122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
import unittest
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import requests
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from sglang.srt.utils import kill_process_tree
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from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
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from sglang.test.kits.eval_accuracy_kit import MMLUMixin
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from sglang.test.test_utils import (
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DEFAULT_MODEL_NAME_FOR_TEST,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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auto_config_device,
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get_benchmark_args,
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is_in_amd_ci,
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is_in_ci,
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popen_launch_server,
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run_benchmark,
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write_github_step_summary,
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)
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register_cuda_ci(est_time=220, stage="base-b", runner_config="1-gpu-large")
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register_amd_ci(est_time=355, suite="stage-b-test-1-gpu-small-amd")
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class TestMultiTokenizer(CustomTestCase, MMLUMixin):
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"""One server covering both worker pools: multi-tokenizer and
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multi-detokenizer (the flags are orthogonal)."""
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mmlu_score_threshold = 0.64
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mmlu_num_examples = 256
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mmlu_num_threads = 32
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@classmethod
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def setUpClass(cls):
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cls.model = DEFAULT_MODEL_NAME_FOR_TEST
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=[
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"--tokenizer-worker-num",
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8,
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"--detokenizer-worker-num",
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4,
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"--mem-fraction-static",
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0.7,
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],
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)
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@classmethod
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def tearDownClass(cls):
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kill_process_tree(cls.process.pid)
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def test_multi_tokenizer_ttft(self):
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# from test_bench_serving.py run_bench_serving
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args = get_benchmark_args(
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base_url=self.base_url,
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dataset_name="random",
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dataset_path="",
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tokenizer=None,
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num_prompts=100,
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random_input_len=4096,
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random_output_len=2048,
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sharegpt_context_len=None,
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request_rate=1,
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disable_stream=False,
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disable_ignore_eos=False,
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seed=0,
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device=auto_config_device(),
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lora_name=None,
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)
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res = run_benchmark(args)
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if is_in_ci():
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write_github_step_summary(
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f"### test_multi_tokenizer_ttft\n"
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f"median_e2e_latency_ms: {res['median_e2e_latency_ms']:.2f} ms\n"
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)
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self.assertLess(res["median_e2e_latency_ms"], 11000)
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# relax for mi300x
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self.assertLess(res["median_ttft_ms"], 130 if is_in_amd_ci() else 86)
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self.assertLess(res["median_itl_ms"], 10)
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def test_batch_input_ids_routing(self):
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# Regression guard for sgl-project/sglang#29878 (introduced by #29214).
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#
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# A batch of pre-tokenized `input_ids` (no text / multimodal) is the one
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# case that takes the batch-tokenization path (_send_batch_request ->
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# BatchTokenizedGenerateReqInput). In multi-tokenizer mode this batch
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# must stamp each sub-request's `http_worker_ipc` so the scheduler can
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# route every reply back to its owning tokenizer worker. If it is missing,
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# the requests hang forever.
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#
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# The existing ttft test only sends *text*, so it never exercises this
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# path — this case does, and uses a short timeout so a routing hang
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# fails fast instead of stalling until the server launch timeout.
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batch_input_ids = [
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[1, 2, 3, 4, 5],
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[10, 11, 12, 13, 14],
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[20, 21, 22, 23, 24],
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[30, 31, 32, 33, 34],
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]
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response = requests.post(
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self.base_url + "/generate",
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json={
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"input_ids": batch_input_ids,
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"sampling_params": {"max_new_tokens": 8, "temperature": 0},
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},
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timeout=60,
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)
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self.assertEqual(response.status_code, 200, response.text)
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results = response.json()
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# Every batched request must get its reply routed back — not hang.
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self.assertEqual(len(results), len(batch_input_ids))
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for result in results:
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self.assertIn("text", result)
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if __name__ == "__main__":
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unittest.main()
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