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add NPU GSM8K accuracy tests for 7 models (#32649)

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This commit is contained in:
Sugar920
2026-08-01 14:22:57 +08:00
committed by GitHub
parent c0d06a6547
commit fd96a35fb0
8 changed files with 486 additions and 0 deletions
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.github/workflows/pr-test-npu.yml
+25
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@@ -463,6 +463,31 @@ jobs:
runner: linux-aarch64-a3-2
test_case: test/registered/ascend/performance/qwen3_6_27b/test_npu_qwen3_6_27b_w8a8_1p_in64k_out1k_50ms.py
test_type: 'perf'
# NPU accuracy tests
- name: glm4_7_flash_1p_gsm8k
runner: linux-aarch64-a3-2
test_case: test/registered/ascend/accuracy/glm4_7_flash/test_npu_glm4_7_flash_1p_gsm8k.py
test_type: 'accuracy'
- name: qwen3_vl_30b_a3b_bf16_2p_gsm8k
runner: linux-aarch64-a3-4
test_case: test/registered/ascend/accuracy/qwen3_vl_30b_a3b/test_npu_qwen3_vl_30b_a3b_bf16_2p_gsm8k.py
test_type: 'accuracy'
- name: glm5_top64_pruned_bf16_8p_gsm8k
runner: linux-aarch64-a3-16
test_case: test/registered/ascend/accuracy/glm5_top64_pruned/test_npu_glm5_top64_pruned_bf16_8p_gsm8k.py
test_type: 'accuracy'
- name: moonshotai_moonlight_16b_a3b_bf16_1p_gsm8k
runner: linux-aarch64-a3-2
test_case: test/registered/ascend/accuracy/moonshotai_moonlight_16b_a3b/test_npu_moonlight_16b_a3b_bf16_1p_gsm8k.py
test_type: 'accuracy'
- name: qwen3_5_9b_bf16_1p_gsm8k
runner: linux-aarch64-a3-2
test_case: test/registered/ascend/accuracy/qwen3_5_9b/test_npu_qwen3_5_9b_bf16_1p_gsm8k.py
test_type: 'accuracy'
- name: qwen3_vl_8b_bf16_2p_gsm8k
runner: linux-aarch64-a3-4
test_case: test/registered/ascend/accuracy/qwen3_vl_8b/test_npu_qwen3_vl_8b_bf16_2p_gsm8k.py
test_type: 'accuracy'
uses: ./.github/workflows/nightly-test-npu-e2e-single-node.yml
with:
runner: ${{ matrix.test_config.runner }}
python/sglang/test/ascend/e2e/test_npu_performance_utils.py
+7
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@@ -150,6 +150,13 @@ KIMI_K2_5_EAGLE3_MODEL_PATH = (
"/root/.cache/modelscope/hub/models/lightseekorg/kimi-k2.5-eagle3"
)
GLM_4_7_FLASH_MODEL_PATH = "/root/.cache/modelscope/hub/models/ZhipuAI/GLM-4.7-Flash"
QWEN3_5_9B_MODEL_PATH = "/root/.cache/modelscope/hub/models/Qwen/Qwen3.5-9B"
MOONLIGHT_16B_A3B_MODEL_PATH = (
"/root/.cache/modelscope/hub/models/moonshotai/Moonlight-16B-A3B-Instruct"
)
GLM5_TOP64_PRUNED_GSM8K_MODEL_PATH = (
"/root/.cache/modelscope/hub/models/yzgjhdxf/GLM-5-top64-pruned-gsm8k"
)
GLM_5_1_W4A8_MODEL_PATH = "/root/.cache/modelscope/hub/models/Eco-Tech/GLM-5.1-w4a8"
MINIMAX_M2_5_W8A8_MODEL_PATH = (
"/root/.cache/modelscope/hub/models/Eco-Tech/MiniMax-M2.5-w8a8-QuaRot"
test/registered/ascend/accuracy/glm4_7_flash/test_npu_glm4_7_flash_1p_gsm8k.py
+76
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@@ -0,0 +1,76 @@
import unittest
from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
TestNpuAccuracyTestCaseBase,
)
from sglang.test.ascend.e2e.test_npu_performance_utils import GLM_4_7_FLASH_MODEL_PATH
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=3600,
suite="",
nightly=True,
disabled="accuracy testcase",
)
ENVS = {
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_BUFFSIZE": "1000",
"HCCL_OP_EXPANSION_MODE": "AIV",
"HCCL_SOCKET_IFNAME": "lo",
"GLOO_SOCKET_IFNAME": "lo",
"SGLANG_SET_CPU_AFFINITY": "1",
}
OTHER_ARGS = [
"--attention-backend",
"ascend",
"--device",
"npu",
"--tp-size",
2,
"--chunked-prefill-size",
16384,
"--max-prefill-tokens",
150000,
"--dtype",
"bfloat16",
"--max-running-requests",
32,
"--trust-remote-code",
"--mem-fraction-static",
0.75,
"--cuda-graph-bs",
1,
2,
4,
8,
16,
32,
"--watchdog-timeout",
9000,
"--reasoning-parser",
"glm45",
"--tool-call-parser",
"glm47",
]
class TestNPUGlm4_7Flash_1P_GSM8K(TestNpuAccuracyTestCaseBase):
model = GLM_4_7_FLASH_MODEL_PATH
envs = ENVS
other_args = OTHER_ARGS
accuracy = 0.9560
datasets = ["gsm8k"]
few_shot_num = 5
generation_config = {"max_tokens": 65536, "temperature": 1.0}
eval_batch_size = 64
def test_gsm8k(self):
self.run_accuracy()
if __name__ == "__main__":
unittest.main()
test/registered/ascend/accuracy/glm5_top64_pruned/test_npu_glm5_top64_pruned_bf16_8p_gsm8k.py
+71
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@@ -0,0 +1,71 @@
import unittest
from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
TestNpuAccuracyTestCaseBase,
)
from sglang.test.ascend.e2e.test_npu_performance_utils import (
GLM5_TOP64_PRUNED_GSM8K_MODEL_PATH,
)
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=3600,
suite="",
nightly=True,
disabled="accuracy testcase",
)
ENVS = {
"SGLANG_SET_CPU_AFFINITY": "1",
"STREAMS_PER_DEVICE": "32",
"SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT": "600",
"SGLANG_ENABLE_SPEC_V2": "1",
"SGLANG_ENABLE_OVERLAP_PLAN_STREAM": "1",
"SGLANG_NPU_USE_MULTI_STREAM": "1",
"HCCL_BUFFSIZE": "1000",
"HCCL_OP_EXPANSION_MODE": "AIV",
"HCCL_SOCKET_IFNAME": "lo",
"GLOO_SOCKET_IFNAME": "lo",
}
OTHER_ARGS = [
"--attention-backend",
"ascend",
"--tp-size",
"16",
"--chunked-prefill-size",
"16384",
"--trust-remote-code",
"--disable-radix-cache",
"--mem-fraction-static",
"0.7",
"--served-model-name",
"glm-5",
"--moe-a2a-backend",
"deepep",
"--deepep-mode",
"auto",
"--cuda-graph-bs",
16,
]
class TestNPUGLM5_Top64_Pruned_GSM8K(TestNpuAccuracyTestCaseBase):
model = GLM5_TOP64_PRUNED_GSM8K_MODEL_PATH
envs = ENVS
other_args = OTHER_ARGS
accuracy = 0.50
datasets = ["gsm8k"]
generation_config = {
"max_tokens": 2048,
"temperature": 0.01,
}
eval_batch_size = 16
limit = 100
def test_gsm8k(self):
self.run_accuracy()
if __name__ == "__main__":
unittest.main()
test/registered/ascend/accuracy/moonshotai_moonlight_16b_a3b/test_npu_moonlight_16b_a3b_bf16_1p_gsm8k.py
+73
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@@ -0,0 +1,73 @@
import unittest
from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
TestNpuAccuracyTestCaseBase,
)
from sglang.test.ascend.e2e.test_npu_performance_utils import (
MOONLIGHT_16B_A3B_MODEL_PATH,
)
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=3600,
suite="",
nightly=True,
disabled="accuracy testcase",
)
MODEL_ENVS = {
"SGLANG_SET_CPU_AFFINITY": "1",
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_SOCKET_IFNAME": "lo",
"GLOO_SOCKET_IFNAME": "lo",
"HCCL_BUFFSIZE": "1536",
"HCCL_OP_EXPANSION_MODE": "AIV",
}
MODEL_OTHER_ARGS = [
"--tp-size",
2,
"--trust-remote-code",
"--attention-backend",
"ascend",
"--device",
"npu",
"--dtype",
"bfloat16",
"--mem-fraction-static",
0.8,
"--disable-radix-cache",
"--chunked-prefill-size",
4096,
"--max-prefill-tokens",
16384,
"--cuda-graph-bs",
1,
2,
4,
8,
16,
"--max-running-requests",
128,
"--watchdog-timeout",
9000,
]
class TestNPUMoonlight16B_A3B_GSM8K(TestNpuAccuracyTestCaseBase):
model = MOONLIGHT_16B_A3B_MODEL_PATH
envs = MODEL_ENVS
other_args = MODEL_OTHER_ARGS
accuracy = 0.8370
datasets = ["gsm8k"]
few_shot_num = 5
generation_config = {"max_tokens": 7168, "temperature": 1.0}
eval_batch_size = 64
def test_gsm8k(self):
self.run_accuracy()
if __name__ == "__main__":
unittest.main()
test/registered/ascend/accuracy/qwen3_5_9b/test_npu_qwen3_5_9b_bf16_1p_gsm8k.py
+76
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@@ -0,0 +1,76 @@
import unittest
from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
TestNpuAccuracyTestCaseBase,
)
from sglang.test.ascend.e2e.test_npu_performance_utils import (
QWEN3_5_9B_MODEL_PATH,
)
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=3600,
suite="stage-b-test-2-npu-a3",
nightly=True,
disabled="accuracy testcase",
)
QWEN3_5_9B_ENVS = {
"SGLANG_SET_CPU_AFFINITY": "1",
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_SOCKET_IFNAME": "lo",
"GLOO_SOCKET_IFNAME": "lo",
"ASCEND_LAUNCH_BLOCKING": "1",
"HCCL_BUFFSIZE": "1536",
"HCCL_OP_EXPANSION_MODE": "AIV",
}
QWEN3_5_9B_OTHER_ARGS = [
"--tp-size",
2,
"--nnodes",
1,
"--attention-backend",
"ascend",
"--device",
"npu",
"--enable-dp-attention",
"--chunked-prefill-size",
4096,
"--max-prefill-tokens",
280000,
"--disable-radix-cache",
"--trust-remote-code",
"--mem-fraction-static",
0.7,
"--cuda-graph-bs",
16,
"--enable-multimodal",
"--mm-attention-backend",
"ascend_attn",
"--dtype",
"bfloat16",
]
class TestNPUQwen3_5_9B_GSM8K(TestNpuAccuracyTestCaseBase):
model = QWEN3_5_9B_MODEL_PATH
envs = QWEN3_5_9B_ENVS
other_args = QWEN3_5_9B_OTHER_ARGS
accuracy = 0.8350
datasets = ["gsm8k"]
few_shot_num = 5
generation_config = {
"max_tokens": 8192,
"temperature": 0.6,
}
eval_batch_size = 64
limit = 100
def test_gsm8k(self):
self.run_accuracy()
if __name__ == "__main__":
unittest.main()
test/registered/ascend/accuracy/qwen3_vl_30b_a3b/test_npu_qwen3_vl_30b_a3b_bf16_2p_gsm8k.py
+75
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@@ -0,0 +1,75 @@
import unittest
from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
TestNpuAccuracyTestCaseBase,
)
from sglang.test.ascend.e2e.test_npu_performance_utils import (
QWEN3_VL_30B_MODEL_PATH,
)
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=3600,
suite="stage-b-test-4-npu-a3",
nightly=True,
disabled="accuracy testcase",
)
QWEN3_VL_30B_A3B_ENVS = {
"SGLANG_SET_CPU_AFFINITY": "1",
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_SOCKET_IFNAME": "lo",
"GLOO_SOCKET_IFNAME": "lo",
"ASCEND_LAUNCH_BLOCKING": "1",
"HCCL_BUFFSIZE": "1536",
"HCCL_OP_EXPANSION_MODE": "AIV",
}
QWEN3_VL_30B_A3B_OTHER_ARGS = [
"--trust-remote-code",
"--attention-backend",
"ascend",
"--dtype",
"bfloat16",
"--device",
"npu",
"--mm-attention-backend",
"ascend_attn",
"--enable-multimodal",
"--chunked-prefill-size",
-1,
"--max-prefill-tokens",
102400,
"--max-running-requests",
512,
"--tp-size",
4,
"--disable-radix-cache",
"--mem-fraction-static",
0.78,
"--sampling-backend",
"ascend",
]
class TestNPUQwen3_VL_30B_A3B_GSM8K(TestNpuAccuracyTestCaseBase):
model = QWEN3_VL_30B_MODEL_PATH
envs = QWEN3_VL_30B_A3B_ENVS
other_args = QWEN3_VL_30B_A3B_OTHER_ARGS
accuracy = 0.9538
datasets = ["gsm8k"]
few_shot_num = 5
generation_config = {
"max_tokens": 40000,
"temperature": 0.0,
"extra_body": {"chat_template_kwargs": {"enable_thinking": False}},
}
eval_batch_size = 64
def test_gsm8k(self):
self.run_accuracy()
if __name__ == "__main__":
unittest.main()
test/registered/ascend/accuracy/qwen3_vl_8b/test_npu_qwen3_vl_8b_bf16_2p_gsm8k.py
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@@ -0,0 +1,83 @@
import unittest
from sglang.test.ascend.e2e.test_npu_accuracy_utils import (
TestNpuAccuracyTestCaseBase,
)
from sglang.test.ascend.e2e.test_npu_performance_utils import (
QWEN3_VL_8B_MODEL_PATH,
)
from sglang.test.ci.ci_register import register_npu_ci
register_npu_ci(
est_time=3600,
suite="stage-b-test-4-npu-a3",
nightly=True,
disabled="accuracy testcase",
)
QWEN3_VL_8B_ENVS = {
"SGLANG_SET_CPU_AFFINITY": "1",
"PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
"STREAMS_PER_DEVICE": "32",
"HCCL_BUFFSIZE": "1536",
"HCCL_OP_EXPANSION_MODE": "AIV",
}
QWEN3_VL_8B_OTHER_ARGS = [
"--enable-multimodal",
"--mm-attention-backend",
"ascend_attn",
"--attention-backend",
"ascend",
"--device",
"npu",
"--trust-remote-code",
"--tp-size",
4,
"--mem-fraction-static",
0.8,
"--disable-radix-cache",
"--chunked-prefill-size",
-1,
"--sampling-backend",
"ascend",
"--tool-call-parser",
"qwen",
"--reasoning-parser",
"qwen3",
"--cuda-graph-bs",
8,
16,
32,
64,
128,
256,
"--dtype",
"bfloat16",
]
class TestNPUQwen3_VL_8B_GSM8K(TestNpuAccuracyTestCaseBase):
model = QWEN3_VL_8B_MODEL_PATH
envs = QWEN3_VL_8B_ENVS
other_args = QWEN3_VL_8B_OTHER_ARGS
accuracy = 0.9553
datasets = ["gsm8k"]
few_shot_num = 5
generation_config = {
"max_tokens": 32768,
"temperature": 1.0,
"top_p": 1.0,
"top_k": 40,
"repetition_penalty": 1.0,
"presence_penalty": 2.0,
"extra_body": {"chat_template_kwargs": {"enable_thinking": False}},
}
eval_batch_size = 64
def test_gsm8k(self):
self.run_accuracy()
if __name__ == "__main__":
unittest.main()