[AMD] CI - Add MI35x nightly/PR tests for kv-cache-fp8 and allreduce-fusion (DeepSeek) (#19834)

Co-authored-by: bingxche <Bingxu.Chen@amd.com>
2026-03-05 07:09:57 -08:00
parent 0de0d74195
commit b5edab57f2
13 changed files with 1614 additions and 177 deletions
@@ -0,0 +1,177 @@
+"""MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model with AIter AllReduce Fusion.
+
+This test benchmarks the DeepSeek-R1-MXFP4 quantized model on MI35x with 8 GPUs
+using --enable-aiter-allreduce-fusion.
+
+The model path can be configured via DEEPSEEK_R1_MXFP4_MODEL_PATH environment variable.
+
+Registry: nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4-ar-fusion suite
+
+Example usage:
+    DEEPSEEK_R1_MXFP4_MODEL_PATH=/data2/models/amd-DeepSeek-R1-MXFP4-Preview python -m pytest test_deepseek_r1_mxfp4_ar_fusion_perf_mi35x.py -v
+"""
+
+import os
+
+# Set HF cache to /data2/models/ for MI35x so HF models download there
+os.environ.setdefault("HF_HOME", "/data2/models/huggingface")
+os.environ.setdefault("HF_HUB_CACHE", "/data2/models/huggingface/hub")
+import unittest
+from typing import List
+
+from sglang.test.ci.ci_register import register_amd_ci
+from sglang.test.nightly_bench_utils import BenchmarkResult
+from sglang.test.nightly_utils import NightlyBenchmarkRunner
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
+
+# Register for AMD CI - DeepSeek-R1-MXFP4 AllReduce Fusion benchmark on MI35x (~300 min)
+register_amd_ci(
+    est_time=18000,
+    suite="nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4-ar-fusion",
+    nightly=True,
+)
+
+
+def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
+    """Generate a simplified markdown report without traces and cost columns.
+
+    Skips the first result if it's a warmup run (duplicate batch_size).
+    """
+    model_header = results[0].model_path
+    if results[0].run_name and results[0].run_name != "default":
+        model_header += f" ({results[0].run_name})"
+
+    gpu_config = os.getenv("GPU_CONFIG", "MI35x")
+    if gpu_config:
+        model_header += f" [{gpu_config}]"
+
+    summary = f"### {model_header}\n"
+    summary += "| batch size | input len | latency (s) | input throughput (tok/s) | output throughput (tok/s) | ITL (ms) |\n"
+    summary += "| ---------- | --------- | ----------- | ------------------------ | ------------------------- | -------- |\n"
+
+    # Skip first result if it's a warmup (same batch_size as second result)
+    report_results = (
+        results[1:]
+        if len(results) > 1 and results[0].batch_size == results[1].batch_size
+        else results
+    )
+
+    for result in report_results:
+        itl = 1 / (result.output_throughput / result.batch_size) * 1000
+        summary += f"| {result.batch_size} | {result.input_len} | {result.latency:.2f} | {result.input_throughput:.2f} | {result.output_throughput:.2f} | {itl:.2f} |\n"
+
+    return summary
+
+
+# Model path configuration for MI35x DeepSeek-R1-MXFP4
+# Priority: 1) env var, 2) local path, 3) HuggingFace model ID
+DEEPSEEK_R1_MXFP4_LOCAL_PATH = "/data2/models/amd-DeepSeek-R1-MXFP4-Preview"
+DEEPSEEK_R1_MXFP4_HF_MODEL_ID = "amd/DeepSeek-R1-MXFP4-Preview"
+PROFILE_DIR = "performance_profiles_deepseek_r1_mxfp4_ar_fusion_mi35x"
+
+
+def get_model_path() -> str:
+    """Get effective model path: env var > local path > HF model ID."""
+    # Check env var first
+    env_path = os.environ.get("DEEPSEEK_R1_MXFP4_MODEL_PATH")
+    if env_path:
+        return env_path
+    # Check local path
+    if os.path.exists(DEEPSEEK_R1_MXFP4_LOCAL_PATH):
+        return DEEPSEEK_R1_MXFP4_LOCAL_PATH
+    # Fall back to HF model ID
+    return DEEPSEEK_R1_MXFP4_HF_MODEL_ID
+
+
+class TestDeepseekR1MXFP4ArFusionPerfMI35x(unittest.TestCase):
+    """MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 with AllReduce Fusion.
+
+    Tests the DeepSeek-R1-MXFP4 quantized model on TP=8 with --enable-aiter-allreduce-fusion.
+    Uses local path if available, otherwise downloads from HuggingFace.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = get_model_path()
+        print(f"Using model path: {cls.model}")
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.batch_sizes = [1, 8, 16, 64]
+        cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
+        cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))
+
+        cls.variants = [
+            {
+                "name": "ar-fusion",
+                "other_args": [
+                    "--trust-remote-code",
+                    "--tp",
+                    "8",
+                    "--chunked-prefill-size",
+                    "131072",
+                    "--disable-radix-cache",
+                    "--mem-fraction-static",
+                    "0.85",
+                    "--enable-aiter-allreduce-fusion",
+                ],
+            },
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+        cls.runner.full_report = f"## {cls.__name__}\n"
+
+    def test_bench_one_batch(self):
+        """Run benchmark across all configured variants."""
+        failed_variants = []
+
+        is_local_path = self.model.startswith("/")
+        if is_local_path and not os.path.exists(self.model):
+            print(f"\n⏭️ SKIPPING: Local model not found at {self.model}")
+            self.runner.full_report += (
+                f"\n⏭️ Test skipped: Local model not found at {self.model}\n"
+            )
+            self.runner.write_final_report()
+            return
+
+        if is_local_path:
+            print(f"📁 Using local model: {self.model}")
+        else:
+            print(
+                f"📥 Using HuggingFace model: {self.model} (will download if not cached)"
+            )
+
+        try:
+            for variant_config in self.variants:
+                with self.subTest(variant=variant_config["name"]):
+                    result_tuple = self.runner.run_benchmark_for_model(
+                        model_path=self.model,
+                        batch_sizes=self.batch_sizes,
+                        input_lens=self.input_lens,
+                        output_lens=self.output_lens,
+                        other_args=variant_config["other_args"],
+                        variant=variant_config["name"],
+                        extra_bench_args=["--trust-remote-code"],
+                        enable_profile=False,
+                    )
+                    results = result_tuple[0]
+                    success = result_tuple[1]
+
+                    if not success:
+                        failed_variants.append(variant_config["name"])
+
+                    if results:
+                        self.runner.full_report += (
+                            generate_simple_markdown_report(results) + "\n"
+                        )
+        finally:
+            self.runner.write_final_report()
+
+        if failed_variants:
+            raise AssertionError(
+                f"Benchmark failed for {self.model} with the following variants: "
+                f"{', '.join(failed_variants)}"
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,178 @@
+"""MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model with KV Cache FP8.
+
+This test benchmarks the DeepSeek-R1-MXFP4 quantized model on MI35x with 8 GPUs
+using --kv-cache-dtype fp8_e4m3.
+
+The model path can be configured via DEEPSEEK_R1_MXFP4_MODEL_PATH environment variable.
+
+Registry: nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4-kv-fp8 suite
+
+Example usage:
+    DEEPSEEK_R1_MXFP4_MODEL_PATH=/data2/models/amd-DeepSeek-R1-MXFP4-Preview python -m pytest test_deepseek_r1_mxfp4_kv_fp8_perf_mi35x.py -v
+"""
+
+import os
+
+# Set HF cache to /data2/models/ for MI35x so HF models download there
+os.environ.setdefault("HF_HOME", "/data2/models/huggingface")
+os.environ.setdefault("HF_HUB_CACHE", "/data2/models/huggingface/hub")
+import unittest
+from typing import List
+
+from sglang.test.ci.ci_register import register_amd_ci
+from sglang.test.nightly_bench_utils import BenchmarkResult
+from sglang.test.nightly_utils import NightlyBenchmarkRunner
+from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env
+
+# Register for AMD CI - DeepSeek-R1-MXFP4 KV FP8 benchmark on MI35x (~300 min)
+register_amd_ci(
+    est_time=18000,
+    suite="nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4-kv-fp8",
+    nightly=True,
+)
+
+
+def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
+    """Generate a simplified markdown report without traces and cost columns.
+
+    Skips the first result if it's a warmup run (duplicate batch_size).
+    """
+    model_header = results[0].model_path
+    if results[0].run_name and results[0].run_name != "default":
+        model_header += f" ({results[0].run_name})"
+
+    gpu_config = os.getenv("GPU_CONFIG", "MI35x")
+    if gpu_config:
+        model_header += f" [{gpu_config}]"
+
+    summary = f"### {model_header}\n"
+    summary += "| batch size | input len | latency (s) | input throughput (tok/s) | output throughput (tok/s) | ITL (ms) |\n"
+    summary += "| ---------- | --------- | ----------- | ------------------------ | ------------------------- | -------- |\n"
+
+    # Skip first result if it's a warmup (same batch_size as second result)
+    report_results = (
+        results[1:]
+        if len(results) > 1 and results[0].batch_size == results[1].batch_size
+        else results
+    )
+
+    for result in report_results:
+        itl = 1 / (result.output_throughput / result.batch_size) * 1000
+        summary += f"| {result.batch_size} | {result.input_len} | {result.latency:.2f} | {result.input_throughput:.2f} | {result.output_throughput:.2f} | {itl:.2f} |\n"
+
+    return summary
+
+
+# Model path configuration for MI35x DeepSeek-R1-MXFP4
+# Priority: 1) env var, 2) local path, 3) HuggingFace model ID
+DEEPSEEK_R1_MXFP4_LOCAL_PATH = "/data2/models/amd-DeepSeek-R1-MXFP4-Preview"
+DEEPSEEK_R1_MXFP4_HF_MODEL_ID = "amd/DeepSeek-R1-MXFP4-Preview"
+PROFILE_DIR = "performance_profiles_deepseek_r1_mxfp4_kv_fp8_mi35x"
+
+
+def get_model_path() -> str:
+    """Get effective model path: env var > local path > HF model ID."""
+    # Check env var first
+    env_path = os.environ.get("DEEPSEEK_R1_MXFP4_MODEL_PATH")
+    if env_path:
+        return env_path
+    # Check local path
+    if os.path.exists(DEEPSEEK_R1_MXFP4_LOCAL_PATH):
+        return DEEPSEEK_R1_MXFP4_LOCAL_PATH
+    # Fall back to HF model ID
+    return DEEPSEEK_R1_MXFP4_HF_MODEL_ID
+
+
+class TestDeepseekR1MXFP4KvFp8PerfMI35x(unittest.TestCase):
+    """MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 with KV Cache FP8.
+
+    Tests the DeepSeek-R1-MXFP4 quantized model on TP=8 with --kv-cache-dtype fp8_e4m3.
+    Uses local path if available, otherwise downloads from HuggingFace.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = get_model_path()
+        print(f"Using model path: {cls.model}")
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.batch_sizes = [1, 8, 16, 64]
+        cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
+        cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))
+
+        cls.variants = [
+            {
+                "name": "kv-fp8",
+                "other_args": [
+                    "--trust-remote-code",
+                    "--tp",
+                    "8",
+                    "--chunked-prefill-size",
+                    "131072",
+                    "--disable-radix-cache",
+                    "--mem-fraction-static",
+                    "0.85",
+                    "--kv-cache-dtype",
+                    "fp8_e4m3",
+                ],
+            },
+        ]
+
+        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
+        cls.runner.setup_profile_directory()
+        cls.runner.full_report = f"## {cls.__name__}\n"
+
+    def test_bench_one_batch(self):
+        """Run benchmark across all configured variants."""
+        failed_variants = []
+
+        is_local_path = self.model.startswith("/")
+        if is_local_path and not os.path.exists(self.model):
+            print(f"\n⏭️ SKIPPING: Local model not found at {self.model}")
+            self.runner.full_report += (
+                f"\n⏭️ Test skipped: Local model not found at {self.model}\n"
+            )
+            self.runner.write_final_report()
+            return
+
+        if is_local_path:
+            print(f"📁 Using local model: {self.model}")
+        else:
+            print(
+                f"📥 Using HuggingFace model: {self.model} (will download if not cached)"
+            )
+
+        try:
+            for variant_config in self.variants:
+                with self.subTest(variant=variant_config["name"]):
+                    result_tuple = self.runner.run_benchmark_for_model(
+                        model_path=self.model,
+                        batch_sizes=self.batch_sizes,
+                        input_lens=self.input_lens,
+                        output_lens=self.output_lens,
+                        other_args=variant_config["other_args"],
+                        variant=variant_config["name"],
+                        extra_bench_args=["--trust-remote-code"],
+                        enable_profile=False,
+                    )
+                    results = result_tuple[0]
+                    success = result_tuple[1]
+
+                    if not success:
+                        failed_variants.append(variant_config["name"])
+
+                    if results:
+                        self.runner.full_report += (
+                            generate_simple_markdown_report(results) + "\n"
+                        )
+        finally:
+            self.runner.write_final_report()
+
+        if failed_variants:
+            raise AssertionError(
+                f"Benchmark failed for {self.model} with the following variants: "
+                f"{', '.join(failed_variants)}"
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()