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[AMD] Add MiniMax-M2.5 nightly perf benchmarks for MI30x and MI35x #21524

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Merged
HaiShaw merged 1 commit into from
Apr 3, 2026
Merged

[AMD] Add MiniMax-M2.5 nightly perf benchmarks for MI30x and MI35x #21524

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28 changes: 26 additions & 2 deletions .github/workflows/nightly-test-amd-rocm720.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -685,7 +685,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# 8-GPU MiniMax-M2.5 (Accuracy) ROCm 7.2
# 8-GPU MiniMax-M2.5 (Accuracy + Performance combined) ROCm 7.2
nightly-8-gpu-minimax-m25-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-minimax-m25-rocm720,'))
runs-on: linux-mi325-8gpu-sglang
Expand Down Expand Up @@ -716,6 +716,18 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

- name: Performance Test ROCm 7.2 (8-GPU MiniMax-M2.5)
timeout-minutes: 120
continue-on-error: true # Perf test failure doesn't fail the job if accuracy passed
run: |
> github_summary.md # Clear summary file
bash scripts/ci/amd/amd_ci_exec.sh -w /sglang-checkout/test \
-e SGLANG_USE_AITER=1 \
-e GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-perf-8-gpu-minimax-m25 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# ============================================== MI30x ROCm 7.2 Diffusion Tests ==============================================
# 1-GPU Z-Image-Turbo (Diffusion T2I) ROCm 7.2
nightly-1-gpu-zimage-turbo-rocm720:
Expand Down Expand Up @@ -1306,7 +1318,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# MI35x 8-GPU MiniMax-M2.5 (Accuracy) ROCm 7.2
# MI35x 8-GPU MiniMax-M2.5 (Accuracy + Performance combined) ROCm 7.2
nightly-8-gpu-mi35x-minimax-m25-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-mi35x-minimax-m25-rocm720,'))
runs-on: linux-mi35x-gpu-8
Expand Down Expand Up @@ -1339,6 +1351,18 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

- name: Performance Test MI35x ROCm 7.2 (8-GPU MiniMax-M2.5)
timeout-minutes: 120
continue-on-error: true # Perf test failure doesn't fail the job if accuracy passed
run: |
> github_summary.md # Clear summary file
bash scripts/ci/amd/amd_ci_exec.sh -w /sglang-checkout/test \
-e SGLANG_USE_AITER=1 \
-e GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-perf-8-gpu-mi35x-minimax-m25 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# MI35x 8-GPU DeepSeek-V3.2 Performance Test (MTP) ROCm 7.2
nightly-perf-8-gpu-mi35x-deepseek-v32-mtp-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-perf-8-gpu-mi35x-deepseek-v32-mtp-rocm720,'))
Expand Down
28 changes: 26 additions & 2 deletions .github/workflows/nightly-test-amd.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -687,7 +687,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# 8-GPU MiniMax-M2.5 (Accuracy)
# 8-GPU MiniMax-M2.5 (Accuracy + Performance combined)
nightly-8-gpu-minimax-m25:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-minimax-m25,'))
runs-on: linux-mi325-8gpu-sglang
Expand Down Expand Up @@ -718,6 +718,18 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

- name: Performance Test (8-GPU MiniMax-M2.5)
timeout-minutes: 120
continue-on-error: true # Perf test failure doesn't fail the job if accuracy passed
run: |
> github_summary.md # Clear summary file
bash scripts/ci/amd/amd_ci_exec.sh -w /sglang-checkout/test \
-e SGLANG_USE_AITER=1 \
-e GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-perf-8-gpu-minimax-m25 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# ============================================== MI30x Diffusion Tests ==============================================
# 1-GPU Z-Image-Turbo (Diffusion T2I)
nightly-1-gpu-zimage-turbo:
Expand Down Expand Up @@ -1278,7 +1290,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# MI35x 8-GPU MiniMax-M2.5 (Accuracy)
# MI35x 8-GPU MiniMax-M2.5 (Accuracy + Performance combined)
nightly-8-gpu-mi35x-minimax-m25:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-mi35x-minimax-m25,'))
runs-on: linux-mi35x-gpu-8
Expand Down Expand Up @@ -1311,6 +1323,18 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

- name: Performance Test MI35x (8-GPU MiniMax-M2.5)
timeout-minutes: 120
continue-on-error: true # Perf test failure doesn't fail the job if accuracy passed
run: |
> github_summary.md # Clear summary file
bash scripts/ci/amd/amd_ci_exec.sh -w /sglang-checkout/test \
-e SGLANG_USE_AITER=1 \
-e GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-perf-8-gpu-mi35x-minimax-m25 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}

# MI35x 8-GPU DeepSeek-V3.2 Performance Test (MTP)
nightly-perf-8-gpu-mi35x-deepseek-v32-mtp:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-perf-8-gpu-mi35x-deepseek-v32-mtp,'))
Expand Down
140 changes: 140 additions & 0 deletions test/registered/amd/perf/mi30x/test_minimax_m25_perf_amd.py
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"""Nightly performance benchmark for MiniMax-M2.5 on MI325/MI300X (8-GPU).

This test benchmarks MiniMax-M2.5 with TP=8 + EP=8 configuration.

The model path can be configured via MINIMAX_M25_MODEL_PATH environment variable.

Registry: nightly-perf-8-gpu-minimax-m25 suite

Example usage:
python -m pytest test_minimax_m25_perf_amd.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_amd_ci(est_time=5400, suite="nightly-perf-8-gpu-minimax-m25", 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", "MI325")
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"

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


MINIMAX_M25_MODEL_PATH = os.environ.get(
"MINIMAX_M25_MODEL_PATH", "MiniMaxAI/MiniMax-M2.5"
)
PROFILE_DIR = "performance_profiles_minimax_m25"


class TestNightlyMiniMaxM25Performance(unittest.TestCase):
"""Nightly performance benchmark for MiniMax-M2.5 on MI325/MI300X.

Tests MiniMax-M2.5 with TP=8 + EP=8 configuration.
"""

@classmethod
def setUpClass(cls):
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.model_config = {
"name": "minimax-m25-tp8-ep8",
"model_path": MINIMAX_M25_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--ep-size",
"8",
"--attention-backend",
"aiter",
"--mem-fraction-static",
"0.85",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
"--watchdog-timeout",
"1200",
],
"env_vars": {
"SGLANG_USE_AITER": "1",
},
}

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_minimax_m25(self):
"""Run benchmark for MiniMax-M2.5."""
old_env = {}
for key, value in self.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=self.model_config["model_path"],
batch_sizes=self.batch_sizes,
input_lens=self.input_lens,
output_lens=self.output_lens,
other_args=self.model_config["other_args"],
variant=self.model_config["name"],
extra_bench_args=["--trust-remote-code"],
enable_profile=False,
timeout=5400,
)
results = result_tuple[0]
success = result_tuple[1]

if results:
self.runner.full_report += (
generate_simple_markdown_report(results) + "\n"
)

self.assertTrue(success, "Benchmark failed for MiniMax-M2.5")
finally:
for key, value in old_env.items():
if value is None:
os.environ.pop(key, None)
else:
os.environ[key] = value
self.runner.write_final_report()


if __name__ == "__main__":
unittest.main()
146 changes: 146 additions & 0 deletions test/registered/amd/perf/mi35x/test_minimax_m25_perf_mi35x.py
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"""MI35x Nightly performance benchmark for MiniMax-M2.5 (8-GPU).

This test benchmarks MiniMax-M2.5 with TP=8 + EP=8 configuration on MI35x.

The model path can be configured via MINIMAX_M25_MODEL_PATH environment variable.

Registry: nightly-perf-8-gpu-mi35x-minimax-m25 suite

Example usage:
python -m pytest test_minimax_m25_perf_mi35x.py -v
"""

import os

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_amd_ci(
est_time=5400, suite="nightly-perf-8-gpu-mi35x-minimax-m25", 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"

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


MINIMAX_M25_MODEL_PATH = os.environ.get(
"MINIMAX_M25_MODEL_PATH", "MiniMaxAI/MiniMax-M2.5"
)
PROFILE_DIR = "performance_profiles_minimax_m25_mi35x"


class TestNightlyMiniMaxM25PerformanceMI35x(unittest.TestCase):
"""MI35x Nightly performance benchmark for MiniMax-M2.5.

Tests MiniMax-M2.5 with TP=8 + EP=8 configuration.
"""

@classmethod
def setUpClass(cls):
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.model_config = {
"name": "minimax-m25-tp8-ep8",
"model_path": MINIMAX_M25_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--ep-size",
"8",
"--attention-backend",
"aiter",
"--mem-fraction-static",
"0.85",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
"--watchdog-timeout",
"1200",
],
"env_vars": {
"SGLANG_USE_AITER": "1",
},
}

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_minimax_m25(self):
"""Run benchmark for MiniMax-M2.5."""
old_env = {}
for key, value in self.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=self.model_config["model_path"],
batch_sizes=self.batch_sizes,
input_lens=self.input_lens,
output_lens=self.output_lens,
other_args=self.model_config["other_args"],
variant=self.model_config["name"],
extra_bench_args=["--trust-remote-code"],
enable_profile=False,
timeout=5400,
)
results = result_tuple[0]
success = result_tuple[1]

if results:
self.runner.full_report += (
generate_simple_markdown_report(results) + "\n"
)

self.assertTrue(success, "Benchmark failed for MiniMax-M2.5 on MI35x")
finally:
for key, value in old_env.items():
if value is None:
os.environ.pop(key, None)
else:
os.environ[key] = value
self.runner.write_final_report()


if __name__ == "__main__":
unittest.main()
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