ec4bdbfa4a
Co-authored-by: cswuyg <cswuyg@gmail.com> Co-authored-by: cswuyg <496090217@qq.com> Co-authored-by: Vedant Jhaveri <vedantjh2@gmail.com> Co-authored-by: Vedant Jhaveri <vjhaveri@linkedin.com>
123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
"""Beam search parity acceptance test (executable API spec).
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- Trigger: sampling_params.beam_width = k (> 1); no server-level beam flag.
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- Response: one response per rid; meta_info.beam_results holds the top-n
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sequences (n <= beam_width, default 1 as in HF/OpenAI), best score first.
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- Acceptance: sequence-set overlap vs HF transformers >= 0.8 for k in {2, 10}.
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Manual test (GPU host): python3 test_beam_parity.py
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"""
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import os
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import unittest
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from typing import List
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import requests
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from sglang.srt.utils import kill_process_tree
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from sglang.test.test_utils import (
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DEFAULT_SMALL_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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popen_launch_server,
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)
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PROMPT = "Hello SGLang"
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MAX_NEW_TOKENS = 10
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OVERLAP_THRESHOLD = 0.8
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def get_transformers_beam_sequences(
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model_path: str, prompt: str, beam_width: int, max_new_tokens: int
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) -> List[str]:
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path, dtype="auto")
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model = model.to("cuda" if torch.cuda.is_available() else "cpu")
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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input_length = inputs["input_ids"].shape[1]
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with torch.no_grad():
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generated = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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num_beams=beam_width,
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num_return_sequences=beam_width,
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do_sample=False,
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)
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sequences = [
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tokenizer.decode(seq[input_length:].cpu().tolist(), skip_special_tokens=True)
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for seq in generated
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]
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del model
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torch.cuda.empty_cache()
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return sequences
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class TestBeamParity(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = os.environ.get(
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"SGLANG_TEST_BEAM_MODEL", DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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)
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cls.base_url = DEFAULT_URL_FOR_TEST
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# No beam-specific server flag. Overlap is pinned off so a parity
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# mismatch can only come from the search, not from scheduling.
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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=["--disable-overlap-schedule"],
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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 _generate_beams(self, beam_width, n=None):
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sampling_params = {"beam_width": beam_width, "max_new_tokens": MAX_NEW_TOKENS}
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if n is not None:
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sampling_params["n"] = n
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resp = requests.post(
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f"{self.base_url}/generate",
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json={"text": PROMPT, "sampling_params": sampling_params},
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timeout=120,
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)
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self.assertEqual(resp.status_code, 200, resp.text)
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beam_results = resp.json().get("meta_info", {}).get("beam_results")
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self.assertIsNotNone(beam_results, "response carries no beam_results")
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return beam_results
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def test_parity_vs_transformers(self):
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for beam_width in [2, 10]:
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# n=beam_width to compare the whole beam set; the API default is 1.
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beam_results = self._generate_beams(beam_width, n=beam_width)
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self.assertEqual(len(beam_results), beam_width)
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scores = [r["meta_info"]["sequence_score"] for r in beam_results]
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self.assertEqual(scores, sorted(scores, reverse=True))
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sglang_sequences = {r["text"] for r in beam_results}
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hf_sequences = set(
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get_transformers_beam_sequences(
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self.model, PROMPT, beam_width, MAX_NEW_TOKENS
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)
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)
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overlap = len(sglang_sequences & hf_sequences) / beam_width
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print(f"beam_width={beam_width} overlap={overlap:.2%}")
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self.assertGreaterEqual(overlap, OVERLAP_THRESHOLD)
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def test_return_top_n(self):
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beam_results = self._generate_beams(beam_width=10, n=3)
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self.assertEqual(len(beam_results), 3)
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def test_default_returns_one_sequence(self):
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self.assertEqual(len(self._generate_beams(beam_width=10)), 1)
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if __name__ == "__main__":
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unittest.main()
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