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

.github/workflows/pr-test-npu.yml

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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()