"""MI35x GLM-5 GSM8K Completion Evaluation Test (8-GPU) Tests GLM-5 with NSA attention backend using few-shot completion benchmark on MI35x. Registry: nightly-amd-8-gpu-mi35x-glm5 suite """ import ast import os # Set HF cache for MI35x os.environ.setdefault("HF_HOME", "/data2/models/huggingface") os.environ.setdefault("HF_HUB_CACHE", "/data2/models/huggingface/hub") import re import time import unittest from dataclasses import dataclass, field from typing import List, Optional, Tuple import numpy as np from sglang.srt.utils import kill_process_tree from sglang.test.ci.ci_register import register_amd_ci from sglang.test.test_utils import ( DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, DEFAULT_URL_FOR_TEST, is_in_ci, popen_launch_server, write_github_step_summary, ) from sglang.utils import download_and_cache_file, read_jsonl # Register for AMD CI - MI35x GLM-5 accuracy test (~90 min) register_amd_ci( est_time=5400, suite="nightly-amd-8-gpu-mi35x-glm5", nightly=True, ) INVALID = -9999999 @dataclass class ModelConfig: """Configuration for a model to test.""" model_path: str tp_size: int = 8 accuracy_threshold: float = 0.50 other_args: List[str] = field(default_factory=list) env_vars: dict = field(default_factory=dict) timeout: Optional[int] = None variant: Optional[str] = None def get_display_name(self) -> str: if self.variant: return f"{self.model_path} ({self.variant})" return self.model_path # GLM-5 models for MI35x - NSA attention backend MI35X_GLM5_MODELS = [ # GLM-5 with NSA attention (TP=8) ModelConfig( model_path="zai-org/GLM-5", tp_size=8, accuracy_threshold=0.93, timeout=5400, variant="nsa", other_args=[ "--trust-remote-code", "--nsa-prefill-backend", "tilelang", "--nsa-decode-backend", "tilelang", "--chunked-prefill-size", "131072", "--mem-fraction-static", "0.80", "--model-loader-extra-config", '{"enable_multithread_load": true}', "--watchdog-timeout", "1200", # 20 minutes for weight loading ], env_vars={}, ), ] def get_one_example(lines, i, include_answer): """Format a single GSM8K example.""" ret = "Question: " + lines[i]["question"] + "\nAnswer:" if include_answer: ret += " " + lines[i]["answer"] return ret def get_few_shot_examples(lines, k): """Get k few-shot examples for prompting.""" ret = "" for i in range(k): ret += get_one_example(lines, i, True) + "\n\n" return ret def get_answer_value(answer_str): """Extract numerical answer from response.""" answer_str = answer_str.replace(",", "") numbers = re.findall(r"\d+", answer_str) if len(numbers) < 1: return INVALID try: return ast.literal_eval(numbers[-1]) except SyntaxError: return INVALID def run_gsm8k_benchmark( base_url: str, num_questions: int = 200, num_shots: int = 5, parallel: int = 64, ) -> Tuple[float, float, float]: """Run GSM8K few-shot completion benchmark.""" import sglang as sgl from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint url = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl" data_path = download_and_cache_file(url) lines = list(read_jsonl(data_path)) few_shot_examples = get_few_shot_examples(lines, num_shots) questions = [] labels = [] for i in range(len(lines[:num_questions])): questions.append(get_one_example(lines, i, False)) labels.append(get_answer_value(lines[i]["answer"])) assert all(l != INVALID for l in labels) arguments = [{"question": q} for q in questions] @sgl.function def few_shot_gsm8k(s, question): s += few_shot_examples + question s += sgl.gen( "answer", max_tokens=512, stop=["Question", "Assistant:", "<|separator|>"] ) backend = RuntimeEndpoint(base_url) sgl.set_default_backend(backend) tic = time.perf_counter() states = few_shot_gsm8k.run_batch( arguments, temperature=0, num_threads=parallel, progress_bar=True ) latency = time.perf_counter() - tic preds = [get_answer_value(states[i]["answer"]) for i in range(len(states))] acc = np.mean(np.array(preds) == np.array(labels)) invalid = np.mean(np.array(preds) == INVALID) return float(acc), float(invalid), float(latency) class TestGLM5EvalMI35x(unittest.TestCase): """GLM-5 GSM8K Completion Evaluation Test for AMD MI35x.""" @classmethod def setUpClass(cls): cls.models = MI35X_GLM5_MODELS cls.base_url = DEFAULT_URL_FOR_TEST cls.num_questions = int(os.environ.get("GSM8K_NUM_QUESTIONS", "200")) def test_glm5_accuracy(self): """Test GLM-5 models with GSM8K completion benchmark.""" all_results = [] summary = "### GLM-5 Models (MI35x)\n\n" summary += "| Model | Variant | TP | Accuracy | Threshold | Status |\n" summary += "| ----- | ------- | -- | -------- | --------- | ------ |\n" for config in self.models: display_name = config.get_display_name() with self.subTest(model=display_name): print(f"\n{'='*60}") print(f"Testing: {display_name}") print(f"{'='*60}") env = os.environ.copy() for key, value in config.env_vars.items(): env[key] = value other_args = list(config.other_args) other_args.extend(["--tp", str(config.tp_size)]) timeout = config.timeout or DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH try: process = popen_launch_server( model=config.model_path, base_url=self.base_url, timeout=timeout, other_args=other_args, env=env, ) try: acc, invalid, latency = run_gsm8k_benchmark( self.base_url, num_questions=self.num_questions ) passed = acc >= config.accuracy_threshold status = "✅ PASS" if passed else "❌ FAIL" print( f" accuracy={acc:.3f} threshold={config.accuracy_threshold} {status}" ) all_results.append( { "model": display_name, "accuracy": acc, "passed": passed, } ) summary += f"| {config.model_path} | {config.variant or 'N/A'} | {config.tp_size} | {acc:.3f} | {config.accuracy_threshold} | {status} |\n" finally: 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()