(self):
if os.path.exists(CHECKPOINT_ROOT_DIR):
shutil.rmtree(CHECKPOINT_ROOT_DIR, ignore_errors=True)
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