[Feature] Support DeepEP normal & Redundant Experts on NPU (#9881)

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

@@ -127,12 +127,48 @@ jobs:
           cd test/srt
           python3 run_suite.py --suite per-commit-4-ascend-npu --timeout-per-file 3600
 
+  per-commit-16-ascend-a3:
+    if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') &&
+        github.event.pull_request.draft == false
+    runs-on: linux-aarch64-a3-16
+    container:
+      image: swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/cann:8.2.rc1-a3-ubuntu22.04-py3.11
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install dependencies
+        run: |
+          # speed up by using infra cache services
+          CACHING_URL="cache-service.nginx-pypi-cache.svc.cluster.local"
+          sed -Ei "s@(ports|archive).ubuntu.com@${CACHING_URL}:8081@g" /etc/apt/sources.list
+          pip config set global.index-url http://${CACHING_URL}/pypi/simple
+          pip config set global.trusted-host ${CACHING_URL}
+
+          bash scripts/ci/npu_ci_install_dependency.sh
+          # copy required file from our daily cache
+          cp ~/.cache/modelscope/hub/datasets/otavia/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json /tmp
+          # copy download through proxy
+          curl -o /tmp/test.jsonl -L https://gh-proxy.test.osinfra.cn/https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl
+
+      - name: Run test
+        timeout-minutes: 90
+        env:
+          SGLANG_USE_MODELSCOPE: true
+          SGLANG_IS_IN_CI: true
+          HF_ENDPOINT: https://hf-mirror.com
+          TORCH_EXTENSIONS_DIR: /tmp/torch_extensions
+        run: |
+          cd test/srt
+          python3 run_suite.py --suite per-commit-16-ascend-a3 --timeout-per-file 5400
+
   pr-test-npu-finish:
     if: always()
     needs:
       - per-commit-1-ascend-npu
       - per-commit-2-ascend-npu
       - per-commit-4-ascend-npu
+      - per-commit-16-ascend-a3
     runs-on: ubuntu-latest
     steps:
       - name: Check all dependent job statuses

.github/workflows/release-docker-npu-nightly.yml

@@ -72,5 +72,6 @@ jobs:
           push: ${{ github.repository == 'sgl-project/sglang' && github.event_name != 'pull_request' }}
           provenance: false
           build-args: |
+            SGLANG_KERNEL_NPU_TAG=20250901
             CANN_VERSION=${{ matrix.cann_version }}
             DEVICE_TYPE=${{ matrix.device_type }}

.github/workflows/release-docker-npu.yml

@@ -54,8 +54,6 @@ jobs:
         run: |
           version=$(cat python/sglang/version.py | cut -d'"' -f2)
           echo "TAG=lmsysorg/sglang:v$version-cann${{ matrix.cann_version }}-${{ matrix.device_type }}" >> $GITHUB_OUTPUT
-          kernel_tag=$(curl -s https://api.github.com/repos/sgl-project/sgl-kernel-npu/tags | jq -r '.[0].name')
-          echo "KERNEL_NPU_TAG=${kernel_tag}" >> $GITHUB_OUTPUT
 
       - name: Build and push Docker image
         id: build-and-push
@@ -70,6 +68,6 @@ jobs:
           push: ${{ github.repository == 'sgl-project/sglang' && github.event_name != 'pull_request' }}
           provenance: false
           build-args: |
-            SGLANG_KERNEL_NPU_TAG=${{ steps.get_version.outputs.KERNEL_NPU_TAG }}
+            SGLANG_KERNEL_NPU_TAG=20250901
             CANN_VERSION=${{ matrix.cann_version }}
             DEVICE_TYPE=${{ matrix.device_type }}

python/sglang/srt/eplb/eplb_manager.py

@@ -55,7 +55,7 @@ class EPLBManager:
         enable_timing = self._rebalance_layers_per_chunk is None
 
         if enable_timing:
-            torch.cuda.synchronize()
+            torch.get_device_module().synchronize()
             time_start = time.time()
 
         dump_record_output = get_global_expert_distribution_recorder().dump_record(
@@ -85,7 +85,7 @@ class EPLBManager:
 
         msg = f"[EPLBManager] rebalance end"
         if enable_timing:
-            torch.cuda.synchronize()
+            torch.get_device_module().synchronize()
             time_end = time.time()
             msg += f" time={time_end - time_start:.3f}s"
         logger.info(msg)

python/sglang/srt/eplb/expert_distribution.py

@@ -30,7 +30,9 @@ import torch.distributed
 
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.server_args import ServerArgs
-from sglang.srt.utils import Withable, get_bool_env_var
+from sglang.srt.utils import Withable, get_bool_env_var, is_npu
+
+_is_npu = is_npu()
 
 if TYPE_CHECKING:
     from sglang.srt.eplb.expert_location import ExpertLocationMetadata
@@ -216,7 +218,9 @@ class _ExpertDistributionRecorderReal(ExpertDistributionRecorder):
     def _on_hook(self, hook_name: str, **kwargs):
         if self._disable_all:
             return
-        if not (self._recording or torch.cuda.is_current_stream_capturing()):
+        if not (
+            self._recording or torch.get_device_module().is_current_stream_capturing()
+        ):
             return
         gatherer = self._single_pass_gatherers[
             self._accumulator.get_single_pass_gatherer_key(
@@ -451,6 +455,10 @@ def _list_sum(a: List, b: List) -> List:
 class _LayerBasedGpuSinglePassGatherer(_SinglePassGatherer):
     def __init__(self, *args, enable_global_physical_experts: bool, **kwargs):
         super().__init__(*args, **kwargs)
+        if not _is_npu:
+            device = "cuda"
+        else:
+            device = "npu"
         self._enable_global_physical_experts = enable_global_physical_experts
         self._data = torch.zeros(
             (
@@ -462,7 +470,7 @@ class _LayerBasedGpuSinglePassGatherer(_SinglePassGatherer):
                 ),
             ),
             dtype=torch.int,
-            device="cuda",
+            device=device,
         )
 
     def reset(self):
@@ -784,7 +792,7 @@ class _StatAccumulator(_UtilizationRateAccumulatorMixin):
 
         if self._first_dump:
             self._first_dump = False
-            torch.cuda.empty_cache()
+            torch.get_device_module().empty_cache()
 
         torch.distributed.all_reduce(
             logical_count_of_buffered_step, op=torch.distributed.ReduceOp.SUM

python/sglang/srt/eplb/expert_location_updater.py

@@ -47,7 +47,7 @@ class ExpertLocationUpdater:
     ):
         if self._first_execution:
             self._first_execution = False
-            torch.cuda.empty_cache()
+            torch.get_device_module().empty_cache()
 
         old_expert_location_metadata = get_global_expert_location_metadata()
         assert old_expert_location_metadata is not None

python/sglang/srt/layers/attention/ascend_backend.py

@@ -10,6 +10,7 @@ from torch.nn.functional import scaled_dot_product_attention
 from sglang.srt.configs.model_config import AttentionArch
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
 from sglang.srt.layers.attention.torch_native_backend import TorchNativeAttnBackend
+from sglang.srt.layers.dp_attention import get_attention_tp_size
 from sglang.srt.layers.radix_attention import AttentionType
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import get_bool_env_var
@@ -33,6 +34,7 @@ class ForwardMetadata:
     extend_seq_lens_cpu_int: Optional[torch.Tensor] = None
     seq_lens_cpu_int: Optional[torch.Tensor] = None
     seq_lens_cpu_list: Optional[List[int]] = None
+    seq_lens_list_cumsum: Optional[List[int]] = None
 
 
 class AscendAttnBackend(AttentionBackend):
@@ -83,6 +85,7 @@ class AscendAttnBackend(AttentionBackend):
 
     def init_forward_metadata(self, forward_batch: ForwardBatch):
         """Init the metadata for a forward pass."""
+        tp_size = get_attention_tp_size()
         self.forward_metadata = ForwardMetadata()
 
         self.forward_metadata.block_tables = (
@@ -96,9 +99,13 @@ class AscendAttnBackend(AttentionBackend):
                 forward_batch.extend_seq_lens.cpu().int()
             )
         self.forward_metadata.seq_lens_cpu_int = forward_batch.seq_lens_cpu.int()
-        self.forward_metadata.seq_lens_list_cumsum = np.cumsum(
+
-            forward_batch.extend_seq_lens_cpu
+        seq_lens_list_cumsum = np.cumsum(forward_batch.extend_seq_lens_cpu)
-        )
+        if forward_batch.is_extend_in_batch:
+            seq_lens_list_cumsum[-1] = (
+                (seq_lens_list_cumsum[-1] - 1) // tp_size + 1
+            ) * tp_size
+        self.forward_metadata.seq_lens_list_cumsum = seq_lens_list_cumsum
 
         self.graph_mode = False
 

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -35,7 +35,6 @@ from sglang.srt.utils import ceil_div, dispose_tensor, get_bool_env_var, is_hip,
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.token_dispatcher import (
-        AscendDeepEPLLOutput,
         DeepEPLLOutput,
         DeepEPNormalOutput,
         DispatchOutput,
@@ -454,7 +453,7 @@ class DeepEPMoE(EPMoE):
             # in forward_aiter, we skip token permutation and unpermutation, which have been fused inside aiter kernel
             return self.forward_aiter(dispatch_output)
         if _is_npu:
-            assert DispatchOutputChecker.format_is_ascent_ll(dispatch_output)
+            assert DispatchOutputChecker.format_is_deepep(dispatch_output)
             return self.forward_npu(dispatch_output)
         if DispatchOutputChecker.format_is_deepep_normal(dispatch_output):
             assert deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and self.use_fp8_w8a8
@@ -718,63 +717,124 @@ class DeepEPMoE(EPMoE):
 
     def forward_npu(
         self,
-        dispatch_output: DeepEPLLOutput,
+        dispatch_output: Union[DeepEPNormalOutput, DeepEPLLOutput],
     ):
-        if TYPE_CHECKING:
-            assert isinstance(dispatch_output, AscendDeepEPLLOutput)
-        hidden_states, topk_idx, topk_weights, _, seg_indptr, _ = dispatch_output
         assert self.quant_method is not None
         assert self.moe_runner_config.activation == "silu"
 
-        # NOTE: Ascend's Dispatch & Combine does not support FP16
-        output_dtype = torch.bfloat16
-
-        pertoken_scale = hidden_states[1]
-        hidden_states = hidden_states[0]
-
-        group_list_type = 1
-        seg_indptr = seg_indptr.to(torch.int64)
-
         import torch_npu
 
-        # gmm1: gate_up_proj
+        from sglang.srt.layers.moe.token_dispatcher import DispatchOutputChecker
-        hidden_states = torch_npu.npu_grouped_matmul(
-            x=[hidden_states],
-            weight=[self.w13_weight],
-            split_item=2,
-            group_list_type=group_list_type,
-            group_type=0,
-            group_list=seg_indptr,
-            output_dtype=torch.int32,
-        )[0]
 
-        # act_fn: swiglu
+        # NOTE: Ascend's Dispatch & Combine does not support FP16
-        hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
+        output_dtype = torch.bfloat16
-            x=hidden_states,
+        group_list_type = 1
-            weight_scale=self.w13_weight_scale.to(torch.float32),
-            activation_scale=pertoken_scale,
-            bias=None,
-            quant_scale=None,
-            quant_offset=None,
-            group_index=seg_indptr,
-            activate_left=True,
-            quant_mode=1,
-        )
 
-        # gmm2: down_proj
+        def _forward_normal(dispatch_output: DeepEPNormalOutput):
-        hidden_states = torch_npu.npu_grouped_matmul(
+            if TYPE_CHECKING:
-            x=[hidden_states],
+                assert isinstance(dispatch_output, DeepEPNormalOutput)
-            weight=[self.w2_weight],
+            hidden_states, _, _, num_recv_tokens_per_expert = dispatch_output
-            scale=[self.w2_weight_scale.to(output_dtype)],
-            per_token_scale=[swiglu_out_scale],
-            split_item=2,
-            group_list_type=group_list_type,
-            group_type=0,
-            group_list=seg_indptr,
-            output_dtype=output_dtype,
-        )[0]
 
-        return hidden_states
+            if isinstance(hidden_states, tuple):
+                per_token_scale = hidden_states[1]
+                hidden_states = hidden_states[0]
+            else:
+                # dynamic quant
+                hidden_states, per_token_scale = torch_npu.npu_dynamic_quant(
+                    hidden_states
+                )
+
+            group_list = torch.tensor(num_recv_tokens_per_expert, dtype=torch.int64).to(
+                hidden_states.device
+            )
+
+            # gmm1: gate_up_proj
+            hidden_states = torch_npu.npu_grouped_matmul(
+                x=[hidden_states],
+                weight=[self.w13_weight],
+                scale=[self.w13_weight_scale.to(output_dtype)],
+                per_token_scale=[per_token_scale],
+                split_item=2,
+                group_list_type=group_list_type,
+                group_type=0,
+                group_list=group_list,
+                output_dtype=output_dtype,
+            )[0]
+
+            # act_fn: swiglu
+            hidden_states = torch_npu.npu_swiglu(hidden_states)
+            hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(hidden_states)
+
+            # gmm2: down_proj
+            hidden_states = torch_npu.npu_grouped_matmul(
+                x=[hidden_states],
+                weight=[self.w2_weight],
+                scale=[self.w2_weight_scale.to(output_dtype)],
+                per_token_scale=[swiglu_out_scale],
+                split_item=2,
+                group_list_type=group_list_type,
+                group_type=0,
+                group_list=group_list,
+                output_dtype=output_dtype,
+            )[0]
+
+            return hidden_states
+
+        def _forward_ll(dispatch_output: DeepEPLLOutput):
+            if TYPE_CHECKING:
+                assert isinstance(dispatch_output, DeepEPLLOutput)
+            hidden_states, topk_idx, topk_weights, group_list, _ = dispatch_output
+
+            per_token_scale = hidden_states[1]
+            hidden_states = hidden_states[0]
+
+            group_list = group_list.to(torch.int64)
+
+            # gmm1: gate_up_proj
+            hidden_states = torch_npu.npu_grouped_matmul(
+                x=[hidden_states],
+                weight=[self.w13_weight],
+                split_item=2,
+                group_list_type=group_list_type,
+                group_type=0,
+                group_list=group_list,
+                output_dtype=torch.int32,
+            )[0]
+
+            # act_fn: swiglu
+            hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
+                x=hidden_states,
+                weight_scale=self.w13_weight_scale.to(torch.float32),
+                activation_scale=per_token_scale,
+                bias=None,
+                quant_scale=None,
+                quant_offset=None,
+                group_index=group_list,
+                activate_left=True,
+                quant_mode=1,
+            )
+
+            # gmm2: down_proj
+            hidden_states = torch_npu.npu_grouped_matmul(
+                x=[hidden_states],
+                weight=[self.w2_weight],
+                scale=[self.w2_weight_scale.to(output_dtype)],
+                per_token_scale=[swiglu_out_scale],
+                split_item=2,
+                group_list_type=group_list_type,
+                group_type=0,
+                group_list=group_list,
+                output_dtype=output_dtype,
+            )[0]
+
+            return hidden_states
+
+        if DispatchOutputChecker.format_is_deepep_normal(dispatch_output):
+            return _forward_normal(dispatch_output)
+        elif DispatchOutputChecker.format_is_deepep_ll(dispatch_output):
+            return _forward_ll(dispatch_output)
+        else:
+            raise ValueError(f"Not Supported DeepEP format {dispatch_output.format}")
 
 
 def get_moe_impl_class(quant_config: Optional[QuantizationConfig] = None):

python/sglang/srt/layers/moe/token_dispatcher/__init__.py

@@ -9,7 +9,6 @@ from sglang.srt.layers.moe.token_dispatcher.base import (
     DispatchOutputFormat,
 )
 from sglang.srt.layers.moe.token_dispatcher.deepep import (
-    AscendDeepEPLLOutput,
     DeepEPConfig,
     DeepEPDispatcher,
     DeepEPLLCombineInput,
@@ -23,7 +22,6 @@ from sglang.srt.layers.moe.token_dispatcher.standard import (
 )
 
 __all__ = [
-    "AscendDeepEPLLOutput",
     "BaseDispatcher",
     "BaseDispatcherConfig",
     "CombineInput",

python/sglang/srt/layers/moe/token_dispatcher/base.py

@@ -8,7 +8,6 @@ import torch
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.token_dispatcher import (
-        AscendDeepEPLLOutput,
         DeepEPLLCombineInput,
         DeepEPLLOutput,
         DeepEPNormalCombineInput,
@@ -47,19 +46,12 @@ class DispatchOutputChecker:
     ) -> TypeGuard[Union[DeepEPNormalOutput, DeepEPLLOutput]]:
         return dispatch_output.format.is_deepep()
 
-    @staticmethod
-    def format_is_ascent_ll(
-        dispatch_output: DispatchOutput,
-    ) -> TypeGuard[AscendDeepEPLLOutput]:
-        return dispatch_output.format.is_ascent_ll()
-
 
 class DispatchOutputFormat(Enum):
 
     STANDARD = "standard"
     DEEPEP_NORMAL = "deepep_normal"
     DEEPEP_LL = "deepep_ll"
-    ASCENT_LL = "ascent_ll"
 
     def is_standard(self) -> bool:
         return self == DispatchOutputFormat.STANDARD
@@ -76,9 +68,6 @@ class DispatchOutputFormat(Enum):
             DispatchOutputFormat.DEEPEP_LL,
         ]
 
-    def is_ascent_ll(self) -> bool:
-        return self == DispatchOutputFormat.ASCENT_LL
-
 
 @runtime_checkable
 class DispatchOutput(Protocol):

python/sglang/srt/layers/moe/token_dispatcher/deepep.py

@@ -77,24 +77,8 @@ class DeepEPLLOutput(NamedTuple):
         return DispatchOutputFormat.DEEPEP_LL
 
 
-class AscendDeepEPLLOutput(NamedTuple):
-    """AscendDeepEP low latency dispatch output."""
-
-    hidden_states_fp8: Tuple[torch.Tensor, torch.Tensor]
-    topk_idx: torch.Tensor
-    topk_weights: torch.Tensor
-    masked_m: torch.Tensor
-    seg_indptr: torch.Tensor
-    expected_m: int
-
-    @property
-    def format(self) -> DispatchOutputFormat:
-        return DispatchOutputFormat.ASCENT_LL
-
-
 assert isinstance(DeepEPNormalOutput, DispatchOutput)
 assert isinstance(DeepEPLLOutput, DispatchOutput)
-assert isinstance(AscendDeepEPLLOutput, DispatchOutput)
 
 
 class DeepEPNormalCombineInput(NamedTuple):
@@ -434,12 +418,11 @@ class _DeepEPDispatcherImplNormal(_DeepEPDispatcherImplBase):
         topk_idx: torch.Tensor,
         topk_weights: torch.Tensor,
     ):
-
         from sglang.srt.layers.moe.ep_moe.kernels import (
             deepep_post_reorder_triton_kernel,
         )
 
-        if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM or _use_aiter:
+        if deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM or _use_aiter or _is_npu:
             output = hidden_states
         else:
             if hidden_states.shape[0] > 0:
@@ -553,23 +536,13 @@ class _DeepEPDispatcherImplLowLatency(_DeepEPDispatcherImplBase):
             masked_m
         )
 
-        if _is_npu:
+        deepep_output = DeepEPLLOutput(
-            deepep_output = AscendDeepEPLLOutput(
+            hidden_states,
-                hidden_states,
+            topk_idx,
-                topk_idx,
+            topk_weights,
-                topk_weights,
+            masked_m,
-                masked_m,
+            expected_m,
-                self.handle[1],
+        )
-                expected_m,
-            )
-        else:
-            deepep_output = DeepEPLLOutput(
-                hidden_states,
-                topk_idx,
-                topk_weights,
-                masked_m,
-                expected_m,
-            )
         return deepep_output
 
     def _dispatch_core(

python/sglang/srt/layers/moe/topk.py

@@ -330,6 +330,14 @@ class TopK(CustomOp):
                 )
                 topk_weights = topk_weights / topk_weights_sum
 
+            if expert_location_dispatch_info is not None:
+                topk_ids = topk_ids_logical_to_physical(
+                    topk_ids, expert_location_dispatch_info
+                )
+            get_global_expert_distribution_recorder().on_select_experts(
+                topk_ids=topk_ids
+            )
+
             return StandardTopKOutput(topk_weights, topk_ids, _)
         else:
             self.topk_config.torch_native = True

scripts/ci/npu_ci_install_dependency.sh

@@ -51,5 +51,11 @@ ${PIP_INSTALL} attrs==24.2.0 numpy==1.26.4 scipy==1.13.1 decorator==5.1.1 psutil
 wget -O "${TRITON_ASCEND_NAME}" "${TRITON_ASCEND_URL}" && ${PIP_INSTALL} "./${TRITON_ASCEND_NAME}"
 
 
+### Install sgl-kernel-npu
+SGL_KERNEL_NPU_TAG="20250901"
+git clone --depth 1 https://github.com/sgl-project/sgl-kernel-npu.git --branch ${SGL_KERNEL_NPU_TAG}
+(cd sgl-kernel-npu && bash ./build.sh -a deepep && pip install output/deep_ep*.whl && cd "$(pip show deep-ep | grep -E '^Location:' | awk '{print $2}')" && ln -s deep_ep/deep_ep_cpp*.so)
+
+
 ### Install SGLang
 ${PIP_INSTALL} -v -e "python[srt_npu]"

test/srt/ascend/test_ascend_deepep.py

@@ -0,0 +1,121 @@
+import os
+import unittest
+from types import SimpleNamespace
+from urllib.parse import urlparse
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.few_shot_gsm8k import run_eval as run_eval_few_shot_gsm8k
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_ci,
+    popen_launch_server,
+    run_bench_offline_throughput,
+)
+
+TEST_MODEL_MATRIX = {
+    "/root/.cache/modelscope/hub/models/vllm-ascend/DeepSeek-R1-0528-W8A8": {
+        "accuracy": 0.95,
+        "latency": 1000,
+        "output_throughput": 6,
+    },
+}
+
+
+class TestAscendDeepEP(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.models = TEST_MODEL_MATRIX.keys()
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.url = urlparse(DEFAULT_URL_FOR_TEST)
+
+        cls.common_args = [
+            "--trust-remote-code",
+            "--attention-backend",
+            "ascend",
+            "--quantization",
+            "w8a8_int8",
+            "--mem-fraction-static",
+            0.9,
+            "--max-running-requests",
+            32,
+            "--disable-radix-cache",
+            "--chunked-prefill-size",
+            32768,
+            "--disable-cuda-graph",
+            "--tp-size",
+            16,
+            "--dp-size",
+            1,
+            "--ep-size",
+            16,
+            "--moe-a2a-backend",
+            "deepep",
+            "--deepep-mode",
+            "auto",
+        ]
+
+        cls.extra_envs = {
+            "HCCL_BUFFSIZE": "500",
+            "SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK": "32",
+        }
+        os.environ.update(cls.extra_envs)
+
+    def test_a_gsm8k(self):
+        for model in self.models:
+            with self.subTest(model=model):
+                print(f"##=== Testing accuracy: {model} ===##")
+
+                process = popen_launch_server(
+                    model,
+                    self.base_url,
+                    timeout=1500,
+                    other_args=[
+                        *self.common_args,
+                    ],
+                )
+
+                try:
+                    args = SimpleNamespace(
+                        num_shots=5,
+                        data_path=None,
+                        num_questions=1319,
+                        max_new_tokens=512,
+                        parallel=128,
+                        host=f"http://{self.url.hostname}",
+                        port=int(self.url.port),
+                    )
+
+                    metrics = run_eval_few_shot_gsm8k(args)
+                    self.assertGreaterEqual(
+                        metrics["accuracy"],
+                        TEST_MODEL_MATRIX[model]["accuracy"],
+                    )
+                finally:
+                    kill_process_tree(process.pid)
+
+    def test_b_throughput(self):
+        for model in self.models:
+            with self.subTest(model=model):
+                print(f"##=== Testing throughput: {model} ===##")
+
+                output_throughput = run_bench_offline_throughput(
+                    model,
+                    [
+                        *self.common_args,
+                    ],
+                )
+
+                print(f"##=== {model} throughput: {output_throughput} ===##")
+
+                if is_in_ci():
+                    self.assertGreater(
+                        output_throughput,
+                        TEST_MODEL_MATRIX[model]["output_throughput"],
+                    )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/run_suite.py

@@ -300,6 +300,9 @@ suite_ascend = {
         TestFile("ascend/test_ascend_mla_w8a8int8.py", 400),
         TestFile("ascend/test_ascend_tp4_bf16.py", 400),
     ],
+    "per-commit-16-ascend-a3": [
+        TestFile("ascend/test_ascend_deepep.py", 400),
+    ],
 }
 
 suites.update(suite_amd)