File size: 5,958 Bytes
61ba51e | 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 | # Transformers fallback model tests
import dataclasses
import multiprocessing as mp
import unittest
from types import SimpleNamespace
from typing import List
import torch
from sglang.srt.utils import is_hip, kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.runners import DEFAULT_PROMPTS, SRTRunner, check_close_model_outputs
from sglang.test.test_utils import (
DEFAULT_MODEL_NAME_FOR_TEST,
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
is_in_ci,
popen_launch_server,
)
register_cuda_ci(est_time=245, suite="stage-b-test-small-1-gpu")
register_amd_ci(est_time=320, suite="stage-b-test-small-1-gpu-amd")
class TestTransformersFallbackEndpoint(CustomTestCase):
@classmethod
def setUpClass(cls):
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=["--model-impl", "transformers"],
)
cls.mmlu_lower_bound = 0.65
cls.gsm8k_lower_bound = 0.65
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_mmlu(self):
args = SimpleNamespace(
base_url=self.base_url,
model=self.model,
eval_name="mmlu",
num_examples=64,
num_threads=32,
)
from sglang.test.run_eval import run_eval
metrics = run_eval(args)
self.assertGreaterEqual(metrics["score"], self.mmlu_lower_bound)
def test_gsm8k(self):
args = SimpleNamespace(
num_shots=5,
data_path=None,
num_questions=200,
max_new_tokens=512,
parallel=128,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
from sglang.test.few_shot_gsm8k import run_eval
metrics = run_eval(args)
print(f"{metrics=}")
self.assertGreater(metrics["accuracy"], self.gsm8k_lower_bound)
@unittest.skipIf(is_hip(), "TorchAO int4wo quantization is not supported on AMD GPUs")
class TestTransformersFallbackTorchAO(TestTransformersFallbackEndpoint):
@classmethod
def setUpClass(cls):
cls.model = DEFAULT_MODEL_NAME_FOR_TEST
cls.base_url = DEFAULT_URL_FOR_TEST
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
other_args=[
"--model-impl",
"transformers",
"--torchao-config",
"int4wo-128",
],
)
cls.mmlu_lower_bound = 0.65
cls.gsm8k_lower_bound = 0.65
@dataclasses.dataclass
class ModelCase:
model_path: str
tp_size: int = 1
prefill_tolerance: float = 5e-2
decode_tolerance: float = 5e-2
rouge_l_tolerance: float = 1
skip_long_prompt: bool = False
trust_remote_code: bool = False
torchao_config: str = None
torch_dtype: torch.dtype = torch.float16
# Popular models that run on the CI
CI_MODELS = [
ModelCase(DEFAULT_MODEL_NAME_FOR_TEST),
]
ALL_OTHER_MODELS = [
ModelCase(DEFAULT_MODEL_NAME_FOR_TEST, tp_size=2),
]
class TestTransformersFallbackEngine(CustomTestCase):
@classmethod
def setUpClass(cls):
mp.set_start_method("spawn", force=True)
def assert_close_logits_and_output_strs(
self,
prompts: List[str],
model_case: ModelCase,
) -> None:
model_path = model_case.model_path
max_new_tokens = 32
# force to use transformers impl
with SRTRunner(
model_path,
tp_size=model_case.tp_size,
torch_dtype=model_case.torch_dtype,
model_type="generation",
model_impl="transformers",
trust_remote_code=model_case.trust_remote_code,
torchao_config=model_case.torchao_config,
) as srt_runner:
srt_outputs = srt_runner.forward(prompts, max_new_tokens=max_new_tokens)
with SRTRunner(
model_path,
tp_size=model_case.tp_size,
torch_dtype=model_case.torch_dtype,
model_type="generation",
trust_remote_code=model_case.trust_remote_code,
torchao_config=model_case.torchao_config,
) as srt_runner:
srt_transformers_outputs = srt_runner.forward(
prompts, max_new_tokens=max_new_tokens
)
check_close_model_outputs(
hf_outputs=srt_transformers_outputs,
srt_outputs=srt_outputs,
prefill_tolerance=model_case.prefill_tolerance,
decode_tolerance=model_case.decode_tolerance,
rouge_l_tolerance=model_case.rouge_l_tolerance,
debug_text=f"model_path={model_path} prompts={prompts}",
)
def test_ci_models(self):
for model_case in CI_MODELS:
# Skip long prompts for models that do not have a long context
prompts = DEFAULT_PROMPTS
if model_case.skip_long_prompt:
prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]
# Assert the logits and output strs are close
self.assert_close_logits_and_output_strs(prompts, model_case)
def test_others(self):
if is_in_ci():
return
# Skip long prompts for models that do not have a long context
prompts = DEFAULT_PROMPTS
for model_case in ALL_OTHER_MODELS:
if model_case.skip_long_prompt:
prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000]
# Assert the logits and output strs are close
self.assert_close_logits_and_output_strs(prompts, model_case)
if __name__ == "__main__":
unittest.main()
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