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[AMD] Add GLM-5-FP8 nightly performance benchmarks for MI30x and MI35x (#21710)

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This commit is contained in:
Michael
2026-04-07 22:43:14 -07:00
committed by GitHub
parent 729b74d8dd
commit db60a620db
6 changed files with 345 additions and 5 deletions
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.github/workflows/nightly-test-amd-rocm720.yml
+25 -1
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@@ -665,7 +665,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# 8-GPU GLM-5 (Accuracy) ROCm 7.2
# 8-GPU GLM-5 (Accuracy + Performance combined) ROCm 7.2
nightly-8-gpu-glm5-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-glm5-rocm720,'))
runs-on: linux-mi325-8gpu-sglang
@@ -697,6 +697,18 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
- name: Performance Test ROCm 7.2 (8-GPU GLM-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-glm5 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# 8-GPU MiniMax-M2.5 (Accuracy + Performance combined) ROCm 7.2
nightly-8-gpu-minimax-m25-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-minimax-m25-rocm720,'))
@@ -1276,6 +1288,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# MI35x 8-GPU GLM-5 (Accuracy + Performance combined) ROCm 7.2
nightly-8-gpu-mi35x-glm5-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-mi35x-glm5-rocm720,'))
runs-on: linux-mi35x-gpu-8
@@ -1309,6 +1322,17 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
- name: Performance Test MI35x ROCm 7.2 (8-GPU GLM-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 GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-perf-8-gpu-mi35x-glm5 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# MI35x 8-GPU GLM-4.7-FP8 (Accuracy) ROCm 7.2
nightly-8-gpu-mi35x-glm47-fp8-rocm720:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-mi35x-glm47-fp8-rocm720,'))
.github/workflows/nightly-test-amd.yml
+25
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@@ -668,6 +668,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# 8-GPU GLM-5 (Accuracy + Performance combined)
nightly-8-gpu-glm5:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-glm5,'))
runs-on: linux-mi325-8gpu-sglang
@@ -699,6 +700,18 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
- name: Performance Test (8-GPU GLM-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-glm5 --nightly --timeout-per-file 5400 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# 8-GPU MiniMax-M2.5 (Accuracy + Performance combined)
nightly-8-gpu-minimax-m25:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-minimax-m25,'))
@@ -1281,6 +1294,7 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# MI35x 8-GPU GLM-5 (Accuracy + Performance combined)
nightly-8-gpu-mi35x-glm5:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') && (!(inputs.job_filter || inputs.job_select) || (inputs.job_filter || inputs.job_select) == 'all' || contains(format(',{0},', inputs.job_filter || inputs.job_select), ',nightly-8-gpu-mi35x-glm5,'))
runs-on: linux-mi35x-gpu-8
@@ -1314,6 +1328,17 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
- name: Performance Test MI35x (8-GPU GLM-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 GITHUB_STEP_SUMMARY="/sglang-checkout/github_summary.md" \
python3 run_suite.py --hw amd --suite nightly-perf-8-gpu-mi35x-glm5 --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 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,'))
test/registered/amd/accuracy/mi30x/test_glm5_eval_amd.py
+6 -2
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@@ -59,13 +59,17 @@ class ModelConfig:
GLM5_MODELS = [
# GLM-5 with NSA attention (TP=8)
ModelConfig(
model_path="zai-org/GLM-5",
model_path="zai-org/GLM-5-FP8",
tp_size=8,
accuracy_threshold=0.93,
timeout=3600,
variant="nsa",
other_args=[
"--trust-remote-code",
"--reasoning-parser",
"glm45",
"--tool-call-parser",
"glm47",
"--nsa-prefill-backend",
"tilelang",
"--nsa-decode-backend",
@@ -77,7 +81,7 @@ GLM5_MODELS = [
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
"--watchdog-timeout",
"1200", # 20 minutes for weight loading
"1200",
],
env_vars={"SGLANG_USE_AITER": "1"},
),
test/registered/amd/accuracy/mi35x/test_glm5_eval_mi35x.py
+6 -2
View File
@@ -64,13 +64,17 @@ class ModelConfig:
MI35X_GLM5_MODELS = [
# GLM-5 with NSA attention (TP=8)
ModelConfig(
model_path="zai-org/GLM-5",
model_path="zai-org/GLM-5-FP8",
tp_size=8,
accuracy_threshold=0.93,
timeout=5400,
variant="nsa",
other_args=[
"--trust-remote-code",
"--reasoning-parser",
"glm45",
"--tool-call-parser",
"glm47",
"--nsa-prefill-backend",
"tilelang",
"--nsa-decode-backend",
@@ -82,7 +86,7 @@ MI35X_GLM5_MODELS = [
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
"--watchdog-timeout",
"1200", # 20 minutes for weight loading
"1200",
],
env_vars={},
),
test/registered/amd/perf/mi30x/test_glm5_perf_amd.py
+140
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@@ -0,0 +1,140 @@
"""Nightly performance benchmark for GLM-5 on MI30x.
Tests GLM-5 with NSA attention backend using bench_one_batch on 8 GPUs.
Model paths can be configured via environment variables:
- GLM5_MODEL_PATH: Path to GLM-5 model (default: zai-org/GLM-5-FP8)
Example usage:
python -m pytest test_glm5_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-glm5", nightly=True)
def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
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
GLM5_MODEL_PATH = os.environ.get("GLM5_MODEL_PATH", "zai-org/GLM-5-FP8")
PROFILE_DIR = "performance_profiles_glm5"
class TestNightlyGLM5Performance(unittest.TestCase):
"""Nightly performance benchmark for GLM-5.
Tests GLM-5 with NSA attention backend on TP=8.
"""
@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": "glm5",
"model_path": GLM5_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--reasoning-parser",
"glm45",
"--tool-call-parser",
"glm47",
"--tp",
"8",
"--nsa-prefill-backend",
"tilelang",
"--nsa-decode-backend",
"tilelang",
"--kv-cache-dtype",
"fp8_e4m3",
"--chunked-prefill-size",
"131072",
"--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_glm5(self):
"""Run benchmark for GLM-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
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, f"Benchmark failed for {GLM5_MODEL_PATH}")
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()
test/registered/amd/perf/mi35x/test_glm5_perf_mi35x.py
+143
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@@ -0,0 +1,143 @@
"""MI35x Nightly performance benchmark for GLM-5.
Tests GLM-5 with NSA attention backend using bench_one_batch on 8 GPUs.
Registry: nightly-perf-8-gpu-mi35x-glm5 suite
"""
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-glm5", nightly=True)
def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
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
GLM5_MODEL_PATH = os.environ.get("GLM5_MODEL_PATH", "zai-org/GLM-5-FP8")
PROFILE_DIR = "performance_profiles_glm5_mi35x"
class TestGLM5PerfMI35x(unittest.TestCase):
"""Nightly performance benchmark for GLM-5 on MI35x.
Tests GLM-5 with NSA attention backend on TP=8.
"""
@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": "glm5-mi35x",
"model_path": GLM5_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--reasoning-parser",
"glm45",
"--tool-call-parser",
"glm47",
"--tp",
"8",
"--nsa-prefill-backend",
"tilelang",
"--nsa-decode-backend",
"tilelang",
"--kv-cache-dtype",
"fp8_e4m3",
"--chunked-prefill-size",
"131072",
"--mem-fraction-static",
"0.85",
"--model-loader-extra-config",
'{"enable_multithread_load": true, "num_threads": 8}',
"--watchdog-timeout",
"1200",
],
"env_vars": {
"SGLANG_ROCM_FUSED_DECODE_MLA": "0",
"ROCM_QUICK_REDUCE_QUANTIZATION": "INT4",
"SAFETENSORS_FAST_GPU": "1",
},
}
os.environ.setdefault("SGLANG_BENCH_TIMEOUT", "3600")
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_glm5_perf(self):
"""Run GLM-5 performance benchmark on MI35x."""
old_env = {}
for key, value in self.model_config.get("env_vars", {}).items():
old_env[key] = os.environ.get(key)
os.environ[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, f"Benchmark failed for {GLM5_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()