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[AMD] Add Kimi-K2.6 in nightly tests for MI30x and MI35x (#23848)

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This commit is contained in:
Michael
2026-05-04 23:37:14 -07:00
committed by GitHub
parent 6279aee716
commit 244531bc4f
6 changed files with 546 additions and 28 deletions
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.github/workflows/nightly-test-amd-rocm720.yml
+14 -14
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@@ -40,7 +40,7 @@ on:
- nightly-8-gpu-deepseek-v32-rocm720
- nightly-8-gpu-deepseek-v32-mtp-rocm720
- nightly-8-gpu-deepseek-v3-kv-fp8-rocm720
- nightly-8-gpu-kimi-k25-rocm720
- nightly-8-gpu-kimi-k26-rocm720
- nightly-8-gpu-qwen3-235b-rocm720
- nightly-8-gpu-qwen35-rocm720
- nightly-8-gpu-glm51-rocm720
@@ -59,7 +59,7 @@ on:
- nightly-perf-8-gpu-mi35x-deepseek-v32-mtp-rocm720
- nightly-8-gpu-mi35x-deepseek-v4-flash-rocm720
- nightly-8-gpu-mi35x-deepseek-v4-pro-rocm720
- nightly-8-gpu-mi35x-kimi-k25-rocm720
- nightly-8-gpu-mi35x-kimi-k26-rocm720
- nightly-8-gpu-mi35x-qwen3-235b-mxfp4-rocm720
- nightly-8-gpu-mi35x-qwen35-rocm720
- nightly-8-gpu-mi35x-glm51-rocm720
@@ -565,9 +565,9 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# 8-GPU Kimi-K2.5 (Accuracy) ROCm 7.2
nightly-8-gpu-kimi-k25-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-kimi-k25-rocm720,'))
# 8-GPU Kimi-K2.6 (Accuracy) ROCm 7.2
nightly-8-gpu-kimi-k26-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-kimi-k26-rocm720,'))
runs-on: linux-mi325-8gpu-sglang
steps:
- name: Checkout code
@@ -585,13 +585,13 @@ jobs:
- name: Install dependencies
run: bash scripts/ci/amd/amd_ci_install_dependency.sh --skip-test-time-deps
- name: Accuracy Test ROCm 7.2 (8-GPU Kimi-K2.5)
- name: Accuracy Test ROCm 7.2 (8-GPU Kimi-K2.6)
timeout-minutes: 120
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-amd-accuracy-8-gpu-kimi-k25 --nightly --timeout-per-file 3600 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
python3 run_suite.py --hw amd --suite nightly-amd-accuracy-8-gpu-kimi-k26 --nightly --timeout-per-file 3600 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
@@ -1181,9 +1181,9 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# MI35x 8-GPU Kimi-K2.5 (Accuracy) ROCm 7.2
nightly-8-gpu-mi35x-kimi-k25-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-kimi-k25-rocm720,'))
# MI35x 8-GPU Kimi-K2.6 (Accuracy) ROCm 7.2
nightly-8-gpu-mi35x-kimi-k26-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-kimi-k26-rocm720,'))
runs-on: linux-mi35x-gpu-8
steps:
- name: Checkout code
@@ -1204,13 +1204,13 @@ jobs:
# Install tabulate for run_suite.py (missing in MI35x container)
bash scripts/ci/amd/amd_ci_exec.sh pip install tabulate
- name: Accuracy Test MI35x ROCm 7.2 (8-GPU Kimi-K2.5)
- name: Accuracy Test MI35x ROCm 7.2 (8-GPU Kimi-K2.6)
timeout-minutes: 180
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-amd-accuracy-8-gpu-mi35x-kimi-k25 --nightly --timeout-per-file 7200 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
python3 run_suite.py --hw amd --suite nightly-amd-accuracy-8-gpu-mi35x-kimi-k26 --nightly --timeout-per-file 7200 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
@@ -1572,7 +1572,7 @@ jobs:
- nightly-8-gpu-deepseek-v32-rocm720
- nightly-8-gpu-deepseek-v32-mtp-rocm720
- nightly-8-gpu-deepseek-v3-kv-fp8-rocm720
- nightly-8-gpu-kimi-k25-rocm720
- nightly-8-gpu-kimi-k26-rocm720
- nightly-8-gpu-qwen3-235b-rocm720
- nightly-8-gpu-qwen35-rocm720
- nightly-8-gpu-glm51-rocm720
@@ -1593,7 +1593,7 @@ jobs:
- nightly-perf-8-gpu-mi35x-deepseek-v32-mtp-rocm720
- nightly-8-gpu-mi35x-deepseek-v4-flash-rocm720
- nightly-8-gpu-mi35x-deepseek-v4-pro-rocm720
- nightly-8-gpu-mi35x-kimi-k25-rocm720
- nightly-8-gpu-mi35x-kimi-k26-rocm720
- nightly-8-gpu-mi35x-qwen3-235b-mxfp4-rocm720
- nightly-8-gpu-mi35x-qwen35-rocm720
- nightly-8-gpu-mi35x-glm51-rocm720
.github/workflows/nightly-test-amd.yml
+14 -14
View File
@@ -40,7 +40,7 @@ on:
- nightly-8-gpu-deepseek-v32
- nightly-8-gpu-deepseek-v32-mtp
- nightly-8-gpu-deepseek-v3-kv-fp8
- nightly-8-gpu-kimi-k25
- nightly-8-gpu-kimi-k26
- nightly-8-gpu-qwen3-235b
- nightly-8-gpu-qwen35
- nightly-8-gpu-glm51
@@ -57,7 +57,7 @@ on:
- nightly-accuracy-8-gpu-mi35x-deepseek-v32-mtp
- nightly-perf-8-gpu-mi35x-deepseek-v32-basic
- nightly-perf-8-gpu-mi35x-deepseek-v32-mtp
- nightly-8-gpu-mi35x-kimi-k25
- nightly-8-gpu-mi35x-kimi-k26
- nightly-8-gpu-mi35x-qwen3-235b-mxfp4
- nightly-8-gpu-mi35x-qwen35
- nightly-8-gpu-mi35x-glm51
@@ -568,9 +568,9 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# 8-GPU Kimi-K2.5 (Accuracy)
nightly-8-gpu-kimi-k25:
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-kimi-k25,'))
# 8-GPU Kimi-K2.6 (Accuracy)
nightly-8-gpu-kimi-k26:
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-kimi-k26,'))
runs-on: linux-mi325-8gpu-sglang
steps:
- name: Checkout code
@@ -588,13 +588,13 @@ jobs:
- name: Install dependencies
run: bash scripts/ci/amd/amd_ci_install_dependency.sh
- name: Accuracy Test (8-GPU Kimi-K2.5)
- name: Accuracy Test (8-GPU Kimi-K2.6)
timeout-minutes: 120
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-amd-accuracy-8-gpu-kimi-k25 --nightly --timeout-per-file 3600 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
python3 run_suite.py --hw amd --suite nightly-amd-accuracy-8-gpu-kimi-k26 --nightly --timeout-per-file 3600 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
@@ -1186,9 +1186,9 @@ jobs:
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
# MI35x 8-GPU Kimi-K2.5 (Accuracy)
nightly-8-gpu-mi35x-kimi-k25:
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-kimi-k25,'))
# MI35x 8-GPU Kimi-K2.6 (Accuracy)
nightly-8-gpu-mi35x-kimi-k26:
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-kimi-k26,'))
runs-on: linux-mi35x-gpu-8
steps:
- name: Checkout code
@@ -1209,13 +1209,13 @@ jobs:
# Install tabulate for run_suite.py (missing in MI35x container)
bash scripts/ci/amd/amd_ci_exec.sh pip install tabulate
- name: Accuracy Test MI35x (8-GPU Kimi-K2.5)
- name: Accuracy Test MI35x (8-GPU Kimi-K2.6)
timeout-minutes: 180
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-amd-accuracy-8-gpu-mi35x-kimi-k25 --nightly --timeout-per-file 7200 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
python3 run_suite.py --hw amd --suite nightly-amd-accuracy-8-gpu-mi35x-kimi-k26 --nightly --timeout-per-file 7200 ${{ inputs.continue_on_error && '--continue-on-error' || '' }} || TEST_EXIT_CODE=$?
echo "$(<github_summary.md )" >> $GITHUB_STEP_SUMMARY || true
exit ${TEST_EXIT_CODE:-0}
@@ -1441,7 +1441,7 @@ jobs:
- nightly-8-gpu-deepseek-v32
- nightly-8-gpu-deepseek-v32-mtp
- nightly-8-gpu-deepseek-v3-kv-fp8
- nightly-8-gpu-kimi-k25
- nightly-8-gpu-kimi-k26
- nightly-8-gpu-qwen3-235b
- nightly-8-gpu-qwen35
- nightly-8-gpu-glm51
@@ -1458,7 +1458,7 @@ jobs:
- nightly-8-gpu-mi35x-deepseek-r1-mxfp4-ar-fusion
- nightly-accuracy-8-gpu-mi35x-deepseek-v32
- nightly-accuracy-8-gpu-mi35x-deepseek-v32-mtp
- nightly-8-gpu-mi35x-kimi-k25
- nightly-8-gpu-mi35x-kimi-k26
- nightly-8-gpu-mi35x-qwen3-235b-mxfp4
- nightly-8-gpu-mi35x-qwen35
- nightly-8-gpu-mi35x-glm51
test/registered/amd/accuracy/mi30x/test_kimi_k26_eval_amd.py
+108
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@@ -0,0 +1,108 @@
"""AMD Kimi-K2.6 GSM8K Completion Evaluation Test (8-GPU)
Tests moonshotai/Kimi-K2.6 with GSM8K few-shot benchmark on MI325.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI30x test.
Registry: nightly-amd-accuracy-8-gpu-kimi-k26 suite
"""
import os
import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.test_utils import (
DEFAULT_URL_FOR_TEST,
CustomTestCase,
is_in_ci,
popen_launch_server,
write_github_step_summary,
)
# Register for AMD CI - Kimi K2.6 accuracy test (~60 min)
register_amd_ci(
est_time=3600, suite="nightly-amd-accuracy-8-gpu-kimi-k26", nightly=True
)
KIMI_K26_MODEL_PATH = "moonshotai/Kimi-K2.6"
SERVER_LAUNCH_TIMEOUT = 3600
ACCURACY_THRESHOLD = 0.92
TP_SIZE = 8
class TestKimiK26EvalAMD(CustomTestCase):
"""Kimi-K2.6 GSM8K Completion Evaluation Test for AMD MI325."""
@classmethod
def setUpClass(cls):
cls.model = KIMI_K26_MODEL_PATH
cls.base_url = DEFAULT_URL_FOR_TEST
other_args = [
"--tp",
str(TP_SIZE),
"--decode-attention-backend",
"triton",
"--prefill-attention-backend",
"aiter",
"--trust-remote-code",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
]
env = os.environ.copy()
env["SGLANG_USE_AITER"] = "1"
env["SGLANG_ROCM_FUSED_DECODE_MLA"] = "0"
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=SERVER_LAUNCH_TIMEOUT,
other_args=other_args,
env=env,
)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid)
def test_kimi_k26_gsm8k_accuracy(self):
"""Test Kimi-K2.6 with GSM8K few-shot completion benchmark."""
requests.get(self.base_url + "/flush_cache")
args = SimpleNamespace(
num_shots=8,
data_path=None,
num_questions=1319,
parallel=1319,
max_new_tokens=512,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
acc = metrics["accuracy"]
passed = acc >= ACCURACY_THRESHOLD
status = "✅ PASS" if passed else "❌ FAIL"
print(f" accuracy={acc:.3f} threshold={ACCURACY_THRESHOLD} {status}")
if is_in_ci():
summary = "### Kimi-K2.6 Model (MI325)\n\n"
summary += "| Model | TP | Accuracy | Threshold | Status |\n"
summary += "| ----- | -- | -------- | --------- | ------ |\n"
summary += f"| {KIMI_K26_MODEL_PATH} | {TP_SIZE} | {acc:.3f} | {ACCURACY_THRESHOLD} | {status} |\n"
write_github_step_summary(summary)
self.assertGreaterEqual(
acc,
ACCURACY_THRESHOLD,
f"Kimi-K2.6 accuracy {acc:.3f} below threshold {ACCURACY_THRESHOLD}",
)
if __name__ == "__main__":
unittest.main()
test/registered/amd/accuracy/mi35x/test_kimi_k26_eval_mi35x.py
+110
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@@ -0,0 +1,110 @@
"""MI35x Kimi-K2.6 GSM8K Completion Evaluation Test (8-GPU)
Tests moonshotai/Kimi-K2.6 with GSM8K few-shot benchmark on MI35x.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI35x test.
Registry: nightly-amd-accuracy-8-gpu-mi35x-kimi-k26 suite
"""
import os
import unittest
from types import SimpleNamespace
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
from sglang.test.test_utils import (
DEFAULT_URL_FOR_TEST,
CustomTestCase,
is_in_ci,
popen_launch_server,
write_github_step_summary,
)
# Register for AMD CI - Kimi K2.6 accuracy test on MI35x (~90 min)
register_amd_ci(
est_time=5400, suite="nightly-amd-accuracy-8-gpu-mi35x-kimi-k26", nightly=True
)
KIMI_K26_MODEL_PATH = "moonshotai/Kimi-K2.6"
SERVER_LAUNCH_TIMEOUT = 5400
ACCURACY_THRESHOLD = 0.92
TP_SIZE = 8
class TestKimiK26EvalMI35x(CustomTestCase):
"""Kimi-K2.6 GSM8K Completion Evaluation Test for AMD MI35x."""
@classmethod
def setUpClass(cls):
cls.base_url = DEFAULT_URL_FOR_TEST
def test_kimi_k26_gsm8k_accuracy(self):
"""Test Kimi-K2.6 with GSM8K few-shot completion benchmark."""
other_args = [
"--tp",
str(TP_SIZE),
"--decode-attention-backend",
"triton",
"--prefill-attention-backend",
"aiter",
"--trust-remote-code",
"--model-loader-extra-config",
'{"enable_multithread_load": true}',
"--watchdog-timeout",
"1200",
]
env = os.environ.copy()
env["SGLANG_USE_AITER"] = "1"
env["SGLANG_ROCM_FUSED_DECODE_MLA"] = "0"
process = popen_launch_server(
KIMI_K26_MODEL_PATH,
self.base_url,
timeout=SERVER_LAUNCH_TIMEOUT,
other_args=other_args,
env=env,
)
try:
requests.get(self.base_url + "/flush_cache")
args = SimpleNamespace(
num_shots=8,
data_path=None,
num_questions=1319,
parallel=1319,
max_new_tokens=512,
host="http://127.0.0.1",
port=int(self.base_url.split(":")[-1]),
)
metrics = run_eval_few_shot_gsm8k(args)
acc = metrics["accuracy"]
passed = acc >= ACCURACY_THRESHOLD
status = "✅ PASS" if passed else "❌ FAIL"
print(f" accuracy={acc:.3f} threshold={ACCURACY_THRESHOLD} {status}")
if is_in_ci():
summary = "### Kimi-K2.6 Model (MI35x)\n\n"
summary += "| Model | TP | Accuracy | Threshold | Status |\n"
summary += "| ----- | -- | -------- | --------- | ------ |\n"
summary += f"| {KIMI_K26_MODEL_PATH} | {TP_SIZE} | {acc:.3f} | {ACCURACY_THRESHOLD} | {status} |\n"
write_github_step_summary(summary)
self.assertGreaterEqual(
acc,
ACCURACY_THRESHOLD,
f"Kimi-K2.6 accuracy {acc:.3f} below threshold {ACCURACY_THRESHOLD}",
)
finally:
kill_process_tree(process.pid)
if __name__ == "__main__":
unittest.main()
test/registered/amd/perf/mi30x/test_kimi_k26_perf_amd.py
+148
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@@ -0,0 +1,148 @@
"""AMD Nightly performance benchmark for Kimi-K2.6 model.
This test benchmarks moonshotai/Kimi-K2.6 with TP=8 on MI325/MI300X.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI30x accuracy test (mixed aiter prefill + triton decode).
The model path can be configured via KIMI_K26_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-kimi-k26 suite
Example usage:
KIMI_K26_MODEL_PATH=moonshotai/Kimi-K2.6 python -m pytest test_kimi_k26_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 for AMD CI - Kimi K2.6 perf benchmark (~90 min)
register_amd_ci(est_time=5400, suite="nightly-perf-8-gpu-kimi-k26", 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
KIMI_K26_MODEL_PATH = os.environ.get("KIMI_K26_MODEL_PATH", "moonshotai/Kimi-K2.6")
PROFILE_DIR = "performance_profiles_kimi_k26"
class TestNightlyKimiK26Performance(unittest.TestCase):
"""AMD Nightly performance benchmark for Kimi-K2.6 model.
Tests Kimi-K2.6 with TP=8 mixed-attention configuration on MI325/MI300X.
"""
@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"))
# Kimi-K2.6 shares Kimi-K2.5's architecture: aiter for prefill,
# triton for decode (aiter ASM MLA decode requires heads_per_gpu % 16,
# but TP=8 with 64 heads gives 8 heads/GPU, so we use triton decode).
cls.model_config = {
"name": "default",
"model_path": KIMI_K26_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--decode-attention-backend",
"triton",
"--prefill-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",
"SGLANG_ROCM_FUSED_DECODE_MLA": "0",
},
}
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_kimi_k26(self):
"""Run benchmark for Kimi-K2.6."""
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 {self.model_config['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_kimi_k26_perf_mi35x.py
+152
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"""MI35x Nightly performance benchmark for Kimi-K2.6 model.
This test benchmarks moonshotai/Kimi-K2.6 with TP=8 on MI35x.
Kimi-K2.6 shares the same architecture as Kimi-K2.5 (per the model card the
deployment method is directly reused), so the AMD server arguments match the
existing Kimi-K2.5 MI35x accuracy test (mixed aiter prefill + triton decode).
The model path can be configured via KIMI_K26_MODEL_PATH environment variable.
Registry: nightly-perf-8-gpu-mi35x-kimi-k26 suite
Example usage:
KIMI_K26_MODEL_PATH=moonshotai/Kimi-K2.6 python -m pytest test_kimi_k26_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 for AMD CI - Kimi K2.6 perf benchmark on MI35x (~90 min)
register_amd_ci(est_time=5400, suite="nightly-perf-8-gpu-mi35x-kimi-k26", 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
KIMI_K26_MODEL_PATH = os.environ.get("KIMI_K26_MODEL_PATH", "moonshotai/Kimi-K2.6")
PROFILE_DIR = "performance_profiles_kimi_k26_mi35x"
class TestNightlyKimiK26PerformanceMI35x(unittest.TestCase):
"""MI35x Nightly performance benchmark for Kimi-K2.6 model.
Tests Kimi-K2.6 with TP=8 mixed-attention configuration on MI35x.
"""
@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"))
# Kimi-K2.6 shares Kimi-K2.5's architecture: aiter for prefill,
# triton for decode (aiter ASM MLA decode requires heads_per_gpu % 16,
# but TP=8 with 64 heads gives 8 heads/GPU, so we use triton decode).
cls.model_config = {
"name": "default",
"model_path": KIMI_K26_MODEL_PATH,
"other_args": [
"--trust-remote-code",
"--tp",
"8",
"--decode-attention-backend",
"triton",
"--prefill-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",
"SGLANG_ROCM_FUSED_DECODE_MLA": "0",
},
}
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_kimi_k26(self):
"""Run benchmark for Kimi-K2.6."""
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 {self.model_config['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()