[feature] Ascend NPU graph support (#9399)

Co-authored-by: ronnie_zheng <zl19940307@163.com>
Co-authored-by: yezhifeng (D) <y00897525@china.huawei.com>
Co-authored-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: Maksim <makcum888e@mail.ru>
Co-authored-by: ssshinigami <44640852+ssshinigami@users.noreply.github.com>

python/sglang/srt/distributed/parallel_state.py

@@ -55,7 +55,7 @@ _is_npu = is_npu()
 
 @dataclass
 class GraphCaptureContext:
-    stream: torch.cuda.Stream
+    stream: torch.cuda.Stream if not _is_npu else torch.npu.Stream
 
 
 TensorMetadata = namedtuple("TensorMetadata", ["device", "dtype", "size"])
@@ -252,8 +252,11 @@ class GroupCoordinator:
 
         if is_cuda_alike():
             self.device = torch.device(f"cuda:{local_rank}")
+        elif _is_npu:
+            self.device = torch.device(f"npu:{local_rank}")
         else:
             self.device = torch.device("cpu")
+        self.device_module = torch.get_device_module(self.device)
 
         self.use_pynccl = use_pynccl
         self.use_pymscclpp = use_pymscclpp
@@ -402,7 +405,7 @@ class GroupCoordinator:
         self, graph_capture_context: Optional[GraphCaptureContext] = None
     ):
         if graph_capture_context is None:
-            stream = torch.cuda.Stream()
+            stream = self.device_module.Stream()
             graph_capture_context = GraphCaptureContext(stream)
         else:
             stream = graph_capture_context.stream
@@ -413,11 +416,11 @@ class GroupCoordinator:
 
         # ensure all initialization operations complete before attempting to
         # capture the graph on another stream
-        curr_stream = torch.cuda.current_stream()
+        curr_stream = self.device_module.current_stream()
         if curr_stream != stream:
             stream.wait_stream(curr_stream)
 
-        with torch.cuda.stream(stream), maybe_ca_context:
+        with self.device_module.stream(stream), maybe_ca_context:
             # In graph mode, we have to be very careful about the collective
             # operations. The current status is:
             #     allreduce \ Mode   |  Eager  |  Graph  |
@@ -1641,6 +1644,8 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
                 )
         elif hasattr(torch, "xpu") and torch.xpu.is_available():
             torch.xpu.empty_cache()
+        elif hasattr(torch, "npu") and torch.npu.is_available():
+            torch.npu.empty_cache()
 
 
 def in_the_same_node_as(pg: ProcessGroup, source_rank: int = 0) -> List[bool]:

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

 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, List, Optional
 
 import torch
 import torch_npu
@@ -27,6 +27,7 @@ class ForwardMetadata:
     # seq len inputs
     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
 
 
 class AscendAttnBackend(AttentionBackend):
@@ -51,7 +52,7 @@ class AscendAttnBackend(AttentionBackend):
 
     def __init__(self, model_runner: ModelRunner):
         super().__init__()
-        self.forward_metadata = ForwardMetadata()
+        self.forward_metadata = None
         self.device = model_runner.device
         self.gen_attention_mask(128, model_runner.dtype)
         self.page_size = model_runner.page_size
@@ -60,9 +61,15 @@ class AscendAttnBackend(AttentionBackend):
             self.kv_lora_rank = model_runner.model_config.kv_lora_rank
             self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
             self.native_attn = TorchNativeAttnBackend(model_runner)
+        self.graph_metadata = {}
+        self.max_context_len = model_runner.model_config.context_len
+        self.req_to_token = model_runner.req_to_token_pool.req_to_token
+        self.graph_mode = False
 
     def init_forward_metadata(self, forward_batch: ForwardBatch):
         """Init the metadata for a forward pass."""
+        self.forward_metadata = ForwardMetadata()
+
         self.forward_metadata.block_tables = (
             forward_batch.req_to_token_pool.req_to_token[
                 forward_batch.req_pool_indices, : forward_batch.seq_lens.max()
@@ -75,6 +82,63 @@ class AscendAttnBackend(AttentionBackend):
             )
         self.forward_metadata.seq_lens_cpu_int = forward_batch.seq_lens_cpu.int()
 
+        self.graph_mode = False
+
+    def init_cuda_graph_state(self, max_bs: int, max_num_tokens: int):
+        self.graph_metadata = {
+            "block_tables": torch.empty(
+                (max_bs, self.max_context_len // self.page_size),
+                dtype=torch.int32,
+                device=self.device,
+            ),
+        }
+
+    def init_forward_metadata_capture_cuda_graph(
+        self,
+        bs: int,
+        num_tokens: int,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        encoder_lens: Optional[torch.Tensor],
+        forward_mode: ForwardMode,
+        spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
+    ):
+        metadata = ForwardMetadata()
+
+        metadata.block_tables = self.graph_metadata["block_tables"][:bs, :]
+        metadata.seq_lens_cpu_list = seq_lens.cpu().int().tolist()
+
+        self.graph_metadata[bs] = metadata
+        self.forward_metadata = metadata
+
+        self.graph_mode = True
+
+    def init_forward_metadata_replay_cuda_graph(
+        self,
+        bs: int,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        seq_lens_sum: int,
+        encoder_lens: Optional[torch.Tensor],
+        forward_mode: ForwardMode,
+        spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
+        seq_lens_cpu: Optional[torch.Tensor],
+    ):
+        metadata = self.graph_metadata[bs]
+        max_len = seq_lens_cpu[:bs].max().item()
+        max_seq_pages = (max_len + self.page_size - 1) // self.page_size
+
+        metadata.block_tables[:bs, :max_seq_pages].copy_(
+            self.req_to_token[req_pool_indices[:bs], :max_len][:, :: self.page_size]
+            // self.page_size
+        )
+        metadata.block_tables[:bs, max_seq_pages:].fill_(0)
+        metadata.block_tables[bs:, :].fill_(0)
+
+        self.forward_metadata = metadata
+
+        self.graph_mode = True
+
     def get_cuda_graph_seq_len_fill_value(self):
         return 1
 
@@ -167,8 +231,54 @@ class AscendAttnBackend(AttentionBackend):
                 layer, forward_batch.out_cache_loc, k, v
             )
         if not self.use_mla:
+            if self.graph_mode:
+                k_cache = forward_batch.token_to_kv_pool.get_key_buffer(
+                    layer.layer_id
+                ).view(-1, self.page_size, layer.tp_k_head_num * layer.qk_head_dim)
+                v_cache = forward_batch.token_to_kv_pool.get_value_buffer(
+                    layer.layer_id
+                ).view(-1, self.page_size, layer.tp_v_head_num * layer.v_head_dim)
+                query = q.view(-1, 1, layer.tp_q_head_num * layer.qk_head_dim)
+                num_tokens = query.shape[0]
+                workspace = (
+                    torch_npu._npu_fused_infer_attention_score_get_max_workspace(
+                        query,
+                        k_cache,
+                        v_cache,
+                        block_table=self.forward_metadata.block_tables,
+                        block_size=self.page_size,
+                        num_heads=layer.tp_q_head_num,
+                        num_key_value_heads=layer.tp_k_head_num,
+                        input_layout="BSH",
+                        scale=layer.scaling,
+                        actual_seq_lengths_kv=self.forward_metadata.seq_lens_cpu_list,
+                    )
+                )
+                output = torch.empty(
+                    (num_tokens, 1, layer.tp_q_head_num * layer.v_head_dim),
+                    dtype=q.dtype,
+                    device=q.device,
+                )
+                softmax_lse = torch.empty(1, dtype=q.dtype, device=q.device)
+                torch_npu.npu_fused_infer_attention_score.out(
+                    query,
+                    k_cache,
+                    v_cache,
+                    block_table=self.forward_metadata.block_tables,
+                    block_size=self.page_size,
+                    num_heads=layer.tp_q_head_num,
+                    num_key_value_heads=layer.tp_k_head_num,
+                    input_layout="BSH",
+                    scale=layer.scaling,
+                    actual_seq_lengths_kv=self.forward_metadata.seq_lens_cpu_list,
+                    workspace=workspace,
+                    out=[output, softmax_lse],
+                )
+            else:
                 k_cache = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
-            v_cache = forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id)
+                v_cache = forward_batch.token_to_kv_pool.get_value_buffer(
+                    layer.layer_id
+                )
 
                 query = q.view(-1, layer.tp_q_head_num, layer.qk_head_dim)
                 num_tokens = query.shape[0]

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -240,6 +240,8 @@ class CudaGraphRunner:
     def __init__(self, model_runner: ModelRunner):
         # Parse args
         self.model_runner = model_runner
+        self.device = model_runner.device
+        self.device_module = torch.get_device_module(self.device)
         self.graphs = {}
         self.output_buffers = {}
         self.enable_torch_compile = model_runner.server_args.enable_torch_compile
@@ -305,13 +307,15 @@ class CudaGraphRunner:
             self.model_runner.lora_manager.init_cuda_graph_batch_info(self.max_bs)
 
         # Graph inputs
-        with torch.device("cuda"):
+        with torch.device(self.device):
             self.input_ids = torch.zeros((self.max_num_token,), dtype=torch.int64)
             self.req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int32)
             self.seq_lens = torch.full(
                 (self.max_bs,), self.seq_len_fill_value, dtype=torch.int32
             )
-            self.out_cache_loc = torch.zeros((self.max_num_token,), dtype=torch.int64)
+            self.out_cache_loc = torch.zeros(
+                (self.max_num_token,), dtype=self._cache_loc_dtype()
+            )
             self.positions = torch.zeros((self.max_num_token,), dtype=torch.int64)
             self.mrope_positions = torch.zeros((3, self.max_bs), dtype=torch.int64)
             self.num_token_non_padded = torch.zeros((1,), dtype=torch.int32)
@@ -366,12 +370,12 @@ class CudaGraphRunner:
                     * self.num_tokens_per_bs
                 ),
                 dtype=torch.bool,
-                device="cuda",
+                device=self.device,
             )
             self.next_token_logits_buffer = torch.zeros(
                 (self.max_num_token, self.model_runner.model_config.vocab_size),
                 dtype=torch.float,
-                device="cuda",
+                device=self.device,
             )
 
         # Capture
@@ -383,6 +387,9 @@ class CudaGraphRunner:
                 f"Capture cuda graph failed: {e}\n{CUDA_GRAPH_CAPTURE_FAILED_MSG}"
             )
 
+    def _cache_loc_dtype(self):
+        return torch.int64
+
     def can_run(self, forward_batch: ForwardBatch):
         if self.require_mlp_tp_gather:
             cuda_graph_bs = (
@@ -502,8 +509,16 @@ class CudaGraphRunner:
             )
             logger.info(log_message)
 
+    def _capture_graph(self, graph, pool, stream, run_once_fn):
+        with self.device_module.graph(graph, pool=pool, stream=stream):
+            out = run_once_fn()
+        return out
+
+    def _create_device_graph(self):
+        return torch.cuda.CUDAGraph()
+
     def capture_one_batch_size(self, bs: int, forward: Callable):
-        graph = torch.cuda.CUDAGraph()
+        graph = self._create_device_graph()
         stream = self.stream
         num_tokens = bs * self.num_tokens_per_bs
 
@@ -643,19 +658,17 @@ class CudaGraphRunner:
             return logits_output_or_pp_proxy_tensors
 
         for _ in range(2):
-            torch.cuda.synchronize()
+            self.device_module.synchronize()
             self.model_runner.tp_group.barrier()
-
             run_once()
 
         if get_global_graph_memory_pool() is None:
-            set_global_graph_memory_pool(torch.cuda.graph_pool_handle())
+            set_global_graph_memory_pool(self.device_module.graph_pool_handle())
         # Set graph pool id globally to be able to use symmetric memory
         set_graph_pool_id(get_global_graph_memory_pool())
-        with torch.cuda.graph(
-            graph, pool=get_global_graph_memory_pool(), stream=stream
-        ):
-            out = run_once()
+        out = self._capture_graph(
+            graph, get_global_graph_memory_pool(), stream, run_once
+        )
 
         return graph, out
 

python/sglang/srt/model_executor/model_runner.py

@@ -91,6 +91,7 @@ from sglang.srt.mem_cache.memory_pool import (
 )
 from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_executor.npu_graph_runner import NPUGraphRunner
 from sglang.srt.model_loader import get_model
 from sglang.srt.model_loader.loader import DefaultModelLoader, get_model_loader
 from sglang.srt.model_loader.utils import set_default_torch_dtype
@@ -341,9 +342,12 @@ class ModelRunner:
         if self.device == "cuda":
             self.init_cublas()
             self.init_attention_backend()
-            self.init_cuda_graphs()
+            self.init_device_graphs()
+        elif self.device == "npu":
+            self.init_attention_backend()
+            self.init_device_graphs()
         else:
-            self.cuda_graph_runner = None
+            self.graph_runner = None
             self.cuda_graph_mem_usage = 0
             self.init_attention_backend()
 
@@ -917,7 +921,8 @@ class ModelRunner:
             )
 
         # We need to get device after patch otherwise the device would be wrong
-        infered_device = torch.cuda.current_device()
+        self.device_module = torch.get_device_module(self.device)
+        infered_device = self.device_module.current_device()
 
         named_tensors = [
             (name, _unwrap_tensor(tensor, tp_rank=self.tp_rank, device=infered_device))
@@ -1592,9 +1597,9 @@ class ModelRunner:
                 .cuda()
             )
 
-    def init_cuda_graphs(self):
+    def init_device_graphs(self):
         """Capture cuda graphs."""
-        self.cuda_graph_runner = None
+        self.graph_runner = None
         self.cuda_graph_mem_usage = 0
 
         if not self.is_generation:
@@ -1609,8 +1614,9 @@ class ModelRunner:
         logger.info(
             f"Capture cuda graph begin. This can take up to several minutes. avail mem={before_mem:.2f} GB"
         )
-        self.cuda_graph_runner = CudaGraphRunner(self)
-
+        self.graph_runner = (
+            CudaGraphRunner(self) if not _is_npu else NPUGraphRunner(self)
+        )
         after_mem = get_available_gpu_memory(self.device, self.gpu_id)
         self.cuda_graph_mem_usage = before_mem - after_mem
         logger.info(
@@ -1762,11 +1768,11 @@ class ModelRunner:
     ) -> Tuple[Union[LogitsProcessorOutput, PPProxyTensors], bool]:
         can_run_cuda_graph = bool(
             forward_batch.forward_mode.is_cuda_graph()
-            and self.cuda_graph_runner
-            and self.cuda_graph_runner.can_run(forward_batch)
+            and self.graph_runner
+            and self.graph_runner.can_run(forward_batch)
         )
         if can_run_cuda_graph:
-            ret = self.cuda_graph_runner.replay(
+            ret = self.graph_runner.replay(
                 forward_batch,
                 skip_attn_backend_init=skip_attn_backend_init,
                 pp_proxy_tensors=pp_proxy_tensors,

python/sglang/srt/model_executor/npu_graph_runner.py

+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Run the model with npu graph and torch.compile."""
+
+from __future__ import annotations
+
+import logging
+import threading
+from typing import TYPE_CHECKING, Optional, Union
+
+import torch
+
+from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from sglang.srt.model_executor.model_runner import ModelRunner
+
+from sglang.srt.layers.logits_processor import LogitsProcessorOutput
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+
+
+class NPUGraphRunner(CudaGraphRunner):
+    """A NPUGraphRunner runs the forward pass of a model with npu graph and torch.compile."""
+
+    def __init__(self, model_runner: ModelRunner):
+        super().__init__(model_runner)
+
+    def _create_device_graph(self):
+        return torch.npu.NPUGraph()
+
+    def _capture_graph(self, graph, pool, stream, run_once_fn):
+        with torch.npu.graph(
+            graph,
+            pool=pool,
+            stream=stream,
+            auto_dispatch_capture=True,
+        ):
+            out = run_once_fn()
+        return out
+
+    def _update_inputs(self, seq_lens):
+        self.graphs[self.bs].update(
+            cpu_update_input=[{"actual_seq_lengths_kv": seq_lens}]
+        )
+
+    def _cache_loc_dtype(self):
+        return torch.int32
+
+    def replay(
+        self,
+        forward_batch: ForwardBatch,
+        skip_attn_backend_init: bool = False,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Union[LogitsProcessorOutput, PPProxyTensors]:
+        if not skip_attn_backend_init:
+            self.replay_prepare(forward_batch, pp_proxy_tensors)
+        else:
+            # In speculative decoding, these two fields are still needed.
+            self.input_ids[: self.raw_num_token].copy_(forward_batch.input_ids)
+            self.positions[: self.raw_num_token].copy_(forward_batch.positions)
+
+        # Replay
+        seq_lens = forward_batch.seq_lens.cpu().tolist() + [0] * (self.bs - self.raw_bs)
+        thread = threading.Thread(target=self._update_inputs, args=(seq_lens,))
+        thread.start()
+        self.graphs[self.bs].replay()
+        thread.join()
+
+        output = self.output_buffers[self.bs]
+        if isinstance(output, LogitsProcessorOutput):
+            return LogitsProcessorOutput(
+                next_token_logits=output.next_token_logits[: self.raw_num_token],
+                hidden_states=(
+                    output.hidden_states[: self.raw_num_token]
+                    if output.hidden_states is not None
+                    else None
+                ),
+            )
+        else:
+            assert isinstance(output, PPProxyTensors)
+            return PPProxyTensors({k: v[: self.bs] for k, v in output.tensors.items()})

test/srt/ascend/test_ascend_graph_tp1_bf16.py

+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 = {
+    "Qwen/Qwen2.5-7B-Instruct": {
+        "accuracy": 0.85,
+        "latency": 150,
+        "output_throughput": 30,
+    },
+}
+
+
+class TestAscendGraphTp1Bf16(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",
+            "--mem-fraction-static",
+            0.8,
+            "--attention-backend",
+            "ascend",
+        ]
+
+    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=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+                    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/ascend/test_ascend_graph_tp2_bf16.py

+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 = {
+    "Qwen/Qwen2.5-7B-Instruct": {
+        "accuracy": 0.85,
+        "latency": 180,
+        "output_throughput": 20,
+    },
+}
+
+
+class TestAscendGraphTp2Bf16(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",
+            "--mem-fraction-static",
+            0.8,
+            "--attention-backend",
+            "ascend",
+            "--tp-size",
+            2,
+        ]
+
+    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=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+                    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

@@ -269,9 +269,11 @@ suite_xeon = {
 suite_ascend = {
     "per-commit-1-ascend-npu": [
         TestFile("ascend/test_ascend_tp1_bf16.py", 400),
+        TestFile("ascend/test_ascend_graph_tp1_bf16.py", 400),
     ],
     "per-commit-2-ascend-npu": [
         TestFile("ascend/test_ascend_tp2_bf16.py", 400),
+        TestFile("ascend/test_ascend_graph_tp2_bf16.py", 400),
     ],
     "per-commit-4-ascend-npu": [
         TestFile("ascend/test_ascend_mla_w8a8int8.py", 400),