Add AMD MI300x Nightly Testing. (#5861)

.github/workflows/nightly-test-amd.yml

+name: Nightly Test (AMD)
+
+on:
+  schedule:
+    - cron: '0 0 * * *'
+  push:
+    branches:
+      - main
+    paths:
+      - "python/sglang/version.py"
+  workflow_dispatch:
+
+concurrency:
+  group: nightly-test-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  nightly-test:
+    if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request'
+    runs-on: linux-mi300-gpu-2
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup docker
+        run: |
+          # Ensure GPU isolation if pod is part of kubernetes setup with DEVICE_FLAG.
+          if [ -f "/etc/podinfo/gha-render-devices" ]; then
+            DEVICE_FLAG=$(cat /etc/podinfo/gha-render-devices)
+          else
+            DEVICE_FLAG="--device /dev/dri"
+          fi
+          touch github_summary.md
+          docker pull ghcr.io/saienduri/sglang-aiter-v0.1.1:428
+          docker run -dt --user root --device=/dev/kfd $DEVICE_FLAG \
+            -v ${{ github.workspace }}:/sglang-checkout --ipc=host --group-add video \
+            --cap-add=SYS_PTRACE -e HF_TOKEN=${HF_TOKEN} --security-opt seccomp=unconfined \
+            -w /sglang-checkout --name ci_sglang \
+            ghcr.io/saienduri/sglang-aiter-v0.1.1:428
+
+      - name: Install dependencies
+        run: |
+          docker exec ci_sglang pip install --upgrade pip
+          docker exec ci_sglang pip uninstall sgl-kernel -y || true
+          docker exec -w /sglang-checkout/sgl-kernel ci_sglang bash -c "rm -f pyproject.toml && mv pyproject_rocm.toml pyproject.toml && python3 setup_rocm.py install"
+          docker exec ci_sglang pip install -e "python[dev_hip]"
+
+          docker exec -w / ci_sglang git clone https://github.com/merrymercy/human-eval.git
+          docker exec -w /human-eval ci_sglang pip install -e .
+
+      - name: Nightly Test
+        run: |
+          docker exec -w /sglang-checkout/test/srt -e SGLANG_IS_IN_CI=1 -e GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" ci_sglang python3 run_suite.py --suite nightly-amd --timeout-per-file 7200
+          echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY

test/srt/run_suite.py

@@ -100,6 +100,9 @@ suites = {
     "nightly": [
         TestFile("test_nightly_gsm8k_eval.py"),
     ],
+    "nightly-amd": [
+        TestFile("test_nightly_gsm8k_eval_amd.py"),
+    ],
     "vllm_dependency_test": [
         TestFile("test_vllm_dependency.py"),
         TestFile("test_awq.py"),

test/srt/test_nightly_gsm8k_eval_amd.py

+import json
+import os
+import unittest
+import warnings
+from datetime import datetime
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.run_eval import run_eval
+from sglang.test.test_utils import (
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1,
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2,
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1,
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2,
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    is_in_ci,
+    popen_launch_server,
+    write_github_step_summary,
+)
+
+MODEL_SCORE_THRESHOLDS = {
+    "meta-llama/Llama-3.1-8B-Instruct": 0.82,
+    "mistralai/Mistral-7B-Instruct-v0.3": 0.56,
+    "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct": 0.85,
+    "meta-llama/Llama-3.1-70B-Instruct": 0.95,
+    "mistralai/Mixtral-8x7B-Instruct-v0.1": 0.64,
+    "Qwen/Qwen2-57B-A14B-Instruct": 0.86,
+    "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8": 0.81,
+    "neuralmagic/Mistral-7B-Instruct-v0.3-FP8": 0.54,
+    "neuralmagic/Meta-Llama-3.1-70B-Instruct-FP8": 0.94,
+    "neuralmagic/Qwen2-72B-Instruct-FP8": 0.94,
+    "neuralmagic/Qwen2-57B-A14B-Instruct-FP8": 0.82,
+}
+
+# Models currently failing on AMD MI300x.
+failing_models = {
+    "google/gemma-2-27b-it",
+    "neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8",
+    "neuralmagic/gemma-2-2b-it-FP8",
+    "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8",
+}
+
+
+def remove_failing_models(model_str):
+    models = model_str.split(",")
+    filtered = [m for m in models if m not in failing_models]
+    return ",".join(filtered)
+
+
+DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1 = remove_failing_models(
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1
+)
+DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2 = remove_failing_models(
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2
+)
+DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1 = remove_failing_models(
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1
+)
+DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2 = remove_failing_models(
+    DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2
+)
+
+
+def parse_models(model_string):
+    return [model.strip() for model in model_string.split(",") if model.strip()]
+
+
+def popen_launch_server_wrapper(base_url, model, is_tp2):
+    other_args = ["--log-level-http", "warning", "--trust-remote-code"]
+    if is_tp2:
+        other_args.extend(["--tp", "2"])
+
+    process = popen_launch_server(
+        model,
+        base_url,
+        timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+        other_args=other_args,
+    )
+    return process
+
+
+def write_results_to_json(model, metrics, mode="a"):
+    result = {
+        "timestamp": datetime.now().isoformat(),
+        "model": model,
+        "metrics": metrics,
+        "score": metrics["score"],
+    }
+
+    existing_results = []
+    if mode == "a" and os.path.exists("results.json"):
+        try:
+            with open("results.json", "r") as f:
+                existing_results = json.load(f)
+        except json.JSONDecodeError:
+            existing_results = []
+
+    if isinstance(existing_results, list):
+        existing_results.append(result)
+    else:
+        existing_results = [result]
+
+    with open("results.json", "w") as f:
+        json.dump(existing_results, f, indent=2)
+
+
+def check_model_scores(results):
+    failed_models = []
+    summary = " | model | score | threshold |\n"
+    summary += "| ----- | ----- | --------- |\n"
+
+    for model, score in results:
+        threshold = MODEL_SCORE_THRESHOLDS.get(model)
+        if threshold is None:
+            print(f"Warning: No threshold defined for model {model}")
+            continue
+
+        if score < threshold:
+            failed_models.append(
+                f"\nScore Check Failed: {model}\n"
+                f"Model {model} score ({score:.4f}) is below threshold ({threshold:.4f})"
+            )
+
+        line = f"| {model} | {score} | {threshold} |\n"
+        summary += line
+
+    print(summary)
+
+    if is_in_ci():
+        write_github_step_summary(f"### TestNightlyGsm8KEval\n{summary}")
+
+    if failed_models:
+        raise AssertionError("\n".join(failed_models))
+
+
+# Do not use `CustomTestCase` since `test_mgsm_en_all_models` does not want retry
+class TestNightlyGsm8KEval(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model_groups = [
+            (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1), False, False),
+            (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2), False, True),
+            (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1), True, False),
+            (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2), True, True),
+        ]
+        cls.base_url = DEFAULT_URL_FOR_TEST
+
+    def test_mgsm_en_all_models(self):
+        warnings.filterwarnings(
+            "ignore", category=ResourceWarning, message="unclosed.*socket"
+        )
+        is_first = True
+        all_results = []
+
+        for model_group, is_fp8, is_tp2 in self.model_groups:
+            for model in model_group:
+                with self.subTest(model=model):
+                    process = popen_launch_server_wrapper(self.base_url, model, is_tp2)
+
+                    args = SimpleNamespace(
+                        base_url=self.base_url,
+                        model=model,
+                        eval_name="mgsm_en",
+                        num_examples=None,
+                        num_threads=1024,
+                    )
+
+                    metrics = run_eval(args)
+                    print(
+                        f"{'=' * 42}\n{model} - metrics={metrics} score={metrics['score']}\n{'=' * 42}\n"
+                    )
+
+                    write_results_to_json(model, metrics, "w" if is_first else "a")
+                    is_first = False
+
+                    all_results.append((model, metrics["score"]))
+                    kill_process_tree(process.pid)
+
+        try:
+            with open("results.json", "r") as f:
+                print("\nFinal Results from results.json:")
+                print(json.dumps(json.load(f), indent=2))
+        except Exception as e:
+            print(f"Error reading results.json: {e}")
+
+        # Check all scores after collecting all results
+        check_model_scores(all_results)
+
+
+if __name__ == "__main__":
+    unittest.main()