v0.10.1rc1

tests/e2e/310p/test_offline_inference_310p.py

@@ -0,0 +1,72 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM 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.
+# This file is a part of the vllm-ascend project.
+import pytest
+import vllm  # noqa: F401
+from vllm import SamplingParams
+
+import vllm_ascend  # noqa: F401
+from tests.e2e.conftest import VllmRunner
+
+MODELS = ["Qwen/Qwen3-0.6B", "Qwen/Qwen2.5-7B-Instruct"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float16"])
+@pytest.mark.parametrize("max_tokens", [5])
+def test_models(model: str, dtype: str, max_tokens: int) -> None:
+    example_prompts = [
+        "Hello, my name is",
+        "The future of AI is",
+    ]
+
+    with VllmRunner(model,
+                    tensor_parallel_size=1,
+                    dtype=dtype,
+                    max_model_len=2048,
+                    enforce_eager=True,
+                    compilation_config={
+                        "custom_ops":
+                        ["none", "+rms_norm", "+rotary_embedding"]
+                    }) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+VL_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float16"])
+def test_vl_model_with_samples(model: str, dtype: str) -> None:
+    example_prompts = [
+        "Hello, my name is",
+        "The future of AI is",
+    ]
+
+    with VllmRunner(model,
+                    tensor_parallel_size=1,
+                    dtype=dtype,
+                    max_model_len=2048,
+                    enforce_eager=True,
+                    compilation_config={
+                        "custom_ops":
+                        ["none", "+rms_norm", "+rotary_embedding"]
+                    }) as vllm_model:
+        sampling_params = SamplingParams(max_tokens=100,
+                                         top_p=0.95,
+                                         top_k=50,
+                                         temperature=0.6)
+        vllm_model.generate(example_prompts, sampling_params)

tests/e2e/310p/test_offline_inference_parallel_310p.py

@@ -0,0 +1,62 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM 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.
+# This file is a part of the vllm-ascend project.
+import pytest
+import vllm  # noqa: F401
+
+import vllm_ascend  # noqa: F401
+from tests.e2e.conftest import VllmRunner
+
+# Pangu local model path
+MODELS = [
+    "IntervitensInc/pangu-pro-moe-model",
+]
+# set additional config for ascend scheduler and torchair graph
+ADDITIONAL_CONFIG = [{
+    "additional_config": {
+        "torchair_graph_config": {
+            "enabled": True
+        },
+        "ascend_scheduler_config": {
+            "enabled": True,
+        }
+    }
+}]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float16"])
+@pytest.mark.parametrize("max_tokens", [5])
+@pytest.mark.parametrize("enfore_eager", [True, False])
+@pytest.mark.parametrize("additional_config", ADDITIONAL_CONFIG)
+def test_pangu_model(model: str, dtype: str, max_tokens: int,
+                     enfore_eager: bool, additional_config: dict) -> None:
+    if enfore_eager:
+        additional_config = {}
+    example_prompts = [
+        "Hello, my name is",
+        "The future of AI is",
+    ]
+
+    with VllmRunner(model,
+                    tensor_parallel_size=4,
+                    dtype=dtype,
+                    max_model_len=1024,
+                    enforce_eager=True,
+                    enable_expert_parallel=True,
+                    additional_config=additional_config,
+                    distributed_executor_backend="mp") as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)