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[AMD][CI] Add MiniMax-M3-MXFP8 MI35x nightly perf benchmark (#36142)

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This commit is contained in:
Michael
2026-08-25 14:05:01 -07:00
committed by GitHub
parent edff717ef0
commit 6569125e3a
2 changed files with 168 additions and 51 deletions
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.github/workflows/nightly-test-amd-rocm720.yml
+17 -51
View File
@@ -96,9 +96,7 @@ on:
- nightly-8-gpu-mi35x-glm52-fp8-rocm720
# 8-GPU GLM-5-MXFP4 (MI35x only)
- nightly-8-gpu-mi35x-glm5-mxfp4-rocm720
# 4-GPU MiniMax-M2.5 (MI35x)
- nightly-4-gpu-mi35x-minimax-m25-rocm720
# 4-GPU MiniMax-M3 MXFP8 (MI35x)
# 4-GPU MiniMax-M3 MXFP8 (MI35x accuracy + performance)
- nightly-4-gpu-mi35x-minimax-m3-rocm720
# 8-GPU MiniMax-M2.7 (MI30x only)
- nightly-8-gpu-minimax-m27-rocm720
@@ -2164,51 +2162,7 @@ jobs:
exit ${TEST_EXIT_CODE:-0}
# ==============================================================================
# 4-GPU MiniMax-M2.5 (MI35x)
# ==============================================================================
nightly-4-gpu-mi35x-minimax-m25-rocm720:
name: ${{ format('nightly-4-gpu-mi35x-minimax-m25 ({0}, linux-mi35x-gpu-8)', matrix.rocm_version) }}
strategy:
fail-fast: false
matrix:
rocm_version: ${{ fromJson(inputs.rocm_version && inputs.rocm_version != 'all' && format('["{0}"]', inputs.rocm_version) || '["rocm724", "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-4-gpu-mi35x-minimax-m25-rocm720,'))
runs-on: linux-mi35x-gpu-8
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ inputs.ref || github.sha }}
- name: Ensure VRAM is clear
run: bash scripts/ci/amd/ensure_vram_clear.sh rocm
- name: Setup docker (${{ matrix.rocm_version }})
run: |
touch github_summary.md
bash scripts/ci/amd/amd_ci_start_container.sh --rocm-version ${{ matrix.rocm_version }}
env:
GITHUB_WORKSPACE: ${{ github.workspace }}
ENABLE_CACHE_HOST: "1"
- name: Install dependencies
run: bash scripts/ci/amd/amd_ci_install_dependency.sh --skip-test-time-deps
- name: Accuracy Test MI35x ROCm 7.2 (4-GPU MiniMax-M2.5)
timeout-minutes: 120
run: |
> github_summary.md
bash scripts/ci/amd/amd_ci_exec.sh -w /sglang-checkout/test \
-e SGLANG_USE_AITER=1 \
-e SGLANG_USE_AITER_UNIFIED_ATTN=1 \
-e GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-amd-4-gpu-mi35x-minimax-m25-tp4 --nightly --timeout-per-file 5400 ${{ (github.event_name == 'schedule' || inputs.continue_on_error) && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# ==============================================================================
# 4-GPU MiniMax-M3 MXFP8 (MI35x)
# 4-GPU MiniMax-M3 MXFP8 (MI35x accuracy + performance)
# ==============================================================================
nightly-4-gpu-mi35x-minimax-m3-rocm720:
@@ -2250,6 +2204,20 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# Run perf only after correctness passes. Keeping both steps in this job
# reuses the cached checkpoint and one scarce MI35x slot.
- name: Performance Test MI35x ROCm 7.2 (4-GPU MiniMax-M3 MXFP8)
timeout-minutes: 120
continue-on-error: true # Perf test failure doesn't fail the job if accuracy passed
run: |
> github_summary.md
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-4-gpu-mi35x-minimax-m3 --nightly --timeout-per-file 5400 ${{ (github.event_name == 'schedule' || inputs.continue_on_error) && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# ==============================================================================
# 8-GPU MiniMax-M2.7 (MI30x only)
# ==============================================================================
@@ -2415,9 +2383,7 @@ jobs:
- nightly-8-gpu-mi35x-glm52-fp8-rocm720
# 8-GPU GLM-5-MXFP4 (MI35x only)
- nightly-8-gpu-mi35x-glm5-mxfp4-rocm720
# 4-GPU MiniMax-M2.5 (MI35x)
- nightly-4-gpu-mi35x-minimax-m25-rocm720
# 4-GPU MiniMax-M3 MXFP8 (MI35x)
# 4-GPU MiniMax-M3 MXFP8 (MI35x accuracy + performance)
- nightly-4-gpu-mi35x-minimax-m3-rocm720
# 8-GPU MiniMax-M2.7 (MI30x only)
- nightly-8-gpu-minimax-m27-rocm720
test/registered/amd/perf/mi35x/test_minimax_m3_perf_mi35x.py
+151
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@@ -0,0 +1,151 @@
"""MI35x nightly performance benchmark for MiniMax-M3-MXFP8 (4-GPU, TP=4).
Benchmarks MiniMaxAI/MiniMax-M3-MXFP8 with the same TP=4 recipe the MI35x
accuracy test validated (aiter attention, fp8 e4m3 KV, block-fp8 linear path,
quick INT4 all-reduce), so a throughput regression cannot be confused with a
configuration difference. MI35x (gfx950 / CDNA4) has hardware MX-scaled matmul,
so the MXFP8 MoE weights are served natively.
This runs as the step after the eval inside nightly-4-gpu-mi35x-minimax-m3-rocm720
rather than as a job of its own, which is how the other combined accuracy plus
performance jobs in that workflow are arranged. Sharing the job reuses the
already-cached checkpoint and one MI35x runner slot, and step ordering supplies
the gate for free: a failed eval fails the job, so this never measures
throughput on a build that got the tokens wrong.
1K-input / 1K-output is the workload shape the cookbook publishes for MI355X, and
it weights decode heavily, which is what matters for a reasoning model that
spends most of its tokens inside `<mm:think>`. That published row is a tp8
bench_serving run (~1678 output tok/s at concurrency 64) while this sweep is tp4
under bench_one_batch_server, so treat it as a shape reference, not a target.
Registry: nightly-perf-4-gpu-mi35x-minimax-m3 suite
"""
import os
import unittest
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.nightly_bench_utils import generate_simple_markdown_report
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-4-gpu-mi35x-minimax-m3",
nightly=True,
)
MINIMAX_M3_MODEL_PATH = os.environ.get(
"MINIMAX_M3_MODEL_PATH", "MiniMaxAI/MiniMax-M3-MXFP8"
)
RESULT_DIR = "performance_results_minimax_m3_mi35x"
MAX_BATCH_SIZE = 64
class TestNightlyMiniMaxM3PerformanceMI35x(unittest.TestCase):
"""MiniMax-M3-MXFP8 TP=4 throughput on AMD MI35x."""
@classmethod
def setUpClass(cls):
cls.base_url = DEFAULT_URL_FOR_TEST
# bench_one_batch_server already warms every unique batch size before it
# times anything, but only at output_len 16. The repeated leading 1 buys
# one throwaway full-length decode -- the report helper drops it -- so
# whatever is first touched over 1024 output tokens lands there instead
# of in a published row.
cls.batch_sizes = [1, 1, 8, 16, MAX_BATCH_SIZE]
cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "1024"))
cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "1024"))
cls.model_config = {
"name": "TP4+MXFP8+aiterAttn+fp8KV+blockFP8+quickAR",
"model_path": MINIMAX_M3_MODEL_PATH,
# Mirrors test_minimax_m3_tp4_eval_mi35x.py, plus multithread weight
# load and a decode graph capped at the largest timed batch (capture
# is not spent on batches this sweep never sends).
"other_args": [
"--quantization",
"mxfp8",
"--dtype",
"bfloat16",
"--trust-remote-code",
"--tp",
"4",
"--attention-backend",
"aiter",
"--kv-cache-dtype",
"fp8_e4m3",
"--disable-radix-cache",
"--chunked-prefill-size",
"8192",
"--mem-fraction-static",
"0.80",
"--cuda-graph-max-bs-decode",
str(MAX_BATCH_SIZE),
"--max-running-requests",
str(MAX_BATCH_SIZE),
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
"--watchdog-timeout",
"1200",
],
# See the accuracy test for what each of these does: the block-fp8
# linear path (#32036) and custom/quick INT4 all-reduce (#32230) are
# both opt-in on gfx950, and the fp32 router GEMM is what ROCm 7.0's
# rocBLAS accepts.
"env_vars": {
"SGLANG_USE_AITER": "1",
"SGLANG_OPT_USE_BF16_ROUTER_GEMM": "0",
"SGLANG_FORCE_MXFP8_BLOCK_CONVERT": "1",
"SGLANG_M3_ALLOW_CUSTOM_AR": "1",
"ROCM_QUICK_REDUCE_QUANTIZATION": "INT4",
"ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": "1",
},
}
cls.runner = NightlyBenchmarkRunner(RESULT_DIR, cls.__name__, cls.base_url)
cls.runner.setup_result_directory()
cls.runner.full_report = f"## {cls.__name__}\n"
def test_bench_minimax_m3(self):
"""Run the MiniMax-M3-MXFP8 batch-size sweep."""
old_env = {}
for key, value in self.model_config["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"],
timeout=5400,
)
results, success = result_tuple[0], result_tuple[1]
if results:
self.runner.full_report += (
generate_simple_markdown_report(results, default_gpu_config="MI35x")
+ "\n"
)
self.assertTrue(
success, f"Benchmark failed for {MINIMAX_M3_MODEL_PATH} 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()