[AMD] Add Qwen3-Coder-Next accuracy and functionality test scripts for MI35x 8-GPU

test/registered/amd/accuracy/mi35x/test_qwen3_coder_next_eval_mi35x.py

@@ -0,0 +1,302 @@
+"""MI35x Qwen3-Coder-Next GSM8K Completion Evaluation Test (8-GPU)
+
+Tests Qwen3-Coder-Next model with basic and MTP configurations
+using few-shot completion benchmark on MI35x.
+
+Registry: nightly-amd-8-gpu-mi35x-qwen3-coder-next 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
+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 Qwen3-Coder-Next accuracy test
+register_amd_ci(est_time=3600, suite="nightly-amd-8-gpu-mi35x", nightly=True)
+
+INVALID = -9999999
+
+# Model path configuration for MI35x Qwen3-Coder-Next
+# Priority: 1) env var, 2) local path
+QWEN3_CODER_NEXT_LOCAL_PATH = "/data/Qwen/Qwen3-Coder-Next/"
+QWEN3_CODER_NEXT_HF_MODEL_ID = "Qwen/Qwen3-Coder-Next"
+
+
+def get_model_path() -> str:
+    """Get effective model path: env var > local path > HF model ID."""
+    env_path = os.environ.get("QWEN3_CODER_NEXT_MODEL_PATH")
+    if env_path:
+        return env_path
+    if os.path.exists(QWEN3_CODER_NEXT_LOCAL_PATH):
+        return QWEN3_CODER_NEXT_LOCAL_PATH
+    return QWEN3_CODER_NEXT_HF_MODEL_ID
+
+
+@dataclass
+class ModelConfig:
+    """Configuration for a model to test."""
+
+    model_path: str
+    tp_size: int = 8
+    accuracy_threshold: float = 0.50
+    other_args: Optional[List[str]] = None
+    env_vars: Optional[dict] = None
+    timeout: Optional[int] = None
+    variant: Optional[str] = None
+
+    def __post_init__(self):
+        if self.other_args is None:
+            self.other_args = []
+        if self.env_vars is None:
+            self.env_vars = {}
+
+    def get_display_name(self) -> str:
+        if self.variant:
+            return f"{self.model_path} ({self.variant})"
+        return self.model_path
+
+
+def get_qwen3_coder_next_models() -> List[ModelConfig]:
+    """Get Qwen3-Coder-Next model configurations for MI35x."""
+    model_path = get_model_path()
+    common_kwargs = {
+        "model_path": model_path,
+        "tp_size": 8,
+        "accuracy_threshold": 0.90,
+        "timeout": 3600,
+    }
+    common_args = [
+        "--attention-backend",
+        "aiter",
+        "--chunked-prefill-size",
+        "131072",
+        "--disable-radix-cache",
+        "--mem-fraction-static",
+        "0.8",
+        "--trust-remote-code",
+    ]
+    return [
+        # Basic — matches run_qwen3-coder-next_spec.sh
+        ModelConfig(
+            **common_kwargs,
+            variant="basic",
+            other_args=common_args
+            + [
+                "--kv-cache-dtype",
+                "fp8_e4m3",
+            ],
+        ),
+        # MTP (speculative decoding)
+        # TODO: Support MTP with fp8 kv cache on gfx950.
+        # Note: no --kv-cache-dtype fp8_e4m3 because Triton extend_attention
+        # used by MTP does not support fp8 kv cache on gfx950.
+        ModelConfig(
+            **common_kwargs,
+            variant="mtp",
+            other_args=common_args
+            + [
+                "--speculative-algorithm",
+                "EAGLE",
+                "--speculative-num-steps",
+                "3",
+                "--speculative-eagle-topk",
+                "1",
+                "--speculative-num-draft-tokens",
+                "4",
+            ],
+        ),
+    ]
+
+
+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 TestQwen3CoderNextEvalMI35x(unittest.TestCase):
+    """Qwen3-Coder-Next GSM8K Completion Evaluation Test for AMD MI35x."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.models = get_qwen3_coder_next_models()
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.num_questions = int(os.environ.get("GSM8K_NUM_QUESTIONS", "200"))
+
+    def test_qwen3_coder_next_accuracy(self):
+        """Test Qwen3-Coder-Next models with GSM8K completion benchmark."""
+        # Check if model exists
+        model_path = get_model_path()
+        is_local_path = model_path.startswith("/")
+        if is_local_path and not os.path.exists(model_path):
+            print(f"\nSKIPPING: Local model not found at {model_path}")
+            self.skipTest(f"Local model not found at {model_path}")
+            return
+
+        if is_local_path:
+            print(f"Using local model: {model_path}")
+        else:
+            print(f"Using HuggingFace model: {model_path}")
+
+        all_results = []
+        summary = "### Qwen3-Coder-Next 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()