Migrate performance, accuracy, and quantization tests to CI registry (#17177)

Co-authored-by: Kangyan-Zhou <zky314343421@gmail.com>
2026-01-18 23:25:24 -08:00
parent a3d9a21882
commit 8916b9d080
20 changed files with 1050 additions and 1020 deletions
@@ -0,0 +1,107 @@
+"""
+Performance tests for 2-GPU that need large GPUs (H200 80GB) - MoE and Pipeline Parallel tests.
+"""
+
+import unittest
+
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import (
+    DEFAULT_MOE_MODEL_NAME_FOR_TEST,
+    CustomTestCase,
+    is_in_amd_ci,
+    is_in_ci,
+    run_bench_serving,
+    write_github_step_summary,
+)
+
+register_cuda_ci(est_time=600, suite="stage-b-test-large-2-gpu-performance")
+
+
+class TestBenchServing2GPU(CustomTestCase):
+    def test_moe_offline_throughput_default(self):
+        res = run_bench_serving(
+            model=DEFAULT_MOE_MODEL_NAME_FOR_TEST,
+            num_prompts=300,
+            request_rate=float("inf"),
+            other_server_args=["--tp", "2"],
+        )
+
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_moe_offline_throughput_default\n"
+                f"Output throughput: {res['output_throughput']:.2f} token/s\n"
+            )
+            if is_in_amd_ci():
+                self.assertGreater(res["output_throughput"], 2100)
+            else:
+                self.assertGreater(res["output_throughput"], 2200)
+
+    def test_moe_offline_throughput_without_radix_cache(self):
+        res = run_bench_serving(
+            model=DEFAULT_MOE_MODEL_NAME_FOR_TEST,
+            num_prompts=300,
+            request_rate=float("inf"),
+            other_server_args=["--tp", "2", "--disable-radix-cache"],
+        )
+
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_moe_offline_throughput_without_radix_cache\n"
+                f"Output throughput: {res['output_throughput']:.2f} token/s\n"
+            )
+            if is_in_amd_ci():
+                self.assertGreater(res["output_throughput"], 2100)
+            else:
+                self.assertGreater(res["output_throughput"], 2200)
+
+    def test_pp_offline_throughput_default_decode(self):
+        res = run_bench_serving(
+            model=DEFAULT_MOE_MODEL_NAME_FOR_TEST,
+            num_prompts=1000,
+            request_rate=float("inf"),
+            random_input_len=1,
+            random_output_len=1024,
+            other_server_args=["--pp-size", "2"],
+            need_warmup=True,
+            seed=42,
+        )
+
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_pp_offline_throughput_default_decode\n"
+                f"Output throughput: {res['output_throughput']:.2f} token/s\n"
+            )
+            self.assertGreater(res["output_throughput"], 6700)
+
+    def test_pp_long_context_prefill(self):
+        res = run_bench_serving(
+            model="meta-llama/Llama-3.3-70B-Instruct",
+            num_prompts=4,
+            request_rate=float("inf"),
+            random_input_len=128000,
+            random_output_len=1,
+            dataset_name="random",
+            other_server_args=[
+                "--quantization",
+                "fp8",
+                "--pp-size",
+                "2",
+            ]
+            + (["--mem-fraction-static", "0.7"] if is_in_amd_ci() else []),
+            need_warmup=False,
+            seed=42,
+        )
+
+        if is_in_ci():
+            write_github_step_summary(
+                f"### test_pp_long_context_latency_prefill\n"
+                f"input_throughput: {res['input_throughput']:.2f} ms\n"
+            )
+            if is_in_amd_ci():
+                self.assertGreater(res["input_throughput"], 3000)
+            else:
+                self.assertGreater(res["input_throughput"], 4000)
+
+
+if __name__ == "__main__":
+    unittest.main()