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[AMD CI] Organize AMD nightly perf test files (#16114)

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Bingxu Chen
2025-12-29 22:09:46 -08:00
committed by GitHub
parent 3946dad6fa
commit 5e20e7a60d
5 changed files with 4 additions and 4 deletions
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test/registered/amd/test_deepseek_v31_perf.py
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"""Nightly performance benchmark for DeepSeek-V3.1 model.
This test benchmarks the DeepSeek-V3.1 model with basic and MTP configurations on 8 GPUs.
The model path can be configured via DEEPSEEK_V31_MODEL_PATH environment variable.
Example usage:
DEEPSEEK_V31_MODEL_PATH=deepseek-ai/DeepSeek-V3.1 python -m pytest test_deepseek_v31_perf.py -v
"""
import os
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-V3.1 benchmark (basic + MTP, ~300 min)
register_amd_ci(est_time=18000, suite="nightly-perf-8-gpu-deepseek-v31", nightly=True)
def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
"""Generate a simplified markdown report without traces and cost columns."""
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", "")
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"
for result in 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 can be overridden via environment variable
DEEPSEEK_V31_MODEL_PATH = os.environ.get(
"DEEPSEEK_V31_MODEL_PATH", "deepseek-ai/DeepSeek-V3.1"
)
PROFILE_DIR = "performance_profiles_deepseek_v31"
class TestNightlyDeepseekV31Performance(unittest.TestCase):
"""Nightly performance benchmark for DeepSeek-V3.1 model.
Tests the DeepSeek-V3.1 model with both basic and MTP configurations on TP=8.
"""
@classmethod
def setUpClass(cls):
cls.model = DEEPSEEK_V31_MODEL_PATH
cls.base_url = DEFAULT_URL_FOR_TEST
cls.batch_sizes = [1, 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"))
# Define variant configurations for DeepSeek-V3.1
cls.variants = [
{
"name": "basic",
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--mem-fraction-static",
"0.85",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
],
},
{
"name": "mtp",
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--speculative-algorithm",
"EAGLE",
"--speculative-num-steps",
"3",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"4",
"--mem-fraction-static",
"0.7",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
],
},
]
cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
cls.runner.setup_profile_directory()
# Override full_report to remove traces help text
cls.runner.full_report = f"## {cls.__name__}\n"
def test_bench_one_batch(self):
"""Run benchmark across all configured variants."""
failed_variants = []
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"],
)
results = result_tuple[0]
success = result_tuple[1]
if not success:
failed_variants.append(variant_config["name"])
# Use simplified report format without traces
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()
test/registered/amd/test_deepseek_v3_perf.py
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"""Nightly performance benchmark for DeepSeek-V3 model.
This test benchmarks the DeepSeek-V3 model with basic and MTP configurations on 8 GPUs.
The model path can be configured via DEEPSEEK_V3_MODEL_PATH environment variable.
Example usage:
DEEPSEEK_V3_MODEL_PATH=deepseek-ai/DeepSeek-V3-0324 python -m pytest test_deepseek_v3_perf.py -v
"""
import os
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-V3 benchmark (basic + MTP, ~300 min)
register_amd_ci(est_time=18000, suite="nightly-perf-8-gpu-deepseek-v3", nightly=True)
def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
"""Generate a simplified markdown report without traces and cost columns."""
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", "")
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"
for result in 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 can be overridden via environment variable
DEEPSEEK_V3_MODEL_PATH = os.environ.get(
"DEEPSEEK_V3_MODEL_PATH", "deepseek-ai/DeepSeek-V3-0324"
)
PROFILE_DIR = "performance_profiles_deepseek_v3"
class TestNightlyDeepseekV3Performance(unittest.TestCase):
"""Nightly performance benchmark for DeepSeek-V3 model.
Tests the DeepSeek-V3 model with both basic and MTP configurations on TP=8.
"""
@classmethod
def setUpClass(cls):
cls.model = DEEPSEEK_V3_MODEL_PATH
cls.base_url = DEFAULT_URL_FOR_TEST
cls.batch_sizes = [1, 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"))
# Define variant configurations for DeepSeek-V3
cls.variants = [
{
"name": "basic",
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--mem-fraction-static",
"0.85",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
],
},
{
"name": "mtp",
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--speculative-algorithm",
"EAGLE",
"--speculative-num-steps",
"3",
"--speculative-eagle-topk",
"1",
"--speculative-num-draft-tokens",
"4",
"--mem-fraction-static",
"0.7",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
],
},
]
cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
cls.runner.setup_profile_directory()
# Override full_report to remove traces help text
cls.runner.full_report = f"## {cls.__name__}\n"
def test_bench_one_batch(self):
"""Run benchmark across all configured variants."""
failed_variants = []
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"],
)
results = result_tuple[0]
success = result_tuple[1]
if not success:
failed_variants.append(variant_config["name"])
# Use simplified report format without traces
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()
test/registered/amd/test_grok_perf.py
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"""Nightly performance benchmark for Grok models (Grok-1 and Grok-2).
This test benchmarks both Grok-1 and Grok-2 models with FP8 quantization on 8 GPUs.
Model paths can be configured via environment variables:
- GROK1_MODEL_PATH: Path to Grok-1 model (default: amd/grok-1-W4A8KV8)
- GROK1_TOKENIZER_PATH: Path to Grok-1 tokenizer (default: Xenova/grok-1-tokenizer)
- GROK2_MODEL_PATH: Path to Grok-2 model (default: xai-org/grok-2)
- GROK2_TOKENIZER_PATH: Path to Grok-2 tokenizer (default: alvarobartt/grok-2-tokenizer)
Example usage:
python -m pytest test_grok_perf.py -v
"""
import os
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 - combined Grok-1 + Grok-2 benchmark (~60 min)
register_amd_ci(est_time=3600, suite="nightly-perf-8-gpu-grok", nightly=True)
def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
"""Generate a simplified markdown report without traces and cost columns."""
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", "")
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"
for result in 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 and tokenizer paths can be overridden via environment variables
GROK1_MODEL_PATH = os.environ.get("GROK1_MODEL_PATH", "amd/grok-1-W4A8KV8")
GROK1_TOKENIZER_PATH = os.environ.get("GROK1_TOKENIZER_PATH", "Xenova/grok-1-tokenizer")
GROK2_MODEL_PATH = os.environ.get("GROK2_MODEL_PATH", "xai-org/grok-2")
GROK2_TOKENIZER_PATH = os.environ.get(
"GROK2_TOKENIZER_PATH", "alvarobartt/grok-2-tokenizer"
)
PROFILE_DIR = "performance_profiles_grok"
class TestNightlyGrokPerformance(unittest.TestCase):
"""Nightly performance benchmark for Grok models (Grok-1 and Grok-2).
Tests both Grok-1 (314B MOE) and Grok-2 models with FP8 quantization on TP=8.
Combined runtime: ~43 minutes (Grok-1: ~23min, Grok-2: ~20min)
"""
@classmethod
def setUpClass(cls):
cls.base_url = DEFAULT_URL_FOR_TEST
cls.batch_sizes = [1, 1, 8, 16, 64]
cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "1024"))
cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))
# Define model configurations for both Grok-1 and Grok-2
cls.models = [
{
"name": "grok1",
"model_path": GROK1_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--quantization",
"fp8",
"--mem-fraction-static",
"0.85",
"--tokenizer-path",
GROK1_TOKENIZER_PATH,
"--attention-backend",
"aiter",
],
"env_vars": {
"RCCL_MSCCL_ENABLE": "0",
"SGLANG_USE_AITER": "1",
"SGLANG_INT4_WEIGHT": "1",
},
},
{
"name": "grok2",
"model_path": GROK2_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--quantization",
"fp8",
"--mem-fraction-static",
"0.85",
"--tokenizer-path",
GROK2_TOKENIZER_PATH,
"--attention-backend",
"aiter",
],
"env_vars": {
"RCCL_MSCCL_ENABLE": "0",
"SGLANG_USE_AITER": "1",
"SGLANG_INT4_WEIGHT": "0",
},
},
]
cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
cls.runner.setup_profile_directory()
# Override full_report to remove traces help text
cls.runner.full_report = f"## {cls.__name__}\n"
def test_bench_one_batch(self):
"""Run benchmark across all Grok models."""
failed_models = []
try:
for model_config in self.models:
with self.subTest(model=model_config["name"]):
# Set environment variables for this model
old_env = {}
for key, value in model_config.get("env_vars", {}).items():
old_env[key] = os.environ.get(key)
os.environ[key] = value
print(f"Setting env: {key}={value}")
try:
result_tuple = self.runner.run_benchmark_for_model(
model_path=model_config["model_path"],
batch_sizes=self.batch_sizes,
input_lens=self.input_lens,
output_lens=self.output_lens,
other_args=model_config["other_args"],
variant=model_config["name"],
extra_bench_args=["--trust-remote-code"],
)
results = result_tuple[0]
success = result_tuple[1]
if not success:
failed_models.append(model_config["name"])
# Use simplified report format without traces
if results:
self.runner.full_report += (
generate_simple_markdown_report(results) + "\n"
)
finally:
# Restore original environment
for key, value in old_env.items():
if value is None:
os.environ.pop(key, None)
else:
os.environ[key] = value
finally:
self.runner.write_final_report()
if failed_models:
raise AssertionError(
f"Benchmark failed for the following models: {', '.join(failed_models)}"
)
if __name__ == "__main__":
unittest.main()