[AMD] [GLM-5 Day 0] Add GLM-5 nightly test (#18911)
This commit is contained in:
Michael
@@ -0,0 +1,244 @@
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"""AMD GLM-5 GSM8K Completion Evaluation Test (8-GPU)
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Tests GLM-5 with NSA attention backend using few-shot completion
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benchmark on MI325/MI300X.
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Registry: nightly-amd-accuracy-8-gpu-glm5 suite
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"""
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import ast
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import os
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import re
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import time
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import unittest
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from dataclasses import dataclass, field
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from typing import List, Optional, Tuple
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import numpy as np
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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
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from sglang.test.test_utils import (
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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is_in_ci,
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popen_launch_server,
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write_github_step_summary,
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)
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from sglang.utils import download_and_cache_file, read_jsonl
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# Register for AMD CI - GLM-5 accuracy test (~60 min)
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register_amd_ci(
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est_time=3600,
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suite="nightly-amd-accuracy-8-gpu-glm5",
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nightly=True,
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)
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INVALID = -9999999
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@dataclass
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class ModelConfig:
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"""Configuration for a model to test."""
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model_path: str
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tp_size: int = 8
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accuracy_threshold: float = 0.50
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other_args: List[str] = field(default_factory=list)
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env_vars: dict = field(default_factory=dict)
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timeout: Optional[int] = None
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variant: Optional[str] = None
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def get_display_name(self) -> str:
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if self.variant:
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return f"{self.model_path} ({self.variant})"
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return self.model_path
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# GLM-5 models for MI325/MI300X - NSA attention backend
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GLM5_MODELS = [
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# GLM-5 with NSA attention (TP=8)
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ModelConfig(
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model_path="zai-org/GLM-5",
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tp_size=8,
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accuracy_threshold=0.93,
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timeout=3600,
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variant="nsa",
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other_args=[
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"--trust-remote-code",
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"--nsa-prefill-backend",
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"tilelang",
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"--nsa-decode-backend",
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"tilelang",
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"--chunked-prefill-size",
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"131072",
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"--mem-fraction-static",
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"0.80",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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"--watchdog-timeout",
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"1200", # 20 minutes for weight loading
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],
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env_vars={"SGLANG_USE_AITER": "1"},
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),
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]
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def get_one_example(lines, i, include_answer):
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"""Format a single GSM8K example."""
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ret = "Question: " + lines[i]["question"] + "\nAnswer:"
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if include_answer:
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ret += " " + lines[i]["answer"]
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return ret
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def get_few_shot_examples(lines, k):
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"""Get k few-shot examples for prompting."""
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ret = ""
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for i in range(k):
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ret += get_one_example(lines, i, True) + "\n\n"
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return ret
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def get_answer_value(answer_str):
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"""Extract numerical answer from response."""
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answer_str = answer_str.replace(",", "")
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numbers = re.findall(r"\d+", answer_str)
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if len(numbers) < 1:
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return INVALID
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try:
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return ast.literal_eval(numbers[-1])
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except SyntaxError:
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return INVALID
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def run_gsm8k_benchmark(
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base_url: str,
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num_questions: int = 200,
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num_shots: int = 5,
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parallel: int = 64,
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) -> Tuple[float, float, float]:
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"""Run GSM8K few-shot completion benchmark."""
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import sglang as sgl
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from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
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url = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl"
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data_path = download_and_cache_file(url)
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lines = list(read_jsonl(data_path))
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few_shot_examples = get_few_shot_examples(lines, num_shots)
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questions = []
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labels = []
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for i in range(len(lines[:num_questions])):
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questions.append(get_one_example(lines, i, False))
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labels.append(get_answer_value(lines[i]["answer"]))
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assert all(l != INVALID for l in labels)
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arguments = [{"question": q} for q in questions]
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@sgl.function
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def few_shot_gsm8k(s, question):
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s += few_shot_examples + question
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s += sgl.gen(
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"answer", max_tokens=512, stop=["Question", "Assistant:", "<|separator|>"]
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)
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backend = RuntimeEndpoint(base_url)
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sgl.set_default_backend(backend)
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tic = time.perf_counter()
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states = few_shot_gsm8k.run_batch(
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arguments, temperature=0, num_threads=parallel, progress_bar=True
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)
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latency = time.perf_counter() - tic
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preds = [get_answer_value(states[i]["answer"]) for i in range(len(states))]
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acc = np.mean(np.array(preds) == np.array(labels))
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invalid = np.mean(np.array(preds) == INVALID)
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return float(acc), float(invalid), float(latency)
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class TestGLM5EvalAMD(unittest.TestCase):
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"""GLM-5 GSM8K Completion Evaluation Test for AMD MI325/MI300X."""
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@classmethod
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def setUpClass(cls):
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cls.models = GLM5_MODELS
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.num_questions = int(os.environ.get("GSM8K_NUM_QUESTIONS", "200"))
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def test_glm5_accuracy(self):
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"""Test GLM-5 models with GSM8K completion benchmark."""
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all_results = []
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summary = "### GLM-5 Models (MI325)\n\n"
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summary += "| Model | Variant | TP | Accuracy | Threshold | Status |\n"
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summary += "| ----- | ------- | -- | -------- | --------- | ------ |\n"
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for config in self.models:
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display_name = config.get_display_name()
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with self.subTest(model=display_name):
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print(f"\n{'='*60}")
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print(f"Testing: {display_name}")
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print(f"{'='*60}")
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env = os.environ.copy()
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for key, value in config.env_vars.items():
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env[key] = value
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other_args = list(config.other_args)
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other_args.extend(["--tp", str(config.tp_size)])
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timeout = config.timeout or DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH
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try:
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process = popen_launch_server(
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model=config.model_path,
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base_url=self.base_url,
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timeout=timeout,
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other_args=other_args,
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env=env,
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)
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try:
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acc, invalid, latency = run_gsm8k_benchmark(
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self.base_url, num_questions=self.num_questions
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)
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passed = acc >= config.accuracy_threshold
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status = "✅ PASS" if passed else "❌ FAIL"
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print(
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f" accuracy={acc:.3f} threshold={config.accuracy_threshold} {status}"
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)
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all_results.append(
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{
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"model": display_name,
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"accuracy": acc,
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"passed": passed,
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}
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)
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summary += f"| {config.model_path} | {config.variant or 'N/A'} | {config.tp_size} | {acc:.3f} | {config.accuracy_threshold} | {status} |\n"
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finally:
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kill_process_tree(process.pid)
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except Exception as e:
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summary += f"| {config.model_path} | {config.variant or 'N/A'} | {config.tp_size} | N/A | {config.accuracy_threshold} | ❌ ERROR |\n"
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all_results.append(
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{
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"model": display_name,
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"accuracy": None,
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"passed": False,
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"error": str(e),
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}
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)
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if is_in_ci():
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write_github_step_summary(summary)
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failed = [r for r in all_results if not r["passed"]]
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if failed:
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raise AssertionError(f"Failed models: {[r['model'] for r in failed]}")
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if __name__ == "__main__":
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unittest.main()
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@@ -42,7 +42,7 @@ MODEL_SCORE_THRESHOLDS = {
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"meta-llama/Llama-3.2-3B-Instruct": 0.55,
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# Mistral series
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"mistralai/Mistral-7B-Instruct-v0.3": 0.55,
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"mistralai/Mixtral-8x7B-Instruct-v0.1": 0.61,
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"mistralai/Mixtral-8x7B-Instruct-v0.1": 0.58,
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# DeepSeek series
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"deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct": 0.85,
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# Qwen2 series
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@@ -0,0 +1,249 @@
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"""MI35x GLM-5 GSM8K Completion Evaluation Test (8-GPU)
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Tests GLM-5 with NSA attention backend using few-shot completion
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benchmark on MI35x.
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Registry: nightly-amd-8-gpu-mi35x-glm5 suite
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"""
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import ast
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import os
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# Set HF cache for MI35x
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os.environ.setdefault("HF_HOME", "/data2/models/huggingface")
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os.environ.setdefault("HF_HUB_CACHE", "/data2/models/huggingface/hub")
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import re
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import time
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import unittest
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from dataclasses import dataclass, field
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from typing import List, Optional, Tuple
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import numpy as np
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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
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from sglang.test.test_utils import (
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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is_in_ci,
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popen_launch_server,
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write_github_step_summary,
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)
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from sglang.utils import download_and_cache_file, read_jsonl
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# Register for AMD CI - MI35x GLM-5 accuracy test (~90 min)
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register_amd_ci(
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est_time=5400,
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suite="nightly-amd-8-gpu-mi35x-glm5",
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nightly=True,
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)
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INVALID = -9999999
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@dataclass
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class ModelConfig:
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"""Configuration for a model to test."""
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model_path: str
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tp_size: int = 8
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accuracy_threshold: float = 0.50
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other_args: List[str] = field(default_factory=list)
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env_vars: dict = field(default_factory=dict)
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timeout: Optional[int] = None
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variant: Optional[str] = None
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def get_display_name(self) -> str:
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if self.variant:
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return f"{self.model_path} ({self.variant})"
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return self.model_path
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# GLM-5 models for MI35x - NSA attention backend
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MI35X_GLM5_MODELS = [
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# GLM-5 with NSA attention (TP=8)
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ModelConfig(
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model_path="zai-org/GLM-5",
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tp_size=8,
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accuracy_threshold=0.93,
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timeout=5400,
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variant="nsa",
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other_args=[
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"--trust-remote-code",
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"--nsa-prefill-backend",
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"tilelang",
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"--nsa-decode-backend",
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"tilelang",
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"--chunked-prefill-size",
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"131072",
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"--mem-fraction-static",
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"0.80",
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"--model-loader-extra-config",
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'{"enable_multithread_load": true}',
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"--watchdog-timeout",
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"1200", # 20 minutes for weight loading
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],
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env_vars={},
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),
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]
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def get_one_example(lines, i, include_answer):
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"""Format a single GSM8K example."""
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ret = "Question: " + lines[i]["question"] + "\nAnswer:"
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if include_answer:
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ret += " " + lines[i]["answer"]
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return ret
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def get_few_shot_examples(lines, k):
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"""Get k few-shot examples for prompting."""
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ret = ""
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for i in range(k):
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ret += get_one_example(lines, i, True) + "\n\n"
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return ret
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def get_answer_value(answer_str):
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"""Extract numerical answer from response."""
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answer_str = answer_str.replace(",", "")
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numbers = re.findall(r"\d+", answer_str)
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if len(numbers) < 1:
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return INVALID
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try:
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return ast.literal_eval(numbers[-1])
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except SyntaxError:
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return INVALID
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def run_gsm8k_benchmark(
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base_url: str,
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num_questions: int = 200,
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num_shots: int = 5,
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parallel: int = 64,
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) -> Tuple[float, float, float]:
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"""Run GSM8K few-shot completion benchmark."""
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import sglang as sgl
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from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
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url = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl"
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data_path = download_and_cache_file(url)
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lines = list(read_jsonl(data_path))
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few_shot_examples = get_few_shot_examples(lines, num_shots)
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questions = []
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labels = []
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for i in range(len(lines[:num_questions])):
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questions.append(get_one_example(lines, i, False))
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labels.append(get_answer_value(lines[i]["answer"]))
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assert all(l != INVALID for l in labels)
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arguments = [{"question": q} for q in questions]
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@sgl.function
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def few_shot_gsm8k(s, question):
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s += few_shot_examples + question
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s += sgl.gen(
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"answer", max_tokens=512, stop=["Question", "Assistant:", "<|separator|>"]
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)
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backend = RuntimeEndpoint(base_url)
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sgl.set_default_backend(backend)
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tic = time.perf_counter()
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states = few_shot_gsm8k.run_batch(
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arguments, temperature=0, num_threads=parallel, progress_bar=True
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)
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latency = time.perf_counter() - tic
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preds = [get_answer_value(states[i]["answer"]) for i in range(len(states))]
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acc = np.mean(np.array(preds) == np.array(labels))
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invalid = np.mean(np.array(preds) == INVALID)
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return float(acc), float(invalid), float(latency)
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class TestGLM5EvalMI35x(unittest.TestCase):
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"""GLM-5 GSM8K Completion Evaluation Test for AMD MI35x."""
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@classmethod
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def setUpClass(cls):
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cls.models = MI35X_GLM5_MODELS
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.num_questions = int(os.environ.get("GSM8K_NUM_QUESTIONS", "200"))
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def test_glm5_accuracy(self):
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"""Test GLM-5 models with GSM8K completion benchmark."""
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all_results = []
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summary = "### GLM-5 Models (MI35x)\n\n"
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summary += "| Model | Variant | TP | Accuracy | Threshold | Status |\n"
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summary += "| ----- | ------- | -- | -------- | --------- | ------ |\n"
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for config in self.models:
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display_name = config.get_display_name()
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with self.subTest(model=display_name):
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print(f"\n{'='*60}")
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print(f"Testing: {display_name}")
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print(f"{'='*60}")
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env = os.environ.copy()
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for key, value in config.env_vars.items():
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env[key] = value
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|
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other_args = list(config.other_args)
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other_args.extend(["--tp", str(config.tp_size)])
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timeout = config.timeout or DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH
|
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|
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try:
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process = popen_launch_server(
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model=config.model_path,
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base_url=self.base_url,
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timeout=timeout,
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other_args=other_args,
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env=env,
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)
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try:
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acc, invalid, latency = run_gsm8k_benchmark(
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self.base_url, num_questions=self.num_questions
|
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)
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passed = acc >= config.accuracy_threshold
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status = "✅ PASS" if passed else "❌ FAIL"
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print(
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f" accuracy={acc:.3f} threshold={config.accuracy_threshold} {status}"
|
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)
|
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|
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all_results.append(
|
||||
{
|
||||
"model": display_name,
|
||||
"accuracy": acc,
|
||||
"passed": passed,
|
||||
}
|
||||
)
|
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summary += f"| {config.model_path} | {config.variant or 'N/A'} | {config.tp_size} | {acc:.3f} | {config.accuracy_threshold} | {status} |\n"
|
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|
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finally:
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kill_process_tree(process.pid)
|
||||
|
||||
except Exception as e:
|
||||
summary += f"| {config.model_path} | {config.variant or 'N/A'} | {config.tp_size} | N/A | {config.accuracy_threshold} | ❌ ERROR |\n"
|
||||
all_results.append(
|
||||
{
|
||||
"model": display_name,
|
||||
"accuracy": None,
|
||||
"passed": False,
|
||||
"error": str(e),
|
||||
}
|
||||
)
|
||||
|
||||
if is_in_ci():
|
||||
write_github_step_summary(summary)
|
||||
|
||||
failed = [r for r in all_results if not r["passed"]]
|
||||
if failed:
|
||||
raise AssertionError(f"Failed models: {[r['model'] for r in failed]}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user